WorldWideScience

Sample records for pattern detection modeling

  1. Detecting Aberrant Response Patterns in the Rasch Model. Rapport 87-3.

    Science.gov (United States)

    Kogut, Jan

    In this paper, the detection of response patterns aberrant from the Rasch model is considered. For this purpose, a new person fit index, recently developed by I. W. Molenaar (1987) and an iterative estimation procedure are used in a simulation study of Rasch model data mixed with aberrant data. Three kinds of aberrant response behavior are…

  2. Enforcing a security pattern in stakeholder goal models

    OpenAIRE

    Yu, Yijun; Kaiya, Haruhiko; Washizaki, Hironori; Xiong, Yingfei; Hu, Zhenjiang; Yoshioka, Nobukazu

    2008-01-01

    Patterns are useful knowledge about recurring problems and solutions. Detecting a security problem using patterns in requirements models may lead to its early solution. In order to facilitate early detection and resolution of security problems, in this paper, we formally describe a role-based access control (RBAC) as a pattern that may occur in stakeholder requirements models. We also implemented in our goal-oriented modeling tool the formally described pattern using model-driven queries and ...

  3. Statistical Methods for Detecting and Modeling General Patterns and Relationships in Lifetime Data

    Energy Technology Data Exchange (ETDEWEB)

    Kvaloey, Jan Terje

    1999-04-01

    In this thesis, the author tries to develop methods of detecting and modeling general patterns and relationships in lifetime data. Tests with power against nonmonotonic trends and nonmonotonic co variate effects are considered, and nonparametric regression methods which allow estimation of fairly general nonlinear relationships are studied. Practical uses of some of the methods are illustrated although in a medical rather than engineering or technological context.

  4. Detecting consistent patterns of directional adaptation using differential selection codon models.

    Science.gov (United States)

    Parto, Sahar; Lartillot, Nicolas

    2017-06-23

    Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors. However, some organisms may be subject to systematic changes in selective pressure, resulting in reproducible molecular adaptations across independent lineages subject to similar conditions. Here, we introduce a codon-based differential selection model, which aims to detect and quantify the fine-grained consistent patterns of adaptation at the protein-coding level, as a function of external conditions experienced by the organism under investigation. The model parameterizes the global mutational pressure, as well as the site- and condition-specific amino acid selective preferences. This phylogenetic model is implemented in a Bayesian MCMC framework. After validation with simulations, we applied our method to a dataset of HIV sequences from patients with known HLA genetic background. Our differential selection model detects and characterizes differentially selected coding positions specifically associated with two different HLA alleles. Our differential selection model is able to identify consistent molecular adaptations as a function of repeated changes in the environment of the organism. These models can be applied to many other problems, ranging from viral adaptation to evolution of life-history strategies in plants or animals.

  5. Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems

    Science.gov (United States)

    Shyu, Mei-Ling; Huang, Zifang; Luo, Hongli

    In recent years, pervasive computing infrastructures have greatly improved the interaction between human and system. As we put more reliance on these computing infrastructures, we also face threats of network intrusion and/or any new forms of undesirable IT-based activities. Hence, network security has become an extremely important issue, which is closely connected with homeland security, business transactions, and people's daily life. Accurate and efficient intrusion detection technologies are required to safeguard the network systems and the critical information transmitted in the network systems. In this chapter, a novel network intrusion detection framework for mining and detecting sequential intrusion patterns is proposed. The proposed framework consists of a Collateral Representative Subspace Projection Modeling (C-RSPM) component for supervised classification, and an inter-transactional association rule mining method based on Layer Divided Modeling (LDM) for temporal pattern analysis. Experiments on the KDD99 data set and the traffic data set generated by a private LAN testbed show promising results with high detection rates, low processing time, and low false alarm rates in mining and detecting sequential intrusion detections.

  6. Time-warp invariant pattern detection with bursting neurons

    International Nuclear Information System (INIS)

    Gollisch, Tim

    2008-01-01

    Sound patterns are defined by the temporal relations of their constituents, individual acoustic cues. Auditory systems need to extract these temporal relations to detect or classify sounds. In various cases, ranging from human speech to communication signals of grasshoppers, this pattern detection has been found to display invariance to temporal stretching or compression of the sound signal ('linear time-warp invariance'). In this work, a four-neuron network model is introduced, designed to solve such a detection task for the example of grasshopper courtship songs. As an essential ingredient, the network contains neurons with intrinsic bursting dynamics, which allow them to encode durations between acoustic events in short, rapid sequences of spikes. As shown by analytical calculations and computer simulations, these neuronal dynamics result in a powerful mechanism for temporal integration. Finally, the network reads out the encoded temporal information by detecting equal activity of two such bursting neurons. This leads to the recognition of rhythmic patterns independent of temporal stretching or compression

  7. [Interactive patterns detection in family communication with adolescents].

    Science.gov (United States)

    Gimeno Collado, Adelina; Anguera Argilaga, M Teresa; Berzosa Sanz, Amparo; Ramírez Ramírez, Luis

    2006-11-01

    Interactive patterns detection in family communication with adolescents. Nondistant communication is a relevant indicator for family functionality valuation. The goal of this study is to analyze this communication in order to identify specific kinds of leadership, interaction patterns and the relation between verbal and nonverbal elements in communication. The observational design exposed is an idiographic one, punctual and multidimensional, which uses field format as observation instrument. Participants were seven standardized families made up of both ancestors and an adolescent son or daughter. According to the family models analyzed, results show a predominantly democratic communication style in adults with recurrent support expressions. The sequential analysis incorporates only categories from the emitter point of view, and detects relevant sequences which show symmetric interaction between all three family members. Verbal and nonverbal channels provide complementary information. Depending on adolescents' gender different patterns in behaviour can be identified as well.

  8. Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models.

    Science.gov (United States)

    Wang, Xinlei; Zang, Miao; Xiao, Guanghua

    2013-06-15

    Epigenetics is the study of changes to the genome that can switch genes on or off and determine which proteins are transcribed without altering the DNA sequence. Recently, epigenetic changes have been linked to the development and progression of disease such as psychiatric disorders. High-throughput epigenetic experiments have enabled researchers to measure genome-wide epigenetic profiles and yield data consisting of intensity ratios of immunoprecipitation versus reference samples. The intensity ratios can provide a view of genomic regions where protein binding occur under one experimental condition and further allow us to detect epigenetic alterations through comparison between two different conditions. However, such experiments can be expensive, with only a few replicates available. Moreover, epigenetic data are often spatially correlated with high noise levels. In this paper, we develop a Bayesian hierarchical model, combined with hidden Markov processes with four states for modeling spatial dependence, to detect genomic sites with epigenetic changes from two-sample experiments with paired internal control. One attractive feature of the proposed method is that the four states of the hidden Markov process have well-defined biological meanings and allow us to directly call the change patterns based on the corresponding posterior probabilities. In contrast, none of existing methods can offer this advantage. In addition, the proposed method offers great power in statistical inference by spatial smoothing (via hidden Markov modeling) and information pooling (via hierarchical modeling). Both simulation studies and real data analysis in a cocaine addiction study illustrate the reliability and success of this method. Copyright © 2012 John Wiley & Sons, Ltd.

  9. Using forbidden ordinal patterns to detect determinism in irregularly sampled time series.

    Science.gov (United States)

    Kulp, C W; Chobot, J M; Niskala, B J; Needhammer, C J

    2016-02-01

    It is known that when symbolizing a time series into ordinal patterns using the Bandt-Pompe (BP) methodology, there will be ordinal patterns called forbidden patterns that do not occur in a deterministic series. The existence of forbidden patterns can be used to identify deterministic dynamics. In this paper, the ability to use forbidden patterns to detect determinism in irregularly sampled time series is tested on data generated from a continuous model system. The study is done in three parts. First, the effects of sampling time on the number of forbidden patterns are studied on regularly sampled time series. The next two parts focus on two types of irregular-sampling, missing data and timing jitter. It is shown that forbidden patterns can be used to detect determinism in irregularly sampled time series for low degrees of sampling irregularity (as defined in the paper). In addition, comments are made about the appropriateness of using the BP methodology to symbolize irregularly sampled time series.

  10. Validating EHR clinical models using ontology patterns.

    Science.gov (United States)

    Martínez-Costa, Catalina; Schulz, Stefan

    2017-12-01

    Clinical models are artefacts that specify how information is structured in electronic health records (EHRs). However, the makeup of clinical models is not guided by any formal constraint beyond a semantically vague information model. We address this gap by advocating ontology design patterns as a mechanism that makes the semantics of clinical models explicit. This paper demonstrates how ontology design patterns can validate existing clinical models using SHACL. Based on the Clinical Information Modelling Initiative (CIMI), we show how ontology patterns detect both modeling and terminology binding errors in CIMI models. SHACL, a W3C constraint language for the validation of RDF graphs, builds on the concept of "Shape", a description of data in terms of expected cardinalities, datatypes and other restrictions. SHACL, as opposed to OWL, subscribes to the Closed World Assumption (CWA) and is therefore more suitable for the validation of clinical models. We have demonstrated the feasibility of the approach by manually describing the correspondences between six CIMI clinical models represented in RDF and two SHACL ontology design patterns. Using a Java-based SHACL implementation, we found at least eleven modeling and binding errors within these CIMI models. This demonstrates the usefulness of ontology design patterns not only as a modeling tool but also as a tool for validation. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Motion Pattern Extraction and Event Detection for Automatic Visual Surveillance

    Directory of Open Access Journals (Sweden)

    Benabbas Yassine

    2011-01-01

    Full Text Available Efficient analysis of human behavior in video surveillance scenes is a very challenging problem. Most traditional approaches fail when applied in real conditions and contexts like amounts of persons, appearance ambiguity, and occlusion. In this work, we propose to deal with this problem by modeling the global motion information obtained from optical flow vectors. The obtained direction and magnitude models learn the dominant motion orientations and magnitudes at each spatial location of the scene and are used to detect the major motion patterns. The applied region-based segmentation algorithm groups local blocks that share the same motion direction and speed and allows a subregion of the scene to appear in different patterns. The second part of the approach consists in the detection of events related to groups of people which are merge, split, walk, run, local dispersion, and evacuation by analyzing the instantaneous optical flow vectors and comparing the learned models. The approach is validated and experimented on standard datasets of the computer vision community. The qualitative and quantitative results are discussed.

  12. Characterizing Dynamic Walking Patterns and Detecting Falls with Wearable Sensors Using Gaussian Process Methods

    Directory of Open Access Journals (Sweden)

    Taehwan Kim

    2017-05-01

    Full Text Available By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches. For the task of characterizing dynamic walking patterns in a low-dimensional latent feature space, we propose a novel approach called auto-encoded Gaussian process dynamical model, in which we combine a GP-based state space modeling method with a nonlinear dimensionality reduction method in a unique manner. The Gaussian process methods are fit for this task because one of the most import strengths of the Gaussian process methods is its capability of handling uncertainty in the model parameters. Also for detecting falls while walking, we propose to recycle the latent samples generated in training the auto-encoded Gaussian process dynamical model for GP-based novelty detection, which can lead to an efficient and seamless solution to the detection task. Experimental results show that the combined use of these GP-based methods can yield promising results for characterizing dynamic walking patterns and detecting falls while walking with the wearable sensors.

  13. A novel approach to describing and detecting performance anti-patterns

    Science.gov (United States)

    Sheng, Jinfang; Wang, Yihan; Hu, Peipei; Wang, Bin

    2017-08-01

    Anti-pattern, as an extension to pattern, describes a widely used poor solution which can bring negative influence to application systems. Aiming at the shortcomings of the existing anti-pattern descriptions, an anti-pattern description method based on first order predicate is proposed. This method synthesizes anti-pattern forms and symptoms, which makes the description more accurate and has good scalability and versatility as well. In order to improve the accuracy of anti-pattern detection, a Bayesian classification method is applied in validation for detection results, which can reduce false negatives and false positives of anti-pattern detection. Finally, the proposed approach in this paper is applied to a small e-commerce system, the feasibility and effectiveness of the approach is demonstrated further through experiments.

  14. Cochlear spike synchronization and neuron coincidence detection model

    Science.gov (United States)

    Bader, Rolf

    2018-02-01

    Coincidence detection of a spike pattern fed from the cochlea into a single neuron is investigated using a physical Finite-Difference model of the cochlea and a physiologically motivated neuron model. Previous studies have shown experimental evidence of increased spike synchronization in the nucleus cochlearis and the trapezoid body [Joris et al., J. Neurophysiol. 71(3), 1022-1036 and 1037-1051 (1994)] and models show tone partial phase synchronization at the transition from mechanical waves on the basilar membrane into spike patterns [Ch. F. Babbs, J. Biophys. 2011, 435135]. Still the traveling speed of waves on the basilar membrane cause a frequency-dependent time delay of simultaneously incoming sound wavefronts up to 10 ms. The present model shows nearly perfect synchronization of multiple spike inputs as neuron outputs with interspike intervals (ISI) at the periodicity of the incoming sound for frequencies from about 30 to 300 Hz for two different amounts of afferent nerve fiber neuron inputs. Coincidence detection serves here as a fusion of multiple inputs into one single event enhancing pitch periodicity detection for low frequencies, impulse detection, or increased sound or speech intelligibility due to dereverberation.

  15. Detecting Beer Intake by Unique Metabolite Patterns.

    Science.gov (United States)

    Gürdeniz, Gözde; Jensen, Morten Georg; Meier, Sebastian; Bech, Lene; Lund, Erik; Dragsted, Lars Ove

    2016-12-02

    Evaluation of the health related effects of beer intake is hampered by the lack of accurate tools for assessing intakes (biomarkers). Therefore, we identified plasma and urine metabolites associated with recent beer intake by untargeted metabolomics and established a characteristic metabolite pattern representing raw materials and beer production as a qualitative biomarker of beer intake. In a randomized, crossover, single-blinded meal study (MSt1), 18 participants were given, one at a time, four different test beverages: strong, regular, and nonalcoholic beers and a soft drink. Four participants were assigned to have two additional beers (MSt2). In addition to plasma and urine samples, test beverages, wort, and hops extract were analyzed by UPLC-QTOF. A unique metabolite pattern reflecting beer metabolome, including metabolites derived from beer raw material (i.e., N-methyl tyramine sulfate and the sum of iso-α-acids and tricyclohumols) and the production process (i.e., pyro-glutamyl proline and 2-ethyl malate), was selected to establish a compliance biomarker model for detection of beer intake based on MSt1. The model predicted the MSt2 samples collected before and up to 12 h after beer intake correctly (AUC = 1). A biomarker model including four metabolites representing both beer raw materials and production steps provided a specific and accurate tool for measurement of beer consumption.

  16. Maximum-entropy networks pattern detection, network reconstruction and graph combinatorics

    CERN Document Server

    Squartini, Tiziano

    2017-01-01

    This book is an introduction to maximum-entropy models of random graphs with given topological properties and their applications. Its original contribution is the reformulation of many seemingly different problems in the study of both real networks and graph theory within the unified framework of maximum entropy. Particular emphasis is put on the detection of structural patterns in real networks, on the reconstruction of the properties of networks from partial information, and on the enumeration and sampling of graphs with given properties.  After a first introductory chapter explaining the motivation, focus, aim and message of the book, chapter 2 introduces the formal construction of maximum-entropy ensembles of graphs with local topological constraints. Chapter 3 focuses on the problem of pattern detection in real networks and provides a powerful way to disentangle nontrivial higher-order structural features from those that can be traced back to simpler local constraints. Chapter 4 focuses on the problem o...

  17. Hotspot detection using image pattern recognition based on higher-order local auto-correlation

    Science.gov (United States)

    Maeda, Shimon; Matsunawa, Tetsuaki; Ogawa, Ryuji; Ichikawa, Hirotaka; Takahata, Kazuhiro; Miyairi, Masahiro; Kotani, Toshiya; Nojima, Shigeki; Tanaka, Satoshi; Nakagawa, Kei; Saito, Tamaki; Mimotogi, Shoji; Inoue, Soichi; Nosato, Hirokazu; Sakanashi, Hidenori; Kobayashi, Takumi; Murakawa, Masahiro; Higuchi, Tetsuya; Takahashi, Eiichi; Otsu, Nobuyuki

    2011-04-01

    Below 40nm design node, systematic variation due to lithography must be taken into consideration during the early stage of design. So far, litho-aware design using lithography simulation models has been widely applied to assure that designs are printed on silicon without any error. However, the lithography simulation approach is very time consuming, and under time-to-market pressure, repetitive redesign by this approach may result in the missing of the market window. This paper proposes a fast hotspot detection support method by flexible and intelligent vision system image pattern recognition based on Higher-Order Local Autocorrelation. Our method learns the geometrical properties of the given design data without any defects as normal patterns, and automatically detects the design patterns with hotspots from the test data as abnormal patterns. The Higher-Order Local Autocorrelation method can extract features from the graphic image of design pattern, and computational cost of the extraction is constant regardless of the number of design pattern polygons. This approach can reduce turnaround time (TAT) dramatically only on 1CPU, compared with the conventional simulation-based approach, and by distributed processing, this has proven to deliver linear scalability with each additional CPU.

  18. Design And Implementation Of Tool For Detecting Anti-Patterns In Relational Database

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar

    2017-07-01

    Full Text Available Anti-patterns are poor solution to design and im-plementation problems. Developers may introduce anti-patterns in their software systems because of time pressure lack of understanding communication and or-skills. Anti-patterns create problems in software maintenance and development. Database anti-patterns lead to complex and time consuming query process-ing and loss of integrity constraints. Detecting anti-patterns could reduce costs efforts and resources. Researchers have proposed approaches to detect anti-patterns in software development. But not much research has been done about database anti-patterns. This report presents two approaches to detect schema design anti-patterns in relational database. Our first approach is based on pattern matchingwe look into potential candidates based on schema patterns. Second approach is a machine learning based approach we generate features of possible anti-patterns and build SVMbased classifier to detect them. Here we look into these four anti-patterns a Multi-valued attribute b Nave tree based c Entity Attribute Value and dPolymorphic Association . We measure precision and recall of each approach and compare the results. SVM-based approach provides more precision and recall with more training dataset.

  19. Using pattern analysis methods to do fast detection of manufacturing pattern failures

    Science.gov (United States)

    Zhao, Evan; Wang, Jessie; Sun, Mason; Wang, Jeff; Zhang, Yifan; Sweis, Jason; Lai, Ya-Chieh; Ding, Hua

    2016-03-01

    At the advanced technology node, logic design has become extremely complex and is getting more challenging as the pattern geometry size decreases. The small sizes of layout patterns are becoming very sensitive to process variations. Meanwhile, the high pressure of yield ramp is always there due to time-to-market competition. The company that achieves patterning maturity earlier than others will have a great advantage and a better chance to realize maximum profit margins. For debugging silicon failures, DFT diagnostics can identify which nets or cells caused the yield loss. But normally, a long time period is needed with many resources to identify which failures are due to one common layout pattern or structure. This paper will present a new yield diagnostic flow, based on preliminary EFA results, to show how pattern analysis can more efficiently detect pattern related systematic defects. Increased visibility on design pattern related failures also allows more precise yield loss estimation.

  20. Attack Pattern Analysis Framework for a Multiagent Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Krzysztof Juszczyszyn

    2008-08-01

    Full Text Available The paper proposes the use of attack pattern ontology and formal framework for network traffic anomalies detection within a distributed multi-agent Intrusion Detection System architecture. Our framework assumes ontology-based attack definition and distributed processing scheme with exchange of communicates between agents. The role of traffic anomalies detection was presented then it has been discussed how some specific values characterizing network communication can be used to detect network anomalies caused by security incidents (worm attack, virus spreading. Finally, it has been defined how to use the proposed techniques in distributed IDS using attack pattern ontology.

  1. Real-time pose invariant logo and pattern detection

    Science.gov (United States)

    Sidla, Oliver; Kottmann, Michal; Benesova, Wanda

    2011-01-01

    The detection of pose invariant planar patterns has many practical applications in computer vision and surveillance systems. The recognition of company logos is used in market studies to examine the visibility and frequency of logos in advertisement. Danger signs on vehicles could be detected to trigger warning systems in tunnels, or brand detection on transport vehicles can be used to count company-specific traffic. We present the results of a study on planar pattern detection which is based on keypoint detection and matching of distortion invariant 2d feature descriptors. Specifically we look at the keypoint detectors of type: i) Lowe's DoG approximation from the SURF algorithm, ii) the Harris Corner Detector, iii) the FAST Corner Detector and iv) Lepetit's keypoint detector. Our study then compares the feature descriptors SURF and compact signatures based on Random Ferns: we use 3 sets of sample images to detect and match 3 logos of different structure to find out which combinations of keypoint detector/feature descriptors work well. A real-world test tries to detect vehicles with a distinctive logo in an outdoor environment under realistic lighting and weather conditions: a camera was mounted on a suitable location for observing the entrance to a parking area so that incoming vehicles could be monitored. In this 2 hour long recording we can successfully detect a specific company logo without false positives.

  2. Modeling seasonal detection patterns for burrowing owl surveys

    Science.gov (United States)

    Quresh S. Latif; Kathleen D. Fleming; Cameron Barrows; John T. Rotenberry

    2012-01-01

    To guide monitoring of burrowing owls (Athene cunicularia) in the Coachella Valley, California, USA, we analyzed survey-method-specific seasonal variation in detectability. Point-based call-broadcast surveys yielded high early season detectability that then declined through time, whereas detectability on driving surveys increased through the season. Point surveys...

  3. Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid

    Directory of Open Access Journals (Sweden)

    Yujia Jiang

    2018-01-01

    Full Text Available Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.

  4. Intelligent-based Structural Damage Detection Model

    International Nuclear Information System (INIS)

    Lee, Eric Wai Ming; Yu, K.F.

    2010-01-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  5. Intelligent-based Structural Damage Detection Model

    Science.gov (United States)

    Lee, Eric Wai Ming; Yu, Kin Fung

    2010-05-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  6. A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

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    He-Yuan Lin

    2008-03-01

    Full Text Available A novel motion-adaptive deinterlacing algorithm with edge-pattern recognition and hybrid motion detection is introduced. The great variety of video contents makes the processing of assorted motion, edges, textures, and the combination of them very difficult with a single algorithm. The edge-pattern recognition algorithm introduced in this paper exhibits the flexibility in processing both textures and edges which need to be separately accomplished by line average and edge-based line average before. Moreover, predicting the neighboring pixels for pattern analysis and interpolation further enhances the adaptability of the edge-pattern recognition unit when motion detection is incorporated. Our hybrid motion detection features accurate detection of fast and slow motion in interlaced video and also the motion with edges. Using only three fields for detection also renders higher temporal correlation for interpolation. The better performance of our deinterlacing algorithm with higher content-adaptability and less memory cost than the state-of-the-art 4-field motion detection algorithms can be seen from the subjective and objective experimental results of the CIF and PAL video sequences.

  7. A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Li Hsin-Te

    2008-01-01

    Full Text Available Abstract A novel motion-adaptive deinterlacing algorithm with edge-pattern recognition and hybrid motion detection is introduced. The great variety of video contents makes the processing of assorted motion, edges, textures, and the combination of them very difficult with a single algorithm. The edge-pattern recognition algorithm introduced in this paper exhibits the flexibility in processing both textures and edges which need to be separately accomplished by line average and edge-based line average before. Moreover, predicting the neighboring pixels for pattern analysis and interpolation further enhances the adaptability of the edge-pattern recognition unit when motion detection is incorporated. Our hybrid motion detection features accurate detection of fast and slow motion in interlaced video and also the motion with edges. Using only three fields for detection also renders higher temporal correlation for interpolation. The better performance of our deinterlacing algorithm with higher content-adaptability and less memory cost than the state-of-the-art 4-field motion detection algorithms can be seen from the subjective and objective experimental results of the CIF and PAL video sequences.

  8. Evaluation of a New Digital Automated Glycemic Pattern Detection Tool.

    Science.gov (United States)

    Comellas, María José; Albiñana, Emma; Artes, Maite; Corcoy, Rosa; Fernández-García, Diego; García-Alemán, Jorge; García-Cuartero, Beatriz; González, Cintia; Rivero, María Teresa; Casamira, Núria; Weissmann, Jörg

    2017-11-01

    Blood glucose meters are reliable devices for data collection, providing electronic logs of historical data easier to interpret than handwritten logbooks. Automated tools to analyze these data are necessary to facilitate glucose pattern detection and support treatment adjustment. These tools emerge in a broad variety in a more or less nonevaluated manner. The aim of this study was to compare eDetecta, a new automated pattern detection tool, to nonautomated pattern analysis in terms of time investment, data interpretation, and clinical utility, with the overarching goal to identify early in development and implementation of tool areas of improvement and potential safety risks. Multicenter web-based evaluation in which 37 endocrinologists were asked to assess glycemic patterns of 4 real reports (2 continuous subcutaneous insulin infusion [CSII] and 2 multiple daily injection [MDI]). Endocrinologist and eDetecta analyses were compared on time spent to analyze each report and agreement on the presence or absence of defined patterns. eDetecta module markedly reduced the time taken to analyze each case on the basis of the emminens eConecta reports (CSII: 18 min; MDI: 12.5), compared to the automatic eDetecta analysis. Agreement between endocrinologists and eDetecta varied depending on the patterns, with high level of agreement in patterns of glycemic variability. Further analysis of low level of agreement led to identifying areas where algorithms used could be improved to optimize trend pattern identification. eDetecta was a useful tool for glycemic pattern detection, helping clinicians to reduce time required to review emminens eConecta glycemic reports. No safety risks were identified during the study.

  9. Mapping vegetation patterns in arable land using the models STICS and DAISY

    Science.gov (United States)

    Heuer, Antje; Casper, Markus

    2010-05-01

    Several statistical methods exist to detect spatial and / or temporal variability with regard to ecological data-analysis: Semivariance-analysis, Trend surface analysis, Kriging, Voronoi polygons, Moran's I and Mantel-test, to mention just some of them. In this contribution, we concentrate on spatial vegetation patterns within the soil-vegetation-atmosphere (SVAT) system. Using variography, spatial analysis with a geographic information system and self-organizing maps, spatial patterns of yield have been isolated in an agro-ecosystem (see poster contribution EGU 2009, EGU2009-8948). Data were derived from two agricultural plots, each about 5 hectare, in the area of Newel, located in Western Palatinate, Germany. The plots have been conventionally cultivated with a crop rotation of winter rape, winter wheat and spring barley. The aim of the present study is to find out if the existing natural spatial patterns can be mapped by means of SVAT models. If so, the discretization of a landscape according to its spatial patterns could be the basis for parameterization of SVAT models in order to model soil-vegetation-atmosphere interaction over a large area, that is for up-scaling. For this purpose the SVAT models STICS (developed by INRA, France) and DAISY (developed at Tåstrup University, Denmark) are applied. After a wide sensitivity analysis both models are parameterized with field data according to the given situation of each of the detected spatial patterns. The results of the simulation per representative location of a pattern are validated first with field data concerning yield, soil water content and soil nitrogen; besides, above ground dry matter, root depth and specific stress indices are used for validation. The conclusions that can be made with regard to up-scaling are discussed in detail. In a second step the results of the STICS model are compared with those of the DAISY model to analyse the models' behaviour, to get further knowledge about the inner structure

  10. Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Jing, E-mail: jing.zhang2@duke.edu; Ghate, Sujata V.; Yoon, Sora C. [Department of Radiology, Duke University School of Medicine, Durham, North Carolina 27705 (United States); Lo, Joseph Y. [Department of Radiology, Duke University School of Medicine, Durham, North Carolina 27705 (United States); Duke Cancer Institute, Durham, North Carolina 27710 (United States); Departments of Biomedical Engineering and Electrical and Computer Engineering, Duke University, Durham, North Carolina 27705 (United States); Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 (United States); Kuzmiak, Cherie M. [Department of Radiology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina 27599 (United States); Mazurowski, Maciej A. [Department of Radiology, Duke University School of Medicine, Durham, North Carolina 27705 (United States); Duke Cancer Institute, Durham, North Carolina 27710 (United States); Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 (United States)

    2014-09-15

    from 0.5 (p < 0.0001). For the 7 residents only, the AUC performance of the models was 0.590 (95% CI,0.537-0.642) and was also significantly higher than 0.5 (p = 0.0009). Therefore, generally the authors’ models were able to predict which masses were detected and which were missed better than chance. Conclusions: The authors proposed an algorithm that was able to predict which masses will be detected and which will be missed by each individual trainee. This confirms existence of error-making patterns in the detection of masses among radiology trainees. Furthermore, the proposed methodology will allow for the optimized selection of difficult cases for the trainees in an automatic and efficient manner.

  11. Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents

    International Nuclear Information System (INIS)

    Zhang, Jing; Ghate, Sujata V.; Yoon, Sora C.; Lo, Joseph Y.; Kuzmiak, Cherie M.; Mazurowski, Maciej A.

    2014-01-01

    from 0.5 (p < 0.0001). For the 7 residents only, the AUC performance of the models was 0.590 (95% CI,0.537-0.642) and was also significantly higher than 0.5 (p = 0.0009). Therefore, generally the authors’ models were able to predict which masses were detected and which were missed better than chance. Conclusions: The authors proposed an algorithm that was able to predict which masses will be detected and which will be missed by each individual trainee. This confirms existence of error-making patterns in the detection of masses among radiology trainees. Furthermore, the proposed methodology will allow for the optimized selection of difficult cases for the trainees in an automatic and efficient manner

  12. Model Building – A Circular Approach to Evaluate Multidimensional Patterns and Operationalized Procedures

    Directory of Open Access Journals (Sweden)

    Franz HAAS

    2017-12-01

    Full Text Available Managers operate in highly different fields. Decision-making can be based on models reflecting in part these differences. The challenge is to connect the respective models without too great a disruption. A threefold procedural approach is proposed by chaining a scheme of modeling in a complex field to an operationalized model to statistical multivariate methods. Multivariate pattern-detecting methods offer the chance to evaluate patterns within the complex field partly. This step completes the cycle of research and improved models can be used in a further cycle.

  13. Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents.

    Science.gov (United States)

    Zhang, Jing; Lo, Joseph Y; Kuzmiak, Cherie M; Ghate, Sujata V; Yoon, Sora C; Mazurowski, Maciej A

    2014-09-01

    Mammography is the most widely accepted and utilized screening modality for early breast cancer detection. Providing high quality mammography education to radiology trainees is essential, since excellent interpretation skills are needed to ensure the highest benefit of screening mammography for patients. The authors have previously proposed a computer-aided education system based on trainee models. Those models relate human-assessed image characteristics to trainee error. In this study, the authors propose to build trainee models that utilize features automatically extracted from images using computer vision algorithms to predict likelihood of missing each mass by the trainee. This computer vision-based approach to trainee modeling will allow for automatically searching large databases of mammograms in order to identify challenging cases for each trainee. The authors' algorithm for predicting the likelihood of missing a mass consists of three steps. First, a mammogram is segmented into air, pectoral muscle, fatty tissue, dense tissue, and mass using automated segmentation algorithms. Second, 43 features are extracted using computer vision algorithms for each abnormality identified by experts. Third, error-making models (classifiers) are applied to predict the likelihood of trainees missing the abnormality based on the extracted features. The models are developed individually for each trainee using his/her previous reading data. The authors evaluated the predictive performance of the proposed algorithm using data from a reader study in which 10 subjects (7 residents and 3 novices) and 3 experts read 100 mammographic cases. Receiver operating characteristic (ROC) methodology was applied for the evaluation. The average area under the ROC curve (AUC) of the error-making models for the task of predicting which masses will be detected and which will be missed was 0.607 (95% CI,0.564-0.650). This value was statistically significantly different from 0.5 (perror

  14. Modeling Patterns of Activities using Activity Curves.

    Science.gov (United States)

    Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen

    2016-06-01

    Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve , which represents an abstraction of an individual's normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.

  15. Patterns of data modeling

    CERN Document Server

    Blaha, Michael

    2010-01-01

    Best-selling author and database expert with more than 25 years of experience modeling application and enterprise data, Dr. Michael Blaha provides tried and tested data model patterns, to help readers avoid common modeling mistakes and unnecessary frustration on their way to building effective data models. Unlike the typical methodology book, "Patterns of Data Modeling" provides advanced techniques for those who have mastered the basics. Recognizing that database representation sets the path for software, determines its flexibility, affects its quality, and influences whether it succ

  16. FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining

    Directory of Open Access Journals (Sweden)

    K. R. Seeja

    2014-01-01

    Full Text Available This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.

  17. FraudMiner: a novel credit card fraud detection model based on frequent itemset mining.

    Science.gov (United States)

    Seeja, K R; Zareapoor, Masoumeh

    2014-01-01

    This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incoming transaction of a particular customer is closer and a decision is made accordingly. In order to handle the anonymous nature of the data, no preference is given to any of the attributes and each attribute is considered equally for finding the patterns. The performance evaluation of the proposed model is done on UCSD Data Mining Contest 2009 Dataset (anonymous and imbalanced) and it is found that the proposed model has very high fraud detection rate, balanced classification rate, Matthews correlation coefficient, and very less false alarm rate than other state-of-the-art classifiers.

  18. Toward unsupervised outbreak detection through visual perception of new patterns

    Directory of Open Access Journals (Sweden)

    Lévy Pierre P

    2009-06-01

    Full Text Available Abstract Background Statistical algorithms are routinely used to detect outbreaks of well-defined syndromes, such as influenza-like illness. These methods cannot be applied to the detection of emerging diseases for which no preexisting information is available. This paper presents a method aimed at facilitating the detection of outbreaks, when there is no a priori knowledge of the clinical presentation of cases. Methods The method uses a visual representation of the symptoms and diseases coded during a patient consultation according to the International Classification of Primary Care 2nd version (ICPC-2. The surveillance data are transformed into color-coded cells, ranging from white to red, reflecting the increasing frequency of observed signs. They are placed in a graphic reference frame mimicking body anatomy. Simple visual observation of color-change patterns over time, concerning a single code or a combination of codes, enables detection in the setting of interest. Results The method is demonstrated through retrospective analyses of two data sets: description of the patients referred to the hospital by their general practitioners (GPs participating in the French Sentinel Network and description of patients directly consulting at a hospital emergency department (HED. Informative image color-change alert patterns emerged in both cases: the health consequences of the August 2003 heat wave were visualized with GPs' data (but passed unnoticed with conventional surveillance systems, and the flu epidemics, which are routinely detected by standard statistical techniques, were recognized visually with HED data. Conclusion Using human visual pattern-recognition capacities to detect the onset of unexpected health events implies a convenient image representation of epidemiological surveillance and well-trained "epidemiology watchers". Once these two conditions are met, one could imagine that the epidemiology watchers could signal epidemiological alerts

  19. Application of DNA Machineries for the Barcode Patterned Detection of Genes or Proteins.

    Science.gov (United States)

    Zhou, Zhixin; Luo, Guofeng; Wulf, Verena; Willner, Itamar

    2018-06-05

    The study introduces an analytical platform for the detection of genes or aptamer-ligand complexes by nucleic acid barcode patterns generated by DNA machineries. The DNA machineries consist of nucleic acid scaffolds that include specific recognition sites for the different genes or aptamer-ligand analytes. The binding of the analytes to the scaffolds initiate, in the presence of the nucleotide mixture, a cyclic polymerization/nicking machinery that yields displaced strands of variable lengths. The electrophoretic separation of the resulting strands provides barcode patterns for the specific detection of the different analytes. Mixtures of DNA machineries that yield, upon sensing of different genes (or aptamer ligands), one-, two-, or three-band barcode patterns are described. The combination of nucleic acid scaffolds acting, in the presence of polymerase/nicking enzyme and nucleotide mixture, as DNA machineries, that generate multiband barcode patterns provide an analytical platform for the detection of an individual gene out of many possible genes. The diversity of genes (or other analytes) that can be analyzed by the DNA machineries and the barcode patterned imaging is given by the Pascal's triangle. As a proof-of-concept, the detection of one of six genes, that is, TP53, Werner syndrome, Tay-Sachs normal gene, BRCA1, Tay-Sachs mutant gene, and cystic fibrosis disorder gene by six two-band barcode patterns is demonstrated. The advantages and limitations of the detection of analytes by polymerase/nicking DNA machineries that yield barcode patterns as imaging readout signals are discussed.

  20. Face Liveness Detection Using Dynamic Local Ternary Pattern (DLTP

    Directory of Open Access Journals (Sweden)

    Sajida Parveen

    2016-05-01

    Full Text Available Face spoofing is considered to be one of the prominent threats to face recognition systems. However, in order to improve the security measures of such biometric systems against deliberate spoof attacks, liveness detection has received significant recent attention from researchers. For this purpose, analysis of facial skin texture properties becomes more popular because of its limited resource requirement and lower processing cost. The traditional method of skin analysis for liveness detection was to use Local Binary Pattern (LBP and its variants. LBP descriptors are effective, but they may exhibit certain limitations in near uniform patterns. Thus, in this paper, we demonstrate the effectiveness of Local Ternary Pattern (LTP as an alternative to LBP. In addition, we adopted Dynamic Local Ternary Pattern (DLTP, which eliminates the manual threshold setting in LTP by using Weber’s law. The proposed method was tested rigorously on four facial spoof databases: three are public domain databases and the other is the Universiti Putra Malaysia (UPM face spoof database, which was compiled through this study. The results obtained from the proposed DLTP texture descriptor attained optimum accuracy and clearly outperformed the reported LBP and LTP texture descriptors.

  1. Modeling Architectural Patterns Using Architectural Primitives

    NARCIS (Netherlands)

    Zdun, Uwe; Avgeriou, Paris

    2005-01-01

    Architectural patterns are a key point in architectural documentation. Regrettably, there is poor support for modeling architectural patterns, because the pattern elements are not directly matched by elements in modeling languages, and, at the same time, patterns support an inherent variability that

  2. Binary pattern analysis for 3D facial action unit detection

    NARCIS (Netherlands)

    Sandbach, Georgia; Zafeiriou, Stefanos; Pantic, Maja

    2012-01-01

    In this paper we propose new binary pattern features for use in the problem of 3D facial action unit (AU) detection. Two representations of 3D facial geometries are employed, the depth map and the Azimuthal Projection Distance Image (APDI). To these the traditional Local Binary Pattern is applied,

  3. Multivariate data-driven modelling and pattern recognition for damage detection and identification for acoustic emission and acousto-ultrasonics

    DEFF Research Database (Denmark)

    Torres-Arredondo, M.A.; Tibaduiza, D.-A.; McGugan, Malcolm

    2013-01-01

    and pattern recognition are evaluated and integrated into the different proposed methodologies. As a contribution to solve the problem, this paper presents results in damage detection and classification using a methodology based on hierarchical nonlinear principal component analysis, square prediction...

  4. Transient pattern analysis for fault detection and diagnosis of HVAC systems

    International Nuclear Information System (INIS)

    Cho, Sung-Hwan; Yang, Hoon-Cheol; Zaheer-uddin, M.; Ahn, Byung-Cheon

    2005-01-01

    Modern building HVAC systems are complex and consist of a large number of interconnected sub-systems and components. In the event of a fault, it becomes very difficult for the operator to locate and isolate the faulty component in such large systems using conventional fault detection methods. In this study, transient pattern analysis is explored as a tool for fault detection and diagnosis of an HVAC system. Several tests involving different fault replications were conducted in an environmental chamber test facility. The results show that the evolution of fault residuals forms clear and distinct patterns that can be used to isolate faults. It was found that the time needed to reach steady state for a typical building HVAC system is at least 50-60 min. This means incorrect diagnosis of faults can happen during online monitoring if the transient pattern responses are not considered in the fault detection and diagnosis analysis

  5. Patterns of detection and capture are associated with cohabiting predators and prey.

    Directory of Open Access Journals (Sweden)

    Billie T Lazenby

    Full Text Available Avoidance behaviour can play an important role in structuring ecosystems but can be difficult to uncover and quantify. Remote cameras have great but as yet unrealized potential to uncover patterns arising from predatory, competitive or other interactions that structure animal communities by detecting species that are active at the same sites and recording their behaviours and times of activity. Here, we use multi-season, two-species occupancy models to test for evidence of interactions between introduced (feral cat Felis catus and native predator (Tasmanian devil Sarcophilus harrisii and predator and small mammal (swamp rat Rattus lutreolus velutinus combinations at baited camera sites in the cool temperate forests of southern Tasmania. In addition, we investigate the capture rates of swamp rats in traps scented with feral cat and devil faecal odours. We observed that one species could reduce the probability of detecting another at a camera site. In particular, feral cats were detected less frequently at camera sites occupied by devils, whereas patterns of swamp rat detection associated with devils or feral cats varied with study site. Captures of swamp rats were not associated with odours on traps, although fewer captures tended to occur in traps scented with the faecal odour of feral cats. The observation that a native carnivorous marsupial, the Tasmanian devil, can suppress the detectability of an introduced eutherian predator, the feral cat, is consistent with a dominant predator-mesopredator relationship. Such a relationship has important implications for the interaction between feral cats and the lower trophic guilds that form their prey, especially if cat activity increases in places where devil populations are declining. More generally, population estimates derived from devices such as remote cameras need to acknowledge the potential for one species to change the detectability of another, and incorporate this in assessments of numbers

  6. Patterns of Detection and Capture Are Associated with Cohabiting Predators and Prey

    Science.gov (United States)

    Lazenby, Billie T.; Dickman, Christopher R.

    2013-01-01

    Avoidance behaviour can play an important role in structuring ecosystems but can be difficult to uncover and quantify. Remote cameras have great but as yet unrealized potential to uncover patterns arising from predatory, competitive or other interactions that structure animal communities by detecting species that are active at the same sites and recording their behaviours and times of activity. Here, we use multi-season, two-species occupancy models to test for evidence of interactions between introduced (feral cat Felis catus) and native predator (Tasmanian devil Sarcophilus harrisii) and predator and small mammal (swamp rat Rattus lutreolus velutinus) combinations at baited camera sites in the cool temperate forests of southern Tasmania. In addition, we investigate the capture rates of swamp rats in traps scented with feral cat and devil faecal odours. We observed that one species could reduce the probability of detecting another at a camera site. In particular, feral cats were detected less frequently at camera sites occupied by devils, whereas patterns of swamp rat detection associated with devils or feral cats varied with study site. Captures of swamp rats were not associated with odours on traps, although fewer captures tended to occur in traps scented with the faecal odour of feral cats. The observation that a native carnivorous marsupial, the Tasmanian devil, can suppress the detectability of an introduced eutherian predator, the feral cat, is consistent with a dominant predator – mesopredator relationship. Such a relationship has important implications for the interaction between feral cats and the lower trophic guilds that form their prey, especially if cat activity increases in places where devil populations are declining. More generally, population estimates derived from devices such as remote cameras need to acknowledge the potential for one species to change the detectability of another, and incorporate this in assessments of numbers and survival

  7. Detection and modelling of time-dependent QTL in animal populations

    DEFF Research Database (Denmark)

    Lund, Mogens S; Sørensen, Peter; Madsen, Per

    2008-01-01

    A longitudinal approach is proposed to map QTL affecting function-valued traits and to estimate their effect over time. The method is based on fitting mixed random regression models. The QTL allelic effects are modelled with random coefficient parametric curves and using a gametic relationship...... matrix. A simulation study was conducted in order to assess the ability of the approach to fit different patterns of QTL over time. It was found that this longitudinal approach was able to adequately fit the simulated variance functions and considerably improved the power of detection of time-varying QTL...... effects compared to the traditional univariate model. This was confirmed by an analysis of protein yield data in dairy cattle, where the model was able to detect QTL with high effect either at the beginning or the end of the lactation, that were not detected with a simple 305 day model....

  8. PatternQuery: web application for fast detection of biomacromolecular structural patterns in the entire Protein Data Bank.

    Science.gov (United States)

    Sehnal, David; Pravda, Lukáš; Svobodová Vařeková, Radka; Ionescu, Crina-Maria; Koča, Jaroslav

    2015-07-01

    Well defined biomacromolecular patterns such as binding sites, catalytic sites, specific protein or nucleic acid sequences, etc. precisely modulate many important biological phenomena. We introduce PatternQuery, a web-based application designed for detection and fast extraction of such patterns. The application uses a unique query language with Python-like syntax to define the patterns that will be extracted from datasets provided by the user, or from the entire Protein Data Bank (PDB). Moreover, the database-wide search can be restricted using a variety of criteria, such as PDB ID, resolution, and organism of origin, to provide only relevant data. The extraction generally takes a few seconds for several hundreds of entries, up to approximately one hour for the whole PDB. The detected patterns are made available for download to enable further processing, as well as presented in a clear tabular and graphical form directly in the browser. The unique design of the language and the provided service could pave the way towards novel PDB-wide analyses, which were either difficult or unfeasible in the past. The application is available free of charge at http://ncbr.muni.cz/PatternQuery. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  9. Detecting Math Anxiety with a Mixture Partial Credit Model

    Science.gov (United States)

    Ölmez, Ibrahim Burak; Cohen, Allan S.

    2017-01-01

    The purpose of this study was to investigate a new methodology for detection of differences in middle grades students' math anxiety. A mixture partial credit model analysis revealed two distinct latent classes based on homogeneities in response patterns within each latent class. Students in Class 1 had less anxiety about apprehension of math…

  10. Generation and Detection of Alignments in Gabor Patterns

    Directory of Open Access Journals (Sweden)

    Samy Blusseau

    2016-11-01

    Full Text Available This paper presents a method to be used in psychophysical experiments to compare directly visual perception to an a contrario algorithm, on a straight patterns detection task. The method is composed of two parts. The first part consists in building a stimulus, namely an array of oriented elements, in which an alignment is present with variable salience. The second part focuses on a detection algorithm, based on the a contrario theory, which is designed to predict which alignment will be considered as the most salient in a given stimulus.

  11. Comparison of detection pattern of HCC by ferumoxide-enhanced MRI and intratumoral blood flow pattern

    International Nuclear Information System (INIS)

    Itou, Naoki; Kotake, Fumio; Saitou, Kazuhiro; Abe, Kimihiko

    2000-01-01

    We compared the detection rate and pattern of ferumoxide-enhanced magnetic resonance imaging (Fe-MRI) with the intratumoral blood flow pattern determined by CT angiography (CTA) and CT portography (CTAP) in 124 nodes (34 cases) diagnosed as hepatocellular carcinoma (HCC) or borderline HCC, based on the clinical course. Sequences to obtain a T1-weighted images (T1W), proton density-weighted images (PDW), T2-weighted images (T2W), T2*-weighted images (T2*W) were used in Fe-MRI. In nodes shown to be hypervascular on CTA, the detection rate by Fe-MRI was 69.7%. In nodes shown to be avascular by CTAP, the detection rate by Fe-MRI was 67.3%. These rates were higher than with other flow patterns. In nodes showing high signal intensity (HSI) on any sequences, arterial blood flow was increased and portal blood flow decreased in comparison with nodes without high signal intensity. All nodes showing HSI, both on Fe-MRI T2W and T2*W, were hypervascular on CTA, and portal blood flow was absent on CTAP. Nodes showing HSI on both T2*W and T2W were considered to have greater arterial blood flow and decreased portal blood flow compared with nodes appearing as HSI on T2*W, but only as iso- or low signal intensity on T2W (Mann-Whitney U-test; p<0.05). (author)

  12. Low contrast detectability for color patterns variation of display images

    International Nuclear Information System (INIS)

    Ogura, Akio

    1998-01-01

    In recent years, the radionuclide images are acquired in digital form and displayed with false colors for signal intensity. This color scales for signal intensity have various patterns. In this study, low contrast detectability was compared the performance of gray scale cording with three color scales: the hot color scale, prism color scale and stripe color scale. SPECT images of brain phantom were displayed using four color patterns. These printed images and display images were evaluated with ROC analysis. Display images were indicated higher detectability than printed images. The hot scale and gray scale images indicated better Az of ROC than prism scale images because the prism scale images showed higher false positive rate. (author)

  13. Detection and Alert of muscle fatigue considering a Surface Electromyography Chaotic Model

    International Nuclear Information System (INIS)

    Herrera, V; Romero, J F; Amestegui, M

    2011-01-01

    This work propose a detection and alert algorithm for muscle fatigue in paraplegic patients undergoing electro-therapy sessions. The procedure is based on a mathematical chaotic model emulating physiological signals and Continuous Wavelet Transform (CWT). The chaotic model developed is based on a logistic map that provides suitable data accomplishing some physiological signal class patterns. The CWT was applied to signals generated by the model and the resulting vector was obtained through Total Wavelet Entropy (TWE). In this sense, the presented work propose a viable and practical alert and detection algorithm for muscle fatigue.

  14. Detection and Alert of muscle fatigue considering a Surface Electromyography Chaotic Model

    Energy Technology Data Exchange (ETDEWEB)

    Herrera, V; Romero, J F [Engineering, Modeling and Applied Social Sciences Center, ABC Federal University, Santo Andr - SP (Brazil); Amestegui, M, E-mail: victoria.herrera@ufabc.edu.br [Engineering Faculty, Electronics Engineering, Universidad Mayor de San Andres, La Paz (Bolivia, Plurinational State of)

    2011-03-01

    This work propose a detection and alert algorithm for muscle fatigue in paraplegic patients undergoing electro-therapy sessions. The procedure is based on a mathematical chaotic model emulating physiological signals and Continuous Wavelet Transform (CWT). The chaotic model developed is based on a logistic map that provides suitable data accomplishing some physiological signal class patterns. The CWT was applied to signals generated by the model and the resulting vector was obtained through Total Wavelet Entropy (TWE). In this sense, the presented work propose a viable and practical alert and detection algorithm for muscle fatigue.

  15. Adaptive hidden Markov model with anomaly States for price manipulation detection.

    Science.gov (United States)

    Cao, Yi; Li, Yuhua; Coleman, Sonya; Belatreche, Ammar; McGinnity, Thomas Martin

    2015-02-01

    Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analyzing and detecting price manipulation in real time are yet to be developed. This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters to price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and 10 simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models.

  16. Intrusion detection in cloud computing based attack patterns and risk assessment

    Directory of Open Access Journals (Sweden)

    Ben Charhi Youssef

    2017-05-01

    Full Text Available This paper is an extension of work originally presented in SYSCO CONF.We extend our previous work by presenting the initial results of the implementation of intrusion detection based on risk assessment on cloud computing. The idea focuses on a novel approach for detecting cyber-attacks on the cloud environment by analyzing attacks pattern using risk assessment methodologies. The aim of our solution is to combine evidences obtained from Intrusion Detection Systems (IDS deployed in a cloud with risk assessment related to each attack pattern. Our approach presents a new qualitative solution for analyzing each symptom, indicator and vulnerability analyzing impact and likelihood of distributed and multi-steps attacks directed to cloud environments. The implementation of this approach will reduce the number of false alerts and will improve the performance of the IDS.

  17. Landscape Pattern Detection in Archaeological Remote Sensing

    Directory of Open Access Journals (Sweden)

    Arianna Traviglia

    2017-12-01

    Full Text Available Automated detection of landscape patterns on Remote Sensing imagery has seen virtually little or no development in the archaeological domain, notwithstanding the fact that large portion of cultural landscapes worldwide are characterized by land engineering applications. The current extraordinary availability of remotely sensed images makes it now urgent to envision and develop automatic methods that can simplify their inspection and the extraction of relevant information from them, as the quantity of information is no longer manageable by traditional “human” visual interpretation. This paper expands on the development of automatic methods for the detection of target landscape features—represented by field system patterns—in very high spatial resolution images, within the framework of an archaeological project focused on the landscape engineering embedded in Roman cadasters. The targets of interest consist of a variety of similarly oriented objects of diverse nature (such as roads, drainage channels, etc. concurring to demark the current landscape organization, which reflects the one imposed by Romans over two millennia ago. The proposed workflow exploits the textural and shape properties of real-world elements forming the field patterns using multiscale analysis of dominant oriented response filters. Trials showed that this approach provides accurate localization of target linear objects and alignments signaled by a wide range of physical entities with very different characteristics.

  18. Workflow Patterns for Business Process Modeling

    NARCIS (Netherlands)

    Thom, Lucineia Heloisa; Lochpe, Cirano; Reichert, M.U.

    For its reuse advantages, workflow patterns (e.g., control flow patterns, data patterns, resource patterns) are increasingly attracting the interest of both researchers and vendors. Frequently, business process or workflow models can be assembeled out of a set of recurrent process fragments (or

  19. Neural network based pattern matching and spike detection tools and services--in the CARMEN neuroinformatics project.

    Science.gov (United States)

    Fletcher, Martyn; Liang, Bojian; Smith, Leslie; Knowles, Alastair; Jackson, Tom; Jessop, Mark; Austin, Jim

    2008-10-01

    In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets.

  20. Pattern detection in stream networks: Quantifying spatialvariability in fish distribution

    Science.gov (United States)

    Torgersen, Christian E.; Gresswell, Robert E.; Bateman, Douglas S.

    2004-01-01

    Biological and physical properties of rivers and streams are inherently difficult to sample and visualize at the resolution and extent necessary to detect fine-scale distributional patterns over large areas. Satellite imagery and broad-scale fish survey methods are effective for quantifying spatial variability in biological and physical variables over a range of scales in marine environments but are often too coarse in resolution to address conservation needs in inland fisheries management. We present methods for sampling and analyzing multiscale, spatially continuous patterns of stream fishes and physical habitat in small- to medium-size watersheds (500–1000 hectares). Geospatial tools, including geographic information system (GIS) software such as ArcInfo dynamic segmentation and ArcScene 3D analyst modules, were used to display complex biological and physical datasets. These tools also provided spatial referencing information (e.g. Cartesian and route-measure coordinates) necessary for conducting geostatistical analyses of spatial patterns (empirical semivariograms and wavelet analysis) in linear stream networks. Graphical depiction of fish distribution along a one-dimensional longitudinal profile and throughout the stream network (superimposed on a 10-metre digital elevation model) provided the spatial context necessary for describing and interpreting the relationship between landscape pattern and the distribution of coastal cutthroat trout (Oncorhynchus clarki clarki) in western Oregon, U.S.A. The distribution of coastal cutthroat trout was highly autocorrelated and exhibited a spherical semivariogram with a defined nugget, sill, and range. Wavelet analysis of the main-stem longitudinal profile revealed periodicity in trout distribution at three nested spatial scales corresponding ostensibly to landscape disturbances and the spacing of tributary junctions.

  1. Simulation of pattern and defect detection in periodic amplitude and phase structures using photorefractive four-wave mixing

    Science.gov (United States)

    Nehmetallah, Georges; Banerjee, Partha; Khoury, Jed

    2015-03-01

    The nonlinearity inherent in four-wave mixing in photorefractive (PR) materials is used for adaptive filtering. Examples include script enhancement on a periodic pattern, scratch and defect cluster enhancement, periodic pattern dislocation enhancement, etc. through intensity filtering image manipulation. Organic PR materials have large space-bandwidth product, which makes them useful in adaptive filtering techniques in quality control systems. For instance, in the case of edge enhancement, phase conjugation via four-wave mixing suppresses the low spatial frequencies of the Fourier spectrum of an aperiodic image and consequently leads to image edge enhancement. In this work, we model, numerically verify, and simulate the performance of a four wave mixing setup used for edge, defect and pattern detection in periodic amplitude and phase structures. The results show that this technique successfully detects the slightest defects clearly even with no enhancement. This technique should facilitate improvements in applications such as image display sharpness utilizing edge enhancement, production line defect inspection of fabrics, textiles, e-beam lithography masks, surface inspection, and materials characterization.

  2. EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

    Science.gov (United States)

    Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

    2017-10-01

    The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Toxicoproteomics: serum proteomic pattern diagnostics for early detection of drug induced cardiac toxicities and cardioprotection.

    Science.gov (United States)

    Petricoin, Emanuel F; Rajapaske, Vinodh; Herman, Eugene H; Arekani, Ali M; Ross, Sally; Johann, Donald; Knapton, Alan; Zhang, J; Hitt, Ben A; Conrads, Thomas P; Veenstra, Timothy D; Liotta, Lance A; Sistare, Frank D

    2004-01-01

    Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry which communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity based processes as cascades of reinforcing information percolate through the system and become reflected in changing proteomic information content of the circulation. Serum Proteomic Pattern Diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. While this approach has shown tremendous promise in early detection of cancers, detection of drug-induced toxicity may also be possible with this same technology. Analysis of serum from rat models of anthracycline and anthracenedione induced cardiotoxicity indicate the potential clinical utility of diagnostic proteomic patterns where low molecular weight peptides and protein fragments may have higher accuracy than traditional biomarkers of cardiotoxicity such as troponins. These fragments may one day be harvested by circulating nanoparticles designed to absorb, enrich and amplify the diagnostic biomarker repertoire generated even at the critical initial stages of toxicity.

  4. Analytical maximum-likelihood method to detect patterns in real networks

    International Nuclear Information System (INIS)

    Squartini, Tiziano; Garlaschelli, Diego

    2011-01-01

    In order to detect patterns in real networks, randomized graph ensembles that preserve only part of the topology of an observed network are systematically used as fundamental null models. However, the generation of them is still problematic. Existing approaches are either computationally demanding and beyond analytic control or analytically accessible but highly approximate. Here, we propose a solution to this long-standing problem by introducing a fast method that allows one to obtain expectation values and standard deviations of any topological property analytically, for any binary, weighted, directed or undirected network. Remarkably, the time required to obtain the expectation value of any property analytically across the entire graph ensemble is as short as that required to compute the same property using the adjacency matrix of the single original network. Our method reveals that the null behavior of various correlation properties is different from what was believed previously, and is highly sensitive to the particular network considered. Moreover, our approach shows that important structural properties (such as the modularity used in community detection problems) are currently based on incorrect expressions, and provides the exact quantities that should replace them.

  5. Modelling biomechanics of bark patterning in grasstrees.

    Science.gov (United States)

    Dale, Holly; Runions, Adam; Hobill, David; Prusinkiewicz, Przemyslaw

    2014-09-01

    Bark patterns are a visually important characteristic of trees, typically attributed to fractures occurring during secondary growth of the trunk and branches. An understanding of bark pattern formation has been hampered by insufficient information regarding the biomechanical properties of bark and the corresponding difficulties in faithfully modelling bark fractures using continuum mechanics. This study focuses on the genus Xanthorrhoea (grasstrees), which have an unusual bark-like structure composed of distinct leaf bases connected by sticky resin. Due to its discrete character, this structure is well suited for computational studies. A dynamic computational model of grasstree development was created. The model captures both the phyllotactic pattern of leaf bases during primary growth and the changes in the trunk's width during secondary growth. A biomechanical representation based on a system of masses connected by springs is used for the surface of the trunk, permitting the emergence of fractures during secondary growth to be simulated. The resulting fracture patterns were analysed statistically and compared with images of real trees. The model reproduces key features of grasstree bark patterns, including their variability, spanning elongated and reticulate forms. The patterns produced by the model have the same statistical character as those seen in real trees. The model was able to support the general hypothesis that the patterns observed in the grasstree bark-like layer may be explained in terms of mechanical fractures driven by secondary growth. Although the generality of the results is limited by the unusual structure of grasstree bark, it supports the hypothesis that bark pattern formation is primarily a biomechanical phenomenon.

  6. Application of pattern recognition techniques to the detection of the Phenix reactor control rods vibrations

    International Nuclear Information System (INIS)

    Zwingelstein, G.; Deat, M.; Le Guillou, G.

    1979-01-01

    The incipient detection of control rods vibrations is very important for the safety of the operating plants. This detection can be achieved by an analysis of the peaks of the power spectrum density of the neutron noise. Pattern Recognition techniques were applied to detect the rod vibrations which occured at the fast breeder Phenix (250MWe). In the first part we give a description of the basic pattern which is used to characterize the behavior of the plant. The pattern is considered as column vector in n dimensional Euclidian space where the components are the samples of the power spectral density of the neutron noise. In the second part, a recursive learning procedure of the normal patterns which provides the mean and the variance of the estimates is described. In the third part the classification problem has been framed in terms of a partitioning procedure in n dimensional space which encloses regions corresponding to normal operations. This pattern recognition scheme was applied to the detection of rod vibrations with neutron data collected at the Phenix site before and after occurence of the vibrations. The analysis was carried out with a 42-dimensional measurement space. The learned pattern was estimated with 150 measurement vectors which correspond to the period without vibrations. The efficiency of the surveillance scheme is then demonstrated by processing separately 119 measurement vectors recorded during the rod vibration period

  7. Bilge dump detection from SAR imagery using local binary patterns

    CSIR Research Space (South Africa)

    Mdakane, LW

    2015-07-01

    Full Text Available 2015: Remote Sensing: Understanding the Earth for a Safer World, Milan, Italy, 26-31 July 2015 Bilge dump detection from SAR imagery using local binary patterns yz L.W. Mdakane,yz W. Kleynhans,yz C.P. Schwegmann yDepartment of Electrical...

  8. T-Pattern Analysis and Cognitive Load Manipulation to Detect Low-Stake Lies: An Exploratory Study.

    Science.gov (United States)

    Diana, Barbara; Zurloni, Valentino; Elia, Massimiliano; Cavalera, Cesare; Realdon, Olivia; Jonsson, Gudberg K; Anguera, M Teresa

    2018-01-01

    Deception has evolved to become a fundamental aspect of human interaction. Despite the prolonged efforts in many disciplines, there has been no definite finding of a univocally "deceptive" signal. This work proposes an approach to deception detection combining cognitive load manipulation and T-pattern methodology with the objective of: (a) testing the efficacy of dual task-procedure in enhancing differences between truth tellers and liars in a low-stakes situation; (b) exploring the efficacy of T-pattern methodology in discriminating truthful reports from deceitful ones in a low-stakes situation; (c) setting the experimental design and procedure for following research. We manipulated cognitive load to enhance differences between truth tellers and liars, because of the low-stakes lies involved in our experiment. We conducted an experimental study with a convenience sample of 40 students. We carried out a first analysis on the behaviors' frequencies coded through the observation software, using SPSS (22). The aim was to describe shape and characteristics of behavior's distributions and explore differences between groups. Datasets were then analyzed with Theme 6.0 software which detects repeated patterns (T-patterns) of coded events (non-verbal behaviors) that regularly or irregularly occur within a period of observation. A descriptive analysis on T-pattern frequencies was carried out to explore differences between groups. An in-depth analysis on more complex patterns was performed to get qualitative information on the behavior structure expressed by the participants. Results show that the dual-task procedure enhances differences observed between liars and truth tellers with T-pattern methodology; moreover, T-pattern detection reveals a higher variety and complexity of behavior in truth tellers than in liars. These findings support the combination of cognitive load manipulation and T-pattern methodology for deception detection in low-stakes situations, suggesting the

  9. T-Pattern Analysis and Cognitive Load Manipulation to Detect Low-Stake Lies: An Exploratory Study

    Directory of Open Access Journals (Sweden)

    Barbara Diana

    2018-03-01

    Full Text Available Deception has evolved to become a fundamental aspect of human interaction. Despite the prolonged efforts in many disciplines, there has been no definite finding of a univocally “deceptive” signal. This work proposes an approach to deception detection combining cognitive load manipulation and T-pattern methodology with the objective of: (a testing the efficacy of dual task-procedure in enhancing differences between truth tellers and liars in a low-stakes situation; (b exploring the efficacy of T-pattern methodology in discriminating truthful reports from deceitful ones in a low-stakes situation; (c setting the experimental design and procedure for following research. We manipulated cognitive load to enhance differences between truth tellers and liars, because of the low-stakes lies involved in our experiment. We conducted an experimental study with a convenience sample of 40 students. We carried out a first analysis on the behaviors’ frequencies coded through the observation software, using SPSS (22. The aim was to describe shape and characteristics of behavior’s distributions and explore differences between groups. Datasets were then analyzed with Theme 6.0 software which detects repeated patterns (T-patterns of coded events (non-verbal behaviors that regularly or irregularly occur within a period of observation. A descriptive analysis on T-pattern frequencies was carried out to explore differences between groups. An in-depth analysis on more complex patterns was performed to get qualitative information on the behavior structure expressed by the participants. Results show that the dual-task procedure enhances differences observed between liars and truth tellers with T-pattern methodology; moreover, T-pattern detection reveals a higher variety and complexity of behavior in truth tellers than in liars. These findings support the combination of cognitive load manipulation and T-pattern methodology for deception detection in low

  10. Chopper model of pattern recognition

    NARCIS (Netherlands)

    van Hemmen, J.L.; Enter, A.C.D. van

    A simple model is proposed that allows an efficient storage and retrieval of random patterns. Also correlated patterns can be handled. The data are stored in an Ising-spin system with ferromagnetic interactions between all the spins and the main idea is to "chop" the system along the boundaries

  11. Mouse V1 population correlates of visual detection rely on heterogeneity within neuronal response patterns

    Science.gov (United States)

    Montijn, Jorrit S; Goltstein, Pieter M; Pennartz, Cyriel MA

    2015-01-01

    Previous studies have demonstrated the importance of the primary sensory cortex for the detection, discrimination, and awareness of visual stimuli, but it is unknown how neuronal populations in this area process detected and undetected stimuli differently. Critical differences may reside in the mean strength of responses to visual stimuli, as reflected in bulk signals detectable in functional magnetic resonance imaging, electro-encephalogram, or magnetoencephalography studies, or may be more subtly composed of differentiated activity of individual sensory neurons. Quantifying single-cell Ca2+ responses to visual stimuli recorded with in vivo two-photon imaging, we found that visual detection correlates more strongly with population response heterogeneity rather than overall response strength. Moreover, neuronal populations showed consistencies in activation patterns across temporally spaced trials in association with hit responses, but not during nondetections. Contrary to models relying on temporally stable networks or bulk signaling, these results suggest that detection depends on transient differentiation in neuronal activity within cortical populations. DOI: http://dx.doi.org/10.7554/eLife.10163.001 PMID:26646184

  12. Fuzzy Pattern Classification Based Detection of Faulty Electronic Fuel Control (EFC Valves Used in Diesel Engines

    Directory of Open Access Journals (Sweden)

    Umut Tugsal

    2014-05-01

    Full Text Available In this paper, we develop mathematical models of a rotary Electronic Fuel Control (EFC valve used in a Diesel engine based on dynamic performance test data and system identification methodology in order to detect the faulty EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. A recursive least squares (RLS type system identification methodology has been utilized to determine the transfer functions of the different types of EFC valves that were investigated in this study. Both in frequency domain and time domain methods have been utilized for this purpose. Based on the characteristic patterns exhibited by the EFC valves, a fuzzy logic based pattern classification method was utilized to evaluate the residuals and identify faulty EFC valves from good ones. The developed methodology has been shown to provide robust diagnostics for a wide range of EFC valves.

  13. EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures

    NARCIS (Netherlands)

    Wang, Lei; Long, Xi; Arends, J.B.A.M.; Aarts, R.M.

    2017-01-01

    Background The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. New method A single-channel

  14. Complex networks from experimental horizontal oil–water flows: Community structure detection versus flow pattern discrimination

    International Nuclear Information System (INIS)

    Gao, Zhong-Ke; Fang, Peng-Cheng; Ding, Mei-Shuang; Yang, Dan; Jin, Ning-De

    2015-01-01

    We propose a complex network-based method to distinguish complex patterns arising from experimental horizontal oil–water two-phase flow. We first use the adaptive optimal kernel time–frequency representation (AOK TFR) to characterize flow pattern behaviors from the energy and frequency point of view. Then, we infer two-phase flow complex networks from experimental measurements and detect the community structures associated with flow patterns. The results suggest that the community detection in two-phase flow complex network allows objectively discriminating complex horizontal oil–water flow patterns, especially for the segregated and dispersed flow patterns, a task that existing method based on AOK TFR fails to work. - Highlights: • We combine time–frequency analysis and complex network to identify flow patterns. • We explore the transitional flow behaviors in terms of betweenness centrality. • Our analysis provides a novel way for recognizing complex flow patterns. • Broader applicability of our method is demonstrated and articulated

  15. Control and near-field detection of surface plasmon interference patterns.

    Science.gov (United States)

    Dvořák, Petr; Neuman, Tomáš; Břínek, Lukáš; Šamořil, Tomáš; Kalousek, Radek; Dub, Petr; Varga, Peter; Šikola, Tomáš

    2013-06-12

    The tailoring of electromagnetic near-field properties is the central task in the field of nanophotonics. In addition to 2D optics for optical nanocircuits, confined and enhanced electric fields are utilized in detection and sensing, photovoltaics, spatially localized spectroscopy (nanoimaging), as well as in nanolithography and nanomanipulation. For practical purposes, it is necessary to develop easy-to-use methods for controlling the electromagnetic near-field distribution. By imaging optical near-fields using a scanning near-field optical microscope, we demonstrate that surface plasmon polaritons propagating from slits along the metal-dielectric interface form tunable interference patterns. We present a simple way how to control the resulting interference patterns both by variation of the angle between two slits and, for a fixed slit geometry, by a proper combination of laser beam polarization and inhomogeneous far-field illumination of the structure. Thus the modulation period of interference patterns has become adjustable and new variable patterns consisting of stripelike and dotlike motifs have been achieved, respectively.

  16. Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

    Science.gov (United States)

    Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

    2017-09-01

    With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

  17. A travel time forecasting model based on change-point detection method

    Science.gov (United States)

    LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei

    2017-06-01

    Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.

  18. Modelling ground deformation patterns associated with volcanic processes at the Okataina Volcanic Centre

    Science.gov (United States)

    Holden, L.; Cas, R.; Fournier, N.; Ailleres, L.

    2017-09-01

    The Okataina Volcanic Centre (OVC) is one of two large active rhyolite centres in the modern Taupo Volcanic Zone (TVZ) in the North Island of New Zealand. It is located in a complex section of the Taupo rift, a tectonically active section of the TVZ. The most recent volcanic unrest at the OVC includes the 1315 CE Kaharoa and 1886 Tarawera eruptions. Current monitoring activity at the OVC includes the use of continuous GPS receivers (cGPS), lake levelling and seismographs. The ground deformation patterns preceding volcanic activity the OVC are poorly constrained and restricted to predictions from basic modelling and comparison to other volcanoes worldwide. A better understanding of the deformation patterns preceding renewed volcanic activity is essential to determine if observed deformation is related to volcanic, tectonic or hydrothermal processes. Such an understanding also means that the ability of the present day cGPS network to detect these deformation patterns can also be assessed. The research presented here uses the finite element (FE) modelling technique to investigate ground deformation patterns associated with magma accumulation and diking processes at the OVC in greater detail. A number of FE models are produced and tested using Pylith software and incorporate characteristics of the 1315 CE Kaharoa and 1886 Tarawera eruptions, summarised from the existing body of research literature. The influence of a simple ring fault structure at the OVC on the modelled deformation is evaluated. The ability of the present-day continuous GPS (cGPS) GeoNet monitoring network to detect or observe the modelled deformation is also considered. The results show the modelled horizontal and vertical displacement fields have a number of key features, which include prominent lobe based regions extending northwest and southeast of the OVC. The results also show that the ring fault structure increases the magnitude of the displacements inside the caldera, in particular in the

  19. A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

    Science.gov (United States)

    de Oliveira, Helder C. R.; Moraes, Diego R.; Reche, Gustavo A.; Borges, Lucas R.; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

    2017-03-01

    This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).

  20. Pattern formation of a nonlocal, anisotropic interaction model

    KAUST Repository

    Burger, Martin

    2017-11-24

    We consider a class of interacting particle models with anisotropic, repulsive–attractive interaction forces whose orientations depend on an underlying tensor field. An example of this class of models is the so-called Kücken–Champod model describing the formation of fingerprint patterns. This class of models can be regarded as a generalization of a gradient flow of a nonlocal interaction potential which has a local repulsion and a long-range attraction structure. In contrast to isotropic interaction models the anisotropic forces in our class of models cannot be derived from a potential. The underlying tensor field introduces an anisotropy leading to complex patterns which do not occur in isotropic models. This anisotropy is characterized by one parameter in the model. We study the variation of this parameter, describing the transition between the isotropic and the anisotropic model, analytically and numerically. We analyze the equilibria of the corresponding mean-field partial differential equation and investigate pattern formation numerically in two dimensions by studying the dependence of the parameters in the model on the resulting patterns.

  1. Pattern formation of a nonlocal, anisotropic interaction model

    KAUST Repository

    Burger, Martin; Dü ring, Bertram; Kreusser, Lisa Maria; Markowich, Peter A.; Schö nlieb, Carola-Bibiane

    2017-01-01

    We consider a class of interacting particle models with anisotropic, repulsive–attractive interaction forces whose orientations depend on an underlying tensor field. An example of this class of models is the so-called Kücken–Champod model describing the formation of fingerprint patterns. This class of models can be regarded as a generalization of a gradient flow of a nonlocal interaction potential which has a local repulsion and a long-range attraction structure. In contrast to isotropic interaction models the anisotropic forces in our class of models cannot be derived from a potential. The underlying tensor field introduces an anisotropy leading to complex patterns which do not occur in isotropic models. This anisotropy is characterized by one parameter in the model. We study the variation of this parameter, describing the transition between the isotropic and the anisotropic model, analytically and numerically. We analyze the equilibria of the corresponding mean-field partial differential equation and investigate pattern formation numerically in two dimensions by studying the dependence of the parameters in the model on the resulting patterns.

  2. RFID-Based Human Behavior Modeling and Anomaly Detection for Elderly Care

    Directory of Open Access Journals (Sweden)

    Hui-Huang Hsu

    2010-01-01

    Full Text Available This research aimed at building an intelligent system that can detect abnormal behavior for the elderly at home. Active RFID tags can be deployed at home to help collect daily movement data of the elderly who carries an RFID reader. When the reader detects the signals from the tags, RSSI values that represent signal strength are obtained. The RSSI values are reversely related to the distance between the tags and the reader and they are recorded following the movement of the user. The movement patterns, not the exact locations, of the user are the major concern. With the movement data (RSSI values, the clustering technique is then used to build a personalized model of normal behavior. After the model is built, any incoming datum outside the model can be viewed as abnormal and an alarm can be raised by the system. In this paper, we present the system architecture for RFID data collection and preprocessing, clustering for anomaly detection, and experimental results. The results show that this novel approach is promising.

  3. Fluid pipeline system leak detection based on neural network and pattern recognition

    International Nuclear Information System (INIS)

    Tang Xiujia

    1998-01-01

    The mechanism of the stress wave propagation along the pipeline system of NPP, caused by turbulent ejection from pipeline leakage, is researched. A series of characteristic index are described in time domain or frequency domain, and compress numerical algorithm is developed for original data compression. A back propagation neural networks (BPNN) with the input matrix composed by stress wave characteristics in time domain or frequency domain is first proposed to classify various situations of the pipeline, in order to detect the leakage in the fluid flow pipelines. The capability of the new method had been demonstrated by experiments and finally used to design a handy instrument for the pipeline leakage detection. Usually a pipeline system has many inner branches and often in adjusting dynamic condition, it is difficult for traditional pipeline diagnosis facilities to identify the difference between inner pipeline operation and pipeline fault. The author first proposed pipeline wave propagation identification by pattern recognition to diagnose pipeline leak. A series of pattern primitives such as peaks, valleys, horizon lines, capstan peaks, dominant relations, slave relations, etc., are used to extract features of the negative pressure wave form. The context-free grammar of symbolic representation of the negative wave form is used, and a negative wave form parsing system with application to structural pattern recognition based on the representation is first proposed to detect and localize leaks of the fluid pipelines

  4. RT-PCR detection of Candida albicans ALS gene expression in the reconstituted human epithelium (RHE) model of oral candidiasis and in model biofilms.

    Science.gov (United States)

    Green, Clayton B; Cheng, Georgina; Chandra, Jyotsna; Mukherjee, Pranab; Ghannoum, Mahmoud A; Hoyer, Lois L

    2004-02-01

    An RT-PCR assay was developed to analyse expression patterns of genes in the Candida albicans ALS (agglutinin-like sequence) family. Inoculation of a reconstituted human buccal epithelium (RHE) model of mucocutaneous candidiasis with strain SC5314 showed destruction of the epithelial layer by C. albicans and also formation of an upper fungal layer that had characteristics similar to a biofilm. RT-PCR analysis of total RNA samples extracted from C. albicans-inoculated buccal RHE showed that ALS1, ALS2, ALS3, ALS4, ALS5 and ALS9 were consistently detected over time as destruction of the RHE progressed. Detection of transcripts from ALS7, and particularly from ALS6, was more sporadic, but not associated with a strictly temporal pattern. The expression pattern of ALS genes in C. albicans cultures used to inoculate the RHE was similar to that observed in the RHE model, suggesting that contact of C. albicans with buccal RHE does little to alter ALS gene expression. RT-PCR analysis of RNA samples extracted from model denture and catheter biofilms showed similar gene expression patterns to the buccal RHE specimens. Results from the RT-PCR analysis of biofilm RNA specimens were consistent between various C. albicans strains during biofilm development and were comparable to gene expression patterns in planktonic cells. The RT-PCR assay described here will be useful for analysis of human clinical specimens and samples from other disease models. The method will provide further insight into the role of ALS genes and their encoded proteins in the diverse interactions between C. albicans and its host.

  5. Generative models versus underlying symmetries to explain biological pattern.

    Science.gov (United States)

    Frank, S A

    2014-06-01

    Mathematical models play an increasingly important role in the interpretation of biological experiments. Studies often present a model that generates the observations, connecting hypothesized process to an observed pattern. Such generative models confirm the plausibility of an explanation and make testable hypotheses for further experiments. However, studies rarely consider the broad family of alternative models that match the same observed pattern. The symmetries that define the broad class of matching models are in fact the only aspects of information truly revealed by observed pattern. Commonly observed patterns derive from simple underlying symmetries. This article illustrates the problem by showing the symmetry associated with the observed rate of increase in fitness in a constant environment. That underlying symmetry reveals how each particular generative model defines a single example within the broad class of matching models. Further progress on the relation between pattern and process requires deeper consideration of the underlying symmetries. © 2014 The Author. Journal of Evolutionary Biology © 2014 European Society For Evolutionary Biology.

  6. Motor skills related to body movement and dance. t-patterns detection Habilidades motrices en expresión corporal y danza. Detección de t-patterns

    Directory of Open Access Journals (Sweden)

    M. Dinušová

    2010-09-01

    Full Text Available

    In the learning processes that promotes the generation of motor actions, teachers usually propose instructions based on kinetic models. The aim of this study is to observe what type of motor answers the subjects generate from kinesic models offered by the teachers. The motor answers to observe refer to the patterns of motor skills of stability, locomotion and manipulation, variations of body-space, time and interaction between participants. 12 Phsysical Activity and Sports Science students without experience in dance participated in the study. 8 sessions were observed focused on space, time, energy and body contact. A specific instrument was created, the observational system of motor skills OSMOS (Castañer, Torrents, Anguera y Dinusôva, 2008,. It was codified with ThemeCoder  (Pattern Vision, 2001 and SDIS-GSEQ   (Bakeman &  Quera, 1996 and THEME (Magnusson, 2000 were used for the reliability and detection of T-patterns .
    Key Words:  Motor Skills, Field Format, Body expression and dance, Motor T-Patterns detection, Kinesic's Model.

    En todo proceso de enseñanza-aprendizaje que promueva la generación de acciones motrices, la elección de los modelos que usan los docentes es una decisión pedagógica de mayor importancia de la que se le suele otorgar.  El objetivo de la investigación es el de observar y constatar qué tipo de respuestas motrices generan los discentes a partir de los modelos de tipo cinésico ofrecidos por los docentes. Las respuestas motrices a observar se refieren a los patrones de habilidades motrices, las variaciones de cuerpo-espacio y tiempo así como de interacción entre los participantes. Han participado en el estudio 12 estudiantes de primer ciclo en Ciencias de la Actividad Física y el deporte con alto bagaje deportivo pero sin experiencia en danza y expresión corporal (EC. Se observaron 8 sesiones

  7. Grid-texture mechanisms in human vision: Contrast detection of regular sparse micro-patterns requires specialist templates.

    Science.gov (United States)

    Baker, Daniel H; Meese, Tim S

    2016-07-27

    Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50-100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.

  8. Blood oxygen level dependent magnetic resonance imaging for detecting pathological patterns in lupus nephritis patients: a preliminary study using a decision tree model.

    Science.gov (United States)

    Shi, Huilan; Jia, Junya; Li, Dong; Wei, Li; Shang, Wenya; Zheng, Zhenfeng

    2018-02-09

    Precise renal histopathological diagnosis will guide therapy strategy in patients with lupus nephritis. Blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) has been applicable noninvasive technique in renal disease. This current study was performed to explore whether BOLD MRI could contribute to diagnose renal pathological pattern. Adult patients with lupus nephritis renal pathological diagnosis were recruited for this study. Renal biopsy tissues were assessed based on the lupus nephritis ISN/RPS 2003 classification. The Blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) was used to obtain functional magnetic resonance parameter, R2* values. Several functions of R2* values were calculated and used to construct algorithmic models for renal pathological patterns. In addition, the algorithmic models were compared as to their diagnostic capability. Both Histopathology and BOLD MRI were used to examine a total of twelve patients. Renal pathological patterns included five classes III (including 3 as class III + V) and seven classes IV (including 4 as class IV + V). Three algorithmic models, including decision tree, line discriminant, and logistic regression, were constructed to distinguish the renal pathological pattern of class III and class IV. The sensitivity of the decision tree model was better than that of the line discriminant model (71.87% vs 59.48%, P decision tree model was equivalent to that of the line discriminant model (63.87% vs 63.73%, P = 0.939) and higher than that of the logistic regression model (63.87% vs 38.0%, P decision tree model was greater than that of the line discriminant model (0.765 vs 0.629, P Decision tree models constructed using functions of R2* values may facilitate the prediction of renal pathological patterns.

  9. A coupled classification - evolutionary optimization model for contamination event detection in water distribution systems.

    Science.gov (United States)

    Oliker, Nurit; Ostfeld, Avi

    2014-03-15

    This study describes a decision support system, alerts for contamination events in water distribution systems. The developed model comprises a weighted support vector machine (SVM) for the detection of outliers, and a following sequence analysis for the classification of contamination events. The contribution of this study is an improvement of contamination events detection ability and a multi-dimensional analysis of the data, differing from the parallel one-dimensional analysis conducted so far. The multivariate analysis examines the relationships between water quality parameters and detects changes in their mutual patterns. The weights of the SVM model accomplish two goals: blurring the difference between sizes of the two classes' data sets (as there are much more normal/regular than event time measurements), and adhering the time factor attribute by a time decay coefficient, ascribing higher importance to recent observations when classifying a time step measurement. All model parameters were determined by data driven optimization so the calibration of the model was completely autonomic. The model was trained and tested on a real water distribution system (WDS) data set with randomly simulated events superimposed on the original measurements. The model is prominent in its ability to detect events that were only partly expressed in the data (i.e., affecting only some of the measured parameters). The model showed high accuracy and better detection ability as compared to previous modeling attempts of contamination event detection. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG.

    Science.gov (United States)

    Roy, Rinku; Sikdar, Debdeep; Mahadevappa, Manjunatha; Kumar, C S

    2018-05-19

    A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with "one against one" multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future. The model to virtualise motor imagery based fingertip force prediction with inherent slippage correction for different grasp types ᅟ.

  11. Graffiti for science: Qualitative detection of erosional patterns through bedrock erosion painting

    Science.gov (United States)

    Beer, Alexander R.; Kirchner, James W.; Turowski, Jens M.

    2016-04-01

    Bedrock erosion is a crucial constraint on stream channel incision, and hence whole landscape evolution, in steep mountainous terrain and tectonically active regions. Several interacting processes lead to bedrock erosion in stream channels, with hydraulic shear detachment, plucking, and abrasion due to sediment impacts generally being the most efficient. Bedrock topography, together with the sediment tools and cover effects, regulate the rate and spatial pattern of in situ surface change. Measurements of natural bedrock erosion rates are valuable for understanding the underlying process physics, as well as for modelling landscape evolution and designing engineered structures. However, quantifying spatially distributed bedrock erosion rates in natural settings is challenging and few such measurements exist. We studied spatial bedrock erosion in a 30m-long bedrock gorge in the Gornera, a glacial meltwater stream above Zermatt. This stream is flushed episodically with sediment-laden streamflow due to hydropower operations upstream, with negligible discharge in the gorge in between these flushing events. We coated several bedrock surface patches with environmentally safe, and water-insoluble outdoor paint to document the spatial pattern of surface abrasion, or to be more precise, to document its driving forces. During four consecutive years, the change of the painted areas was recorded repeatedly with photographs before the painting was renewed. These photographs visually documented the spatial patterns of vertical erosion (channel incision), of lateral erosion (channel widening) and of downstream-directed erosion (channel clearance). The observed qualitative patterns were verified through comparison to quantitative change detection analyses based on annual high-resolution terrestrial laser scanning surveys of the bedrock surfaces. Comparison of repeated photographs indicated a temporal cover effect and a general height limit of the tools effect above the streambed

  12. Model-based fault detection and isolation of a PWR nuclear power plant using neural networks

    International Nuclear Information System (INIS)

    Far, R.R.; Davilu, H.; Lucas, C.

    2008-01-01

    The proper and timely fault detection and isolation of industrial plant is of premier importance to guarantee the safe and reliable operation of industrial plants. The paper presents application of a neural networks-based scheme for fault detection and isolation, for the pressurizer of a PWR nuclear power plant. The scheme is constituted by 2 components: residual generation and fault isolation. The first component generates residuals via the discrepancy between measurements coming from the plant and a nominal model. The neutral network estimator is trained with healthy data collected from a full-scale simulator. For the second component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. These patterns are stored in an associative memory based on a recurrent neutral network. The proposed fault diagnosis tool is evaluated on-line via a full-scale simulator detected and isolate the main faults appearing in the pressurizer of a PWR. (orig.)

  13. Information geometric analysis of phase transitions in complex patterns: the case of the Gray-Scott reaction–diffusion model

    International Nuclear Information System (INIS)

    Har-Shemesh, Omri; Quax, Rick; Hoekstra, Alfons G; Sloot, Peter M A

    2016-01-01

    The Fisher–Rao metric from information geometry is related to phase transition phenomena in classical statistical mechanics. Several studies propose to extend the use of information geometry to study more general phase transitions in complex systems. However, it is unclear whether the Fisher–Rao metric does indeed detect these more general transitions, especially in the absence of a statistical model. In this paper we study the transitions between patterns in the Gray-Scott reaction–diffusion model using Fisher information. We describe the system by a probability density function that represents the size distribution of blobs in the patterns and compute its Fisher information with respect to changing the two rate parameters of the underlying model. We estimate the distribution non-parametrically so that we do not assume any statistical model. The resulting Fisher map can be interpreted as a phase-map of the different patterns. Lines with high Fisher information can be considered as boundaries between regions of parameter space where patterns with similar characteristics appear. These lines of high Fisher information can be interpreted as phase transitions between complex patterns. (paper: disordered systems, classical and quantum)

  14. Emergent patterns of social affiliation in primates, a model.

    Directory of Open Access Journals (Sweden)

    Ivan Puga-Gonzalez

    2009-12-01

    Full Text Available Many patterns of affiliative behaviour have been described for primates, for instance: reciprocation and exchange of grooming, grooming others of similar rank, reconciliation of fights, and preferential reconciliation with more valuable partners. For these patterns several functions and underlying cognitive processes have been suggested. It is, however, difficult to imagine how animals may combine these diverse considerations in their mind. Although the co-variation hypothesis, by limiting the social possibilities an individual has, constrains the number of cognitive considerations an individual has to take, it does not present an integrated theory of affiliative patterns either. In the present paper, after surveying patterns of affiliation in egalitarian and despotic macaques, we use an individual-based model with a high potential for self-organisation as a starting point for such an integrative approach. In our model, called GrooFiWorld, individuals group and, upon meeting each other, may perform a dominance interaction of which the outcomes of winning and losing are self-reinforcing. Besides, if individuals think they will be defeated, they consider grooming others. Here, the greater their anxiety is, the greater their "motivation" to groom others. Our model generates patterns similar to many affiliative patterns of empirical data. By merely increasing the intensity of aggression, affiliative patterns in the model change from those resembling egalitarian macaques to those resembling despotic ones. Our model produces such patterns without assuming in the mind of the individual the specific cognitive processes that are usually thought to underlie these patterns (such as recordkeeping of the acts given and received, a tendency to exchange, memory of the former fight, selective attraction to the former opponent, and estimation of the value of a relationship. Our model can be used as a null model to increase our understanding of affiliative

  15. Detection of Group B Streptococcus in Brazilian pregnant women and antimicrobial susceptibility patterns

    Directory of Open Access Journals (Sweden)

    Didier Silveira Castellano-Filho

    2010-12-01

    Full Text Available Group B Streptococcus (GBS is still not routinely screened during pregnancy in Brazil, being prophylaxis and empirical treatment based on identification of risk groups. This study aimed to investigate GBS prevalence in Brazilian pregnant women by culture or polymerase chain reaction (PCR associated to the enrichment culture, and to determine the antimicrobial susceptibility patterns of isolated bacteria, so as to support public health policies and empirical prophylaxis. After an epidemiological survey, vaginal and anorectal specimens were collected from 221 consenting laboring women. Each sample was submitted to enrichment culture and sheep blood agar was used to isolate suggestive GBS. Alternatively, specific PCR was performed from enrichment cultures. Antimicrobial susceptibility patterns were determined for isolated bacteria by agar diffusion method. No risk groups were identified. Considering the culture-based methodology, GBS was detected in 9.5% of the donors. Twenty five bacterial strains were isolated and identified. Through the culture-PCR methodology, GBS was detected in 32.6% specimens. Bacterial resistance was not detected against ampicillin, cephazolin, vancomycin and ciprofloxacin, whereas 22.7% were resistant to erythromycin and 50% were resistant to clindamycin. GBS detection may be improved by the association of PCR and enrichment culture. Considering that colony selection in agar plates may be laboring and technician-dependent, it may not reflect the real prevalence of streptococci. As in Brazil prevention strategies to reduce the GBS associated diseases have not been adopted, prospective studies are needed to anchor public health policies especially considering the regional GBS antimicrobial susceptibility patterns.

  16. Vision-Based Bicycle Detection Using Multiscale Block Local Binary Pattern

    Directory of Open Access Journals (Sweden)

    Hongyu Hu

    2014-01-01

    Full Text Available Bicycle traffic has heavy proportion among all travel modes in some developing countries, which is crucial for urban traffic control and management as well as facility design. This paper proposes a real-time multiple bicycle detection algorithm based on video. At first, an effective feature called multiscale block local binary pattern (MBLBP is extracted for representing the moving object, which is a well-classified feature to distinguish between bicycles and nonbicycles; then, a cascaded bicycle classifier trained by AdaBoost algorithm is proposed, which has a good computation efficiency. Finally, the method is tested with video sequence captured from the real-world traffic scenario. The bicycles in the test scenario are successfully detected.

  17. Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

    Science.gov (United States)

    Ren, W. X.; Lin, Y. Q.; Fang, S. E.

    2011-11-01

    One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

  18. Ngram-derived pattern recognition for the detection and prediction of epileptic seizures.

    Directory of Open Access Journals (Sweden)

    Amir Eftekhar

    Full Text Available This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70-100% and low false predictions (dependant on training procedure. The cases of highest false predictions are found in the frontal origin with 0.31-0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40-50% for a false prediction rate of less than 0.15/hour.

  19. An evidence accumulation model for conflict detection performance in a simulated air traffic control task.

    Science.gov (United States)

    Neal, Andrew; Kwantes, Peter J

    2009-04-01

    The aim of this article is to develop a formal model of conflict detection performance. Our model assumes that participants iteratively sample evidence regarding the state of the world and accumulate it over time. A decision is made when the evidence reaches a threshold that changes over time in response to the increasing urgency of the task. Two experiments were conducted to examine the effects of conflict geometry and timing on response proportions and response time. The model is able to predict the observed pattern of response times, including a nonmonotonic relationship between distance at point of closest approach and response time, as well as effects of angle of approach and relative velocity. The results demonstrate that evidence accumulation models provide a good account of performance on a conflict detection task. Evidence accumulation models are a form of dynamic signal detection theory, allowing for the analysis of response times as well as response proportions, and can be used for simulating human performance on dynamic decision tasks.

  20. Detecting Rumors Through Modeling Information Propagation Networks in a Social Media Environment.

    Science.gov (United States)

    Liu, Yang; Xu, Songhua; Tourassi, Georgia

    2015-01-01

    In the midst of today's pervasive influence of social media content and activities, information credibility has increasingly become a major issue. Accordingly, identifying false information, e.g. rumors circulated in social media environments, attracts expanding research attention and growing interests. Many previous studies have exploited user-independent features for rumor detection. These prior investigations uniformly treat all users relevant to the propagation of a social media message as instances of a generic entity. Such a modeling approach usually adopts a homogeneous network to represent all users, the practice of which ignores the variety across an entire user population in a social media environment. Recognizing this limitation of modeling methodologies, this study explores user-specific features in a social media environment for rumor detection. The new approach hypothesizes that whether a user tends to spread a rumor is dependent upon specific attributes of the user in addition to content characteristics of the message itself. Under this hypothesis, information propagation patterns of rumors versus those of credible messages in a social media environment are systematically differentiable. To explore and exploit this hypothesis, we develop a new information propagation model based on a heterogeneous user representation for rumor recognition. The new approach is capable of differentiating rumors from credible messages through observing distinctions in their respective propagation patterns in social media. Experimental results show that the new information propagation model based on heterogeneous user representation can effectively distinguish rumors from credible social media content.

  1. Partitioning detectability components in populations subject to within-season temporary emigration using binomial mixture models.

    Science.gov (United States)

    O'Donnell, Katherine M; Thompson, Frank R; Semlitsch, Raymond D

    2015-01-01

    Detectability of individual animals is highly variable and nearly always binomial mixture models to account for multiple sources of variation in detectability. The state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. We illustrate our model's potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders Plethodon serratus. We fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3-5 surveys each spring and fall 2010-2012. Our models generated similar parameter estimates to standard binomial mixture models. Aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling), while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling). By explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. We stress the importance of choosing survey locations and protocols that maximize species availability and conditional detection probability to increase population parameter estimate reliability.

  2. Turing patterns in a modified Lotka-Volterra model

    International Nuclear Information System (INIS)

    McGehee, Edward A.; Peacock-Lopez, Enrique

    2005-01-01

    In this Letter we consider a modified Lotka-Volterra model widely known as the Bazykin model, which is the MacArthur-Rosenzweig (MR) model that includes a prey-dependent response function and is modified with the inclusion of intraspecies interactions. We show that a quadratic intra-prey interaction term, which is the most realistic nonlinearity, yields sufficient conditions for Turing patterns. For the Bazykin model we find the Turing region in parameter space and Turing patterns in one dimension

  3. Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis

    Science.gov (United States)

    Wang, Xiao; Gao, Feng; Dong, Junyu; Qi, Qiang

    2018-04-01

    Synthetic aperture radar (SAR) image is independent on atmospheric conditions, and it is the ideal image source for change detection. Existing methods directly analysis all the regions in the speckle noise contaminated difference image. The performance of these methods is easily affected by small noisy regions. In this paper, we proposed a novel change detection framework for saliency-guided change detection based on pattern and intensity distinctiveness analysis. The saliency analysis step can remove small noisy regions, and therefore makes the proposed method more robust to the speckle noise. In the proposed method, the log-ratio operator is first utilized to obtain a difference image (DI). Then, the saliency detection method based on pattern and intensity distinctiveness analysis is utilized to obtain the changed region candidates. Finally, principal component analysis and k-means clustering are employed to analysis pixels in the changed region candidates. Thus, the final change map can be obtained by classifying these pixels into changed or unchanged class. The experiment results on two real SAR images datasets have demonstrated the effectiveness of the proposed method.

  4. Locating sensors for detecting source-to-target patterns of special nuclear material smuggling: a spatial information theoretic approach.

    Science.gov (United States)

    Przybyla, Jay; Taylor, Jeffrey; Zhou, Xuesong

    2010-01-01

    In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

  5. Locating Sensors for Detecting Source-to-Target Patterns of Special Nuclear Material Smuggling: A Spatial Information Theoretic Approach

    Directory of Open Access Journals (Sweden)

    Xuesong Zhou

    2010-08-01

    Full Text Available In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM smuggling. In order to ship the nuclear materials from a source location with SNM production to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.

  6. A Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis

    Directory of Open Access Journals (Sweden)

    Iman Veisi

    2010-03-01

    Full Text Available Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is contaminated with noise is investigated and compared with some traditional chaos-based measures. Materials and Methods: In the proposed method, the phase space of the time series is reconstructed and then partitioned using ordinal patterns. The partitions can be labeled using a set of symbols. Therefore, the state trajectory is converted to a symbol sequence. A finite state machine is then constructed to model the sequence. A new complexity measure is proposed to detect dynamical changes using the state transition matrix of the state machine. The proposed complexity measure was applied to detect epilepsy in short and noisy EEG signals and the results were compared with some chaotic measures. Results: The results indicate that this complexity measure can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 75% for highly noised EEG signals. Discussion and Conclusion: The complexity measure can be computed in a very fast and easy way and, unlike traditional chaotic measures, is robust with respect to noise corrupting the data. This measure is also capable of dynamical change detection in short time series data.

  7. Partitioning detectability components in populations subject to within-season temporary emigration using binomial mixture models.

    Directory of Open Access Journals (Sweden)

    Katherine M O'Donnell

    Full Text Available Detectability of individual animals is highly variable and nearly always < 1; imperfect detection must be accounted for to reliably estimate population sizes and trends. Hierarchical models can simultaneously estimate abundance and effective detection probability, but there are several different mechanisms that cause variation in detectability. Neglecting temporary emigration can lead to biased population estimates because availability and conditional detection probability are confounded. In this study, we extend previous hierarchical binomial mixture models to account for multiple sources of variation in detectability. The state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. We illustrate our model's potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders Plethodon serratus. We fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3-5 surveys each spring and fall 2010-2012. Our models generated similar parameter estimates to standard binomial mixture models. Aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling, while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling. By explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. We stress the importance of choosing survey locations and

  8. Detecting Recombination Hotspots from Patterns of Linkage Disequilibrium.

    Science.gov (United States)

    Wall, Jeffrey D; Stevison, Laurie S

    2016-08-09

    With recent advances in DNA sequencing technologies, it has become increasingly easy to use whole-genome sequencing of unrelated individuals to assay patterns of linkage disequilibrium (LD) across the genome. One type of analysis that is commonly performed is to estimate local recombination rates and identify recombination hotspots from patterns of LD. One method for detecting recombination hotspots, LDhot, has been used in a handful of species to further our understanding of the basic biology of recombination. For the most part, the effectiveness of this method (e.g., power and false positive rate) is unknown. In this study, we run extensive simulations to compare the effectiveness of three different implementations of LDhot. We find large differences in the power and false positive rates of these different approaches, as well as a strong sensitivity to the window size used (with smaller window sizes leading to more accurate estimation of hotspot locations). We also compared our LDhot simulation results with comparable simulation results obtained from a Bayesian maximum-likelihood approach for identifying hotspots. Surprisingly, we found that the latter computationally intensive approach had substantially lower power over the parameter values considered in our simulations. Copyright © 2016 Wall and Stevison.

  9. Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology.

    Science.gov (United States)

    Grimm, Volker; Railsback, Steven F

    2012-01-19

    Modern ecology recognizes that modelling systems across scales and at multiple levels-especially to link population and ecosystem dynamics to individual adaptive behaviour-is essential for making the science predictive. 'Pattern-oriented modelling' (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.

  10. Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction.

    Science.gov (United States)

    Beaulieu, Jeremy M; O'Meara, Brian C

    2016-07-01

    The distribution of diversity can vary considerably from clade to clade. Attempts to understand these patterns often employ state-dependent speciation and extinction models to determine whether the evolution of a particular novel trait has increased speciation rates and/or decreased extinction rates. It is still unclear, however, whether these models are uncovering important drivers of diversification, or whether they are simply pointing to more complex patterns involving many unmeasured and co-distributed factors. Here we describe an extension to the popular state-dependent speciation and extinction models that specifically accounts for the presence of unmeasured factors that could impact diversification rates estimated for the states of any observed trait, addressing at least one major criticism of BiSSE (Binary State Speciation and Extinction) methods. Specifically, our model, which we refer to as HiSSE (Hidden State Speciation and Extinction), assumes that related to each observed state in the model are "hidden" states that exhibit potentially distinct diversification dynamics and transition rates than the observed states in isolation. We also demonstrate how our model can be used as character-independent diversification models that allow for a complex diversification process that is independent of the evolution of a character. Under rigorous simulation tests and when applied to empirical data, we find that HiSSE performs reasonably well, and can at least detect net diversification rate differences between observed and hidden states and detect when diversification rate differences do not correlate with the observed states. We discuss the remaining issues with state-dependent speciation and extinction models in general, and the important ways in which HiSSE provides a more nuanced understanding of trait-dependent diversification. © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved

  11. Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

    International Nuclear Information System (INIS)

    Liu, Chao; Sarkar, Soumik; Gong, Yongqiang; Laflamme, Simon; Phares, Brent

    2017-01-01

    The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density. (paper)

  12. A system for learning statistical motion patterns.

    Science.gov (United States)

    Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

    2006-09-01

    Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

  13. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer.

    Science.gov (United States)

    Petricoin, Emanuel F; Liotta, Lance A

    2004-02-01

    Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity-based processes. Serum proteomic pattern diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. This approach has recently shown tremendous promise in the detection of early-stage cancers. The biomarkers found by SELDI-TOF-based pattern recognition analysis are mostly low molecular weight fragments produced at the specific tumor microenvironment.

  14. Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study.

    Science.gov (United States)

    Becker, A S; Blüthgen, C; Phi van, V D; Sekaggya-Wiltshire, C; Castelnuovo, B; Kambugu, A; Fehr, J; Frauenfelder, T

    2018-03-01

    To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients. In this prospective, observational study, patients with previously diagnosed TB were enrolled. Photographs of their CXRs were taken using a consumer-grade digital still camera. The images were stratified by pathological patterns into classes: cavity, consolidation, effusion, interstitial changes, miliary pattern or normal examination. Image analysis was performed with commercially available Deep Learning software in two steps. Pathological areas were first localised; detected areas were then classified. Detection was assessed using receiver operating characteristics (ROC) analysis, and classification using a confusion matrix. The study cohort was 138 patients with human immunodeficiency virus (HIV) and TB co-infection (median age 34 years, IQR 28-40); 54 patients were female. Localisation of pathological areas was excellent (area under the ROC curve 0.82). The software could perfectly distinguish pleural effusions from intraparenchymal changes. The most frequent misclassifications were consolidations as cavitations, and miliary patterns as interstitial patterns (and vice versa). Deep Learning analysis of CXR photographs is a promising tool. Further efforts are needed to build larger, high-quality data sets to achieve better diagnostic performance.

  15. Cascading walks model for human mobility patterns.

    Science.gov (United States)

    Han, Xiao-Pu; Wang, Xiang-Wen; Yan, Xiao-Yong; Wang, Bing-Hong

    2015-01-01

    Uncovering the mechanism behind the scaling laws and series of anomalies in human trajectories is of fundamental significance in understanding many spatio-temporal phenomena. Recently, several models, e.g. the explorations-returns model (Song et al., 2010) and the radiation model for intercity travels (Simini et al., 2012), have been proposed to study the origin of these anomalies and the prediction of human movements. However, an agent-based model that could reproduce most of empirical observations without priori is still lacking. In this paper, considering the empirical findings on the correlations of move-lengths and staying time in human trips, we propose a simple model which is mainly based on the cascading processes to capture the human mobility patterns. In this model, each long-range movement activates series of shorter movements that are organized by the law of localized explorations and preferential returns in prescribed region. Based on the numerical simulations and analytical studies, we show more than five statistical characters that are well consistent with the empirical observations, including several types of scaling anomalies and the ultraslow diffusion properties, implying the cascading processes associated with the localized exploration and preferential returns are indeed a key in the understanding of human mobility activities. Moreover, the model shows both of the diverse individual mobility and aggregated scaling displacements, bridging the micro and macro patterns in human mobility. In summary, our model successfully explains most of empirical findings and provides deeper understandings on the emergence of human mobility patterns.

  16. A hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recorders.

    Directory of Open Access Journals (Sweden)

    Robert M Dorazio

    Full Text Available Several spatial capture-recapture (SCR models have been developed to estimate animal abundance by analyzing the detections of individuals in a spatial array of traps. Most of these models do not use the actual dates and times of detection, even though this information is readily available when using continuous-time recorders, such as microphones or motion-activated cameras. Instead most SCR models either partition the period of trap operation into a set of subjectively chosen discrete intervals and ignore multiple detections of the same individual within each interval, or they simply use the frequency of detections during the period of trap operation and ignore the observed times of detection. Both practices make inefficient use of potentially important information in the data.We developed a hierarchical SCR model to estimate the spatial distribution and abundance of animals detected with continuous-time recorders. Our model includes two kinds of point processes: a spatial process to specify the distribution of latent activity centers of individuals within the region of sampling and a temporal process to specify temporal patterns in the detections of individuals. We illustrated this SCR model by analyzing spatial and temporal patterns evident in the camera-trap detections of tigers living in and around the Nagarahole Tiger Reserve in India. We also conducted a simulation study to examine the performance of our model when analyzing data sets of greater complexity than the tiger data.Our approach provides three important benefits: First, it exploits all of the information in SCR data obtained using continuous-time recorders. Second, it is sufficiently versatile to allow the effects of both space use and behavior of animals to be specified as functions of covariates that vary over space and time. Third, it allows both the spatial distribution and abundance of individuals to be estimated, effectively providing a species distribution model, even in

  17. Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology.

    Science.gov (United States)

    Fürbass, F; Hartmann, M M; Halford, J J; Koren, J; Herta, J; Gruber, A; Baumgartner, C; Kluge, T

    2015-09-01

    Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports. First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report. In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures. The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  18. REMOTE SENSING-BASED DETECTION AND SPATIAL PATTERN ANALYSIS FOR GEO-ECOLOGICAL NICHE MODELING OF TILLANDSIA SPP. IN THE ATACAMA, CHILE

    Directory of Open Access Journals (Sweden)

    N. Wolf

    2016-06-01

    Full Text Available In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called ‘fog oases’ dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

  19. Remote Sensing-Based Detection and Spatial Pattern Analysis for Geo-Ecological Niche Modeling of Tillandsia SPP. In the Atacama, Chile

    Science.gov (United States)

    Wolf, N.; Siegmund, A.; del Río, C.; Osses, P.; García, J. L.

    2016-06-01

    In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called `fog oases' dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

  20. Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

    Directory of Open Access Journals (Sweden)

    Pietro Quaglio

    2017-05-01

    Full Text Available Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs. STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons. In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST. We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE analysis.

  1. Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

    Science.gov (United States)

    Okamoto, Hiroshi

    2016-08-01

    Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  2. Pattern formation in superdiffusion Oregonator model

    Science.gov (United States)

    Feng, Fan; Yan, Jia; Liu, Fu-Cheng; He, Ya-Feng

    2016-10-01

    Pattern formations in an Oregonator model with superdiffusion are studied in two-dimensional (2D) numerical simulations. Stability analyses are performed by applying Fourier and Laplace transforms to the space fractional reaction-diffusion systems. Antispiral, stable turing patterns, and travelling patterns are observed by changing the diffusion index of the activator. Analyses of Floquet multipliers show that the limit cycle solution loses stability at the wave number of the primitive vector of the travelling hexagonal pattern. We also observed a transition between antispiral and spiral by changing the diffusion index of the inhibitor. Project supported by the National Natural Science Foundation of China (Grant Nos. 11205044 and 11405042), the Research Foundation of Education Bureau of Hebei Province, China (Grant Nos. Y2012009 and ZD2015025), the Program for Young Principal Investigators of Hebei Province, China, and the Midwest Universities Comprehensive Strength Promotion Project.

  3. Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales

    Directory of Open Access Journals (Sweden)

    Ludovico Frate

    2014-09-01

    Full Text Available We propose a procedure to detect significant changes in forest spatial patterns and relevant scales. Our approach consists of four sequential steps. First, based on a series of multi-temporal forest maps, a set of geographic windows of increasing extents are extracted. Second, for each extent and date, specific stochastic simulations that replicate real-world spatial pattern characteristics are run. Third, by computing pattern metrics on both simulated and real maps, their empirical distributions and confidence intervals are derived. Finally, multi-temporal scalograms are built for each metric. Based on cover maps (1954, 2011 with a resolution of 10 m we analyze forest pattern changes in a central Apennines (Italy reserve at multiple spatial extents (128, 256 and 512 pixels. We identify three types of multi-temporal scalograms, depending on pattern metric behaviors, describing different dynamics of natural reforestation process. The statistical distribution and variability of pattern metrics at multiple extents offers a new and powerful tool to detect forest variations over time. Similar procedures can (i help to identify significant changes in spatial patterns and provide the bases to relate them to landscape processes; (ii minimize the bias when comparing pattern metrics at a single extent and (iii be extended to other landscapes and scales.

  4. Spatio-temporal patterns in simple models of marine systems

    Science.gov (United States)

    Feudel, U.; Baurmann, M.; Gross, T.

    2009-04-01

    Spatio-temporal patterns in marine systems are a result of the interaction of population dynamics with physical transport processes. These physical transport processes can be either diffusion processes in marine sediments or in the water column. We study the dynamics of one population of bacteria and its nutrient in in a simplified model of a marine sediments, taking into account that the considered bacteria possess an active as well as an inactive state, where activation is processed by signal molecules. Furthermore the nutrients are transported actively by bioirrigation and passively by diffusion. It is shown that under certain conditions Turing patterns can occur which yield heterogeneous spatial patterns of the species. The influence of bioirrigation on Turing patterns leads to the emergence of ''hot spots``, i.e. localized regions of enhanced bacterial activity. All obtained patterns fit quite well to observed patterns in laboratory experiments. Spatio-temporal patterns appear in a predator-prey model, used to describe plankton dynamics. These patterns appear due to the simultaneous emergence of Turing patterns and oscillations in the species abundance in the neighborhood of a Turing-Hopf bifurcation. We observe a large variety of different patterns where i) stationary heterogeneous patterns (e.g. hot and cold spots) compete with spatio-temporal patterns ii) slowly moving patterns are embedded in an oscillatory background iii) moving fronts and spiral waves appear.

  5. Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Olivier Aycard

    2004-12-01

    Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

  6. Modelling of Patterns in Space and Time

    CERN Document Server

    Murray, James

    1984-01-01

    This volume contains a selection of papers presented at the work­ shop "Modelling of Patterns in Space and Time", organized by the 80nderforschungsbereich 123, "8tochastische Mathematische Modelle", in Heidelberg, July 4-8, 1983. The main aim of this workshop was to bring together physicists, chemists, biologists and mathematicians for an exchange of ideas and results in modelling patterns. Since the mathe­ matical problems arising depend only partially on the particular field of applications the interdisciplinary cooperation proved very useful. The workshop mainly treated phenomena showing spatial structures. The special areas covered were morphogenesis, growth in cell cultures, competition systems, structured populations, chemotaxis, chemical precipitation, space-time oscillations in chemical reactors, patterns in flames and fluids and mathematical methods. The discussions between experimentalists and theoreticians were especially interesting and effective. The editors hope that these proceedings reflect ...

  7. Modifications of center-surround, spot detection and dot-pattern selective operators

    NARCIS (Netherlands)

    Petkov, Nicolai; Visser, Wicher T.

    2005-01-01

    This paper describes modifications of the models of center-surround and dot-pattern selective cells proposed previously. These modifications concern mainly the normalization of the difference of Gaussians (DoG) function used to model center-surround receptive fields, the normalization of

  8. Detection of viability of micro-algae cells by optofluidic hologram pattern.

    Science.gov (United States)

    Wang, Junsheng; Yu, Xiaomei; Wang, Yanjuan; Pan, Xinxiang; Li, Dongqing

    2018-03-01

    A rapid detection of micro-algae activity is critical for analysis of ship ballast water. A new method for detecting micro-algae activity based on lens-free optofluidic holographic imaging is presented in this paper. A compact lens-free optofluidic holographic imaging device was developed. This device is mainly composed of a light source, a small through-hole, a light propagation module, a microfluidic chip, and an image acquisition and processing module. The excited light from the light source passes through a small hole to reach the surface of the micro-algae cells in the microfluidic chip, and a holographic image is formed by the diffraction light of surface of micro-algae cells. The relation between the characteristics in the hologram pattern and the activity of micro-algae cells was investigated by using this device. The characteristics of the hologram pattern were extracted to represent the activity of micro-algae cells. To demonstrate the accuracy of the presented method and device, four species of micro-algae cells were employed as the test samples and the comparison experiments between the alive and dead cells of four species of micro-algae were conducted. The results show that the developed method and device can determine live/dead microalgae cells accurately.

  9. Modeling and analyzing stripe patterns in fish skin

    Science.gov (United States)

    Zheng, Yibo; Zhang, Lei; Wang, Yuan; Liang, Ping; Kang, Junjian

    2009-11-01

    The formation mechanism of stripe patterns in the skin of tropical fishes has been investigated by a coupled two variable reaction diffusion model. Two types of spatial inhomogeneities have been introduced into a homogenous system. Several Turing modes pumped by the Turing instability give rise to a simple stripe pattern. It is found that the Turing mechanism can only determine the wavelength of stripe pattern. The orientation of stripe pattern is determined by the spatial inhomogeneity. Our numerical results suggest that it may be the most possible mechanism for the forming process of fish skin patterns.

  10. SENSING URBAN LAND-USE PATTERNS BY INTEGRATING GOOGLE TENSORFLOW AND SCENE-CLASSIFICATION MODELS

    Directory of Open Access Journals (Sweden)

    Y. Yao

    2017-09-01

    Full Text Available With the rapid progress of China’s urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model’s ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI. Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs. To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737 for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

  11. [Blood Test Patterns for Blood Donors after Nucleic Acid Detection in the Blood Center].

    Science.gov (United States)

    Men, Shou-Shan; Lv, Lian-Zhi; Chen, Yuan-Feng; Han, Chun-Hua; Liu, Hong-Yu; Yan, Yan

    2017-12-01

    To investigate the blood test patterns for blood donors after nucleic acid detection in blood center. The collected blood samples after voluntary blood donors first were detected by conventional ELISA, then 31981 negative samples were detected via HBV/HCV/HIV combined nucleic acid test of 6 mixed samples(22716 cases) or single samples(9265 cases) by means of Roche cobas s201 instrument. The combined detection method as follows: the blood samples were assayed by conventional nucleic acid test of 6 mixed samples, at same time, 6 mixed samples were treated with polyethylene glycol precipitation method to concentrate the virus, then the nucleic acid test of blood samples was performed; the single detection method as follows: firstly the conventional nucleic acid test of single sample was performed, then the positive reactive samples after re-examination were 6-fold diluted to simulate the nucleic acid test of 6-mixed samples. The positive rate of positive samples detected by combined nucleic acid test, positive samples detected by nucleic acid test of mixed virus concentration and positive samples detected by single nucleic acid test was statistically analyzed. In addition, for HBV + persons the serological test yet should be performed. In 22 716 samples detected by nucleic acid test of 6 mixed samples (MP-6-NAT) , 9 cases were HBV + (0.40‰, 9/22716); at same time, the detection of same samples by nucleic acid test of mixed sample virus concentration showed 29 cases of HBV + (1.28‰, 29/22716). In 9265 samples detected by single nucleic acid test(ID-NAT) 12 cases showed HBV + (1.30‰, 12/9265), meanwhile the detection of these 12 samples with HBV + by 6-fold dilution for virus concentration found only 4 samples with HBV + . In serological qualified samples, ID-NAT unqualified rate was 1.28‰, which was higher than that of MP-6-NAT(0.4‰) (χ 2 =8.11, P0.05). In 41 samples with HBsAg - HBV DNA + detected by ELISA, 36 samples were confirmed to be occult HBV

  12. Detection of dependence patterns with delay.

    Science.gov (United States)

    Chevallier, Julien; Laloë, Thomas

    2015-11-01

    The Unitary Events (UE) method is a popular and efficient method used this last decade to detect dependence patterns of joint spike activity among simultaneously recorded neurons. The first introduced method is based on binned coincidence count (Grün, 1996) and can be applied on two or more simultaneously recorded neurons. Among the improvements of the methods, a transposition to the continuous framework has recently been proposed by Muiño and Borgelt (2014) and fully investigated by Tuleau-Malot et al. (2014) for two neurons. The goal of the present paper is to extend this study to more than two neurons. The main result is the determination of the limit distribution of the coincidence count. This leads to the construction of an independence test between L≥2 neurons. Finally, we propose a multiple test procedure via a Benjamini and Hochberg approach (Benjamini and Hochberg, 1995). All the theoretical results are illustrated by a simulation study, and compared to the UE method proposed by Grün et al. (2002). Furthermore our method is applied on real data. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Comparison of bioassays with different exposure time patterns: the added value of dynamic modelling in predictive ecotoxicology.

    Science.gov (United States)

    Billoir, Elise; Delhaye, Hèlène; Forfait, Carole; Clément, Bernard; Triffault-Bouchet, Gaëlle; Charles, Sandrine; Delignette-Muller, Marie Laure

    2012-01-01

    The purpose of this study was to compare Daphnia magna responses to cadmium between two toxicity experiments performed in static and flow-through conditions. As a consequence of how water was renewed, the two experiments were characterised by two different exposure time patterns for daphnids, time-varying and constant, respectively. Basing on survival, growth and reproduction, we addressed the questions of organism development and sensitivity to cadmium. Classical analysis methods are not designed to deal with the time dimension and therefore not suitable to compare effects of different exposure time patterns. We used instead a dynamic modelling framework taking all timepoints and the time course of exposure into account, making comparable the results obtained from our two experiments. This modelling framework enabled us to detect an improvement of organism development in flow-through conditions compared to static ones and infer similar sensitivity to cadmium for both exposure time patterns. Copyright © 2011 Elsevier Inc. All rights reserved.

  14. Colorimetric sensor arrays based on pattern recognition for the detection of nitroaromatic molecules

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Wei; Dong, Xiao [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Qiu, Lili, E-mail: qiulili@bit.edu.cn [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Yan, Zequn [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Meng, Zihui, E-mail: m_zihui@yahoo.com [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Xue, Min [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); He, Xuan; Liu, Xueyong [Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900 (China)

    2017-03-15

    Graphical abstract: A colorimetric sensor array based on four kinds molecularly imprinted photonic crystal (MIPC) was explored for the selective visual detection of TNT, 2,6-DNT, 2,4-DNT and 4-MNT. The color of individual sensor changed with the increasing concentration of the analytes, and a cross-responsive platform was evaluated by a “radar” pattern. With the assistance of principal component analysis (PCA), a separate response region contained 95.25% of significant characteristics for the detection of nitroaromatics was generated, which also promised high potential for the customized visual detection system of other harmful chemicals. - Highlights: • Nitroaromatics were visually detected by molecularly imprinted photonic crystal. • The adsorption capacity was calculated. • The cross responsive platform of sensor array was established and discussed. • The discrimination capability was promoted by principal component analysis. • This system had high potential to be used in other customed visual detection. - Abstract: This research demonstrated that, in a colorimetric sensor array, 2,4,6-trinitrotoluene (TNT), 2,6-dinitrotoluene (2,6-DNT), 2,4-dinitrotoluene (2,4-DNT) and 4-nitrotoluene (4-MNT) were identifiable through a unique pattern in a qualitative and semi-quantitative manner. The adsorption capacity of the molecularly imprinted colloidal particles (MICs) for their corresponding templates was 0.27 mmol TNT/g, 0.22 mmol 2,6-DNT/g, 0.31 mmol 2,4-DNT/g and 0.16 mmol 4-MNT/g, respectively. Every optical sensor utilized in the arrays contained three-dimensional molecularly imprinted photonic crystal (MIPC) sensor with different imprinted templates. The intelligent materials can display different colors from green to red to 20 mM corresponding nitroaromatics with varying diffraction red shifts of 84 nm (TNT), 46 nm (2,6-DNT), 54 nm (2,4-DNT) and 35 nm (4-MNT), respectively. With the assistance of principal component analysis (PCA) and rational design

  15. Colorimetric sensor arrays based on pattern recognition for the detection of nitroaromatic molecules

    International Nuclear Information System (INIS)

    Lu, Wei; Dong, Xiao; Qiu, Lili; Yan, Zequn; Meng, Zihui; Xue, Min; He, Xuan; Liu, Xueyong

    2017-01-01

    Graphical abstract: A colorimetric sensor array based on four kinds molecularly imprinted photonic crystal (MIPC) was explored for the selective visual detection of TNT, 2,6-DNT, 2,4-DNT and 4-MNT. The color of individual sensor changed with the increasing concentration of the analytes, and a cross-responsive platform was evaluated by a “radar” pattern. With the assistance of principal component analysis (PCA), a separate response region contained 95.25% of significant characteristics for the detection of nitroaromatics was generated, which also promised high potential for the customized visual detection system of other harmful chemicals. - Highlights: • Nitroaromatics were visually detected by molecularly imprinted photonic crystal. • The adsorption capacity was calculated. • The cross responsive platform of sensor array was established and discussed. • The discrimination capability was promoted by principal component analysis. • This system had high potential to be used in other customed visual detection. - Abstract: This research demonstrated that, in a colorimetric sensor array, 2,4,6-trinitrotoluene (TNT), 2,6-dinitrotoluene (2,6-DNT), 2,4-dinitrotoluene (2,4-DNT) and 4-nitrotoluene (4-MNT) were identifiable through a unique pattern in a qualitative and semi-quantitative manner. The adsorption capacity of the molecularly imprinted colloidal particles (MICs) for their corresponding templates was 0.27 mmol TNT/g, 0.22 mmol 2,6-DNT/g, 0.31 mmol 2,4-DNT/g and 0.16 mmol 4-MNT/g, respectively. Every optical sensor utilized in the arrays contained three-dimensional molecularly imprinted photonic crystal (MIPC) sensor with different imprinted templates. The intelligent materials can display different colors from green to red to 20 mM corresponding nitroaromatics with varying diffraction red shifts of 84 nm (TNT), 46 nm (2,6-DNT), 54 nm (2,4-DNT) and 35 nm (4-MNT), respectively. With the assistance of principal component analysis (PCA) and rational design

  16. Occupancy models for monitoring marine fish: a bayesian hierarchical approach to model imperfect detection with a novel gear combination.

    Directory of Open Access Journals (Sweden)

    Lewis G Coggins

    Full Text Available Occupancy models using incidence data collected repeatedly at sites across the range of a population are increasingly employed to infer patterns and processes influencing population distribution and dynamics. While such work is common in terrestrial systems, fewer examples exist in marine applications. This disparity likely exists because the replicate samples required by these models to account for imperfect detection are often impractical to obtain when surveying aquatic organisms, particularly fishes. We employ simultaneous sampling using fish traps and novel underwater camera observations to generate the requisite replicate samples for occupancy models of red snapper, a reef fish species. Since the replicate samples are collected simultaneously by multiple sampling devices, many typical problems encountered when obtaining replicate observations are avoided. Our results suggest that augmenting traditional fish trap sampling with camera observations not only doubled the probability of detecting red snapper in reef habitats off the Southeast coast of the United States, but supplied the necessary observations to infer factors influencing population distribution and abundance while accounting for imperfect detection. We found that detection probabilities tended to be higher for camera traps than traditional fish traps. Furthermore, camera trap detections were influenced by the current direction and turbidity of the water, indicating that collecting data on these variables is important for future monitoring. These models indicate that the distribution and abundance of this species is more heavily influenced by latitude and depth than by micro-scale reef characteristics lending credence to previous characterizations of red snapper as a reef habitat generalist. This study demonstrates the utility of simultaneous sampling devices, including camera traps, in aquatic environments to inform occupancy models and account for imperfect detection when

  17. Occupancy models for monitoring marine fish: a bayesian hierarchical approach to model imperfect detection with a novel gear combination.

    Science.gov (United States)

    Coggins, Lewis G; Bacheler, Nathan M; Gwinn, Daniel C

    2014-01-01

    Occupancy models using incidence data collected repeatedly at sites across the range of a population are increasingly employed to infer patterns and processes influencing population distribution and dynamics. While such work is common in terrestrial systems, fewer examples exist in marine applications. This disparity likely exists because the replicate samples required by these models to account for imperfect detection are often impractical to obtain when surveying aquatic organisms, particularly fishes. We employ simultaneous sampling using fish traps and novel underwater camera observations to generate the requisite replicate samples for occupancy models of red snapper, a reef fish species. Since the replicate samples are collected simultaneously by multiple sampling devices, many typical problems encountered when obtaining replicate observations are avoided. Our results suggest that augmenting traditional fish trap sampling with camera observations not only doubled the probability of detecting red snapper in reef habitats off the Southeast coast of the United States, but supplied the necessary observations to infer factors influencing population distribution and abundance while accounting for imperfect detection. We found that detection probabilities tended to be higher for camera traps than traditional fish traps. Furthermore, camera trap detections were influenced by the current direction and turbidity of the water, indicating that collecting data on these variables is important for future monitoring. These models indicate that the distribution and abundance of this species is more heavily influenced by latitude and depth than by micro-scale reef characteristics lending credence to previous characterizations of red snapper as a reef habitat generalist. This study demonstrates the utility of simultaneous sampling devices, including camera traps, in aquatic environments to inform occupancy models and account for imperfect detection when describing factors

  18. Occupancy Models for Monitoring Marine Fish: A Bayesian Hierarchical Approach to Model Imperfect Detection with a Novel Gear Combination

    Science.gov (United States)

    Coggins, Lewis G.; Bacheler, Nathan M.; Gwinn, Daniel C.

    2014-01-01

    Occupancy models using incidence data collected repeatedly at sites across the range of a population are increasingly employed to infer patterns and processes influencing population distribution and dynamics. While such work is common in terrestrial systems, fewer examples exist in marine applications. This disparity likely exists because the replicate samples required by these models to account for imperfect detection are often impractical to obtain when surveying aquatic organisms, particularly fishes. We employ simultaneous sampling using fish traps and novel underwater camera observations to generate the requisite replicate samples for occupancy models of red snapper, a reef fish species. Since the replicate samples are collected simultaneously by multiple sampling devices, many typical problems encountered when obtaining replicate observations are avoided. Our results suggest that augmenting traditional fish trap sampling with camera observations not only doubled the probability of detecting red snapper in reef habitats off the Southeast coast of the United States, but supplied the necessary observations to infer factors influencing population distribution and abundance while accounting for imperfect detection. We found that detection probabilities tended to be higher for camera traps than traditional fish traps. Furthermore, camera trap detections were influenced by the current direction and turbidity of the water, indicating that collecting data on these variables is important for future monitoring. These models indicate that the distribution and abundance of this species is more heavily influenced by latitude and depth than by micro-scale reef characteristics lending credence to previous characterizations of red snapper as a reef habitat generalist. This study demonstrates the utility of simultaneous sampling devices, including camera traps, in aquatic environments to inform occupancy models and account for imperfect detection when describing factors

  19. Using Clustering Techniques To Detect Usage Patterns in a Web-based Information System.

    Science.gov (United States)

    Chen, Hui-Min; Cooper, Michael D.

    2001-01-01

    This study developed an analytical approach to detecting groups with homogenous usage patterns in a Web-based information system. Principal component analysis was used for data reduction, cluster analysis for categorizing usage into groups. The methodology was demonstrated and tested using two independent samples of user sessions from the…

  20. Applications of pattern recognition techniques to online fault detection

    International Nuclear Information System (INIS)

    Singer, R.M.; Gross, K.C.; King, R.W.

    1993-01-01

    A common problem to operators of complex industrial systems is the early detection of incipient degradation of sensors and components in order to avoid unplanned outages, to orderly plan for anticipated maintenance activities and to assure continued safe operation. In such systems, there usually are a large number of sensors (upwards of several thousand is not uncommon) serving many functions, ranging from input to control systems, monitoring of safety parameters and component performance limits, system environmental conditions, etc. Although sensors deemed to measure important process conditions are generally alarmed, the alarm set points usually are just high-low limits and the operator's response to such alarms is based on written procedures and his or her experience and training. In many systems this approach has been successful, but in situations where the cost of a forced outage is high an improved method is needed. In such cases it is desirable, if not necessary, to detect disturbances in either sensors or the process prior to any actual failure that could either shut down the process or challenge any safety system that is present. Recent advances in various artificial intelligence techniques have provided the opportunity to perform such functions of early detection and diagnosis. In this paper, the experience gained through the application of several pattern-recognition techniques to the on-line monitoring and incipient disturbance detection of several coolant pumps and numerous sensors at the Experimental Breeder Reactor-II (EBR-II) which is located at the Idaho National Engineering Laboratory is presented

  1. Observation of Communication by Physical Education Teachers: Detecting Patterns in Verbal Behavior.

    Science.gov (United States)

    García-Fariña, Abraham; Jiménez-Jiménez, F; Anguera, M Teresa

    2018-01-01

    The aim of this study was to analyze the verbal behavior of primary school physical education teachers in a natural classroom setting in order to investigate patterns in social constructivist communication strategies before and after participation in a training program designed to familiarize teachers with these strategies. The participants were three experienced physical education teachers interacting separately with 65 students over a series of classes. Written informed consent was obtained from all the students' parents or legal guardians. An indirect observation tool (ADDEF) was designed specifically for the study within the theoretical framework, and consisted of a combined field format, with three dimensions, and category systems. Each dimension formed the basis for building a subsequent system of exhaustive and mutually exclusive categories. Twenty-nine sessions, grouped into two separate modules, were coded using the Atlas.ti 7 program, and a total of 1991 units (messages containing constructivist discursive strategies) were recorded. Analysis of intraobserver reliability showed almost perfect agreement. Lag sequential analysis, which is a powerful statistical technique based on the calculation of conditional and unconditional probabilities in prospective and retrospective lags, was performed in GSEQ5 software to search for verbal behavior patterns before and after the training program. At both time points, we detected a pattern formed by requests for information combined with the incorporation of students' contributions into the teachers' discourse and re-elaborations of answers. In the post-training phase, we detected new and stronger patterns in certain sessions, indicating that programs combining theoretical and practical knowledge can effectively increase teachers' repertoire of discursive strategies and ultimately promote active engagement in learning. This has important implications for the evaluation and development of teacher effectiveness in

  2. New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data

    Directory of Open Access Journals (Sweden)

    Kyeong-Sang Lee

    2017-01-01

    Full Text Available Snow cover plays an important role in climate and hydrology, at both global and regional scales. Most previous studies have used static threshold techniques to detect snow cover, which can lead to errors such as misclassification of snow and clouds, because the reflectance of snow cover exhibits variability and is affected by several factors. Therefore, we present a simple new algorithm for mapping snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS data using dynamic wavelength warping (DWW, which is based on dynamic time warping (DTW. DTW is a pattern recognition technique that is widely used in various fields such as human action recognition, anomaly detection, and clustering. Before performing DWW, we constructed 49 snow reflectance spectral libraries as reference data for various solar zenith angle and digital elevation model conditions using approximately 1.6 million sampled data. To verify the algorithm, we compared our results with the MODIS swath snow cover product (MOD10_L2. Producer’s accuracy, user’s accuracy, and overall accuracy values were 92.92%, 78.41%, and 92.24%, respectively, indicating good overall classification accuracy. The proposed algorithm is more useful for discriminating between snow cover and clouds than threshold techniques in some areas, such as those with a high viewing zenith angle.

  3. A Multiscale Survival Process for Modeling Human Activity Patterns.

    Science.gov (United States)

    Zhang, Tianyang; Cui, Peng; Song, Chaoming; Zhu, Wenwu; Yang, Shiqiang

    2016-01-01

    Human activity plays a central role in understanding large-scale social dynamics. It is well documented that individual activity pattern follows bursty dynamics characterized by heavy-tailed interevent time distributions. Here we study a large-scale online chatting dataset consisting of 5,549,570 users, finding that individual activity pattern varies with timescales whereas existing models only approximate empirical observations within a limited timescale. We propose a novel approach that models the intensity rate of an individual triggering an activity. We demonstrate that the model precisely captures corresponding human dynamics across multiple timescales over five orders of magnitudes. Our model also allows extracting the population heterogeneity of activity patterns, characterized by a set of individual-specific ingredients. Integrating our approach with social interactions leads to a wide range of implications.

  4. Automated Detection of Selective Logging in Amazon Forests Using Airborne Lidar Data and Pattern Recognition Algorithms

    Science.gov (United States)

    Keller, M. M.; d'Oliveira, M. N.; Takemura, C. M.; Vitoria, D.; Araujo, L. S.; Morton, D. C.

    2012-12-01

    Selective logging, the removal of several valuable timber trees per hectare, is an important land use in the Brazilian Amazon and may degrade forests through long term changes in structure, loss of forest carbon and species diversity. Similar to deforestation, the annual area affected by selected logging has declined significantly in the past decade. Nonetheless, this land use affects several thousand km2 per year in Brazil. We studied a 1000 ha area of the Antimary State Forest (FEA) in the State of Acre, Brazil (9.304 ○S, 68.281 ○W) that has a basal area of 22.5 m2 ha-1 and an above-ground biomass of 231 Mg ha-1. Logging intensity was low, approximately 10 to 15 m3 ha-1. We collected small-footprint airborne lidar data using an Optech ALTM 3100EA over the study area once each in 2010 and 2011. The study area contained both recent and older logging that used both conventional and technologically advanced logging techniques. Lidar return density averaged over 20 m-2 for both collection periods with estimated horizontal and vertical precision of 0.30 and 0.15 m. A relative density model comparing returns from 0 to 1 m elevation to returns in 1-5 m elevation range revealed the pattern of roads and skid trails. These patterns were confirmed by ground-based GPS survey. A GIS model of the road and skid network was built using lidar and ground data. We tested and compared two pattern recognition approaches used to automate logging detection. Both segmentation using commercial eCognition segmentation and a Frangi filter algorithm identified the road and skid trail network compared to the GIS model. We report on the effectiveness of these two techniques.

  5. Pattern Extraction Algorithm for NetFlow-Based Botnet Activities Detection

    Directory of Open Access Journals (Sweden)

    Rafał Kozik

    2017-01-01

    Full Text Available As computer and network technologies evolve, the complexity of cybersecurity has dramatically increased. Advanced cyber threats have led to current approaches to cyber-attack detection becoming ineffective. Many currently used computer systems and applications have never been deeply tested from a cybersecurity point of view and are an easy target for cyber criminals. The paradigm of security by design is still more of a wish than a reality, especially in the context of constantly evolving systems. On the other hand, protection technologies have also improved. Recently, Big Data technologies have given network administrators a wide spectrum of tools to combat cyber threats. In this paper, we present an innovative system for network traffic analysis and anomalies detection to utilise these tools. The systems architecture is based on a Big Data processing framework, data mining, and innovative machine learning techniques. So far, the proposed system implements pattern extraction strategies that leverage batch processing methods. As a use case we consider the problem of botnet detection by means of data in the form of NetFlows. Results are promising and show that the proposed system can be a useful tool to improve cybersecurity.

  6. Hopfield's Model of Patterns Recognition and Laws of Artistic Perception

    Science.gov (United States)

    Yevin, Igor; Koblyakov, Alexander

    The model of patterns recognition or attractor network model of associative memory, offered by J.Hopfield 1982, is the most known model in theoretical neuroscience. This paper aims to show, that such well-known laws of art perception as the Wundt curve, perception of visual ambiguity in art, and also the model perception of musical tonalities are nothing else than special cases of the Hopfield’s model of patterns recognition.

  7. Voronoi cell patterns: Theoretical model and applications

    Science.gov (United States)

    González, Diego Luis; Einstein, T. L.

    2011-11-01

    We use a simple fragmentation model to describe the statistical behavior of the Voronoi cell patterns generated by a homogeneous and isotropic set of points in 1D and in 2D. In particular, we are interested in the distribution of sizes of these Voronoi cells. Our model is completely defined by two probability distributions in 1D and again in 2D, the probability to add a new point inside an existing cell and the probability that this new point is at a particular position relative to the preexisting point inside this cell. In 1D the first distribution depends on a single parameter while the second distribution is defined through a fragmentation kernel; in 2D both distributions depend on a single parameter. The fragmentation kernel and the control parameters are closely related to the physical properties of the specific system under study. We use our model to describe the Voronoi cell patterns of several systems. Specifically, we study the island nucleation with irreversible attachment, the 1D car-parking problem, the formation of second-level administrative divisions, and the pattern formed by the Paris Métro stations.

  8. Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology

    Science.gov (United States)

    Grimm, Volker; Railsback, Steven F.

    2012-01-01

    Modern ecology recognizes that modelling systems across scales and at multiple levels—especially to link population and ecosystem dynamics to individual adaptive behaviour—is essential for making the science predictive. ‘Pattern-oriented modelling’ (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions. PMID:22144392

  9. Strain detection in crystalline heterostructures using bidimensional blocking patterns of channelled particles

    Science.gov (United States)

    Redondo-Cubero, A.; David-Bosne, E.; Wahl, U.; Miranda, P.; da Silva, M. R.; Correia, J. G.; Lorenz, K.

    2018-03-01

    Strain is a critical parameter affecting the growth and the performance of many semiconductor systems but, at the same time, the accurate determination of strain profiles in heterostructures can be challenging, especially at the nanoscale. Ion channelling/blocking is a powerful technique for the detection of the strain state of thin films, normally carried out through angular scans with conventional particle detectors. Here we report the novel application of position sensitive detectors for the evaluation of the strain in a series of AlInN/GaN heterostructures with different compositions and thicknesses. The tetragonal strain is varied from compressive to tensile and analysed through bidimensional blocking patterns. The results demonstrate that strain can be correctly quantified when compared to Monte Carlo channelling simulations, which are essential because of the presence of ion steering effects at the interface between the layer and the substrate. Despite this physical limitation caused by ion steering, our results show that full bidimensional patterns can be applied to detect fingerprints and enhance the accuracy for most critical cases, in which the angular shift associated to the lattice distortion is below the critical angle for channelling.

  10. Numerical approaches to model perturbation fire in turing pattern formations

    Science.gov (United States)

    Campagna, R.; Brancaccio, M.; Cuomo, S.; Mazzoleni, S.; Russo, L.; Siettos, K.; Giannino, F.

    2017-11-01

    Turing patterns were observed in chemical, physical and biological systems described by coupled reaction-diffusion equations. Several models have been formulated proposing the water as the causal mechanism of vegetation pattern formation, but this isn't an exhaustive hypothesis in some natural environments. An alternative explanation has been related to the plant-soil negative feedback. In Marasco et al. [1] the authors explored the hypothesis that both mechanisms contribute in the formation of regular and irregular vegetation patterns. The mathematical model consists in three partial differential equations (PDEs) that take into account for a dynamic balance between biomass, water and toxic compounds. A numerical approach is mandatory also to investigate on the predictions of this kind of models. In this paper we start from the mathematical model described in [1], set the model parameters such that the biomass reaches a stable spatial pattern (spots) and present preliminary studies about the occurrence of perturbing events, such as wildfire, that can affect the regularity of the biomass configuration.

  11. Molecular detection of hematozoa infections in tundra swans relative to migration patterns and ecological conditions at breeding grounds.

    Directory of Open Access Journals (Sweden)

    Andrew M Ramey

    Full Text Available Tundra swans (Cygnus columbianus are broadly distributed in North America, use a wide variety of habitats, and exhibit diverse migration strategies. We investigated patterns of hematozoa infection in three populations of tundra swans that breed in Alaska using satellite tracking to infer host movement and molecular techniques to assess the prevalence and genetic diversity of parasites. We evaluated whether migratory patterns and environmental conditions at breeding areas explain the prevalence of blood parasites in migratory birds by contrasting the fit of competing models formulated in an occupancy modeling framework and calculating the detection probability of the top model using Akaike Information Criterion (AIC. We described genetic diversity of blood parasites in each population of swans by calculating the number of unique parasite haplotypes observed. Blood parasite infection was significantly different between populations of Alaska tundra swans, with the highest estimated prevalence occurring among birds occupying breeding areas with lower mean daily wind speeds and higher daily summer temperatures. Models including covariates of wind speed and temperature during summer months at breeding grounds better predicted hematozoa prevalence than those that included annual migration distance or duration. Genetic diversity of blood parasites in populations of tundra swans appeared to be relative to hematozoa prevalence. Our results suggest ecological conditions at breeding grounds may explain differences of hematozoa infection among populations of tundra swans that breed in Alaska.

  12. Molecular detection of hematozoa infections in tundra swans relative to migration patterns and ecological conditions at breeding grounds.

    Science.gov (United States)

    Ramey, Andrew M; Ely, Craig R; Schmutz, Joel A; Pearce, John M; Heard, Darryl J

    2012-01-01

    Tundra swans (Cygnus columbianus) are broadly distributed in North America, use a wide variety of habitats, and exhibit diverse migration strategies. We investigated patterns of hematozoa infection in three populations of tundra swans that breed in Alaska using satellite tracking to infer host movement and molecular techniques to assess the prevalence and genetic diversity of parasites. We evaluated whether migratory patterns and environmental conditions at breeding areas explain the prevalence of blood parasites in migratory birds by contrasting the fit of competing models formulated in an occupancy modeling framework and calculating the detection probability of the top model using Akaike Information Criterion (AIC). We described genetic diversity of blood parasites in each population of swans by calculating the number of unique parasite haplotypes observed. Blood parasite infection was significantly different between populations of Alaska tundra swans, with the highest estimated prevalence occurring among birds occupying breeding areas with lower mean daily wind speeds and higher daily summer temperatures. Models including covariates of wind speed and temperature during summer months at breeding grounds better predicted hematozoa prevalence than those that included annual migration distance or duration. Genetic diversity of blood parasites in populations of tundra swans appeared to be relative to hematozoa prevalence. Our results suggest ecological conditions at breeding grounds may explain differences of hematozoa infection among populations of tundra swans that breed in Alaska.

  13. Molecular detection of hematozoa infections in tundra swans relative to migration patterns and ecological conditions at breeding grounds

    Science.gov (United States)

    Ramey, Andrew M.; Ely, Craig R.; Schmutz, Joel A.; Pearce, John M.; Heard, Darryl J.

    2012-01-01

    Tundra swans (Cygnus columbianus) are broadly distributed in North America, use a wide variety of habitats, and exhibit diverse migration strategies. We investigated patterns of hematozoa infection in three populations of tundra swans that breed in Alaska using satellite tracking to infer host movement and molecular techniques to assess the prevalence and genetic diversity of parasites. We evaluated whether migratory patterns and environmental conditions at breeding areas explain the prevalence of blood parasites in migratory birds by contrasting the fit of competing models formulated in an occupancy modeling framework and calculating the detection probability of the top model using Akaike Information Criterion (AIC). We described genetic diversity of blood parasites in each population of swans by calculating the number of unique parasite haplotypes observed. Blood parasite infection was significantly different between populations of Alaska tundra swans, with the highest estimated prevalence occurring among birds occupying breeding areas with lower mean daily wind speeds and higher daily summer temperatures. Models including covariates of wind speed and temperature during summer months at breeding grounds better predicted hematozoa prevalence than those that included annual migration distance or duration. Genetic diversity of blood parasites in populations of tundra swans appeared to be relative to hematozoa prevalence. Our results suggest ecological conditions at breeding grounds may explain differences of hematozoa infection among populations of tundra swans that breed in Alaska.

  14. Differences in Movement Pattern and Detectability between Males and Females Influence How Common Sampling Methods Estimate Sex Ratio.

    Directory of Open Access Journals (Sweden)

    João Fabrício Mota Rodrigues

    Full Text Available Sampling the biodiversity is an essential step for conservation, and understanding the efficiency of sampling methods allows us to estimate the quality of our biodiversity data. Sex ratio is an important population characteristic, but until now, no study has evaluated how efficient are the sampling methods commonly used in biodiversity surveys in estimating the sex ratio of populations. We used a virtual ecologist approach to investigate whether active and passive capture methods are able to accurately sample a population's sex ratio and whether differences in movement pattern and detectability between males and females produce biased estimates of sex-ratios when using these methods. Our simulation allowed the recognition of individuals, similar to mark-recapture studies. We found that differences in both movement patterns and detectability between males and females produce biased estimates of sex ratios. However, increasing the sampling effort or the number of sampling days improves the ability of passive or active capture methods to properly sample sex ratio. Thus, prior knowledge regarding movement patterns and detectability for species is important information to guide field studies aiming to understand sex ratio related patterns.

  15. Differences in Movement Pattern and Detectability between Males and Females Influence How Common Sampling Methods Estimate Sex Ratio.

    Science.gov (United States)

    Rodrigues, João Fabrício Mota; Coelho, Marco Túlio Pacheco

    2016-01-01

    Sampling the biodiversity is an essential step for conservation, and understanding the efficiency of sampling methods allows us to estimate the quality of our biodiversity data. Sex ratio is an important population characteristic, but until now, no study has evaluated how efficient are the sampling methods commonly used in biodiversity surveys in estimating the sex ratio of populations. We used a virtual ecologist approach to investigate whether active and passive capture methods are able to accurately sample a population's sex ratio and whether differences in movement pattern and detectability between males and females produce biased estimates of sex-ratios when using these methods. Our simulation allowed the recognition of individuals, similar to mark-recapture studies. We found that differences in both movement patterns and detectability between males and females produce biased estimates of sex ratios. However, increasing the sampling effort or the number of sampling days improves the ability of passive or active capture methods to properly sample sex ratio. Thus, prior knowledge regarding movement patterns and detectability for species is important information to guide field studies aiming to understand sex ratio related patterns.

  16. Point-Mass Model for Nano-Patterning Using Dip-Pen Nanolithography (DPN

    Directory of Open Access Journals (Sweden)

    Seok-Won Kang

    2011-04-01

    Full Text Available Micro-cantilevers are frequently used as scanning probes and sensors in micro-electromechanical systems (MEMS. Usually micro-cantilever based sensors operate by detecting changes in cantilever vibration modes (e.g., bending or torsional vibration frequency or surface stresses - when a target analyte is adsorbed on the surface. The catalyst for chemical reactions (i.e., for a specific analyte can be deposited on micro-cantilevers by using Dip-Pen Nanolithography (DPN technique. In this study, we simulate the vibration mode in nano-patterning processes by using a Point-Mass Model (or Lumped Parameter Model. The results from the simulations are used to derive the stability of writing and reading mode for a particular driving frequency during the DPN process. In addition, we analyze the sensitivity of the tip-sample interaction forces in fluid (ink solution by utilizing the Derjaguin-Muller-Toporov (DMT contact theory.

  17. Detecting causality from online psychiatric texts using inter-sentential language patterns

    Directory of Open Access Journals (Sweden)

    Wu Jheng-Long

    2012-07-01

    Full Text Available Abstract Background Online psychiatric texts are natural language texts expressing depressive problems, published by Internet users via community-based web services such as web forums, message boards and blogs. Understanding the cause-effect relations embedded in these psychiatric texts can provide insight into the authors’ problems, thus increasing the effectiveness of online psychiatric services. Methods Previous studies have proposed the use of word pairs extracted from a set of sentence pairs to identify cause-effect relations between sentences. A word pair is made up of two words, with one coming from the cause text span and the other from the effect text span. Analysis of the relationship between these words can be used to capture individual word associations between cause and effect sentences. For instance, (broke up, life and (boyfriend, meaningless are two word pairs extracted from the sentence pair: “I broke up with my boyfriend. Life is now meaningless to me”. The major limitation of word pairs is that individual words in sentences usually cannot reflect the exact meaning of the cause and effect events, and thus may produce semantically incomplete word pairs, as the previous examples show. Therefore, this study proposes the use of inter-sentential language patterns such as ≪broke up, boyfriend>, Results Performance was evaluated on a corpus of texts collected from PsychPark (http://www.psychpark.org, a virtual psychiatric clinic maintained by a group of volunteer professionals from the Taiwan Association of Mental Health Informatics. Experimental results show that the use of inter-sentential language patterns outperformed the use of word pairs proposed in previous studies. Conclusions This study demonstrates the acquisition of inter-sentential language patterns for causality detection from online psychiatric texts. Such semantically more complete and precise features can improve causality detection performance.

  18. Plasmonic detection and visualization of directed adsorption of charged single nanoparticles to patterned surfaces

    International Nuclear Information System (INIS)

    Scherbahn, Vitali; Nizamov, Shavkat; Mirsky, Vladimir M.

    2016-01-01

    It has recently been shown that surface plasmon microscopy (SPM) allows single nanoparticles (NPs) on sensor surfaces to be detected and analyzed. The authors have applied this technique to study the adsorption of single metallic and plastic NPs. Binding of gold NPs (40, 60 and 100 nm in size) and of 100 nm polystyrene NPs to gold surfaces modified by differently ω-functionalized alkyl thiols was studied first. Self-assembled monolayers (SAM) with varying terminal functions including amino, carboxy, oligo(ethylene glycol), methyl, or trimethylammonium groups were deposited on gold films to form surfaces possessing different charge and hydrophobicity. The affinity of NPs to these surfaces depends strongly on the type of coating. SAMs terminated with trimethylammonium groups and carboxy group display highly different affinity and therefore were preferred when creating patterned charged surfaces. Citrate-stabilized gold NPs and sulfate-terminated polystyrene NPs were used as negatively charged NPs, while branched polyethylenimine-coated silver NPs were used as positively charged NPs. It is shown that the charged patterned areas on the gold films are capable of selectively adsorbing oppositely charged NPs that can be detected and analyzed with an ∼1 ng⋅mL −1 detection limit. (author)

  19. Modeling and inferring cleavage patterns in proliferating epithelia.

    Directory of Open Access Journals (Sweden)

    Ankit B Patel

    2009-06-01

    Full Text Available The regulation of cleavage plane orientation is one of the key mechanisms driving epithelial morphogenesis. Still, many aspects of the relationship between local cleavage patterns and tissue-level properties remain poorly understood. Here we develop a topological model that simulates the dynamics of a 2D proliferating epithelium from generation to generation, enabling the exploration of a wide variety of biologically plausible cleavage patterns. We investigate a spectrum of models that incorporate the spatial impact of neighboring cells and the temporal influence of parent cells on the choice of cleavage plane. Our findings show that cleavage patterns generate "signature" equilibrium distributions of polygonal cell shapes. These signatures enable the inference of local cleavage parameters such as neighbor impact, maternal influence, and division symmetry from global observations of the distribution of cell shape. Applying these insights to the proliferating epithelia of five diverse organisms, we find that strong division symmetry and moderate neighbor/maternal influence are required to reproduce the predominance of hexagonal cells and low variability in cell shape seen empirically. Furthermore, we present two distinct cleavage pattern models, one stochastic and one deterministic, that can reproduce the empirical distribution of cell shapes. Although the proliferating epithelia of the five diverse organisms show a highly conserved cell shape distribution, there are multiple plausible cleavage patterns that can generate this distribution, and experimental evidence suggests that indeed plants and fruitflies use distinct division mechanisms.

  20. Modelling survival: exposure pattern, species sensitivity and uncertainty.

    Science.gov (United States)

    Ashauer, Roman; Albert, Carlo; Augustine, Starrlight; Cedergreen, Nina; Charles, Sandrine; Ducrot, Virginie; Focks, Andreas; Gabsi, Faten; Gergs, André; Goussen, Benoit; Jager, Tjalling; Kramer, Nynke I; Nyman, Anna-Maija; Poulsen, Veronique; Reichenberger, Stefan; Schäfer, Ralf B; Van den Brink, Paul J; Veltman, Karin; Vogel, Sören; Zimmer, Elke I; Preuss, Thomas G

    2016-07-06

    The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

  1. Failure detection by adaptive lattice modelling using Kalman filtering methodology : application to NPP

    International Nuclear Information System (INIS)

    Ciftcioglu, O.

    1991-03-01

    Detection of failure in the operational status of a NPP is described. The method uses lattice form of the signal modelling established by means of Kalman filtering methodology. In this approach each lattice parameter is considered to be a state and the minimum variance estimate of the states is performed adaptively by optimal parameter estimation together with fast convergence and favourable statistical properties. In particular, the state covariance is also the covariance of the error committed by that estimate of the state value and the Mahalanobis distance formed for pattern comparison takes x 2 distribution for normally distributed signals. The failure detection is performed after a decision making process by probabilistic assessments based on the statistical information provided. The failure detection system is implemented in multi-channel signal environment of Borssele NPP and its favourable features are demonstrated. (author). 29 refs.; 7 figs

  2. Temporal diagnostic analysis of the SWAT model to detect dominant periods of poor model performance

    Science.gov (United States)

    Guse, Björn; Reusser, Dominik E.; Fohrer, Nicola

    2013-04-01

    four reoccurring patterns of typical model performance, which can be related to different phases of the hydrograph. Overall, the baseflow cluster has the lowest performance. By combining the periods with poor model performance with the dominant model components during these phases, the groundwater module was detected as the model part with the highest potential for model improvements. The detection of dominant processes in periods of poor model performance enhances the understanding of the SWAT model. Based on this, concepts how to improve the SWAT model structure for the application in German lowland catchment are derived.

  3. Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks

    Science.gov (United States)

    Chen, Bo; Zang, Chuanzhi

    2009-03-01

    This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune recognition and learning mechanisms. The structure damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to generate recognition feature vectors that are able to match the training data. In addition, the immune learning algorithm can learn and remember various data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) Structural Health Monitoring Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms.

  4. Stripe patterns in a model for block polymers

    NARCIS (Netherlands)

    Peletier, M.A.; Veneroni, M.

    2009-01-01

    We consider a pattern-forming system in two space dimensions defined by an energy Ge. The functional Ge models strong phase separation in AB diblock copolymer melts, and patterns are represented by {0, 1}-valued functions; the values 0 and 1 correspond to the A and B phases. The parameter e is the

  5. Stripe patterns in a model for block polymers

    NARCIS (Netherlands)

    Peletier, M.A.; Veneroni, M.

    2010-01-01

    We consider a pattern-forming system in two space dimensions defined by an energy Ge. The functional Ge models strong phase separation in AB diblock copolymer melts, and patterns are represented by {0, 1}-valued functions; the values 0 and 1 correspond to the A and B phases. The parameter e is the

  6. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    Directory of Open Access Journals (Sweden)

    Pudjo Sukarno

    2007-05-01

    Full Text Available Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained from oil fields. Therefore, for training purposes hypothetical data are developed using the transmission pipeline model, by applying various physical configuration of pipeline and applying oil properties correlations to estimate the value of oil density and viscosity. The various leak locations and leak rates are also represented in this model. The prediction of those two leak parameters will be completed until the total error is less than certain value of tolerance, or until iterations level is reached. To recognize the pattern, forward procedure is conducted. The application of this approach produces conclusion that for certain pipeline network configuration, the higher number of iterations will produce accurate result. The number of iterations depend on the leakage rate, the smaller leakage rate, the higher number of iterations are required. The accuracy of this approach is clearly determined by the quality of training data. Therefore, in the preparation of training data the results of pressure drop calculations should be validated by the real measurement of pressure drop along the pipeline. For the accuracy purposes, there are possibility to change the pressure drop and fluid properties correlations, to get the better results. The results of this research are expected to give real contribution for giving an early detection of oil-spill in oil fields.

  7. [Analysis of dietary pattern and diabetes mellitus influencing factors identified by classification tree model in adults of Fujian].

    Science.gov (United States)

    Yu, F L; Ye, Y; Yan, Y S

    2017-05-10

    Objective: To find out the dietary patterns and explore the relationship between environmental factors (especially dietary patterns) and diabetes mellitus in the adults of Fujian. Methods: Multi-stage sampling method were used to survey residents aged ≥18 years by questionnaire, physical examination and laboratory detection in 10 disease surveillance points in Fujian. Factor analysis was used to identify the dietary patterns, while logistic regression model was applied to analyze relationship between dietary patterns and diabetes mellitus, and classification tree model was adopted to identify the influencing factors for diabetes mellitus. Results: There were four dietary patterns in the population, including meat, plant, high-quality protein, and fried food and beverages patterns. The result of logistic analysis showed that plant pattern, which has higher factor loading of fresh fruit-vegetables and cereal-tubers, was a protective factor for non-diabetes mellitus. The risk of diabetes mellitus in the population at T2 and T3 levels of factor score were 0.727 (95 %CI: 0.561-0.943) times and 0.736 (95 %CI : 0.573-0.944) times higher, respectively, than those whose factor score was in lowest quartile. Thirteen influencing factors and eleven group at high-risk for diabetes mellitus were identified by classification tree model. The influencing factors were dyslipidemia, age, family history of diabetes, hypertension, physical activity, career, sex, sedentary time, abdominal adiposity, BMI, marital status, sleep time and high-quality protein pattern. Conclusion: There is a close association between dietary patterns and diabetes mellitus. It is necessary to promote healthy and reasonable diet, strengthen the monitoring and control of blood lipids, blood pressure and body weight, and have good lifestyle for the prevention and control of diabetes mellitus.

  8. Water-Tree Modelling and Detection for Underground Cables

    Science.gov (United States)

    Chen, Qi

    is used to model water-tree in large system. Both empirical measurements and the mathematical model show that the impedance of early-stage water-tree is extremely large. As the result, traditional detection methods such Tan-Delta or Partial Discharge are not effective due to the excessively high accuracy requirement. A high-frequency pulse detection method is developed instead. The water-tree impedance is capacitive in nature and it can be reduced to manageable level by high-frequency inputs. The method is able to determine the location of early-stage water-tree in long-distance cables using economically feasible equipment. A pattern recognition method is developed to estimate the severity of water-tree using its pulse response from the high-frequency test method. The early-warning system for water-tree appearance is a tool developed to assist the practical implementation of the high-frequency pulse detection method. Although the equipment used by the detection method is economically feasible, it is still a specialized test and not designed for constant monitoring of the system. The test also place heavy stress on the cable and it is most effective when the cable is taken offline. As the result, utilities need a method to estimate the likelihood of water-tree presence before subjecting the cable to the specialized test. The early-warning system takes advantage of naturally occurring high-frequency events in the system and uses a deviation-comparison method to estimate the probability of water-tree presence on the cable. If the likelihood is high, then the utility can use the high-frequency pulse detection method to obtain accurate results. Specific pulse response patterns can be used to calculate the capacitance of water-tree. The calculated result, however, is subjected to margins of error due to limitations from the real system. There are both long-term and short-term methods to improve the accuracy. Computation algorithm improvement allows immediate improvement on

  9. BUSINESS MODEL PATTERNS FOR DISRUPTIVE TECHNOLOGIES

    OpenAIRE

    BENJAMIN AMSHOFF; CHRISTIAN DÜLME; JULIAN ECHTERFELD; JÜRGEN GAUSEMEIER

    2015-01-01

    Companies nowadays face a myriad of business opportunities as a direct consequence of manifold disruptive technology developments. As a basic characteristic, disruptive technologies lead to a severe shift in value-creation networks giving rise to new market segments. One of the key challenges is to anticipate the business logics within these nascent and formerly unknown markets. Business model patterns promise to tackle this challenge. They can be interpreted as proven business model elements...

  10. Pattern Formation in Predator-Prey Model with Delay and Cross Diffusion

    Directory of Open Access Journals (Sweden)

    Xinze Lian

    2013-01-01

    Full Text Available We consider the effect of time delay and cross diffusion on the dynamics of a modified Leslie-Gower predator-prey model incorporating a prey refuge. Based on the stability analysis, we demonstrate that delayed feedback may generate Hopf and Turing instability under some conditions, resulting in spatial patterns. One of the most interesting findings is that the model exhibits complex pattern replication: the model dynamics exhibits a delay and diffusion controlled formation growth not only to spots, stripes, and holes, but also to spiral pattern self-replication. The results indicate that time delay and cross diffusion play important roles in pattern formation.

  11. Effective and efficient model clone detection

    DEFF Research Database (Denmark)

    Störrle, Harald

    2015-01-01

    Code clones are a major source of software defects. Thus, it is likely that model clones (i.e., duplicate fragments of models) have a significant negative impact on model quality, and thus, on any software created based on those models, irrespective of whether the software is generated fully...... automatically (“MDD-style”) or hand-crafted following the blueprint defined by the model (“MBSD-style”). Unfortunately, however, model clones are much less well studied than code clones. In this paper, we present a clone detection algorithm for UML domain models. Our approach covers a much greater variety...... of model types than existing approaches while providing high clone detection rates at high speed....

  12. Large-scale hydrology in Europe : observed patterns and model performance

    Energy Technology Data Exchange (ETDEWEB)

    Gudmundsson, Lukas

    2011-06-15

    In a changing climate, terrestrial water storages are of great interest as water availability impacts key aspects of ecosystem functioning. Thus, a better understanding of the variations of wet and dry periods will contribute to fully grasp processes of the earth system such as nutrient cycling and vegetation dynamics. Currently, river runoff from small, nearly natural, catchments is one of the few variables of the terrestrial water balance that is regularly monitored with detailed spatial and temporal coverage on large scales. River runoff, therefore, provides a foundation to approach European hydrology with respect to observed patterns on large scales, with regard to the ability of models to capture these.The analysis of observed river flow from small catchments, focused on the identification and description of spatial patterns of simultaneous temporal variations of runoff. These are dominated by large-scale variations of climatic variables but also altered by catchment processes. It was shown that time series of annual low, mean and high flows follow the same atmospheric drivers. The observation that high flows are more closely coupled to large scale atmospheric drivers than low flows, indicates the increasing influence of catchment properties on runoff under dry conditions. Further, it was shown that the low-frequency variability of European runoff is dominated by two opposing centres of simultaneous variations, such that dry years in the north are accompanied by wet years in the south.Large-scale hydrological models are simplified representations of our current perception of the terrestrial water balance on large scales. Quantification of the models strengths and weaknesses is the prerequisite for a reliable interpretation of simulation results. Model evaluations may also enable to detect shortcomings with model assumptions and thus enable a refinement of the current perception of hydrological systems. The ability of a multi model ensemble of nine large

  13. From sounds to words: a neurocomputational model of adaptation, inhibition and memory processes in auditory change detection.

    Science.gov (United States)

    Garagnani, Max; Pulvermüller, Friedemann

    2011-01-01

    Most animals detect sudden changes in trains of repeated stimuli but only some can learn a wide range of sensory patterns and recognise them later, a skill crucial for the evolutionary success of higher mammals. Here we use a neural model mimicking the cortical anatomy of sensory and motor areas and their connections to explain brain activity indexing auditory change and memory access. Our simulations indicate that while neuronal adaptation and local inhibition of cortical activity can explain aspects of change detection as observed when a repeated unfamiliar sound changes in frequency, the brain dynamics elicited by auditory stimulation with well-known patterns (such as meaningful words) cannot be accounted for on the basis of adaptation and inhibition alone. Specifically, we show that the stronger brain responses observed to familiar stimuli in passive oddball tasks are best explained in terms of activation of memory circuits that emerged in the cortex during the learning of these stimuli. Such memory circuits, and the activation enhancement they entail, are absent for unfamiliar stimuli. The model illustrates how basic neurobiological mechanisms, including neuronal adaptation, lateral inhibition, and Hebbian learning, underlie neuronal assembly formation and dynamics, and differentially contribute to the brain's major change detection response, the mismatch negativity. Copyright © 2010 Elsevier Inc. All rights reserved.

  14. Large-Scale Patterns in a Minimal Cognitive Flocking Model: Incidental Leaders, Nematic Patterns, and Aggregates

    Science.gov (United States)

    Barberis, Lucas; Peruani, Fernando

    2016-12-01

    We study a minimal cognitive flocking model, which assumes that the moving entities navigate using the available instantaneous visual information exclusively. The model consists of active particles, with no memory, that interact by a short-ranged, position-based, attractive force, which acts inside a vision cone (VC), and lack velocity-velocity alignment. We show that this active system can exhibit—due to the VC that breaks Newton's third law—various complex, large-scale, self-organized patterns. Depending on parameter values, we observe the emergence of aggregates or millinglike patterns, the formation of moving—locally polar—files with particles at the front of these structures acting as effective leaders, and the self-organization of particles into macroscopic nematic structures leading to long-ranged nematic order. Combining simulations and nonlinear field equations, we show that position-based active models, as the one analyzed here, represent a new class of active systems fundamentally different from other active systems, including velocity-alignment-based flocking systems. The reported results are of prime importance in the study, interpretation, and modeling of collective motion patterns in living and nonliving active systems.

  15. A Comparative Study of Frequent and Maximal Periodic Pattern Mining Algorithms in Spatiotemporal Databases

    Science.gov (United States)

    Obulesu, O.; Rama Mohan Reddy, A., Dr; Mahendra, M.

    2017-08-01

    Detecting regular and efficient cyclic models is the demanding activity for data analysts due to unstructured, vigorous and enormous raw information produced from web. Many existing approaches generate large candidate patterns in the occurrence of huge and complex databases. In this work, two novel algorithms are proposed and a comparative examination is performed by considering scalability and performance parameters. The first algorithm is, EFPMA (Extended Regular Model Detection Algorithm) used to find frequent sequential patterns from the spatiotemporal dataset and the second one is, ETMA (Enhanced Tree-based Mining Algorithm) for detecting effective cyclic models with symbolic database representation. EFPMA is an algorithm grows models from both ends (prefixes and suffixes) of detected patterns, which results in faster pattern growth because of less levels of database projection compared to existing approaches such as Prefixspan and SPADE. ETMA uses distinct notions to store and manage transactions data horizontally such as segment, sequence and individual symbols. ETMA exploits a partition-and-conquer method to find maximal patterns by using symbolic notations. Using this algorithm, we can mine cyclic models in full-series sequential patterns including subsection series also. ETMA reduces the memory consumption and makes use of the efficient symbolic operation. Furthermore, ETMA only records time-series instances dynamically, in terms of character, series and section approaches respectively. The extent of the pattern and proving efficiency of the reducing and retrieval techniques from synthetic and actual datasets is a really open & challenging mining problem. These techniques are useful in data streams, traffic risk analysis, medical diagnosis, DNA sequence Mining, Earthquake prediction applications. Extensive investigational outcomes illustrates that the algorithms outperforms well towards efficiency and scalability than ECLAT, STNR and MAFIA approaches.

  16. Cylindrical SUV distribution model for detecting skin lesions in body trunk FDG-PET/CT images

    International Nuclear Information System (INIS)

    Nemoto, Mitsutaka; Nomura, Yukihiro; Masutani, Yoshitaka; Yoshikawa, Takeharu; Hayashi, Naoto; Yoshioka, Naoki; Ohtomo, Kuni; Hanaoka, Shouhei

    2010-01-01

    We have been developing a computerized detection method for skin lesions in body trunk fluorodeoxyglucose-positron emission tomography (FDG-PET)/CT images. Spots on the skin with a high standard uptake value (SUV) are due not only to glucose metabolism in skin lesions but also to the physiological metabolism of organs near the skin. The distribution pattern of regional SUV on the skin is important information for the differential diagnosis of such high-SUV spots. In this study, we have developed a new skin lesion detection method based on a cylindrical SUV distribution model of the skin. The shape of the SUV distribution model is an approximation of the body trunk, and the SUV distribution model includes standard values for regional skin SUV. Classifier ensembles based on CT image features, SUV features, and subtraction features between the SUVs in FDG-PET images and the values in the SUV distribution model are used to extract and classify candidate regions for skin lesions. In a study of skin lesion detection using FDG-PET/CT images in 36 clinical cases, the true-positive rate was 61.7%, with 11.7 false-positive regions per case. The training results of the classifier ensemble for extracting and classifying candidate regions showed the effective features for detecting skin lesions in the study. (author)

  17. Crossover patterning by the beam-film model: analysis and implications.

    Directory of Open Access Journals (Sweden)

    Liangran Zhang

    2014-01-01

    Full Text Available Crossing-over is a central feature of meiosis. Meiotic crossover (CO sites are spatially patterned along chromosomes. CO-designation at one position disfavors subsequent CO-designation(s nearby, as described by the classical phenomenon of CO interference. If multiple designations occur, COs tend to be evenly spaced. We have previously proposed a mechanical model by which CO patterning could occur. The central feature of a mechanical mechanism is that communication along the chromosomes, as required for CO interference, can occur by redistribution of mechanical stress. Here we further explore the nature of the beam-film model, its ability to quantitatively explain CO patterns in detail in several organisms, and its implications for three important patterning-related phenomena: CO homeostasis, the fact that the level of zero-CO bivalents can be low (the "obligatory CO", and the occurrence of non-interfering COs. Relationships to other models are discussed.

  18. Nuclear emulsions for the detection of micrometric-scale fringe patterns: an application to positron interferometry

    Science.gov (United States)

    Aghion, S.; Ariga, A.; Bollani, M.; Ereditato, A.; Ferragut, R.; Giammarchi, M.; Lodari, M.; Pistillo, C.; Sala, S.; Scampoli, P.; Vladymyrov, M.

    2018-05-01

    Nuclear emulsions are capable of very high position resolution in the detection of ionizing particles. This feature can be exploited to directly resolve the micrometric-scale fringe pattern produced by a matter-wave interferometer for low energy positrons (in the 10–20 keV range). We have tested the performance of emulsion films in this specific scenario. Exploiting silicon nitride diffraction gratings as absorption masks, we produced periodic patterns with features comparable to the expected interferometer signal. Test samples with periodicities of 6, 7 and 20 μ m were exposed to the positron beam, and the patterns clearly reconstructed. Our results support the feasibility of matter-wave interferometry experiments with positrons.

  19. Tilted dipole model for bias-dependent photoluminescence pattern

    Energy Technology Data Exchange (ETDEWEB)

    Fujieda, Ichiro, E-mail: fujieda@se.ritsumei.ac.jp; Suzuki, Daisuke; Masuda, Taishi [Department of Electrical and Electronic Engineering, Ritsumeikan University, Kusatsu 525-8577 (Japan)

    2014-12-14

    In a guest-host system containing elongated dyes and a nematic liquid crystal, both molecules are aligned to each other. An external bias tilts these molecules and the radiation pattern of the system is altered. A model is proposed to describe this bias-dependent photoluminescence patterns. It divides the liquid crystal/dye layer into sub-layers that contain electric dipoles with specific tilt angles. Each sub-layer emits linearly polarized light. Its radiation pattern is toroidal and is determined by the tilt angle. Its intensity is assumed to be proportional to the power of excitation light absorbed by the sub-layer. This is calculated by the Lambert-Beer's Law. The absorption coefficient is assumed to be proportional to the cross-section of the tilted dipole moment, in analogy to the ellipsoid of refractive index, to evaluate the cross-section for each polarized component of the excitation light. Contributions from all the sub-layers are added to give a final expression for the radiation pattern. Self-absorption is neglected. The model is simplified by reducing the number of sub-layers. Analytical expressions are derived for a simple case that consists of a single layer with tilted dipoles sandwiched by two layers with horizontally-aligned dipoles. All the parameters except for the tilt angle can be determined by measuring transmittance of the excitation light. The model roughly reproduces the bias-dependent photoluminescence patterns of a cell containing 0.5 wt. % coumarin 6. It breaks down at large emission angles. Measured spectral changes suggest that the discrepancy is due to self-absorption and re-emission.

  20. Research in Model-Based Change Detection and Site Model Updating

    National Research Council Canada - National Science Library

    Nevatia, R

    1998-01-01

    .... Some of these techniques also are applicable to automatic site modeling and some of our change detection techniques may apply to detection of larger mobile objects, such as airplanes. We have implemented an interactive modeling system that works in conjunction with our automatic system to minimize the need for tedious interaction.

  1. Turing patterns and long-time behavior in a three-species food-chain model

    KAUST Repository

    Parshad, Rana D.

    2014-08-01

    We consider a spatially explicit three-species food chain model, describing generalist top predator-specialist middle predator-prey dynamics. We investigate the long-time dynamics of the model and show the existence of a finite dimensional global attractor in the product space, L2(Ω). We perform linear stability analysis and show that the model exhibits the phenomenon of Turing instability, as well as diffusion induced chaos. Various Turing patterns such as stripe patterns, mesh patterns, spot patterns, labyrinth patterns and weaving patterns are obtained, via numerical simulations in 1d as well as in 2d. The Turing and non-Turing space, in terms of model parameters, is also explored. Finally, we use methods from nonlinear time series analysis to reconstruct a low dimensional chaotic attractor of the model, and estimate its fractal dimension. This provides a lower bound, for the fractal dimension of the attractor, of the spatially explicit model. © 2014 Elsevier Inc.

  2. A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data

    KAUST Repository

    Sepúlveda, Nuno

    2013-02-26

    Background: The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to describe different data patterns, whilst robust against deviations from the often assumed Poisson model.Results: Using sequence coverage data of 7 Plasmodium falciparum malaria genomes (3D7 reference strain, HB3, DD2, 7G8, GB4, OX005, and OX006), we showed that empirical coverage distributions are intrinsically asymmetric and overdispersed in relation to the Poisson model. We also demonstrated a low baseline false positive rate for the proposed methodology using 3D7 resequencing data and simulation. When applied to the non-reference isolate data, our approach detected known CNV hits, including an amplification of the PfMDR1 locus in DD2 and a large deletion in the CLAG3.2 gene in GB4, and putative novel CNV regions. When compared to the recently available FREEC and cn.MOPS approaches, our findings were more concordant with putative hits from the highest quality array data for the 7G8 and GB4 isolates.Conclusions: In summary, the proposed methodology brings an increase in flexibility, robustness, accuracy and statistical rigour to CNV detection using sequence coverage data. 2013 Seplveda et al.; licensee BioMed Central Ltd.

  3. A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data.

    Science.gov (United States)

    Sepúlveda, Nuno; Campino, Susana G; Assefa, Samuel A; Sutherland, Colin J; Pain, Arnab; Clark, Taane G

    2013-02-26

    The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to describe different data patterns, whilst robust against deviations from the often assumed Poisson model. Using sequence coverage data of 7 Plasmodium falciparum malaria genomes (3D7 reference strain, HB3, DD2, 7G8, GB4, OX005, and OX006), we showed that empirical coverage distributions are intrinsically asymmetric and overdispersed in relation to the Poisson model. We also demonstrated a low baseline false positive rate for the proposed methodology using 3D7 resequencing data and simulation. When applied to the non-reference isolate data, our approach detected known CNV hits, including an amplification of the PfMDR1 locus in DD2 and a large deletion in the CLAG3.2 gene in GB4, and putative novel CNV regions. When compared to the recently available FREEC and cn.MOPS approaches, our findings were more concordant with putative hits from the highest quality array data for the 7G8 and GB4 isolates. In summary, the proposed methodology brings an increase in flexibility, robustness, accuracy and statistical rigour to CNV detection using sequence coverage data.

  4. A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data

    KAUST Repository

    Sepú lveda, Nuno; Campino, Susana G; Assefa, Samuel A; Sutherland, Colin J; Pain, Arnab; Clark, Taane G

    2013-01-01

    Background: The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to describe different data patterns, whilst robust against deviations from the often assumed Poisson model.Results: Using sequence coverage data of 7 Plasmodium falciparum malaria genomes (3D7 reference strain, HB3, DD2, 7G8, GB4, OX005, and OX006), we showed that empirical coverage distributions are intrinsically asymmetric and overdispersed in relation to the Poisson model. We also demonstrated a low baseline false positive rate for the proposed methodology using 3D7 resequencing data and simulation. When applied to the non-reference isolate data, our approach detected known CNV hits, including an amplification of the PfMDR1 locus in DD2 and a large deletion in the CLAG3.2 gene in GB4, and putative novel CNV regions. When compared to the recently available FREEC and cn.MOPS approaches, our findings were more concordant with putative hits from the highest quality array data for the 7G8 and GB4 isolates.Conclusions: In summary, the proposed methodology brings an increase in flexibility, robustness, accuracy and statistical rigour to CNV detection using sequence coverage data. 2013 Seplveda et al.; licensee BioMed Central Ltd.

  5. Space debris: modeling and detectability

    Science.gov (United States)

    Wiedemann, C.; Lorenz, J.; Radtke, J.; Kebschull, C.; Horstmann, A.; Stoll, E.

    2017-01-01

    High precision orbit determination is required for the detection and removal of space debris. Knowledge of the distribution of debris objects in orbit is necessary for orbit determination by active or passive sensors. The results can be used to investigate the orbits on which objects of a certain size at a certain frequency can be found. The knowledge of the orbital distribution of the objects as well as their properties in accordance with sensor performance models provide the basis for estimating the expected detection rates. Comprehensive modeling of the space debris environment is required for this. This paper provides an overview of the current state of knowledge about the space debris environment. In particular non-cataloged small objects are evaluated. Furthermore, improvements concerning the update of the current space debris model are addressed. The model of the space debris environment is based on the simulation of historical events, such as fragmentations due to explosions and collisions that actually occurred in Earth orbits. The orbital distribution of debris is simulated by propagating the orbits considering all perturbing forces up to a reference epoch. The modeled object population is compared with measured data and validated. The model provides a statistical distribution of space objects, according to their size and number. This distribution is based on the correct consideration of orbital mechanics. This allows for a realistic description of the space debris environment. Subsequently, a realistic prediction can be provided concerning the question, how many pieces of debris can be expected on certain orbits. To validate the model, a software tool has been developed which allows the simulation of the observation behavior of ground-based or space-based sensors. Thus, it is possible to compare the results of published measurement data with simulated detections. This tool can also be used for the simulation of sensor measurement campaigns. It is

  6. Type-2 fuzzy graphical models for pattern recognition

    CERN Document Server

    Zeng, Jia

    2015-01-01

    This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.

  7. Cellular automaton modeling of biological pattern formation characterization, examples, and analysis

    CERN Document Server

    Deutsch, Andreas

    2017-01-01

    This text explores the use of cellular automata in modeling pattern formation in biological systems. It describes several mathematical modeling approaches utilizing cellular automata that can be used to study the dynamics of interacting cell systems both in simulation and in practice. New in this edition are chapters covering cell migration, tissue development, and cancer dynamics, as well as updated references and new research topic suggestions that reflect the rapid development of the field. The book begins with an introduction to pattern-forming principles in biology and the various mathematical modeling techniques that can be used to analyze them. Cellular automaton models are then discussed in detail for different types of cellular processes and interactions, including random movement, cell migration, adhesive cell interaction, alignment and cellular swarming, growth processes, pigment cell pattern formation, tissue development, tumor growth and invasion, and Turing-type patterns and excitable media. In ...

  8. Spatiotemporal Patterns in a Ratio-Dependent Food Chain Model with Reaction-Diffusion

    Directory of Open Access Journals (Sweden)

    Lei Zhang

    2014-01-01

    Full Text Available Predator-prey models describe biological phenomena of pursuit-evasion interaction. And this interaction exists widely in the world for the necessary energy supplement of species. In this paper, we have investigated a ratio-dependent spatially extended food chain model. Based on the bifurcation analysis (Hopf and Turing, we give the spatial pattern formation via numerical simulation, that is, the evolution process of the system near the coexistence equilibrium point (u2*,v2*,w2*, and find that the model dynamics exhibits complex pattern replication. For fixed parameters, on increasing the control parameter c1, the sequence “holes → holes-stripe mixtures → stripes → spots-stripe mixtures → spots” pattern is observed. And in the case of pure Hopf instability, the model exhibits chaotic wave pattern replication. Furthermore, we consider the pattern formation in the case of which the top predator is extinct, that is, the evolution process of the system near the equilibrium point (u1*,v1*,0, and find that the model dynamics exhibits stripes-spots pattern replication. Our results show that reaction-diffusion model is an appropriate tool for investigating fundamental mechanism of complex spatiotemporal dynamics. It will be useful for studying the dynamic complexity of ecosystems.

  9. Patterns in coupled water and energy cycle: Modeling, synthesis with observations, and assessing the subsurface-landsurface interactions

    Science.gov (United States)

    Rahman, A.; Kollet, S. J.; Sulis, M.

    2013-12-01

    In the terrestrial hydrological cycle, the atmosphere and the free groundwater table act as the upper and lower boundary condition, respectively, in the non-linear two-way exchange of mass and energy across the land surface. Identifying and quantifying the interactions among various atmospheric-subsurface-landsurface processes is complicated due to the diverse spatiotemporal scales associated with these processes. In this study, the coupled subsurface-landsurface model ParFlow.CLM was applied over a ~28,000 km2 model domain encompassing the Rur catchment, Germany, to simulate the fluxes of the coupled water and energy cycle. The model was forced by hourly atmospheric data from the COSMO-DE model (numerical weather prediction system of the German Weather Service) over one year. Following a spinup period, the model results were synthesized with observed river discharge, soil moisture, groundwater table depth, temperature, and landsurface energy flux data at different sites in the Rur catchment. It was shown that the model is able to reproduce reasonably the dynamics and also absolute values in observed fluxes and state variables without calibration. The spatiotemporal patterns in simulated water and energy fluxes as well as the interactions were studied using statistical, geostatistical and wavelet transform methods. While spatial patterns in the mass and energy fluxes can be predicted from atmospheric forcing and power law scaling in the transition and winter months, it appears that, in the summer months, the spatial patterns are determined by the spatially correlated variability in groundwater table depth. Continuous wavelet transform techniques were applied to study the variability of the catchment average mass and energy fluxes at varying time scales. From this analysis, the time scales associated with significant interactions among different mass and energy balance components were identified. The memory of precipitation variability in subsurface hydrodynamics

  10. A Study on the Model of Detecting the Variation of Geomagnetic Intensity Based on an Adapted Motion Strategy

    Directory of Open Access Journals (Sweden)

    Hong Li

    2017-12-01

    Full Text Available By simulating the geomagnetic fields and analyzing thevariation of intensities, this paper presents a model for calculating the objective function ofan Autonomous Underwater Vehicle (AUVgeomagnetic navigation task. By investigating the biologically inspired strategies, the AUV successfullyreachesthe destination duringgeomagnetic navigation without using the priori geomagnetic map. Similar to the pattern of a flatworm, the proposed algorithm relies on a motion pattern to trigger a local searching strategy by detecting the real-time geomagnetic intensity. An adapted strategy is then implemented, which is biased on the specific target. The results show thereliabilityandeffectivenessofthe proposed algorithm.

  11. Detection of Propagating Fast Sausage Waves through Detailed Analysis of a Zebra-pattern Fine Structure in a Solar Radio Burst

    Science.gov (United States)

    Kaneda, K.; Misawa, H.; Iwai, K.; Masuda, S.; Tsuchiya, F.; Katoh, Y.; Obara, T.

    2018-03-01

    Various magnetohydrodynamic (MHD) waves have recently been detected in the solar corona and investigated intensively in the context of coronal heating and coronal seismology. In this Letter, we report the first detection of short-period propagating fast sausage mode waves in a metric radio spectral fine structure observed with the Assembly of Metric-band Aperture Telescope and Real-time Analysis System. Analysis of Zebra patterns (ZPs) in a type-IV burst revealed a quasi-periodic modulation in the frequency separation between the adjacent stripes of the ZPs (Δf ). The observed quasi-periodic modulation had a period of 1–2 s and exhibited a characteristic negative frequency drift with a rate of 3–8 MHz s‑1. Based on the double plasma resonance model, the most accepted generation model of ZPs, the observed quasi-periodic modulation of the ZP can be interpreted in terms of fast sausage mode waves propagating upward at phase speeds of 3000–8000 km s‑1. These results provide us with new insights for probing the fine structure of coronal loops.

  12. Gait pattern recognition in cerebral palsy patients using neural network modelling

    International Nuclear Information System (INIS)

    Muhammad, J.; Gibbs, S.; Abboud, R.; Anand, S.

    2015-01-01

    Interpretation of gait data obtained from modern 3D gait analysis is a challenging and time consuming task. The aim of this study was to create neural network models which can recognise the gait patterns from pre- and post-treatment and the normal ones. Neural network is a method which works on the principle of learning from experience and then uses the obtained knowledge to predict the unknown. Methods: Twenty-eight patients with cerebral palsy were recruited as subjects whose gait was analysed in pre- and post-treatment. A group of twenty-six normal subjects also participated in this study as control group. All subjects gait was analysed using Vicon Nexus to obtain the gait parameters and kinetic and kinematic parameters of hip, knee and ankle joints in three planes of both limbs. The gait data was used as input to create neural network models. A total of approximately 300 trials were split into 70% and 30% to train and test the models, respectively. Different models were built using different parameters. The gait was categorised as three patterns, i.e., normal, pre- and post-treatments. Result: The results showed that the models using all parameters or using the joint angles and moments could predict the gait patterns with approximately 95% accuracy. Some of the models e.g., the models using joint power and moments, had lower rate in recognition of gait patterns with approximately 70-90% successful ratio. Conclusion: Neural network model can be used in clinical practice to recognise the gait pattern for cerebral palsy patients. (author)

  13. Reticular pattern detection in dermoscopy: an approach using Curvelet Transform

    Directory of Open Access Journals (Sweden)

    Marlene Machado

    Full Text Available Abstract Introduction Dermoscopy is a non-invasive in vivo imaging technique, used in dermatology in feature identification, among pigmented melanocytic neoplasms, from suspicious skin lesions. Often, in the skin exam is possible to ascertain markers, whose identification and proper characterization is difficult, even when it is used a magnifying lens and a source of light. Dermoscopic images are thus a challenging source of a wide range of digital features, frequently with clinical correlation. Among these markers, one of particular interest to diagnosis in skin evaluation is the reticular pattern. Methods This paper presents a novel approach (avoiding pre-processing, e.g. segmentation and filtering for reticular pattern detection in dermoscopic images, using texture spectral analysis. The proposed methodology involves a Curvelet Transform procedure to identify features. Results Feature extraction is applied to identify a set of discriminant characteristics in the reticular pattern, and it is also employed in the automatic classification task. The results obtained are encouraging, presenting Sensitivity and Specificity of 82.35% and 76.79%, respectively. Conclusions These results highlight the use of automatic classification, in the context of artificial intelligence, within a computer-aided diagnosis strategy, as a strong tool to help the human decision making task in clinical practice. Moreover, the results were obtained using images from three different sources, without previous lesion segmentation, achieving to a rapid, robust and low complexity methodology. These properties boost the presented approach to be easily used in clinical practice as an aid to the diagnostic process.

  14. Early Obstacle Detection and Avoidance for All to All Traffic Pattern in Wireless Sensor Networks

    Science.gov (United States)

    Huc, Florian; Jarry, Aubin; Leone, Pierre; Moraru, Luminita; Nikoletseas, Sotiris; Rolim, Jose

    This paper deals with early obstacles recognition in wireless sensor networks under various traffic patterns. In the presence of obstacles, the efficiency of routing algorithms is increased by voluntarily avoiding some regions in the vicinity of obstacles, areas which we call dead-ends. In this paper, we first propose a fast convergent routing algorithm with proactive dead-end detection together with a formal definition and description of dead-ends. Secondly, we present a generalization of this algorithm which improves performances in all to many and all to all traffic patterns. In a third part we prove that this algorithm produces paths that are optimal up to a constant factor of 2π + 1. In a fourth part we consider the reactive version of the algorithm which is an extension of a previously known early obstacle detection algorithm. Finally we give experimental results to illustrate the efficiency of our algorithms in different scenarios.

  15. A model for optimizing file access patterns using spatio-temporal parallelism

    Energy Technology Data Exchange (ETDEWEB)

    Boonthanome, Nouanesengsy [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Patchett, John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Geveci, Berk [Kitware Inc., Clifton Park, NY (United States); Ahrens, James [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Bauer, Andy [Kitware Inc., Clifton Park, NY (United States); Chaudhary, Aashish [Kitware Inc., Clifton Park, NY (United States); Miller, Ross G. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Shipman, Galen M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2013-01-01

    For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible file access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.

  16. Neural communication patterns underlying conflict detection, resolution, and adaptation.

    Science.gov (United States)

    Oehrn, Carina R; Hanslmayr, Simon; Fell, Juergen; Deuker, Lorena; Kremers, Nico A; Do Lam, Anne T; Elger, Christian E; Axmacher, Nikolai

    2014-07-30

    In an ever-changing environment, selecting appropriate responses in conflicting situations is essential for biological survival and social success and requires cognitive control, which is mediated by dorsomedial prefrontal cortex (DMPFC) and dorsolateral prefrontal cortex (DLPFC). How these brain regions communicate during conflict processing (detection, resolution, and adaptation), however, is still unknown. The Stroop task provides a well-established paradigm to investigate the cognitive mechanisms mediating such response conflict. Here, we explore the oscillatory patterns within and between the DMPFC and DLPFC in human epilepsy patients with intracranial EEG electrodes during an auditory Stroop experiment. Data from the DLPFC were obtained from 12 patients. Thereof four patients had additional DMPFC electrodes available for interaction analyses. Our results show that an early θ (4-8 Hz) modulated enhancement of DLPFC γ-band (30-100 Hz) activity constituted a prerequisite for later successful conflict processing. Subsequent conflict detection was reflected in a DMPFC θ power increase that causally entrained DLPFC θ activity (DMPFC to DLPFC). Conflict resolution was thereafter completed by coupling of DLPFC γ power to DMPFC θ oscillations. Finally, conflict adaptation was related to increased postresponse DLPFC γ-band activity and to θ coupling in the reverse direction (DLPFC to DMPFC). These results draw a detailed picture on how two regions in the prefrontal cortex communicate to resolve cognitive conflicts. In conclusion, our data show that conflict detection, control, and adaptation are supported by a sequence of processes that use the interplay of θ and γ oscillations within and between DMPFC and DLPFC. Copyright © 2014 the authors 0270-6474/14/3410438-15$15.00/0.

  17. Simulating discrete models of pattern formation by ion beam sputtering

    International Nuclear Information System (INIS)

    Hartmann, Alexander K; Kree, Reiner; Yasseri, Taha

    2009-01-01

    A class of simple, (2+1)-dimensional, discrete models is reviewed, which allow us to study the evolution of surface patterns on solid substrates during ion beam sputtering (IBS). The models are based on the same assumptions about the erosion process as the existing continuum theories. Several distinct physical mechanisms of surface diffusion are added, which allow us to study the interplay of erosion-driven and diffusion-driven pattern formation. We present results from our own work on evolution scenarios of ripple patterns, especially for longer timescales, where nonlinear effects become important. Furthermore we review kinetic phase diagrams, both with and without sample rotation, which depict the systematic dependence of surface patterns on the shape of energy depositing collision cascades after ion impact. Finally, we discuss some results from more recent work on surface diffusion with Ehrlich-Schwoebel barriers as the driving force for pattern formation during IBS and on Monte Carlo simulations of IBS with codeposition of surfactant atoms.

  18. Transitional Probabilities Are Prioritized over Stimulus/Pattern Probabilities in Auditory Deviance Detection: Memory Basis for Predictive Sound Processing.

    Science.gov (United States)

    Mittag, Maria; Takegata, Rika; Winkler, István

    2016-09-14

    Representations encoding the probabilities of auditory events do not directly support predictive processing. In contrast, information about the probability with which a given sound follows another (transitional probability) allows predictions of upcoming sounds. We tested whether behavioral and cortical auditory deviance detection (the latter indexed by the mismatch negativity event-related potential) relies on probabilities of sound patterns or on transitional probabilities. We presented healthy adult volunteers with three types of rare tone-triplets among frequent standard triplets of high-low-high (H-L-H) or L-H-L pitch structure: proximity deviant (H-H-H/L-L-L), reversal deviant (L-H-L/H-L-H), and first-tone deviant (L-L-H/H-H-L). If deviance detection was based on pattern probability, reversal and first-tone deviants should be detected with similar latency because both differ from the standard at the first pattern position. If deviance detection was based on transitional probabilities, then reversal deviants should be the most difficult to detect because, unlike the other two deviants, they contain no low-probability pitch transitions. The data clearly showed that both behavioral and cortical auditory deviance detection uses transitional probabilities. Thus, the memory traces underlying cortical deviance detection may provide a link between stimulus probability-based change/novelty detectors operating at lower levels of the auditory system and higher auditory cognitive functions that involve predictive processing. Our research presents the first definite evidence for the auditory system prioritizing transitional probabilities over probabilities of individual sensory events. Forming representations for transitional probabilities paves the way for predictions of upcoming sounds. Several recent theories suggest that predictive processing provides the general basis of human perception, including important auditory functions, such as auditory scene analysis. Our

  19. Auditory Pattern Memory and Group Signal Detection

    National Research Council Canada - National Science Library

    Sorkin, Robert

    1997-01-01

    .... The experiments with temporally-coded auditory patterns showed how listeners' attention is influenced by the position and the amount of information carried by different segments of the pattern...

  20. Sentence Level Information Patterns for Novelty Detection

    National Research Council Canada - National Science Library

    Li, Xiaoyan

    2006-01-01

    .... Given a user's information need, some information patterns in sentences such as combinations of query words, sentence lengths, named entities and phrases, and other sentence patterns, may contain...

  1. Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson's disease.

    Science.gov (United States)

    Iakovakis, Dimitrios; Hadjidimitriou, Stelios; Charisis, Vasileios; Bostantzopoulou, Sevasti; Katsarou, Zoe; Hadjileontiadis, Leontios J

    2018-05-16

    Parkinson's disease (PD) is a degenerative movement disorder causing progressive disability that severely affects patients' quality of life. While early treatment can produce significant benefits for patients, the mildness of many early signs combined with the lack of accessible high-frequency monitoring tools may delay clinical diagnosis. To meet this need, user interaction data from consumer technologies have recently been exploited towards unsupervised screening for PD symptoms in daily life. Similarly, this work proposes a method for detecting fine motor skills decline in early PD patients via analysis of patterns emerging from finger interaction with touchscreen smartphones during natural typing. Our approach relies on low-/higher-order statistical features of keystrokes timing and pressure variables, computed from short typing sessions. Features are fed into a two-stage multi-model classification pipeline that reaches a decision on the subject's status (PD patient/control) by gradually fusing prediction probabilities obtained for individual typing sessions and keystroke variables. This method achieved an AUC = 0.92 and 0.82/0.81 sensitivity/specificity (matched groups of 18 early PD patients/15 controls) with discriminant features plausibly correlating with clinical scores of relevant PD motor symptoms. These findings suggest an improvement over similar approaches, thereby constituting a further step towards unobtrusive early PD detection from routine activities.

  2. Breast cancer early detection via tracking of skin back-scattered secondary speckle patterns

    Science.gov (United States)

    Bennett, Aviya; Sirkis, Talia; Beiderman, Yevgeny; Agdarov, Sergey; Beiderman, Yafim; Zalevsky, Zeev

    2018-02-01

    Breast cancer has become a major cause of death among women. The lifetime risk of a woman developing this disease has been established as one in eight. The most useful way to reduce breast cancer death is to treat the disease as early as possible. The existing methods of early diagnostics of breast cancer are mainly based on screening mammography or Magnetic Resonance Imaging (MRI) periodically conducted at medical facilities. In this paper the authors proposing a new approach for simple breast cancer detection. It is based on skin stimulation by sound waves, illuminating it by laser beam and tracking the reflected secondary speckle patterns. As first approach, plastic balls of different sizes were placed under the skin of chicken breast and detected by the proposed method.

  3. From lag synchronization to pattern formation in one-dimensional open flow models

    International Nuclear Information System (INIS)

    Liu Zengrong; Luo Jigui

    2006-01-01

    In this paper, the relation between synchronization and pattern formation in one-dimensional discrete and continuous open flow models is investigated in detail. Firstly a sufficient condition for globally asymptotical stability of lag/anticipating synchronization among lattices of these models is proved by analytic method. Then, by analyzing and simulating lag/anticipating synchronization in discrete case, three kinds of pattern of wave (it is called wave pattern) travelling in the lattices are discovered. Finally, a proper definition for these kinds of pattern is proposed

  4. Trojan detection model based on network behavior analysis

    International Nuclear Information System (INIS)

    Liu Junrong; Liu Baoxu; Wang Wenjin

    2012-01-01

    Based on the analysis of existing Trojan detection technology, this paper presents a Trojan detection model based on network behavior analysis. First of all, we abstract description of the Trojan network behavior, then according to certain rules to establish the characteristic behavior library, and then use the support vector machine algorithm to determine whether a Trojan invasion. Finally, through the intrusion detection experiments, shows that this model can effectively detect Trojans. (authors)

  5. Long-time integration methods for mesoscopic models of pattern-forming systems

    International Nuclear Information System (INIS)

    Abukhdeir, Nasser Mohieddin; Vlachos, Dionisios G.; Katsoulakis, Markos; Plexousakis, Michael

    2011-01-01

    Spectral methods for simulation of a mesoscopic diffusion model of surface pattern formation are evaluated for long simulation times. Backwards-differencing time-integration, coupled with an underlying Newton-Krylov nonlinear solver (SUNDIALS-CVODE), is found to substantially accelerate simulations, without the typical requirement of preconditioning. Quasi-equilibrium simulations of patterned phases predicted by the model are shown to agree well with linear stability analysis. Simulation results of the effect of repulsive particle-particle interactions on pattern relaxation time and short/long-range order are discussed.

  6. A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

    Science.gov (United States)

    Bernardes, Juliana S; Carbone, Alessandra; Zaverucha, Gerson

    2011-03-23

    Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

  7. Simulating pattern-process relationships to validate landscape genetic models

    Science.gov (United States)

    A. J. Shirk; S. A. Cushman; E. L. Landguth

    2012-01-01

    Landscapes may resist gene flow and thereby give rise to a pattern of genetic isolation within a population. The mechanism by which a landscape resists gene flow can be inferred by evaluating the relationship between landscape models and an observed pattern of genetic isolation. This approach risks false inferences because researchers can never feasibly test all...

  8. a Landsat Time-Series Stacks Model for Detection of Cropland Change

    Science.gov (United States)

    Chen, J.; Chen, J.; Zhang, J.

    2017-09-01

    Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

  9. A Visual Detection Learning Model

    Science.gov (United States)

    Beard, Bettina L.; Ahumada, Albert J., Jr.; Trejo, Leonard (Technical Monitor)

    1998-01-01

    Our learning model has memory templates representing the target-plus-noise and noise-alone stimulus sets. The best correlating template determines the response. The correlations and the feedback participate in the additive template updating rule. The model can predict the relative thresholds for detection in random, fixed and twin noise.

  10. ISS Destiny Laboratory Smoke Detection Model

    Science.gov (United States)

    Brooker, John E.; Urban, David L.; Ruff, Gary A.

    2007-01-01

    Smoke transport and detection were modeled numerically in the ISS Destiny module using the NIST, Fire Dynamics Simulator code. The airflows in Destiny were modeled using the existing flow conditions and the module geometry included obstructions that simulate the currently installed hardware on orbit. The smoke source was modeled as a 0.152 by 0.152 m region that emitted smoke particulate ranging from 1.46 to 8.47 mg/s. In the module domain, the smoke source was placed in the center of each Destiny rack location and the model was run to determine the time required for the two smoke detectors to alarm. Overall the detection times were dominated by the circumferential flow, the axial flow from the intermodule ventilation and the smoke source strength.

  11. Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics.

    Science.gov (United States)

    Mahajan, Ruhi; Viangteeravat, Teeradache; Akbilgic, Oguz

    2017-12-01

    A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to detect CHF in subjects from their cardiac interbeat (R-R) intervals. PSPR discretizes each continuous R-R interval time series by mapping them onto an eight-symbol alphabet and then models the pattern transition behavior in the symbolic representation of the series. The PSPR-based analysis of the discretized series from 107 subjects (69 normal and 38 CHF subjects) yielded discernible features to distinguish normal subjects and subjects with CHF. In addition to PSPR features, we also extracted features using the time-domain heart rate variability measures such as average and standard deviation of R-R intervals. An ensemble of bagged decision trees was used to classify two groups resulting in a five-fold cross-validation accuracy, specificity, and sensitivity of 98.1%, 100%, and 94.7%, respectively. However, a 20% holdout validation yielded an accuracy, specificity, and sensitivity of 99.5%, 100%, and 98.57%, respectively. Results from this study suggest that features obtained with the combination of PSPR and long-term heart rate variability measures can be used in developing automated CHF diagnosis tools. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. AFM imaging and analysis of local mechanical properties for detection of surface pattern of functional groups

    Energy Technology Data Exchange (ETDEWEB)

    Knotek, Petr, E-mail: petr.knotek@upce.cz [University of Pardubice, Faculty of Chemical Technology, Joint Laboratory of Solid State Chemistry of IMC ASCR and University of Pardubice, Studentska 573, 532 10 Pardubice (Czech Republic); Chanova, Eliska; Rypacek, Frantisek [Institute of Macromolecular Chemistry, Academy of Sciences of the Czech Republic, Heyrovskeho sq. 2, 162 06 Prague (Czech Republic)

    2013-05-01

    In this work we evaluate the applicability of different atomic force microscopy (AFM) modes, such as Phase Shift Imaging, Atomic Force Acoustic Microscopy (AFAM) and Force Spectroscopy, for mapping of the distribution pattern of low-molecular-weight biomimetic groups on polymer biomaterial surfaces. Patterns with either random or clustered spatial distribution of bioactive peptide group derived from fibronectin were prepared by surface deposition of functional block copolymer nano-colloids and grafted with RGDS peptide containing the sequence of amino acids arginine–glycine–aspartic acid–serine (conventionally labeled as RGDS) and carrying biotin as a tag. The biotin-tagged peptides were labeled with 40 nm streptavidin-modified Au nanospheres. The peptide molecules were localized through the detection of bound Au nanospheres by AFM, and thus, the surface distribution of peptides was revealed. AFM techniques capable of monitoring local mechanical properties of the surface were proved to be the most efficient for identification of Au nano-markers. The efficiency was successfully demonstrated on two different patterns, i.e. random and clustered distribution of RGDS peptides on structured surface of the polymer biomaterial. Highlights: ► Bioactive peptides for cell adhesion on PLA-b-PEO biomimetic surface were visualized. ► The biotin-tagged RGDS peptides were labeled with streptavidin-Au nanospheres. ► The RGDS pattern was detected using different atomic force microscopy (AFM) modes. ► Phase Shift Image was proved to be suitable method for studying peptide distribution.

  13. An Evaluation of ADLs on Modeling Patterns for Software Architecture

    NARCIS (Netherlands)

    Waqas Kamal, Ahmad; Avgeriou, Paris

    2007-01-01

    Architecture patterns provide solutions to recurring design problems at the architecture level. In order to model patterns during software architecture design, one may use a number of existing Architecture Description Languages (ADLs), including the UML, a generic language but also a de facto

  14. A prescription fraud detection model.

    Science.gov (United States)

    Aral, Karca Duru; Güvenir, Halil Altay; Sabuncuoğlu, Ihsan; Akar, Ahmet Ruchan

    2012-04-01

    Prescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database. Conventionally, prescription fraud detection is conducted on random samples by human experts. However, the samples might be misleading and manual detection is costly. We propose a novel distance based on data-mining approach for assessing the fraudulent risk of prescriptions regarding cross-features. Final tests have been conducted on adult cardiac surgery database. The results obtained from experiments reveal that the proposed model works considerably well with a true positive rate of 77.4% and a false positive rate of 6% for the fraudulent medical prescriptions. The proposed model has the potential advantages including on-line risk prediction for prescription fraud, off-line analysis of high-risk prescriptions by human experts, and self-learning ability by regular updates of the integrative data sets. We conclude that incorporating such a system in health authorities, social security agencies and insurance companies would improve efficiency of internal review to ensure compliance with the law, and radically decrease human-expert auditing costs. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  15. Accounting for detectability in fish distribution models: an approach based on time-to-first-detection

    Directory of Open Access Journals (Sweden)

    Mário Ferreira

    2015-12-01

    Full Text Available Imperfect detection (i.e., failure to detect a species when the species is present is increasingly recognized as an important source of uncertainty and bias in species distribution modeling. Although methods have been developed to solve this problem by explicitly incorporating variation in detectability in the modeling procedure, their use in freshwater systems remains limited. This is probably because most methods imply repeated sampling (≥ 2 of each location within a short time frame, which may be impractical or too expensive in most studies. Here we explore a novel approach to control for detectability based on the time-to-first-detection, which requires only a single sampling occasion and so may find more general applicability in freshwaters. The approach uses a Bayesian framework to combine conventional occupancy modeling with techniques borrowed from parametric survival analysis, jointly modeling factors affecting the probability of occupancy and the time required to detect a species. To illustrate the method, we modeled large scale factors (elevation, stream order and precipitation affecting the distribution of six fish species in a catchment located in north-eastern Portugal, while accounting for factors potentially affecting detectability at sampling points (stream depth and width. Species detectability was most influenced by depth and to lesser extent by stream width and tended to increase over time for most species. Occupancy was consistently affected by stream order, elevation and annual precipitation. These species presented a widespread distribution with higher uncertainty in tributaries and upper stream reaches. This approach can be used to estimate sampling efficiency and provide a practical framework to incorporate variations in the detection rate in fish distribution models.

  16. Systematic analysis of stability patterns in plant primary metabolism.

    Directory of Open Access Journals (Sweden)

    Dorothee Girbig

    Full Text Available Metabolic networks are characterized by complex interactions and regulatory mechanisms between many individual components. These interactions determine whether a steady state is stable to perturbations. Structural kinetic modeling (SKM is a framework to analyze the stability of metabolic steady states that allows the study of the system Jacobian without requiring detailed knowledge about individual rate equations. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. Until now, SKM experiments applied univariate tests to detect the network components with the largest influence on stability. In this work, we present an extended SKM approach relying on supervised machine learning to detect patterns of enzyme-metabolite interactions that act together in an orchestrated manner to ensure stability. We demonstrate its application on a detailed SK-model of the Calvin-Benson cycle and connected pathways. The identified stability patterns are highly complex reflecting that changes in dynamic properties depend on concerted interactions between several network components. In total, we find more patterns that reliably ensure stability than patterns ensuring instability. This shows that the design of this system is strongly targeted towards maintaining stability. We also investigate the effect of allosteric regulators revealing that the tendency to stability is significantly increased by including experimentally determined regulatory mechanisms that have not yet been integrated into existing kinetic models.

  17. Detection of viruses and the spatial and temporal spread patterns of viral diseases of cucurbits (Cucurbitaceae spp.) in the coastal savannah zone of Ghana

    International Nuclear Information System (INIS)

    Gyamena, A. E

    2013-07-01

    Cucurbits are susceptible to over 35 plant viruses; each of these viruses is capable of causing total crop failure in a poorly managed virus pathosystem. The objectives of this study were to detect the viruses that infect six cucurbit species in the coastal savannah zone of Ghana and to describe the spatial and temporal spread patterns of virus epidemics in zucchini squash (Cucurbita pepo L.) by the use of mathematical and geostatistical models. Cucumber (Cucumis sativus L.), watermelon (Citrullus lanatus Thunb.), zucchini squash (Cucurbita pepo L.), butternut squash (Cucurbita moschata Duchesne), egushi (Citrullus colocynthis L. Schrad.) and melon (Cucumis melo L.) were grown on an experimental field in the coastal savannah zone of Ghana and were monitored for the expression of virus and virus-like symptoms. The observed symptoms were further confirmed by Double Antibody Sandwich Enzyme-Linked Immunosorbent Assay (DAS ELISA) and mechanical inoculation of indicator plants. The temporal spread patterns of virus disease in zucchini squash were analyzed by exponential logistic, monomolecular and gompertz mechanistic models. The spatial patterns of virus disease spread in zucchini squash field were analyzed by semivariograms and inverse distance weighing (IDW) methods. Cucumber, zucchini squash, melon and butternut squash were infected by both Cucumber mosaic virus (CMW) and Papaya ringspot virus (PRSV-W). Egushi was infected by CMW but not PRSV-W. None of the six cucurbit species were infected by Watermelon mosaic virus (WMV) or Zucchini yellow mosaic virus (ZYMV). The temporal pattern of disease incidence in the zucchini squash field followed the gompertz function with an average apparent infection rate of 0.026 per day. The temporal pattern of disease severity was best described by the exponential model with coefficient of determination of 94.38 % and rate of progress disease severity of 0.114 per day. As at 49 days after planting (DAP), disease incidence and

  18. Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

    Directory of Open Access Journals (Sweden)

    Shaonian Huang

    2018-01-01

    Full Text Available Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns. Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events. The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods. The experimental results show its competitive performance for anomaly event detection in video surveillance.

  19. Pattern-based approach for logical traffic isolation forensic modelling

    CSIR Research Space (South Africa)

    Dlamini, I

    2009-08-01

    Full Text Available reusability and flexibility of the LTI model. This model is viewed as a three-tier architecture, which for experimental purposes is composed of the following components: traffic generator, DiffServ network and the sink server. The Mediator pattern is used...

  20. A Physics-Inspired Mechanistic Model of Migratory Movement Patterns in Birds.

    Science.gov (United States)

    Revell, Christopher; Somveille, Marius

    2017-08-29

    In this paper, we introduce a mechanistic model of migratory movement patterns in birds, inspired by ideas and methods from physics. Previous studies have shed light on the factors influencing bird migration but have mainly relied on statistical correlative analysis of tracking data. Our novel method offers a bottom up explanation of population-level migratory movement patterns. It differs from previous mechanistic models of animal migration and enables predictions of pathways and destinations from a given starting location. We define an environmental potential landscape from environmental data and simulate bird movement within this landscape based on simple decision rules drawn from statistical mechanics. We explore the capacity of the model by qualitatively comparing simulation results to the non-breeding migration patterns of a seabird species, the Black-browed Albatross (Thalassarche melanophris). This minimal, two-parameter model was able to capture remarkably well the previously documented migration patterns of the Black-browed Albatross, with the best combination of parameter values conserved across multiple geographically separate populations. Our physics-inspired mechanistic model could be applied to other bird and highly-mobile species, improving our understanding of the relative importance of various factors driving migration and making predictions that could be useful for conservation.

  1. Modeling of gene expression pattern alteration by p,p′-DDE and dieldrin in largemouth bass

    Science.gov (United States)

    Garcia-Reyero, Natalia; Barber, David; Gross, Timothy; Denslow, Nancy

    2006-01-01

    In this study, largemouth bass (LMB) were subchronically exposed to p,p′-DDE or dieldrin in their diet to evaluate the effect of exposure on expression of genes involved in reproduction and steroid homeostasis. Using real-time PCR, we detected a different gene expression pattern for each OCP, suggesting that they each affect LMB in a different way. We also detected a different expression pattern among sexes, suggesting that sexes are affected differently by OCPs perhaps reflecting the different adaptive responses of each sex to dysregulation caused by OCP exposure.

  2. Improved twin detection via tracking of individual Kikuchi band intensity of EBSD patterns.

    Science.gov (United States)

    Rampton, Travis M; Wright, Stuart I; Miles, Michael P; Homer, Eric R; Wagoner, Robert H; Fullwood, David T

    2018-02-01

    Twin detection via EBSD can be particularly challenging due to the fine scale of certain twin types - for example, compression and double twins in Mg. Even when a grid of sufficient resolution is chosen to ensure scan points within the twins, the interaction volume of the electron beam often encapsulates a region that contains both the parent grain and the twin, confusing the twin identification process. The degradation of the EBSD pattern results in a lower image quality metric, which has long been used to imply potential twins. However, not all bands within the pattern are degraded equally. This paper exploits the fact that parent and twin lattices share common planes that lead to the quality of the associated bands not degrading; i.e. common planes that exist in both grains lead to bands of consistent intensity for scan points adjacent to twin boundaries. Hence, twin boundaries in a microstructure can be recognized, even when they are associated with thin twins. Proof of concept was performed on known twins in Inconel 600, Tantalum, and Magnesium AZ31. This method was then used to search for undetected twins in a Mg AZ31 structure, revealing nearly double the number of twins compared with those initially detected by standard procedures. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. General Business Model Patterns for Local Energy Management Concepts

    International Nuclear Information System (INIS)

    Facchinetti, Emanuele; Sulzer, Sabine

    2016-01-01

    The transition toward a more sustainable global energy system, significantly relying on renewable energies and decentralized energy systems, requires a deep reorganization of the energy sector. The way how energy services are generated, delivered, and traded is expected to be very different in the coming years. Business model innovation is recognized as a key driver for the successful implementation of the energy turnaround. This work contributes to this topic by introducing a heuristic methodology easing the identification of general business model patterns best suited for Local Energy Management concepts such as Energy Hubs. A conceptual framework characterizing the Local Energy Management business model solution space is developed. Three reference business model patterns providing orientation across the defined solution space are identified, analyzed, and compared. Through a market review, a number of successfully implemented innovative business models have been analyzed and allocated within the defined solution space. The outcomes of this work offer to potential stakeholders a starting point and guidelines for the business model innovation process, as well as insights for policy makers on challenges and opportunities related to Local Energy Management concepts.

  4. General Business Model Patterns for Local Energy Management Concepts

    Energy Technology Data Exchange (ETDEWEB)

    Facchinetti, Emanuele, E-mail: emanuele.facchinetti@hslu.ch; Sulzer, Sabine [Lucerne Competence Center for Energy Research, Lucerne University of Applied Science and Arts, Horw (Switzerland)

    2016-03-03

    The transition toward a more sustainable global energy system, significantly relying on renewable energies and decentralized energy systems, requires a deep reorganization of the energy sector. The way how energy services are generated, delivered, and traded is expected to be very different in the coming years. Business model innovation is recognized as a key driver for the successful implementation of the energy turnaround. This work contributes to this topic by introducing a heuristic methodology easing the identification of general business model patterns best suited for Local Energy Management concepts such as Energy Hubs. A conceptual framework characterizing the Local Energy Management business model solution space is developed. Three reference business model patterns providing orientation across the defined solution space are identified, analyzed, and compared. Through a market review, a number of successfully implemented innovative business models have been analyzed and allocated within the defined solution space. The outcomes of this work offer to potential stakeholders a starting point and guidelines for the business model innovation process, as well as insights for policy makers on challenges and opportunities related to Local Energy Management concepts.

  5. Analyzing Unsaturated Flow Patterns in Fractured Rock Using an Integrated Modeling Approach

    International Nuclear Information System (INIS)

    Y.S. Wu; G. Lu; K. Zhang; L. Pan; G.S. Bodvarsson

    2006-01-01

    Characterizing percolation patterns in unsaturated fractured rock has posed a greater challenge to modeling investigations than comparable saturated zone studies, because of the heterogeneous nature of unsaturated media and the great number of variables impacting unsaturated flow. This paper presents an integrated modeling methodology for quantitatively characterizing percolation patterns in the unsaturated zone of Yucca Mountain, Nevada, a proposed underground repository site for storing high-level radioactive waste. The modeling approach integrates a wide variety of moisture, pneumatic, thermal, and isotopic geochemical field data into a comprehensive three-dimensional numerical model for modeling analyses. It takes into account the coupled processes of fluid and heat flow and chemical isotopic transport in Yucca Mountain's highly heterogeneous, unsaturated fractured tuffs. Modeling results are examined against different types of field-measured data and then used to evaluate different hydrogeological conceptualizations and their results of flow patterns in the unsaturated zone. In particular, this model provides a much clearer understanding of percolation patterns and flow behavior through the unsaturated zone, both crucial issues in assessing repository performance. The integrated approach for quantifying Yucca Mountain's flow system is demonstrated to provide a practical modeling tool for characterizing flow and transport processes in complex subsurface systems

  6. PASSion: a pattern growth algorithm-based pipeline for splice junction detection in paired-end RNA-Seq data.

    Science.gov (United States)

    Zhang, Yanju; Lameijer, Eric-Wubbo; 't Hoen, Peter A C; Ning, Zemin; Slagboom, P Eline; Ye, Kai

    2012-02-15

    RNA-seq is a powerful technology for the study of transcriptome profiles that uses deep-sequencing technologies. Moreover, it may be used for cellular phenotyping and help establishing the etiology of diseases characterized by abnormal splicing patterns. In RNA-Seq, the exact nature of splicing events is buried in the reads that span exon-exon boundaries. The accurate and efficient mapping of these reads to the reference genome is a major challenge. We developed PASSion, a pattern growth algorithm-based pipeline for splice site detection in paired-end RNA-Seq reads. Comparing the performance of PASSion to three existing RNA-Seq analysis pipelines, TopHat, MapSplice and HMMSplicer, revealed that PASSion is competitive with these packages. Moreover, the performance of PASSion is not affected by read length and coverage. It performs better than the other three approaches when detecting junctions in highly abundant transcripts. PASSion has the ability to detect junctions that do not have known splicing motifs, which cannot be found by the other tools. Of the two public RNA-Seq datasets, PASSion predicted ≈ 137,000 and 173,000 splicing events, of which on average 82 are known junctions annotated in the Ensembl transcript database and 18% are novel. In addition, our package can discover differential and shared splicing patterns among multiple samples. The code and utilities can be freely downloaded from https://trac.nbic.nl/passion and ftp://ftp.sanger.ac.uk/pub/zn1/passion.

  7. Algorithmic detectability threshold of the stochastic block model

    Science.gov (United States)

    Kawamoto, Tatsuro

    2018-03-01

    The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

  8. Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis.

    Science.gov (United States)

    Kaya, Yılmaz

    2015-09-01

    This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100% were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.

  9. Detection of heavy-metal ions using liquid crystal droplet patterns modulated by interaction between negatively charged carboxylate and heavy-metal cations.

    Science.gov (United States)

    Han, Gyeo-Re; Jang, Chang-Hyun

    2014-10-01

    Herein, we demonstrated a simple, sensitive, and rapid label-free detection method for heavy-metal (HM) ions using liquid crystal (LC) droplet patterns on a solid surface. Stearic-acid-doped LC droplet patterns were spontaneously generated on an n-octyltrichlorosilane (OTS)-treated glass substrate by evaporating a solution of the nematic LC, 4-cyano-4'-pentylbiphenyl (5CB), dissolved in heptane. The optical appearance of the droplet patterns was a dark crossed texture when in contact with air, which represents the homeotropic orientation of the LC. This was caused by the steric interaction between the LC molecules and the alkyl chains of the OTS-treated surface. The dark crossed appearance of the acid-doped LC patterns was maintained after the addition of phosphate buffered saline (PBS) solution (pH 8.1 at 25°C). The deprotonated stearic-acid molecules self-assembled through the LC/aqueous interface, thereby supporting the homeotropic anchoring of 5CB. However, the optical image of the acid-doped LC droplet patterns incubated with PBS containing HM ions appeared bright, indicating a planar orientation of 5CB at the aqueous/LC droplet interface. This dark to bright transition of the LC patterns was caused by HM ions attached to the deprotonated carboxylate moiety, followed by the sequential interruption of the self-assembly of the stearic acid at the LC/aqueous interface. The results showed that the acid-doped LC pattern system not only enabled the highly sensitive detection of HM ions at a sub-nanomolar concentration but it also facilitated rapid detection (<10 min) with simple procedures. Copyright © 2014 Elsevier B.V. All rights reserved.

  10. Testing a Conceptual Model of Working through Self-Defeating Patterns

    Science.gov (United States)

    Wei, Meifen; Ku, Tsun-Yao

    2007-01-01

    The present study developed and examined a conceptual model of working through self-defeating patterns. Participants were 390 college students at a large midwestern university. Results indicated that self-defeating patterns mediated the relations between attachment and distress. Also, self-esteem mediated the link between self-defeating patterns…

  11. On Improving Face Detection Performance by Modelling Contextual Information

    OpenAIRE

    Atanasoaei, Cosmin; McCool, Chris; Marcel, Sébastien

    2010-01-01

    In this paper we present a new method to enhance object detection by removing false alarms and merging multiple detections in a principled way with few parameters. The method models the output of an object classiï¬er which we consider as the context. A hierarchical model is built using the detection distribution around a target sub-window to discriminate between false alarms and true detections. Next the context is used to iteratively reï¬ne the detections. Finally the detections are clustere...

  12. Clustering Of Left Ventricular Wall Motion Patterns

    Science.gov (United States)

    Bjelogrlic, Z.; Jakopin, J.; Gyergyek, L.

    1982-11-01

    A method for detection of wall regions with similar motion was presented. A model based on local direction information was used to measure the left ventricular wall motion from cineangiographic sequence. Three time functions were used to define segmental motion patterns: distance of a ventricular contour segment from the mean contour, the velocity of a segment and its acceleration. Motion patterns were clustered by the UPGMA algorithm and by an algorithm based on K-nearest neighboor classification rule.

  13. Detecting oscillatory patterns and time lags from proxy records with non-uniform sampling: Some pitfalls and possible solutions

    Science.gov (United States)

    Donner, Reik

    2013-04-01

    Time series analysis offers a rich toolbox for deciphering information from high-resolution geological and geomorphological archives and linking the thus obtained results to distinct climate and environmental processes. Specifically, on various time-scales from inter-annual to multi-millenial, underlying driving forces exhibit more or less periodic oscillations, the detection of which in proxy records often allows linking them to specific mechanisms by which the corresponding drivers may have affected the archive under study. A persistent problem in geomorphology is that available records do not present a clear signal of the variability of environmental conditions, but exhibit considerable uncertainties of both the measured proxy variables and the associated age model. Particularly, time-scale uncertainty as well as the heterogeneity of sampling in the time domain are source of severe conceptual problems that may lead to false conclusions about the presence or absence of oscillatory patterns and their mutual phasing in different archives. In my presentation, I will discuss how one can cope with non-uniformly sampled proxy records to detect and quantify oscillatory patterns in one or more data sets. For this purpose, correlation analysis is reformulated using kernel estimates which are found superior to classical estimators based on interpolation or Fourier transform techniques. In order to characterize non-stationary or noisy periodicities and their relative phasing between different records, an extension of continuous wavelet transform is utilized. The performance of both methods is illustrated for different case studies. An extension to explicitly considering time-scale uncertainties by means of Bayesian techniques is briefly outlined.

  14. Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis.

    Science.gov (United States)

    Zhou, Wei; Wen, Junhao; Koh, Yun Sing; Xiong, Qingyu; Gao, Min; Dobbie, Gillian; Alam, Shafiq

    2015-01-01

    Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim' based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks.

  15. Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis

    Science.gov (United States)

    Zhou, Wei; Wen, Junhao; Koh, Yun Sing; Xiong, Qingyu; Gao, Min; Dobbie, Gillian; Alam, Shafiq

    2015-01-01

    Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim’ based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks. PMID:26222882

  16. A stochastic differential equation model of diurnal cortisol patterns

    Science.gov (United States)

    Brown, E. N.; Meehan, P. M.; Dempster, A. P.

    2001-01-01

    Circadian modulation of episodic bursts is recognized as the normal physiological pattern of diurnal variation in plasma cortisol levels. The primary physiological factors underlying these diurnal patterns are the ultradian timing of secretory events, circadian modulation of the amplitude of secretory events, infusion of the hormone from the adrenal gland into the plasma, and clearance of the hormone from the plasma by the liver. Each measured plasma cortisol level has an error arising from the cortisol immunoassay. We demonstrate that all of these three physiological principles can be succinctly summarized in a single stochastic differential equation plus measurement error model and show that physiologically consistent ranges of the model parameters can be determined from published reports. We summarize the model parameters in terms of the multivariate Gaussian probability density and establish the plausibility of the model with a series of simulation studies. Our framework makes possible a sensitivity analysis in which all model parameters are allowed to vary simultaneously. The model offers an approach for simultaneously representing cortisol's ultradian, circadian, and kinetic properties. Our modeling paradigm provides a framework for simulation studies and data analysis that should be readily adaptable to the analysis of other endocrine hormone systems.

  17. Outlier Detection in Structural Time Series Models

    DEFF Research Database (Denmark)

    Marczak, Martyna; Proietti, Tommaso

    investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality......Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general......–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse– and step–indicator saturation, we...

  18. A novel spatial performance metric for robust pattern optimization of distributed hydrological models

    Science.gov (United States)

    Stisen, S.; Demirel, C.; Koch, J.

    2017-12-01

    Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing

  19. A Pattern-Oriented Approach to a Methodical Evaluation of Modeling Methods

    Directory of Open Access Journals (Sweden)

    Michael Amberg

    1996-11-01

    Full Text Available The paper describes a pattern-oriented approach to evaluate modeling methods and to compare various methods with each other from a methodical viewpoint. A specific set of principles (the patterns is defined by investigating the notations and the documentation of comparable modeling methods. Each principle helps to examine some parts of the methods from a specific point of view. All principles together lead to an overall picture of the method under examination. First the core ("method neutral" meaning of each principle is described. Then the methods are examined regarding the principle. Afterwards the method specific interpretations are compared with each other and with the core meaning of the principle. By this procedure, the strengths and weaknesses of modeling methods regarding methodical aspects are identified. The principles are described uniformly using a principle description template according to descriptions of object oriented design patterns. The approach is demonstrated by evaluating a business process modeling method.

  20. Higgs detectability in the extended supersymmetric standard model

    International Nuclear Information System (INIS)

    Kamoshita, Jun-ichi

    1995-01-01

    Higgs detectability at a future linear collider are discussed in the minimal supersymmetric standard model (MSSM) and a supersymmetric standard model with a gauge singlet Higgs field (NMSSM). First, in the MSSM at least one of the neutral scalar Higgs is shown to be detectable irrespective of parameters of the model in a future e + e - linear collider at √s = 300-500 GeV. Next the Higgs sector of the NMSSM is considered, since the lightest Higgs boson can be singlet dominated and therefore decouple from Z 0 boson it is important to consider the production of heavier Higgses. It is shown that also in this case at least one of the neutral scalar Higgs will be detectable in a future linear collider. We extend the analysis and show that the same is true even if three singlets are included. Thus the detectability of these Higgs bosons of these models is guaranteed. (author)

  1. Specimen-specific modeling of hip fracture pattern and repair.

    Science.gov (United States)

    Ali, Azhar A; Cristofolini, Luca; Schileo, Enrico; Hu, Haixiang; Taddei, Fulvia; Kim, Raymond H; Rullkoetter, Paul J; Laz, Peter J

    2014-01-22

    Hip fracture remains a major health problem for the elderly. Clinical studies have assessed fracture risk based on bone quality in the aging population and cadaveric testing has quantified bone strength and fracture loads. Prior modeling has primarily focused on quantifying the strain distribution in bone as an indicator of fracture risk. Recent advances in the extended finite element method (XFEM) enable prediction of the initiation and propagation of cracks without requiring a priori knowledge of the crack path. Accordingly, the objectives of this study were to predict femoral fracture in specimen-specific models using the XFEM approach, to perform one-to-one comparisons of predicted and in vitro fracture patterns, and to develop a framework to assess the mechanics and load transfer in the fractured femur when it is repaired with an osteosynthesis implant. Five specimen-specific femur models were developed from in vitro experiments under a simulated stance loading condition. Predicted fracture patterns closely matched the in vitro patterns; however, predictions of fracture load differed by approximately 50% due to sensitivity to local material properties. Specimen-specific intertrochanteric fractures were induced by subjecting the femur models to a sideways fall and repaired with a contemporary implant. Under a post-surgical stance loading, model-predicted load sharing between the implant and bone across the fracture surface varied from 59%:41% to 89%:11%, underscoring the importance of considering anatomic and fracture variability in the evaluation of implants. XFEM modeling shows potential as a macro-level analysis enabling fracture investigations of clinical cohorts, including at-risk groups, and the design of robust implants. © 2013 Published by Elsevier Ltd.

  2. Understanding Activation Patterns in Shared Circuits: Toward a Value Driven Model

    Directory of Open Access Journals (Sweden)

    Lisa Aziz-Zadeh

    2018-05-01

    Full Text Available Over the past decade many studies indicate that we utilize our own motor system to understand the actions of other people. This mirror neuron system (MNS has been proposed to be involved in social cognition and motor learning. However, conflicting findings regarding the underlying mechanisms that drive these shared circuits make it difficult to decipher a common model of their function. Here we propose adapting a “value-driven” model to explain discrepancies in the human mirror system literature and to incorporate this model with existing models. We will use this model to explain discrepant activation patterns in multiple shared circuits in the human data, such that a unified model may explain reported activation patterns from previous studies as a function of value.

  3. Image decomposition model Shearlet-Hilbert-L2 with better performance for denoising in ESPI fringe patterns.

    Science.gov (United States)

    Xu, Wenjun; Tang, Chen; Su, Yonggang; Li, Biyuan; Lei, Zhenkun

    2018-02-01

    In this paper, we propose an image decomposition model Shearlet-Hilbert-L 2 with better performance for denoising in electronic speckle pattern interferometry (ESPI) fringe patterns. In our model, the low-density fringes, high-density fringes, and noise are, respectively, described by shearlet smoothness spaces, adaptive Hilbert space, and L 2 space and processed individually. Because the shearlet transform has superior directional sensitivity, our proposed Shearlet-Hilbert-L 2 model achieves commendable filtering results for various types of ESPI fringe patterns, including uniform density fringe patterns, moderately variable density fringe patterns, and greatly variable density fringe patterns. We evaluate the performance of our proposed Shearlet-Hilbert-L 2 model via application to two computer-simulated and nine experimentally obtained ESPI fringe patterns with various densities and poor quality. Furthermore, we compare our proposed model with windowed Fourier filtering and coherence-enhancing diffusion, both of which are the state-of-the-art methods for ESPI fringe patterns denoising in transform domain and spatial domain, respectively. We also compare our proposed model with the previous image decomposition model BL-Hilbert-L 2 .

  4. Computational modeling and experimental characterization of bacterial microcolonies for rapid detection using light scattering

    Science.gov (United States)

    Bai, Nan

    A label-free and nondestructive optical elastic forward light scattering method has been extended for the analysis of microcolonies for food-borne bacteria detection and identification. To understand the forward light scattering phenomenon, a model based on the scalar diffraction theory has been employed: a bacterial colony is considered as a biological spatial light modulator with amplitude and phase modulation to the incoming light, which continues to propagate to the far-field to form a distinct scattering 'fingerprint'. Numerical implementation via angular spectrum method (ASM) and Fresnel approximation have been carried out through Fast Fourier Transform (FFT) to simulate this optical model. Sampling criteria to achieve unbiased and un-aliased simulation results have been derived and the effects of violating these conditions have been studied. Diffraction patterns predicted by these two methods (ASM and Fresnel) have been compared to show their applicability to different simulation settings. Through the simulation work, the correlation between the colony morphology and its forward scattering pattern has been established to link the number of diffraction rings and the half cone angle with the diameter and the central height of the Gaussian-shaped colonies. In order to experimentally prove the correlation, a colony morphology analyzer has been built and used to characterize the morphology of different bacteria genera and investigate their growth dynamics. The experimental measurements have demonstrated the possibility of differentiating bacteria Salmonella, Listeria, Escherichia in their early growth stage (100˜500 µm) based on their phenotypic characteristics. This conclusion has important implications in microcolony detection, as most bacteria of our interest need much less incubation time (8˜12 hours) to grow into this size range. The original forward light scatterometer has been updated to capture scattering patterns from microcolonies. Experiments have

  5. Web-based GIS for spatial pattern detection: application to malaria incidence in Vietnam.

    Science.gov (United States)

    Bui, Thanh Quang; Pham, Hai Minh

    2016-01-01

    There is a great concern on how to build up an interoperable health information system of public health and health information technology within the development of public information and health surveillance programme. Technically, some major issues remain regarding to health data visualization, spatial processing of health data, health information dissemination, data sharing and the access of local communities to health information. In combination with GIS, we propose a technical framework for web-based health data visualization and spatial analysis. Data was collected from open map-servers and geocoded by open data kit package and data geocoding tools. The Web-based system is designed based on Open-source frameworks and libraries. The system provides Web-based analyst tool for pattern detection through three spatial tests: Nearest neighbour, K function, and Spatial Autocorrelation. The result is a web-based GIS, through which end users can detect disease patterns via selecting area, spatial test parameters and contribute to managers and decision makers. The end users can be health practitioners, educators, local communities, health sector authorities and decision makers. This web-based system allows for the improvement of health related services to public sector users as well as citizens in a secure manner. The combination of spatial statistics and web-based GIS can be a solution that helps empower health practitioners in direct and specific intersectional actions, thus provide for better analysis, control and decision-making.

  6. In vitro burn model illustrating heat conduction patterns using compressed thermal papers.

    Science.gov (United States)

    Lee, Jun Yong; Jung, Sung-No; Kwon, Ho

    2015-01-01

    To date, heat conduction from heat sources to tissue has been estimated by complex mathematical modeling. In the present study, we developed an intuitive in vitro skin burn model that illustrates heat conduction patterns inside the skin. This was composed of tightly compressed thermal papers with compression frames. Heat flow through the model left a trace by changing the color of thermal papers. These were digitized and three-dimensionally reconstituted to reproduce the heat conduction patterns in the skin. For standardization, we validated K91HG-CE thermal paper using a printout test and bivariate correlation analysis. We measured the papers' physical properties and calculated the estimated depth of heat conduction using Fourier's equation. Through contact burns of 5, 10, 15, 20, and 30 seconds on porcine skin and our burn model using a heated brass comb, and comparing the burn wound and heat conduction trace, we validated our model. The heat conduction pattern correlation analysis (intraclass correlation coefficient: 0.846, p < 0.001) and the heat conduction depth correlation analysis (intraclass correlation coefficient: 0.93, p < 0.001) showed statistically significant high correlations between the porcine burn wound and our model. Our model showed good correlation with porcine skin burn injury and replicated its heat conduction patterns. © 2014 by the Wound Healing Society.

  7. Prioritizing alarms from sensor-based detection models in livestock production

    DEFF Research Database (Denmark)

    Dominiak, Katarina Nielsen; Kristensen, Anders Ringgaard

    2017-01-01

    The objective of this review is to present, evaluate and discuss methods for reducing false alarms in sensor-based detection models developed for livestock production as described in the scientific literature. Papers included in this review are all peer-reviewed and present sensor-based detection...... models developed for modern livestock production with the purpose of optimizing animal health or managerial routines. The papers must present a performance for the model, but no criteria were specified for animal species or the condition sought to be detected. 34 papers published during the last 20 years...... (NBN) and Hidden phase-type Markov model, the NBN shows the greatest potential for future reduction of alerts from sensor-based detection models in livestock production. The included detection models are evaluated on three criteria; performance, time-window and similarity to determine whether...

  8. A TESSELLATION MODEL FOR CRACK PATTERNS ON SURFACES

    Directory of Open Access Journals (Sweden)

    Werner Nagel

    2011-05-01

    Full Text Available This paper presents a model of random tessellations that reflect several features of crack pattern. There are already several theoretical results derivedwhich indicate that thismodel can be an appropriate referencemodel. Some potential applications are presented in a tentative statistical study.

  9. Rejecting probability summation for radial frequency patterns, not so Quick!

    Science.gov (United States)

    Baldwin, Alex S; Schmidtmann, Gunnar; Kingdom, Frederick A A; Hess, Robert F

    2016-05-01

    Radial frequency (RF) patterns are used to assess how the visual system processes shape. They are thought to be detected globally. This is supported by studies that have found summation for RF patterns to be greater than what is possible if the parts were being independently detected and performance only then improved with an increasing number of cycles by probability summation between them. However, the model of probability summation employed in these previous studies was based on High Threshold Theory (HTT), rather than Signal Detection Theory (SDT). We conducted rating scale experiments to investigate the receiver operating characteristics. We find these are of the curved form predicted by SDT, rather than the straight lines predicted by HTT. This means that to test probability summation we must use a model based on SDT. We conducted a set of summation experiments finding that thresholds decrease as the number of modulated cycles increases at approximately the same rate as previously found. As this could be consistent with either additive or probability summation, we performed maximum-likelihood fitting of a set of summation models (Matlab code provided in our Supplementary material) and assessed the fits using cross validation. We find we are not able to distinguish whether the responses to the parts of an RF pattern are combined by additive or probability summation, because the predictions are too similar. We present similar results for summation between separate RF patterns, suggesting that the summation process there may be the same as that within a single RF. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Infrared interference patterns for new capabilities in laser end point detection

    International Nuclear Information System (INIS)

    Heason, D J; Spencer, A G

    2003-01-01

    Standard laser interferometry is used in dry etch fabrication of semiconductor and MEMS devices to measure etch depth, rate and to detect the process end point. However, many wafer materials, such as silicon are absorbing at probing wavelengths in the visible, severely limiting the amount of information that can be obtained using this technique. At infrared (IR) wavelengths around 1500 nm and above, silicon is highly transparent. In this paper we describe an instrument that can be used to monitor etch depth throughout a thru-wafer etch. The provision of this information could eliminate the requirement of an 'etch stop' layer and improve the performance of fabricated devices. We have added a further new capability by using tuneable lasers to scan through wavelengths in the near IR to generate an interference pattern. Fitting a theoretical curve to this interference pattern gives in situ measurement of film thickness. Whereas conventional interferometry would only allow etch depth to be monitored in real time, we can use a pre-etch thickness measurement to terminate the etch on a remaining thickness of film material. This paper discusses the capabilities of, and the opportunities offered by, this new technique and gives examples of applications in MEMS and waveguides

  11. Clone Detection for Graph-Based Model Transformation Languages

    DEFF Research Database (Denmark)

    Strüber, Daniel; Plöger, Jennifer; Acretoaie, Vlad

    2016-01-01

    and analytical quality assurance. From these use cases, we derive a set of key requirements. We describe our customization of existing model clone detection techniques allowing us to address these requirements. Finally, we provide an experimental evaluation, indicating that our customization of ConQAT, one......Cloning is a convenient mechanism to enable reuse across and within software artifacts. On the downside, it is also a practice related to significant long-term maintainability impediments, thus generating a need to identify clones in affected artifacts. A large variety of clone detection techniques...... has been proposed for programming and modeling languages; yet no specific ones have emerged for model transformation languages. In this paper, we explore clone detection for graph-based model transformation languages. We introduce potential use cases for such techniques in the context of constructive...

  12. Particle size distribution models of small angle neutron scattering pattern on ferro fluids

    International Nuclear Information System (INIS)

    Sistin Asri Ani; Darminto; Edy Giri Rachman Putra

    2009-01-01

    The Fe 3 O 4 ferro fluids samples were synthesized by a co-precipitation method. The investigation of ferro fluids microstructure is known to be one of the most important problems because the presence of aggregates and their internal structure influence greatly the properties of ferro fluids. The size and the size dispersion of particle in ferro fluids were determined assuming a log normal distribution of particle radius. The scattering pattern of the measurement by small angle neutron scattering were fitted by the theoretical scattering function of two limitation models are log normal sphere distribution and fractal aggregate. Two types of particle are detected, which are presumably primary particle of 30 Armstrong in radius and secondary fractal aggregate of 200 Armstrong with polydispersity of 0.47 up to 0.53. (author)

  13. Simulating Visual Pattern Detection and Brightness Perception Based on Implicit Masking

    Directory of Open Access Journals (Sweden)

    Yang Jian

    2007-01-01

    Full Text Available A quantitative model of implicit masking, with a front-end low-pass filter, a retinal local compressive nonlinearity described by a modified Naka-Rushton equation, a cortical representation of the image in the Fourier domain, and a frequency-dependent compressive nonlinearity, was developed to simulate visual image processing. The model algorithm was used to estimate contrast sensitivity functions over 7 mean illuminance levels ranging from 0.0009 to 900 trolands, and fit to the contrast thresholds of 43 spatial patterns in the Modelfest study. The RMS errors between model estimations and experimental data in the literature were about 0.1 log unit. In addition, the same model was used to simulate the effects of simultaneous contrast, assimilation, and crispening. The model results matched the visual percepts qualitatively, showing the value of integrating the three diverse perceptual phenomena under a common theoretical framework.

  14. An incremental anomaly detection model for virtual machines.

    Directory of Open Access Journals (Sweden)

    Hancui Zhang

    Full Text Available Self-Organizing Map (SOM algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

  15. An incremental anomaly detection model for virtual machines

    Science.gov (United States)

    Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

    2017-01-01

    Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform. PMID:29117245

  16. Spatial filtering precedes motion detection.

    Science.gov (United States)

    Morgan, M J

    1992-01-23

    When we perceive motion on a television or cinema screen, there must be some process that allows us to track moving objects over time: if not, the result would be a conflicting mass of motion signals in all directions. A possible mechanism, suggested by studies of motion displacement in spatially random patterns, is that low-level motion detectors have a limited spatial range, which ensures that they tend to be stimulated over time by the same object. This model predicts that the direction of displacement of random patterns cannot be detected reliably above a critical absolute displacement value (Dmax) that is independent of the size or density of elements in the display. It has been inferred that Dmax is a measure of the size of motion detectors in the visual pathway. Other studies, however, have shown that Dmax increases with element size, in which case the most likely interpretation is that Dmax depends on the probability of false matches between pattern elements following a displacement. These conflicting accounts are reconciled here by showing that Dmax is indeed determined by the spacing between the elements in the pattern, but only after fine detail has been removed by a physiological prefiltering stage: the filter required to explain the data has a similar size to the receptive field of neurons in the primate magnocellular pathway. The model explains why Dmax can be increased by removing high spatial frequencies from random patterns, and simplifies our view of early motion detection.

  17. Simple models for studying complex spatiotemporal patterns of animal behavior

    Science.gov (United States)

    Tyutyunov, Yuri V.; Titova, Lyudmila I.

    2017-06-01

    Minimal mathematical models able to explain complex patterns of animal behavior are essential parts of simulation systems describing large-scale spatiotemporal dynamics of trophic communities, particularly those with wide-ranging species, such as occur in pelagic environments. We present results obtained with three different modelling approaches: (i) an individual-based model of animal spatial behavior; (ii) a continuous taxis-diffusion-reaction system of partial-difference equations; (iii) a 'hybrid' approach combining the individual-based algorithm of organism movements with explicit description of decay and diffusion of the movement stimuli. Though the models are based on extremely simple rules, they all allow description of spatial movements of animals in a predator-prey system within a closed habitat, reproducing some typical patterns of the pursuit-evasion behavior observed in natural populations. In all three models, at each spatial position the animal movements are determined by local conditions only, so the pattern of collective behavior emerges due to self-organization. The movement velocities of animals are proportional to the density gradients of specific cues emitted by individuals of the antagonistic species (pheromones, exometabolites or mechanical waves of the media, e.g., sound). These cues play a role of taxis stimuli: prey attract predators, while predators repel prey. Depending on the nature and the properties of the movement stimulus we propose using either a simplified individual-based model, a continuous taxis pursuit-evasion system, or a little more detailed 'hybrid' approach that combines simulation of the individual movements with the continuous model describing diffusion and decay of the stimuli in an explicit way. These can be used to improve movement models for many species, including large marine predators.

  18. Computational design of patterned interfaces using reduced order models

    International Nuclear Information System (INIS)

    Vattre, A.J.; Abdolrahim, N.; Kolluri, K.; Demkowicz, M.J.

    2014-01-01

    Patterning is a familiar approach for imparting novel functionalities to free surfaces. We extend the patterning paradigm to interfaces between crystalline solids. Many interfaces have non-uniform internal structures comprised of misfit dislocations, which in turn govern interface properties. We develop and validate a computational strategy for designing interfaces with controlled misfit dislocation patterns by tailoring interface crystallography and composition. Our approach relies on a novel method for predicting the internal structure of interfaces: rather than obtaining it from resource-intensive atomistic simulations, we compute it using an efficient reduced order model based on anisotropic elasticity theory. Moreover, our strategy incorporates interface synthesis as a constraint on the design process. As an illustration, we apply our approach to the design of interfaces with rapid, 1-D point defect diffusion. Patterned interfaces may be integrated into the microstructure of composite materials, markedly improving performance. (authors)

  19. Modelling Global Pattern Formations for Collaborative Learning Environments

    DEFF Research Database (Denmark)

    Grappiolo, Corrado; Cheong, Yun-Gyung; Khaled, Rilla

    2012-01-01

    We present our research towards the design of a computational framework capable of modelling the formation and evolution of global patterns (i.e. group structures) in a population of social individuals. The framework is intended to be used in collaborative environments, e.g. social serious games...

  20. Modeling patterns of hot water use in households

    Energy Technology Data Exchange (ETDEWEB)

    Lutz, James D.; Liu, Xiaomin; McMahon, James E.; Dunham, Camilla; Shown, Leslie J.; McCure, Quandra T.

    1996-01-01

    This report presents a detailed model of hot water use patterns in individual households. The model improves upon an existing model by including the effects of four conditions that were previously unaccounted for: the absence of a clothes washer; the absence of a dishwasher; a household consisting of seniors only; and a household that does not pay for its own hot water use. Although these four conditions can significantly affect residential hot water use, and have been noted in other studies, this is the first time that they have been incorporated into a detailed model. This model allows detailed evaluation of the impact of potential efficiency standards for water heaters and other market transformation policies.

  1. Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

    Science.gov (United States)

    Sierra-Pérez, Julián; Torres-Arredondo, M.-A.; Alvarez-Montoya, Joham

    2018-01-01

    Structural health monitoring consists of using sensors integrated within structures together with algorithms to perform load monitoring, damage detection, damage location, damage size and severity, and prognosis. One possibility is to use strain sensors to infer structural integrity by comparing patterns in the strain field between the pristine and damaged conditions. In previous works, the authors have demonstrated that it is possible to detect small defects based on strain field pattern recognition by using robust machine learning techniques. They have focused on methodologies based on principal component analysis (PCA) and on the development of several unfolding and standardization techniques, which allow dealing with multiple load conditions. However, before a real implementation of this approach in engineering structures, changes in the strain field due to conditions different from damage occurrence need to be isolated. Since load conditions may vary in most engineering structures and promote significant changes in the strain field, it is necessary to implement novel techniques for uncoupling such changes from those produced by damage occurrence. A damage detection methodology based on optimal baseline selection (OBS) by means of clustering techniques is presented. The methodology includes the use of hierarchical nonlinear PCA as a nonlinear modeling technique in conjunction with Q and nonlinear-T 2 damage indices. The methodology is experimentally validated using strain measurements obtained by 32 fiber Bragg grating sensors bonded to an aluminum beam under dynamic bending loads and simultaneously submitted to variations in its pitch angle. The results demonstrated the capability of the methodology for clustering data according to 13 different load conditions (pitch angles), performing the OBS and detecting six different damages induced in a cumulative way. The proposed methodology showed a true positive rate of 100% and a false positive rate of 1.28% for a

  2. Patterns of glycogen turnover in liver characterized by computer modeling

    International Nuclear Information System (INIS)

    Youn, J.H.; Bergman, R.N.

    1987-01-01

    The authors used a computer model of liver glycogen turnover to reexamine the data of Devos and Hers, who reported the time course of accumulation in and loss from glycogen of label originating in [1- 14 C]galactose injected at different times after the start of refeeding of 40-h fasted mice or rats. In the present study computer representation of individual glycogen molecules was utilized to account for growth and degradation of glycogen according to specific hypothetical patterns. Using this model they could predict the accumulation and localization within glycogen of labeled glucose residues and compare the predictions with the previously published data. They considered three specific hypotheses of glycogen accumulation during refeeding: (1) simultaneous, (2) sequential, and (3) accelerating growth. Hypothetical patterns of glycogen degradation were (1) ordered and (2) random degradation. The pattern of glycogen synthesis consistent with experimental data was a steadily increasing number of growing glycogen molecules, whereas during degradation glycogen molecules are exposed to degrading enzymes randomly, rather than in a specific reverse order of synthesis. These patterns predict the existence of a specific mechanism for the steadily increasing seeding of new glycogen molecules during synthesis

  3. Patterns of flavor signals in supersymmetric models

    Energy Technology Data Exchange (ETDEWEB)

    Goto, T. [KEK National High Energy Physics, Tsukuba (Japan)]|[Kyoto Univ. (Japan). YITP; Okada, Y. [KEK National High Energy Physics, Tsukuba (Japan)]|[Graduate Univ. for Advanced Studies, Tsukuba (Japan). Dept. of Particle and Nucelar Physics; Shindou, T. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)]|[International School for Advanced Studies, Trieste (Italy); Tanaka, M. [Osaka Univ., Toyonaka (Japan). Dept. of Physics

    2007-11-15

    Quark and lepton flavor signals are studied in four supersymmetric models, namely the minimal supergravity model, the minimal supersymmetric standard model with right-handed neutrinos, SU(5) supersymmetric grand unified theory with right-handed neutrinos and the minimal supersymmetric standard model with U(2) flavor symmetry. We calculate b{yields}s(d) transition observables in B{sub d} and B{sub s} decays, taking the constraint from the B{sub s}- anti B{sub s} mixing recently observed at Tevatron into account. We also calculate lepton flavor violating processes {mu} {yields} e{gamma}, {tau} {yields} {mu}{gamma} and {tau} {yields} e{gamma} for the models with right-handed neutrinos. We investigate possibilities to distinguish the flavor structure of the supersymmetry breaking sector with use of patterns of various flavor signals which are expected to be measured in experiments such as MEG, LHCb and a future Super B Factory. (orig.)

  4. Patterns of flavor signals in supersymmetric models

    International Nuclear Information System (INIS)

    Goto, T.; Tanaka, M.

    2007-11-01

    Quark and lepton flavor signals are studied in four supersymmetric models, namely the minimal supergravity model, the minimal supersymmetric standard model with right-handed neutrinos, SU(5) supersymmetric grand unified theory with right-handed neutrinos and the minimal supersymmetric standard model with U(2) flavor symmetry. We calculate b→s(d) transition observables in B d and B s decays, taking the constraint from the B s - anti B s mixing recently observed at Tevatron into account. We also calculate lepton flavor violating processes μ → eγ, τ → μγ and τ → eγ for the models with right-handed neutrinos. We investigate possibilities to distinguish the flavor structure of the supersymmetry breaking sector with use of patterns of various flavor signals which are expected to be measured in experiments such as MEG, LHCb and a future Super B Factory. (orig.)

  5. Robust pattern decoding in shape-coded structured light

    Science.gov (United States)

    Tang, Suming; Zhang, Xu; Song, Zhan; Song, Lifang; Zeng, Hai

    2017-09-01

    Decoding is a challenging and complex problem in a coded structured light system. In this paper, a robust pattern decoding method is proposed for the shape-coded structured light in which the pattern is designed as grid shape with embedded geometrical shapes. In our decoding method, advancements are made at three steps. First, a multi-template feature detection algorithm is introduced to detect the feature point which is the intersection of each two orthogonal grid-lines. Second, pattern element identification is modelled as a supervised classification problem and the deep neural network technique is applied for the accurate classification of pattern elements. Before that, a training dataset is established, which contains a mass of pattern elements with various blurring and distortions. Third, an error correction mechanism based on epipolar constraint, coplanarity constraint and topological constraint is presented to reduce the false matches. In the experiments, several complex objects including human hand are chosen to test the accuracy and robustness of the proposed method. The experimental results show that our decoding method not only has high decoding accuracy, but also owns strong robustness to surface color and complex textures.

  6. Hidden Markov model analysis of maternal behavior patterns in inbred and reciprocal hybrid mice.

    Directory of Open Access Journals (Sweden)

    Valeria Carola

    Full Text Available Individual variation in maternal care in mammals shows a significant heritable component, with the maternal behavior of daughters resembling that of their mothers. In laboratory mice, genetically distinct inbred strains show stable differences in maternal care during the first postnatal week. Moreover, cross fostering and reciprocal breeding studies demonstrate that differences in maternal care between inbred strains persist in the absence of genetic differences, demonstrating a non-genetic or epigenetic contribution to maternal behavior. In this study we applied a mathematical tool, called hidden Markov model (HMM, to analyze the behavior of female mice in the presence of their young. The frequency of several maternal behaviors in mice has been previously described, including nursing/grooming pups and tending to the nest. However, the ordering, clustering, and transitions between these behaviors have not been systematically described and thus a global description of maternal behavior is lacking. Here we used HMM to describe maternal behavior patterns in two genetically distinct mouse strains, C57BL/6 and BALB/c, and their genetically identical reciprocal hybrid female offspring. HMM analysis is a powerful tool to identify patterns of events that cluster in time and to determine transitions between these clusters, or hidden states. For the HMM analysis we defined seven states: arched-backed nursing, blanket nursing, licking/grooming pups, grooming, activity, eating, and sleeping. By quantifying the frequency, duration, composition, and transition probabilities of these states we were able to describe the pattern of maternal behavior in mouse and identify aspects of these patterns that are under genetic and nongenetic inheritance. Differences in these patterns observed in the experimental groups (inbred and hybrid females were detected only after the application of HMM analysis whereas classical statistical methods and analyses were not able to

  7. Simulation Model of Mobile Detection Systems

    International Nuclear Information System (INIS)

    Edmunds, T.; Faissol, D.; Yao, Y.

    2009-01-01

    In this paper, we consider a mobile source that we attempt to detect with man-portable, vehicle-mounted or boat-mounted radiation detectors. The source is assumed to transit an area populated with these mobile detectors, and the objective is to detect the source before it reaches a perimeter. We describe a simulation model developed to estimate the probability that one of the mobile detectors will come in to close proximity of the moving source and detect it. We illustrate with a maritime simulation example. Our simulation takes place in a 10 km by 5 km rectangular bay patrolled by boats equipped with 2-inch x 4-inch x 16-inch NaI detectors. Boats to be inspected enter the bay and randomly proceed to one of seven harbors on the shore. A source-bearing boat enters the mouth of the bay and proceeds to a pier on the opposite side. We wish to determine the probability that the source is detected and its range from target when detected. Patrol boats select the nearest in-bound boat for inspection and initiate an intercept course. Once within an operational range for the detection system, a detection algorithm is started. If the patrol boat confirms the source is not present, it selects the next nearest boat for inspection. Each run of the simulation ends either when a patrol successfully detects a source or when the source reaches its target. Several statistical detection algorithms have been implemented in the simulation model. First, a simple k-sigma algorithm, which alarms with the counts in a time window exceeds the mean background plus k times the standard deviation of background, is available to the user. The time window used is optimized with respect to the signal-to-background ratio for that range and relative speed. Second, a sequential probability ratio test [Wald 1947] is available, and configured in this simulation with a target false positive probability of 0.001 and false negative probability of 0.1. This test is utilized when the mobile detector maintains

  8. Simulation Model of Mobile Detection Systems

    Energy Technology Data Exchange (ETDEWEB)

    Edmunds, T; Faissol, D; Yao, Y

    2009-01-27

    In this paper, we consider a mobile source that we attempt to detect with man-portable, vehicle-mounted or boat-mounted radiation detectors. The source is assumed to transit an area populated with these mobile detectors, and the objective is to detect the source before it reaches a perimeter. We describe a simulation model developed to estimate the probability that one of the mobile detectors will come in to close proximity of the moving source and detect it. We illustrate with a maritime simulation example. Our simulation takes place in a 10 km by 5 km rectangular bay patrolled by boats equipped with 2-inch x 4-inch x 16-inch NaI detectors. Boats to be inspected enter the bay and randomly proceed to one of seven harbors on the shore. A source-bearing boat enters the mouth of the bay and proceeds to a pier on the opposite side. We wish to determine the probability that the source is detected and its range from target when detected. Patrol boats select the nearest in-bound boat for inspection and initiate an intercept course. Once within an operational range for the detection system, a detection algorithm is started. If the patrol boat confirms the source is not present, it selects the next nearest boat for inspection. Each run of the simulation ends either when a patrol successfully detects a source or when the source reaches its target. Several statistical detection algorithms have been implemented in the simulation model. First, a simple k-sigma algorithm, which alarms with the counts in a time window exceeds the mean background plus k times the standard deviation of background, is available to the user. The time window used is optimized with respect to the signal-to-background ratio for that range and relative speed. Second, a sequential probability ratio test [Wald 1947] is available, and configured in this simulation with a target false positive probability of 0.001 and false negative probability of 0.1. This test is utilized when the mobile detector maintains

  9. Measurement agreement between a newly developed sensing insole and traditional laboratory-based method for footstrike pattern detection in runners

    Science.gov (United States)

    Cheung, Roy T. H.; An, Winko W.; Au, Ivan P. H.; Zhang, Janet H.; Chan, Zoe Y. S.; Man, Alfred; Lau, Fannie O. Y.; Lam, Melody K. Y.; Lau, K. K.; Leung, C. Y.; Tsang, N. W.; Sze, Louis K. Y.; Lam, Gilbert W. K.

    2017-01-01

    This study introduced a novel but simple method to continuously measure footstrike patterns in runners using inexpensive force sensors. Two force sensing resistors were firmly affixed at the heel and second toe of both insoles to collect the time signal of foot contact. A total of 109 healthy young adults (42 males and 67 females) were recruited in this study. They ran on an instrumented treadmill at 0°, +10°, and -10° inclinations and attempted rearfoot, midfoot, and forefoot landings using real time visual biofeedback. Intra-step strike index and onset time difference between two force sensors were measured and analyzed with univariate linear regression. We analyzed 25,655 footfalls and found that onset time difference between two sensors explained 80–84% of variation in the prediction model of strike index (R-squared = 0.799–0.836, p<0.001). However, the time windows to detect footstrike patterns on different surface inclinations were not consistent. These findings may allow laboratory-based gait retraining to be implemented in natural running environments to aid in both injury prevention and performance enhancement. PMID:28599003

  10. Measurement agreement between a newly developed sensing insole and traditional laboratory-based method for footstrike pattern detection in runners.

    Directory of Open Access Journals (Sweden)

    Roy T H Cheung

    Full Text Available This study introduced a novel but simple method to continuously measure footstrike patterns in runners using inexpensive force sensors. Two force sensing resistors were firmly affixed at the heel and second toe of both insoles to collect the time signal of foot contact. A total of 109 healthy young adults (42 males and 67 females were recruited in this study. They ran on an instrumented treadmill at 0°, +10°, and -10° inclinations and attempted rearfoot, midfoot, and forefoot landings using real time visual biofeedback. Intra-step strike index and onset time difference between two force sensors were measured and analyzed with univariate linear regression. We analyzed 25,655 footfalls and found that onset time difference between two sensors explained 80-84% of variation in the prediction model of strike index (R-squared = 0.799-0.836, p<0.001. However, the time windows to detect footstrike patterns on different surface inclinations were not consistent. These findings may allow laboratory-based gait retraining to be implemented in natural running environments to aid in both injury prevention and performance enhancement.

  11. An expanded Notch-Delta model exhibiting long-range patterning and incorporating MicroRNA regulation.

    Directory of Open Access Journals (Sweden)

    Jerry S Chen

    2014-06-01

    Full Text Available Notch-Delta signaling is a fundamental cell-cell communication mechanism that governs the differentiation of many cell types. Most existing mathematical models of Notch-Delta signaling are based on a feedback loop between Notch and Delta leading to lateral inhibition of neighboring cells. These models result in a checkerboard spatial pattern whereby adjacent cells express opposing levels of Notch and Delta, leading to alternate cell fates. However, a growing body of biological evidence suggests that Notch-Delta signaling produces other patterns that are not checkerboard, and therefore a new model is needed. Here, we present an expanded Notch-Delta model that builds upon previous models, adding a local Notch activity gradient, which affects long-range patterning, and the activity of a regulatory microRNA. This model is motivated by our experiments in the ascidian Ciona intestinalis showing that the peripheral sensory neurons, whose specification is in part regulated by the coordinate activity of Notch-Delta signaling and the microRNA miR-124, exhibit a sparse spatial pattern whereby consecutive neurons may be spaced over a dozen cells apart. We perform rigorous stability and bifurcation analyses, and demonstrate that our model is able to accurately explain and reproduce the neuronal pattern in Ciona. Using Monte Carlo simulations of our model along with miR-124 transgene over-expression assays, we demonstrate that the activity of miR-124 can be incorporated into the Notch decay rate parameter of our model. Finally, we motivate the general applicability of our model to Notch-Delta signaling in other animals by providing evidence that microRNAs regulate Notch-Delta signaling in analogous cell types in other organisms, and by discussing evidence in other organisms of sparse spatial patterns in tissues where Notch-Delta signaling is active.

  12. A kinematic method for footstrike pattern detection in barefoot and shod runners.

    Science.gov (United States)

    Altman, Allison R; Davis, Irene S

    2012-02-01

    Footstrike patterns during running can be classified discretely into a rearfoot strike, midfoot strike and forefoot strike by visual observation. However, the footstrike pattern can also be classified on a continuum, ranging from 0% to 100% (extreme rearfoot to extreme forefoot) using the strike index, a measure requiring force plate data. When force data are not available, an alternative method to quantify the strike pattern must be used. The purpose of this paper was to quantify the continuum of foot strike patterns using an easily attainable kinematic measure, and compare it to the strike index measure. Force and kinematic data from twenty subjects were collected as they ran across an embedded force plate. Strike index and the footstrike angle were identified for the four running conditions of rearfoot strike, midfoot strike and forefoot strike, as well as barefoot. The footstrike angle was calculated as the angle of the foot with respect to the ground in the sagittal plane. Results indicated that the footstrike angle was significantly correlated with strike index. The linear regression model suggested that strike index can be accurately estimated, in both barefoot and shod conditions, in the absence of force data. Copyright © 2011 Elsevier B.V. All rights reserved.

  13. A simplified memory network model based on pattern formations

    Science.gov (United States)

    Xu, Kesheng; Zhang, Xiyun; Wang, Chaoqing; Liu, Zonghua

    2014-12-01

    Many experiments have evidenced the transition with different time scales from short-term memory (STM) to long-term memory (LTM) in mammalian brains, while its theoretical understanding is still under debate. To understand its underlying mechanism, it has recently been shown that it is possible to have a long-period rhythmic synchronous firing in a scale-free network, provided the existence of both the high-degree hubs and the loops formed by low-degree nodes. We here present a simplified memory network model to show that the self-sustained synchronous firing can be observed even without these two necessary conditions. This simplified network consists of two loops of coupled excitable neurons with different synaptic conductance and with one node being the sensory neuron to receive an external stimulus signal. This model can be further used to show how the diversity of firing patterns can be selectively formed by varying the signal frequency, duration of the stimulus and network topology, which corresponds to the patterns of STM and LTM with different time scales. A theoretical analysis is presented to explain the underlying mechanism of firing patterns.

  14. Multivariate data-driven modelling and pattern recognition for damage detection and identification for acoustic emission and acousto-ultrasonics

    International Nuclear Information System (INIS)

    Torres-Arredondo, M-A; Fritzen, C-P; Tibaduiza, D-A; Mujica, L E; Rodellar, J; McGugan, M; Toftegaard, H; Borum, K-K

    2013-01-01

    Different methods are commonly used for non-destructive testing in structures; among others, acoustic emission and ultrasonic inspections are widely used to assess structures. The research presented in this paper is motivated by the need to improve the inspection capabilities and reliability of structural health monitoring (SHM) systems based on ultrasonic guided waves with focus on the acoustic emission and acousto-ultrasonics techniques. The use of a guided wave based approach is driven by the fact that these waves are able to propagate over relatively long distances, and interact sensitively and uniquely with different types of defect. Special attention is paid here to the development of efficient SHM methodologies. This requires robust signal processing techniques for the correct interpretation of the complex ultrasonic waves. Therefore, a variety of existing algorithms for signal processing and pattern recognition are evaluated and integrated into the different proposed methodologies. As a contribution to solve the problem, this paper presents results in damage detection and classification using a methodology based on hierarchical nonlinear principal component analysis, square prediction measurements and self-organizing maps, which are applied to data from acoustic emission tests and acousto-ultrasonic inspections. At the end, the efficiency of these methodologies is experimentally evaluated in diverse anisotropic composite structures. (paper)

  15. Spatiotemporal patterns, triggers and anatomies of seismically detected rockfalls

    Directory of Open Access Journals (Sweden)

    M. Dietze

    2017-11-01

    Full Text Available Rockfalls are a ubiquitous geomorphic process and a natural hazard in steep landscapes across the globe. Seismic monitoring can provide precise information on the timing, location and event anatomy of rockfalls, which are parameters that are otherwise hard to constrain. By pairing data from 49 seismically detected rockfalls in the Lauterbrunnen Valley in the Swiss Alps with auxiliary meteorologic and seismic data of potential triggers during autumn 2014 and spring 2015, we are able to (i analyse the evolution of single rockfalls and their common properties, (ii identify spatial changes in activity hotspots (iii and explore temporal activity patterns on different scales ranging from months to minutes to quantify relevant trigger mechanisms. Seismic data allow for the classification of rockfall activity into two distinct phenomenological types. The signals can be used to discern multiple rock mass releases from the same spot, identify rockfalls that trigger further rockfalls and resolve modes of subsequent talus slope activity. In contrast to findings based on discontinuous methods with integration times of several months, rockfall in the monitored limestone cliff is not spatially uniform but shows a systematic downward shift of a rock mass release zone following an exponential law, most likely driven by a continuously lowering water table. Freeze–thaw transitions, approximated at first order from air temperature time series, account for only 5 out of the 49 rockfalls, whereas 19 rockfalls were triggered by rainfall events with a peak lag time of 1 h. Another 17 rockfalls were triggered by diurnal temperature changes and occurred during the coldest hours of the day and during the highest temperature change rates. This study is thus the first to show direct links between proposed rockfall triggers and the spatiotemporal distribution of rockfalls under natural conditions; it extends existing models by providing seismic observations of the

  16. Modeling patterns of hot water use in households

    Energy Technology Data Exchange (ETDEWEB)

    Lutz, J.D.; Liu, Xiaomin; McMahon, J.E. [and others

    1996-11-01

    This report presents a detailed model of hot water use patterns in individual household. The model improves upon an existing model by including the effects of four conditions that were previously unaccounted for: the absence of a clothes washer; the absence of a dishwasher; a household consisting of seniors only; and a household that does not pay for its own hot water use. Although these four conditions can significantly affect residential hot water use, and have been noted in other studies, this is the first time that they have been incorporated into a detailed model. This model allows detailed evaluation of the impact of potential efficiency standards for water heaters and other market transformation policies. 21 refs., 3 figs., 10 tabs.

  17. Mechanochemical pattern formation in simple models of active viscoelastic fluids and solids

    Science.gov (United States)

    Alonso, Sergio; Radszuweit, Markus; Engel, Harald; Bär, Markus

    2017-11-01

    The cytoskeleton of the organism Physarum polycephalum is a prominent example of a complex active viscoelastic material wherein stresses induce flows along the organism as a result of the action of molecular motors and their regulation by calcium ions. Experiments in Physarum polycephalum have revealed a rich variety of mechanochemical patterns including standing, traveling and rotating waves that arise from instabilities of spatially homogeneous states without gradients in stresses and resulting flows. Herein, we investigate simple models where an active stress induced by molecular motors is coupled to a model describing the passive viscoelastic properties of the cellular material. Specifically, two models for viscoelastic fluids (Maxwell and Jeffrey model) and two models for viscoelastic solids (Kelvin-Voigt and Standard model) are investigated. Our focus is on the analysis of the conditions that cause destabilization of spatially homogeneous states and the related onset of mechano-chemical waves and patterns. We carry out linear stability analyses and numerical simulations in one spatial dimension for different models. In general, sufficiently strong activity leads to waves and patterns. The primary instability is stationary for all active fluids considered, whereas all active solids have an oscillatory primary instability. All instabilities found are of long-wavelength nature reflecting the conservation of the total calcium concentration in the models studied.

  18. Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching.

    Science.gov (United States)

    Du, Pan; Kibbe, Warren A; Lin, Simon M

    2006-09-01

    A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. The algorithm is implemented in R and will be

  19. Imbalance aware lithography hotspot detection: a deep learning approach

    Science.gov (United States)

    Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

    2017-07-01

    With the advancement of very large scale integrated circuits (VLSI) technology nodes, lithographic hotspots become a serious problem that affects manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with the extreme scaling of transistor feature size and layout patterns growing in complexity, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. We present a deep convolutional neural network (CNN) that targets representative feature learning in lithography hotspot detection. We carefully analyze the impact and effectiveness of different CNN hyperparameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always in the minority in VLSI mask design, the training dataset is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from a high number of false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply hotspot upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

  20. Mathematical modeling of high-rate Anammox UASB reactor based on granular packing patterns

    International Nuclear Information System (INIS)

    Tang, Chong-Jian; He, Rui; Zheng, Ping; Chai, Li-Yuan; Min, Xiao-Bo

    2013-01-01

    Highlights: ► A novel model was conducted to estimate volumetric nitrogen conversion rates. ► The packing patterns of the granules in Anammox reactor are investigated. ► The simple cubic packing pattern was simulated in high-rate Anammox UASB reactor. ► Operational strategies concerning sludge concentration were proposed by the modeling. -- Abstract: A novel mathematical model was developed to estimate the volumetric nitrogen conversion rates of a high-rate Anammox UASB reactor based on the packing patterns of granular sludge. A series of relationships among granular packing density, sludge concentration, hydraulic retention time and volumetric conversion rate were constructed to correlate Anammox reactor performance with granular packing patterns. It was suggested that the Anammox granules packed as the equivalent simple cubic pattern in high-rate UASB reactor with packing density of 50–55%, which not only accommodated a high concentration of sludge inside the reactor, but also provided large pore volume, thus prolonging the actual substrate conversion time. Results also indicated that it was necessary to improve Anammox reactor performance by enhancing substrate loading when sludge concentration was higher than 37.8 gVSS/L. The established model was carefully calibrated and verified, and it well simulated the performance of granule-based high-rate Anammox UASB reactor

  1. Mathematical modeling of high-rate Anammox UASB reactor based on granular packing patterns

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Chong-Jian, E-mail: chjtangzju@yahoo.com.cn [Department of Environmental Engineering, School of Metallurgical Science and Engineering, Central South University, Changsha 410083 (China); National Engineering Research Center for Control and Treatment of Heavy Metal Pollution, Changsha 410083 (China); He, Rui; Zheng, Ping [Department of Environmental Engineering, Zhejiang University, Zijingang Campus, Hangzhou 310058 (China); Chai, Li-Yuan; Min, Xiao-Bo [Department of Environmental Engineering, School of Metallurgical Science and Engineering, Central South University, Changsha 410083 (China); National Engineering Research Center for Control and Treatment of Heavy Metal Pollution, Changsha 410083 (China)

    2013-04-15

    Highlights: ► A novel model was conducted to estimate volumetric nitrogen conversion rates. ► The packing patterns of the granules in Anammox reactor are investigated. ► The simple cubic packing pattern was simulated in high-rate Anammox UASB reactor. ► Operational strategies concerning sludge concentration were proposed by the modeling. -- Abstract: A novel mathematical model was developed to estimate the volumetric nitrogen conversion rates of a high-rate Anammox UASB reactor based on the packing patterns of granular sludge. A series of relationships among granular packing density, sludge concentration, hydraulic retention time and volumetric conversion rate were constructed to correlate Anammox reactor performance with granular packing patterns. It was suggested that the Anammox granules packed as the equivalent simple cubic pattern in high-rate UASB reactor with packing density of 50–55%, which not only accommodated a high concentration of sludge inside the reactor, but also provided large pore volume, thus prolonging the actual substrate conversion time. Results also indicated that it was necessary to improve Anammox reactor performance by enhancing substrate loading when sludge concentration was higher than 37.8 gVSS/L. The established model was carefully calibrated and verified, and it well simulated the performance of granule-based high-rate Anammox UASB reactor.

  2. Modeling the detectability of vesicoureteral reflux using microwave radiometry

    International Nuclear Information System (INIS)

    Arunachalam, Kavitha; Maccarini, Paolo F; Stauffer, Paul R; De Luca, Valeria; Bardati, Fernando; Snow, Brent W

    2010-01-01

    We present the modeling efforts on antenna design, frequency selection and receiver sensitivity estimation to detect vesicoureteral reflux (VUR) using microwave (MW) radiometry as warm urine from the bladder maintained at fever range temperature using a MW hyperthermia device reflows into the kidneys. The radiometer center frequency (f c ), frequency band (Δf) and aperture radius (r a ) of the physical antenna for kidney temperature monitoring are determined using a simplified universal antenna model with a circular aperture. Anatomical information extracted from the computed tomography (CT) images of children aged 4-6 years is used to construct a layered 3D tissue model. Radiometric antenna efficiency is evaluated in terms of the ratio of the power collected from the target at depth to the total power received by the antenna (η). The power ratio of the theoretical antenna is used to design a microstrip log spiral antenna with directional radiation pattern over f c ± Δf/2. Power received by the log spiral from the deep target is enhanced using a thin low-loss dielectric matching layer. A cylindrical metal cup is proposed to shield the antenna from electromagnetic interference (EMI). Transient thermal simulations are carried out to determine the minimum detectable change in the antenna brightness temperature (δT B ) for 15-25 mL urine refluxes at 40-42 0 C located 35 mm from the skin surface. Theoretical antenna simulations indicate maximum η over 1.1-1.6 GHz for r a = 30-40 mm. Simulations of the 35 mm radius tapered log spiral yielded a higher power ratio over f c ± Δf/2 for the 35-40 mm deep targets in the presence of an optimal matching layer. Radiometric temperature calculations indicate δT B ≥ 0.1 K for the 15 mL urine at 40 0 C and 35 mm depth. Higher η and δT B were observed for the antenna and matching layer inside the metal cup. Reflection measurements of the log spiral in a saline phantom are in agreement with the simulation data. The

  3. Modeling the detectability of vesicoureteral reflux using microwave radiometry

    Energy Technology Data Exchange (ETDEWEB)

    Arunachalam, Kavitha [Department of Engineering Design, Indian Institute of Technology Madras, Chennai (India); Maccarini, Paolo F; Stauffer, Paul R [Department of Radiation Oncology, Duke University Medical Center, Durham, NC (United States); De Luca, Valeria [Department of Information Tech and Electrical Eng., ETH Zurich (Switzerland); Bardati, Fernando [Department of Computer Science, Systems and Production, University of Rome, Tor Vergata, Roma (Italy); Snow, Brent W, E-mail: akavitha@iitm.ac.i [University of Utah and Primary Children' s Medical Center, Salt Lake City, UT (United States)

    2010-09-21

    We present the modeling efforts on antenna design, frequency selection and receiver sensitivity estimation to detect vesicoureteral reflux (VUR) using microwave (MW) radiometry as warm urine from the bladder maintained at fever range temperature using a MW hyperthermia device reflows into the kidneys. The radiometer center frequency (f{sub c}), frequency band ({Delta}f) and aperture radius (r{sub a}) of the physical antenna for kidney temperature monitoring are determined using a simplified universal antenna model with a circular aperture. Anatomical information extracted from the computed tomography (CT) images of children aged 4-6 years is used to construct a layered 3D tissue model. Radiometric antenna efficiency is evaluated in terms of the ratio of the power collected from the target at depth to the total power received by the antenna ({eta}). The power ratio of the theoretical antenna is used to design a microstrip log spiral antenna with directional radiation pattern over f{sub c} {+-} {Delta}f/2. Power received by the log spiral from the deep target is enhanced using a thin low-loss dielectric matching layer. A cylindrical metal cup is proposed to shield the antenna from electromagnetic interference (EMI). Transient thermal simulations are carried out to determine the minimum detectable change in the antenna brightness temperature ({delta}T{sub B}) for 15-25 mL urine refluxes at 40-42 {sup 0}C located 35 mm from the skin surface. Theoretical antenna simulations indicate maximum {eta} over 1.1-1.6 GHz for r{sub a} = 30-40 mm. Simulations of the 35 mm radius tapered log spiral yielded a higher power ratio over f{sub c} {+-} {Delta}f/2 for the 35-40 mm deep targets in the presence of an optimal matching layer. Radiometric temperature calculations indicate {delta}T{sub B} {>=} 0.1 K for the 15 mL urine at 40 {sup 0}C and 35 mm depth. Higher {eta} and {delta}T{sub B} were observed for the antenna and matching layer inside the metal cup. Reflection measurements

  4. Pattern solutions of the Klausmeier Model for banded vegetation in semi-arid environments I

    International Nuclear Information System (INIS)

    Sherratt, Jonathan A

    2010-01-01

    In many semi-arid environments, vegetation cover is sparse, and is self-organized into large-scale spatial patterns. In particular, banded vegetation is typical on hillsides. Mathematical modelling is widely used to study these banded patterns, and many models are effectively extensions of a coupled reaction–diffusion–advection system proposed by Klausmeier (1999 Science 284 1826–8). However, there is currently very little mathematical theory on pattern solutions of these equations. This paper is the first in a series whose aim is a comprehensive understanding of these solutions, which can act as a springboard both for future simulation-based studies of the Klausmeier model, and for analysis of model extensions. The author focusses on a particular part of parameter space, and derives expressions for the boundaries of the parameter region in which patterns occur. The calculations are valid to leading order at large values of the 'slope parameter', which reflects a comparison of the rate of water flow downhill with the rate of vegetation dispersal. The form of the corresponding patterns is also studied, and the author shows that the leading order equations change close to one boundary of the parameter region in which there are patterns, leading to a homoclinic solution. Conclusions are drawn on the way in which changes in mean annual rainfall affect pattern properties, including overall biomass productivity

  5. An equilibrium-point model of electromyographic patterns during single-joint movements based on experimentally reconstructed control signals.

    Science.gov (United States)

    Latash, M L; Goodman, S R

    1994-01-01

    The purpose of this work has been to develop a model of electromyographic (EMG) patterns during single-joint movements based on a version of the equilibrium-point hypothesis, a method for experimental reconstruction of the joint compliant characteristics, the dual-strategy hypothesis, and a kinematic model of movement trajectory. EMG patterns are considered emergent properties of hypothetical control patterns that are equally affected by the control signals and peripheral feedback reflecting actual movement trajectory. A computer model generated the EMG patterns based on simulated movement kinematics and hypothetical control signals derived from the reconstructed joint compliant characteristics. The model predictions have been compared to published recordings of movement kinematics and EMG patterns in a variety of movement conditions, including movements over different distances, at different speeds, against different-known inertial loads, and in conditions of possible unexpected decrease in the inertial load. Changes in task parameters within the model led to simulated EMG patterns qualitatively similar to the experimentally recorded EMG patterns. The model's predictive power compares it favourably to the existing models of the EMG patterns. Copyright © 1994. Published by Elsevier Ltd.

  6. Detecting Faults By Use Of Hidden Markov Models

    Science.gov (United States)

    Smyth, Padhraic J.

    1995-01-01

    Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).

  7. PATTERN BASED DETECTION OF POTENTIALLY DRUGGABLE BINDING SITES BY LIGAND SCREENING

    Directory of Open Access Journals (Sweden)

    Uttam Pal

    2018-03-01

    Full Text Available This article describes an innovative way of finding the potentially druggable sites on a target protein, which can be used for orthosteric and allosteric lead detection in a single virtual screening setup. Druggability estimation for an alternate binding site other than the canonical ligand-binding pocket of an enzyme is rewarding for several inherent benefits. Allostery is a direct and efficient way of regulating biomacromolecule function. The allosteric modulators can fine-tune protein mechanics. Besides, allosteric sites are evolutionarily less conserved/more diverse even in very similarly related proteins, thus, provides high degree of specificity in targeting a particular protein. Therefore, targeting of allosteric sites is gaining attention as an emerging strategy in rational drug design. However, the experimental approaches provide a limited degree of characterization of new allosteric sites. Computational approaches are useful to analyze and select potential allosteric sites for drug discovery. Here, the use of molecular docking, which has become an integral part of the drug discovery process, has been discussed to predict the druggability of novel allosteric sites as well as the active site on target proteins by ligand screening. Genetic algorithm was used for docking and the whole protein was placed in the search space. For each ligand in the library of small molecules, the genetic algorithm was run for multiple times to populate all the druggable sites in the target protein, which was then translated into two dimensional density maps or “patterns”. High density clusters were observed for lead like molecules in these pattern diagrams. Each cluster in such a pattern diagram indicated a plausible binding site and the density gave its druggability score in terms of weighted probabilities. The patterns were filtered to find the leads for each of the druggable sites on the target protein. Such a novel pattern based analysis of the

  8. Location Contexts of User Check-Ins to Model Urban Geo Life-Style Patterns

    Science.gov (United States)

    Hasan, Samiul; Ukkusuri, Satish V.

    2015-01-01

    Geo-location data from social media offers us information, in new ways, to understand people's attitudes and interests through their activity choices. In this paper, we explore the idea of inferring individual life-style patterns from activity-location choices revealed in social media. We present a model to understand life-style patterns using the contextual information (e. g. location categories) of user check-ins. Probabilistic topic models are developed to infer individual geo life-style patterns from two perspectives: i) to characterize the patterns of user interests to different types of places and ii) to characterize the patterns of user visits to different neighborhoods. The method is applied to a dataset of Foursquare check-ins of the users from New York City. The co-existence of several location contexts and the corresponding probabilities in a given pattern provide useful information about user interests and choices. It is found that geo life-style patterns have similar items—either nearby neighborhoods or similar location categories. The semantic and geographic proximity of the items in a pattern reflects the hidden regularity in user preferences and location choice behavior. PMID:25970430

  9. SOA Modeling Patterns for Service Oriented Discovery and Analysis

    CERN Document Server

    Bell, Michael

    2010-01-01

    Learn the essential tools for developing a sound service-oriented architecture. SOA Modeling Patterns for Service-Oriented Discovery and Analysis introduces a universal, easy-to-use, and nimble SOA modeling language to facilitate the service identification and examination life cycle stage. This business and technological vocabulary will benefit your service development endeavors and foster organizational software asset reuse and consolidation, and reduction of expenditure. Whether you are a developer, business architect, technical architect, modeler, business analyst, team leader, or manager,

  10. Abnormal network flow detection based on application execution patterns from Web of Things (WoT) platforms.

    Science.gov (United States)

    Yoon, Young; Jung, Hyunwoo; Lee, Hana

    2018-01-01

    In this paper, we present a research work on a novel methodology of identifying abnormal behaviors at the underlying network monitor layer during runtime based on the execution patterns of Web of Things (WoT) applications. An execution pattern of a WoT application is a sequence of profiled time delays between the invocations of involved Web services, and it can be obtained from WoT platforms. We convert the execution pattern to a time sequence of network flows that are generated when the WoT applications are executed. We consider such time sequences as a whitelist. This whitelist reflects the valid application execution patterns. At the network monitor layer, our applied RETE algorithm examines whether any given runtime sequence of network flow instances does not conform to the whitelist. Through this approach, it is possible to interpret a sequence of network flows with regard to application logic. Given such contextual information, we believe that the administrators can detect and reason about any abnormal behaviors more effectively. Our empirical evaluation shows that our RETE-based algorithm outperforms the baseline algorithm in terms of memory usage.

  11. Morphogenesis and pattern formation in biological systems experiments and models

    CERN Document Server

    Noji, Sumihare; Ueno, Naoto; Maini, Philip

    2003-01-01

    A central goal of current biology is to decode the mechanisms that underlie the processes of morphogenesis and pattern formation. Concerned with the analysis of those phenomena, this book covers a broad range of research fields, including developmental biology, molecular biology, plant morphogenesis, ecology, epidemiology, medicine, paleontology, evolutionary biology, mathematical biology, and computational biology. In Morphogenesis and Pattern Formation in Biological Systems: Experiments and Models, experimental and theoretical aspects of biology are integrated for the construction and investigation of models of complex processes. This collection of articles on the latest advances by leading researchers not only brings together work from a wide spectrum of disciplines, but also provides a stepping-stone to the creation of new areas of discovery.

  12. Multisensor analyzer detector (MSAD) for low cost chemical and aerosol detection and pattern fusion

    Science.gov (United States)

    Swanson, David C.; Merdes, Daniel W.; Lysak, Daniel B., Jr.; Curtis, Richard C.; Lang, Derek C.; Mazzara, Andrew F.; Nicholas, Nicholas C.

    2002-08-01

    MSAD is being developed as a low-cost point detection chemical and biological sensor system designed around an information fusion inference engine that also allows additional sensors to be included in the detection process. The MSAD concept is based on probable cause detection of hazardous chemical vapors and aerosols of either chemical or biological composition using a small portable unit containing an embedded computer system and several integrated sensors with complementary capabilities. The configuration currently envisioned includes a Surface-Enhanced Raman Spectroscopy (SERS) sensor of chemical vapors and a detector of respirable aerosols based on Fraunhofer diffraction. Additional sensors employing Ion Mobility Spectrometry (IMS), Surface Acoustic Wave (SAW) detection, Flame Photometric Detection (FPD), and other principles are candidates for integration into the device; also, available commercial detectors implementing IMS, SAW, and FPD will be made accessible to the unit through RS232 ports. Both feature and decision level information fusion is supported using a Continuous Inference Network (CINET) of fuzzy logic. Each class of agents has a unique CINET with information inputs from a number of available sensors. Missing or low confidence sensor information is gracefully blended out of the output confidence for the particular agent. This approach constitutes a plug and play arrangement between the sensors and the information pattern recognition algorithms. We are currently doing simulant testing and developing out CINETs for actual agent testing at Edgewood Chemical and Biological Center (ECBC) later this year.

  13. Calibration of a distributed hydrologic model using observed spatial patterns from MODIS data

    Science.gov (United States)

    Demirel, Mehmet C.; González, Gorka M.; Mai, Juliane; Stisen, Simon

    2016-04-01

    Distributed hydrologic models are typically calibrated against streamflow observations at the outlet of the basin. Along with these observations from gauging stations, satellite based estimates offer independent evaluation data such as remotely sensed actual evapotranspiration (aET) and land surface temperature. The primary objective of the study is to compare model calibrations against traditional downstream discharge measurements with calibrations against simulated spatial patterns and combinations of both types of observations. While the discharge based model calibration typically improves the temporal dynamics of the model, it seems to give rise to minimum improvement of the simulated spatial patterns. In contrast, objective functions specifically targeting the spatial pattern performance could potentially increase the spatial model performance. However, most modeling studies, including the model formulations and parameterization, are not designed to actually change the simulated spatial pattern during calibration. This study investigates the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale hydrologic model (mHM). This model is selected as it allows for a change in the spatial distribution of key soil parameters through the optimization of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) values directly as input. In addition the simulated aET can be estimated at a spatial resolution suitable for comparison to the spatial patterns observed with MODIS data. To increase our control on spatial calibration we introduced three additional parameters to the model. These new parameters are part of an empirical equation to the calculate crop coefficient (Kc) from daily LAI maps and used to update potential evapotranspiration (PET) as model inputs. This is done instead of correcting/updating PET with just a uniform (or aspect driven) factor used in the mHM model

  14. Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.

    Science.gov (United States)

    Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

    2017-05-01

    Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P  sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  15. Post-decomposition optimizations using pattern matching and rule-based clustering for multi-patterning technology

    Science.gov (United States)

    Wang, Lynn T.-N.; Madhavan, Sriram

    2018-03-01

    A pattern matching and rule-based polygon clustering methodology with DFM scoring is proposed to detect decomposition-induced manufacturability detractors and fix the layout designs prior to manufacturing. A pattern matcher scans the layout for pre-characterized patterns from a library. If a pattern were detected, rule-based clustering identifies the neighboring polygons that interact with those captured by the pattern. Then, DFM scores are computed for the possible layout fixes: the fix with the best score is applied. The proposed methodology was applied to two 20nm products with a chip area of 11 mm2 on the metal 2 layer. All the hotspots were resolved. The number of DFM spacing violations decreased by 7-15%.

  16. Rapid detection of drug resistance and mutational patterns of extensively drug-resistant strains by a novel GenoType® MTBDRsl assay

    Directory of Open Access Journals (Sweden)

    A K Singh

    2013-01-01

    Full Text Available Background: The emergence of extensively drug-resistant tuberculosis (XDR-TB is a major concern in the India. The burden of XDR-TB is increasing due to inadequate monitoring, lack of proper diagnosis, and treatment. The GenoType ® Mycobacterium tuberculosis drug resistance second line (MTBDRsl assay is a novel line probe assay used for the rapid detection of mutational patterns conferring resistance to XDR-TB. Aim: The aim of this study was to study the rapid detection of drug resistance and mutational patterns of the XDR-TB by a novel GenoType ® MTBDRsl assay. Materials and Methods: We evaluated 98 multidrug-resistant (MDR M. tuberculosis isolates for second line drugs susceptibility testing by 1% proportion method (BacT/ALERT 3D system and GenoType ® MTBDRsl assay for rapid detection of conferring drug resistance to XDR-TB. Results: A total of seven (17.4% were identified as XDR-TB by using standard phenotypic method. The concordance between phenotypic and GenoType ® MTBDRsl assay was 91.7-100% for different antibiotics. The sensitivity and specificity of the MTBDRsl assay were 100% and 100% for aminoglycosides; 100% and 100% for fluoroquinolones; 91.7% and 100% for ethambutol. The most frequent mutations and patterns were gyrA MUT1 (A90V in seven (41.2% and gyrA + WT1-3 + MUT1 in four (23.5%; rrs MUT1 (A1401G in 11 (64.7%, and rrs WT1-2 + MUT1 in eight (47.1%; and embB MUT1B (M306V in 11 (64.7% strains. Conclusions: These data suggest that the GenoType ® MTBDRsl assay is rapid, novel test for detection of resistance to second line anti-tubercular drugs. This assay provides additional information about the frequency and mutational patterns responsible for XDR-TB resistance.

  17. A simple model for research interest evolution patterns

    Science.gov (United States)

    Jia, Tao; Wang, Dashun; Szymanski, Boleslaw

    Sir Isaac Newton supposedly remarked that in his scientific career he was like ``...a boy playing on the sea-shore ...finding a smoother pebble or a prettier shell than ordinary''. His remarkable modesty and famous understatement motivate us to seek regularities in how scientists shift their research focus as the career develops. Indeed, despite intensive investigations on how microscopic factors, such as incentives and risks, would influence a scientist's choice of research agenda, little is known on the macroscopic patterns in the research interest change undertaken by individual scientists throughout their careers. Here we make use of over 14,000 authors' publication records in physics. By quantifying statistical characteristics in the interest evolution, we model scientific research as a random walk, which reproduces patterns in individuals' careers observed empirically. Despite myriad of factors that shape and influence individual choices of research subjects, we identified regularities in this dynamical process that are well captured by a simple statistical model. The results advance our understanding of scientists' behaviors during their careers and open up avenues for future studies in the science of science.

  18. Spatial pattern evaluation of a calibrated national hydrological model - a remote-sensing-based diagnostic approach

    Science.gov (United States)

    Mendiguren, Gorka; Koch, Julian; Stisen, Simon

    2017-11-01

    Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds

  19. Detection of cytokine expression patterns in the peripheral blood of patients with acute leukemia by antibody microarray analysis.

    Science.gov (United States)

    Li, Qing; Li, Mei; Wu, Yao-hui; Zhu, Xiao-jian; Zeng, Chen; Zou, Ping; Chen, Zhi-chao

    2014-04-01

    The cytokines of acute leukemia (AL) patients have certain expression patterns, forming a complex network involved in diagnosis, progression, and prognosis. We collected the serum of different AL patients before and after complete remission (CR) for detection of cytokines by using an antibody chip. The expression patterns of cytokines were determined by using bioinformatics computational analysis. The results showed that there were significant differences in the cytokine expression patterns between AL patients and normal controls, as well as between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). In confirmatory test, ELISA revealed the expression of uPAR in AL. Moreover, the bioinformatic analysis showed that the differentially expressed cytokines among the AL groups were involved in different biological behaviors and were closely related with the development of the disease. It was concluded that the cytokine expression pattern of AL patients is significantly different from that of healthy volunteers. Also, differences of cytokine expression patterns exist between AML and ALL, and between before and after CR in the same subtype of AL, which holds important clinical significance for revealing disease progression.

  20. Replication Banding Patterns in Human Chromosomes Detected Using 5-ethynyl-2'-deoxyuridine Incorporation

    International Nuclear Information System (INIS)

    Hoshi, Osamu; Ushiki, Tatsuo

    2011-01-01

    A novel technique using the incorporation of 5-ethynyl-2'-deoxyuridine (EdU) into replicating DNA is described for the analysis of replicating banding patterns of human metaphase chromosomes. Human lymphocytes were synchronized with excess thymidine and treated with EdU during the late S phase of the cell cycle. The incorporated EdU was then detected in metaphase chromosomes using Alexa Fluor® 488 azides, through the 1,3-dipolar cycloaddition reaction of organic azides with the terminal acetylene group of EdU. Chromosomes with incorporated EdU showed a banding pattern similar to G-banding of normal human chromosomes. Imaging by atomic force microscopy (AFM) in liquid conditions showed that the structure of the chromosomes was well preserved even after EdU treatment. Comparison between fluorescence microscopy and AFM images of the same chromosome 1 indicated the presence of ridges and grooves in the chromatid arm, features that have been previously reported in relation to G-banding. These results suggest an intimate relationship between EdU-induced replication bands and G- or R-bands in human chromosomes. This technique is thus useful for analyzing the structure of chromosomes in relation to their banding patterns following DNA replication in the S phase

  1. A Theoretical Model of Jigsaw-Puzzle Pattern Formation by Plant Leaf Epidermal Cells.

    Science.gov (United States)

    Higaki, Takumi; Kutsuna, Natsumaro; Akita, Kae; Takigawa-Imamura, Hisako; Yoshimura, Kenji; Miura, Takashi

    2016-04-01

    Plant leaf epidermal cells exhibit a jigsaw puzzle-like pattern that is generated by interdigitation of the cell wall during leaf development. The contribution of two ROP GTPases, ROP2 and ROP6, to the cytoskeletal dynamics that regulate epidermal cell wall interdigitation has already been examined; however, how interactions between these molecules result in pattern formation remains to be elucidated. Here, we propose a simple interface equation model that incorporates both the cell wall remodeling activity of ROP GTPases and the diffusible signaling molecules by which they are regulated. This model successfully reproduces pattern formation observed in vivo, and explains the counterintuitive experimental results of decreased cellulose production and increased thickness. Our model also reproduces the dynamics of three-way cell wall junctions. Therefore, this model provides a possible mechanism for cell wall interdigitation formation in vivo.

  2. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

    DEFF Research Database (Denmark)

    Demirel, Mehmet C.; Mai, Juliane; Mendiguren Gonzalez, Gorka

    2018-01-01

    Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target...... and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance...

  3. Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model

    Energy Technology Data Exchange (ETDEWEB)

    Rossi, R; Gallagher, B; Neville, J; Henderson, K

    2011-11-11

    Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.

  4. Modeling on bubbly to churn flow pattern transition in narrow rectangular channel

    International Nuclear Information System (INIS)

    Wang Yanlin; Chen Bingde; Huang Yanping; Wang Junfeng

    2012-01-01

    A theoretical model based on some reasonable concepts was developed to predict the bubbly flow to churn flow pattern transition in vertical narrow rectangular channel under flow boiling condition. The maximum size of ideal bubble in narrow rectangular channel was calculated based on previous literature. The thermal hydraulics boundary condition of bubbly to churn flow pattern transition was exported from Helmholtz and maximum size of ideal bubble. The theoretical model was validated by existent experimental data. (authors)

  5. Spatial pattern detection modeling of thrips (Thrips tabaci on onion fields Detecção de padrões espaciais na ocorrência do tripes (Thrips tabaci na cultura da cebola

    Directory of Open Access Journals (Sweden)

    Paulo Justiniano Ribeiro Jr

    2009-02-01

    Full Text Available Onion (Allium cepa is one of the most cultivated and consumed vegetables in Brazil and its importance is due to the large laborforce involved. One of the main pests that affect this crop is the Onion Thrips (Thrips tabaci, but the spatial distribution of this insect, although important, has not been considered in crop management recommendations, experimental planning or sampling procedures. Our purpose here is to consider statistical tools to detect and model spatial patterns of the occurrence of the onion thrips. In order to characterize the spatial distribution pattern of the Onion Thrips a survey was carried out to record the number of insects in each development phase on onion plant leaves, on different dates and sample locations, in four rural properties with neighboring farms under different infestation levels and planting methods. The Mantel randomization test proved to be a useful tool to test for spatial correlation which, when detected, was described by a mixed spatial Poisson model with a geostatistical random component and parameters allowing for a characterization of the spatial pattern, as well as the production of prediction maps of susceptibility to levels of infestation throughout the area.A cebola é uma das hortaliças mais cultivadas e consumidas no Brasil e sua importância social se deve à grande demanda por mão-de-obra. Uma das principais pragas que afeta essa cultura é o tripes do prateamento (Thrips tabaci e sua distribuição espacial, embora importante, não tem sido considerada nas recomendações de manejo da cultura, planejamento de experimentos ou estudos amostrais. O objetivo desse artigo foi considerar métodos estatísticos para detectar e modelar padrões espaciais na ocorrência do tripes do prateamento da cebola. Para caracterizar o padrão espacial da dispersão do tripes do prateamento da cebola foi feito um levantamento anotando-se o número de insetos por fase de desenvolvimento em folhas de plantas de

  6. Neural network approach to radiologic lesion detection

    International Nuclear Information System (INIS)

    Newman, F.D.; Raff, U.; Stroud, D.

    1989-01-01

    An area of artificial intelligence that has gained recent attention is the neural network approach to pattern recognition. The authors explore the use of neural networks in radiologic lesion detection with what is known in the literature as the novelty filter. This filter uses a linear model; images of normal patterns become training vectors and are stored as columns of a matrix. An image of an abnormal pattern is introduced and the abnormality or novelty is extracted. A VAX 750 was used to encode the novelty filter, and two experiments have been examined

  7. Spiking patterns of a hippocampus model in electric fields

    International Nuclear Information System (INIS)

    Men Cong; Wang Jiang; Qin Ying-Mei; Wei Xi-Le; Deng Bin; Che Yan-Qiu

    2011-01-01

    We develop a model of CA3 neurons embedded in a resistive array to mimic the effects of electric fields from a new perspective. Effects of DC and sinusoidal electric fields on firing patterns in CA3 neurons are investigated in this study. The firing patterns can be switched from no firing pattern to burst or from burst to fast periodic firing pattern with the increase of DC electric field intensity. It is also found that the firing activities are sensitive to the frequency and amplitude of the sinusoidal electric field. Different phase-locking states and chaotic firing regions are observed in the parameter space of frequency and amplitude. These findings are qualitatively in accordance with the results of relevant experimental and numerical studies. It is implied that the external or endogenous electric field can modulate the neural code in the brain. Furthermore, it is helpful to develop control strategies based on electric fields to control neural diseases such as epilepsy. (interdisciplinary physics and related areas of science and technology)

  8. Deep Learning @15 Petaflops/second: Semi-supervised pattern detection for 15 Terabytes of climate data

    Science.gov (United States)

    Collins, W. D.; Wehner, M. F.; Prabhat, M.; Kurth, T.; Satish, N.; Mitliagkas, I.; Zhang, J.; Racah, E.; Patwary, M.; Sundaram, N.; Dubey, P.

    2017-12-01

    Anthropogenically-forced climate changes in the number and character of extreme storms have the potential to significantly impact human and natural systems. Current high-performance computing enables multidecadal simulations with global climate models at resolutions of 25km or finer. Such high-resolution simulations are demonstrably superior in simulating extreme storms such as tropical cyclones than the coarser simulations available in the Coupled Model Intercomparison Project (CMIP5) and provide the capability to more credibly project future changes in extreme storm statistics and properties. The identification and tracking of storms in the voluminous model output is very challenging as it is impractical to manually identify storms due to the enormous size of the datasets, and therefore automated procedures are used. Traditionally, these procedures are based on a multi-variate set of physical conditions based on known properties of the class of storms in question. In recent years, we have successfully demonstrated that Deep Learning produces state of the art results for pattern detection in climate data. We have developed supervised and semi-supervised convolutional architectures for detecting and localizing tropical cyclones, extra-tropical cyclones and atmospheric rivers in simulation data. One of the primary challenges in the applicability of Deep Learning to climate data is in the expensive training phase. Typical networks may take days to converge on 10GB-sized datasets, while the climate science community has ready access to O(10 TB)-O(PB) sized datasets. In this work, we present the most scalable implementation of Deep Learning to date. We successfully scale a unified, semi-supervised convolutional architecture on all of the Cori Phase II supercomputer at NERSC. We use IntelCaffe, MKL and MLSL libraries. We have optimized single node MKL libraries to obtain 1-4 TF on single KNL nodes. We have developed a novel hybrid parameter update strategy to improve

  9. Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method

    Directory of Open Access Journals (Sweden)

    Minhwan Seo

    2017-01-01

    Full Text Available Early detection of an internal short circuit (ISCr in a Li-ion battery can prevent it from undergoing thermal runaway, and thereby ensure battery safety. In this paper, a model-based switching model method (SMM is proposed to detect the ISCr in the Li-ion battery. The SMM updates the model of the Li-ion battery with ISCr to improve the accuracy of ISCr resistance R I S C f estimates. The open circuit voltage (OCV and the state of charge (SOC are estimated by applying the equivalent circuit model, and by using the recursive least squares algorithm and the relation between OCV and SOC. As a fault index, the R I S C f is estimated from the estimated OCVs and SOCs to detect the ISCr, and used to update the model; this process yields accurate estimates of OCV and R I S C f . Then the next R I S C f is estimated and used to update the model iteratively. Simulation data from a MATLAB/Simulink model and experimental data verify that this algorithm shows high accuracy of R I S C f estimates to detect the ISCr, thereby helping the battery management system to fulfill early detection of the ISCr.

  10. Modeling how shark and dolphin skin patterns control transitional wall-turbulence vorticity patterns using spatiotemporal phase reset mechanisms.

    Science.gov (United States)

    Bandyopadhyay, Promode R; Hellum, Aren M

    2014-10-23

    Many slow-moving biological systems like seashells and zebrafish that do not contend with wall turbulence have somewhat organized pigmentation patterns flush with their outer surfaces that are formed by underlying autonomous reaction-diffusion (RD) mechanisms. In contrast, sharks and dolphins contend with wall turbulence, are fast swimmers, and have more organized skin patterns that are proud and sometimes vibrate. A nonlinear spatiotemporal analytical model is not available that explains the mechanism underlying control of flow with such proud patterns, despite the fact that shark and dolphin skins are major targets of reverse engineering mechanisms of drag and noise reduction. Comparable to RD, a minimal self-regulation model is given for wall turbulence regeneration in the transitional regime--laterally coupled, diffusively--which, although restricted to pre-breakdown durations and to a plane close and parallel to the wall, correctly reproduces many experimentally observed spatiotemporal organizations of vorticity in both laminar-to-turbulence transitioning and very low Reynolds number but turbulent regions. We further show that the onset of vorticity disorganization is delayed if the skin organization is treated as a spatiotemporal template of olivo-cerebellar phase reset mechanism. The model shows that the adaptation mechanisms of sharks and dolphins to their fluid environment have much in common.

  11. Laser spot detection based on reaction diffusion

    Czech Academy of Sciences Publication Activity Database

    Vázquez-Otero, Alejandro; Khikhlukha, Danila; Solano-Altamirano, J. M.; Dormido, R.; Duro, N.

    2016-01-01

    Roč. 16, č. 3 (2016), s. 1-11, č. článku 315. ISSN 1424-8220 R&D Projects: GA MŠk EF15_008/0000162 Grant - others:ELI Beamlines(XE) CZ.02.1.01/0.0/0.0/15_008/0000162 Institutional support: RVO:68378271 Keywords : laser spot detection * laser beam detection * reaction diffusion models * Fitzhugh-Nagumo model * reaction diffusion computation * Turing patterns Subject RIV: BL - Plasma and Gas Discharge Physics OBOR OECD: Fluids and plasma physics (including surface physics) Impact factor: 2.677, year: 2016

  12. Detecting spatial patterns with the cumulant function – Part 1: The theory

    Directory of Open Access Journals (Sweden)

    P. Naveau

    2008-02-01

    Full Text Available In climate studies, detecting spatial patterns that largely deviate from the sample mean still remains a statistical challenge. Although a Principal Component Analysis (PCA, or equivalently a Empirical Orthogonal Functions (EOF decomposition, is often applied for this purpose, it provides meaningful results only if the underlying multivariate distribution is Gaussian. Indeed, PCA is based on optimizing second order moments, and the covariance matrix captures the full dependence structure of multivariate Gaussian vectors. Whenever the application at hand can not satisfy this normality hypothesis (e.g. precipitation data, alternatives and/or improvements to PCA have to be developed and studied. To go beyond this second order statistics constraint, that limits the applicability of the PCA, we take advantage of the cumulant function that can produce higher order moments information. The cumulant function, well-known in the statistical literature, allows us to propose a new, simple and fast procedure to identify spatial patterns for non-Gaussian data. Our algorithm consists in maximizing the cumulant function. Three families of multivariate random vectors, for which explicit computations are obtained, are implemented to illustrate our approach. In addition, we show that our algorithm corresponds to selecting the directions along which projected data display the largest spread over the marginal probability density tails.

  13. A new code for automatic detection and analysis of the lineament patterns for geophysical and geological purposes (ADALGEO)

    Science.gov (United States)

    Soto-Pinto, C.; Arellano-Baeza, A.; Sánchez, G.

    2013-08-01

    We present a new numerical method for automatic detection and analysis of changes in lineament patterns caused by seismic and volcanic activities. The method is implemented as a series of modules: (i) normalization of the image contrast, (ii) extraction of small linear features (stripes) through convolution of the part of the image in the vicinity of each pixel with a circular mask or through Canny algorithm, and (iii) posterior detection of main lineaments using the Hough transform. We demonstrate that our code reliably detects changes in the lineament patterns related to the stress evolution in the Earth's crust: specifically, a significant number of new lineaments appear approximately one month before an earthquake, while one month after the earthquake the lineament configuration returns to its initial state. Application of our software to the deformations caused by volcanic activity yields the opposite results: the number of lineaments decreases with the onset of microseismicity. This discrepancy can be explained assuming that the plate tectonic earthquakes are caused by the compression and accumulation of stress in the Earth's crust due to subduction of tectonic plates, whereas in the case of volcanic activity we deal with the inflation of a volcano edifice due to elevation of pressure and magma intrusion and the resulting stretching of the surface.

  14. Turing and Non-Turing patterns in diffusive plankton model

    Directory of Open Access Journals (Sweden)

    N. K. Thakur

    2015-03-01

    Full Text Available In this paper, we investigate a Rosenzweig-McAurthur model and its variant for phytoplankton, zooplankton and fish population dynamics with Holling type II and III functional responses. We present the theoretical analysis of processes of pattern formation that involves organism distribution and their interaction of spatially distributed population with local diffusion. The choice of parameter values is important to study the effect of diffusion, also it depends more on the nonlinearity of the system. With the help of numerical simulations, we observe the formation of spatiotemporal patterns both inside and outside the Turing space.

  15. Finite element model updating and damage detection for bridges using vibration measurement.

    Science.gov (United States)

    2013-12-01

    In this report, the results of a study on developing a damage detection methodology based on Statistical Pattern Recognition are : presented. This methodology uses a new damage sensitive feature developed in this study that relies entirely on modal :...

  16. Spatial statistics detect clustering patterns of kidney diseases in south-eastern Romania

    Directory of Open Access Journals (Sweden)

    Ruben I.

    2016-02-01

    Full Text Available Medical geography was conceptualized almost ten years ago due to its obvious usefulness in epidemiological research. Still, numerous diseases in many regions were neglected in these aspects of research, and the prevalence of kidney diseases in Eastern Europe is such an example. We evaluated the spatial patterns of main kidney diseases in south-eastern Romania, and highlighted the importance of spatial modeling in medical management in Romania. We found two statistically significant hotspots of kidney diseases prevalence. We also found differences in the spatial patterns between categories of diseases. We propose to speed up the process of creating a national database of records on kidney diseases. Offering the researchers access to a national database will allow further epidemiology studies in Romania and finally lead to a better management of medical services.

  17. Modeling of metal nanocluster growth on patterned substrates and surface pattern formation under ion bombardment

    Energy Technology Data Exchange (ETDEWEB)

    Numazawa, Satoshi

    2012-11-01

    This work addresses the metal nanocluster growth process on prepatterned substrates, the development of atomistic simulation method with respect to an acceleration of the atomistic transition states, and the continuum model of the ion-beam inducing semiconductor surface pattern formation mechanism. Experimentally, highly ordered Ag nanocluster structures have been grown on pre-patterned amorphous SiO{sub 2} surfaces by oblique angle physical vapor deposition at room temperature. Despite the small undulation of the rippled surface, the stripe-like Ag nanoclusters are very pronounced, reproducible and well-separated. The first topic is the investigation of this growth process with a continuum theoretical approach to the surface gas condensation as well as an atomistic cluster growth model. The atomistic simulation model is a lattice-based kinetic Monte-Carlo (KMC) method using a combination of a simplified inter-atomic potential and experimental transition barriers taken from the literature. An effective transition event classification method is introduced which allows a boost factor of several thousand compared to a traditional KMC approach, thus allowing experimental time scales to be modeled. The simulation predicts a low sticking probability for the arriving atoms, millisecond order lifetimes for single Ag monomers and {approx}1 nm square surface migration ranges of Ag monomers. The simulations give excellent reproduction of the experimentally observed nanocluster growth patterns. The second topic specifies the acceleration scheme utilized in the metallic cluster growth model. Concerning the atomistic movements, a classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements

  18. Modeling of metal nanocluster growth on patterned substrates and surface pattern formation under ion bombardment

    Energy Technology Data Exchange (ETDEWEB)

    Numazawa, Satoshi

    2012-11-01

    This work addresses the metal nanocluster growth process on prepatterned substrates, the development of atomistic simulation method with respect to an acceleration of the atomistic transition states, and the continuum model of the ion-beam inducing semiconductor surface pattern formation mechanism. Experimentally, highly ordered Ag nanocluster structures have been grown on pre-patterned amorphous SiO{sub 2} surfaces by oblique angle physical vapor deposition at room temperature. Despite the small undulation of the rippled surface, the stripe-like Ag nanoclusters are very pronounced, reproducible and well-separated. The first topic is the investigation of this growth process with a continuum theoretical approach to the surface gas condensation as well as an atomistic cluster growth model. The atomistic simulation model is a lattice-based kinetic Monte-Carlo (KMC) method using a combination of a simplified inter-atomic potential and experimental transition barriers taken from the literature. An effective transition event classification method is introduced which allows a boost factor of several thousand compared to a traditional KMC approach, thus allowing experimental time scales to be modeled. The simulation predicts a low sticking probability for the arriving atoms, millisecond order lifetimes for single Ag monomers and {approx}1 nm square surface migration ranges of Ag monomers. The simulations give excellent reproduction of the experimentally observed nanocluster growth patterns. The second topic specifies the acceleration scheme utilized in the metallic cluster growth model. Concerning the atomistic movements, a classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements

  19. Neuromorphic Modeling of Moving Target Detection in Insects

    Science.gov (United States)

    2007-12-31

    Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39, 18 Grants FA9550-04-1-0283 and FA9550-04-1-0294 Neuromorphic Modeling of Moving Target Detection...natural for neuromorphic sensory processing. We developed visual motion detection circuitry, including photodetectors, early vision, and models for both...Lincoln Labs 3DM2 run, Tanner Research reserved and utilized space corresponding to two MOSIS ’tiny chips ’ (2mm square each), each with three interconnected

  20. Using statistical anomaly detection models to find clinical decision support malfunctions.

    Science.gov (United States)

    Ray, Soumi; McEvoy, Dustin S; Aaron, Skye; Hickman, Thu-Trang; Wright, Adam

    2018-05-11

    Malfunctions in Clinical Decision Support (CDS) systems occur due to a multitude of reasons, and often go unnoticed, leading to potentially poor outcomes. Our goal was to identify malfunctions within CDS systems. We evaluated 6 anomaly detection models: (1) Poisson Changepoint Model, (2) Autoregressive Integrated Moving Average (ARIMA) Model, (3) Hierarchical Divisive Changepoint (HDC) Model, (4) Bayesian Changepoint Model, (5) Seasonal Hybrid Extreme Studentized Deviate (SHESD) Model, and (6) E-Divisive with Median (EDM) Model and characterized their ability to find known anomalies. We analyzed 4 CDS alerts with known malfunctions from the Longitudinal Medical Record (LMR) and Epic® (Epic Systems Corporation, Madison, WI, USA) at Brigham and Women's Hospital, Boston, MA. The 4 rules recommend lead testing in children, aspirin therapy in patients with coronary artery disease, pneumococcal vaccination in immunocompromised adults and thyroid testing in patients taking amiodarone. Poisson changepoint, ARIMA, HDC, Bayesian changepoint and the SHESD model were able to detect anomalies in an alert for lead screening in children and in an alert for pneumococcal conjugate vaccine in immunocompromised adults. EDM was able to detect anomalies in an alert for monitoring thyroid function in patients on amiodarone. Malfunctions/anomalies occur frequently in CDS alert systems. It is important to be able to detect such anomalies promptly. Anomaly detection models are useful tools to aid such detections.

  1. Clinical patterns associated with the concurrent detection of anti-HBs and HBV DNA.

    Science.gov (United States)

    Anastasiou, Olympia E; Widera, Marek; Korth, Johannes; Kefalakes, Helenie; Katsounas, Antonios; Hilgard, Gudrun; Gerken, Guido; Canbay, Ali; Ciesek, Sandra; Verheyen, Jens

    2018-02-01

    Simultaneous detection of anti-HBs and HBV DNA is a rare serological combination and has been described in acute and chronic HBV infection. To scrutinize viral and clinical patterns associated with concurrent detection of anti-HBs and HBV DNA. Simultaneous detection of anti-HBs and HBV DNA was observed in 64/1444 (4.4%) patients treated for HBV infection at the University Hospital of Essen from 2006 to 2016 (8 with acute, 20 with reactivated, and 36 chronic HBV infection). Clinical data and laboratory parameters were analyzed. Regions of the small hepatitis B surface antigen (SHB) and the reverse transcriptase (RT) were sequenced using next generation sequencing (NGS). Among the 64 patients with detectable HBV DNA and anti-HBs, 17 were HBsAg negative (HBsAg[-]), and two had acute liver failure. Patients with acute HBV infection had fewer genotype specific amino acid substitutions in the SHB region than patients with reactivated HBV infection (4 [4.5] vs 9 [16.25], P = 0.043). However, we could observe a significantly higher number of mutations in the a-determinant region when comparing chronically infected patients to patients with acute infection (0 [1] vs 1 [1], P = 0.044). The ratio of nonsynonymous to synonymous mutations (Ka/Ks) was on average >1 for the SHB region and 1) in the SHB region indicates that anti-HBs might have exerted selection pressure on the HBsAg. In three cases the diagnosis of acute HBV infection would have been at least delayed by only focusing on HBsAg testing. © 2017 Wiley Periodicals, Inc.

  2. Spatial patterns of breeding success of grizzly bears derived from hierarchical multistate models.

    Science.gov (United States)

    Fisher, Jason T; Wheatley, Matthew; Mackenzie, Darryl

    2014-10-01

    Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low-elevation wetlands or mid-elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy-herbaceous alpine ecotones-were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. © 2014 Society

  3. Biogeographic patterns in ocean microbes emerge in a neutral agent-based model.

    Science.gov (United States)

    Hellweger, Ferdi L; van Sebille, Erik; Fredrick, Neil D

    2014-09-12

    A key question in ecology and evolution is the relative role of natural selection and neutral evolution in producing biogeographic patterns. We quantify the role of neutral processes by simulating division, mutation, and death of 100,000 individual marine bacteria cells with full 1 million-base-pair genomes in a global surface ocean circulation model. The model is run for up to 100,000 years and output is analyzed using BLAST (Basic Local Alignment Search Tool) alignment and metagenomics fragment recruitment. Simulations show the production and maintenance of biogeographic patterns, characterized by distinct provinces subject to mixing and periodic takeovers by neighbors (coalescence), after which neutral evolution reestablishes the province and the patterns reorganize. The emergent patterns are substantial (e.g., down to 99.5% DNA identity between North and Central Pacific provinces) and suggest that microbes evolve faster than ocean currents can disperse them. This approach can also be used to explore environmental selection. Copyright © 2014, American Association for the Advancement of Science.

  4. Blood oxygen level-dependent magnetic resonance imaging for detecting pathological patterns in patients with lupus nephritis: a preliminary study using gray-level co-occurrence matrix analysis.

    Science.gov (United States)

    Shi, Huilan; Jia, Junya; Li, Dong; Wei, Li; Shang, Wenya; Zheng, Zhenfeng

    2018-01-01

    Objective Blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) is a noninvasive technique useful in patients with renal disease. The current study was performed to determine whether BOLD MRI can contribute to the diagnosis of renal pathological patterns. Methods BOLD MRI was used to obtain functional magnetic resonance parameter R2* values. Gray-level co-occurrence matrixes (GLCMs) were generated for gray-scale maps. Several GLCM parameters were calculated and used to construct algorithmic models for renal pathological patterns. Results Histopathology and BOLD MRI were used to examine 12 patients. Two GLCM parameters, including correlation and energy, revealed differences among four groups of renal pathological patterns. Four Fisher's linear discriminant formulas were constructed using two variables, including the correlation at 45° and correlation at 90°. A cross-validation test showed that the formulas correctly predicted 28 of 36 samples, and the rate of correct prediction was 77.8%. Conclusions Differences in the texture characteristics of BOLD MRI in patients with lupus nephritis may be detected by GLCM analysis. Discriminant formulas constructed using GLCM parameters may facilitate prediction of renal pathological patterns.

  5. Optimization of single photon detection model based on GM-APD

    Science.gov (United States)

    Chen, Yu; Yang, Yi; Hao, Peiyu

    2017-11-01

    One hundred kilometers high precision laser ranging hopes the detector has very strong detection ability for very weak light. At present, Geiger-Mode of Avalanche Photodiode has more use. It has high sensitivity and high photoelectric conversion efficiency. Selecting and designing the detector parameters according to the system index is of great importance to the improvement of photon detection efficiency. Design optimization requires a good model. In this paper, we research the existing Poisson distribution model, and consider the important detector parameters of dark count rate, dead time, quantum efficiency and so on. We improve the optimization of detection model, select the appropriate parameters to achieve optimal photon detection efficiency. The simulation is carried out by using Matlab and compared with the actual test results. The rationality of the model is verified. It has certain reference value in engineering applications.

  6. Physical optics modeling of modal patterns in a crossed porro prism resonator

    CSIR Research Space (South Africa)

    Litvin, IA

    2006-07-01

    Full Text Available A physical optics model is proposed to describe the transverse modal patterns in crossed Porro prism resonators. The model departs from earlier attempts in that the prisms are modeled as non-classical rotating elements with amplitude and phase...

  7. An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

    Science.gov (United States)

    Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

    2015-01-01

    Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

  8. Detecting anthropogenic climate change with an optimal fingerprint method

    International Nuclear Information System (INIS)

    Hegerl, G.C.; Storch, H. von; Hasselmann, K.; Santer, B.D.; Jones, P.D.

    1994-01-01

    We propose a general fingerprint strategy to detect anthropogenic climate change and present application to near surface temperature trends. An expected time-space-variable pattern of anthropogenic climate change (the 'signal') is identified through application of an appropriate optimally matched space-time filter (the 'fingerprint') to the observations. The signal and the fingerprint are represented in a space with sufficient observed and simulated data. The signal pattern is derived from a model-generated prediction of anthropogenic climate change. Application of the fingerprint filter to the data yields a scalar detection variable. The statistically optimal fingerprint is obtained by weighting the model-predicted pattern towards low-noise directions. A combination of model output and observations is used to estimate the noise characteristics of the detection variable, arising from the natural variability of climate in the absence of external forcing. We test then the null hypothesis that the observed climate change is part of natural climate variability. We conclude that a statistically significant externally induced warming has been observed, with the caveat of a possibly inadequate estimate of the internal climate variability. In order to attribute this warming uniquely to anthropogenic greenhouse gas forcing, more information on the climate's response to other forcing mechanisms (e.g. changes in solar radiation, volcanic or anthropogenic aerosols) and their interaction is needed. (orig./KW)

  9. Statistical Texture Model for mass Detection in Mammography

    Directory of Open Access Journals (Sweden)

    Nicolás Gallego-Ortiz

    2013-12-01

    Full Text Available In the context of image processing algorithms for mass detection in mammography, texture is a key feature to be used to distinguish abnormal tissue from normal tissue. Recently, a texture model based on a multivariate Gaussian mixture was proposed, of which the parameters are learned in an unsupervised way from the pixel intensities of images. The model produces images that are probabilistic maps of texture normality and it was proposed as a visualization aid for diagnostic by clinical experts. In this paper, the usability of the model is studied for automatic mass detection. A segmentation strategy is proposed and evaluated using 79 mammography cases.

  10. SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events

    DEFF Research Database (Denmark)

    Cuttone, Andrea; Bækgaard, Per; Sekara, Vedran

    2017-01-01

    We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400...... to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient....

  11. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

    Science.gov (United States)

    Demirel, Mehmet C.; Mai, Juliane; Mendiguren, Gorka; Koch, Julian; Samaniego, Luis; Stisen, Simon

    2018-02-01

    Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex

  12. Use of nonstatistical techniques for pattern recognition to detect risk groups among liquidators of the Chernobyl NPP accident aftereffects

    International Nuclear Information System (INIS)

    Blinov, N.N.; Guslistyj, V.P.; Misyurev, A.V.; Novitskaya, N.N.; Snigireva, G.P.

    1993-01-01

    Attempt of using of the nonstatistical techniques for pattern recognition to detect the risk groups among liquidators of the Chernobyl NPP accident aftereffects was described. 14 hematologic, biochemical and biophysical blood serum parameters of the group of liquidators of the Chernobyl NPP accident impact as well as the group of donors free of any radiation dose (controlled group) were taken as the diagnostic parameters. Modification of the nonstatistical techniques for pattern recognition based on the assessment calculations were used. The patients were divided into risk group at the truth ∼ 80%

  13. Modelling Behaviour Patterns of Pedestrians for Mobile Robot Trajectory Generation

    Directory of Open Access Journals (Sweden)

    Yusuke Tamura

    2013-08-01

    Full Text Available Robots are expected to be operated in environments where they coexist with humans, such as shopping malls and offices. Both the safety and efficiency of a robot are necessary in such environments. To achieve this, pedestrian behaviour should be accurately predicted. However, the behaviour is uncertain and cannot be easily predicted. This paper proposes a probabilistic method of determining pedestrian trajectory based on an estimation of pedestrian behaviour patterns. The proposed method focuses on the specific behaviour of pedestrians around the robot. The proposed model classifies the behaviours of pedestrians into definite patterns. The behaviour patterns, distribution of the positions of the pedestrians, and the direction of each behaviour pattern are determined by learning through observation. The behaviour pattern of a pedestrian can be estimated correctly by a likelihood calculation. A robot decides to move with an emphasis on either safety or efficiency depending on the result of the pattern estimation. If the pedestrian trajectory follows a known behaviour pattern, the robot would move with an emphasis on efficiency because the pedestrian trajectory can be predicted. Otherwise, the robot would move with an emphasis on safety because the behaviour of the pedestrian cannot be predicted. Experimental results show that robots can move efficiently and safely when passing by a pedestrian by applying the proposed method.

  14. Leak rate models and leak detection

    International Nuclear Information System (INIS)

    1992-01-01

    Leak detection may be carried out by a number of detection systems, but selection of the systems must be carefully adapted to the fluid state and the location of the leak in the reactor coolant system. Computer programs for the calculation of leak rates contain different models to take into account the fluid state before its entrance into the crack, and they have to be verified by experiments; agreement between experiments and calculations is generally not satisfactory for very small leak rates resulting from narrow cracks or from a closing bending moment

  15. Group-ICA model order highlights patterns of functional brain connectivity

    Directory of Open Access Journals (Sweden)

    Ahmed eAbou Elseoud

    2011-06-01

    Full Text Available Resting-state networks (RSNs can be reliably and reproducibly detected using independent component analysis (ICA at both individual subject and group levels. Altering ICA dimensionality (model order estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual-regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.

  16. Detecting Hotspot Information Using Multi-Attribute Based Topic Model.

    Directory of Open Access Journals (Sweden)

    Jing Wang

    Full Text Available Microblogging as a kind of social network has become more and more important in our daily lives. Enormous amounts of information are produced and shared on a daily basis. Detecting hot topics in the mountains of information can help people get to the essential information more quickly. However, due to short and sparse features, a large number of meaningless tweets and other characteristics of microblogs, traditional topic detection methods are often ineffective in detecting hot topics. In this paper, we propose a new topic model named multi-attribute latent dirichlet allocation (MA-LDA, in which the time and hashtag attributes of microblogs are incorporated into LDA model. By introducing time attribute, MA-LDA model can decide whether a word should appear in hot topics or not. Meanwhile, compared with the traditional LDA model, applying hashtag attribute in MA-LDA model gives the core words an artificially high ranking in results meaning the expressiveness of outcomes can be improved. Empirical evaluations on real data sets demonstrate that our method is able to detect hot topics more accurately and efficiently compared with several baselines. Our method provides strong evidence of the importance of the temporal factor in extracting hot topics.

  17. Detecting Hotspot Information Using Multi-Attribute Based Topic Model

    Science.gov (United States)

    Wang, Jing; Li, Li; Tan, Feng; Zhu, Ying; Feng, Weisi

    2015-01-01

    Microblogging as a kind of social network has become more and more important in our daily lives. Enormous amounts of information are produced and shared on a daily basis. Detecting hot topics in the mountains of information can help people get to the essential information more quickly. However, due to short and sparse features, a large number of meaningless tweets and other characteristics of microblogs, traditional topic detection methods are often ineffective in detecting hot topics. In this paper, we propose a new topic model named multi-attribute latent dirichlet allocation (MA-LDA), in which the time and hashtag attributes of microblogs are incorporated into LDA model. By introducing time attribute, MA-LDA model can decide whether a word should appear in hot topics or not. Meanwhile, compared with the traditional LDA model, applying hashtag attribute in MA-LDA model gives the core words an artificially high ranking in results meaning the expressiveness of outcomes can be improved. Empirical evaluations on real data sets demonstrate that our method is able to detect hot topics more accurately and efficiently compared with several baselines. Our method provides strong evidence of the importance of the temporal factor in extracting hot topics. PMID:26496635

  18. Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

    OpenAIRE

    Iddamalgoda, Lahiru; Das, Partha S.; Aponso, Achala; Sundararajan, Vijayaraghava S.; Suravajhala, Prashanth; Valadi, Jayaraman K.

    2016-01-01

    Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited ...

  19. Simple model for polar cap convection patterns and generation of theta auroras

    International Nuclear Information System (INIS)

    Lyons, L.R.

    1985-01-01

    The simple addition of a uniform interplanetary magnetic field and the Earth's dipole magnetic field is used to evaluate electric field convection patterns over the polar caps that result from solar wind flow across open geomagnetic field lines. This model is found to account for observed polar-cap convection patterns as a function of the interplanetary magnetic field components B/sub y/ and B/sub z/. In particular, the model offers an explanation for sunward and antisunward convection over the polar caps for B/sub z/>0. Observed field-aligned current patterns within the polar cap and observed auroral arcs across the polar cap are also explained by the model. In addition, the model gives several predictions concerning the polar cap that should be testable. Effects of solar wind pressure and magnetospheric currents on magnetospheric electric and magnetic fields are neglected. That observed polar cap features are reproduced suggests that the neglected effects do not modify the large-scale topology of magnetospheric electric and magnetic fields along open polar cap field lines. Of course, the neglected effects significantly modify the magnetic geometry, so that the results of this paper are not quantitatively realistic and many details may be incorrect. Nevertheless, the model provides a simple explanation for many qualitative features of polar cap convection

  20. Distance-dependent pattern blending can camouflage salient aposematic signals.

    Science.gov (United States)

    Barnett, James B; Cuthill, Innes C; Scott-Samuel, Nicholas E

    2017-07-12

    The effect of viewing distance on the perception of visual texture is well known: spatial frequencies higher than the resolution limit of an observer's visual system will be summed and perceived as a single combined colour. In animal defensive colour patterns, distance-dependent pattern blending may allow aposematic patterns, salient at close range, to match the background to distant observers. Indeed, recent research has indicated that reducing the distance from which a salient signal can be detected can increase survival over camouflage or conspicuous aposematism alone. We investigated whether the spatial frequency of conspicuous and cryptically coloured stripes affects the rate of avian predation. Our results are consistent with pattern blending acting to camouflage salient aposematic signals effectively at a distance. Experiments into the relative rate of avian predation on edible model caterpillars found that increasing spatial frequency (thinner stripes) increased survival. Similarly, visual modelling of avian predators showed that pattern blending increased the similarity between caterpillar and background. These results show how a colour pattern can be tuned to reveal or conceal different information at different distances, and produce tangible survival benefits. © 2017 The Author(s).

  1. The Role of Different Plant Soil-Water Feedbacks in Models of Dryland Vegetation Patterns

    Science.gov (United States)

    Silber, M.; Bonetti, S.; Gandhi, P.; Gowda, K.; Iams, S.; Porporato, A. M.

    2017-12-01

    Understanding the processes underlying the formation of regular vegetation patterns in arid and semi-arid regions is important to assessing desertification risk under increasing anthropogenic pressure. Various modeling frameworks have been proposed, which are all capable of generating similar patterns through self-organizing mechanisms that stem from assumptions about plant feedbacks on surface/subsurface water transport. We critically discuss a hierarchy of hydrology-vegetation models for the coupled dynamics of surface water, soil moisture, and vegetation biomass on a hillslope. We identify distinguishing features and trends for the periodic traveling wave solutions when there is an imposed idealized topography and make some comparisons to satellite images of large-scale banded vegetation patterns in drylands of Africa, Australia and North America. This work highlights the potential for constraining models by considerations of where the patterns may lie on a landscape, such as whether on a ridge or in a valley.

  2. Algorithm for real-time detection of signal patterns using phase synchrony: an application to an electrode array

    Science.gov (United States)

    Sadeghi, Saman; MacKay, William A.; van Dam, R. Michael; Thompson, Michael

    2011-02-01

    Real-time analysis of multi-channel spatio-temporal sensor data presents a considerable technical challenge for a number of applications. For example, in brain-computer interfaces, signal patterns originating on a time-dependent basis from an array of electrodes on the scalp (i.e. electroencephalography) must be analyzed in real time to recognize mental states and translate these to commands which control operations in a machine. In this paper we describe a new technique for recognition of spatio-temporal patterns based on performing online discrimination of time-resolved events through the use of correlation of phase dynamics between various channels in a multi-channel system. The algorithm extracts unique sensor signature patterns associated with each event during a training period and ranks importance of sensor pairs in order to distinguish between time-resolved stimuli to which the system may be exposed during real-time operation. We apply the algorithm to electroencephalographic signals obtained from subjects tested in the neurophysiology laboratories at the University of Toronto. The extension of this algorithm for rapid detection of patterns in other sensing applications, including chemical identification via chemical or bio-chemical sensor arrays, is also discussed.

  3. Detection of artifacts from high energy bursts in neonatal EEG.

    Science.gov (United States)

    Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

    2013-11-01

    Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the

  4. Analytical Model-based Fault Detection and Isolation in Control Systems

    DEFF Research Database (Denmark)

    Vukic, Z.; Ozbolt, H.; Blanke, M.

    1998-01-01

    The paper gives an introduction and an overview of the field of fault detection and isolation for control systems. The summary of analytical (quantitative model-based) methodds and their implementation are presented. The focus is given to mthe analytical model-based fault-detection and fault...

  5. Antagonism pattern detection between microRNA and target expression in Ewing's sarcoma.

    Directory of Open Access Journals (Sweden)

    Loredana Martignetti

    Full Text Available MicroRNAs (miRNAs have emerged as fundamental regulators that silence gene expression at the post-transcriptional and translational levels. The identification of their targets is a major challenge to elucidate the regulated biological processes. The overall effect of miRNA is reflected on target mRNA expression, suggesting the design of new investigative methods based on high-throughput experimental data such as miRNA and transcriptome profiles. We propose a novel statistical measure of non-linear dependence between miRNA and mRNA expression, in order to infer miRNA-target interactions. This approach, which we name antagonism pattern detection, is based on the statistical recognition of a triangular-shaped pattern in miRNA-target expression profiles. This pattern is observed in miRNA-target expression measurements since their simultaneously elevated expression is statistically under-represented in the case of miRNA silencing effect. The proposed method enables miRNA target prediction to strongly rely on cellular context and physiological conditions reflected by expression data. The procedure has been assessed on synthetic datasets and tested on a set of real positive controls. Then it has been applied to analyze expression data from Ewing's sarcoma patients. The antagonism relationship is evaluated as a good indicator of real miRNA-target biological interaction. The predicted targets are consistently enriched for miRNA binding site motifs in their 3'UTR. Moreover, we reveal sets of predicted targets for each miRNA sharing important biological function. The procedure allows us to infer crucial miRNA regulators and their potential targets in Ewing's sarcoma disease. It can be considered as a valid statistical approach to discover new insights in the miRNA regulatory mechanisms.

  6. iSentenizer-μ: multilingual sentence boundary detection model.

    Science.gov (United States)

    Wong, Derek F; Chao, Lidia S; Zeng, Xiaodong

    2014-01-01

    Sentence boundary detection (SBD) system is normally quite sensitive to genres of data that the system is trained on. The genres of data are often referred to the shifts of text topics and new languages domains. Although new detection models can be retrained for different languages or new text genres, previous model has to be thrown away and the creation process has to be restarted from scratch. In this paper, we present a multilingual sentence boundary detection system (iSentenizer-μ) for Danish, German, English, Spanish, Dutch, French, Italian, Portuguese, Greek, Finnish, and Swedish languages. The proposed system is able to detect the sentence boundaries of a mixture of different text genres and languages with high accuracy. We employ i (+)Learning algorithm, an incremental tree learning architecture, for constructing the system. iSentenizer-μ, under the incremental learning framework, is adaptable to text of different topics and Roman-alphabet languages, by merging new data into existing model to learn the new knowledge incrementally by revision instead of retraining. The system has been extensively evaluated on different languages and text genres and has been compared against two state-of-the-art SBD systems, Punkt and MaxEnt. The experimental results show that the proposed system outperforms the other systems on all datasets.

  7. iSentenizer-μ: Multilingual Sentence Boundary Detection Model

    Directory of Open Access Journals (Sweden)

    Derek F. Wong

    2014-01-01

    Full Text Available Sentence boundary detection (SBD system is normally quite sensitive to genres of data that the system is trained on. The genres of data are often referred to the shifts of text topics and new languages domains. Although new detection models can be retrained for different languages or new text genres, previous model has to be thrown away and the creation process has to be restarted from scratch. In this paper, we present a multilingual sentence boundary detection system (iSentenizer-μ for Danish, German, English, Spanish, Dutch, French, Italian, Portuguese, Greek, Finnish, and Swedish languages. The proposed system is able to detect the sentence boundaries of a mixture of different text genres and languages with high accuracy. We employ i+Learning algorithm, an incremental tree learning architecture, for constructing the system. iSentenizer-μ, under the incremental learning framework, is adaptable to text of different topics and Roman-alphabet languages, by merging new data into existing model to learn the new knowledge incrementally by revision instead of retraining. The system has been extensively evaluated on different languages and text genres and has been compared against two state-of-the-art SBD systems, Punkt and MaxEnt. The experimental results show that the proposed system outperforms the other systems on all datasets.

  8. Output-Sensitive Pattern Extraction in Sequences

    DEFF Research Database (Denmark)

    Grossi, Roberto; Menconi, Giulia; Pisanti, Nadia

    2014-01-01

    Genomic Analysis, Plagiarism Detection, Data Mining, Intrusion Detection, Spam Fighting and Time Series Analysis are just some examples of applications where extraction of recurring patterns in sequences of objects is one of the main computational challenges. Several notions of patterns exist...... or extend them causes a loss of significant information (where the number of occurrences changes). Output-sensitive algorithms have been proposed to enumerate and list these patterns, taking polynomial time O(nc) per pattern for constant c > 1, which is impractical for massive sequences of very large length...

  9. Seasonal Habitat Patterns of Japanese Common Squid (Todarodes Pacificus Inferred from Satellite-Based Species Distribution Models

    Directory of Open Access Journals (Sweden)

    Irene D. Alabia

    2016-11-01

    Full Text Available The understanding of the spatio-temporal distributions of the species habitat in the marine environment is central to effectual resource management and conservation. Here, we examined the potential habitat distributions of Japanese common squid (Todarodes pacificus in the Sea of Japan during a four-year period. The seasonal patterns of preferential habitat were inferred from species distribution models, built using squid occurrences detected from night-time visible images and remotely-sensed environmental factors. The predicted squid habitat (i.e., areas with high habitat suitability revealed strong seasonal variability, characterized by a reduction of potential habitat, confined off of the southern part of the basin during the winter–spring period (December–May. Apparent expansion of preferential habitat occurred during summer–autumn months (June–November, concurrent with the formation of highly suitable habitat patches in certain regions of the Sea of Japan. These habitat distribution patterns were in response to changes in oceanographic conditions and synchronous with seasonal migration of squid. Moreover, the most important variables regulating the spatio-temporal patterns of suitable habitat were sea surface temperature, depth, sea surface height anomaly, and eddy kinetic energy. These variables could affect the habitat distributions through their impacts on growth and survival of squid, local nutrient transport, and the availability of favorable spawning and feeding grounds.

  10. Using hyper-spectral indices to detect soil phosphorus concentration for various land use patterns.

    Science.gov (United States)

    Lin, Chen; Ma, Ronghua; Zhu, Qing; Li, Jingtao

    2015-01-01

    The management of nonpoint source pollution requires accurate information regarding soil phosphorus concentrations for different land use patterns. The use of remotely sensed information provides an important opportunity for such studies, and the previous studies showed that soil phosphorus shows no clear spectral response feature, while the phosphorus concentrations can be indirectly detected from the normalised difference vegetation indices (NDVI). Therefore, this study uses an optimised index in the RED and near-infrared (NIR) wavelengths to estimate total phosphorus and Olsen-P concentrations. The prediction accuracy is not entirely satisfactory with respect to a mixed land use dataset in which the determination coefficient was maintained at approximately 0.6, with particularly poor performance obtained for forest land group. However, the prediction accuracy increases markedly with the separation of samples into broad land use categories, even the R(2) was exceeded 0.8 for tea plantation group. The soil phosphorus prediction effect showed obvious variance for different land use patterns, which was related to vegetation growth conditions and critical soil properties including soil organic matter and mechanical composition.

  11. Introducing a Model for Suspicious Behaviors Detection in Electronic Banking by Using Decision Tree Algorithms

    Directory of Open Access Journals (Sweden)

    Rohulla Kosari Langari

    2014-02-01

    Full Text Available Change the world through information technology and Internet development, has created competitive knowledge in the field of electronic commerce, lead to increasing in competitive potential among organizations. In this condition The increasing rate of commercial deals developing guaranteed with speed and light quality is due to provide dynamic system of electronic banking until by using modern technology to facilitate electronic business process. Internet banking is enumerate as a potential opportunity the fundamental pillars and determinates of e-banking that in cyber space has been faced with various obstacles and threats. One of this challenge is complete uncertainty in security guarantee of financial transactions also exist of suspicious and unusual behavior with mail fraud for financial abuse. Now various systems because of intelligence mechanical methods and data mining technique has been designed for fraud detection in users’ behaviors and applied in various industrial such as insurance, medicine and banking. Main of article has been recognizing of unusual users behaviors in e-banking system. Therefore, detection behavior user and categories of emerged patterns to paper the conditions for predicting unauthorized penetration and detection of suspicious behavior. Since detection behavior user in internet system has been uncertainty and records of transactions can be useful to understand these movement and therefore among machine method, decision tree technique is considered common tool for classification and prediction, therefore in this research at first has determinate banking effective variable and weight of everything in internet behaviors production and in continuation combining of various behaviors manner draw out such as the model of inductive rules to provide ability recognizing of different behaviors. At least trend of four algorithm Chaid, ex_Chaid, C4.5, C5.0 has compared and evaluated for classification and detection of exist

  12. Voronoi Cell Patterns: theoretical model and application to submonolayer growth

    Science.gov (United States)

    González, Diego Luis; Einstein, T. L.

    2012-02-01

    We use a simple fragmentation model to describe the statistical behavior of the Voronoi cell patterns generated by a homogeneous and isotropic set of points in 1D and in 2D. In particular, we are interested in the distribution of sizes of these Voronoi cells. Our model is completely defined by two probability distributions in 1D and again in 2D, the probability to add a new point inside an existing cell and the probability that this new point is at a particular position relative to the preexisting point inside this cell. In 1D the first distribution depends on a single parameter while the second distribution is defined through a fragmentation kernel; in 2D both distributions depend on a single parameter. The fragmentation kernel and the control parameters are closely related to the physical properties of the specific system under study. We apply our model to describe the Voronoi cell patterns of island nucleation for critical island sizes i=0,1,2,3. Experimental results for the Voronoi cells of InAs/GaAs quantum dots are also described by our model.

  13. Detection and Growth Pattern of Arcuate Fasciculus from Newborn to Adult

    Directory of Open Access Journals (Sweden)

    Molly Wilkinson

    2017-07-01

    Full Text Available Fractional anisotropy (FA threshold is commonly used to perform diffusion MRI tractography. However, FA threshold may be one aspect of tractography that needs additional scrutiny in accurately assessing pathways in immature, developing brains, as well as in adult brains. Using high-angular resolution diffusion MRI (HARDI tractography without an FA threshold, we identified the arcuate fasciculus (AF of 83 healthy subjects ranging in age from 40 gestational weeks (GW (newborns to 28-year-old adults. The AF was identified in both hemispheres in all subjects with high inter-rater reliability. The detected AF included regions with very low FA values. The entire AF was segmented into anterior, posterior, and long tracts. Growth and laterality patterns were investigated using tract count (number of detected streamlines, total volume of imaging voxels (touched by the detected streamlines, mean length, mean FA, and mean apparent diffusion coefficient (ADC. Comparison of subjects under 3 years old, to those that were older, revealed the three AF tracts that took different developmental courses. As expected, the anterior and long tracts showed lower ADC values in subjects over 3 years old, while the posterior tract showed higher ADC in that same age range. The posterior tract did not show age-related effect in terms of FA, tract count, length, and volume. These results suggest that the posterior AF tract shows a matured state, indexed by most of the used measurements in early postnatal developmental ages, and ADC is a measurement that can detect further maturation of the posterior tract. Interestingly, in all tracts, hemispheric asymmetries were found in raw (leftright tract count, as well as in raw volume (left

  14. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

    Directory of Open Access Journals (Sweden)

    M. C. Demirel

    2018-02-01

    Full Text Available Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the

  15. Sequential super-stereotypy of an instinctive fixed action pattern in hyper-dopaminergic mutant mice: a model of obsessive compulsive disorder and Tourette's

    Directory of Open Access Journals (Sweden)

    Houchard Kimberly R

    2005-02-01

    Full Text Available Abstract Background Excessive sequential stereotypy of behavioral patterns (sequential super-stereotypy in Tourette's syndrome and obsessive compulsive disorder (OCD is thought to involve dysfunction in nigrostriatal dopamine systems. In sequential super-stereotypy, patients become trapped in overly rigid sequential patterns of action, language, or thought. Some instinctive behavioral patterns of animals, such as the syntactic grooming chain pattern of rodents, have sufficiently complex and stereotyped serial structure to detect potential production of overly-rigid sequential patterns. A syntactic grooming chain is a fixed action pattern that serially links up to 25 grooming movements into 4 predictable phases that follow 1 syntactic rule. New mutant mouse models allow gene-based manipulation of brain function relevant to sequential patterns, but no current animal model of spontaneous OCD-like behaviors has so far been reported to exhibit sequential super-stereotypy in the sense of a whole complex serial pattern that becomes stronger and excessively rigid. Here we used a hyper-dopaminergic mutant mouse to examine whether an OCD-like behavioral sequence in animals shows sequential super-stereotypy. Knockdown mutation of the dopamine transporter gene (DAT causes extracellular dopamine levels in the neostriatum of these adult mutant mice to rise to 170% of wild-type control levels. Results We found that the serial pattern of this instinctive behavioral sequence becomes strengthened as an entire entity in hyper-dopaminergic mutants, and more resistant to interruption. Hyper-dopaminergic mutant mice have stronger and more rigid syntactic grooming chain patterns than wild-type control mice. Mutants showed sequential super-stereotypy in the sense of having more stereotyped and predictable syntactic grooming sequences, and were also more likely to resist disruption of the pattern en route, by returning after a disruption to complete the pattern from the

  16. Detection and Modeling of Cyber Attacks with Petri Nets

    Directory of Open Access Journals (Sweden)

    Bartosz Jasiul

    2014-12-01

    Full Text Available The aim of this article is to present an approach to develop and verify a method of formal modeling of cyber threats directed at computer systems. Moreover, the goal is to prove that the method enables one to create models resembling the behavior of malware that support the detection process of selected cyber attacks and facilitate the application of countermeasures. The most common cyber threats targeting end users and terminals are caused by malicious software, called malware. The malware detection process can be performed either by matching their digital signatures or analyzing their behavioral models. As the obfuscation techniques make the malware almost undetectable, the classic signature-based anti-virus tools must be supported with behavioral analysis. The proposed approach to modeling of malware behavior is based on colored Petri nets. This article is addressed to cyber defense researchers, security architects and developers solving up-to-date problems regarding the detection and prevention of advanced persistent threats.

  17. Gas leak detection in infrared video with background modeling

    Science.gov (United States)

    Zeng, Xiaoxia; Huang, Likun

    2018-03-01

    Background modeling plays an important role in the task of gas detection based on infrared video. VIBE algorithm is a widely used background modeling algorithm in recent years. However, the processing speed of the VIBE algorithm sometimes cannot meet the requirements of some real time detection applications. Therefore, based on the traditional VIBE algorithm, we propose a fast prospect model and optimize the results by combining the connected domain algorithm and the nine-spaces algorithm in the following processing steps. Experiments show the effectiveness of the proposed method.

  18. Immediate effects of modified landing pattern on a probabilistic tibial stress fracture model in runners.

    Science.gov (United States)

    Chen, T L; An, W W; Chan, Z Y S; Au, I P H; Zhang, Z H; Cheung, R T H

    2016-03-01

    Tibial stress fracture is a common injury in runners. This condition has been associated with increased impact loading. Since vertical loading rates are related to the landing pattern, many heelstrike runners attempt to modify their footfalls for a lower risk of tibial stress fracture. Such effect of modified landing pattern remains unknown. This study examined the immediate effects of landing pattern modification on the probability of tibial stress fracture. Fourteen experienced heelstrike runners ran on an instrumented treadmill and they were given augmented feedback for landing pattern switch. We measured their running kinematics and kinetics during different landing patterns. Ankle joint contact force and peak tibial strains were estimated using computational models. We used an established mathematical model to determine the effect of landing pattern on stress fracture probability. Heelstrike runners experienced greater impact loading immediately after landing pattern switch (Ptibial strains and the risk of tibial stress fracture in runners with different landing patterns (P>0.986). Immediate transitioning of the landing pattern in heelstrike runners may not offer timely protection against tibial stress fracture, despite a reduction of impact loading. Long-term effects of landing pattern switch remains unknown. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Condition Parameter Modeling for Anomaly Detection in Wind Turbines

    Directory of Open Access Journals (Sweden)

    Yonglong Yan

    2014-05-01

    Full Text Available Data collected from the supervisory control and data acquisition (SCADA system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs, is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified.

  20. On the Modelling of Biological Patterns with Mechanochemical Models: Insights from Analysis and Computation

    KAUST Repository

    Moreo, P.

    2009-11-14

    The diversity of biological form is generated by a relatively small number of underlying mechanisms. Consequently, mathematical and computational modelling can, and does, provide insight into how cellular level interactions ultimately give rise to higher level structure. Given cells respond to mechanical stimuli, it is therefore important to consider the effects of these responses within biological self-organisation models. Here, we consider the self-organisation properties of a mechanochemical model previously developed by three of the authors in Acta Biomater. 4, 613-621 (2008), which is capable of reproducing the behaviour of a population of cells cultured on an elastic substrate in response to a variety of stimuli. In particular, we examine the conditions under which stable spatial patterns can emerge with this model, focusing on the influence of mechanical stimuli and the interplay of non-local phenomena. To this end, we have performed a linear stability analysis and numerical simulations based on a mixed finite element formulation, which have allowed us to study the dynamical behaviour of the system in terms of the qualitative shape of the dispersion relation. We show that the consideration of mechanotaxis, namely changes in migration speeds and directions in response to mechanical stimuli alters the conditions for pattern formation in a singular manner. Furthermore without non-local effects, responses to mechanical stimuli are observed to result in dispersion relations with positive growth rates at arbitrarily large wavenumbers, in turn yielding heterogeneity at the cellular level in model predictions. This highlights the sensitivity and necessity of non-local effects in mechanically influenced biological pattern formation models and the ultimate failure of the continuum approximation in their absence. © 2009 Society for Mathematical Biology.

  1. Craniopharyngioma: identification of different semiological patterns by magnetic resonance

    International Nuclear Information System (INIS)

    Molla, E.; Marti-Bonmati, L.; Casillas, C.; Poyatos, C.; Menor, F.; Arana, E.

    1998-01-01

    To study craniopharyngiomas using different MR sequences to detect semiological patterns that aid in the characterization of the different components. We performed a retrospective MR study of 17 patients with confirmed craniopharyngioma. T1-weighted spin-echo, proton density-weighted, T2-weighted gradient-echo, T1-weighted (after administration of gadolinium), T1-weighted inversion recovery and phase and opposed-phase gradient echo sequences were employed to distinguish the different patterns. The semiologic patterns considered in MR were: solid-tissue, blood, protein, fat and fluid. A solid pole was detected in all the patients. There was a cystic component in 88.2% of cases; the protein pattern was observed in 52.9%, blood in 29.4%, fluid in 23.5% and fat in 11.7%. The coexistence of three patterns was detected in 29.4% and of two patterns in 58.8%. The calcium pattern was viewed in 75% of the patients studied with CT, with four patterns coexisting in 25%, three patterns in 41.6% and two patterns in 25%. MR detects different semiologic components in craniopharyngiomas, although it is necessary to employ certain unusual sequences in order to distinguish some patterns from others. (Author) 22 refs

  2. Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.

    Science.gov (United States)

    Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo

    2013-01-01

    Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

  3. A hydroclimatic model of global fire patterns

    Science.gov (United States)

    Boer, Matthias

    2015-04-01

    Satellite-based earth observation is providing an increasingly accurate picture of global fire patterns. The highest fire activity is observed in seasonally dry (sub-)tropical environments of South America, Africa and Australia, but fires occur with varying frequency, intensity and seasonality in almost all biomes on Earth. The particular combination of these fire characteristics, or fire regime, is known to emerge from the combined influences of climate, vegetation, terrain and land use, but has so far proven difficult to reproduce by global models. Uncertainty about the biophysical drivers and constraints that underlie current global fire patterns is propagated in model predictions of how ecosystems, fire regimes and biogeochemical cycles may respond to projected future climates. Here, I present a hydroclimatic model of global fire patterns that predicts the mean annual burned area fraction (F) of 0.25° x 0.25° grid cells as a function of the climatic water balance. Following Bradstock's four-switch model, long-term fire activity levels were assumed to be controlled by fuel productivity rates and the likelihood that the extant fuel is dry enough to burn. The frequency of ignitions and favourable fire weather were assumed to be non-limiting at long time scales. Fundamentally, fuel productivity and fuel dryness are a function of the local water and energy budgets available for the production and desiccation of plant biomass. The climatic water balance summarizes the simultaneous availability of biologically usable energy and water at a site, and may therefore be expected to explain a significant proportion of global variation in F. To capture the effect of the climatic water balance on fire activity I focused on the upper quantiles of F, i.e. the maximum level of fire activity for a given climatic water balance. Analysing GFED4 data for annual burned area together with gridded climate data, I found that nearly 80% of the global variation in the 0.99 quantile of F

  4. A model-based exploration of the role of pattern generating circuits during locomotor adaptation.

    Science.gov (United States)

    Marjaninejad, Ali; Finley, James M

    2016-08-01

    In this study, we used a model-based approach to explore the potential contributions of central pattern generating circuits (CPGs) during adaptation to external perturbations during locomotion. We constructed a neuromechanical modeled of locomotion using a reduced-phase CPG controller and an inverted pendulum mechanical model. Two different forms of locomotor adaptation were examined in this study: split-belt treadmill adaptation and adaptation to a unilateral, elastic force field. For each simulation, we first examined the effects of phase resetting and varying the model's initial conditions on the resulting adaptation. After evaluating the effect of phase resetting on the adaptation of step length symmetry, we examined the extent to which the results from these simple models could explain previous experimental observations. We found that adaptation of step length symmetry during split-belt treadmill walking could be reproduced using our model, but this model failed to replicate patterns of adaptation observed in response to force field perturbations. Given that spinal animal models can adapt to both of these types of perturbations, our findings suggest that there may be distinct features of pattern generating circuits that mediate each form of adaptation.

  5. Studies on modeling to failed fuel detection system response in LMFBR

    International Nuclear Information System (INIS)

    Miyazawa, T.; Saji, G.; Mitsuzuku, N.; Hikichi, T.; Odo, T.; Rindo, H.

    1981-05-01

    Failed Fuel Detection (FFD) system with Fission Products (FP) detection is considered to be the most promissing method, since FP provides direct information against fuel element failure. For designing FFD system and for evaluating FFD signals, some adequate FFD signal response to fuel failure have been required. But few models are available in nowadays. Thus Power Reactor and Nuclear Fuel Development Corporation (PNC) had developed FFD response model with computer codes, based on several fundamental investigations on FP release and FP behavior, and referred to foreign country experiences on fuel failure. In developing the model, noble gas and halogen FP release and behavior were considered, since FFD system would be composed of both cover gas monitoring and delayed neutron monitoring. The developed model can provide typical fuel failure response and detection limit which depends on various background signals at cover gas monitoring and delayed neutron monitoring. According to the FFD response model, we tried to assume fuel failure response and detection limit at Japan experimental fast reactor ''JOYO''. The detection limit of JOYO FFD system was estimated by measuring the background signals. Followed on the studies, a complete computer code has been now made with some improvement. On the paper, the details of the model, out line of developed computer code, status of JOYO FFD system, and trial assumption of JOYO FFD response and detection limit. (author)

  6. Random regression models for detection of gene by environment interaction

    Directory of Open Access Journals (Sweden)

    Meuwissen Theo HE

    2007-02-01

    Full Text Available Abstract Two random regression models, where the effect of a putative QTL was regressed on an environmental gradient, are described. The first model estimates the correlation between intercept and slope of the random regression, while the other model restricts this correlation to 1 or -1, which is expected under a bi-allelic QTL model. The random regression models were compared to a model assuming no gene by environment interactions. The comparison was done with regards to the models ability to detect QTL, to position them accurately and to detect possible QTL by environment interactions. A simulation study based on a granddaughter design was conducted, and QTL were assumed, either by assigning an effect independent of the environment or as a linear function of a simulated environmental gradient. It was concluded that the random regression models were suitable for detection of QTL effects, in the presence and absence of interactions with environmental gradients. Fixing the correlation between intercept and slope of the random regression had a positive effect on power when the QTL effects re-ranked between environments.

  7. Conditional Stochastic Models in Reduced Space: Towards Efficient Simulation of Tropical Cyclone Precipitation Patterns

    Science.gov (United States)

    Dodov, B.

    2017-12-01

    Stochastic simulation of realistic and statistically robust patterns of Tropical Cyclone (TC) induced precipitation is a challenging task. It is even more challenging in a catastrophe modeling context, where tens of thousands of typhoon seasons need to be simulated in order to provide a complete view of flood risk. Ultimately, one could run a coupled global climate model and regional Numerical Weather Prediction (NWP) model, but this approach is not feasible in the catastrophe modeling context and, most importantly, may not provide TC track patterns consistent with observations. Rather, we propose to leverage NWP output for the observed TC precipitation patterns (in terms of downscaled reanalysis 1979-2015) collected on a Lagrangian frame along the historical TC tracks and reduced to the leading spatial principal components of the data. The reduced data from all TCs is then grouped according to timing, storm evolution stage (developing, mature, dissipating, ETC transitioning) and central pressure and used to build a dictionary of stationary (within a group) and non-stationary (for transitions between groups) covariance models. Provided that the stochastic storm tracks with all the parameters describing the TC evolution are already simulated, a sequence of conditional samples from the covariance models chosen according to the TC characteristics at a given moment in time are concatenated, producing a continuous non-stationary precipitation pattern in a Lagrangian framework. The simulated precipitation for each event is finally distributed along the stochastic TC track and blended with a non-TC background precipitation using a data assimilation technique. The proposed framework provides means of efficient simulation (10000 seasons simulated in a couple of days) and robust typhoon precipitation patterns consistent with observed regional climate and visually undistinguishable from high resolution NWP output. The framework is used to simulate a catalog of 10000 typhoon

  8. Lectin binding patterns and immunohistochemical antigen detection ...

    African Journals Online (AJOL)

    Ibrahim Eldaghayes

    2018-02-09

    Feb 9, 2018 ... placenta and lungs of Brucella abortus-bovine infected fetuses. María Andrea ... The present lectin histochemical study revealed a distinctive pattern of oligosaccharide .... tissue was used as a positive control and nonimmune.

  9. A collision avoidance model for two-pedestrian groups: Considering random avoidance patterns

    Science.gov (United States)

    Zhou, Zhuping; Cai, Yifei; Ke, Ruimin; Yang, Jiwei

    2017-06-01

    Grouping is a common phenomenon in pedestrian crowds and group modeling is still an open challenging problem. When grouping pedestrians avoid each other, different patterns can be observed. Pedestrians can keep close with group members and avoid other groups in cluster. Also, they can avoid other groups separately. Considering this randomness in avoidance patterns, we propose a collision avoidance model for two-pedestrian groups. In our model, the avoidance model is proposed based on velocity obstacle method at first. Then grouping model is established using Distance constrained line (DCL), by transforming DCL into the framework of velocity obstacle, the avoidance model and grouping model are successfully put into one unified calculation structure. Within this structure, an algorithm is developed to solve the problem when solutions of the two models conflict with each other. Two groups of bidirectional pedestrian experiments are designed to verify the model. The accuracy of avoidance behavior and grouping behavior is validated in the microscopic level, while the lane formation phenomenon and fundamental diagrams is validated in the macroscopic level. The experiments results show our model is convincing and has a good expansibility to describe three or more pedestrian groups.

  10. SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events

    DEFF Research Database (Denmark)

    Cuttone, Andrea; Bækgaard, Per; Sekara, Vedran

    2017-01-01

    We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400...... to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient....... participants from two different datasets, and we verify the results against ground truth from dedicated armband sleep trackers. We show that the model is able to produce reliable sleep estimates with an accuracy of 0.89, both at the individual and at the collective level. Moreover the Bayesian model is able...

  11. SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events.

    Science.gov (United States)

    Cuttone, Andrea; Bækgaard, Per; Sekara, Vedran; Jonsson, Håkan; Larsen, Jakob Eg; Lehmann, Sune

    2017-01-01

    We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400 participants from two different datasets, and we verify the results against ground truth from dedicated armband sleep trackers. We show that the model is able to produce reliable sleep estimates with an accuracy of 0.89, both at the individual and at the collective level. Moreover the Bayesian model is able to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient.

  12. SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events.

    Directory of Open Access Journals (Sweden)

    Andrea Cuttone

    Full Text Available We propose a Bayesian model for extracting sleep patterns from smartphone events. Our method is able to identify individuals' daily sleep periods and their evolution over time, and provides an estimation of the probability of sleep and wake transitions. The model is fitted to more than 400 participants from two different datasets, and we verify the results against ground truth from dedicated armband sleep trackers. We show that the model is able to produce reliable sleep estimates with an accuracy of 0.89, both at the individual and at the collective level. Moreover the Bayesian model is able to quantify uncertainty and encode prior knowledge about sleep patterns. Compared with existing smartphone-based systems, our method requires only screen on/off events, and is therefore much less intrusive in terms of privacy and more battery-efficient.

  13. Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil.

    Science.gov (United States)

    Chen, Bin; Wang, Yanan; Yan, Zhaoli

    2018-01-29

    Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method.

  14. Automatic pattern identification of rock moisture based on the Staff-RF model

    Science.gov (United States)

    Zheng, Wei; Tao, Kai; Jiang, Wei

    2018-04-01

    Studies on the moisture and damage state of rocks generally focus on the qualitative description and mechanical information of rocks. This method is not applicable to the real-time safety monitoring of rock mass. In this study, a musical staff computing model is used to quantify the acoustic emission signals of rocks with different moisture patterns. Then, the random forest (RF) method is adopted to form the staff-RF model for the real-time pattern identification of rock moisture. The entire process requires only the computing information of the AE signal and does not require the mechanical conditions of rocks.

  15. Latent Feature Models for Uncovering Human Mobility Patterns from Anonymized User Location Traces with Metadata

    KAUST Repository

    Alharbi, Basma Mohammed

    2017-04-10

    In the mobile era, data capturing individuals’ locations have become unprecedentedly available. Data from Location-Based Social Networks is one example of large-scale user-location data. Such data provide a valuable source for understanding patterns governing human mobility, and thus enable a wide range of research. However, mining and utilizing raw user-location data is a challenging task. This is mainly due to the sparsity of data (at the user level), the imbalance of data with power-law users and locations check-ins degree (at the global level), and more importantly the lack of a uniform low-dimensional feature space describing users. Three latent feature models are proposed in this dissertation. Each proposed model takes as an input a collection of user-location check-ins, and outputs a new representation space for users and locations respectively. To avoid invading users privacy, the proposed models are designed to learn from anonymized location data where only IDs - not geophysical positioning or category - of locations are utilized. To enrich the inferred mobility patterns, the proposed models incorporate metadata, often associated with user-location data, into the inference process. In this dissertation, two types of metadata are utilized to enrich the inferred patterns, timestamps and social ties. Time adds context to the inferred patterns, while social ties amplifies incomplete user-location check-ins. The first proposed model incorporates timestamps by learning from collections of users’ locations sharing the same discretized time. The second proposed model also incorporates time into the learning model, yet takes a further step by considering time at different scales (hour of a day, day of a week, month, and so on). This change in modeling time allows for capturing meaningful patterns over different times scales. The last proposed model incorporates social ties into the learning process to compensate for inactive users who contribute a large volume

  16. Modelling and predicting biogeographical patterns in river networks

    Directory of Open Access Journals (Sweden)

    Sabela Lois

    2016-04-01

    Full Text Available Statistical analysis and interpretation of biogeographical phenomena in rivers is now possible using a spatially explicit modelling framework, which has seen significant developments in the past decade. I used this approach to identify a spatial extent (geostatistical range in which the abundance of the parasitic freshwater pearl mussel (Margaritifera margaritifera L. is spatially autocorrelated in river networks. I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. Although I used a variety of modelling approaches in my thesis, I focus here on the details of this relatively new spatial stream network model, thus advancing the study of biogeographical patterns in river networks.

  17. DEPTracker – Sleep Pattern Tracking with Accelerometer Technology

    DEFF Research Database (Denmark)

    Grode, Jesper Nicolai Riis; Havn, Ib; Svane Hansen, Lars

    2015-01-01

    REM (Rapid Eye Movement) sleep pattern changes are known to be an early indicator of effective medical treatment of patients with a depression diagnosis. Existing methods to detect REM sleep pattern changes are known to be inaccurate, costly, or otherwise inadequate in normal settings...... of this patient group. In this paper, we demonstrate DEPTracker, a system capable of detecting sleep patterns, and in particular REM sleep. We show that DEPTracker is an accurate, cost-effective and suitable approach for sleep pattern detection in general. Details of the technology used, combining accelerometer...... technology with digital signal analysis is given and illustrates that the system is able to successfully detect REM sleep. The project demonstrates that accelerometers can be mounted on an eye lid and eye movements can be detected, sampled and stored in a database for online real-time analysis or post-sleep...

  18. 3D physical modeling for patterning process development

    Science.gov (United States)

    Sarma, Chandra; Abdo, Amr; Bailey, Todd; Conley, Will; Dunn, Derren; Marokkey, Sajan; Talbi, Mohamed

    2010-03-01

    In this paper we will demonstrate how a 3D physical patterning model can act as a forensic tool for OPC and ground-rule development. We discuss examples where the 2D modeling shows no issues in printing gate lines but 3D modeling shows severe resist loss in the middle. In absence of corrective measure, there is a high likelihood of line discontinuity post etch. Such early insight into process limitations of prospective ground rules can be invaluable for early technology development. We will also demonstrate how the root cause of broken poly-line after etch could be traced to resist necking in the region of STI step with the help of 3D models. We discuss different cases of metal and contact layouts where 3D modeling gives an early insight in to technology limitations. In addition such a 3D physical model could be used for early resist evaluation and selection for required ground-rule challenges, which can substantially reduce the cycle time for process development.

  19. Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry.

    Science.gov (United States)

    Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H

    2014-12-30

    For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivated by a clinical cancer registry application, where complex event patterns have to be dealt with and variable selection is needed at the same time, we propose a general approach for linking variable selection between several Cox models. Specifically, we combine score statistics for each covariate across models by Fisher's method as a basis for variable selection. This principle is implemented for a stepwise forward selection approach as well as for a regularized regression technique. In an application to data from hepatocellular carcinoma patients, the coupled stepwise approach is seen to facilitate joint interpretation of the different cause-specific Cox models. In conditional survival models at landmark times, which address updates of prediction as time progresses and both treatment and other potential explanatory variables may change, the coupled regularized regression approach identifies potentially important, stably selected covariates together with their effect time pattern, despite having only a small number of events. These results highlight the promise of the proposed approach for coupling variable selection between Cox models, which is particularly relevant for modeling for clinical cancer registries with their complex event patterns. Copyright © 2014 John Wiley & Sons

  20. Detecting beer intake by unique metabolite patterns

    DEFF Research Database (Denmark)

    Gürdeniz, Gözde; Jensen, Morten Georg; Meier, Sebastian

    2016-01-01

    Evaluation of health related effects of beer intake is hampered by the lack of accurate tools for assessing intakes (biomarkers). Therefore, we identified plasma and urine metabolites associated with recent beer intake by untargeted metabolomics and established a characteristic metabolite pattern...... representing raw materials and beer production as a qualitative biomarker of beer intake. In a randomized, crossover, single-blinded meal study (MSt1) 18 participants were given one at a time four different test beverages: strong, regular and non-alcoholic beers and a soft drink. Four participants were...... assigned to have two additional beers (MSt2). In addition to plasma and urine samples, test beverages, wort and hops extract were analyzed by UPLC-QTOF. A unique metabolite pattern reflecting beer metabolome, including metabolites derived from beer raw material (i.e. N-methyl tyramine sulfate and the sum...

  1. In Vivo Measurement of Glenohumeral Joint Contact Patterns

    Directory of Open Access Journals (Sweden)

    Bey MichaelJ

    2010-01-01

    Full Text Available The objectives of this study were to describe a technique for measuring in-vivo glenohumeral joint contact patterns during dynamic activities and to demonstrate application of this technique. The experimental technique calculated joint contact patterns by combining CT-based 3D bone models with joint motion data that were accurately measured from biplane x-ray images. Joint contact patterns were calculated for the repaired and contralateral shoulders of 20 patients who had undergone rotator cuff repair. Significant differences in joint contact patterns were detected due to abduction angle and shoulder condition (i.e., repaired versus contralateral. Abduction angle had a significant effect on the superior/inferior contact center position, with the average joint contact center of the repaired shoulder 12.1% higher on the glenoid than the contralateral shoulder. This technique provides clinically relevant information by calculating in-vivo joint contact patterns during dynamic conditions and overcomes many limitations associated with conventional techniques for quantifying joint mechanics.

  2. Application of pattern mixture models to address missing data in longitudinal data analysis using SPSS.

    Science.gov (United States)

    Son, Heesook; Friedmann, Erika; Thomas, Sue A

    2012-01-01

    Longitudinal studies are used in nursing research to examine changes over time in health indicators. Traditional approaches to longitudinal analysis of means, such as analysis of variance with repeated measures, are limited to analyzing complete cases. This limitation can lead to biased results due to withdrawal or data omission bias or to imputation of missing data, which can lead to bias toward the null if data are not missing completely at random. Pattern mixture models are useful to evaluate the informativeness of missing data and to adjust linear mixed model (LMM) analyses if missing data are informative. The aim of this study was to provide an example of statistical procedures for applying a pattern mixture model to evaluate the informativeness of missing data and conduct analyses of data with informative missingness in longitudinal studies using SPSS. The data set from the Patients' and Families' Psychological Response to Home Automated External Defibrillator Trial was used as an example to examine informativeness of missing data with pattern mixture models and to use a missing data pattern in analysis of longitudinal data. Prevention of withdrawal bias, omitted data bias, and bias toward the null in longitudinal LMMs requires the assessment of the informativeness of the occurrence of missing data. Missing data patterns can be incorporated as fixed effects into LMMs to evaluate the contribution of the presence of informative missingness to and control for the effects of missingness on outcomes. Pattern mixture models are a useful method to address the presence and effect of informative missingness in longitudinal studies.

  3. Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.

    Science.gov (United States)

    Anderson, Andrew James; Binder, Jeffrey R; Fernandino, Leonardo; Humphries, Colin J; Conant, Lisa L; Aguilar, Mario; Wang, Xixi; Doko, Donias; Raizada, Rajeev D S

    2017-09-01

    We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern

    Directory of Open Access Journals (Sweden)

    Sylvia Jane Annatje Sumarauw

    2007-06-01

    Full Text Available Abstract Capital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern. Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition

  5. Post-Hoc Pattern-Oriented Testing and Tuning of an Existing Large Model: Lessons from the Field Vole

    DEFF Research Database (Denmark)

    Topping, Christopher John; Dalkvist, Trine; Grimm, Volker

    2012-01-01

    Pattern-oriented modeling (POM) is a general strategy for modeling complex systems. In POM, multiple patterns observed at different scales and hierarchical levels are used to optimize model structure, to test and select sub-models of key processes, and for calibration. So far, POM has been used f...

  6. Onset patterns in a simple model of localized parametric forcing.

    Science.gov (United States)

    Porter, J; Tinao, I; Laverón-Simavilla, A; Rodríguez, J

    2013-10-01

    We investigate pattern selection at onset in a parametrically and inhomogeneously forced partial differential equation obtained by generalizing Mathieu's equation to include spatial interactions. No separation of scales is assumed. The proposed model is directly relevant to the case of parametrically forced surface waves, such as cross-waves, excited by the horizontal vibration of a fluid, where the forcing is localized to a finite region near the endwall or wavemaker. The availability of analytical solutions in the limit of piecewise constant forcing allows us investigate in detail the dependence of selected eigenfunctions on spatial detuning, forcing width, damping, boundary conditions, and container size. A wide range of onset patterns are located and described, many of which are rotated, modulated, or both, and deviate far from simple crosswise oriented standing waves. The linear selection mechanisms governing this multiplicity of potential onset patterns are discussed.

  7. Detecting altered connectivity patterns in HIV associated neurocognitive impairment using mutual connectivity analysis

    Science.gov (United States)

    Abidin, Anas Zainul; D'Souza, Adora M.; Nagarajan, Mahesh B.; Wismüller, Axel

    2016-03-01

    The use of functional Magnetic Resonance Imaging (fMRI) has provided interesting insights into our understanding of the brain. In clinical setups these scans have been used to detect and study changes in the brain network properties in various neurological disorders. A large percentage of subjects infected with HIV present cognitive deficits, which are known as HIV associated neurocognitive disorder (HAND). In this study we propose to use our novel technique named Mutual Connectivity Analysis (MCA) to detect differences in brain networks in subjects with and without HIV infection. Resting state functional MRI scans acquired from 10 subjects (5 HIV+ and 5 HIV-) were subject to standard preprocessing routines. Subsequently, the average time-series for each brain region of the Automated Anatomic Labeling (AAL) atlas are extracted and used with the MCA framework to obtain a graph characterizing the interactions between them. The network graphs obtained for different subjects are then compared using Network-Based Statistics (NBS), which is an approach to detect differences between graphs edges while controlling for the family-wise error rate when mass univariate testing is performed. Applying this approach on the graphs obtained yields a single network encompassing 42 nodes and 65 edges, which is significantly different between the two subject groups. Specifically connections to the regions in and around the basal ganglia are significantly decreased. Also some nodes corresponding to the posterior cingulate cortex are affected. These results are inline with our current understanding of pathophysiological mechanisms of HIV associated neurocognitive disease (HAND) and other HIV based fMRI connectivity studies. Hence, we illustrate the applicability of our novel approach with network-based statistics in a clinical case-control study to detect differences connectivity patterns.

  8. Study on Analysis and Pattern Recognition of the Manifestation of the Pulse Detection of Cerebrovascular Disease

    Energy Technology Data Exchange (ETDEWEB)

    Jing, J; Wang, Y C; Hong, W X; Zhang, W P [Department of Biomedical Engineering, University of Yanshan, Qinhuangdao, Hebei Province, 066004 (China)

    2006-10-15

    Cerebrovascular Disease (CVD) is also called stroke in Traditional Chinese Medicine (TCM). CVD is a kind of frequent diseases with high incidence, high death rate, high deformity rate and high relapse rate. The pathogenesis of CVD has relation to many factors. In modern medicine, we can make use of various instruments to check many biochemical parameters. However, at present, the early detection of CVD can mostly be done artificially by specialists. In TCM the salted expert can detect the state of a CVD patient by felling his (or her) pulse. It is significant to apply the modern information and engineering techniques to the early discovery of CVD. It is also a challenge to do this in fact. In this paper, the authors presented a detection method of CVD basing on analysis and pattern recognition of Manifestation of the Pulse of TCM using wavelet technology and Neural Networks. Pulse signals from normal health persons and CVD patients were studied comparatively. This research method is flexible to deal with other physiological signals.

  9. Characteristic LDH isozyme electrophoretic patterns in six flatfish species in the Trondheimsfjord, Norway and their utility for the detection of natural species hybrids

    KAUST Repository

    He, Song

    2014-11-19

    Abstract: LDH isozyme electrophoretic patterns among 621 specimens of six different flatfish species collected in the Trondheimsfjord, Norway, were characterized by using the isoelectric focusing in polyacrylamide gel (IFPAG) technique. The LDH locus appears to be a reliable tool for species identification in the Trondheimsfjord flatfishes. Hence, these patterns were used to detect and identify potential hybrids, together with morphological traits. Among all the specimens collected during this study no hybrids were detected. From the actual numbers analysed, the natural hybridization rate between European plaice and European flounder in the Trondheimsfjord can be roughly estimated to be less than 1%. This is substantially lower than corresponding values reported from Baltic and Danish waters.

  10. Characteristic LDH isozyme electrophoretic patterns in six flatfish species in the Trondheimsfjord, Norway and their utility for the detection of natural species hybrids

    KAUST Repository

    He, Song; Mork, Jarle

    2014-01-01

    Abstract: LDH isozyme electrophoretic patterns among 621 specimens of six different flatfish species collected in the Trondheimsfjord, Norway, were characterized by using the isoelectric focusing in polyacrylamide gel (IFPAG) technique. The LDH locus appears to be a reliable tool for species identification in the Trondheimsfjord flatfishes. Hence, these patterns were used to detect and identify potential hybrids, together with morphological traits. Among all the specimens collected during this study no hybrids were detected. From the actual numbers analysed, the natural hybridization rate between European plaice and European flounder in the Trondheimsfjord can be roughly estimated to be less than 1%. This is substantially lower than corresponding values reported from Baltic and Danish waters.

  11. [Patterns of detection of mild cognitive impairment in nursing].

    Science.gov (United States)

    Sebastián Hernández, Ana J; Arranz Santamaría, Luís Carlos

    2017-06-01

    Mild cognitive impairment (MCI) is characterized by an acquired cognitive loss that places individuals, mainly older adults, in an intermediate stage between normal cognitive functioning and dementia. This impairment has a high risk of progression to dementia and is suitable for screening, which allows more effective early intervention. Nursing professionals, especially community-based primary care nurses, play an important role in the detection and follow-up of MCI and in interventions for this condition. The first step should be to take a thorough history from both the patient and his or her carers, which should assess the changes occurring in the patient's daily, family and social life through functional patterns. In subsequent assessment of cognitive function, brief screening tests can be used such as the Mini Mental State Examination (MMSE) or other similar tests. Special attention should be paid to the presence of affective or depressive symptoms, sensory deficits, polypharmacy, decompensated cardiovascular risk factors, and rapid functional deterioration, given their particular influence on MCI. Finally, various nurse-led, non-pharmacological interventions that are effective in MCI can be recommended, based on cardiovascular risk factor control, physical exercise, and cognitive and psychosocial interventions. Copyright © 2017 Sociedad Española de Geriatría y Gerontología. Publicado por Elsevier España, S.L.U. All rights reserved.

  12. [Purity Detection Model Update of Maize Seeds Based on Active Learning].

    Science.gov (United States)

    Tang, Jin-ya; Huang, Min; Zhu, Qi-bing

    2015-08-01

    Seed purity reflects the degree of seed varieties in typical consistent characteristics, so it is great important to improve the reliability and accuracy of seed purity detection to guarantee the quality of seeds. Hyperspectral imaging can reflect the internal and external characteristics of seeds at the same time, which has been widely used in nondestructive detection of agricultural products. The essence of nondestructive detection of agricultural products using hyperspectral imaging technique is to establish the mathematical model between the spectral information and the quality of agricultural products. Since the spectral information is easily affected by the sample growth environment, the stability and generalization of model would weaken when the test samples harvested from different origin and year. Active learning algorithm was investigated to add representative samples to expand the sample space for the original model, so as to implement the rapid update of the model's ability. Random selection (RS) and Kennard-Stone algorithm (KS) were performed to compare the model update effect with active learning algorithm. The experimental results indicated that in the division of different proportion of sample set (1:1, 3:1, 4:1), the updated purity detection model for maize seeds from 2010 year which was added 40 samples selected by active learning algorithm from 2011 year increased the prediction accuracy for 2011 new samples from 47%, 33.75%, 49% to 98.89%, 98.33%, 98.33%. For the updated purity detection model of 2011 year, its prediction accuracy for 2010 new samples increased by 50.83%, 54.58%, 53.75% to 94.57%, 94.02%, 94.57% after adding 56 new samples from 2010 year. Meanwhile the effect of model updated by active learning algorithm was better than that of RS and KS. Therefore, the update for purity detection model of maize seeds is feasible by active learning algorithm.

  13. Detecting misinformation and knowledge conflicts in relational data

    Science.gov (United States)

    Levchuk, Georgiy; Jackobsen, Matthew; Riordan, Brian

    2014-06-01

    Information fusion is required for many mission-critical intelligence analysis tasks. Using knowledge extracted from various sources, including entities, relations, and events, intelligence analysts respond to commander's information requests, integrate facts into summaries about current situations, augment existing knowledge with inferred information, make predictions about the future, and develop action plans. However, information fusion solutions often fail because of conflicting and redundant knowledge contained in multiple sources. Most knowledge conflicts in the past were due to translation errors and reporter bias, and thus could be managed. Current and future intelligence analysis, especially in denied areas, must deal with open source data processing, where there is much greater presence of intentional misinformation. In this paper, we describe a model for detecting conflicts in multi-source textual knowledge. Our model is based on constructing semantic graphs representing patterns of multi-source knowledge conflicts and anomalies, and detecting these conflicts by matching pattern graphs against the data graph constructed using soft co-reference between entities and events in multiple sources. The conflict detection process maintains the uncertainty throughout all phases, providing full traceability and enabling incremental updates of the detection results as new knowledge or modification to previously analyzed information are obtained. Detected conflicts are presented to analysts for further investigation. In the experimental study with SYNCOIN dataset, our algorithms achieved perfect conflict detection in ideal situation (no missing data) while producing 82% recall and 90% precision in realistic noise situation (15% of missing attributes).

  14. Drone Detection with Chirp‐Pulse Radar Based on Target Fluctuation Models

    Directory of Open Access Journals (Sweden)

    Byung‐Kwan Kim

    2018-04-01

    Full Text Available This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non‐conducting materials, their radar cross‐section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal‐to‐noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.

  15. Simulated small-angle scattering patterns for a plastically deformed model composite material

    NARCIS (Netherlands)

    Shenoy, V.B.; Cleveringa, H.H.M.; Phillips, R.; Giessen, E. van der; Needleman, A.

    2000-01-01

    The small-angle scattering patterns predicted by discrete dislocation plasticity versus local and non-local continuum plasticity theory are compared in a model problem. The problem considered is a two-dimensional model composite with elastic reinforcements in a crystalline matrix subject to

  16. Detection of Subtle Context-Dependent Model Inaccuracies in High-Dimensional Robot Domains.

    Science.gov (United States)

    Mendoza, Juan Pablo; Simmons, Reid; Veloso, Manuela

    2016-12-01

    Autonomous robots often rely on models of their sensing and actions for intelligent decision making. However, when operating in unconstrained environments, the complexity of the world makes it infeasible to create models that are accurate in every situation. This article addresses the problem of using potentially large and high-dimensional sets of robot execution data to detect situations in which a robot model is inaccurate-that is, detecting context-dependent model inaccuracies in a high-dimensional context space. To find inaccuracies tractably, the robot conducts an informed search through low-dimensional projections of execution data to find parametric Regions of Inaccurate Modeling (RIMs). Empirical evidence from two robot domains shows that this approach significantly enhances the detection power of existing RIM-detection algorithms in high-dimensional spaces.

  17. Diversity of chimera-like patterns from a model of 2D arrays of neurons with nonlocal coupling

    Science.gov (United States)

    Tian, Chang-Hai; Zhang, Xi-Yun; Wang, Zhen-Hua; Liu, Zong-Hua

    2017-06-01

    Chimera states have been studied in 1D arrays, and a variety of different chimera states have been found using different models. Research has recently been extended to 2D arrays but only to phase models of them. Here, we extend it to a nonphase model of 2D arrays of neurons and focus on the influence of nonlocal coupling. Using extensive numerical simulations, we find, surprisingly, that this system can show most types of previously observed chimera states, in contrast to previous models, where only one or a few types of chimera states can be observed in each model. We also find that this model can show some special chimera-like patterns such as gridding and multicolumn patterns, which were previously observed only in phase models. Further, we present an effective approach, i.e., removing some of the coupling links, to generate heterogeneous coupling, which results in diverse chimera-like patterns and even induces transformations from one chimera-like pattern to another.

  18. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

    Energy Technology Data Exchange (ETDEWEB)

    Grimm, Lars J., E-mail: Lars.grimm@duke.edu; Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie [Department of Radiology, Duke University Medical Center, Box 3808, Durham, North Carolina 27710 (United States); Kuzmiak, Cherie M. [Department of Radiology, University of North Carolina School of Medicine, 2006 Old Clinic, CB No. 7510, Chapel Hill, North Carolina 27599 (United States); Mazurowski, Maciej A. [Duke University Medical Center, Box 2731 Medical Center, Durham, North Carolina 27710 (United States)

    2014-03-15

    Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

  19. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

    International Nuclear Information System (INIS)

    Grimm, Lars J.; Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie; Kuzmiak, Cherie M.; Mazurowski, Maciej A.

    2014-01-01

    Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees

  20. Pipe fracture evaluations for leak-rate detection: Probabilistic models

    International Nuclear Information System (INIS)

    Rahman, S.; Wilkowski, G.; Ghadiali, N.

    1993-01-01

    This is the second in series of three papers generated from studies on nuclear pipe fracture evaluations for leak-rate detection. This paper focuses on the development of novel probabilistic models for stochastic performance evaluation of degraded nuclear piping systems. It was accomplished here in three distinct stages. First, a statistical analysis was conducted to characterize various input variables for thermo-hydraulic analysis and elastic-plastic fracture mechanics, such as material properties of pipe, crack morphology variables, and location of cracks found in nuclear piping. Second, a new stochastic model was developed to evaluate performance of degraded piping systems. It is based on accurate deterministic models for thermo-hydraulic and fracture mechanics analyses described in the first paper, statistical characterization of various input variables, and state-of-the-art methods of modem structural reliability theory. From this model. the conditional probability of failure as a function of leak-rate detection capability of the piping systems can be predicted. Third, a numerical example was presented to illustrate the proposed model for piping reliability analyses. Results clearly showed that the model provides satisfactory estimates of conditional failure probability with much less computational effort when compared with those obtained from Monte Carlo simulation. The probabilistic model developed in this paper will be applied to various piping in boiling water reactor and pressurized water reactor plants for leak-rate detection applications

  1. Evaluating the predictive abilities of community occupancy models using AUC while accounting for imperfect detection

    Science.gov (United States)

    Zipkin, Elise F.; Grant, Evan H. Campbell; Fagan, William F.

    2012-01-01

    The ability to accurately predict patterns of species' occurrences is fundamental to the successful management of animal communities. To determine optimal management strategies, it is essential to understand species-habitat relationships and how species habitat use is related to natural or human-induced environmental changes. Using five years of monitoring data in the Chesapeake and Ohio Canal National Historical Park, Maryland, USA, we developed four multi-species hierarchical models for estimating amphibian wetland use that account for imperfect detection during sampling. The models were designed to determine which factors (wetland habitat characteristics, annual trend effects, spring/summer precipitation, and previous wetland occupancy) were most important for predicting future habitat use. We used the models to make predictions of species occurrences in sampled and unsampled wetlands and evaluated model projections using additional data. Using a Bayesian approach, we calculated a posterior distribution of receiver operating characteristic area under the curve (ROC AUC) values, which allowed us to explicitly quantify the uncertainty in the quality of our predictions and to account for false negatives in the evaluation dataset. We found that wetland hydroperiod (the length of time that a wetland holds water) as well as the occurrence state in the prior year were generally the most important factors in determining occupancy. The model with only habitat covariates predicted species occurrences well; however, knowledge of wetland use in the previous year significantly improved predictive ability at the community level and for two of 12 species/species complexes. Our results demonstrate the utility of multi-species models for understanding which factors affect species habitat use of an entire community (of species) and provide an improved methodology using AUC that is helpful for quantifying the uncertainty in model predictions while explicitly accounting for

  2. Application of Remote-Sensing Observations for Detecting Patterns of Localization of Cu-Ni Mineralization of the Norilsk Ore Region

    Science.gov (United States)

    Milovsky, G. A.; Ishmukhametova, V. T.; Shemyakina, E. M.

    2017-12-01

    The methods of a complex analysis of materials of space, gravimetric, and magnetometric surveys were developed on the basis of a study of reference fields of the Norilsk ore region (Imangda, etc.) for detection patterns of the localization of Cu-Ni (with PGMs) mineralization in intrusive complexes of the northwestern frame of the Siberian Platform.

  3. Simulation of organ patterning on the floral meristem using a polar auxin transport model.

    Directory of Open Access Journals (Sweden)

    Simon van Mourik

    Full Text Available An intriguing phenomenon in plant development is the timing and positioning of lateral organ initiation, which is a fundamental aspect of plant architecture. Although important progress has been made in elucidating the role of auxin transport in the vegetative shoot to explain the phyllotaxis of leaf formation in a spiral fashion, a model study of the role of auxin transport in whorled organ patterning in the expanding floral meristem is not available yet. We present an initial simulation approach to study the mechanisms that are expected to play an important role. Starting point is a confocal imaging study of Arabidopsis floral meristems at consecutive time points during flower development. These images reveal auxin accumulation patterns at the positions of the organs, which strongly suggests that the role of auxin in the floral meristem is similar to the role it plays in the shoot apical meristem. This is the basis for a simulation study of auxin transport through a growing floral meristem, which may answer the question whether auxin transport can in itself be responsible for the typical whorled floral pattern. We combined a cellular growth model for the meristem with a polar auxin transport model. The model predicts that sepals are initiated by auxin maxima arising early during meristem outgrowth. These form a pre-pattern relative to which a series of smaller auxin maxima are positioned, which partially overlap with the anlagen of petals, stamens, and carpels. We adjusted the model parameters corresponding to properties of floral mutants and found that the model predictions agree with the observed mutant patterns. The predicted timing of the primordia outgrowth and the timing and positioning of the sepal primordia show remarkable similarities with a developing flower in nature.

  4. A Time-Frequency Auditory Model Using Wavelet Packets

    DEFF Research Database (Denmark)

    Agerkvist, Finn

    1996-01-01

    A time-frequency auditory model is presented. The model uses the wavelet packet analysis as the preprocessor. The auditory filters are modelled by the rounded exponential filters, and the excitation is smoothed by a window function. By comparing time-frequency excitation patterns it is shown...... that the change in the time-frequency excitation pattern introduced when a test tone at masked threshold is added to the masker is approximately equal to 7 dB for all types of maskers. The classic detection ratio therefore overrates the detection efficiency of the auditory system....

  5. Model uncertainties do not affect observed patterns of species richness in the Amazon

    Science.gov (United States)

    Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo

    2017-01-01

    Background Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. Methods We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. Results The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. Conclusions From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of

  6. Model Based Fault Detection in a Centrifugal Pump Application

    DEFF Research Database (Denmark)

    Kallesøe, Carsten; Cocquempot, Vincent; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural considerations...

  7. Patterns of cytosine methylation in an elite rice hybrid and its parental lines, detected by a methylation-sensitive amplification polymorphism technique.

    Science.gov (United States)

    Xiong, L Z; Xu, C G; Saghai Maroof, M A; Zhang, Q

    1999-04-01

    DNA methylation is known to play an important role in the regulation of gene expression in eukaryotes. In this study, we assessed the extent and pattern of cytosine methylation in the rice genome, using the technique of methylation-sensitive amplified polymorphism (MSAP), which is a modification of the amplified fragment length polymorphism (AFLP) method that makes use of the differential sensitivity of a pair of isoschizomers to cytosine methylation. The tissues assayed included seedlings and flag leaves of an elite rice hybrid, Shanyou 63, and the parental lines Zhenshan 97 and Minghui 63. In all, 1076 fragments, each representing a recognition site cleaved by either or both of the isoschizomers, were amplified using 16 pairs of selective primers. A total of 195 sites were found to be methylated at cytosines in one or both parents, and the two parents showed approximately the same overall degree of methylation (16.3%), as revealed by the incidence of differential digestion by the isoschizomers. Four classes of patterns were identified in a comparative assay of cytosine methylation in the parents and hybrid; increased methylation was detected in the hybrid compared to the parents at some of the recognition sites, while decreased methylation in the hybrid was detected at other sites. A small proportion of the sites was found to be differentially methylated in seedlings and flag leaves; DNA from young seedlings was methylated to a greater extent than that from flag leaves. Almost all of the methylation patterns detected by MSAP could be confirmed by Southern analysis using the isolated amplified fragments as probes. The results clearly demonstrate that the MSAP technique is highly efficient for large-scale detection of cytosine methylation in the rice genome. We believe that the technique can be adapted for use in other plant species.

  8. Prefiltering Model for Homology Detection Algorithms on GPU.

    Science.gov (United States)

    Retamosa, Germán; de Pedro, Luis; González, Ivan; Tamames, Javier

    2016-01-01

    Homology detection has evolved over the time from heavy algorithms based on dynamic programming approaches to lightweight alternatives based on different heuristic models. However, the main problem with these algorithms is that they use complex statistical models, which makes it difficult to achieve a relevant speedup and find exact matches with the original results. Thus, their acceleration is essential. The aim of this article was to prefilter a sequence database. To make this work, we have implemented a groundbreaking heuristic model based on NVIDIA's graphics processing units (GPUs) and multicore processors. Depending on the sensitivity settings, this makes it possible to quickly reduce the sequence database by factors between 50% and 95%, while rejecting no significant sequences. Furthermore, this prefiltering application can be used together with multiple homology detection algorithms as a part of a next-generation sequencing system. Extensive performance and accuracy tests have been carried out in the Spanish National Centre for Biotechnology (NCB). The results show that GPU hardware can accelerate the execution times of former homology detection applications, such as National Centre for Biotechnology Information (NCBI), Basic Local Alignment Search Tool for Proteins (BLASTP), up to a factor of 4.

  9. Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models

    Science.gov (United States)

    Keeler, James D.

    1987-01-01

    The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.

  10. A hidden Markov model approach to neuron firing patterns.

    Science.gov (United States)

    Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G

    1996-11-01

    Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.

  11. Change Detection Analysis of Water Pollution in Coimbatore Region using Different Color Models

    Science.gov (United States)

    Jiji, G. Wiselin; Devi, R. Naveena

    2017-12-01

    The data acquired through remote sensing satellites furnish facts about the land and water at varying resolutions and has been widely used for several change detection studies. Apart from the existence of many change detection methodologies and techniques, emergence of new ones continues to subsist. Existing change detection techniques exploit images that are either in gray scale or RGB color model. In this paper we introduced color models for performing change detection for water pollution. Here the polluted lakes are classified and post-classification change detection techniques are applied to RGB images and results obtained are analysed for changes to exist or not. Furthermore RGB images obtained after classification when converted to any of the two color models YCbCr and YIQ is found to produce the same results as that of the RGB model images. Thus it can be concluded that other color models like YCbCr, YIQ can be used as substitution to RGB color model for analysing change detection with regard to water pollution.

  12. Stochastic modeling of a hazard detection and avoidance maneuver—The planetary landing case

    International Nuclear Information System (INIS)

    Witte, Lars

    2013-01-01

    Hazard Detection and Avoidance (HDA) functionalities, thus the ability to recognize and avoid potential hazardous terrain features, is regarded as an enabling technology for upcoming robotic planetary landing missions. In the forefront of any landing mission the landing site safety assessment is an important task in the systems and mission engineering process. To contribute to this task, this paper presents a mathematical framework to consider the HDA strategy and system constraints in this mission engineering aspect. Therefore the HDA maneuver is modeled as a stochastic decision process based on Markov chains to map an initial dispersion at an arrival gate to a new dispersion pattern affected by the divert decision-making and system constraints. The implications for an efficient numerical implementation are addressed. An example case study is given to demonstrate the implementation and use of the proposed scheme

  13. A mechanical model for deformable and mesh pattern wheel of lunar roving vehicle

    Science.gov (United States)

    Liang, Zhongchao; Wang, Yongfu; Chen, Gang (Sheng); Gao, Haibo

    2015-12-01

    As an indispensable tool for astronauts on lunar surface, the lunar roving vehicle (LRV) is of great significance for manned lunar exploration. An LRV moves on loose and soft lunar soil, so the mechanical property of its wheels directly affects the mobility performance. The wheels used for LRV have deformable and mesh pattern, therefore, the existing mechanical theory of vehicle wheel cannot be used directly for analyzing the property of LRV wheels. In this paper, a new mechanical model for LRV wheel is proposed. At first, a mechanical model for a rigid normal wheel is presented, which involves in multiple conventional parameters such as vertical load, tangential traction force, lateral force, and slip ratio. Secondly, six equivalent coefficients are introduced to amend the rigid normal wheel model to fit for the wheels with deformable and mesh-pattern in LRV application. Thirdly, the values of the six equivalent coefficients are identified by using experimental data obtained in an LRV's single wheel testing. Finally, the identified mechanical model for LRV's wheel with deformable and mesh pattern are further verified and validated by using additional experimental results.

  14. CHARACTERISTIC FEATURES OF MUELLER MATRIX PATTERNS FOR POLARIZATION SCATTERING MODEL OF BIOLOGICAL TISSUES

    Directory of Open Access Journals (Sweden)

    E DU

    2014-01-01

    Full Text Available We developed a model to describe polarized photon scattering in biological tissues. In this model, tissues are simplified to a mixture of scatterers and surrounding medium. There are two types of scatterers in the model: solid spheres and infinitely long solid cylinders. Variables related to the scatterers include: the densities and sizes of the spheres and cylinders, the orientation and angular distribution of cylinders. Variables related to the surrounding medium include: the refractive index, absorption coefficient and birefringence. In this paper, as a development we introduce an optical activity effect to the model. By comparing experiments and Monte Carlo simulations, we analyze the backscattering Mueller matrix patterns of several tissue-like media, and summarize the different effects coming from anisotropic scattering and optical properties. In addition, we propose a possible method to extract the optical activity values for tissues. Both the experimental and simulated results show that, by analyzing the Mueller matrix patterns, the microstructure and optical properties of the medium can be obtained. The characteristic features of Mueller matrix patterns are potentially powerful tools for studying the contrast mechanisms of polarization imaging for medical diagnosis.

  15. Dynamic Delayed Duplicate Detection for External Memory Model Checking

    DEFF Research Database (Denmark)

    Evangelista, Sami

    2008-01-01

    Duplicate detection is an expensive operation of disk-based model checkers. It consists of comparing some potentially new states, the candidate states, to previous visited states. We propose a new approach to this technique called dynamic delayed duplicate detection. This one exploits some typical...

  16. Integument pattern formation involves genetic and epigenetic controls: feather arrays simulated by digital hormone models.

    Science.gov (United States)

    Jiang, Ting-Xin; Widelitz, Randall B; Shen, Wei-Min; Will, Peter; Wu, Da-Yu; Lin, Chih-Min; Jung, Han-Sung; Chuong, Cheng-Ming

    2004-01-01

    Pattern formation is a fundamental morphogenetic process. Models based on genetic and epigenetic control have been proposed but remain controversial. Here we use feather morphogenesis for further evaluation. Adhesion molecules and/or signaling molecules were first expressed homogenously in feather tracts (restrictive mode, appear earlier) or directly in bud or inter-bud regions ( de novo mode, appear later). They either activate or inhibit bud formation, but paradoxically colocalize in the bud. Using feather bud reconstitution, we showed that completely dissociated cells can reform periodic patterns without reference to previous positional codes. The patterning process has the characteristics of being self-organizing, dynamic and plastic. The final pattern is an equilibrium state reached by competition, and the number and size of buds can be altered based on cell number and activator/inhibitor ratio, respectively. We developed a Digital Hormone Model which consists of (1) competent cells without identity that move randomly in a space, (2) extracellular signaling hormones which diffuse by a reaction-diffusion mechanism and activate or inhibit cell adhesion, and (3) cells which respond with topological stochastic actions manifested as changes in cell adhesion. Based on probability, the results are cell clusters arranged in dots or stripes. Thus genetic control provides combinational molecular information which defines the properties of the cells but not the final pattern. Epigenetic control governs interactions among cells and their environment based on physical-chemical rules (such as those described in the Digital Hormone Model). Complex integument patterning is the sum of these two components of control and that is why integument patterns are usually similar but non-identical. These principles may be shared by other pattern formation processes such as barb ridge formation, fingerprints, pigmentation patterning, etc. The Digital Hormone Model can also be applied to

  17. Dynamic model based on voltage transfer curve for pattern formation in dielectric barrier glow discharge

    Energy Technology Data Exchange (ETDEWEB)

    Li, Ben; He, Feng; Ouyang, Jiting, E-mail: jtouyang@bit.edu.cn [School of Physics, Beijing Institute of Technology, Beijing 100081 (China); Duan, Xiaoxi [Research Center of Laser Fusion, CAEP, Mianyang 621900 (China)

    2015-12-15

    Simulation work is very important for understanding the formation of self-organized discharge patterns. Previous works have witnessed different models derived from other systems for simulation of discharge pattern, but most of these models are complicated and time-consuming. In this paper, we introduce a convenient phenomenological dynamic model based on the basic dynamic process of glow discharge and the voltage transfer curve (VTC) to study the dielectric barrier glow discharge (DBGD) pattern. VTC is an important characteristic of DBGD, which plots the change of wall voltage after a discharge as a function of the initial total gap voltage. In the modeling, the combined effect of the discharge conditions is included in VTC, and the activation-inhibition effect is expressed by a spatial interaction term. Besides, the model reduces the dimensionality of the system by just considering the integration effect of current flow. All these greatly facilitate the construction of this model. Numerical simulations turn out to be in good accordance with our previous fluid modeling and experimental result.

  18. Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis

    Directory of Open Access Journals (Sweden)

    Andres eOrtiz

    2015-11-01

    Full Text Available Alzheimer’s Disease (AD is the most common neurodegenerative disease in elderly people. Itsdevelopment has been shown to be closely related to changes in the brain connectivity networkand in the brain activation patterns along with structural changes caused by the neurodegenerativeprocess.Methods to infer dependence between brain regions are usually derived from the analysis ofcovariance between activation levels in the different areas. However, these covariance-basedmethods are not able to estimate conditional independence between variables to factor out theinfluence of other regions. Conversely, models based on the inverse covariance, or precisionmatrix, such as Sparse Gaussian Graphical Models allow revealing conditional independencebetween regions by estimating the covariance between two variables given the rest as constant.This paper uses Sparse Inverse Covariance Estimation (SICE methods to learn undirectedgraphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose(18F-FDG Position Emission Tomography (PET data and segmented Magnetic Resonanceimages (MRI, drawn from the ADNI database, for Control, MCI (Mild Cognitive ImpairmentSubjects and AD subjects. Sparse computation fits perfectly here as brain regions usually onlyinteract with a few other areas.The models clearly show different metabolic covariation patters between subject groups, revealingthe loss of strong connections in AD and MCI subjects when compared to Controls. Similarly,the variance between GM (Grey Matter densities of different regions reveals different structuralcovariation patterns between the different groups. Thus, the different connectivity patterns forcontrols and AD are used in this paper to select regions of interest in PET and GM images withdiscriminative power for early AD diagnosis. Finally, functional an structural models are combinedto leverage the classification accuracy.The results obtained in this work show the usefulness

  19. Leak Detection Modeling and Simulation for Oil Pipeline with Artificial Intelligence Method

    OpenAIRE

    Sukarno, Pudjo; Sidarto, Kuntjoro Adji; Trisnobudi, Amoranto; Setyoadi, Delint Ira; Rohani, Nancy; Darmadi, Darmadi

    2007-01-01

    Leak detection is always interesting research topic, where leak location and leak rate are two pipeline leaking parameters that should be determined accurately to overcome pipe leaking problems. In this research those two parameters are investigated by developing transmission pipeline model and the leak detection model which is developed using Artificial Neural Network. The mathematical approach needs actual leak data to train the leak detection model, however such data could not be obtained ...

  20. OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL

    Directory of Open Access Journals (Sweden)

    JUN ZHAO

    Full Text Available The weighed total least square (WTLS estimate is very sensitive to the outliers in the partial EIV model. A new procedure for detecting outliers based on the data-snooping is presented in this paper. Firstly, a two-step iterated method of computing the WTLS estimates for the partial EIV model based on the standard LS theory is proposed. Secondly, the corresponding w-test statistics are constructed to detect outliers while the observations and coefficient matrix are contaminated with outliers, and a specific algorithm for detecting outliers is suggested. When the variance factor is unknown, it may be estimated by the least median squares (LMS method. At last, the simulated data and real data about two-dimensional affine transformation are analyzed. The numerical results show that the new test procedure is able to judge that the outliers locate in x component, y component or both components in coordinates while the observations and coefficient matrix are contaminated with outliers

  1. Multivariate Cholesky models of human female fertility patterns in the NLSY.

    Science.gov (United States)

    Rodgers, Joseph Lee; Bard, David E; Miller, Warren B

    2007-03-01

    Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene-gene and gene-environment interactions for which we must account to better understand individual differences in fertility outcomes.

  2. Salient regions detection using convolutional neural networks and color volume

    Science.gov (United States)

    Liu, Guang-Hai; Hou, Yingkun

    2018-03-01

    Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

  3. Complex microcirculation patterns detected by confocal indocyanine green angiography predict time to growth of small choroidal melanocytic tumors: MuSIC Report II.

    Science.gov (United States)

    Mueller, Arthur J; Freeman, William R; Schaller, Ulrich C; Kampik, Anselm; Folberg, Robert

    2002-12-01

    Multiple independent laboratories have confirmed the histologic observation that some tumor microcirculation patterns (MCPs) in uveal melanomas are associated strongly with death resulting from metastatic disease. Because these patterns are imageable with confocal indocyanine green angiography (ICG), we designed a prospective study to evaluate whether these angiographically detectable MCPs predict time to tumor growth. Observational case series, prospective, non-randomized. Ninety-eight patients with unilateral, small, choroidal melanocytic tumors. The following information and tumor characteristics were recorded for each patient: demographic parameters, best-corrected visual acuity, intraocular pressure, related visual symptoms, location and dimension of tumor, pigmentation, orange pigment, drusen, tumor-associated hemorrhage, subretinal fluid, and confocal ICG angiographically determined microcirculation patterns-silent (avascularity), normal (preexisting normal choroidal vessels within the tumor), straight vessels, parallel without and with cross-linking, arcs without and with branching, loops, and networks. Time to growth of the tumor, with growth defined as an increase in the maximal apical tumor height of 0.5 mm measured by standardized A-scan ultrasonography, photographic documentation of an increase of the largest basal diameter of at least 1.5 mm, advancement of one tumor border of at least 0.75 mm, or a combination thereof. Twenty-eight of the 98 tumors in this study (29%) met the predetermined criteria for tumor growth. The median time to growth was 127 days (range, 51-625 days). The following tumor characteristics were significantly associated with time to tumor growth: flashes (P = 0.0224), orange pigment (P = 0.012), subretinal fluid (P < 0.001), maximum basal tumor diameter at initial examination (P = 0.015), maximum apical tumor height (P < 0.001), parallel with cross-linking MCP (P < 0.001), arcs with branching MCP (P = 0.006), loops (P < 0

  4. Estimating species occurrence, abundance, and detection probability using zero-inflated distributions.

    Science.gov (United States)

    Wenger, Seth J; Freeman, Mary C

    2008-10-01

    Researchers have developed methods to account for imperfect detection of species with either occupancy (presence absence) or count data using replicated sampling. We show how these approaches can be combined to simultaneously estimate occurrence, abundance, and detection probability by specifying a zero-inflated distribution for abundance. This approach may be particularly appropriate when patterns of occurrence and abundance arise from distinct processes operating at differing spatial or temporal scales. We apply the model to two data sets: (1) previously published data for a species of duck, Anas platyrhynchos, and (2) data for a stream fish species, Etheostoma scotti. We show that in these cases, an incomplete-detection zero-inflated modeling approach yields a superior fit to the data than other models. We propose that zero-inflated abundance models accounting for incomplete detection be considered when replicate count data are available.

  5. Describing Growth Pattern of Bali Cows Using Non-linear Regression Models

    Directory of Open Access Journals (Sweden)

    Mohd. Hafiz A.W

    2016-12-01

    Full Text Available The objective of this study was to evaluate the best fit non-linear regression model to describe the growth pattern of Bali cows. Estimates of asymptotic mature weight, rate of maturing and constant of integration were derived from Brody, von Bertalanffy, Gompertz and Logistic models which were fitted to cross-sectional data of body weight taken from 74 Bali cows raised in MARDI Research Station Muadzam Shah Pahang. Coefficient of determination (R2 and residual mean squares (MSE were used to determine the best fit model in describing the growth pattern of Bali cows. Von Bertalanffy model was the best model among the four growth functions evaluated to determine the mature weight of Bali cattle as shown by the highest R2 and lowest MSE values (0.973 and 601.9, respectively, followed by Gompertz (0.972 and 621.2, respectively, Logistic (0.971 and 648.4, respectively and Brody (0.932 and 660.5, respectively models. The correlation between rate of maturing and mature weight was found to be negative in the range of -0.170 to -0.929 for all models, indicating that animals of heavier mature weight had lower rate of maturing. The use of non-linear model could summarize the weight-age relationship into several biologically interpreted parameters compared to the entire lifespan weight-age data points that are difficult and time consuming to interpret.

  6. HyDe: a Python Package for Genome-Scale Hybridization Detection.

    Science.gov (United States)

    Blischak, Paul D; Chifman, Julia; Wolfe, Andrea D; Kubatko, Laura S

    2018-03-19

    The analysis of hybridization and gene flow among closely related taxa is a common goal for researchers studying speciation and phylogeography. Many methods for hybridization detection use simple site pattern frequencies from observed genomic data and compare them to null models that predict an absence of gene flow. The theory underlying the detection of hybridization using these site pattern probabilities exploits the relationship between the coalescent process for gene trees within population trees and the process of mutation along the branches of the gene trees. For certain models, site patterns are predicted to occur in equal frequency (i.e., their difference is 0), producing a set of functions called phylogenetic invariants. In this paper we introduce HyDe, a software package for detecting hybridization using phylogenetic invariants arising under the coalescent model with hybridization. HyDe is written in Python, and can be used interactively or through the command line using pre-packaged scripts. We demonstrate the use of HyDe on simulated data, as well as on two empirical data sets from the literature. We focus in particular on identifying individual hybrids within population samples and on distinguishing between hybrid speciation and gene flow. HyDe is freely available as an open source Python package under the GNU GPL v3 on both GitHub (https://github.com/pblischak/HyDe) and the Python Package Index (PyPI: https://pypi.python.org/pypi/phyde).

  7. Post-hoc pattern-oriented testing and tuning of an existing large model: lessons from the field vole.

    Directory of Open Access Journals (Sweden)

    Christopher J Topping

    Full Text Available Pattern-oriented modeling (POM is a general strategy for modeling complex systems. In POM, multiple patterns observed at different scales and hierarchical levels are used to optimize model structure, to test and select sub-models of key processes, and for calibration. So far, POM has been used for developing new models and for models of low to moderate complexity. It remains unclear, though, whether the basic idea of POM to utilize multiple patterns, could also be used to test and possibly develop existing and established models of high complexity. Here, we use POM to test, calibrate, and further develop an existing agent-based model of the field vole (Microtus agrestis, which was developed and tested within the ALMaSS framework. This framework is complex because it includes a high-resolution representation of the landscape and its dynamics, of the individual's behavior, and of the interaction between landscape and individual behavior. Results of fitting to the range of patterns chosen were generally very good, but the procedure required to achieve this was long and complicated. To obtain good correspondence between model and the real world it was often necessary to model the real world environment closely. We therefore conclude that post-hoc POM is a useful and viable way to test a highly complex simulation model, but also warn against the dangers of over-fitting to real world patterns that lack details in their explanatory driving factors. To overcome some of these obstacles we suggest the adoption of open-science and open-source approaches to ecological simulation modeling.

  8. Sensitivity analysis of discharge patterns of subthalamic nucleus in the model of basal ganglia in Parkinson disease.

    Science.gov (United States)

    Singh, Jyotsna; Singh, Phool; Malik, Vikas

    2017-01-01

    Parkinson disease alters the information patterns in movement related pathways in brain. Experimental results performed on rats show that the activity patterns changes from single spike activity to mixed burst mode in Parkinson disease. However the cause of this change in activity pattern is not yet completely understood. Subthalamic nucleus is one of the main nuclei involved in the origin of motor dysfunction in Parkinson disease. In this paper, a single compartment conductance based model is considered which focuses on subthalamic nucleus and synaptic input from globus pallidus (external). This model shows highly nonlinear behavior with respect to various intrinsic parameters. Behavior of model has been presented with the help of activity patterns generated in healthy and Parkinson condition. These patterns have been compared by calculating their correlation coefficient for different values of intrinsic parameters. Results display that the activity patterns are very sensitive to various intrinsic parameters and calcium shows some promising results which provide insights into the motor dysfunction.

  9. Evaluation of the Johnson AG-1007-7 (G-7) microwave motion detection system

    Energy Technology Data Exchange (ETDEWEB)

    1979-01-01

    A series of tests was performed on the Johnson Model AG-1007-7 motion detection system. The primary objectives of these tests were to determine sensor detection patterns and to quantitate the effects of intruder velocity. System susceptibility to fluorescent lights, oscillatory motion, and environmental factors was also examined.

  10. Evaluation of the Johnson AG-1007-7 (G-7) microwave motion detection system

    International Nuclear Information System (INIS)

    1979-01-01

    A series of tests was performed on the Johnson Model AG-1007-7 motion detection system. The primary objectives of these tests were to determine sensor detection patterns and to quantitate the effects of intruder velocity. System susceptibility to fluorescent lights, oscillatory motion, and environmental factors was also examined

  11. Using species distribution modeling to delineate the botanical richness patterns and phytogeographical regions of China

    Science.gov (United States)

    Zhang, Ming-Gang; Slik, J. W. Ferry; Ma, Ke-Ping

    2016-03-01

    The millions of plant specimens that have been collected and stored in Chinese herbaria over the past ~110 years have recently been digitized and geo-referenced. Here we use this unique collection data set for species distribution modeling exercise aiming at mapping & explaining the botanical richness; delineating China’s phytogeographical regions and investigating the environmental drivers of the dissimilarity patterns. We modeled distributions of 6,828 woody plants using MaxEnt and remove the collection bias using null model. The continental China was divided into different phytogeographical regions based on the dissimilarity patterns. An ordination and Getis-Ord Gi* hotspot spatial statistics were used to analysis the environmental drivers of the dissimilarity patterns. We found that the annual precipitation and temperature stability were responsible for observed species diversity. The mechanisms causing dissimilarity pattern seems differ among biogeographical regions. The identified environmental drivers of the dissimilarity patterns for southeast, southwest, northwest and northeast are annual precipitation, topographic & temperature stability, water deficit and temperature instability, respectively. For effective conservation of China’s plant diversity, identifying the historical refuge and protection of high diversity areas in each of the identified floristic regions and their subdivisions will be essential.

  12. Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

    Science.gov (United States)

    Huntsberger, Terry

    2011-01-01

    The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

  13. Swallowing sound detection using hidden markov modeling of recurrence plot features

    International Nuclear Information System (INIS)

    Aboofazeli, Mohammad; Moussavi, Zahra

    2009-01-01

    Automated detection of swallowing sounds in swallowing and breath sound recordings is of importance for monitoring purposes in which the recording durations are long. This paper presents a novel method for swallowing sound detection using hidden Markov modeling of recurrence plot features. Tracheal sound recordings of 15 healthy and nine dysphagic subjects were studied. The multidimensional state space trajectory of each signal was reconstructed using the Taken method of delays. The sequences of three recurrence plot features of the reconstructed trajectories (which have shown discriminating capability between swallowing and breath sounds) were modeled by three hidden Markov models. The Viterbi algorithm was used for swallowing sound detection. The results were validated manually by inspection of the simultaneously recorded airflow signal and spectrogram of the sounds, and also by auditory means. The experimental results suggested that the performance of the proposed method using hidden Markov modeling of recurrence plot features was superior to the previous swallowing sound detection methods.

  14. Swallowing sound detection using hidden markov modeling of recurrence plot features

    Energy Technology Data Exchange (ETDEWEB)

    Aboofazeli, Mohammad [Faculty of Engineering, Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T 5V6 (Canada)], E-mail: umaboofa@cc.umanitoba.ca; Moussavi, Zahra [Faculty of Engineering, Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T 5V6 (Canada)], E-mail: mousavi@ee.umanitoba.ca

    2009-01-30

    Automated detection of swallowing sounds in swallowing and breath sound recordings is of importance for monitoring purposes in which the recording durations are long. This paper presents a novel method for swallowing sound detection using hidden Markov modeling of recurrence plot features. Tracheal sound recordings of 15 healthy and nine dysphagic subjects were studied. The multidimensional state space trajectory of each signal was reconstructed using the Taken method of delays. The sequences of three recurrence plot features of the reconstructed trajectories (which have shown discriminating capability between swallowing and breath sounds) were modeled by three hidden Markov models. The Viterbi algorithm was used for swallowing sound detection. The results were validated manually by inspection of the simultaneously recorded airflow signal and spectrogram of the sounds, and also by auditory means. The experimental results suggested that the performance of the proposed method using hidden Markov modeling of recurrence plot features was superior to the previous swallowing sound detection methods.

  15. DYNAMIC MATHEMATICAL MODEL OF URBAN SPATIAL PATTERN (RESIDENTIAL CHOICE OF LOCATION: MOBILITY VS EXTERNALITY

    Directory of Open Access Journals (Sweden)

    Rahma Fitriani

    2015-01-01

    Full Text Available Household’s residential choice of location determines urban spatial pattern (e.g sprawl. The static model which assumes that the choice has been affected by distance to the CBD and location specific externality, fails to capture the evoution of the pattern over time. Therefore this study proposes a dynamic version of the model. It analyses the effects of externalities on the optimal solution of development decision as function of time. It also derives the effect of mobility and externality on the rate of change of development pattern through time. When the increasing rate of utility is not as significant as the increasing rate of income, the externalities will delay the change of urban spatial pattern over time. If the mobility costs increase by large amount relative to the increase of income and inflation rate, then the mobility effect dominates the effects of externalities in delaying the urban expansion.

  16. Automated image based prominent nucleoli detection.

    Science.gov (United States)

    Yap, Choon K; Kalaw, Emarene M; Singh, Malay; Chong, Kian T; Giron, Danilo M; Huang, Chao-Hui; Cheng, Li; Law, Yan N; Lee, Hwee Kuan

    2015-01-01

    Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

  17. Automated image based prominent nucleoli detection

    Directory of Open Access Journals (Sweden)

    Choon K Yap

    2015-01-01

    Full Text Available Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

  18. Nonlinear Model-Based Fault Detection for a Hydraulic Actuator

    NARCIS (Netherlands)

    Van Eykeren, L.; Chu, Q.P.

    2011-01-01

    This paper presents a model-based fault detection algorithm for a specific fault scenario of the ADDSAFE project. The fault considered is the disconnection of a control surface from its hydraulic actuator. Detecting this type of fault as fast as possible helps to operate an aircraft more cost

  19. Specification for a surface-search radar-detection-range model

    Science.gov (United States)

    Hattan, Claude P.

    1990-09-01

    A model that predicts surface-search radar detection range versus a variety of combatants has been developed at the Naval Ocean Systems Center. This model uses a simplified ship radar cross section (RCS) model and the U.S. Navy Oceanographic and Atmospheric Mission Library Standard Electromagnetic Propagation Model. It provides the user with a method of assessing the effects of the environment of the performance of a surface-search radar system. The software implementation of the model is written in ANSI FORTRAN 77, with MIL-STD-1753 extensions. The program provides the user with a table of expected detection ranges when the model is supplied with the proper environmental radar system inputs. The target model includes the variation in RCS as a function of aspect angle and the distribution of reflected radar energy as a function of height above the waterline. The modeled propagation effects include refraction caused by a multisegmented refractivity profile, sea-surface roughness caused by local winds, evaporation ducting, and surface-based ducts caused by atmospheric layering.

  20. Analysis of Wave Velocity Patterns in Black Cherry Trees and its Effect on Internal Decay Detection

    Science.gov (United States)

    Guanghui Li; Xiping Wang; Jan Wiedenbeck; Robert J. Ross

    2013-01-01

    In this study, we examined stress wave velocity patterns in the cross sections of black cherry trees, developed analytical models of stress wave velocity in sound healthy trees, and then tested the effectiveness of the models as a tool for tree decay diagnosis. Acoustic tomography data of the tree cross sections were collected from 12 black cherry trees at a production...

  1. Investigation Model for DDoS Attack Detection in Real-Time

    Directory of Open Access Journals (Sweden)

    Abdulghani Ali Ahmed

    2015-02-01

    Full Text Available Investigating traffic of distributed denial of services (DDoS attack requires extra overhead which mostly results in network performance degradation. This study proposes an investigation model for detecting DDoS attack in real-time without causing negative degradation against network performance. The model investigates network traffic in a scalable way to detect user violations on quality of service regulations. Traffic investigation is triggered only when the network is congested; at that exact moment, burst gateways actually generate a congestion notification to misbehaving users. The misbehaving users are thus further investigated by measuring their consumption ratios of bandwidth. By exceeding the service level agreement bandwidth ratio, user traffic is filtered as DDoS traffic. Simulation results demonstrate that the proposed model efficiently monitors intrusive traffic and precisely detects DDoS attack.

  2. APPROACH TO SYNTHESIS OF PASSIVE INFRARED DETECTORS BASED ON QUASI-POINT MODEL OF QUALIFIED INTRUDER

    Directory of Open Access Journals (Sweden)

    I. V. Bilizhenko

    2017-01-01

    Full Text Available Subject of Research. The paper deals with synthesis of passive infra red (PIR detectors with enhanced detection capability of qualified intruder who uses different types of detection countermeasures: the choice of specific movement direction and disguise in infrared band. Methods. We propose an approach based on quasi-point model of qualified intruder. It includes: separation of model priority parameters, formation of partial detection patterns adapted to those parameters and multi channel signal processing. Main Results. Quasi-pointmodel of qualified intruder consisting of different fragments was suggested. Power density difference was used for model parameters estimation. Criteria were formulated for detection pattern parameters choice on the basis of model parameters. Pyroelectric sensor with nine sensitive elements was applied for increasing the signal information content. Multi-channel processing with multiple partial detection patterns was proposed optimized for detection of intruder's specific movement direction. Practical Relevance. Developed functional device diagram can be realized both by hardware and software and is applicable as one of detection channels for dual technology passive infrared and microwave detectors.

  3. Inter-dependent tissue growth and Turing patterning in a model for long bone development

    International Nuclear Information System (INIS)

    Tanaka, Simon; Iber, Dagmar

    2013-01-01

    The development of long bones requires a sophisticated spatial organization of cellular signalling, proliferation, and differentiation programs. How such spatial organization emerges on the growing long bone domain is still unresolved. Based on the reported biochemical interactions we developed a regulatory model for the core signalling factors IHH, PTCH1, and PTHrP and included two cell types, proliferating/resting chondrocytes and (pre-)hypertrophic chondrocytes. We show that the reported IHH–PTCH1 interaction gives rise to a Schnakenberg-type Turing kinetics, and that inclusion of PTHrP is important to achieve robust patterning when coupling patterning and tissue dynamics. The model reproduces relevant spatiotemporal gene expression patterns, as well as a number of relevant mutant phenotypes. In summary, we propose that a ligand–receptor based Turing mechanism may control the emergence of patterns during long bone development, with PTHrP as an important mediator to confer patterning robustness when the sensitive Turing system is coupled to the dynamics of a growing and differentiating tissue. We have previously shown that ligand–receptor based Turing mechanisms can also result from BMP–receptor, SHH–receptor, and GDNF–receptor interactions, and that these reproduce the wildtype and mutant patterns during digit formation in limbs and branching morphogenesis in lung and kidneys. Receptor–ligand interactions may thus constitute a general mechanism to generate Turing patterns in nature. (paper)

  4. Inter-dependent tissue growth and Turing patterning in a model for long bone development

    Science.gov (United States)

    Tanaka, Simon; Iber, Dagmar

    2013-10-01

    The development of long bones requires a sophisticated spatial organization of cellular signalling, proliferation, and differentiation programs. How such spatial organization emerges on the growing long bone domain is still unresolved. Based on the reported biochemical interactions we developed a regulatory model for the core signalling factors IHH, PTCH1, and PTHrP and included two cell types, proliferating/resting chondrocytes and (pre-)hypertrophic chondrocytes. We show that the reported IHH-PTCH1 interaction gives rise to a Schnakenberg-type Turing kinetics, and that inclusion of PTHrP is important to achieve robust patterning when coupling patterning and tissue dynamics. The model reproduces relevant spatiotemporal gene expression patterns, as well as a number of relevant mutant phenotypes. In summary, we propose that a ligand-receptor based Turing mechanism may control the emergence of patterns during long bone development, with PTHrP as an important mediator to confer patterning robustness when the sensitive Turing system is coupled to the dynamics of a growing and differentiating tissue. We have previously shown that ligand-receptor based Turing mechanisms can also result from BMP-receptor, SHH-receptor, and GDNF-receptor interactions, and that these reproduce the wildtype and mutant patterns during digit formation in limbs and branching morphogenesis in lung and kidneys. Receptor-ligand interactions may thus constitute a general mechanism to generate Turing patterns in nature.

  5. Modeling the Geographic Consequence and Pattern of Dengue Fever Transmission in Thailand.

    Science.gov (United States)

    Bekoe, Collins; Pansombut, Tatdow; Riyapan, Pakwan; Kakchapati, Sampurna; Phon-On, Aniruth

    2017-05-04

    Dengue fever is one of the infectious diseases that is still a public health problem in Thailand. This study considers in detail, the geographic consequence, seasonal and pattern of dengue fever transmission among the 76 provinces of Thailand from 2003 to 2015. A cross-sectional study. The data for the study was from the Department of Disease Control under the Bureau of Epidemiology, Thailand. The quarterly effects and location on the transmission of dengue was modeled using an alternative additive log-linear model. The model fitted well as illustrated by the residual plots and the  Again, the model showed that dengue fever is high in the second quarter of every year from May to August. There was an evidence of an increase in the trend of dengue annually from 2003 to 2015. There was a difference in the distribution of dengue fever within and between provinces. The areas of high risks were the central and southern regions of Thailand. The log-linear model provided a simple medium of modeling dengue fever transmission. The results are very important in the geographic distribution of dengue fever patterns.

  6. Detecting spatial patterns with the cumulant function – Part 2: An application to El Niño

    Directory of Open Access Journals (Sweden)

    P. Yiou

    2008-02-01

    Full Text Available The spatial coherence of a measured variable (e.g. temperature or pressure is often studied to determine the regions of high variability or to find teleconnections, i.e. correlations between specific regions. While usual methods to find spatial patterns, such as Principal Components Analysis (PCA, are constrained by linear symmetries, the dependence of variables such as temperature or pressure at different locations is generally nonlinear. In particular, large deviations from the sample mean are expected to be strongly affected by such nonlinearities. Here we apply a newly developed nonlinear technique (Maxima of Cumulant Function, MCF for detection of typical spatial patterns that largely deviate from the mean. In order to test the technique and to introduce the methodology, we focus on the El Niño/Southern Oscillation and its spatial patterns. We find nonsymmetric temperature patterns corresponding to El Niño and La Niña, and we compare the results of MCF with other techniques, such as the symmetric solutions of PCA, and the nonsymmetric solutions of Nonlinear PCA (NLPCA. We found that MCF solutions are more reliable than the NLPCA fits, and can capture mixtures of principal components. Finally, we apply Extreme Value Theory on the temporal variations extracted from our methodology. We find that the tails of the distribution of extreme temperatures during La Niña episodes is bounded, while the tail during El Niños is less likely to be bounded. This implies that the mean spatial patterns of the two phases are asymmetric, as well as the behaviour of their extremes.

  7. A generalized linear-quadratic model incorporating reciprocal time pattern of radiation damage repair

    International Nuclear Information System (INIS)

    Huang, Zhibin; Mayr, Nina A.; Lo, Simon S.; Wang, Jian Z.; Jia Guang; Yuh, William T. C.; Johnke, Roberta

    2012-01-01

    Purpose: It has been conventionally assumed that the repair rate for sublethal damage (SLD) remains constant during the entire radiation course. However, increasing evidence from animal studies suggest that this may not the case. Rather, it appears that the repair rate for radiation-induced SLD slows down with increasing time. Such a slowdown in repair would suggest that the exponential repair pattern would not necessarily accurately predict repair process. As a result, the purpose of this study was to investigate a new generalized linear-quadratic (LQ) model incorporating a repair pattern with reciprocal time. The new formulas were tested with published experimental data. Methods: The LQ model has been widely used in radiation therapy, and the parameter G in the surviving fraction represents the repair process of sublethal damage with T r as the repair half-time. When a reciprocal pattern of repair process was adopted, a closed form of G was derived analytically for arbitrary radiation schemes. The published animal data adopted to test the reciprocal formulas. Results: A generalized LQ model to describe the repair process in a reciprocal pattern was obtained. Subsequently, formulas for special cases were derived from this general form. The reciprocal model showed a better fit to the animal data than the exponential model, particularly for the ED50 data (reduced χ 2 min of 2.0 vs 4.3, p = 0.11 vs 0.006), with the following gLQ parameters: α/β = 2.6-4.8 Gy, T r = 3.2-3.9 h for rat feet skin, and α/β = 0.9 Gy, T r = 1.1 h for rat spinal cord. Conclusions: These results of repair process following a reciprocal time suggest that the generalized LQ model incorporating the reciprocal time of sublethal damage repair shows a better fit than the exponential repair model. These formulas can be used to analyze the experimental and clinical data, where a slowing-down repair process appears during the course of radiation therapy.

  8. Multi-Model Estimation Based Moving Object Detection for Aerial Video

    Directory of Open Access Journals (Sweden)

    Yanning Zhang

    2015-04-01

    Full Text Available With the wide development of UAV (Unmanned Aerial Vehicle technology, moving target detection for aerial video has become a popular research topic in the computer field. Most of the existing methods are under the registration-detection framework and can only deal with simple background scenes. They tend to go wrong in the complex multi background scenarios, such as viaducts, buildings and trees. In this paper, we break through the single background constraint and perceive the complex scene accurately by automatic estimation of multiple background models. First, we segment the scene into several color blocks and estimate the dense optical flow. Then, we calculate an affine transformation model for each block with large area and merge the consistent models. Finally, we calculate subordinate degree to multi-background models pixel to pixel for all small area blocks. Moving objects are segmented by means of energy optimization method solved via Graph Cuts. The extensive experimental results on public aerial videos show that, due to multi background models estimation, analyzing each pixel’s subordinate relationship to multi models by energy minimization, our method can effectively remove buildings, trees and other false alarms and detect moving objects correctly.

  9. Dynamic Delayed Duplicate Detection for External Memory Model Checking

    DEFF Research Database (Denmark)

    Evangelista, Sami

    2008-01-01

    Duplicate detection is an expensive operation of disk-based model checkers. It consists of comparing some potentially new states, the candidate states, to previous visited states. We propose a new approach to this technique called dynamic delayed duplicate detection. This one exploits some typica...... significantly better than some previously published algorithms....

  10. Formalization of the classification pattern: survey of classification modeling in information systems engineering.

    Science.gov (United States)

    Partridge, Chris; de Cesare, Sergio; Mitchell, Andrew; Odell, James

    2018-01-01

    Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling. The classification pattern that underlies these systems provides a good case study of the move toward formalization in part because it illustrates some of the barriers to formalization, including the formal complexity of the pattern and the ontological issues surrounding the "one and the many." Powersets are a way of characterizing the (complex) formal structure of the classification pattern, and their formalization has been extensively studied in mathematics since Cantor's work in the late nineteenth century. One can use this formalization to develop a useful benchmark. There are various communities within information systems engineering (ISE) that are gradually working toward a formalization of the classification pattern. However, for most of these communities, this work is incomplete, in that they have not yet arrived at a solution with the expressiveness of the powerset benchmark. This contrasts with the early smooth adoption of powerset by other information systems communities to, for example, formalize relations. One way of understanding the varying rates of adoption is recognizing that the different communities have different historical baggage. Many conceptual modeling communities emerged from work done on database design, and this creates hurdles to the adoption of the high level of expressiveness of powersets. Another relevant factor is that these communities also often feel, particularly in the case of domain modeling, a responsibility to explain the semantics of whatever formal structures they adopt. This paper aims to make sense of the formalization of the classification pattern in ISE and surveys its history through the literature, starting from the relevant theoretical works of the mathematical literature and gradually shifting focus

  11. Anomaly Detection and Life Pattern Estimation for the Elderly Based on Categorization of Accumulated Data

    Science.gov (United States)

    Mori, Taketoshi; Ishino, Takahito; Noguchi, Hiroshi; Shimosaka, Masamichi; Sato, Tomomasa

    2011-06-01

    We propose a life pattern estimation method and an anomaly detection method for elderly people living alone. In our observation system for such people, we deploy some pyroelectric sensors into the house and measure the person's activities all the time in order to grasp the person's life pattern. The data are transferred successively to the operation center and displayed to the nurses in the center in a precise way. Then, the nurses decide whether the data is the anomaly or not. In the system, the people whose features in their life resemble each other are categorized as the same group. Anomalies occurred in the past are shared in the group and utilized in the anomaly detection algorithm. This algorithm is based on "anomaly score." The "anomaly score" is figured out by utilizing the activeness of the person. This activeness is approximately proportional to the frequency of the sensor response in a minute. The "anomaly score" is calculated from the difference between the activeness in the present and the past one averaged in the long term. Thus, the score is positive if the activeness in the present is higher than the average in the past, and the score is negative if the value in the present is lower than the average. If the score exceeds a certain threshold, it means that an anomaly event occurs. Moreover, we developed an activity estimation algorithm. This algorithm estimates the residents' basic activities such as uprising, outing, and so on. The estimation is shown to the nurses with the "anomaly score" of the residents. The nurses can understand the residents' health conditions by combining these two information.

  12. a Novel Ship Detection Method for Large-Scale Optical Satellite Images Based on Visual Lbp Feature and Visual Attention Model

    Science.gov (United States)

    Haigang, Sui; Zhina, Song

    2016-06-01

    Reliably ship detection in optical satellite images has a wide application in both military and civil fields. However, this problem is very difficult in complex backgrounds, such as waves, clouds, and small islands. Aiming at these issues, this paper explores an automatic and robust model for ship detection in large-scale optical satellite images, which relies on detecting statistical signatures of ship targets, in terms of biologically-inspired visual features. This model first selects salient candidate regions across large-scale images by using a mechanism based on biologically-inspired visual features, combined with visual attention model with local binary pattern (CVLBP). Different from traditional studies, the proposed algorithm is high-speed and helpful to focus on the suspected ship areas avoiding the separation step of land and sea. Largearea images are cut into small image chips and analyzed in two complementary ways: Sparse saliency using visual attention model and detail signatures using LBP features, thus accordant with sparseness of ship distribution on images. Then these features are employed to classify each chip as containing ship targets or not, using a support vector machine (SVM). After getting the suspicious areas, there are still some false alarms such as microwaves and small ribbon clouds, thus simple shape and texture analysis are adopted to distinguish between ships and nonships in suspicious areas. Experimental results show the proposed method is insensitive to waves, clouds, illumination and ship size.

  13. Classifier models and architectures for EEG-based neonatal seizure detection

    International Nuclear Information System (INIS)

    Greene, B R; Marnane, W P; Lightbody, G; Reilly, R B; Boylan, G B

    2008-01-01

    Neonatal seizures are the most common neurological emergency in the neonatal period and are associated with a poor long-term outcome. Early detection and treatment may improve prognosis. This paper aims to develop an optimal set of parameters and a comprehensive scheme for patient-independent multi-channel EEG-based neonatal seizure detection. We employed a dataset containing 411 neonatal seizures. The dataset consists of multi-channel EEG recordings with a mean duration of 14.8 h from 17 neonatal patients. Early-integration and late-integration classifier architectures were considered for the combination of information across EEG channels. Three classifier models based on linear discriminants, quadratic discriminants and regularized discriminants were employed. Furthermore, the effect of electrode montage was considered. The best performing seizure detection system was found to be an early integration configuration employing a regularized discriminant classifier model. A referential EEG montage was found to outperform the more standard bipolar electrode montage for automated neonatal seizure detection. A cross-fold validation estimate of the classifier performance for the best performing system yielded 81.03% of seizures correctly detected with a false detection rate of 3.82%. With post-processing, the false detection rate was reduced to 1.30% with 59.49% of seizures correctly detected. These results represent a comprehensive illustration that robust reliable patient-independent neonatal seizure detection is possible using multi-channel EEG

  14. Detection and 3d Modelling of Vehicles from Terrestrial Stereo Image Pairs

    Science.gov (United States)

    Coenen, M.; Rottensteiner, F.; Heipke, C.

    2017-05-01

    The detection and pose estimation of vehicles plays an important role for automated and autonomous moving objects e.g. in autonomous driving environments. We tackle that problem on the basis of street level stereo images, obtained from a moving vehicle. Processing every stereo pair individually, our approach is divided into two subsequent steps: the vehicle detection and the modelling step. For the detection, we make use of the 3D stereo information and incorporate geometric assumptions on vehicle inherent properties in a firstly applied generic 3D object detection. By combining our generic detection approach with a state of the art vehicle detector, we are able to achieve satisfying detection results with values for completeness and correctness up to more than 86%. By fitting an object specific vehicle model into the vehicle detections, we are able to reconstruct the vehicles in 3D and to derive pose estimations as well as shape parameters for each vehicle. To deal with the intra-class variability of vehicles, we make use of a deformable 3D active shape model learned from 3D CAD vehicle data in our model fitting approach. While we achieve encouraging values up to 67.2% for correct position estimations, we are facing larger problems concerning the orientation estimation. The evaluation is done by using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012).

  15. A model of hygiene practices and consumption patterns in the consumer phase

    DEFF Research Database (Denmark)

    Christensen, Bjarke Bak; Rosenquist, Hanne; Sommer, Helle Mølgaard

    2005-01-01

    A mathematical model is presented, which addresses individual hygiene practices during food preparation and consumption patterns in private homes. Further, the model links food preparers and consumers based on their relationship to household types. For different age and gender groups, the model...... was highest for young males (aged 18-29 years) and lowest for the elderly above 60 years of age. Children aged 0-4 years had a higher probability of ingesting a risk meal than children aged 5-17 years. This difference between age and gender groups was ascribed to the variations in the hygiene levels of food....... The simulated results show that the probability of ingesting a chicken risk meal at home does not only depend on the hygiene practices of the persons preparing the food, but also on the consumption patterns of consumers, and the relationship between people preparing and ingesting food. This finding supports...

  16. An economic theory-based explanatory model of agricultural land-use patterns

    NARCIS (Netherlands)

    Diogo, V.; Koomen, E.; Kuhlman, T.

    2015-01-01

    An economic theory-based land-use modelling framework is presented aiming to explain the causal link between economic decisions and resulting spatial patterns of agricultural land use. The framework assumes that farmers pursue utility maximisation in agricultural production systems, while

  17. Correlation between theoretical anatomical patterns of lymphatic drainage and lymphoscintigraphy findings during sentinel node detection in head and neck melanomas

    Energy Technology Data Exchange (ETDEWEB)

    Vidal, Monica; Ruiz, Diana Milena [Hospital Clinic de Barcelona, Nuclear Medicine Department, Barcelona (Spain); Vidal-Sicart, Sergi; Paredes, Pilar; Pons, Francesca [Hospital Clinic de Barcelona, Nuclear Medicine Department, Barcelona (Spain); Institut d' Investigacions Biomediques Agusti Pi i Sunyer (IDIBAPS), Barcelona (Spain); Torres, Ferran [Hospital Clinic Barcelona, Statistical of Biostatistics and Data Management Core Facility, IDIBAPS, Barcelona (Spain); Universitat Autonoma de Barcelona, Biostatistics Unit, Faculty of Medicine, Barcelona (Spain)

    2016-04-15

    In the diagnosis of head and neck melanoma, lymphatic drainage is complex and highly variable. As regional lymph node metastasis is one of the most important prognostic factors, lymphoscintigraphy can help map individual drainage patterns. The aim of this study was to compare the results of lymphoscintigraphy and sentinel lymph node (SLN) detection with theoretical anatomical patterns of lymphatic drainage based on the location of the primary tumour lesion in patients with head and neck melanoma. We also determined the percentage of discrepancies between our lymphoscintigraphy and the theoretical location of nodal drainage predicted by a large lymphoscintigraphic database, in order to explain recurrence and false-negative SLN biopsies. In this retrospective study of 152 patients with head and neck melanoma, the locations of the SLNs on lymphoscintigraphy and detected intraoperatively were compared with the lymphatic drainage predicted by on-line software based on a large melanoma database. All patients showed lymphatic drainage and in all patients at least one SLN was identified by lymphoscintigraphy. Of the 152 patients, 4 had a primary lesion in areas that were not described in the Sydney Melanoma Unit database, so agreement could only be evaluated in 148 patients. Agreement between lymphoscintigraphic findings and the theoretical lymphatic drainage predicted by the software was completely concordant in 119 of the 148 patients (80.4 %, 95 % CI 73.3 - 86 %). However, this concordance was partial (some concordant nodes and others not) in 18 patients (12.2 %, 95 % CI 7.8 - 18.4 %). Discordance was complete in 11 patients (7.4 %, 95 % CI 4.2 - 12.8 %). In melanoma of the head and neck there is a high correlation between lymphatic drainage found by lymphoscintigraphy and the predicted drainage pattern and basins provided by a large reference database. Due to unpredictable drainage, preoperative lymphoscintigraphy is essential to accurately detect the SLNs in head and

  18. Exploring the patterns and evolution of self-organized urban street networks through modeling

    Science.gov (United States)

    Rui, Yikang; Ban, Yifang; Wang, Jiechen; Haas, Jan

    2013-03-01

    As one of the most important subsystems in cities, urban street networks have recently been well studied by using the approach of complex networks. This paper proposes a growing model for self-organized urban street networks. The model involves a competition among new centers with different values of attraction radius and a local optimal principle of both geometrical and topological factors. We find that with the model growth, the local optimization in the connection process and appropriate probability for the loop construction well reflect the evolution strategy in real-world cities. Moreover, different values of attraction radius in centers competition process lead to morphological change in patterns including urban network, polycentric and monocentric structures. The model succeeds in reproducing a large diversity of road network patterns by varying parameters. The similarity between the properties of our model and empirical results implies that a simple universal growth mechanism exists in self-organized cities.

  19. Modelling the effect of temperature on hatching and settlement patterns of meroplanktonic organisms: the case of octopus

    Directory of Open Access Journals (Sweden)

    Stelios Katsanevakis

    2006-12-01

    Full Text Available The duration of embryonic development and the planktonic stage of meroplanktonic species is highly temperature dependent and thus the seasonal temperature oscillations of temperate regions greatly affect the patterns of hatching and benthic settlement. Based on data from the literature on embryonic development and planktonic duration of Octopus vulgaris (common octopus in relation to temperature, and on observed temperature patterns, several models of hatching and settlement patterns were created. There was a good fit between observed settlement patterns and model predictions. Based on these models we concluded that in temperate regions: (1 when temperature is increasing (from early spring to mid summer the hatching and settlement periods tend to shorten, while when the temperature is decreasing (during autumn the hatching and settlement periods tend to lengthen; (2 hatching and settlement peaks are narrower and more intense than a spring spawning peak but wider and less intense than an autumn spawning peak; (3 at lower latitudes, hatching and settlement patterns tend to follow the spawning pattern more closely, (4 the periodic temperature pattern of temperate areas has the potential to cause a convergence of hatching during spring.

  20. Exchange bias of patterned systems: Model and numerical simulation

    International Nuclear Information System (INIS)

    Garcia, Griselda; Kiwi, Miguel; Mejia-Lopez, Jose; Ramirez, Ricardo

    2010-01-01

    The magnitude of the exchange bias field of patterned systems exhibits a notable increase in relation to the usual bilayer systems, where a continuous ferromagnetic film is deposited on an antiferromagnet insulator. Here we develop a model, and implement a Monte Carlo calculation, to interpret the experimental observations which is consistent with experimental results, on the basis of assuming a small fraction of spins pinned ferromagnetically in the antiferromagnetic interface layer.

  1. Patterns of flavour violation in models with vector-like quarks

    Energy Technology Data Exchange (ETDEWEB)

    Bobeth, Christoph [TUM Institute for Advanced Study, Lichtenbergstr. 2a, D-85748 Garching (Germany); Physik Department, TU München, James-Franck-Straße, D-85748 Garching (Germany); Excellence Cluster Universe, Technische Universität München,Boltzmannstr. 2, D-85748 Garching (Germany); Buras, Andrzej J. [TUM Institute for Advanced Study, Lichtenbergstr. 2a, D-85748 Garching (Germany); Physik Department, TU München, James-Franck-Straße, D-85748 Garching (Germany); Celis, Alejandro [Ludwig-Maximilians-Universität München, Fakultät für Physik,Arnold Sommerfeld Center for Theoretical Physics, 80333 München (Germany); Jung, Martin [TUM Institute for Advanced Study, Lichtenbergstr. 2a, D-85748 Garching (Germany); Excellence Cluster Universe, Technische Universität München,Boltzmannstr. 2, D-85748 Garching (Germany)

    2017-04-13

    We study the patterns of flavour violation in renormalisable extensions of the Standard Model (SM) that contain vector-like quarks (VLQs) in a single complex representation of either the SM gauge group G{sub SM} or G{sub SM}{sup ′}≡G{sub SM}⊗U(1){sub L{sub μ−L{sub τ}}}. We first decouple VLQs in the M=(1−10) TeV range and then at the electroweak scale also Z,Z{sup ′} gauge bosons and additional scalars to study the phenomenology. The results depend on the relative size of Z- and Z{sup ′}-induced flavour-changing neutral currents, as well as the size of |ΔF|=2 contributions including the effects of renormalisation group Yukawa evolution from M to the electroweak scale that turn out to be very important for models with right-handed currents through the generation of left-right operators. In addition to rare decays like P→ℓℓ̄, P→P{sup ′}ℓℓ̄, P→P{sup ′}νν̄ with P=K,B{sub s},B{sub d} and |ΔF|=2 observables we analyze the ratio ε{sup ′}/ε which appears in the SM to be significantly below the data. We study patterns and correlations between these observables which taken together should in the future allow for differentiating between VLQ models. In particular the patterns in models with left-handed and right-handed currents are markedly different from each other. Among the highlights are large Z-mediated new physics effects in Kaon observables in some of the models and significant effects in B{sub s,d}-observables. ε{sup ′}/ε can easily be made consistent with the data, implying then uniquely the suppression of K{sub L}→π{sup 0}νν̄. Significant enhancements of Br(K{sup +}→π{sup +}νν̄) are still possible. We point out that the combination of NP effects to |ΔF|=2 and |ΔF|=1 observables in a given meson system generally allows to determine the masses of VLQs in a given representation independently of the size of VLQ couplings.

  2. Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

    Directory of Open Access Journals (Sweden)

    Sumaiya Thaseen Ikram

    2016-06-01

    Full Text Available Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA and support vector machine (SVM. The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C and kernel parameter gamma (γ, thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

  3. Computational neural network regression model for Host based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Gautam

    2016-09-01

    Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

  4. A software tool to model genetic regulatory networks. Applications to the modeling of threshold phenomena and of spatial patterning in Drosophila.

    Directory of Open Access Journals (Sweden)

    Rui Dilão

    Full Text Available We present a general methodology in order to build mathematical models of genetic regulatory networks. This approach is based on the mass action law and on the Jacob and Monod operon model. The mathematical models are built symbolically by the Mathematica software package GeneticNetworks. This package accepts as input the interaction graphs of the transcriptional activators and repressors of a biological process and, as output, gives the mathematical model in the form of a system of ordinary differential equations. All the relevant biological parameters are chosen automatically by the software. Within this framework, we show that concentration dependent threshold effects in biology emerge from the catalytic properties of genes and its associated conservation laws. We apply this methodology to the segment patterning in Drosophila early development and we calibrate the genetic transcriptional network responsible for the patterning of the gap gene proteins Hunchback and Knirps, along the antero-posterior axis of the Drosophila embryo. In this approach, the zygotically produced proteins Hunchback and Knirps do not diffuse along the antero-posterior axis of the embryo of Drosophila, developing a spatial pattern due to concentration dependent thresholds. This shows that patterning at the gap genes stage can be explained by the concentration gradients along the embryo of the transcriptional regulators.

  5. Biochemical transport modeling, estimation, and detection in realistic environments

    Science.gov (United States)

    Ortner, Mathias; Nehorai, Arye

    2006-05-01

    Early detection and estimation of the spread of a biochemical contaminant are major issues for homeland security applications. We present an integrated approach combining the measurements given by an array of biochemical sensors with a physical model of the dispersion and statistical analysis to solve these problems and provide system performance measures. We approximate the dispersion model of the contaminant in a realistic environment through numerical simulations of reflected stochastic diffusions describing the microscopic transport phenomena due to wind and chemical diffusion using the Feynman-Kac formula. We consider arbitrary complex geometries and account for wind turbulence. Localizing the dispersive sources is useful for decontamination purposes and estimation of the cloud evolution. To solve the associated inverse problem, we propose a Bayesian framework based on a random field that is particularly powerful for localizing multiple sources with small amounts of measurements. We also develop a sequential detector using the numerical transport model we propose. Sequential detection allows on-line analysis and detecting wether a change has occurred. We first focus on the formulation of a suitable sequential detector that overcomes the presence of unknown parameters (e.g. release time, intensity and location). We compute a bound on the expected delay before false detection in order to decide the threshold of the test. For a fixed false-alarm rate, we obtain the detection probability of a substance release as a function of its location and initial concentration. Numerical examples are presented for two real-world scenarios: an urban area and an indoor ventilation duct.

  6. The numerical model of multi-layer insulation with a defined wrapping pattern immersed in superfluid helium

    Science.gov (United States)

    Malecha, Ziemowit; Lubryka, Eliza

    2017-11-01

    The numerical model of thin layers, characterized by a defined wrapping pattern can be a crucial element of many computational problems related to engineering and science. A motivating example is found in multilayer electrical insulation, which is an important component of superconducting magnets and other cryogenic installations. The wrapping pattern of the insulation can significantly affect heat transport and the performance of the considered instruments. The major objective of this study is to develop the numerical boundary conditions (BC) needed to model the wrapping pattern of thin insulation. An example of the practical application of the proposed BC includes the heat transfer of Rutherford NbTi cables immersed in super-fluid helium (He II) across thin layers of electrical insulation. The proposed BC and a mathematical model of heat transfer in He II are implemented in the open source CFD toolbox OpenFOAM. The implemented mathematical model and the BC are compared in the experiments. The study confirms that the thermal resistance of electrical insulation can be lowered by implementing the proper wrapping pattern. The proposed BC can be useful in the study of new patterns for wrapping schemes. The work has been supported by statutory funds from Polish Ministry for Science and Higher Education for the year of 2017.

  7. CHF Enhancement by Surface Patterning based on Hydrodynamic Instability Model

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Han; Bang, In Cheol [UNIST, Ulsan (Korea, Republic of)

    2015-05-15

    If the power density of a device exceeds the CHF point, bubbles and vapor films will be covered on the whole heater surface. Because vapor films have much lower heat transfer capabilities compared to the liquid layer, the temperature of the heater surface will increase rapidly, and the device could be damaged due to the heater burnout. Therefore, the prediction and the enhancement of the CHF are essential to maximizing the efficient heat removal region. Numerous studies have been conducted to describe the CHF phenomenon, such as hydrodynamic instability theory, macrolayer dryout theory, hot/dry spot theory, and bubble interaction theory. The hydrodynamic instability model, proposed by Zuber, is the predominant CHF model that Helmholtz instability attributed to the CHF. Zuber assumed that the Rayleigh-Taylor (RT) instability wavelength is related to the Helmholtz wavelength. Lienhard and Dhir proposed a CHF model that Helmholtz instability wavelength is equal to the most dangerous RT wavelength. In addition, they showed the heater size effect using various heater surfaces. Lu et al. proposed a modified hydrodynamic theory that the Helmholtz instability was assumed to be the heater size and the area of the vapor column was used as a fitting factor. The modified hydrodynamic theories were based on the change of Helmholtz wavelength related to the RT instability wavelength. In the present study, the change of the RT instability wavelength, based on the heater surface modification, was conducted to show the CHF enhancement based on the heater surface patterning in a plate pool boiling. Sapphire glass was used as a base heater substrate, and the Pt film was used as a heating source. The patterning surface was based on the change of RT instability wavelength. In the present work the study of the CHF was conducted using bare Pt and patterned heating surfaces.

  8. Evaluation of Spatial Pattern of Altered Flow Regimes on a River Network Using a Distributed Hydrological Model.

    Science.gov (United States)

    Ryo, Masahiro; Iwasaki, Yuichi; Yoshimura, Chihiro; Saavedra V, Oliver C

    2015-01-01

    Alteration of the spatial variability of natural flow regimes has been less studied than that of the temporal variability, despite its ecological importance for river ecosystems. Here, we aimed to quantify the spatial patterns of flow regime alterations along a river network in the Sagami River, Japan, by estimating river discharge under natural and altered flow conditions. We used a distributed hydrological model, which simulates hydrological processes spatiotemporally, to estimate 20-year daily river discharge along the river network. Then, 33 hydrologic indices (i.e., Indicators of Hydrologic Alteration) were calculated from the simulated discharge to estimate the spatial patterns of their alterations. Some hydrologic indices were relatively well estimated such as the magnitude and timing of maximum flows, monthly median flows, and the frequency of low and high flow pulses. The accuracy was evaluated with correlation analysis (r > 0.4) and the Kolmogorov-Smirnov test (α = 0.05) by comparing these indices calculated from both observed and simulated discharge. The spatial patterns of the flow regime alterations varied depending on the hydrologic indices. For example, both the median flow in August and the frequency of high flow pulses were reduced by the maximum of approximately 70%, but these strongest alterations were detected at different locations (i.e., on the mainstream and the tributary, respectively). These results are likely caused by different operational purposes of multiple water control facilities. The results imply that the evaluation only at discharge gauges is insufficient to capture the alteration of the flow regime. Our findings clearly emphasize the importance of evaluating the spatial pattern of flow regime alteration on a river network where its discharge is affected by multiple water control facilities.

  9. PolyaPeak: Detecting Transcription Factor Binding Sites from ChIP-seq Using Peak Shape Information

    Science.gov (United States)

    Wu, Hao; Ji, Hongkai

    2014-01-01

    ChIP-seq is a powerful technology for detecting genomic regions where a protein of interest interacts with DNA. ChIP-seq data for mapping transcription factor binding sites (TFBSs) have a characteristic pattern: around each binding site, sequence reads aligned to the forward and reverse strands of the reference genome form two separate peaks shifted away from each other, and the true binding site is located in between these two peaks. While it has been shown previously that the accuracy and resolution of binding site detection can be improved by modeling the pattern, efficient methods are unavailable to fully utilize that information in TFBS detection procedure. We present PolyaPeak, a new method to improve TFBS detection by incorporating the peak shape information. PolyaPeak describes peak shapes using a flexible Pólya model. The shapes are automatically learnt from the data using Minorization-Maximization (MM) algorithm, then integrated with the read count information via a hierarchical model to distinguish true binding sites from background noises. Extensive real data analyses show that PolyaPeak is capable of robustly improving TFBS detection compared with existing methods. An R package is freely available. PMID:24608116

  10. Color-pattern analysis of eyespots in butterfly wings: a critical examination of morphogen gradient models.

    Science.gov (United States)

    Otaki, Joji M

    2011-06-01

    Butterfly wing color patterns consist of many color-pattern elements such as eyespots. It is believed that eyespot patterns are determined by a concentration gradient of a single morphogen species released by diffusion from the prospective eyespot focus in conjunction with multiple thresholds in signal-receiving cells. As alternatives to this single-morphogen model, more flexible multiple-morphogen model and induction model can be proposed. However, the relevance of these conceptual models to actual eyespots has not been examined systematically. Here, representative eyespots from nymphalid butterflies were analyzed morphologically to determine if they are consistent with these models. Measurement of ring widths of serial eyespots from a single wing surface showed that the proportion of each ring in an eyespot is quite different among homologous rings of serial eyespots of different sizes. In asymmetric eyespots, each ring is distorted to varying degrees. In extreme cases, only a portion of rings is expressed remotely from the focus. Similarly, there are many eyespots where only certain rings are deleted, added, or expanded. In an unusual case, the central area of an eyespot is composed of multiple "miniature eyespots," but the overall macroscopic eyespot structure is maintained. These results indicate that each eyespot ring has independence and flexibility to a certain degree, which is less consistent with the single-morphogen model. Considering a "periodic eyespot", which has repeats of a set of rings, damage-induced eyespots in mutants, and a scale-size distribution pattern in an eyespot, the induction model is the least incompatible with the actual eyespot diversity.

  11. Aggregation patterns from nonlocal interactions: Discrete stochastic and continuum modeling

    KAUST Repository

    Hackett-Jones, Emily J.

    2012-04-17

    Conservation equations governed by a nonlocal interaction potential generate aggregates from an initial uniform distribution of particles. We address the evolution and formation of these aggregating steady states when the interaction potential has both attractive and repulsive singularities. Currently, no existence theory for such potentials is available. We develop and compare two complementary solution methods, a continuous pseudoinverse method and a discrete stochastic lattice approach, and formally show a connection between the two. Interesting aggregation patterns involving multiple peaks for a simple doubly singular attractive-repulsive potential are determined. For a swarming Morse potential, characteristic slow-fast dynamics in the scaled inverse energy is observed in the evolution to steady state in both the continuous and discrete approaches. The discrete approach is found to be remarkably robust to modifications in movement rules, related to the potential function. The comparable evolution dynamics and steady states of the discrete model with the continuum model suggest that the discrete stochastic approach is a promising way of probing aggregation patterns arising from two- and three-dimensional nonlocal interaction conservation equations. © 2012 American Physical Society.

  12. Moving object detection using dynamic motion modelling from UAV aerial images.

    Science.gov (United States)

    Saif, A F M Saifuddin; Prabuwono, Anton Satria; Mahayuddin, Zainal Rasyid

    2014-01-01

    Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

  13. Toward robust phase-locking in Melibe swim central pattern generator models

    Science.gov (United States)

    Jalil, Sajiya; Allen, Dane; Youker, Joseph; Shilnikov, Andrey

    2013-12-01

    Small groups of interneurons, abbreviated by CPG for central pattern generators, are arranged into neural networks to generate a variety of core bursting rhythms with specific phase-locked states, on distinct time scales, which govern vital motor behaviors in invertebrates such as chewing and swimming. These movements in lower level animals mimic motions of organs in higher animals due to evolutionarily conserved mechanisms. Hence, various neurological diseases can be linked to abnormal movement of body parts that are regulated by a malfunctioning CPG. In this paper, we, being inspired by recent experimental studies of neuronal activity patterns recorded from a swimming motion CPG of the sea slug Melibe leonina, examine a mathematical model of a 4-cell network that can plausibly and stably underlie the observed bursting rhythm. We develop a dynamical systems framework for explaining the existence and robustness of phase-locked states in activity patterns produced by the modeled CPGs. The proposed tools can be used for identifying core components for other CPG networks with reliable bursting outcomes and specific phase relationships between the interneurons. Our findings can be employed for identifying or implementing the conditions for normal and pathological functioning of basic CPGs of animals and artificially intelligent prosthetics that can regulate various movements.

  14. Efficient Personalized Mispronunciation Detection of Taiwanese-Accented English Speech Based on Unsupervised Model Adaptation and Dynamic Sentence Selection

    Science.gov (United States)

    Wu, Chung-Hsien; Su, Hung-Yu; Liu, Chao-Hong

    2013-01-01

    This study presents an efficient approach to personalized mispronunciation detection of Taiwanese-accented English. The main goal of this study was to detect frequently occurring mispronunciation patterns of Taiwanese-accented English instead of scoring English pronunciations directly. The proposed approach quickly identifies personalized…

  15. Accurate modeling and maximum power point detection of ...

    African Journals Online (AJOL)

    Accurate modeling and maximum power point detection of photovoltaic ... Determination of MPP enables the PV system to deliver maximum available power. ..... adaptive artificial neural network: Proposition for a new sizing procedure.

  16. Spatial patterns of biodiversity conservation in a multiregional general equilibrium model

    NARCIS (Netherlands)

    Eppink, F.V.; Withagen, C.A.A.M.

    2009-01-01

    Migration dynamics and local biodiversity are interrelated in a way that is likely to affect patterns of regional specialisation. We assess this relationship with a New Economic Geography model that has been extended with biodiversity. Biodiversity is heterogeneous, and responds to habitat

  17. A Cyber-Attack Detection Model Based on Multivariate Analyses

    Science.gov (United States)

    Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi

    In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.

  18. Ontology and modeling patterns for state-based behavior representation

    Science.gov (United States)

    Castet, Jean-Francois; Rozek, Matthew L.; Ingham, Michel D.; Rouquette, Nicolas F.; Chung, Seung H.; Kerzhner, Aleksandr A.; Donahue, Kenneth M.; Jenkins, J. Steven; Wagner, David A.; Dvorak, Daniel L.; hide

    2015-01-01

    This paper provides an approach to capture state-based behavior of elements, that is, the specification of their state evolution in time, and the interactions amongst them. Elements can be components (e.g., sensors, actuators) or environments, and are characterized by state variables that vary with time. The behaviors of these elements, as well as interactions among them are represented through constraints on state variables. This paper discusses the concepts and relationships introduced in this behavior ontology, and the modeling patterns associated with it. Two example cases are provided to illustrate their usage, as well as to demonstrate the flexibility and scalability of the behavior ontology: a simple flashlight electrical model and a more complex spacecraft model involving instruments, power and data behaviors. Finally, an implementation in a SysML profile is provided.

  19. Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

    Science.gov (United States)

    Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto

    2017-09-01

    Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.

  20. Activation Detection in fMRI Using Jeffrey Divergence

    Science.gov (United States)

    Seghouane, Abd-Krim

    2009-12-01

    A statistical test for detecting activated pixels in functional MRI (fMRI) data is proposed. For the derivation of this test, the fMRI time series measured at each voxel is modeled as the sum of a response signal which arises due to the experimentally controlled activation-baseline pattern, a nuisance component representing effects of no interest, and Gaussian white noise. The test is based on comparing the dimension of the voxels fMRI time series fitted data models with and without controlled activation-baseline pattern. The Jeffrey divergence is used for this comparison. The test has the advantage of not requiring a level of significance or a threshold to be provided.

  1. Human Mobility Patterns and Cholera Epidemics: a Spatially Explicit Modeling Approach

    Science.gov (United States)

    Mari, L.; Bertuzzo, E.; Righetto, L.; Casagrandi, R.; Gatto, M.; Rodriguez-Iturbe, I.; Rinaldo, A.

    2010-12-01

    Cholera is an acute enteric disease caused by the ingestion of water or food contaminated by the bacterium Vibrio cholerae. Although most infected individuals do not develop severe symptoms, their stool may contain huge quantities of V.~cholerae cells. Therefore, while traveling or commuting, asymptomatic carriers can be responsible for the long-range dissemination of the disease. As a consequence, human mobility is an alternative and efficient driver for the spread of cholera, whose primary propagation pathway is hydrological transport through river networks. We present a multi-layer network model that accounts for the interplay between epidemiological dynamics, hydrological transport and long-distance dissemination of V.~cholerae due to human movement. In particular, building on top of state-of-the-art spatially explicit models for cholera spread through surface waters, we describe human movement and its effects on the propagation of the disease by means of a gravity-model approach borrowed from transportation theory. Gravity-like contact processes have been widely used in epidemiology, because they can satisfactorily depict human movement when data on actual mobility patterns are not available. We test our model against epidemiological data recorded during the cholera outbreak occurred in the KwaZulu-Natal province of South Africa during years 2000--2001. We show that human mobility does actually play an important role in the formation of the spatiotemporal patterns of cholera epidemics. In particular, long-range human movement may determine inter-catchment dissemination of V.~cholerae cells, thus in turn explaining the emergence of epidemic patterns that cannot be produced by hydrological transport alone. We also show that particular attention has to be devoted to study how heterogeneously distributed drinking water supplies and sanitation conditions may affect cholera transmission.

  2. Observer and data-driven model based fault detection in Power Plant Coal Mills

    DEFF Research Database (Denmark)

    Fogh Odgaard, Peter; Lin, Bao; Jørgensen, Sten Bay

    2008-01-01

    model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual...... between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault......This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles...

  3. An integrated logit model for contamination event detection in water distribution systems.

    Science.gov (United States)

    Housh, Mashor; Ostfeld, Avi

    2015-05-15

    The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Multivariate pattern dependence.

    Directory of Open Access Journals (Sweden)

    Stefano Anzellotti

    2017-11-01

    Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.

  5. How to detect a cuckoo egg : A signal-detection theory model for recognition and learning

    NARCIS (Netherlands)

    Rodriguez-Girones, MA; Lotem, A

    This article presents a model of egg rejection in cases of brood parasitism. The model is developed in three stages in the framework of signal-detection theory. We first assume that the behavior of host females is adapted to the relevant parameters concerning the appearance of the eggs they lay. In

  6. Digital Elevation Models of Patterned Ground in the Canadian Arctic and Implications for the Study of Mars

    Science.gov (United States)

    Knightly, P.; Murakami, Y.; Clarke, J.; Sizemore, H.; Siegler, M.; Rupert, S.; Chevrier, V.

    2017-12-01

    Patterned ground forms in periglacial zones from both expansion and contraction of permafrost by freeze-thaw and sub-freezing temperature changes and has been observed on both Earth and Mars from orbital and the surface at the Phoneix and Viking 2 landing sites. The Phoenix mission to Mars studied patterned ground in the vicinity of the spacecraft including the excavation of a trench revealing water permafrost beneath the surface. A study of patterned ground at the Haughton Impact structure on Devon Island used stereo-pair imaging and three-dimensional photographic models to catalog the type and occurrence of patterned ground in the study area. This image catalog was then used to provide new insight into photographic observations gathered by Phoenix. Stereo-pair imagery has been a valuable geoscience tool for decades and it is an ideal tool for comparative planetary geology studies. Stereo-pair images captured on Devon Island were turned into digital elevation models (DEMs) and comparisons were noted between the permafrost and patterned ground environment of Earth and Mars including variations in grain sorting, active layer thickness, and ice table depth. Recent advances in 360° cameras also enabled the creation of a detailed, immersive site models of patterned ground at selected sites in Haughton crater on Devon Island. The information from this ground truth study will enable the development and refinement of existing models to better evaluate patterned ground on Mars and predict its evolution.

  7. A Novel Biped Pattern Generator Based on Extended ZMP and Extended Cart-Table Model

    Directory of Open Access Journals (Sweden)

    Guangbin Sun

    2015-07-01

    Full Text Available This paper focuses on planning patterns for biped walking on complex terrains. Two problems are solved: ZMP (zero moment point cannot be used on uneven terrain, and the conventional cart-table model does not allow vertical CM (centre of mass motion. For the ZMP definition problem, we propose the extended ZMP (EZMP concept as an extension of ZMP to uneven terrains. It can be used to judge dynamic balance on universal terrains. We achieve a deeper insight into the connection and difference between ZMP and EZMP by adding different constraints. For the model problem, we extend the cart-table model by using a dynamic constraint instead of constant height constraint, which results in a mathematically symmetric set of three equations. In this way, the vertical motion is enabled and the resultant equations are still linear. Based on the extended ZMP concept and extended cart-table model, a biped pattern generator using triple preview controllers is constructed and implemented simultaneously to three dimensions. Using the proposed pattern generator, the Atlas robot is simulated. The simulation results show the robot can walk stably on rather complex terrains by accurately tracking extended ZMP.

  8. On Input Vector Representation for the SVR model of Reactor Core Loading Pattern Critical Parameters

    International Nuclear Information System (INIS)

    Trontl, K.; Pevec, D.; Smuc, T.

    2008-01-01

    Determination and optimization of reactor core loading pattern is an important factor in nuclear power plant operation. The goal is to minimize the amount of enriched uranium (fresh fuel) and burnable absorbers placed in the core, while maintaining nuclear power plant operational and safety characteristics. The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. Recently, we proposed a new method for fast loading pattern evaluation based on general robust regression model relying on the state of the art research in the field of machine learning. We employed Support Vector Regression (SVR) technique. SVR is a supervised learning method in which model parameters are automatically determined by solving a quadratic optimization problem. The preliminary tests revealed a good potential of the SVR method application for fast and accurate reactor core loading pattern evaluation. However, some aspects of model development are still unresolved. The main objective of the work reported in this paper was to conduct additional tests and analyses required for full clarification of the SVR applicability for loading pattern evaluation. We focused our attention on the parameters defining input vector, primarily its structure and complexity, and parameters defining kernel functions. All the tests were conducted on the NPP Krsko reactor core, using MCRAC code for the calculation of reactor core loading pattern critical parameters. The tested input vector structures did not influence the accuracy of the models suggesting that the initially tested input vector, consisted of the number of IFBAs and the k-inf at the beginning of the cycle, is adequate. The influence of kernel function specific parameters (σ for RBF kernel

  9. A Multi-Agent Modelling Approach to Simulate Dynamic Activity-Travel Patterns

    NARCIS (Netherlands)

    Han, Q.; Arentze, T.A.; Timmermans, H.J.P.; Janssens, D.; Wets, G.; Bazzan, A.L.C.; Klügl, F.

    2009-01-01

    Contributing to the recent interest in the dynamics of activity-travel patterns, this chapter discusses a framework of an agent-based modeling approach focusing on the dynamic formation of (location) choice sets. Individual travelers are represented as agents, each with their cognition of the

  10. An equilibrium-point model for fast, single-joint movement: I. Emergence of strategy-dependent EMG patterns.

    Science.gov (United States)

    Latash, M L; Gottlieb, G L

    1991-09-01

    We describe a model for the regulation of fast, single-joint movements, based on the equilibrium-point hypothesis. Limb movement follows constant rate shifts of independently regulated neuromuscular variables. The independently regulated variables are tentatively identified as thresholds of a length sensitive reflex for each of the participating muscles. We use the model to predict EMG patterns associated with changes in the conditions of movement execution, specifically, changes in movement times, velocities, amplitudes, and moments of limb inertia. The approach provides a theoretical neural framework for the dual-strategy hypothesis, which considers certain movements to be results of one of two basic, speed-sensitive or speed-insensitive strategies. This model is advanced as an alternative to pattern-imposing models based on explicit regulation of timing and amplitudes of signals that are explicitly manifest in the EMG patterns.

  11. Thin Cloud Detection Method by Linear Combination Model of Cloud Image

    Science.gov (United States)

    Liu, L.; Li, J.; Wang, Y.; Xiao, Y.; Zhang, W.; Zhang, S.

    2018-04-01

    The existing cloud detection methods in photogrammetry often extract the image features from remote sensing images directly, and then use them to classify images into cloud or other things. But when the cloud is thin and small, these methods will be inaccurate. In this paper, a linear combination model of cloud images is proposed, by using this model, the underlying surface information of remote sensing images can be removed. So the cloud detection result can become more accurate. Firstly, the automatic cloud detection program in this paper uses the linear combination model to split the cloud information and surface information in the transparent cloud images, then uses different image features to recognize the cloud parts. In consideration of the computational efficiency, AdaBoost Classifier was introduced to combine the different features to establish a cloud classifier. AdaBoost Classifier can select the most effective features from many normal features, so the calculation time is largely reduced. Finally, we selected a cloud detection method based on tree structure and a multiple feature detection method using SVM classifier to compare with the proposed method, the experimental data shows that the proposed cloud detection program in this paper has high accuracy and fast calculation speed.

  12. Modeling and forecasting rainfall patterns of southwest monsoons in North-East India as a SARIMA process

    Science.gov (United States)

    Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.

    2018-02-01

    Weather forecasting is an important issue in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. India experiences the precious Southwest monsoon season for four months from June to September. The present paper describes an empirical study for modeling and forecasting the time series of Southwest monsoon rainfall patterns in the North-East India. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The study has shown that the SARIMA (0, 1, 1) (1, 0, 1)4 model is appropriate for analyzing and forecasting the future rainfall patterns. The Analysis of Means (ANOM) is a useful alternative to the analysis of variance (ANOVA) for comparing the group of treatments to study the variations and critical comparisons of rainfall patterns in different months of the season.

  13. The RiverFish Approach to Business Process Modeling: Linking Business Steps to Control-Flow Patterns

    Science.gov (United States)

    Zuliane, Devanir; Oikawa, Marcio K.; Malkowski, Simon; Alcazar, José Perez; Ferreira, João Eduardo

    Despite the recent advances in the area of Business Process Management (BPM), today’s business processes have largely been implemented without clearly defined conceptual modeling. This results in growing difficulties for identification, maintenance, and reuse of rules, processes, and control-flow patterns. To mitigate these problems in future implementations, we propose a new approach to business process modeling using conceptual schemas, which represent hierarchies of concepts for rules and processes shared among collaborating information systems. This methodology bridges the gap between conceptual model description and identification of actual control-flow patterns for workflow implementation. We identify modeling guidelines that are characterized by clear phase separation, step-by-step execution, and process building through diagrams and tables. The separation of business process modeling in seven mutually exclusive phases clearly delimits information technology from business expertise. The sequential execution of these phases leads to the step-by-step creation of complex control-flow graphs. The process model is refined through intuitive table and diagram generation in each phase. Not only does the rigorous application of our modeling framework minimize the impact of rule and process changes, but it also facilitates the identification and maintenance of control-flow patterns in BPM-based information system architectures.

  14. Differing patterns of P-selectin expression in lung injury

    DEFF Research Database (Denmark)

    Bless, N M; Tojo, S J; Kawarai, H

    1998-01-01

    Using two models of acute lung inflammatory injury in rats (intrapulmonary deposition of immunoglobulin G immune complexes and systemic activation of complement after infusion of purified cobra venom factor), we have analyzed the requirements and patterns for upregulation of lung vascular P......-selectin. In the immune complex model, upregulation of P-selectin was defined by Northern and Western blot analysis of lung homogenates, by immunostaining of lung tissue, and by vascular fixation of 125I-labeled anti-P-selectin. P-selectin protein was detected by 1 hour (long before detection of mRNA) and expression......-selectin was dependent on an intact complement system, and the presence of blood neutrophils was susceptible to the antioxidant dimethyl sulfoxide and required C5a but not tumor necrosis factor alpha. In contrast, in the cobra venom factor model, upregulation of P-selectin, which is C5a dependent, was also dimethyl...

  15. Deep Learning-Based Data Forgery Detection in Automatic Generation Control

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Fengli [Univ. of Arkansas, Fayetteville, AR (United States); Li, Qinghua [Univ. of Arkansas, Fayetteville, AR (United States)

    2017-10-09

    Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Network and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.

  16. An efficient background modeling approach based on vehicle detection

    Science.gov (United States)

    Wang, Jia-yan; Song, Li-mei; Xi, Jiang-tao; Guo, Qing-hua

    2015-10-01

    The existing Gaussian Mixture Model(GMM) which is widely used in vehicle detection suffers inefficiency in detecting foreground image during the model phase, because it needs quite a long time to blend the shadows in the background. In order to overcome this problem, an improved method is proposed in this paper. First of all, each frame is divided into several areas(A, B, C and D), Where area A, B, C and D are decided by the frequency and the scale of the vehicle access. For each area, different new learning rate including weight, mean and variance is applied to accelerate the elimination of shadows. At the same time, the measure of adaptive change for Gaussian distribution is taken to decrease the total number of distributions and save memory space effectively. With this method, different threshold value and different number of Gaussian distribution are adopted for different areas. The results show that the speed of learning and the accuracy of the model using our proposed algorithm surpass the traditional GMM. Probably to the 50th frame, interference with the vehicle has been eliminated basically, and the model number only 35% to 43% of the standard, the processing speed for every frame approximately has a 20% increase than the standard. The proposed algorithm has good performance in terms of elimination of shadow and processing speed for vehicle detection, it can promote the development of intelligent transportation, which is very meaningful to the other Background modeling methods.

  17. The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models

    Science.gov (United States)

    Koch, Julian; Cüneyd Demirel, Mehmet; Stisen, Simon

    2018-05-01

    The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.

  18. Modeling concentration patterns of agricultural and urban micropollutants in surface waters in catchment of mixed land use

    Science.gov (United States)

    Stamm, C.; Scheidegger, R.; Bader, H. P.

    2012-04-01

    Organic micropollutants detected in surface waters can originate from agricultural and urban sources. Depending on the use of the compounds, the temporal loss patterns vary substantially. Therefore models that simulate water quality in watersheds of mixed land use have to account for all relevant sources. We present here simulation results of a transport model that describes the dynamic of several biocidal compounds as well as the behaviour of human pharmaceuticals. The model consists of the sub-model Rexpo simulating the transfer of the compounds from the point of application to the stream in semi-lumped manner. The river sub-model, which is programmed in the Aquasim software, describes the fate of the compounds in the stream. Both sub-models are process-based. The Rexpo sub-model was calibrated at the scale of a small catchment of 25 km2, which is inhabited by about 12'000 people. Based on the resulting model parameters the loss dynamics of two herbicides (atrazine, isoproturon) and a compound of mixed urban and agricultural use (diuron) were predicted for two nested catchment of 212 and 1696 km2, respectively. The model output was compared to observed time-series of concentrations and loads obtained for the entire year 2009. Additionally, the fate of two pharmaceuticals with constant input (carbamazepine, diclofenac) was simulated for improving the understanding of possible degradation processes. The simulated loads and concentrations of the biocidal compounds differed by a factor of 2 to 3 from the observations. In general, the seasonal patterns were well captured by the model. However, a detailed analysis of the seasonality revealed substantial input uncertainty for the application of the compounds. The model results also demonstrated that for the dynamics of rain-driven losses of biocidal compounds the semi-lumped approach of the Rexpo sub-model was sufficient. Only for simulating the photolytic degradation of diclofenac in the stream the detailed

  19. Morphodynamic modeling of the river pattern continuum (Invited)

    Science.gov (United States)

    Nicholas, A. P.

    2013-12-01

    Numerical models provide valuable tools for integrating understanding of fluvial processes and morphology. Moreover, they have considerable potential for use in investigating river responses to environmental change and catchment management, and for aiding the interpretation of alluvial deposits and landforms. For this potential to be realised fully, such models must be capable of representing diverse river styles and the spatial and temporal transitions between styles that are driven by changes in environmental forcing. However, while numerical modeling of rivers has advanced considerable over the past few decades, this has been accomplished largely by developing separate approaches to modeling single and multi-thread channels. Results are presented here from numerical simulations undertaken using a new model of river and floodplain co-evolution, applied to investigate the morphodynamics of large sand-bed rivers. This model solves the two-dimensional depth-averaged shallow water equations using a Godunov-type finite volume scheme, with a two-fraction representation of sediment transport, and includes the effects of secondary circulation, bank erosion and floodplain development due to the colonization of bar surfaces by vegetation. Simulation results demonstrate the feasibility of representing a wide range of fluvial styles (including braiding, meandering and anabranching channels) using relatively simple physics-based models, and provide insight into the controls on channel pattern diversity in large sand-bed rivers. Analysis of model sensitivity illustrates the important role of upstream boundary conditions as a control on channel dynamics. Moreover, this analysis highlights key uncertainties in model process representation and their implications for modelling river evolution in response to natural and anthropogenic-induced river disturbance.

  20. Estimating indices of range shifts in birds using dynamic models when detection is imperfect

    Science.gov (United States)

    Clement, Matthew J.; Hines, James E.; Nichols, James D.; Pardieck, Keith L.; Ziolkowski, David J.

    2016-01-01

    There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997–2013 study period. Near-term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection – a new extension to correlated detection occupancy models – were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.