WorldWideScience

Sample records for pattern detection modeling

  1. 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...

  2. 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…

  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. 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.

  6. 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...

  7. Detecting memory and structure in human navigation patterns using Markov chain models of varying order.

    Science.gov (United States)

    Singer, Philipp; Helic, Denis; Taraghi, Behnam; Strohmaier, Markus

    2014-01-01

    One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.

  8. Detecting memory and structure in human navigation patterns using Markov chain models of varying order.

    Directory of Open Access Journals (Sweden)

    Philipp Singer

    Full Text Available One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.

  9. 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

  10. 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.

  11. 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)

  12. 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

  13. 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.

  14. 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 ...

  15. 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

  16. 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.

  17. 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

  18. 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.

  19. [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.

  20. 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.

  1. 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...

  2. 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

  3. 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.

  4. 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...

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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...

  12. 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.

  13. 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

  14. 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,

  15. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    interesting property of many biological networks that was recently brought to attention of the scientific community [3, 4, 5] is an extremely broad distribution of node connectivities defined as the number of immediate neighbors of a given node in the network. While the majority of nodes have just a few edges connecting them to other nodes in the network, there exist some nodes, that we will refer to as ''hubs'', with an unusually large number of neighbors. The connectivity of the most connected hub in such a network is typically several orders of magnitude larger than the average connectivity in the network. Often the distribution of connectivities of individual nodes can be approximated by a scale-free power law form [3] in which case the network is referred to as scale-free. Among biological networks distributions of node connectivities in metabolic [4], protein interaction [5], and brain functional [6] networks can be reasonably approximated by a power law extending for several orders of magnitude. The set of connectivities of individual nodes is an example of a low-level (single-node) topological property of a network. While it answers the question about how many neighbors a given node has, it gives no information about the identity of those neighbors. It is clear that most functional properties of networks are defined at a higher topological level in the exact pattern of connections of nodes to each other. However, such multi-node connectivity patterns are rather difficult to quantify and compare between networks. In this work we concentrate on multi-node topological properties of protein networks. These networks (as any other biological networks) lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as mutations within individual genes, and gene duplications. As a result their connections are characterized by a large degree of randomness. One may wonder which

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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...

  3. 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.

  4. 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.

  5. 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)

  6. 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.

  7. 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.

  8. A male-specific QTL for social interaction behavior in mice mapped with automated pattern detection by a hidden Markov model incorporated into newly developed freeware.

    Science.gov (United States)

    Arakawa, Toshiya; Tanave, Akira; Ikeuchi, Shiho; Takahashi, Aki; Kakihara, Satoshi; Kimura, Shingo; Sugimoto, Hiroki; Asada, Nobuhiko; Shiroishi, Toshihiko; Tomihara, Kazuya; Tsuchiya, Takashi; Koide, Tsuyoshi

    2014-08-30

    Owing to their complex nature, social interaction tests normally require the observation of video data by a human researcher, and thus are difficult to use in large-scale studies. We previously established a statistical method, a hidden Markov model (HMM), which enables the differentiation of two social states ("interaction" and "indifference"), and three social states ("sniffing", "following", and "indifference"), automatically in silico. Here, we developed freeware called DuoMouse for the rapid evaluation of social interaction behavior. This software incorporates five steps: (1) settings, (2) video recording, (3) tracking from the video data, (4) HMM analysis, and (5) visualization of the results. Using DuoMouse, we mapped a genetic locus related to social interaction. We previously reported that a consomic strain, B6-Chr6C(MSM), with its chromosome 6 substituted for one from MSM/Ms, showed more social interaction than C57BL/6 (B6). We made four subconsomic strains, C3, C5, C6, and C7, each of which has a shorter segment of chromosome 6 derived from B6-Chr6C, and conducted social interaction tests on these strains. DuoMouse indicated that C6, but not C3, C5, and C7, showed higher interaction, sniffing, and following than B6, specifically in males. The data obtained by human observation showed high concordance to those from DuoMouse. The results indicated that the MSM-derived chromosomal region present in C6-but not in C3, C5, and C7-associated with increased social behavior. This method to analyze social interaction will aid primary screening for difference in social behavior in mice. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. 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

  10. 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

  11. 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.

  12. 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.

  13. 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)

  14. 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.

  15. 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

  16. 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.

  17. 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 ...

  18. 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.

  19. 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.

  20. 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.

  1. 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...

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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.)

  7. 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.)

  8. 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.

  9. 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.

  10. [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.

  11. 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...

  12. Detecting deviating behaviors without models

    NARCIS (Netherlands)

    Lu, X.; Fahland, D.; van den Biggelaar, F.J.H.M.; van der Aalst, W.M.P.; Reichert, M.; Reijers, H.A.

    2016-01-01

    Deviation detection is a set of techniques that identify deviations from normative processes in real process executions. These diagnostics are used to derive recommendations for improving business processes. Existing detection techniques identify deviations either only on the process instance level

  13. 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.

  14. 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…

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. Pattern of interstitial lung disease detected by high resolution ...

    African Journals Online (AJOL)

    Background: Diffuse lung diseases constitute a major cause of morbidity and mortality worldwide. High Resolution Computed Tomography (HRCT) is the recommended imaging technique in the diagnosis, assessment and followup of these diseases. Objectives: To describe the pattern of HRCT findings in patients with ...

  4. Modeling urbanization patterns with generative adversarial networks

    OpenAIRE

    Albert, Adrian; Strano, Emanuele; Kaur, Jasleen; Gonzalez, Marta

    2018-01-01

    In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory. We generated a synthetic urban "universe" that qualitatively reproduces the complex spatial organization observed in global urban patterns, while being able to quantitatively recover certain key high-level urban spatial metrics.

  5. Detecting and Sonifying Temporal Patterns of Body Segments When Batting

    Directory of Open Access Journals (Sweden)

    Akemi Kobayashi

    2018-02-01

    Full Text Available To improve skill in sport activities it is essential to discern the temporal patterns of one’s own movements. Our previous motion capture experiment involving elite female softball players identified key differences in the temporal body movements between the top players and young players against fastballs/change-ups. In this paper, we found that key features could be extracted from the rotation of the pelvis and we developed a sonification feedback system with two nine-axes inertial sensors. Rotation patterns are converted into two synthesized sounds to represent the time at peak trunk rotation speed and impact time. We conducted a pilot experiment with this feedback proposal using expert and novice batters, male and female whether the participants can pace of the rotational motion in batting. As a result, this feedback approach may allow the user to alter the time of peak trunk rotation speed to more closely match the cue provided by the training sound.

  6. The Michelson interferometer-how to detect invisible interference patterns

    International Nuclear Information System (INIS)

    Verovnik, Ivo; Likar, Andrej

    2004-01-01

    In a Michelson interferometer, the contrast of the interference pattern fades away due to incoherence of light when the mirrors are not in equidistant positions. We propose an experiment where the distance between the interference fringes can be determined, even when the difference in length of the interferometer arms is far beyond the coherence length of the light, i.e. when the interference pattern disappears completely for the naked eye. We used a semiconductor laser with two photodiodes as sensors, which enabled us to follow the fluctuations of the light intensity on the screen. The distance between invisible interference fringes was determined from periodic changes of the summed fluctuating signal, obtained by changing the distance between the two sensors

  7. Pattern Discovery and Change Detection of Online Music Query Streams

    Science.gov (United States)

    Li, Hua-Fu

    In this paper, an efficient stream mining algorithm, called FTP-stream (Frequent Temporal Pattern mining of streams), is proposed to find the frequent temporal patterns over melody sequence streams. In the framework of our proposed algorithm, an effective bit-sequence representation is used to reduce the time and memory needed to slide the windows. The FTP-stream algorithm can calculate the support threshold in only a single pass based on the concept of bit-sequence representation. It takes the advantage of "left" and "and" operations of the representation. Experiments show that the proposed algorithm only scans the music query stream once, and runs significant faster and consumes less memory than existing algorithms, such as SWFI-stream and Moment.

  8. 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

  9. 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.

  10. Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition

    NARCIS (Netherlands)

    Azzopardi, George; Petkov, Nicolai

    Background: Keypoint detection is important for many computer vision applications. Existing methods suffer from insufficient selectivity regarding the shape properties of features and are vulnerable to contrast variations and to the presence of noise or texture. Methods: We propose a trainable

  11. Trainable COSFIRE Filters for Keypoint Detection and Pattern Recognition

    NARCIS (Netherlands)

    Azzopardi, G.; Petkov, N.

    2013-01-01

    Background: Keypoint detection is important for many computer vision applications. Existing methods suffer from insufficient selectivity regarding the shape properties of features and are vulnerable to contrast variations and to the presence of noise or texture. Methods: We propose a trainable

  12. Resistance pattern and detection of metallo‑beta‑lactamase genes ...

    African Journals Online (AJOL)

    Materials and Methods: Two hundred nonduplicate, consecutive isolates of P. aeruginosa from clinical samples submitted to the Medical Microbiology Laboratory of National Hospital, Abuja were screened for carbapenem resistance using imipenem and meropenem. Phenotypic detection of MBL‑producing strains was ...

  13. Dysphonia Detected by Pattern Recognition of Spectral Composition.

    Science.gov (United States)

    Leinonen, Lea; And Others

    1992-01-01

    This study analyzed production of a long vowel sound within Finnish words by normal or dysphonic voices, using the Self-Organizing Map, the artificial neural network algorithm of T. Kohonen which produces two-dimensional representations of speech. The method was found to be both sensitive and specific in the detection of dysphonia. (Author/JDD)

  14. Threat Detection in Tweets with Trigger Patterns and Contextual Cues

    NARCIS (Netherlands)

    Spitters, M.M.; Eendebak, P.T.; Worm, D.T.H.; Bouma, H.

    2014-01-01

    Many threats in the real world can be related to activities in open sources on the internet. Early detection of threats based on internet information could assist in the prevention of incidents. However, the amount of data in social media, blogs and forums rapidly increases and it is time consuming

  15. Searching for Complex Patterns Using Disjunctive Anomaly Detection

    OpenAIRE

    Sabhnani, Maheshkumar; Dubrawski, Artur; Schneider, Jeff

    2013-01-01

    Objective Disjunctive anomaly detection (DAD) algorithm [1] can efficiently search across multidimensional biosurveillance data to find multiple simultaneously occurring (in time) and overlapping (across different data dimensions) anomalous clusters. We introduce extensions of DAD to handle rich cluster interactions and diverse data distributions. Introduction Modern biosurveillance data contains thousands of unique time series defined across various categorical dimensions (zipcode, age group...

  16. 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.

  17. 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.

  18. 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.

  19. Detecting regularities in soccer dynamics: A T-pattern approach

    Directory of Open Access Journals (Sweden)

    Valentino Zurloni

    2014-01-01

    Full Text Available La dinámica del juego en partidos de fútbol profesional es un fenómeno complejo que no ha estado resuelto de forma óptima a través delas vías tradicionales que han pretendido la cuantificación en deportes de equipo. El objetivo de este estudio es el de detectar la dinámica existente mediante un análisis de patrones temporales. Específicamente, se pretenden revelar las estructuras ocultas pero estables que subyacen a las situaciones interactivas que determinan las acciones de ataque en el fútbol. El planteamiento metodológico se basa en un diseño observacional, y con apoyo de registros digitales y análisis informatizados. Los datos se analizaron mediante el programa Theme 6 beta, el cual permite detectar la estructura temporaly secuencial de las series de datos, poniendo de manifiesto patrones que regular o irregularmente ocurren repetidamente en un período de observación. El Theme ha detectado muchos patrones temporales (T-patterns en los partidos de fútbol analizados. Se hallaron notables diferencias entre los partidos ganados y perdidos. El número de distintos T-patterns detectados fue mayor para los partidos perdidos, y menor para los ganados, mientras que el número de eventos codificados fue similar. El programa Theme y los T-patterns mejoran las posibilidades investigadoras respecto a un análisis de rendimiento basado en la frecuencia, y hacen que esta metodología sea eficaz para la investigación y constituya un apoyo procedimental en el análisis del deporte. Nuestros resultados indican que se requieren posteriores investigaciones relativas a posibles conexiones entre la detección de estas estructuras temporales y las observaciones humanas respecto al rendimiento en el fútbol. Este planteamiento sería un apoyo tanto para los miembros de los equipos como para los entrenadores, permitiendo alcanzar una mejor comprensión de la dinámica del juego y aportando una información que no ofrecen los métodos tradicionales.

  20. 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.

  1. 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....

  2. 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.

  3. 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.

  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. 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

  6. 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.

  7. 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

  8. 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

  9. Inhomogeneous Markov Models for Describing Driving Patterns

    DEFF Research Database (Denmark)

    Iversen, Emil Banning; Møller, Jan K.; Morales, Juan Miguel

    2017-01-01

    . Specifically, an inhomogeneous Markov model that captures the diurnal variation in the use of a vehicle is presented. The model is defined by the time-varying probabilities of starting and ending a trip, and is justified due to the uncertainty associated with the use of the vehicle. The model is fitted to data...... collected from the actual utilization of a vehicle. Inhomogeneous Markov models imply a large number of parameters. The number of parameters in the proposed model is reduced using B-splines....

  10. Inhomogeneous Markov Models for Describing Driving Patterns

    DEFF Research Database (Denmark)

    Iversen, Jan Emil Banning; Møller, Jan Kloppenborg; Morales González, Juan Miguel

    . Specically, an inhomogeneous Markov model that captures the diurnal variation in the use of a vehicle is presented. The model is dened by the time-varying probabilities of starting and ending a trip and is justied due to the uncertainty associated with the use of the vehicle. The model is tted to data...... collected from the actual utilization of a vehicle. Inhomogeneous Markov models imply a large number of parameters. The number of parameters in the proposed model is reduced using B-splines....

  11. 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...

  12. 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

  13. Modeling of glutamate-induced dynamical patterns

    DEFF Research Database (Denmark)

    Faurby-Bentzen, Christian Krefeld; Zhabotinsky, A.M.; Laugesen, Jakob Lund

    2009-01-01

    Based on established physiological mechanisms, the paper presents a detailed computer model, which supports the hypothesis that temporal lobe epilepsy may be caused by failure of glutamate reuptake from the extracellular space. The elevated glutamate concentration causes an increased activation...

  14. 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...

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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

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

    Directory of Open Access Journals (Sweden)

    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.

  1. 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.

  2. 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.

  3. Temporal-pattern learning in neural models

    CERN Document Server

    Genís, Carme Torras

    1985-01-01

    While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experi­ mental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specifica­ tion of the pacemaker neuron model proposed together with its valida­ tion through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying seve­ ral factors r...

  4. Evaluating spatial patterns in hydrological modelling

    DEFF Research Database (Denmark)

    Koch, Julian

    the contiguous United Sates (10^6 km2). To this end, the thesis at hand applies a set of spatial performance metrics on various hydrological variables, namely land-surface-temperature (LST), evapotranspiration (ET) and soil moisture. The inspiration for the applied metrics is found in related fields...... is not fully exploited by current modelling frameworks due to the lack of suitable spatial performance metrics. Furthermore, the traditional model evaluation using discharge is found unsuitable to lay confidence on the predicted catchment inherent spatial variability of hydrological processes in a fully...

  5. Modelling point patterns with linear structures

    DEFF Research Database (Denmark)

    Møller, Jesper; Rasmussen, Jakob Gulddahl

    2009-01-01

    processes whose realizations contain such linear structures. Such a point process is constructed sequentially by placing one point at a time. The points are placed in such a way that new points are often placed close to previously placed points, and the points form roughly line shaped structures. We...... consider simulations of this model and compare with real data....

  6. Modelling point patterns with linear structures

    DEFF Research Database (Denmark)

    Møller, Jesper; Rasmussen, Jakob Gulddahl

    processes whose realizations contain such linear structures. Such a point process is constructed sequentially by placing one point at a time. The points are placed in such a way that new points are often placed close to previously placed points, and the points form roughly line shaped structures. We...... consider simulations of this model and compare with real data....

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. Citation-based plagiarism detection detecting disguised and cross-language plagiarism using citation pattern analysis

    CERN Document Server

    Gipp, Bela

    2014-01-01

    Plagiarism is a problem with far-reaching consequences for the sciences. However, even today's best software-based systems can only reliably identify copy & paste plagiarism. Disguised plagiarism forms, including paraphrased text, cross-language plagiarism, as well as structural and idea plagiarism often remain undetected. This weakness of current systems results in a large percentage of scientific plagiarism going undetected. Bela Gipp provides an overview of the state-of-the art in plagiarism detection and an analysis of why these approaches fail to detect disguised plagiarism forms. The aut

  12. 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)

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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…

  18. 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.

  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. 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.

  1. 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.

  2. 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

  3. 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

  4. 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%).

  5. 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,

  6. Affective topic model for social emotion detection.

    Science.gov (United States)

    Rao, Yanghui; Li, Qing; Wenyin, Liu; Wu, Qingyuan; Quan, Xiaojun

    2014-10-01

    The rapid development of social media services has been a great boon for the communication of emotions through blogs, microblogs/tweets, instant-messaging tools, news portals, and so forth. This paper is concerned with the detection of emotions evoked in a reader by social media. Compared to classical sentiment analysis conducted from the writer's perspective, analysis from the reader's perspective can be more meaningful when applied to social media. We propose an affective topic model with the intention to bridge the gap between social media materials and a reader's emotions by introducing an intermediate layer. The proposed model can be used to classify the social emotions of unlabeled documents and to generate a social emotion lexicon. Extensive evaluations using real-world data validate the effectiveness of the proposed model for both these applications. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. 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.

  8. 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)

  9. 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.

  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. 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

  12. 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...

  13. 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.

  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. Robotic Detection of Marine Litter Using Deep Visual Detection Models

    OpenAIRE

    Fulton, Michael; Hong, Jungseok; Islam, Md Jahidul; Sattar, Junaed

    2018-01-01

    Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. A step towards this goal is the successful detection of trash in underwater environments. This paper evaluates a number of deep-learning algorithms to the task of visually detecting trash in realistic underwater envi...

  16. 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.

  17. 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...

  18. 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.

  19. Modeling patterns in count data using loglinear and related models

    International Nuclear Information System (INIS)

    Atwood, C.L.

    1995-12-01

    This report explains the use of loglinear and logit models, for analyzing Poisson and binomial counts in the presence of explanatory variables. The explanatory variables may be unordered categorical variables or numerical variables, or both. The report shows how to construct models to fit data, and how to test whether a model is too simple or too complex. The appropriateness of the methods with small data sets is discussed. Several example analyses, using the SAS computer package, illustrate the methods

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. Emotion detection model of Filipino music

    Science.gov (United States)

    Noblejas, Kathleen Alexis; Isidro, Daryl Arvin; Samonte, Mary Jane C.

    2017-02-01

    This research explored the creation of a model to detect emotion from Filipino songs. The emotion model used was based from Paul Ekman's six basic emotions. The songs were classified into the following genres: kundiman, novelty, pop, and rock. The songs were annotated by a group of music experts based on the emotion the song induces to the listener. Musical features of the songs were extracted using jAudio while the lyric features were extracted by Bag-of- Words feature representation. The audio and lyric features of the Filipino songs were extracted for classification by the chosen three classifiers, Naïve Bayes, Support Vector Machines, and k-Nearest Neighbors. The goal of the research was to know which classifier would work best for Filipino music. Evaluation was done by 10-fold cross validation and accuracy, precision, recall, and F-measure results were compared. Models were also tested with unknown test data to further determine the models' accuracy through the prediction results.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Modeling patterns in data using linear and related models

    International Nuclear Information System (INIS)

    Engelhardt, M.E.

    1996-06-01

    This report considers the use of linear models for analyzing data related to reliability and safety issues of the type usually associated with nuclear power plants. The report discusses some of the general results of linear regression analysis, such as the model assumptions and properties of the estimators of the parameters. The results are motivated with examples of operational data. Results about the important case of a linear regression model with one covariate are covered in detail. This case includes analysis of time trends. The analysis is applied with two different sets of time trend data. Diagnostic procedures and tests for the adequacy of the model are discussed. Some related methods such as weighted regression and nonlinear models are also considered. A discussion of the general linear model is also included. Appendix A gives some basic SAS programs and outputs for some of the analyses discussed in the body of the report. Appendix B is a review of some of the matrix theoretic results which are useful in the development of linear models

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Modeling of Viral Aerosol Transmission and Detection

    KAUST Repository

    Khalid, Maryam; Amin, Osama; Ahmed, Sajid; Alouini, Mohamed-Slim

    2018-01-01

    The objective of this work is to investigate the spread mechanism of diseases in the atmosphere as an engineering problem. Among the viral transmission mechanisms that do not include physical contact, aerosol transmission is the most significant mode of transmission where virus-laden droplets are carried over long distances by wind. In this work, we focus on aerosol transmission of virus and introduce the idea of viewing virus transmission through aerosols and their transport as a molecular communication problem, where one has no control over transmission source but a robust receiver can be designed using nano-biosensors. To investigate this idea, a complete system is presented and end-toend mathematical model for the aerosol transmission channel is derived under certain constraints and boundary conditions. In addition to transmitter and channel, a receiver architecture composed of air sampler and Silicon Nanowire field effect transistor is also discussed. Furthermore, a detection problem is formulated for which maximum likelihood decision rule and the corresponding missed detection probability is discussed. At the end, simulation results are presented to investigate the parameters that affect the performance and justify the feasibility of proposed setup in related applications.

  18. [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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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)

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. Using Petri nets for modeling enterprise integration patterns

    NARCIS (Netherlands)

    Fahland, D.; Gierds, C.

    2012-01-01

    Enterprise Integration Patterns are a collection of widely used patterns for integrating enterprise applications and business processes. These patterns informally represent typical design decisions for connecting enterprise applications. For the set of patterns collected by Hohpe and Woolf in

  4. 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.

  5. 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.

  6. 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.

  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. 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

  9. 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

  10. 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

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Stochastic pattern recognition techniques and artificial intelligence for nuclear power plant surveillance and anomaly detection

    Energy Technology Data Exchange (ETDEWEB)

    Kemeny, L.G

    1998-12-31

    In this paper a theoretical and system conceptual model is outlined for the instrumentation, core assessment and surveillance and anomaly detection of a nuclear power plant. The system specified is based on the statistical on-line analysis of optimally placed instrumentation sensed fluctuating signals in terms of such variates as coherence, correlation function, zero-crossing and spectral density

  3. Stochastic pattern recognition techniques and artificial intelligence for nuclear power plant surveillance and anomaly detection

    International Nuclear Information System (INIS)

    Kemeny, L.G.

    1998-01-01

    In this paper a theoretical and system conceptual model is outlined for the instrumentation, core assessment and surveillance and anomaly detection of a nuclear power plant. The system specified is based on the statistical on-line analysis of optimally placed instrumentation sensed fluctuating signals in terms of such variates as coherence, correlation function, zero-crossing and spectral density

  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. Analog electronic model of the lobster pyloric central pattern generator

    Energy Technology Data Exchange (ETDEWEB)

    Volkovskii, A [Institute for Nonlinear Science, University of California San Diego, CA (United States); Brugioni, S [Institute for Nonlinear Science, University of California San Diego, CA (United States); Istituto Nazionale di Ottica Applicata Largo E. Fermi 6 50125 Florence (Italy); Levi, R [Institute for Nonlinear Science, University of California San Diego, CA (United States); Rabinovich, M [Institute for Nonlinear Science, University of California San Diego, CA (United States); Selverston, A [Institute for Nonlinear Science, University of California San Diego, CA (United States); Abarbane, H D I [Institute for Nonlinear Science, University of California San Diego, CA (United States)

    2005-01-01

    An electronic circuit intended to simulate the nonlinear dynamics of a simplified 3-cell model of the pyloric central pattern generator in California spiny lobster stomato gastric ganglion is presented. The model employs the synaptic phase locked loop (SPLL) concept where the frequency of oscillations of a postsynaptic cell is mainly controlled by the synaptic current which depends on the phase shift between the oscillations. The theoretical study showed that the system has a stable steady state with correct phase shifts between the oscillations and that this regime is stable when the frequency of the pacemaker cell is varied over a wide range. The main bifurcations in the system were studied analytically, in computer simulations, and in experiments with the electronic circuit. The experimental measurements are in good agreement with the expectations of the theoretical model.

  6. 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.

  7. 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.

  8. 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

  9. 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...

  10. 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.

  11. 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.

  12. 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.

  13. Mathematics in Nature Modeling Patterns in the Natural World

    CERN Document Server

    Adam, John A

    2011-01-01

    From rainbows, river meanders, and shadows to spider webs, honeycombs, and the markings on animal coats, the visible world is full of patterns that can be described mathematically. Examining such readily observable phenomena, this book introduces readers to the beauty of nature as revealed by mathematics and the beauty of mathematics as revealed in nature.Generously illustrated, written in an informal style, and replete with examples from everyday life, Mathematics in Nature is an excellent and undaunting introduction to the ideas and methods of mathematical modeling. It illustrates how mathem

  14. Active patterning and asymmetric transport in a model actomyosin network

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Shenshen [Department of Chemical Engineering and Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Wolynes, Peter G. [Department of Chemistry and Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005 (United States)

    2013-12-21

    Cytoskeletal networks, which are essentially motor-filament assemblies, play a major role in many developmental processes involving structural remodeling and shape changes. These are achieved by nonequilibrium self-organization processes that generate functional patterns and drive intracellular transport. We construct a minimal physical model that incorporates the coupling between nonlinear elastic responses of individual filaments and force-dependent motor action. By performing stochastic simulations we show that the interplay of motor processes, described as driving anti-correlated motion of the network vertices, and the network connectivity, which determines the percolation character of the structure, can indeed capture the dynamical and structural cooperativity which gives rise to diverse patterns observed experimentally. The buckling instability of individual filaments is found to play a key role in localizing collapse events due to local force imbalance. Motor-driven buckling-induced node aggregation provides a dynamic mechanism that stabilizes the two-dimensional patterns below the apparent static percolation limit. Coordinated motor action is also shown to suppress random thermal noise on large time scales, the two-dimensional configuration that the system starts with thus remaining planar during the structural development. By carrying out similar simulations on a three-dimensional anchored network, we find that the myosin-driven isotropic contraction of a well-connected actin network, when combined with mechanical anchoring that confers directionality to the collective motion, may represent a novel mechanism of intracellular transport, as revealed by chromosome translocation in the starfish oocyte.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.)

  10. 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.

  11. Enhanced leak detection risk model development

    Energy Technology Data Exchange (ETDEWEB)

    Harron, Lorna; Barlow, Rick; Farquhar, Ted [Enbridge Pipelines Inc., Edmonton, Alberta (Canada)

    2010-07-01

    Increasing concerns and attention to pipeline safety have engaged pipeline companies and regulatory agencies to extend their approaches to pipeline integrity. The implementation of High Consequence Areas (HCAs) has especially had an impact on the development of integrity management protocols (IMPs) for pipelines. These IMPs can require that a risk based assessment of integrity issues be applied to specific HCA risk factors. This paper addresses the development of an operational risk assessment approach for pipeline leak detection requirements for HCAs. A detailed risk assessment algorithm that includes 25 risk variables and 28 consequence variables was developed for application to all HCA areas. This paper describes the consultative process that was used to workshop the development of this algorithm. Included in this description is how the process addressed various methods of leak detection across a wide variety of pipelines. The paper also looks at development challenges and future steps in applying operation risk assessment techniques to mainline leak detection risk management.

  12. Detection of Upscale-Crop and Partial Manipulation in Surveillance Video Based on Sensor Pattern Noise

    Science.gov (United States)

    Hyun, Dai-Kyung; Ryu, Seung-Jin; Lee, Hae-Yeoun; Lee, Heung-Kyu

    2013-01-01

    In many court cases, surveillance videos are used as significant court evidence. As these surveillance videos can easily be forged, it may cause serious social issues, such as convicting an innocent person. Nevertheless, there is little research being done on forgery of surveillance videos. This paper proposes a forensic technique to detect forgeries of surveillance video based on sensor pattern noise (SPN). We exploit the scaling invariance of the minimum average correlation energy Mellin radial harmonic (MACE-MRH) correlation filter to reliably unveil traces of upscaling in videos. By excluding the high-frequency components of the investigated video and adaptively choosing the size of the local search window, the proposed method effectively localizes partially manipulated regions. Empirical evidence from a large database of test videos, including RGB (Red, Green, Blue)/infrared video, dynamic-/static-scene video and compressed video, indicates the superior performance of the proposed method. PMID:24051524

  13. Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes.

    Science.gov (United States)

    Pampouchidou, Anastasia; Marias, Kostas; Tsiknakis, Manolis; Simos, Panagiotis; Fan Yang; Lemaitre, Guillaume; Meriaudeau, Fabrice

    2016-08-01

    Depression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns. The main advantage of these methods is that they have significantly lower computational requirements, as the extracted features are of very low dimensionality. This is achieved by modifying the previously proposed algorithm which considers Three-Orthogonal-Planes, to only Pairwise-Orthogonal-Planes. Performance of the algorithms was tested on the benchmark dataset provided by the Audio/Visual Emotion Challenge 2014, with the person-specific system achieving 97.6% classification accuracy, and the person-independed one yielding promising preliminary results of 74.5% accuracy. The paper concludes with open issues, proposed solutions, and future plans.

  14. 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.

  15. Drought Patterns Forecasting using an Auto-Regressive Logistic Model

    Science.gov (United States)

    del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.

    2014-12-01

    Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.

  16. 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.

  17. Pattern of Breast Cancer Distribution in Ghana: A Survey to Enhance Early Detection, Diagnosis, and Treatment

    Directory of Open Access Journals (Sweden)

    Frank Naku Ghartey Jnr

    2016-01-01

    Full Text Available Background. Nearly 70% of women diagnosed with breast cancer in Ghana are in advanced stages of the disease due especially to low awareness, resulting in limited treatment success and high death rate. With limited epidemiological studies on breast cancer in Ghana, the aim of this study is to assess and understand the pattern of breast cancer distribution for enhancing early detection and treatment. Methods. We randomly selected and screened 3000 women for clinical palpable breast lumps and used univariate and bivariate analysis for description and exploration of variables, respectively, in relation to incidence of breast cancer. Results. We diagnosed 23 (0.76% breast cancer cases out of 194 (6.46% participants with clinically palpable breast lumps. Seventeen out of these 23 (0.56% were premenopausal (<46.6 years with 7 (0.23% being below 35 years. With an overall breast cancer incidence of 0.76% in this study, our observation that about 30% of these cancer cases were below 35 years may indicate a relative possible shift of cancer burden to women in their early thirties in Ghana, compared to Western countries. Conclusion. These results suggest an age adjustment for breast cancer screening to early twenties for Ghanaian women and the need for a nationwide breast cancer screening to understand completely the pattern of breast cancer distribution in Ghana.

  18. 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

  19. 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.

  20. 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.

  1. A Semiparametric Model for Hyperspectral Anomaly Detection

    Science.gov (United States)

    2012-01-01

    treeline ) in the presence of natural background clutter (e.g., trees, dirt roads, grasses). Each target consists of about 7 × 4 pixels, and each pixel...vehicles near the treeline in Cube 1 (Figure 1) constitutes the target set, but, since anomaly detectors are not designed to detect a particular target

  2. 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....

  3. 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.

  4. 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

  5. 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

  6. 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

  7. Towards Clone Detection in UML Domain Models

    DEFF Research Database (Denmark)

    Störrle, Harald

    2013-01-01

    Code clones (i.e., duplicate fragments of code) have been studied for long, and there is strong evidence that they are a major source of software faults. Anecdotal evidence suggests that this phenomenon occurs similarly in models, suggesting that model clones are as detrimental to model quality...... as they are to code quality. However, programming language code and visual models have significant differences that make it difficult to directly transfer notions and algorithms developed in the code clone arena to model clones. In this article, we develop and propose a definition of the notion of “model clone” based...... we believe that our approach advances the state of the art significantly, it is restricted to UML models, its results leave room for improvements, and there is no validation by field studies....

  8. Shedding and serological patterns of dairy cows following abortions associated with Coxiella burnetii DNA detection.

    Science.gov (United States)

    Guatteo, R; Joly, A; Beaudeau, F

    2012-03-23

    To describe both shedding and serological patterns following abortions detected as being associated with Coxiella burnetii (Cb), 24 cows experiencing an abortion due to Cb were followed over a one month period. Samples taken on the day of abortion (D0) were followed 3-fold by weekly samplings from day 14 (D14) to D28 after the abortion. Milk and vaginal mucus were collected at each weekly sampling and tested using real-time PCR while blood samples were collected 2-fold on D21 and D28 and tested using ELISA. We found a very short duration of C. burnetii shedding in vaginal mucus after abortion, highlighting the need to collect samples as rapidly as possible following an abortion to avoid false negative results. In contrast with previous results, concomitancy of vaginal and mucus shedding was frequent, especially for cows shedding a high bacterial load on DO leading to the hypothesis that the clinical onset of the infection influences the modalities of Cb shedding. Lastly, serological results indicating a lack of sensitivity to detect Cb shedder cows (especially for cows for which Ct values were high) suggest that ELISA is not a useful tool to diagnose abortions at the individual level. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. Sleep patterning changes in a prenatal stress model of depression

    DEFF Research Database (Denmark)

    Sickmann, Helle Mark; Skoven, C; Bastlund, Jesper F

    2018-01-01

    /wakefulness behavior around the change from light-to-dark phase. Control and PNS Sprague-Dawley rats were implanted with electrodes for continuous monitoring of electroencephalic activity used to determine behavioral state. The distribution of slow-wave sleep (SWS), rapid eye movement sleep (REMS) and wakefulness......Clinical depression is accompanied by changes in sleep patterning, which is controlled in a circadian fashion. It is thus desirable that animal models of depression mirror such diurnally-specific state alterations, along with other behavioral and physiological changes. We previously found several...... changes in behavior indicative of a depression-like phenotype in offspring of rats subjected to repeated, variable prenatal stress (PNS), including increased locomotor activity during specific periods of the circadian cycle. We, therefore, investigated whether PNS rats also exhibit alterations in sleep...

  10. Modelling human mobility patterns using photographic data shared online.

    Science.gov (United States)

    Barchiesi, Daniele; Preis, Tobias; Bishop, Steven; Moat, Helen Susannah

    2015-08-01

    Humans are inherently mobile creatures. The way we move around our environment has consequences for a wide range of problems, including the design of efficient transportation systems and the planning of urban areas. Here, we gather data about the position in space and time of about 16 000 individuals who uploaded geo-tagged images from locations within the UK to the Flickr photo-sharing website. Inspired by the theory of Lévy flights, which has previously been used to describe the statistical properties of human mobility, we design a machine learning algorithm to infer the probability of finding people in geographical locations and the probability of movement between pairs of locations. Our findings are in general agreement with official figures in the UK and on travel flows between pairs of major cities, suggesting that online data sources may be used to quantify and model large-scale human mobility patterns.

  11. 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

  12. 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

  13. 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

  14. 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.

  15. 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

  16. Contextual snowflake modelling for pattern warehouse logical design

    Indian Academy of Sciences (India)

    being managed by the pattern warehouse management system (PWMS) ... The authors pointed out that the necessity to find out the relationship between patterns .... (i) Some customer queries can only be satisfied by specific DM technique.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. Accurate means of detecting and characterizing abnormal patterns of ventricular activation by phase image analysis

    Energy Technology Data Exchange (ETDEWEB)

    Botvinick, E.H.; Frais, M.A.; Shosa, D.W.; O' Connell, J.W.; Pacheco-Alvarez, J.A.; Scheinman, M.; Hattner, R.S.; Morady, F.; Faulkner, D.B.

    1982-08-01

    The ability of scintigraphic phase image analysis to characterize patterns of abnormal ventricular activation was investigated. The pattern of phase distribution and sequential phase changes over both right and left ventricular regions of interest were evaluated in 16 patients with normal electrical activation and wall motion and compared with those in 8 patients with an artificial pacemaker and 4 patients with sinus rhythm with the Wolff-Parkinson-White syndrome and delta waves. Normally, the site of earliest phase angle was seen at the base of the interventricular septum, with sequential change affecting the body of the septum and the cardiac apex and then spreading laterally to involve the body of both ventricles. The site of earliest phase angle was located at the apex of the right ventricle in seven patients with a right ventricular endocardial pacemaker and on the lateral left ventricular wall in one patient with a left ventricular epicardial pacemaker. In each case the site corresponded exactly to the position of the pacing electrode as seen on posteroanterior and left lateral chest X-ray films, and sequential phase changes spread from the initial focus to affect both ventricles. In each of the patients with the Wolff-Parkinson-White syndrome, the site of earliest ventricular phase angle was located, and it corresponded exactly to the site of the bypass tract as determined by endocardial mapping. In this way, four bypass pathways, two posterior left paraseptal, one left lateral and one right lateral, were correctly localized scintigraphically. On the basis of the sequence of mechanical contraction, phase image analysis provides an accurate noninvasive method of detecting abnormal foci of ventricular activation.

  3. Accurate means of detecting and characterizing abnormal patterns of ventricular activation by phase image analysis

    International Nuclear Information System (INIS)

    Botvinick, E.H.; Frais, M.A.; Shosa, D.W.; O'Connell, J.W.; Pacheco-Alvarez, J.A.; Scheinman, M.; Hattner, R.S.; Morady, F.; Faulkner, D.B.

    1982-01-01

    The ability of scintigraphic phase image analysis to characterize patterns of abnormal ventricular activation was investigated. The pattern of phase distribution and sequential phase changes over both right and left ventricular regions of interest were evaluated in 16 patients with normal electrical activation and wall motion and compared with those in 8 patients with an artificial pacemaker and 4 patients with sinus rhythm with the Wolff-Parkinson-White syndrome and delta waves. Normally, the site of earliest phase angle was seen at the base of the interventricular septum, with sequential change affecting the body of the septum and the cardiac apex and then spreading laterally to involve the body of both ventricles. The site of earliest phase angle was located at the apex of the right ventricle in seven patients with a right ventricular endocardial pacemaker and on the lateral left ventricular wall in one patient with a left ventricular epicardial pacemaker. In each case the site corresponded exactly to the position of the pacing electrode as seen on posteroanterior and left lateral chest X-ray films, and sequential phase changes spread from the initial focus to affect both ventricles. In each of the patients with the Wolff-Parkinson-White syndrome, the site of earliest ventricular phase angle was located, and it corresponded exactly to the site of the bypass tract as determined by endocardial mapping. In this way, four bypass pathways, two posterior left paraseptal, one left lateral and one right lateral, were correctly localized scintigraphically. On the basis of the sequence of mechanical contraction, phase image analysis provides an accurate noninvasive method of detecting abnormal foci of ventricular activation

  4. DNA methylation patterns in bladder cancer and washing cell sediments: a perspective for tumor recurrence detection

    Directory of Open Access Journals (Sweden)

    Goldberg José

    2008-08-01

    Full Text Available Abstract Background Epigenetic alterations are a hallmark of human cancer. In this study, we aimed to investigate whether aberrant DNA methylation of cancer-associated genes is related to urinary bladder cancer recurrence. Methods A set of 4 genes, including CDH1 (E-cadherin, SFN (stratifin, RARB (retinoic acid receptor, beta and RASSF1A (Ras association (RalGDS/AF-6 domain family 1, had their methylation patterns evaluated by MSP (Methylation-Specific Polymerase Chain Reaction analysis in 49 fresh urinary bladder carcinoma tissues (including 14 cases paired with adjacent normal bladder epithelium, 3 squamous cell carcinomas and 2 adenocarcinomas and 24 cell sediment samples from bladder washings of patients classified as cancer-free by cytological analysis (control group. A third set of samples included 39 archived tumor fragments and 23 matched washouts from 20 urinary bladder cancer patients in post-surgical monitoring. After genomic DNA isolation and sodium bisulfite modification, methylation patterns were determined and correlated with standard clinic-histopathological parameters. Results CDH1 and SFN genes were methylated at high frequencies in bladder cancer as well as in paired normal adjacent tissue and exfoliated cells from cancer-free patients. Although no statistically significant differences were found between RARB and RASSF1A methylation and the clinical and histopathological parameters in bladder cancer, a sensitivity of 95% and a specificity of 71% were observed for RARB methylation (Fisher's Exact test (p RASSF1A gene, respectively, in relation to the control group. Conclusion Indistinct DNA hypermethylation of CDH1 and SFN genes between tumoral and normal urinary bladder samples suggests that these epigenetic features are not suitable biomarkers for urinary bladder cancer. However, RARB and RASSF1A gene methylation appears to be an initial event in urinary bladder carcinogenesis and should be considered as defining a panel of

  5. 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

  6. Detecting Structural Breaks using Hidden Markov Models

    DEFF Research Database (Denmark)

    Ntantamis, Christos

    Testing for structural breaks and identifying their location is essential for econometric modeling. In this paper, a Hidden Markov Model (HMM) approach is used in order to perform these tasks. Breaks are defined as the data points where the underlying Markov Chain switches from one state to another....... The estimation of the HMM is conducted using a variant of the Iterative Conditional Expectation-Generalized Mixture (ICE-GEMI) algorithm proposed by Delignon et al. (1997), that permits analysis of the conditional distributions of economic data and allows for different functional forms across regimes...

  7. Towards Clone Detection in UML Domain Models

    DEFF Research Database (Denmark)

    Störrle, Harald

    2010-01-01

    Code clones - that is, duplicate fragments of code - have been studied for a long time. There is strong evidence that code clones are a major source of software faults. Anecdotal evidence suggests that this phenomenon is not restricted to code, but occurs in models in a very similar way. So it is...

  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. Detection of superparticles beyond the standard model

    International Nuclear Information System (INIS)

    Bornhauser, Sascha

    2008-07-01

    This Phd thesis deals with supersymmetric particles within the context of astroparticle and collider physics. The first part is about the detection of UHE cosmic particles; it based on the use of the matter of Earth and Moon as detector volume, where in the case of UHE neutralino LSPs the Earth acts in addition as a filter against the background of UHE neutrinos. We present the solutions of the transport equations regarding UHE neutralino LSP and neutrino fluxes; these solutions are given for processes where the total cross section is dominated by t- or s- channel scattering. The last section of the first part provides the final formulas for the calculation of event rates with respect to the Earth, including the background of UHE neutrinos, and the Moon. Here, we are taking into account the energy loss of tau leptons in matter, before they decay back into neutrinos. We then find detectable event rates in experiments of several teratons scale, like a future satellite experiment as EUSO or OWL only if the following conditions are satisfied: the lightest neutralino must be a higgsino, rather than a bino; the X particle must decay via a mode which results in a large ratio of neutralino LSP and proton flux; the X particle mass must be rather close to its lower bound; the experiment must be able to detect Cerenkov light. The second part deals with electroweak contributions, being the result of neutralinos and chargino exchange in the t- and/or u-channel as well as electroweak gauge bosons in the s-channel, to squark pair production at the CERN LHC. The reason for the partly sizable electroweak contributions is the interference between electroweak and QCD interactions. These contributions are most important for two final state SU(2) doublet (L-type) squarks; if one has at least one SU(2) singlet (R-type) squark, the change of the total cross sections decreases to only a few percent. We found that higher squark masses give rise to higher relative electroweak contributions

  10. Detection of superparticles beyond the standard model

    Energy Technology Data Exchange (ETDEWEB)

    Bornhauser, Sascha

    2008-07-15

    This Phd thesis deals with supersymmetric particles within the context of astroparticle and collider physics. The first part is about the detection of UHE cosmic particles; it based on the use of the matter of Earth and Moon as detector volume, where in the case of UHE neutralino LSPs the Earth acts in addition as a filter against the background of UHE neutrinos. We present the solutions of the transport equations regarding UHE neutralino LSP and neutrino fluxes; these solutions are given for processes where the total cross section is dominated by t- or s- channel scattering. The last section of the first part provides the final formulas for the calculation of event rates with respect to the Earth, including the background of UHE neutrinos, and the Moon. Here, we are taking into account the energy loss of tau leptons in matter, before they decay back into neutrinos. We then find detectable event rates in experiments of several teratons scale, like a future satellite experiment as EUSO or OWL only if the following conditions are satisfied: the lightest neutralino must be a higgsino, rather than a bino; the X particle must decay via a mode which results in a large ratio of neutralino LSP and proton flux; the X particle mass must be rather close to its lower bound; the experiment must be able to detect Cerenkov light. The second part deals with electroweak contributions, being the result of neutralinos and chargino exchange in the t- and/or u-channel as well as electroweak gauge bosons in the s-channel, to squark pair production at the CERN LHC. The reason for the partly sizable electroweak contributions is the interference between electroweak and QCD interactions. These contributions are most important for two final state SU(2) doublet (L-type) squarks; if one has at least one SU(2) singlet (R-type) squark, the change of the total cross sections decreases to only a few percent. We found that higher squark masses give rise to higher relative electroweak contributions

  11. A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

    Energy Technology Data Exchange (ETDEWEB)

    Choudhury, Sutanay; Holder, Larry; Chin, George; Agarwal, Khushbu; Feo, John T.

    2015-05-27

    Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a ``Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named ``Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over non-incremental, selectivity agnostic approaches.

  12. [Alcohol consumption patterns among patients in primary health care and detection by health professionals].

    Science.gov (United States)

    Taufick, Maíra Lemos de Castro; Evangelista, Lays Aparecida; Silva, Michelle da; Oliveira, Luiz Carlos Marques de

    2014-02-01

    This cross-sectional study investigated patterns of alcohol consumption among patients enrolled in the Family Health Program (FHP) in a city in Southeast Brazil, as well as the detection of such consumption by FHP professionals. A total of 932 adult patients were evaluated from November 2010 to November 2011. Of this total, 17.5% were considered at risk for hazardous drinking (AUDIT ≥ 8); increased risk was associated with male gender, younger age, and chronic illness. The CAGE questionnaire was positive in 98 patients (10.5%), with a higher proportion in men. Health professionals were more likely to ask about alcohol consumption in men, individuals aged ≥ 55 years, those with chronic illnesses, and heavier drinkers (438/932; 47.8%). Positive diagnosis of alcoholism was more frequent in men, individuals aged 35-54 years, and those with serious alcohol abuse (22/175; 12.6%). The study concluded that alcohol consumption is common among patients treated by FHP teams (although insufficiently recognized by professionals) and that a minority of alcoholics is instructed on the risks of drinking.

  13. Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

    Directory of Open Access Journals (Sweden)

    Andres M. Alvarez-Meza

    2017-10-01

    Full Text Available We introduce Enhanced Kernel-based Relevance Analysis (EKRA that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand.

  14. Characterization of Antimicrobial Resistance Patterns and Detection of Virulence Genes in Campylobacter Isolates in Italy

    Science.gov (United States)

    Di Giannatale, Elisabetta; Di Serafino, Gabriella; Zilli, Katiuscia; Alessiani, Alessandra; Sacchini, Lorena; Garofolo, Giuliano; Aprea, Giuseppe; Marotta, Francesca

    2014-01-01

    Campylobacter has developed resistance to several antimicrobial agents over the years, including macrolides, quinolones and fluoroquinolones, becoming a significant public health hazard. A total of 145 strains derived from raw milk, chicken faeces, chicken carcasses, cattle faeces and human faeces collected from various Italian regions, were screened for antimicrobial susceptibility, molecular characterization (SmaI pulsed-field gel electrophoresis) and detection of virulence genes (sequencing and DNA microarray analysis). The prevalence of C. jejuni and C. coli was 62.75% and 37.24% respectively. Antimicrobial susceptibility revealed a high level of resistance for ciprofloxacin (62.76%), tetracycline (55.86%) and nalidixic acid (55.17%). Genotyping of Campylobacter isolates using PFGE revealed a total of 86 unique SmaI patterns. Virulence gene profiles were determined using a new microbial diagnostic microarray composed of 70-mer oligonucleotide probes targeting genes implicated in Campylobacter pathogenicity. Correspondence between PFGE and microarray clusters was observed. Comparisons of PFGE and virulence profiles reflected the high genetic diversity of the strains examined, leading us to speculate different degrees of pathogenicity inside Campylobacter populations. PMID:24556669

  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. 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...

  17. 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...

  18. 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....

  19. 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

  20. 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.

  1. 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)

  2. Fault Management: Degradation Signature Detection, Modeling, and Processing, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Fault to Failure Progression (FFP) signature modeling and processing is a new method for applying condition-based signal data to detect degradation, to identify...

  3. Molecular modelling of a chemodosimeter for the selective detection ...

    Indian Academy of Sciences (India)

    Wintec

    Molecular modelling of a chemodosimeter for the selective detection of. As(III) ion in water. † ... high levels of arsenic cause severe skin diseases in- cluding skin cancer ..... Special Attention to Groundwater in SE Asia (eds) D. Chakraborti, A ...

  4. Review of Literature for Model Assisted Probability of Detection

    Energy Technology Data Exchange (ETDEWEB)

    Meyer, Ryan M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Crawford, Susan L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lareau, John P. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Anderson, Michael T. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2014-09-30

    This is a draft technical letter report for NRC client documenting a literature review of model assisted probability of detection (MAPOD) for potential application to nuclear power plant components for improvement of field NDE performance estimations.

  5. 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.

  6. Non-Parametric Model Drift Detection

    Science.gov (United States)

    2016-07-01

    framework on two tasks in NLP domain, topic modeling, and machine translation. Our main findings are summarized as follows: • We can measure important...thank,us,me,hope,today Group num: 4, TC(X;Y_j): 0.407 4:republic,palestinian,israel, arab ,israeli,democratic,congo,mr,president,occupied Group num: 5...support,change,lessons,partnerships,l earned Group num: 35, TC(X;Y_j): 0.094 35:russian,federation,spoke,you,french,spanish, arabic ,your,chinese,sir

  7. 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

  8. 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

  9. Agent-based modelling of shifting cultivation field patterns, Vietnam

    DEFF Research Database (Denmark)

    Jepsen, Martin Rudbeck; Leisz, S.; Rasmussen, K.

    2006-01-01

    Shifting cultivation in the Nghe An Province of Vietnam's Northern Mountain Region produces a characteristic land-cover pattern of small and larger fields. The pattern is the result of farmers cultivating either individually or in spatially clustered groups. Using spatially explicit agent...

  10. 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.

  11. DNA methylation patterns in bladder cancer and washing cell sediments: a perspective for tumor recurrence detection

    International Nuclear Information System (INIS)

    Negraes, Priscilla D; Favaro, Francine P; Camargo, João Lauro V; Oliveira, Maria Luiza CS; Goldberg, José; Rainho, Cláudia A; Salvadori, Daisy MF

    2008-01-01

    Epigenetic alterations are a hallmark of human cancer. In this study, we aimed to investigate whether aberrant DNA methylation of cancer-associated genes is related to urinary bladder cancer recurrence. A set of 4 genes, including CDH1 (E-cadherin), SFN (stratifin), RARB (retinoic acid receptor, beta) and RASSF1A (Ras association (RalGDS/AF-6) domain family 1), had their methylation patterns evaluated by MSP (Methylation-Specific Polymerase Chain Reaction) analysis in 49 fresh urinary bladder carcinoma tissues (including 14 cases paired with adjacent normal bladder epithelium, 3 squamous cell carcinomas and 2 adenocarcinomas) and 24 cell sediment samples from bladder washings of patients classified as cancer-free by cytological analysis (control group). A third set of samples included 39 archived tumor fragments and 23 matched washouts from 20 urinary bladder cancer patients in post-surgical monitoring. After genomic DNA isolation and sodium bisulfite modification, methylation patterns were determined and correlated with standard clinic-histopathological parameters. CDH1 and SFN genes were methylated at high frequencies in bladder cancer as well as in paired normal adjacent tissue and exfoliated cells from cancer-free patients. Although no statistically significant differences were found between RARB and RASSF1A methylation and the clinical and histopathological parameters in bladder cancer, a sensitivity of 95% and a specificity of 71% were observed for RARB methylation (Fisher's Exact test (p < 0.0001; OR = 48.89) and, 58% and 17% (p < 0.05; OR = 0.29) for RASSF1A gene, respectively, in relation to the control group. Indistinct DNA hypermethylation of CDH1 and SFN genes between tumoral and normal urinary bladder samples suggests that these epigenetic features are not suitable biomarkers for urinary bladder cancer. However, RARB and RASSF1A gene methylation appears to be an initial event in urinary bladder carcinogenesis and should be considered as defining a

  12. Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning.

    Directory of Open Access Journals (Sweden)

    Erico N de Souza

    Full Text Available A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with uncertainty, especially in remote areas and the High Seas. Better understanding of the behavior of the global fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS are now commonly installed on most ocean-going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. Here we present approaches to identify fishing activity from S-AIS data for three dominant fishing gear types: trawl, longline and purse seine. Using a large dataset containing worldwide fishing vessel tracks from 2011-2015, we developed three methods to detect and map fishing activities: for trawlers we produced a Hidden Markov Model (HMM using vessel speed as observation variable. For longliners we have designed a Data Mining (DM approach using an algorithm inspired from studies on animal movement. For purse seiners a multi-layered filtering strategy based on vessel speed and operation time was implemented. Validation against expert-labeled datasets showed average detection accuracies of 83% for trawler and longliner, and 97% for purse seiner. Our study represents the first comprehensive approach to detect and identify potential fishing behavior for three major gear types operating on a global scale. We hope that this work will enable new efforts to assess the spatial and temporal distribution of global fishing effort and make global fisheries activities transparent to ocean scientists, managers and the public.

  13. Pattern formation through spatial interactions in a modified Daisyworld model

    Science.gov (United States)

    Alberti, Tommaso; Primavera, Leonardo; Lepreti, Fabio; Vecchio, Antonio; Carbone, Vincenzo

    2015-04-01

    The Daisyworld model is based on a hypothetical planet, like the Earth, which receives the radiant energy coming from a Sun-like star, and populated by two kinds of identical plants differing by their colour: white daisies reflecting light and black daisies absorbing light. The interactions and feedbacks between the collective biota of the planet and the incoming radiation form a self-regulating system where the conditions for life are maintained. We investigate a modified version of the Daisyworld model where a spatial dependency on latitude is introduced, and both a variable heat diffusivity along latitude and a simple greenhouse model are included. We show that the spatial interactions between the variables of the system can generate some equilibrium patterns which can locally stabilize the coexistence of the two vegetation types. The feedback on albedo is able to generate new equilibrium solutions which can efficiently self-regulate the planet climate, even for values of the solar luminosity relatively far from the current Earth conditions. The extension to spatial Daisyworld gives room to the possibility of inhomogeneous solar forcing in a curved planet, with explicit differences between poles and equator and the direct use of the heat diffusion equation. As a first approach, to describe a spherical planet, we consider the temperature T(θ,t) and the surface coverage as depending only on time and on latitude θ (-90° ≤ θ ≤ 90°). A second step is the introduction of the greenhouse effect in the model, the process by which outgoing infrared radiation is partly screened by greenhouse gases. This effect can be described by relaxing the black-body radiation hypothesis and by introducing a grayness function g(T) in the heat equation. As a third step, we consider a latitude dependence of the Earth's conductivity, χ = χ(θ). Considering these terms, using spherical coordinates and symmetry with respect to θ, the modified Daisyworld equations reduce to the

  14. 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.

  15. 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

  16. Using soft-hard fusion for misinformation detection and pattern of life analysis in OSINT

    Science.gov (United States)

    Levchuk, Georgiy; Shabarekh, Charlotte

    2017-05-01

    Today's battlefields are shifting to "denied areas", where the use of U.S. Military air and ground assets is limited. To succeed, the U.S. intelligence analysts increasingly rely on available open-source intelligence (OSINT) which is fraught with inconsistencies, biased reporting and fake news. Analysts need automated tools for retrieval of information from OSINT sources, and these solutions must identify and resolve conflicting and deceptive information. In this paper, we present a misinformation detection model (MDM) which converts text to attributed knowledge graphs and runs graph-based analytics to identify misinformation. At the core of our solution is identification of knowledge conflicts in the fused multi-source knowledge graph, and semi-supervised learning to compute locally consistent reliability and credibility scores for the documents and sources, respectively. We present validation of proposed method using an open source dataset constructed from the online investigations of MH17 downing in Eastern Ukraine.

  17. 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.

  18. Asteroid families from cratering: Detection and models

    Science.gov (United States)

    Milani, A.; Cellino, A.; Knežević, Z.; Novaković, B.; Spoto, F.; Paolicchi, P.

    2014-07-01

    A new asteroid families classification, more efficient in the inclusion of smaller family members, shows how relevant the cratering impacts are on large asteroids. These do not disrupt the target, but just form families with the ejecta from large craters. Of the 12 largest asteroids, 8 have cratering families: number (2), (4), (5), (10), (87), (15), (3), and (31). At least another 7 cratering families can be identified. Of the cratering families identified so far, 7 have >1000 members. This imposes a remarkable change from the focus on fragmentation families of previous classifications. Such a large dataset of asteroids believed to be crater ejecta opens a new challenge: to model the crater and family forming event(s) generating them. The first problem is to identify which cratering families, found by the similarity of proper elements, can be formed at once, with a single collision. We have identified as a likely outcome of multiple collisions the families of (4), (10), (15), and (20). Of the ejecta generated by cratering, only a fraction reaches the escape velocity from the surviving parent body. The distribution of velocities at infinity, giving to the resulting family an initial position and shape in the proper elements space, is highly asymmetric with respect to the parent body. This shape is deformed by the Yarkovsky effect and by the interaction with resonances. All the largest asteroids have been subjected to large cratering events, thus the lack of a family needs to be interpreted. The most interesting case is (1) Ceres, which is not the parent body of the nearby family of (93). Two possible interpretations of the low family forming efficiency are based on either the composition of Ceres with a significant fraction of ice, protected by a thin crust, or with the larger escape velocity of ~500 m/s.

  19. [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.

  20. 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.

  1. Assessing elders using the functional health pattern assessment model.

    Science.gov (United States)

    Beyea, S; Matzo, M

    1989-01-01

    The impact of older Americans on the health care system requires we increase our students' awareness of their unique needs. The authors discuss strategies to develop skills using Gordon's Functional Health Patterns Assessment for assessing older clients.

  2. 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.

  3. A Kinematic Method for Footstrike Pattern Detection in Barefoot and Shod Runners

    OpenAIRE

    Altman, Allison R.; Davis, Irene S.

    2011-01-01

    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 0–100% (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 o...

  4. 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.

  5. 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.

  6. A new binaural detection model based on contralateral inhibition

    NARCIS (Netherlands)

    Breebaart, D.J.; Kohlrausch, A.G.; Dau, T.; Hohmann, V.; Kollmeier, B.

    1999-01-01

    Binaural models attempt to explain binaural phenomena in terms of neural mechanisms that extract binaural information from accoustic stimuli. In this paper, a model setup is presented that can be used to simulate binaural detection tasks. In contrast to the most often used cross correlation between

  7. 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)

  8. 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.

  9. 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.

  10. 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).

  11. 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.

  12. 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...

  13. 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.

  14. 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.

  15. Seizure pattern-specific epileptic epoch detection in patients with intellectual disability

    NARCIS (Netherlands)

    Wang, L.; Arends, J.B.A.M.; Long, X.; Cluitmans, P.J.M.; van Dijk, J.P.

    Electroencephalogram (EEG) features are crucial for the seizure detection performance. Traditional algorithms are designed for a population with normal brain development. However, for patients with an intellectual disability the seizure detection performance is still largely unknown. In addition,

  16. 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

  17. 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.

  18. A Bi-centre Study of the Pattern and Evolution of readily detectable ...

    African Journals Online (AJOL)

    The pattern and evolution of obvious post-meningitic sequelae were determined in 187 post-neonatal children followed up at two tertiary centres. The pattern of sequelae was classified using previously described schemes, as well as by the number of deficits per child. One hundred and eighty-seven children were assessed ...

  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. A model to explain joint patterns found in ignimbrite deposits

    Science.gov (United States)

    Tibaldi, A.; Bonali, F. L.

    2018-03-01

    The study of fracture systems is of paramount importance for economic applications, such as CO2 storage in rock successions, geothermal and hydrocarbon exploration and exploitation, and also for a better knowledge of seismogenic fault formation. Understanding the origin of joints can be useful for tectonic studies and for a geotechnical characterisation of rock masses. Here, we illustrate a joint pattern discovered in ignimbrite deposits of South America, which can be confused with conjugate tectonic joint sets but which have another origin. The pattern is probably common, but recognisable only in plan view and before tectonic deformation obscures and overprints it. Key sites have been mostly studied by field surveys in Bolivia and Chile. The pattern is represented by hundreds-of-meters up to kilometre-long swarms of master joints, which show circular to semi-circular geometries and intersections that have "X" and "Y" patterns. Inside each swarm, joints are systematic, rectilinear or curvilinear in plan view, and as much as 900 m long. In section view, they are from sub-vertical to vertical and do not affect the underlying deposits. Joints with different orientation mostly interrupt each other, suggesting they have the same age. This joint architecture is here interpreted as resulting from differential contraction after emplacement of the ignimbrite deposit above a complex topography. The set of the joint pattern that has suitable orientation with respect to tectonic stresses may act to nucleate faults.

  1. Detection of Outliers in Regression Model for Medical Data

    Directory of Open Access Journals (Sweden)

    Stephen Raj S

    2017-07-01

    Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.

  2. Detection of temporal behaviour patterns of free-ranging cattle by means of diversity spectra

    Directory of Open Access Journals (Sweden)

    de Miguel, J. M.

    1991-06-01

    Full Text Available The aim of this paper is to detect temporal patterns of cattle behaviour. The method, diversity spectra, provides, on the one hand, the number of parts into which a temporary transect should be divided in order to understand the maximum segregation of cattle activities and, on the other, the clarity with which each segregation is defined. In the case under study (a 'dehesa' pasture-land in central Spain the maximum segregation of fundamental activities in cattle behaviour is reached by considering the year as divided into two periods: spring-summer and autumn-winter. Cattle behaviour shows an annual "coarse grain" pattern, which is associated with management activities and with the meteorological seasonality of the Mediterranean climate. However, within each of the two annual periods, maximum segregation is reached considering separately the days of observation. This "fine grain" pattern indicates within each season, a certain capacity for response to a fluctuating environment and determines very different behaviour on close days. During autumn-winter period cattle show seasonal and daily activity segregations which are clearer than during spring-summer. In the former period, the lack of grass, more severe climatic conditions and management would seem to be determining factors of this temporal behaviour pattern.

    [es] El objetivo del trabajo es identificar patrones temporales de comportamiento del ganado. El procedimiento utilizado, espectros de diversidad, permite apreciar, por un lado, el número de partes en que debe dividirse un transecto temporal para detectar la máxima segregación de las actividades del ganado y, por otro, el grado de definición con que se manifiesta dicha segregación. En el caso estudiado (una dehesa del centro de España la máxima segregación de las actividades fundamentales de comportamiento del ganado se produce al considerar el año dividido en dos periodos: primavera-verano y otoño-invierno. El

  3. 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%

  4. 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)

  5. Spatio-Temporal Diffusion Pattern and Hotspot Detection of Dengue in Chachoengsao Province, Thailand

    Directory of Open Access Journals (Sweden)

    Phaisarn Jeefoo

    2010-12-01

    Full Text Available In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999–2007 was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group, data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall, spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999–2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well.

  6. 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

  7. Chimera patterns in the Kuramoto–Battogtokh model

    International Nuclear Information System (INIS)

    Smirnov, Lev; Osipov, Grigory; Pikovsky, Arkady

    2017-01-01

    Kuramoto and Battogtokh (2002 Nonlinear Phenom. Complex Syst . 5 380) discovered chimera states represented by stable coexisting synchrony and asynchrony domains in a lattice of coupled oscillators. After a reformulation in terms of a local order parameter, the problem can be reduced to partial differential equations. We find uniformly rotating, spatially periodic chimera patterns as solutions of a reversible ordinary differential equation, and demonstrate a plethora of such states. In the limit of neutral coupling they reduce to analytical solutions in the form of one- and two-point chimera patterns as well as localized chimera solitons. Patterns at weakly attracting coupling are characterized by virtue of a perturbative approach. Stability analysis reveals that only the simplest chimeras with one synchronous region are stable. (letter)

  8. Chimera patterns in the Kuramoto-Battogtokh model

    Science.gov (United States)

    Smirnov, Lev; Osipov, Grigory; Pikovsky, Arkady

    2017-02-01

    Kuramoto and Battogtokh (2002 Nonlinear Phenom. Complex Syst. 5 380) discovered chimera states represented by stable coexisting synchrony and asynchrony domains in a lattice of coupled oscillators. After a reformulation in terms of a local order parameter, the problem can be reduced to partial differential equations. We find uniformly rotating, spatially periodic chimera patterns as solutions of a reversible ordinary differential equation, and demonstrate a plethora of such states. In the limit of neutral coupling they reduce to analytical solutions in the form of one- and two-point chimera patterns as well as localized chimera solitons. Patterns at weakly attracting coupling are characterized by virtue of a perturbative approach. Stability analysis reveals that only the simplest chimeras with one synchronous region are stable.

  9. Modeling activity patterns of wildlife using time-series analysis.

    Science.gov (United States)

    Zhang, Jindong; Hull, Vanessa; Ouyang, Zhiyun; He, Liang; Connor, Thomas; Yang, Hongbo; Huang, Jinyan; Zhou, Shiqiang; Zhang, Zejun; Zhou, Caiquan; Zhang, Hemin; Liu, Jianguo

    2017-04-01

    The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency-based time-series analysis, with high-resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e.g., pregnancy) and external factors (e.g., seasonal dynamics of resources and weather) on activity patterns of the endangered giant panda ( Ailuropoda melanoleuca ). Giant pandas exhibited higher frequency cycles during the winter when resources (e.g., water and forage) were relatively poor, as well as during spring, which includes the giant panda's mating season. During the summer and autumn when resources were abundant, pandas exhibited a regular activity pattern with activity peaks every 24 hr. A pregnant individual showed distinct differences in her activity pattern from other giant pandas for several months following parturition. These results indicate that animals adjust activity cycles to adapt to seasonal variation of the resources and unique physiological periods. Wavelet coherency analysis also verified the synchronization of giant panda activity level with air temperature and solar radiation at the 24-hr band. Our study also shows that wavelet analysis is an effective tool for analyzing high-resolution activity pattern data and its relationship to internal and external states, an approach that has the potential to inform wildlife conservation and management across species.

  10. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.

    Science.gov (United States)

    Liao, Shih-Cheng; Wu, Chien-Te; Huang, Hao-Chuan; Cheng, Wei-Teng; Liu, Yi-Hung

    2017-06-14

    Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP

  11. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

    Directory of Open Access Journals (Sweden)

    Shih-Cheng Liao

    2017-06-01

    Full Text Available Major depressive disorder (MDD has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP. The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total. Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be

  12. 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.

  13. 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

  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. Prediction of Annual Rainfall Pattern Using Hidden Markov Model ...

    African Journals Online (AJOL)

    ADOWIE PERE

    Hidden Markov model is very influential in stochastic world because of its ... the earth from the clouds. The usual ... Rainfall modelling and ... Markov Models have become popular tools ... environment sciences, University of Jos, plateau state,.

  16. Detectable elements in a particles pattern of suspended urban matter analysed by neutron activation

    International Nuclear Information System (INIS)

    Herrera, L.; Beltran, C.; Alemon, E.; Ortiz, M.E.

    2001-01-01

    The multielement composition of a Standard Reference Material 1648 pattern certified is reported and it is used for the suspended in air aerosol samples analysis from urban localities of the Valley of Mexico, which was irradiated in the same geometry of the sample. The bottom of laboratory is analysed where was made the gamma spectrometry and it is compared the ratio of country up of bottom photo peaks with pattern photo peaks in nearer interest regions. The bottom natural gamma transmitters were identified and those of the activated pattern in the TRIGA Mark III nuclear reactor. (Author)

  17. Pattern formation in the bistable Gray-Scott model

    DEFF Research Database (Denmark)

    Mazin, W.; Rasmussen, K.E.; Mosekilde, Erik

    1996-01-01

    The paper presents a computer simulation study of a variety of far-from-equilibrium phenomena that can arise in a bistable chemical reaction-diffusion system which also displays Turing and Hopf instabilities. The Turing bifurcation curve and the wave number for the patterns of maximum linear grow...

  18. 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.

  19. 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.

  20. Models and detection of spontaneous recurrent seizures in laboratory rodents

    Directory of Open Access Journals (Sweden)

    Bin Gu

    2017-07-01

    Full Text Available Epilepsy, characterized by spontaneous recurrent seizures (SRS, is a serious and common neurological disorder afflicting an estimated 1% of the population worldwide. Animal experiments, especially those utilizing small laboratory rodents, remain essential to understanding the fundamental mechanisms underlying epilepsy and to prevent, diagnose, and treat this disease. While much attention has been focused on epileptogenesis in animal models of epilepsy, there is little discussion on SRS, the hallmark of epilepsy. This is in part due to the technical difficulties of rigorous SRS detection. In this review, we comprehensively summarize both genetic and acquired models of SRS and discuss the methodology used to monitor and detect SRS in mice and rats.

  1. 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.

  2. Detection of macro-ecological patterns in South American hummingbirds is affected by spatial scale

    DEFF Research Database (Denmark)

    Rahbek, Carsten; Graves, Gary R.

    2000-01-01

    Scale is widely recognized as a fundamental conceptual problem in biology, but the question of whether species-richness patterns vary with scale is often ignored in macro-ecological analyses, despite the increasing application of such data in international conservation programmes. We tested for s...... peaks, decreasing the power of statistical tests to discriminate the causal agents of regional richness gradients. Ideally, the scale of analysis should be varied systematically to provide the optimal resolution of macro-ecological pattern....

  3. 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.

  4. 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.

  5. 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

  6. Molecular and metabolic pattern classification for detection of brain glioma progression

    Energy Technology Data Exchange (ETDEWEB)

    Imani, Farzin, E-mail: imanif@upmc.edu [Department of Radiology, University of Pittsburgh Medical Center, PA (United States); Boada, Fernando E. [Department of Radiology, University of Pittsburgh Medical Center, PA (United States); Lieberman, Frank S. [Department of Neurology, University of Pittsburgh Medical Center, PA (United States); Davis, Denise K.; Mountz, James M. [Department of Radiology, University of Pittsburgh Medical Center, PA (United States)

    2014-02-15

    %. Conclusion: This study suggests that SVM models may improve detection of glioma progression more accurately than single parametric imaging methods. Research support: National Cancer Institute, Cancer Center Support Grant Supplement Award, Imaging Response Assessment Teams.

  7. 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

  8. 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.

  9. Revisiting the direct detection of dark matter in simplified models

    OpenAIRE

    Li, Tong

    2018-01-01

    In this work we numerically re-examine the loop-induced WIMP-nucleon scattering cross section for the simplified dark matter models and the constraint set by the latest direct detection experiment. We consider a fermion, scalar or vector dark matter component from five simplified models with leptophobic spin-0 mediators coupled only to Standard Model quarks and dark matter particles. The tree-level WIMP-nucleon cross sections in these models are all momentum-suppressed. We calculate the non-s...

  10. Model driven design of distribution patterns for web service compositions

    OpenAIRE

    2006-01-01

    Increasingly, distributed systems are being constructed by composing a number of discrete components. This practice, termed composition, is particularly prevalent within the Web service domain. Here, enterprise systems are built from many existing discrete applications, often legacy applications exposed using Web service interfaces. There are a number of architectural configurations or distribution patterns, which express how a composed system is to be deployed. However, the amount o...

  11. Fusion of optical flow based motion pattern analysis and silhouette classification for person tracking and detection

    NARCIS (Netherlands)

    Tangelder, J.W.H.; Lebert, E.; Burghouts, G.J.; Zon, K. van; Den Uyl, M.J.

    2014-01-01

    This paper presents a novel approach to detect persons in video by combining optical flow based motion analysis and silhouette based recognition. A new fast optical flow computation method is described, and its application in a motion based analysis framework unifying human tracking and detection is

  12. 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.

  13. Modelling and interpretation of gas detection using remote laser pointers.

    Science.gov (United States)

    Hodgkinson, J; van Well, B; Padgett, M; Pride, R D

    2006-04-01

    We have developed a quantitative model of the performance of laser pointer style gas leak detectors, which are based on remote detection of backscattered radiation. The model incorporates instrumental noise limits, the reflectivity of the target background surface and a mathematical description of gas leak dispersion in constant wind speed and turbulence conditions. We have investigated optimum instrument performance and limits of detection in simulated leak detection situations. We predict that the optimum height for instruments is at eye level or above, giving an operating range of 10 m or more for most background surfaces, in wind speeds of up to 2.5 ms(-1). For ground based leak sources, we find laser pointer measurements are dominated by gas concentrations over a short distance close to the target surface, making their readings intuitive to end users in most cases. This finding is consistent with the results of field trials.

  14. 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

  15. 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

  16. 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

  17. 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

  18. Modeling spatial pattern of deforestation using GIS and logistic ...

    African Journals Online (AJOL)

    This study aimed to predict spatial distribution of deforestation and detects factors influencing forest degradation of Northern forests of Ilam province. For this purpose, effects of six factors including distance from road and settlement areas, forest fragmentation index, elevation, slope and distance from the forest edge on the ...

  19. 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

  20. An empirical probability model of detecting species at low densities.

    Science.gov (United States)

    Delaney, David G; Leung, Brian

    2010-06-01

    False negatives, not detecting things that are actually present, are an important but understudied problem. False negatives are the result of our inability to perfectly detect species, especially those at low density such as endangered species or newly arriving introduced species. They reduce our ability to interpret presence-absence survey data and make sound management decisions (e.g., rapid response). To reduce the probability of false negatives, we need to compare the efficacy and sensitivity of different sampling approaches and quantify an unbiased estimate of the probability of detection. We conducted field experiments in the intertidal zone of New England and New York to test the sensitivity of two sampling approaches (quadrat vs. total area search, TAS), given different target characteristics (mobile vs. sessile). Using logistic regression we built detection curves for each sampling approach that related the sampling intensity and the density of targets to the probability of detection. The TAS approach reduced the probability of false negatives and detected targets faster than the quadrat approach. Mobility of targets increased the time to detection but did not affect detection success. Finally, we interpreted two years of presence-absence data on the distribution of the Asian shore crab (Hemigrapsus sanguineus) in New England and New York, using our probability model for false negatives. The type of experimental approach in this paper can help to reduce false negatives and increase our ability to detect species at low densities by refining sampling approaches, which can guide conservation strategies and management decisions in various areas of ecology such as conservation biology and invasion ecology.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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)

  6. Assessment of errors and uncertainty patterns in GIA modeling

    DEFF Research Database (Denmark)

    Barletta, Valentina Roberta; Spada, G.

    2012-01-01

    During the last decade many efforts have been devoted to the assessment of global sea level rise and to the determination of the mass balance of continental ice sheets. In this context, the important role of glacial-isostatic adjustment (GIA) has been clearly recognized. Yet, in many cases only one......, such as time-evolving shorelines and paleo-coastlines. In this study we quantify these uncertainties and their propagation in GIA response using a Monte Carlo approach to obtain spatio-temporal patterns of GIA errors. A direct application is the error estimates in ice mass balance in Antarctica and Greenland...

  7. Rail Track Detection and Modelling in Mobile Laser Scanner Data

    Directory of Open Access Journals (Sweden)

    S. Oude Elberink

    2013-10-01

    Full Text Available We present a method for detecting and modelling rails in mobile laser scanner data. The detection is based on the properties of the rail tracks and contact wires such as relative height, linearity and relative position with respect to other objects. Points classified as rail track are used in a 3D modelling algorithm. The modelling is done by first fitting a parametric model of a rail piece to the points along each track, and estimating the position and orientation parameters of each piece model. For each position and orientation parameter a smooth low-order Fourier curve is interpolated. Using all interpolated parameters a mesh model of the rail is reconstructed. The method is explained using two areas from a dataset acquired by a LYNX mobile mapping system in a mountainous area. Residuals between railway laser points and 3D models are in the range of 2 cm. It is concluded that a curve fitting algorithm is essential to reliably and accurately model the rail tracks by using the knowledge that railways are following a continuous and smooth path.

  8. People detection in crowded scenes using active contour models

    Science.gov (United States)

    Sidla, Oliver

    2009-01-01

    The detection of pedestrians in real-world scenes is a daunting task, especially in crowded situations. Our experience over the last years has shown that active shape models (ASM) can contribute significantly to a robust pedestrian detection system. The paper starts with an overview of shape model approaches, it then explains our approach which builds on top of Eigenshape models which have been trained using real-world data. These models are placed over candidate regions and matched to image gradients using a scoring function which integrates i) point distribution, ii) local gradient orientations iii) local image gradient strengths. A matching and shape model update process is iteratively applied in order to fit the flexible models to the local image content. The weights of the scoring function have a significant impact on the ASM performance. We analyze different settings of scoring weights for gradient magnitude, relative orientation differences, distance between model and gradient in an experiment which uses real-world data. Although for only one pedestrian model in an image computation time is low, the number of necessary processing cycles which is needed to track many people in crowded scenes can become the bottleneck in a real-time application. We describe the measures which have been taken in order to improve the speed of the ASM implementation and make it real-time capable.

  9. On-Line Detection of Distributed Attacks from Space-Time Network Flow Patterns

    National Research Council Canada - National Science Library

    Baras, J. S; Cardenas, A. A; Ramezani, V

    2003-01-01

    .... The directionality of the change in a network flow is assumed to have an objective or target. The particular problem of detecting distributed denial of service attacks from distributed observations is presented as a working framework...

  10. Hardware-software face detection system based on multi-block local binary patterns

    Science.gov (United States)

    Acasandrei, Laurentiu; Barriga, Angel

    2015-03-01

    Face detection is an important aspect for biometrics, video surveillance and human computer interaction. Due to the complexity of the detection algorithms any face detection system requires a huge amount of computational and memory resources. In this communication an accelerated implementation of MB LBP face detection algorithm targeting low frequency, low memory and low power embedded system is presented. The resulted implementation is time deterministic and uses a customizable AMBA IP hardware accelerator. The IP implements the kernel operations of the MB-LBP algorithm and can be used as universal accelerator for MB LBP based applications. The IP employs 8 parallel MB-LBP feature evaluators cores, uses a deterministic bandwidth, has a low area profile and the power consumption is ~95 mW on a Virtex5 XC5VLX50T. The resulted implementation acceleration gain is between 5 to 8 times, while the hardware MB-LBP feature evaluation gain is between 69 and 139 times.

  11. Gamma and neutron detection modeling in the nuclear detection figure of merit (NDFOM) portal

    International Nuclear Information System (INIS)

    Stroud, Phillip D.; Saeger, Kevin J.

    2009-01-01

    The Nuclear Detection Figure Of Merit (NDFOM) portal is a database of objects and algorithms for evaluating the performance of radiation detectors to detect nuclear material. This paper describes the algorithms used to model the physics and mathematics of radiation detection. As a first-principles end-to-end analysis system, it starts with the representation of the gamma and neutron spectral fluxes, which are computed with the particle and radiation transport code MCNPX. The gamma spectra emitted by uranium, plutonium, and several other materials of interest are described. The impact of shielding and other intervening material is computed by the method of build-up factors. The interaction of radiation with the detector material is computed by a detector response function approach. The construction of detector response function matrices based on MCNPX simulation runs is described in detail. Neutron fluxes are represented in a three group formulation to treat differences in detector sensitivities to thermal, epithermal, and fast neutrons.

  12. Employment, Production and Consumption model: Patterns of phase transitions

    Science.gov (United States)

    Lavička, H.; Lin, L.; Novotný, J.

    2010-04-01

    We have simulated the model of Employment, Production and Consumption (EPC) using Monte Carlo. The EPC model is an agent based model that mimics very basic rules of industrial economy. From the perspective of physics, the nature of the interactions in the EPC model represents multi-agent interactions where the relations among agents follow the key laws for circulation of capital and money. Monte Carlo simulations of the stochastic model reveal phase transition in the model economy. The two phases are the phase with full unemployment and the phase with nearly full employment. The economy switches between these two states suddenly as a reaction to a slight variation in the exogenous parameter, thus the system exhibits strong non-linear behavior as a response to the change of the exogenous parameters.

  13. 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.

  14. Fault detection in IRIS reactor secondary loop using inferential models

    International Nuclear Information System (INIS)

    Perillo, Sergio R.P.; Upadhyaya, Belle R.; Hines, J. Wesley

    2013-01-01

    The development of fault detection algorithms is well-suited for remote deployment of small and medium reactors, such as the IRIS, and the development of new small modular reactors (SMR). However, an extensive number of tests are still to be performed for new engineering aspects and components that are not yet proven technology in the current PWRs, and present some technological challenges for its deployment since many of its features cannot be proven until a prototype plant is built. In this work, an IRIS plant simulation platform was developed using a Simulink® model. The dynamic simulation was utilized in obtaining inferential models that were used to detect faults artificially added to the secondary system simulations. The implementation of data-driven models and the results are discussed. (author)

  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. 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

  17. Spiking and bursting patterns of fractional-order Izhikevich model

    Science.gov (United States)

    Teka, Wondimu W.; Upadhyay, Ranjit Kumar; Mondal, Argha

    2018-03-01

    Bursting and spiking oscillations play major roles in processing and transmitting information in the brain through cortical neurons that respond differently to the same signal. These oscillations display complex dynamics that might be produced by using neuronal models and varying many model parameters. Recent studies have shown that models with fractional order can produce several types of history-dependent neuronal activities without the adjustment of several parameters. We studied the fractional-order Izhikevich model and analyzed different kinds of oscillations that emerge from the fractional dynamics. The model produces a wide range of neuronal spike responses, including regular spiking, fast spiking, intrinsic bursting, mixed mode oscillations, regular bursting and chattering, by adjusting only the fractional order. Both the active and silent phase of the burst increase when the fractional-order model further deviates from the classical model. For smaller fractional order, the model produces memory dependent spiking activity after the pulse signal turned off. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. On the network level, the response of the neuronal network shifts from random to scale-free spiking. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

  18. 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.

  19. 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.

  20. Detecting physics beyond the Standard Model with the REDTOP experiment

    Science.gov (United States)

    González, D.; León, D.; Fabela, B.; Pedraza, M. I.

    2017-10-01

    REDTOP is an experiment at its proposal stage. It belongs to the High Intensity class of experiments. REDTOP will use a 1.8 GeV continuous proton beam impinging on a fixed target. It is expected to produce about 1013 η mesons per year. The main goal of REDTOP is to look for physics beyond the Standard Model by detecting rare η decays. The detector is designed with innovative technologies based on the detection of prompt Cherenkov light, such that interesting events can be observed and the background events are efficiently rejected. The experimental design, the physics program and the running plan of the experiment is presented.

  1. Evading direct dark matter detection in Higgs portal models

    Energy Technology Data Exchange (ETDEWEB)

    Arcadi, Giorgio [Max Planck Institut für Kernphysik, Saupfercheckweg 1, D-69117 Heidelberg (Germany); Gross, Christian, E-mail: christian.gross@helsinki.fi [Department of Physics and Helsinki Institute of Physics, Gustaf Hällströmin katu 2, FI-00014 Helsinki (Finland); Lebedev, Oleg [Department of Physics and Helsinki Institute of Physics, Gustaf Hällströmin katu 2, FI-00014 Helsinki (Finland); Pokorski, Stefan [Institute of Theoretical Physics, University of Warsaw, Pasteura 5, PL-02-093 Warsaw (Poland); Toma, Takashi [Physik-Department T30d, Technische Universität München, James-Franck-Straße, D-85748 Garching (Germany)

    2017-06-10

    Many models of Higgs portal Dark Matter (DM) find themselves under pressure from increasingly tight direct detection constraints. In the framework of gauge field DM, we study how such bounds can be relaxed while retaining the thermal WIMP paradigm. When the hidden sector gauge symmetry is broken via the Higgs mechanism, the hidden sector generally contains unstable states which are lighter than dark matter. These states provide DM with an efficient annihilation channel. As a result, the DM relic abundance and the direct detection limits are controlled by different parameters, and the two can easily be reconciled. This simple setup realizes the idea of “secluded” dark matter naturally.

  2. 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.

  3. 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.

  4. Color Mixing Correction for Post-printed Patterns on Colored Background Using Modified Particle Density Model

    OpenAIRE

    Suwa , Misako; Fujimoto , Katsuhito

    2006-01-01

    http://www.suvisoft.com; Color mixing occurs between background and foreground colors when a pattern is post-printed on a colored area because ink is not completely opaque. This paper proposes a new method for the correction of color mixing in line pattern such as characters and stamps, by using a modified particle density model. Parameters of the color correction can be calculated from two sets of foreground and background colors. By employing this method, the colors of foreground patterns o...

  5. Stationary Patterns in One-Predator Two-Prey Models

    DEFF Research Database (Denmark)

    Pedersen, Michael; Zhigui, Lin

    1999-01-01

    Weakly-coupled elliptic system decribing models of simple three-species food webs such as the one-predator, two-prey model is discussed. We show that there is no non-constant solution if diffusions or inter-specific competitions are strong, or if the intrinsic growths of the prey are slow...

  6. Analyzing Interaction Patterns to Verify a Simulation/Game Model

    Science.gov (United States)

    Myers, Rodney Dean

    2012-01-01

    In order for simulations and games to be effective for learning, instructional designers must verify that the underlying computational models being used have an appropriate degree of fidelity to the conceptual models of their real-world counterparts. A simulation/game that provides incorrect feedback is likely to promote misunderstanding and…

  7. Pattern formation in flocking models: A hydrodynamic description.

    Science.gov (United States)

    Solon, Alexandre P; Caussin, Jean-Baptiste; Bartolo, Denis; Chaté, Hugues; Tailleur, Julien

    2015-12-01

    We study in detail the hydrodynamic theories describing the transition to collective motion in polar active matter, exemplified by the Vicsek and active Ising models. Using a simple phenomenological theory, we show the existence of an infinity of propagative solutions, describing both phase and microphase separation, that we fully characterize. We also show that the same results hold specifically in the hydrodynamic equations derived in the literature for the active Ising model and for a simplified version of the Vicsek model. We then study numerically the linear stability of these solutions. We show that stable ones constitute only a small fraction of them, which, however, includes all existing types. We further argue that, in practice, a coarsening mechanism leads towards phase-separated solutions. Finally, we construct the phase diagrams of the hydrodynamic equations proposed to qualitatively describe the Vicsek and active Ising models and connect our results to the phenomenology of the corresponding microscopic models.

  8. 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.

  9. Detecting Different Road Infrastructural Elements Based on the Stochastic Characterization of Speed Patterns

    Directory of Open Access Journals (Sweden)

    Mario Muñoz-Organero

    2017-01-01

    Full Text Available The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the total variation distance instead of the statistical moments to improve the classification accuracy. The algorithm is validated for detecting traffic lights, roundabouts, and street-crossings in a real scenario and the obtained accuracy (0.75 improves the best results using previous approaches based on statistical moments based features (0.71. Each road element to be detected is characterized as a vector of speeds measured when a driver goes through it. We first eliminate the speed samples in congested traffic conditions which are not comparable with clear traffic conditions and would contaminate the dataset. Then, we calculate the probability mass function for the speed (in 1 m/s intervals at each point. The total variation distance is then used to find the similarity among different points of interest (which can contain a similar road element or a different one. Finally, a k-NN approach is used for assigning a class to each unlabelled element.

  10. Model-Based Design of Tree WSNs for Decentralized Detection

    Directory of Open Access Journals (Sweden)

    Ashraf Tantawy

    2015-08-01

    Full Text Available The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches.

  11. Model-based fault detection algorithm for photovoltaic system monitoring

    KAUST Repository

    Harrou, Fouzi

    2018-02-12

    Reliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a statistical approach. Specifically, a simulation model that mimics the theoretical performances of the inspected PV system is designed. Residuals, which are the difference between the measured and estimated output data, are used as a fault indicator. Indeed, residuals are used as the input for the Multivariate CUmulative SUM (MCUSUM) algorithm to detect potential faults. We evaluated the proposed method by using data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.

  12. 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.

  13. Pattern-recognition software detecting the onset of failures in complex systems

    International Nuclear Information System (INIS)

    Mott, J.; King, R.

    1987-01-01

    A very general mathematical framework for embodying learned data from a complex system and combining it with a current observation to estimate the true current state of the system has been implemented using nearly universal pattern-recognition algorithms and applied to surveillance of the EBR-II power plant. In this application the methodology can provide signal validation and replacement of faulty signals on a near-real-time basis for hundreds of plant parameters. The mathematical framework, the pattern-recognition algorithms, examples of the learning and estimating process, and plant operating decisions made using this methodology are discussed. The entire methodology has been reduced to a set of FORTRAN subroutines which are small, fast, robust and executable on a personal computer with a serial link to the system's data acquisition computer, or on the data acquisition computer itself

  14. Knowledge Management through the Equilibrium Pattern Model for Learning

    Science.gov (United States)

    Sarirete, Akila; Noble, Elizabeth; Chikh, Azeddine

    Contemporary students are characterized by having very applied learning styles and methods of acquiring knowledge. This behavior is consistent with the constructivist models where students are co-partners in the learning process. In the present work the authors developed a new model of learning based on the constructivist theory coupled with the cognitive development theory of Piaget. The model considers the level of learning based on several stages and the move from one stage to another requires learners' challenge. At each time a new concept is introduced creates a disequilibrium that needs to be worked out to return back to its equilibrium stage. This process of "disequilibrium/equilibrium" has been analyzed and validated using a course in computer networking as part of Cisco Networking Academy Program at Effat College, a women college in Saudi Arabia. The model provides a theoretical foundation for teaching especially in a complex knowledge domain such as engineering and can be used in a knowledge economy.

  15. Rib Fracture Patterns and Radiologic Detection – A Restraint-Based Comparison

    OpenAIRE

    Crandall, Jeff; Kent, Richard; Patrie, James; Fertile, Jay; Martin, Peter

    2000-01-01

    This paper presents a study of the rib fracture patterns generated in simulated frontal collisions and the visibility of the rib fractures on plain film radiographs. Using 29 cadaver subjects, rib fractures were identified on oblique, lateral, and anteroposterior chest films by five radiologists independently and were compared with fractures found during a detailed necropsy. Physical, geometric, and experimental factors demonstrated an influence on the ability of a radiologist to identify rib...

  16. The patterning of retinal horizontal cells: normalizing the regularity index enhances the detection of genomic linkage

    Directory of Open Access Journals (Sweden)

    Patrick W. Keeley

    2014-10-01

    Full Text Available Retinal neurons are often arranged as non-random distributions called mosaics, as their somata minimize proximity to neighboring cells of the same type. The horizontal cells serve as an example of such a mosaic, but little is known about the developmental mechanisms that underlie their patterning. To identify genes involved in this process, we have used three different spatial statistics to assess the patterning of the horizontal cell mosaic across a panel of genetically distinct recombinant inbred strains. To avoid the confounding effect cell density, which varies two-fold across these different strains, we computed the real/random regularity ratio, expressing the regularity of a mosaic relative to a randomly distributed simulation of similarly sized cells. To test whether this latter statistic better reflects the variation in biological processes that contribute to horizontal cell spacing, we subsequently compared the genetic linkage for each of these two traits, the regularity index and the real/random regularity ratio, each computed from the distribution of nearest neighbor (NN distances and from the Voronoi domain (VD areas. Finally, we compared each of these analyses with another index of patterning, the packing factor. Variation in the regularity indexes, as well as their real/random regularity ratios, and the packing factor, mapped quantitative trait loci (QTL to the distal ends of Chromosomes 1 and 14. For the NN and VD analyses, we found that the degree of linkage was greater when using the real/random regularity ratio rather than the respective regularity index. Using informatic resources, we narrow the list of prospective genes positioned at these two intervals to a small collection of six genes that warrant further investigation to determine their potential role in shaping the patterning of the horizontal cell mosaic.

  17. Mixture models with entropy regularization for community detection in networks

    Science.gov (United States)

    Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

    2018-04-01

    Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

  18. 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.

  19. 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.

  20. 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.

  1. 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…

  2. 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.

  3. Interactive collision detection for deformable models using streaming AABBs.

    Science.gov (United States)

    Zhang, Xinyu; Kim, Young J

    2007-01-01

    We present an interactive and accurate collision detection algorithm for deformable, polygonal objects based on the streaming computational model. Our algorithm can detect all possible pairwise primitive-level intersections between two severely deforming models at highly interactive rates. In our streaming computational model, we consider a set of axis aligned bounding boxes (AABBs) that bound each of the given deformable objects as an input stream and perform massively-parallel pairwise, overlapping tests onto the incoming streams. As a result, we are able to prevent performance stalls in the streaming pipeline that can be caused by expensive indexing mechanism required by bounding volume hierarchy-based streaming algorithms. At runtime, as the underlying models deform over time, we employ a novel, streaming algorithm to update the geometric changes in the AABB streams. Moreover, in order to get only the computed result (i.e., collision results between AABBs) without reading back the entire output streams, we propose a streaming en/decoding strategy that can be performed in a hierarchical fashion. After determining overlapped AABBs, we perform a primitive-level (e.g., triangle) intersection checking on a serial computational model such as CPUs. We implemented the entire pipeline of our algorithm using off-the-shelf graphics processors (GPUs), such as nVIDIA GeForce 7800 GTX, for streaming computations, and Intel Dual Core 3.4G processors for serial computations. We benchmarked our algorithm with different models of varying complexities, ranging from 15K up to 50K triangles, under various deformation motions, and the timings were obtained as 30 approximately 100 FPS depending on the complexity of models and their relative configurations. Finally, we made comparisons with a well-known GPU-based collision detection algorithm, CULLIDE [4] and observed about three times performance improvement over the earlier approach. We also made comparisons with a SW-based AABB

  4. Modeling future power plant location patterns. Final report

    International Nuclear Information System (INIS)

    Eagles, T.W.; Cohon, J.L.; ReVelle, C.

    1979-04-01

    The locations of future energy facilities must be specified to assess the potential environmental impact of those facilities. A computer model was developed to generate probable locations for the energy facilities needed to meet postulated future energy requirements. The model is designed to cover a very large geographical region. The regional demand for baseload electric generating capacity associated with a postulated demand growth rate over any desired time horizon is specified by the user as an input to the model. The model uses linear programming to select the most probable locations within the region, based on physical and political factors. The linear program is multi-objective, with four objective functions based on transmission, coal supply, population proximity, and water supply considerations. Minimizing each objective function leads to a distinct set of locations. The user can select the objective function or weighted combination of objective functions most appropriate to his interest. Users with disparate interests can use the model to see the locational changes which result from varying weighting of the objective functions. The model has been implemented in a six-state mid-Atlantic region. The year 2000 was chosen as the study year, and a test scenario postulating 2.25% growth in baseload generating capacity between 1977 and 2000 was chosen. The scenario stipulatedthat this capacity be 50% nuclear and 50% coal-fired. Initial utility reaction indicates the objective based on transmission costs is most important for such a large-scale analysis

  5. 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.

  6. 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.

  7. Metabolic pattern analysis of early detection in Alzheimer's disease from other types of dementias and correlated with cognitive function

    International Nuclear Information System (INIS)

    Ju, R. H.; Lee, C. W.; Jung, Y. A.; Sohn, H. S.; Kim, S. H.; Seo, T. S

    2004-01-01

    PET/CT studies have demonstrated temporoparietal hypometabolism in probable and definite Alzheimer's disease (AD), a pattern that may help differentiate AD from other types of dementias. Seeking to distinguish Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), we examined brain glucose metabolism of DLB and AD. Identification of individual differences in patterns of regional cerebral glucose metabolism (rCMRglc) interactions may be important for early detection of AD. We elucidate the relationship between reduced cognitive function and cerebral metabolism. Ten patients with the diagnosis of AD, 3 DLB patients underwent 18F-FDG PET CT. We applied statistical mapping procedure to evaluate the diagnostic power of rCMRglc patterns for differentiation and also correlated with Korean-mini mental status exam (K-MMSE) score include orientation time, place, registration, attention, calculation, recaIl, language and visuospatial function. Glucose metabolic pattern analysis confirmed AD and DLB patients showed significant metabolic reductions involving parietotemporal association, posterior cingulate, and frontal association cortex. DLB patients showed significant metabolic reductions in the occipital cortex, particularly in the primary visual cortex. Covariate analysis revealed that occipital metabolic changes in DLB were independent from those in the adjacent parietotemporal cortices. AnaIysis of clinically diagnosed probable AD patients showed a significantly higher frequency of primary visual metabolic reduction among patients who fulfilled clinical criteria for DLB. occipital hypometabolism is a potential discriminate marker to distinguish DLB versus AD

  8. Java Architecture for Detect and Avoid Extensibility and Modeling

    Science.gov (United States)

    Santiago, Confesor; Mueller, Eric Richard; Johnson, Marcus A.; Abramson, Michael; Snow, James William

    2015-01-01

    Unmanned aircraft will equip with a detect-and-avoid (DAA) system that enables them to comply with the requirement to "see and avoid" other aircraft, an important layer in the overall set of procedural, strategic and tactical separation methods designed to prevent mid-air collisions. This paper describes a capability called Java Architecture for Detect and Avoid Extensibility and Modeling (JADEM), developed to prototype and help evaluate various DAA technological requirements by providing a flexible and extensible software platform that models all major detect-and-avoid functions. Figure 1 illustrates JADEM's architecture. The surveillance module can be actual equipment on the unmanned aircraft or simulators that model the process by which sensors on-board detect other aircraft and provide track data to the traffic display. The track evaluation function evaluates each detected aircraft and decides whether to provide an alert to the pilot and its severity. Guidance is a combination of intruder track information, alerting, and avoidance/advisory algorithms behind the tools shown on the traffic display to aid the pilot in determining a maneuver to avoid a loss of well clear. All these functions are designed with a common interface and configurable implementation, which is critical in exploring DAA requirements. To date, JADEM has been utilized in three computer simulations of the National Airspace System, three pilot-in-the-loop experiments using a total of 37 professional UAS pilots, and two flight tests using NASA's Predator-B unmanned aircraft, named Ikhana. The data collected has directly informed the quantitative separation standard for "well clear", safety case, requirements development, and the operational environment for the DAA minimum operational performance standards. This work was performed by the Separation Assurance/Sense and Avoid Interoperability team under NASA's UAS Integration in the NAS project.

  9. 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.

  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. Fuzzy model-based observers for fault detection in CSTR.

    Science.gov (United States)

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  12. 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

  13. Pattern-based feature extraction for fault detection in quality relevant process control

    NARCIS (Netherlands)

    Peruzzo, S.; Holenderski, M.J.; Lukkien, J.J.

    2017-01-01

    Statistical quality control (SQC) applies multivariate statistics to monitor production processes over time and detect changes in their performance in terms of meeting specification limits on key product quality metrics. These limits are imposed by customers and typically assumed to be a single

  14. Mass detection, localization and estimation for wind turbine blades based on statistical pattern recognition

    DEFF Research Database (Denmark)

    Colone, L.; Hovgaard, K.; Glavind, Lars

    2018-01-01

    A method for mass change detection on wind turbine blades using natural frequencies is presented. The approach is based on two statistical tests. The first test decides if there is a significant mass change and the second test is a statistical group classification based on Linear Discriminant Ana...

  15. 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

  16. 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.

  17. Pipeline Processing with an Iterative, Context-Based Detection Model

    Science.gov (United States)

    2016-01-22

    wave precursor artifacts. Distortion definitely is reduced with the addition of more channels to the processed data stream (comparing trace 3 to...limitations of fully automatic hypothesis evaluation with a test case of two events in Central Asia – a deep Hindu Kush earthquake and a shallow earthquake in...AFRL-RV-PS- AFRL-RV-PS- TR-2016-0080 TR-2016-0080 PIPELINE PROCESSING WITH AN ITERATIVE, CONTEXT-BASED DETECTION MODEL T. Kværna, et al

  18. Efficient image duplicated region detection model using sequential block clustering

    Czech Academy of Sciences Publication Activity Database

    Sekeh, M. A.; Maarof, M. A.; Rohani, M. F.; Mahdian, Babak

    2013-01-01

    Roč. 10, č. 1 (2013), s. 73-84 ISSN 1742-2876 Institutional support: RVO:67985556 Keywords : Image forensic * Copy–paste forgery * Local block matching Subject RIV: IN - Informatics, Computer Science Impact factor: 0.986, year: 2013 http://library.utia.cas.cz/separaty/2013/ZOI/mahdian-efficient image duplicated region detection model using sequential block clustering.pdf

  19. AMC Model for Denial of Sleep Attack Detection

    OpenAIRE

    Bhattasali, Tapalina; Chaki, Rituparna

    2012-01-01

    Due to deployment in hostile environment, wireless sensor network is vulnerable to various attacks. Exhausted sensor nodes in sensor network become a challenging issue because it disrupts the normal connectivity of the network. Affected nodes give rise to denial of service that resists to get the objective of sensor network in real life. A mathematical model based on Absorbing Markov Chain (AMC)is proposed for Denial of Sleep attack detection in sensor network. In this mechanism, whether sens...

  20. 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 ...

  1. 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.

  2. Computer modeling of dosimetric pattern in aquatic environment of ...

    African Journals Online (AJOL)

    ... solving the dose rates to aquatic organisms with emphasis on the coastal areas of Nigeria where oil exploration activities involve the use of radioactive materials. Solution of the dose function representing the baseline have been modeled the result of which can be employed in assessing future contamination in the area.

  3. Employment, Production and Consumption model: Patterns of phase transitions

    Czech Academy of Sciences Publication Activity Database

    Lavička, H.; Lin, L.; Novotný, Jan

    2010-01-01

    Roč. 389, č. 8 (2010), s. 1708-1720 ISSN 0378-4371 Institutional research plan: CEZ:AV0Z10480505 Keywords : EPC * Agent based model * Phase transition Subject RIV: BG - Nuclear, Atomic and Molecular Physics, Colliders Impact factor: 1.521, year: 2010

  4. Stationary Patterns in One-Predator Two-Prey Models

    DEFF Research Database (Denmark)

    Pedersen, Michael; Zhigui, Lin

    1999-01-01

    Weakly-coupled elliptic system decribing models of simple three-species food webs such as the one-predator, two-prey modelis discussed. We show thatthere is no non-constant solution if diffusions or inter-specific competitions are strong, or if the intrinsic growths of the prey are slow...

  5. Demand pattern analysis of taxi trip data for anomalies detection and explanation

    DEFF Research Database (Denmark)

    Markou, Ioulia; Rodrigues, Filipe; Pereira, Francisco Camara

    2017-01-01

    Due to environmental and economic stress, strong investment exists now towards adaptive transport systems that can efficiently utilize capacity, minimizing costs and environmental impacts. The common vision is a system that dynamically changes itself (the supply) to anticipate traveler needs (the...... demand). In some occasions, unexpected and unwanted demand patterns are noticed in the traffic network that lead to system failures and cost implications. Significantly low speeds or excessively low flows at an unforeseeable time are only some of the phenomena that are often noticed and need...

  6. POD Model Reconstruction for Gray-Box Fault Detection

    Science.gov (United States)

    Park, Han; Zak, Michail

    2007-01-01

    Proper orthogonal decomposition (POD) is the mathematical basis of a method of constructing low-order mathematical models for the "gray-box" fault-detection algorithm that is a component of a diagnostic system known as beacon-based exception analysis for multi-missions (BEAM). POD has been successfully applied in reducing computational complexity by generating simple models that can be used for control and simulation for complex systems such as fluid flows. In the present application to BEAM, POD brings the same benefits to automated diagnosis. BEAM is a method of real-time or offline, automated diagnosis of a complex dynamic system.The gray-box approach makes it possible to utilize incomplete or approximate knowledge of the dynamics of the system that one seeks to diagnose. In the gray-box approach, a deterministic model of the system is used to filter a time series of system sensor data to remove the deterministic components of the time series from further examination. What is left after the filtering operation is a time series of residual quantities that represent the unknown (or at least unmodeled) aspects of the behavior of the system. Stochastic modeling techniques are then applied to the residual time series. The procedure for detecting abnormal behavior of the system then becomes one of looking for statistical differences between the residual time series and the predictions of the stochastic model.

  7. Fuzzy modeling of analytical redundancy for sensor failure detection

    International Nuclear Information System (INIS)

    Tsai, T.M.; Chou, H.P.

    1991-01-01

    Failure detection and isolation (FDI) in dynamic systems may be accomplished by testing the consistency of the system via analytically redundant relations. The redundant relation is basically a mathematical model relating system inputs and dissimilar sensor outputs from which information is extracted and subsequently examined for the presence of failure signatures. Performance of the approach is often jeopardized by inherent modeling error and noise interference. To mitigate such effects, techniques such as Kalman filtering, auto-regression-moving-average (ARMA) modeling in conjunction with probability tests are often employed. These conventional techniques treat the stochastic nature of uncertainties in a deterministic manner to generate best-estimated model and sensor outputs by minimizing uncertainties. In this paper, the authors present a different approach by treating the effect of uncertainties with fuzzy numbers. Coefficients in redundant relations derived from first-principle physical models are considered as fuzzy parameters and on-line updated according to system behaviors. Failure detection is accomplished by examining the possibility that a sensor signal occurred in an estimated fuzzy domain. To facilitate failure isolation, individual FDI monitors are designed for each interested sensor

  8. 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)

  9. 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 ...

  10. Effects of Modeling and Tempo Patterns as Practice Techniques on the Performance of High School Instrumentalists.

    Science.gov (United States)

    Henley, Paul T.

    2001-01-01

    Examines the effect of modeling conditions and tempo patterns on high school instrumentalists' performance. Focuses on high school students (n=60) who play wind instruments. Reports that the with-model condition was superior in rhythm and tempo percentage gain when compared to the no-model condition. Includes references. (CMK)

  11. 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...

  12. 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

  13. NEUROIMAGING AND PATTERN RECOGNITION TECHNIQUES FOR AUTOMATIC DETECTION OF ALZHEIMER’S DISEASE: A REVIEW

    Directory of Open Access Journals (Sweden)

    Rupali Kamathe

    2017-08-01

    Full Text Available Alzheimer’s disease (AD is the most common form of dementia with currently unavailable firm treatments that can stop or reverse the disease progression. A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD. In recent years, Neuroimaging techniques combined with machine learning algorithms have received lot of attention in this field. There is a need for development of automated techniques to detect the disease well before patient suffers from irreversible loss. This paper is about the review of such semi or fully automatic techniques with detail comparison of methods implemented, class labels considered, data base used and the results obtained for related study. This review provides detailed comparison of different Neuroimaging techniques and reveals potential application of machine learning algorithms in medical image analysis; particularly in AD enabling even the early detection of the disease- the class labelled as Multiple Cognitive Impairment.

  14. 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.

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

    OpenAIRE

    Kozik, Rafał; Choraś, Michał

    2017-01-01

    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 sys...

  16. Pipeline Structural Damage Detection Using Self-Sensing Technology and PNN-Based Pattern Recognition

    International Nuclear Information System (INIS)

    Lee, Chang Gil; Park, Woong Ki; Park, Seung Hee

    2011-01-01

    In a structure, damage can occur at several scales from micro-cracking to corrosion or loose bolts. This makes the identification of damage difficult with one mode of sensing. Hence, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In the self sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this study, an experimental study on the pipeline system is carried out to verify the effectiveness and the robustness of the proposed structural health monitoring approach. Different types of structural damage are artificially inflicted on the pipeline system. To classify the multiple types of structural damage, a supervised learning-based statistical pattern recognition is implemented by composing a two-dimensional space using the damage indices extracted from the impedance and guided wave features. For more systematic damage classification, several control parameters to determine an optimal decision boundary for the supervised learning-based pattern recognition are optimized. Finally, further research issues will be discussed for real-world implementation of the proposed approach

  17. A Malicious Pattern Detection Engine for Embedded Security Systems in the Internet of Things

    Directory of Open Access Journals (Sweden)

    Doohwan Oh

    2014-12-01

    Full Text Available With the emergence of the Internet of Things (IoT, a large number of physical objects in daily life have been aggressively connected to the Internet. As the number of objects connected to networks increases, the security systems face a critical challenge due to the global connectivity and accessibility of the IoT. However, it is difficult to adapt traditional security systems to the objects in the IoT, because of their limited computing power and memory size. In light of this, we present a lightweight security system that uses a novel malicious pattern-matching engine. We limit the memory usage of the proposed system in order to make it work on resource-constrained devices. To mitigate performance degradation due to limitations of computation power and memory, we propose two novel techniques, auxiliary shifting and early decision. Through both techniques, we can efficiently reduce the number of matching operations on resource-constrained systems. Experiments and performance analyses show that our proposed system achieves a maximum speedup of 2.14 with an IoT object and provides scalable performance for a large number of patterns.

  18. A malicious pattern detection engine for embedded security systems in the Internet of Things.

    Science.gov (United States)

    Oh, Doohwan; Kim, Deokho; Ro, Won Woo

    2014-12-16

    With the emergence of the Internet of Things (IoT), a large number of physical objects in daily life have been aggressively connected to the Internet. As the number of objects connected to networks increases, the security systems face a critical challenge due to the global connectivity and accessibility of the IoT. However, it is difficult to adapt traditional security systems to the objects in the IoT, because of their limited computing power and memory size. In light of this, we present a lightweight security system that uses a novel malicious pattern-matching engine. We limit the memory usage of the proposed system in order to make it work on resource-constrained devices. To mitigate performance degradation due to limitations of computation power and memory, we propose two novel techniques, auxiliary shifting and early decision. Through both techniques, we can efficiently reduce the number of matching operations on resource-constrained systems. Experiments and performance analyses show that our proposed system achieves a maximum speedup of 2.14 with an IoT object and provides scalable performance for a large number of patterns.

  19. A Malicious Pattern Detection Engine for Embedded Security Systems in the Internet of Things

    Science.gov (United States)

    Oh, Doohwan; Kim, Deokho; Ro, Won Woo

    2014-01-01

    With the emergence of the Internet of Things (IoT), a large number of physical objects in daily life have been aggressively connected to the Internet. As the number of objects connected to networks increases, the security systems face a critical challenge due to the global connectivity and accessibility of the IoT. However, it is difficult to adapt traditional security systems to the objects in the IoT, because of their limited computing power and memory size. In light of this, we present a lightweight security system that uses a novel malicious pattern-matching engine. We limit the memory usage of the proposed system in order to make it work on resource-constrained devices. To mitigate performance degradation due to limitations of computation power and memory, we propose two novel techniques, auxiliary shifting and early decision. Through both techniques, we can efficiently reduce the number of matching operations on resource-constrained systems. Experiments and performance analyses show that our proposed system achieves a maximum speedup of 2.14 with an IoT object and provides scalable performance for a large number of patterns. PMID:25521382

  20. Marker detection evaluation by phantom and cadaver experiments for C-arm pose estimation pattern

    Science.gov (United States)

    Steger, Teena; Hoßbach, Martin; Wesarg, Stefan

    2013-03-01

    C-arm fluoroscopy is used for guidance during several clinical exams, e.g. in bronchoscopy to locate the bronchoscope inside the airways. Unfortunately, these images provide only 2D information. However, if the C-arm pose is known, it can be used to overlay the intrainterventional fluoroscopy images with 3D visualizations of airways, acquired from preinterventional CT images. Thus, the physician's view is enhanced and localization of the instrument at the correct position inside the bronchial tree is facilitated. We present a novel method for C-arm pose estimation introducing a marker-based pattern, which is placed on the patient table. The steel markers form a pattern, allowing to deduce the C-arm pose by use of the projective invariant cross-ratio. Simulations show that the C-arm pose estimation is reliable and accurate for translations inside an imaging area of 30 cm x 50 cm and rotations up to 30°. Mean error values are 0.33 mm in 3D space and 0.48 px in the 2D imaging plane. First tests on C-arm images resulted in similarly compelling accuracy values and high reliability in an imaging area of 30 cm x 42.5 cm. Even in the presence of interfering structures, tested both with anatomy phantoms and a turkey cadaver, high success rates over 90% and fully satisfying execution times below 4 sec for 1024 px × 1024 px images could be achieved.

  1. The importance of scaling for detecting community patterns: success and failure in assemblages of introduced species

    Science.gov (United States)

    Allen, Craig R.; Angeler, David G.; Moulton, Michael P.; Holling, Crawford S.

    2015-01-01

    Community saturation can help to explain why biological invasions fail. However, previous research has documented inconsistent relationships between failed invasions (i.e., an invasive species colonizes but goes extinct) and the number of species present in the invaded community. We use data from bird communities of the Hawaiian island of Oahu, which supports a community of 38 successfully established introduced birds and where 37 species were introduced but went extinct (failed invasions). We develop a modified approach to evaluate the effects of community saturation on invasion failure. Our method accounts (1) for the number of species present (NSP) when the species goes extinct rather than during its introduction; and (2) scaling patterns in bird body mass distributions that accounts for the hierarchical organization of ecosystems and the fact that interaction strength amongst species varies with scale. We found that when using NSP at the time of extinction, NSP was higher for failed introductions as compared to successful introductions, supporting the idea that increasing species richness and putative community saturation mediate invasion resistance. Accounting for scale-specific patterns in body size distributions further improved the relationship between NSP and introduction failure. Results show that a better understanding of invasion outcomes can be obtained when scale-specific community structure is accounted for in the analysis.

  2. Patterned Array of Poly(ethylene glycol Silane Monolayer for Label-Free Detection of Dengue

    Directory of Open Access Journals (Sweden)

    Nor Zida Rosly

    2016-08-01

    Full Text Available In the present study, the construction of arrays on silicon for naked-eye detection of DNA dengue was demonstrated. The array was created by exposing a polyethylene glycol (PEG silane monolayer to 254 nm ultraviolet (UV light through a photomask. Formation of the PEG silane monolayer and photomodifed surface properties was thoroughly characterized by using atomic force microscopy (AFM, X-ray photoelectron spectroscopy (XPS, and contact angle measurements. The results of XPS confirmed that irradiation of ultraviolet (UV light generates an aldehyde functional group that offers conjugation sites of amino DNA probe for detection of a specific dengue virus target DNA. Employing a gold enhancement process after inducing the electrostatic interaction between positively charged gold nanoparticles and the negatively charged target DNA hybridized to the DNA capture probe allowed to visualize the array with naked eye. The developed arrays demonstrated excellent performance in diagnosis of dengue with a detection limit as low as 10 pM. The selectivity of DNA arrays was also examined using a single base mismatch and noncomplementary target DNA.

  3. Charging stations location model based on spatiotemporal electromobility use patterns

    Science.gov (United States)

    Pagany, Raphaela; Marquardt, Anna; Zink, Roland

    2016-04-01

    One of the major challenges for mainstream adoption of electric vehicles is the provision of infrastructure for charging the batteries of the vehicles. The charging stations must not only be located dense enough to allow users to complete their journeys, but the electric energy must also be provided from renewable sources in order to truly offer a transportation with less CO2 emissions. The examination of potential locations for the charging of electric vehicles can facilitate the adaption of electromobility and the integration of electronic vehicles in everyday life. A geographic information system (GIS) based model for optimal location of charging stations in a small and regional scale is presented. This considers parameters such as the forecast of electric vehicle use penetration, the relevant weight of diverse point of interests and the distance between parking area and destination for different vehicle users. In addition to the spatial scale the temporal modelling of the energy demand at the different charging locations has to be considerate. Depending on different user profiles (commuters, short haul drivers etc.) the frequency of charging vary during the day, the week and the year. In consequence, the spatiotemporal variability is a challenge for a reliable energy supply inside a decentralized renewable energy system. The presented model delivers on the one side the most adequate identified locations for charging stations and on the other side the interaction between energy supply and demand for electromobility under the consideration of temporal aspects. Using ESRI ArcGIS Desktop, first results for the case study region of Lower Bavaria are generated. The aim of the concept is to keep the model transferable to other regions and also open to integrate further and more detailed user profiles, derived from social studies about i.e. the daily behavior and the perception of electromobility in a next step.

  4. A scan for models with realistic fermion mass patterns

    International Nuclear Information System (INIS)

    Bijnens, J.; Wetterich, C.

    1986-03-01

    We consider models which have no small Yukawa couplings unrelated to symmetry. This situation is generic in higher dimensional unification where Yukawa couplings are predicted to have strength similar to the gauge couplings. Generations have then to be differentiated by symmetry properties and the structure of fermion mass matrices is given in terms of quantum numbers alone. We scan possible symmetries leading to realistic mass matrices. (orig.)

  5. 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.

  6. 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

  7. 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

  8. Roof planes detection via a second-order variational model

    Science.gov (United States)

    Benciolini, Battista; Ruggiero, Valeria; Vitti, Alfonso; Zanetti, Massimo

    2018-04-01

    The paper describes a unified automatic procedure for the detection of roof planes in gridded height data. The procedure exploits the Blake-Zisserman (BZ) model for segmentation in both 2D and 1D, and aims to detect, to model and to label roof planes. The BZ model relies on the minimization of a functional that depends on first- and second-order derivatives, free discontinuities and free gradient discontinuities. During the minimization, the relative strength of each competitor is controlled by a set of weight parameters. By finding the minimum of the approximated BZ functional, one obtains: (1) an approximation of the data that is smoothed solely within regions of homogeneous gradient, and (2) an explicit detection of the discontinuities and gradient discontinuities of the approximation. Firstly, input data is segmented using the 2D BZ. The maps of data and gradient discontinuities are used to isolate building candidates and planar patches (i.e. regions with homogeneous gradient) that correspond to roof planes. Connected regions that can not be considered as buildings are filtered according to both patch dimension and distribution of the directions of the normals to the boundary. The 1D BZ model is applied to the curvilinear coordinates of boundary points of building candidates in order to reduce the effect of data granularity when the normals are evaluated. In particular, corners are preserved and can be detected by means of gradient discontinuity. Lastly, a total least squares model is applied to estimate the parameters of the plane that best fits the points of each planar patch (orthogonal regression with planar model). Refinement of planar patches is performed by assigning those points that are close to the boundaries to the planar patch for which a given proximity measure assumes the smallest value. The proximity measure is defined to account for the variance of a fitting plane and a weighted distance of a point from the plane. The effectiveness of the

  9. 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.

  10. 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

  11. 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

  12. 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...

  13. 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.

  14. Mathematical modeling of a steam generator for sensor fault detection

    International Nuclear Information System (INIS)

    Prock, J.

    1988-01-01

    A dynamic model for a nuclear power plant steam generator (vertical, preheated, U-tube recirculation-type) is formulated as a sixth-order nonlinear system. The model integrates nodal mass and energy balances for the primary water, the U-tube metal and the secondary water and steam. The downcomer flow is determined by a static balance of momentum. The mathematical system is solved using transient input data from the Philippsburg 2 (FRG) nuclear power plant. The results of the calculation are compared with actual measured values. The proposed model provides a low-cost tool for the automatic control and simulation of the steam generating process. The ''parity-space'' algorithm is used to demonstrate the applicability of the mathematical model for sensor fault detection and identification purposes. This technique provides a powerful means of generating temporal analytical redundancy between sensor signals. It demonstrates good detection rates of sensor errors using relatively few steps of scanning time and allows the reconfiguration of faulty signals. (author)

  15. Predictive modeling of neuroanatomic structures for brain atrophy detection

    Science.gov (United States)

    Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

    2010-03-01

    In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

  16. Plutonium detection in humans using octagonal computer-generated color patterns

    International Nuclear Information System (INIS)

    Phillips, W.G.; Curtis, S.P.

    1985-01-01

    Routine analysis of humans for plutonium lung burdens is accomplished with two phoswich low-energy gamma detectors. The analysis of data from each detector provides the spectroscopist with a total of eight parameters. These parameters are normalized and displayed as an octagonal histogram over laid against the historical analyses of uncontaminated humans similar in body geometry, i.e., weight, height, and chest thickness. Subjects containing lung burdens of plutonium within (one standard deviation) of the historical average yield data which are displayed on a color graphics terminal as a green octagon. Analyses which yield values greater than 1 sigma above the historical average produce a distorted yellow, orange, or red display. Thus, through color and pattern recognition, the analyst may see at a glance if the current data statistically indicate human contamination

  17. 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

  18. Pitfalls in the detection of cholesterol in Huntington's disease models.

    Science.gov (United States)

    Marullo, Manuela; Valenza, Marta; Leoni, Valerio; Caccia, Claudio; Scarlatti, Chiara; De Mario, Agnese; Zuccato, Chiara; Di Donato, Stefano; Carafoli, Ernesto; Cattaneo, Elena

    2012-10-11

    Background Abnormalities in brain cholesterol homeostasis have been reported in Huntington's disease (HD), an adult-onset neurodegenerative disorder caused by an expansion in the number of CAG repeats in the huntingtin (HTT) gene. However, the results have been contradictory with respect to whether cholesterol levels increase or decrease in HD models. Biochemical and mass spectrometry methods show reduced levels of cholesterol precursors and cholesterol in HD cells and in the brains of several HD animal models. Abnormal brain cholesterol homeostasis was also inferred from studies in HD patients. In contrast, colorimetric and enzymatic methods indicate cholesterol accumulation in HD cells and tissues. Here we used several methods to investigate cholesterol levels in cultured cells in the presence or absence of mutant HTT protein. Results Colorimetric and enzymatic methods with low sensitivity gave variable results, whereas results from a sensitive analytical method, gas chromatography-mass spectrometry, were more reliable. Sample preparation, high cell density and cell clonality also influenced the detection of intracellular cholesterol. Conclusions Detection of cholesterol in HD samples by colorimetric and enzymatic assays should be supplemented by detection using more sensitive analytical methods. Care must be taken to prepare the sample appropriately. By evaluating lathosterol levels using isotopic dilution mass spectrometry, we confirmed reduced cholesterol biosynthesis in knock-in cells expressing the polyQ mutation in a constitutive or inducible manner. *Correspondence should be addressed to Elena Cattaneo: elena.cattaneo@unimi.it.

  19. Development of anomaly detection models for deep subsurface monitoring

    Science.gov (United States)

    Sun, A. Y.

    2017-12-01

    Deep subsurface repositories are used for waste disposal and carbon sequestration. Monitoring deep subsurface repositories for potential anomalies is challenging, not only because the number of sensor networks and the quality of data are often limited, but also because of the lack of labeled data needed to train and validate machine learning (ML) algorithms. Although physical simulation models may be applied to predict anomalies (or the system's nominal state for that sake), the accuracy of such predictions may be limited by inherent conceptual and parameter uncertainties. The main objective of this study was to demonstrate the potential of data-driven models for leakage detection in carbon sequestration repositories. Monitoring data collected during an artificial CO2 release test at a carbon sequestration repository were used, which include both scalar time series (pressure) and vector time series (distributed temperature sensing). For each type of data, separate online anomaly detection algorithms were developed using the baseline experiment data (no leak) and then tested on the leak experiment data. Performance of a number of different online algorithms was compared. Results show the importance of including contextual information in the dataset to mitigate the impact of reservoir noise and reduce false positive rate. The developed algorithms were integrated into a generic Web-based platform for real-time anomaly detection.

  20. 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.

  1. 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.

  2. Human movement onset detection from isometric force and torque measurements: a supervised pattern recognition approach.

    Science.gov (United States)

    Soda, Paolo; Mazzoleni, Stefano; Cavallo, Giuseppe; Guglielmelli, Eugenio; Iannello, Giulio

    2010-09-01

    Recent research has successfully introduced the application of robotics and mechatronics to functional assessment and motor therapy. Measurements of movement initiation in isometric conditions are widely used in clinical rehabilitation and their importance in functional assessment has been demonstrated for specific parts of the human body. The determination of the voluntary movement initiation time, also referred to as onset time, represents a challenging issue since the time window characterizing the movement onset is of particular relevance for the understanding of recovery mechanisms after a neurological damage. Establishing it manually as well as a troublesome task may also introduce oversight errors and loss of information. The most commonly used methods for automatic onset time detection compare the raw signal, or some extracted measures such as its derivatives (i.e., velocity and acceleration) with a chosen threshold. However, they suffer from high variability and systematic errors because of the weakness of the signal, the abnormality of response profiles as well as the variability of movement initiation times among patients. In this paper, we introduce a technique to optimise onset detection according to each input signal. It is based on a classification system that enables us to establish which deterministic method provides the most accurate onset time on the basis of information directly derived from the raw signal. The approach was tested on annotated force and torque datasets. Each dataset is constituted by 768 signals acquired from eight anatomical districts in 96 patients who carried out six tasks related to common daily activities. The results show that the proposed technique improves not only on the performance achieved by each of the deterministic methods, but also on that attained by a group of clinical experts. The paper describes a classification system detecting the voluntary movement initiation time and adaptable to different signals. By

  3. 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

  4. 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.

  5. 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

  6. Using the eServices platform for detecting behavior patterns deviation in the elderly assisted living: a case study.

    Science.gov (United States)

    Marcelino, Isabel; Lopes, David; Reis, Michael; Silva, Fernando; Laza, Rosalía; Pereira, António

    2015-01-01

    World's aging population is rising and the elderly are increasingly isolated socially and geographically. As a consequence, in many situations, they need assistance that is not granted in time. In this paper, we present a solution that follows the CRISP-DM methodology to detect the elderly's behavior pattern deviations that may indicate possible risk situations. To obtain these patterns, many variables are aggregated to ensure the alert system reliability and minimize eventual false positive alert situations. These variables comprehend information provided by body area network (BAN), by environment sensors, and also by the elderly's interaction in a service provider platform, called eServices--Elderly Support Service Platform. eServices is a scalable platform aggregating a service ecosystem developed specially for elderly people. This pattern recognition will further activate the adequate response. With the system evolution, it will learn to predict potential danger situations for a specified user, acting preventively and ensuring the elderly's safety and well-being. As the eServices platform is still in development, synthetic data, based on real data sample and empiric knowledge, is being used to populate the initial dataset. The presented work is a proof of concept of knowledge extraction using the eServices platform information. Regardless of not using real data, this work proves to be an asset, achieving a good performance in preventing alert situations.

  7. Using the eServices Platform for Detecting Behavior Patterns Deviation in the Elderly Assisted Living: A Case Study

    Directory of Open Access Journals (Sweden)

    Isabel Marcelino

    2015-01-01

    Full Text Available World’s aging population is rising and the elderly are increasingly isolated socially and geographically. As a consequence, in many situations, they need assistance that is not granted in time. In this paper, we present a solution that follows the CRISP-DM methodology to detect the elderly’s behavior pattern deviations that may indicate possible risk situations. To obtain these patterns, many variables are aggregated to ensure the alert system reliability and minimize eventual false positive alert situations. These variables comprehend information provided by body area network (BAN, by environment sensors, and also by the elderly’s interaction in a service provider platform, called eServices—Elderly Support Service Platform. eServices is a scalable platform aggregating a service ecosystem developed specially for elderly people. This pattern recognition will further activate the adequate response. With the system evolution, it will learn to predict potential danger situations for a specified user, acting preventively and ensuring the elderly’s safety and well-being. As the eServices platform is still in development, synthetic data, based on real data sample and empiric knowledge, is being used to populate the initial dataset. The presented work is a proof of concept of knowledge extraction using the eServices platform information. Regardless of not using real data, this work proves to be an asset, achieving a good performance in preventing alert situations.

  8. Vortex rope patterns at different load of hydro turbine model

    Directory of Open Access Journals (Sweden)

    Skripkin Sergey

    2017-01-01

    Full Text Available Operation of hydraulic turbines beyond optimal conditions leads to formation of precessing vortex core in a draft tube that generates powerful pressure pulsations in a hydraulic system. In case of resonance it leads to stability decreasing of hydraulic unit and electrical grid on the whole. In present work, such regimes are explored in a conical part of simplified turbine model. Studies are performed at constant flowrate Q = 70 m3/h and varying the runner rotational speed to explore different loads of the hydroturbine unit. The experiments involve pressure measurements, high speed-visualization and velocity measurements by means of laser Doppler anemometer technique. Interesting finding is related with abrupt increasing precession frequency at low swirl parameter of flow near optimal regime.

  9. Numerical modelling of flow pattern for high swirling flows

    Directory of Open Access Journals (Sweden)

    Parra Teresa

    2015-01-01

    Full Text Available This work focuses on the interaction of two coaxial swirling jets. High swirl burners are suitable for lean flames and produce low emissions. Computational Fluid Dynamics has been used to study the isothermal behaviour of two confined jets whose setup and operating conditions are those of the benchmark of Roback and Johnson. Numerical model is a Total Variation Diminishing and PISO is used to pressure velocity coupling. Transient analysis let identify the non-axisymmetric region of reverse flow. The center of instantaneous azimuthal velocities is not located in the axis of the chamber. The temporal sampling evidences this center spins around the axis of the device forming the precessing vortex core (PVC whose Strouhal numbers are more than two for Swirl numbers of one. Influence of swirl number evidences strong swirl numbers are precursor of large vortex breakdown. Influence of conical diffusers evidence the reduction of secondary flows associated to boundary layer separation.

  10. EWAS: Modeling Application for Early Detection of Terrorist Threats

    Science.gov (United States)

    Qureshi, Pir Abdul Rasool; Memon, Nasrullah; Wiil, Uffe Kock

    This paper presents a model and system architecture for an early warning system to detect terrorist threats. The paper discusses the shortcomings of state-of-the-art systems and outlines the functional requirements that must to be met by an ideal system working in the counterterrorism domain. The concept of generation of early warnings to predict terrorist threats is presented. The model relies on data collection from open data sources, information retrieval, information extraction for preparing structured workable data sets from available unstructured data, and finally detailed investigation. The conducted investigation includes social network analysis, investigative data mining, and heuristic rules for the study of complex covert networks for terrorist threat indication. The presented model and system architecture can be used as a core framework for an early warning system.

  11. Neural network pattern recognition of lingual-palatal pressure for automated detection of swallow.

    Science.gov (United States)

    Hadley, Aaron J; Krival, Kate R; Ridgel, Angela L; Hahn, Elizabeth C; Tyler, Dustin J

    2015-04-01

    We describe a novel device and method for real-time measurement of lingual-palatal pressure and automatic identification of the oral transfer phase of deglutition. Clinical measurement of the oral transport phase of swallowing is a complicated process requiring either placement of obstructive sensors or sitting within a fluoroscope or articulograph for recording. Existing detection algorithms distinguish oral events with EMG, sound, and pressure signals from the head and neck, but are imprecise and frequently result in false detection. We placed seven pressure sensors on a molded mouthpiece fitting over the upper teeth and hard palate and recorded pressure during a variety of swallow and non-swallow activities. Pressure measures and swallow times from 12 healthy and 7 Parkinson's subjects provided training data for a time-delay artificial neural network to categorize the recordings as swallow or non-swallow events. User-specific neural networks properly categorized 96 % of swallow and non-swallow events, while a generalized population-trained network was able to properly categorize 93 % of swallow and non-swallow events across all recordings. Lingual-palatal pressure signals are sufficient to selectively and specifically recognize the initiation of swallowing in healthy and dysphagic patients.

  12. Detecting brain growth patterns in normal children using tensor-based morphometry.

    Science.gov (United States)

    Hua, Xue; Leow, Alex D; Levitt, Jennifer G; Caplan, Rochelle; Thompson, Paul M; Toga, Arthur W

    2009-01-01

    Previous magnetic resonance imaging (MRI)-based volumetric studies have shown age-related increases in the volume of total white matter and decreases in the volume of total gray matter of normal children. Recent adaptations of image analysis strategies enable the detection of human brain growth with improved spatial resolution. In this article, we further explore the spatio-temporal complexity of adolescent brain maturation with tensor-based morphometry. By utilizing a novel non-linear elastic intensity-based registration algorithm on the serial structural MRI scans of 13 healthy children, individual Jacobian growth maps are generated and then registered to a common anatomical space. Statistical analyses reveal significant tissue growth in cerebral white matter, contrasted with gray matter loss in parietal, temporal, and occipital lobe. In addition, a linear regression with age and gender suggests a slowing down of the growth rate in regions with the greatest white matter growth. We demonstrate that a tensor-based Jacobian map is a sensitive and reliable method to detect regional tissue changes during development. (c) 2007 Wiley-Liss, Inc.

  13. A deviation based assessment methodology for multiple machine health patterns classification and fault detection

    Science.gov (United States)

    Jia, Xiaodong; Jin, Chao; Buzza, Matt; Di, Yuan; Siegel, David; Lee, Jay

    2018-01-01

    Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.

  14. NIR detection of honey adulteration reveals differences in water spectral pattern.

    Science.gov (United States)

    Bázár, György; Romvári, Róbert; Szabó, András; Somogyi, Tamás; Éles, Viktória; Tsenkova, Roumiana

    2016-03-01

    High fructose corn syrup (HFCS) was mixed with four artisanal Robinia honeys at various ratios (0-40%) and near infrared (NIR) spectra were recorded with a fiber optic immersion probe. Levels of HFCS adulteration could be detected accurately using leave-one-honey-out cross-validation (RMSECV=1.48; R(2)CV=0.987), partial least squares regression and the 1300-1800nm spectral interval containing absorption bands related to both water and carbohydrates. Aquaphotomics-based evaluations showed that unifloral honeys contained more highly organized water than the industrial sugar syrup, supposedly because of the greater variety of molecules dissolved in the multi-component honeys. Adulteration with HFCS caused a gradual reduction of water molecular structures, especially water trimers, which facilitate interaction with other molecules. Quick, non-destructive NIR spectroscopy combined with aquaphotomics could be used to describe water molecular structures in honey and to detect a rather common form of adulteration. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Model-Based Detection of Pipe Leakage at Joints

    International Nuclear Information System (INIS)

    Kim, Taejin; Youn, Byeng D.; Woo, Sihyong

    2015-01-01

    Time domain reflectometry (TDR) is widely used for wire failure detection. It transmits a pulse that is reflected at the boundaries of different characteristic impedances. By analyzing the reflected signal, TDR makes it possible to locate the failure. In this study, TDR was used to detect the water leakage at a pipe joint. A wire attached to the pipe surface was soaked by water when a leak occurred, which affected the characteristic impedance of the wet part, resulting in a change in the reflected signal. To infer the leakage from the TDR signal, we first developed a finite difference time domain-based forward model that provided the output of the TDR signal given the configuration of the transmission line. Then, by solving the inverse problem, the locations of the leaks were found

  16. Animated pose templates for modeling and detecting human actions.

    Science.gov (United States)

    Yao, Benjamin Z; Nie, Bruce X; Liu, Zicheng; Zhu, Song-Chun

    2014-03-01

    This paper presents animated pose templates (APTs) for detecting short-term, long-term, and contextual actions from cluttered scenes in videos. Each pose template consists of two components: 1) a shape template with deformable parts represented in an And-node whose appearances are represented by the Histogram of Oriented Gradient (HOG) features, and 2) a motion template specifying the motion of the parts by the Histogram of Optical-Flows (HOF) features. A shape template may have more than one motion template represented by an Or-node. Therefore, each action is defined as a mixture (Or-node) of pose templates in an And-Or tree structure. While this pose template is suitable for detecting short-term action snippets in two to five frames, we extend it in two ways: 1) For long-term actions, we animate the pose templates by adding temporal constraints in a Hidden Markov Model (HMM), and 2) for contextual actions, we treat contextual objects as additional parts of the pose templates and add constraints that encode spatial correlations between parts. To train the model, we manually annotate part locations on several keyframes of each video and cluster them into pose templates using EM. This leaves the unknown parameters for our learning algorithm in two groups: 1) latent variables for the unannotated frames including pose-IDs and part locations, 2) model parameters shared by all training samples such as weights for HOG and HOF features, canonical part locations of each pose, coefficients penalizing pose-transition and part-deformation. To learn these parameters, we introduce a semi-supervised structural SVM algorithm that iterates between two steps: 1) learning (updating) model parameters using labeled data by solving a structural SVM optimization, and 2) imputing missing variables (i.e., detecting actions on unlabeled frames) with parameters learned from the previous step and progressively accepting high-score frames as newly labeled examples. This algorithm belongs to a

  17. Detecting Seismic Events Using a Supervised Hidden Markov Model

    Science.gov (United States)

    Burks, L.; Forrest, R.; Ray, J.; Young, C.

    2017-12-01

    We explore the use of supervised hidden Markov models (HMMs) to detect seismic events in streaming seismogram data. Current methods for seismic event detection include simple triggering algorithms, such as STA/LTA and the Z-statistic, which can lead to large numbers of false positives that must be investigated by an analyst. The hypothesis of this study is that more advanced detection methods, such as HMMs, may decreases false positives while maintaining accuracy similar to current methods. We train a binary HMM classifier using 2 weeks of 3-component waveform data from the International Monitoring System (IMS) that was carefully reviewed by an expert analyst to pick all seismic events. Using an ensemble of simple and discrete features, such as the triggering of STA/LTA, the HMM predicts the time at which transition occurs from noise to signal. Compared to the STA/LTA detection algorithm, the HMM detects more true events, but the false positive rate remains unacceptably high. Future work to potentially decrease the false positive rate may include using continuous features, a Gaussian HMM, and multi-class HMMs to distinguish between types of seismic waves (e.g., P-waves and S-waves). Acknowledgement: Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.SAND No: SAND2017-8154 A

  18. 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

  19. Multilevel regression models describing regional patterns of invertebrate and algal responses to urbanization across the USA

    Science.gov (United States)

    Cuffney, T.F.; Kashuba, R.; Qian, S.S.; Alameddine, I.; Cha, Y.K.; Lee, B.; Coles, J.F.; McMahon, G.

    2011-01-01

    Multilevel hierarchical regression was used to examine regional patterns in the responses of benthic macroinvertebrates and algae to urbanization across 9 metropolitan areas of the conterminous USA. Linear regressions established that responses (intercepts and slopes) to urbanization of invertebrates and algae varied among metropolitan areas. Multilevel hierarchical regression models were able to explain these differences on the basis of region-scale predictors. Regional differences in the type of land cover (agriculture or forest) being converted to urban and climatic factors (precipitation and air temperature) accounted for the differences in the response of macroinvertebrates to urbanization based on ordination scores, total richness, Ephemeroptera, Plecoptera, Trichoptera richness, and average tolerance. Regional differences in climate and antecedent agriculture also accounted for differences in the responses of salt-tolerant diatoms, but differences in the responses of other diatom metrics (% eutraphenic, % sensitive, and % silt tolerant) were best explained by regional differences in soils (mean % clay soils). The effects of urbanization were most readily detected in regions where forest lands were being converted to urban land because agricultural development significantly degraded assemblages before urbanization and made detection of urban effects difficult. The effects of climatic factors (temperature, precipitation) on background conditions (biogeographic differences) and rates of response to urbanization were most apparent after accounting for the effects of agricultural development. The effects of climate and land cover on responses to urbanization provide strong evidence that monitoring, mitigation, and restoration efforts must be tailored for specific regions and that attainment goals (background conditions) may not be possible in regions with high levels of prior disturbance (e.g., agricultural development). ?? 2011 by The North American

  20. 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.