WorldWideScience

Sample records for anomalies detected prior

  1. A new prior for bayesian anomaly detection: application to biosurveillance.

    Science.gov (United States)

    Shen, Y; Cooper, G F

    2010-01-01

    Bayesian anomaly detection computes posterior probabilities of anomalous events by combining prior beliefs and evidence from data. However, the specification of prior probabilities can be challenging. This paper describes a Bayesian prior in the context of disease outbreak detection. The goal is to provide a meaningful, easy-to-use prior that yields a posterior probability of an outbreak that performs at least as well as a standard frequentist approach. If this goal is achieved, the resulting posterior could be usefully incorporated into a decision analysis about how to act in light of a possible disease outbreak. This paper describes a Bayesian method for anomaly detection that combines learning from data with a semi-informative prior probability over patterns of anomalous events. A univariate version of the algorithm is presented here for ease of illustration of the essential ideas. The paper describes the algorithm in the context of disease-outbreak detection, but it is general and can be used in other anomaly detection applications. For this application, the semi-informative prior specifies that an increased count over baseline is expected for the variable being monitored, such as the number of respiratory chief complaints per day at a given emergency department. The semi-informative prior is derived based on the baseline prior, which is estimated from using historical data. The evaluation reported here used semi-synthetic data to evaluate the detection performance of the proposed Bayesian method and a control chart method, which is a standard frequentist algorithm that is closest to the Bayesian method in terms of the type of data it uses. The disease-outbreak detection performance of the Bayesian method was statistically significantly better than that of the control chart method when proper baseline periods were used to estimate the baseline behavior to avoid seasonal effects. When using longer baseline periods, the Bayesian method performed as well as the

  2. Anomaly Detection in Sequences

    Data.gov (United States)

    National Aeronautics and Space Administration — We present a set of novel algorithms which we call sequenceMiner, that detect and characterize anomalies in large sets of high-dimensional symbol sequences that...

  3. Theoretically Optimal Distributed Anomaly Detection

    Data.gov (United States)

    National Aeronautics and Space Administration — A novel general framework for distributed anomaly detection with theoretical performance guarantees is proposed. Our algorithmic approach combines existing anomaly...

  4. Ferret Workflow Anomaly Detection System

    National Research Council Canada - National Science Library

    Smith, Timothy J; Bryant, Stephany

    2005-01-01

    The Ferret workflow anomaly detection system project 2003-2004 has provided validation and anomaly detection in accredited workflows in secure knowledge management systems through the use of continuous, automated audits...

  5. Signal anomaly detection and characterization

    International Nuclear Information System (INIS)

    Morgenstern, V.M.; Upadhyaya, B.R.; Gloeckler, O.

    1988-08-01

    As part of a comprehensive signal validation system, we have developed a signal anomaly detector, without specifically establishing the cause of the anomaly. A signal recorded from process instrumentation is said to have an anomaly, if during steady-state operation, the deviation in the level of the signal, its root-mean-square (RMS) value, or its statistical distribution changes by a preset value. This deviation could be an unacceptable increase or a decrease in the quantity being monitored. An anomaly in a signal may be characterized by wideband or single-frequency noise, bias error, pulse-type error, nonsymmetric behavior, or a change in the signal bandwidth. Various signatures can be easily computed from data samples and compared against specified threshold values. We want to point out that in real processes, pulses can appear with different time widths, and at different rates of change of the signal. Thus, in characterizing an anomaly as a pulse-type, the fastest pulse width is constrained by the signal sampling interval. For example, if a signal is sampled at 100 Hz, we will not be able to detect pulses occurring at kHz rates. Discussion with utility and Combustion Engineering personnel indicated that it is not practical to detect pulses having a narrow time width. 9 refs., 11 figs., 8 tabs

  6. HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS

    Directory of Open Access Journals (Sweden)

    J. G. Rejas Ayuga

    2016-06-01

    Full Text Available We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR, shortwave infrared (SWIR and thermal infrared (TIR from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares (Spain and San José (Costa Rica respectively, have been used. In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal Background based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction information processes are discussed.

  7. Anomaly detection in diurnal data

    NARCIS (Netherlands)

    Mata, F.; Zuraniewski, P.W.; Mandjes, M.; Mellia, M.

    2014-01-01

    In this paper we present methodological advances in anomaly detection tailored to discover abnormal traffic patterns under the presence of seasonal trends in data. In our setup we impose specific assumptions on the traffic type and nature; our study features VoIP call counts, for which several

  8. Fusion and normalization to enhance anomaly detection

    Science.gov (United States)

    Mayer, R.; Atkinson, G.; Antoniades, J.; Baumback, M.; Chester, D.; Edwards, J.; Goldstein, A.; Haas, D.; Henderson, S.; Liu, L.

    2009-05-01

    This study examines normalizing the imagery and the optimization metrics to enhance anomaly and change detection, respectively. The RX algorithm, the standard anomaly detector for hyperspectral imagery, more successfully extracts bright rather than dark man-made objects when applied to visible hyperspectral imagery. However, normalizing the imagery prior to applying the anomaly detector can help detect some of the problematic dark objects, but can also miss some bright objects. This study jointly fuses images of RX applied to normalized and unnormalized imagery and has a single decision surface. The technique was tested using imagery of commercial vehicles in urban environment gathered by a hyperspectral visible/near IR sensor mounted in an airborne platform. Combining detections first requires converting the detector output to a target probability. The observed anomaly detections were fitted with a linear combination of chi square distributions and these weights were used to help compute the target probability. Receiver Operator Characteristic (ROC) quantitatively assessed the target detection performance. The target detection performance is highly variable depending on the relative number of candidate bright and dark targets and false alarms and controlled in this study by using vegetation and street line masks. The joint Boolean OR and AND operations also generate variable performance depending on the scene. The joint SUM operation provides a reasonable compromise between OR and AND operations and has good target detection performance. In addition, new transforms based on normalizing correlation coefficient and least squares generate new transforms related to canonical correlation analysis (CCA) and a normalized image regression (NIR). Transforms based on CCA and NIR performed better than the standard approaches. Only RX detection of the unnormalized of the difference imagery in change detection provides adequate change detection performance.

  9. Network anomaly detection a machine learning perspective

    CERN Document Server

    Bhattacharyya, Dhruba Kumar

    2013-01-01

    With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents mach

  10. Anomaly Detection in Dynamic Networks

    Energy Technology Data Exchange (ETDEWEB)

    Turcotte, Melissa [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2014-10-14

    Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the

  11. Algorithms for Anomaly Detection - Lecture 1

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detec...

  12. Algorithms for Anomaly Detection - Lecture 2

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detec...

  13. Radon anomalies prior to earthquakes (1). Review of previous studies

    International Nuclear Information System (INIS)

    Ishikawa, Tetsuo; Tokonami, Shinji; Yasuoka, Yumi; Shinogi, Masaki; Nagahama, Hiroyuki; Omori, Yasutaka; Kawada, Yusuke

    2008-01-01

    The relationship between radon anomalies and earthquakes has been studied for more than 30 years. However, most of the studies dealt with radon in soil gas or in groundwater. Before the 1995 Hyogoken-Nanbu earthquake, an anomalous increase of atmospheric radon was observed at Kobe Pharmaceutical University. The increase was well fitted with a mathematical model related to earthquake fault dynamics. This paper reports the significance of this observation, reviewing previous studies on radon anomaly before earthquakes. Groundwater/soil radon measurements for earthquake prediction began in 1970's in Japan as well as foreign countries. One of the most famous studies in Japan is groundwater radon anomaly before the 1978 Izu-Oshima-kinkai earthquake. We have recognized the significance of radon in earthquake prediction research, but recently its limitation was also pointed out. Some researchers are looking for a better indicator for precursors; simultaneous measurements of radon and other gases are new trials in recent studies. Contrary to soil/groundwater radon, we have not paid much attention to atmospheric radon before earthquakes. However, it might be possible to detect precursors in atmospheric radon before a large earthquake. In the next issues, we will discuss the details of the anomalous atmospheric radon data observed before the Hyogoken-Nanbu earthquake. (author)

  14. Data Mining for Anomaly Detection

    Science.gov (United States)

    Biswas, Gautam; Mack, Daniel; Mylaraswamy, Dinkar; Bharadwaj, Raj

    2013-01-01

    The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

  15. Anomaly Detection from Hyperspectral Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Qiandong Guo

    2016-12-01

    Full Text Available Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a great potential for classification and anomaly detection. In this study, two real hyperspectral data sets were used for anomaly detection. One data set was an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS data covering the post-attack World Trade Center (WTC and anomalies are fire spots. The other data set called SpecTIR contained fabric panels as anomalies compared to their background. Existing anomaly detection algorithms including the Reed–Xiaoli detector (RXD, the blocked adaptive computation efficient outlier nominator (BACON, the random selection based anomaly detector (RSAD, the weighted-RXD (W-RXD, and the probabilistic anomaly detector (PAD are reviewed here. The RXD generally sets strict assumptions to the background, which cannot be met in many scenarios, while BACON, RSAD, and W-RXD employ strategies to optimize the estimation of background information. The PAD firstly estimates both background information and anomaly information and then uses the information to conduct anomaly detection. Here, the BACON, RSAD, W-RXD, and PAD outperformed the RXD in terms of detection accuracy, and W-RXD and PAD required less time than BACON and RSAD.

  16. Improved prenatal detection of chromosomal anomalies

    DEFF Research Database (Denmark)

    Frøslev-Friis, Christina; Hjort-Pedersen, Karina; Henriques, Carsten U

    2011-01-01

    Prenatal screening for karyotype anomalies takes place in most European countries. In Denmark, the screening method was changed in 2005. The aim of this study was to study the trends in prevalence and prenatal detection rates of chromosome anomalies and Down syndrome (DS) over a 22-year period....

  17. Quantum machine learning for quantum anomaly detection

    Science.gov (United States)

    Liu, Nana; Rebentrost, Patrick

    2018-04-01

    Anomaly detection is used for identifying data that deviate from "normal" data patterns. Its usage on classical data finds diverse applications in many important areas such as finance, fraud detection, medical diagnoses, data cleaning, and surveillance. With the advent of quantum technologies, anomaly detection of quantum data, in the form of quantum states, may become an important component of quantum applications. Machine-learning algorithms are playing pivotal roles in anomaly detection using classical data. Two widely used algorithms are the kernel principal component analysis and the one-class support vector machine. We find corresponding quantum algorithms to detect anomalies in quantum states. We show that these two quantum algorithms can be performed using resources that are logarithmic in the dimensionality of quantum states. For pure quantum states, these resources can also be logarithmic in the number of quantum states used for training the machine-learning algorithm. This makes these algorithms potentially applicable to big quantum data applications.

  18. Comparison of Unsupervised Anomaly Detection Methods

    Data.gov (United States)

    National Aeronautics and Space Administration — Several different unsupervised anomaly detection algorithms have been applied to Space Shuttle Main Engine (SSME) data to serve the purpose of developing a...

  19. Radon anomalies prior to earthquakes (2). Atmospheric radon anomaly observed before the Hyogoken-Nanbu earthquake

    International Nuclear Information System (INIS)

    Ishikawa, Tetsuo; Tokonami, Shinji; Yasuoka, Yumi; Shinogi, Masaki; Nagahama, Hiroyuki; Omori, Yasutaka; Kawada, Yusuke

    2008-01-01

    Before the 1995 Hyogoken-Nanbu earthquake, various geochemical precursors were observed in the aftershock area: chloride ion concentration, groundwater discharge rate, groundwater radon concentration and so on. Kobe Pharmaceutical University (KPU) is located about 25 km northeast from the epicenter and within the aftershock area. Atmospheric radon concentration had been continuously measured from 1984 at KPU, using a flow-type ionization chamber. The radon concentration data were analyzed using the smoothed residual values which represent the daily minimum of radon concentration with the exclusion of normalized seasonal variation. The radon concentration (smoothed residual values) demonstrated an upward trend about two months before the Hyogoken-Nanbu earthquake. The trend can be well fitted to a log-periodic model related to earthquake fault dynamics. As a result of model fitting, a critical point was calculated to be between 13 and 27 January 1995, which was in good agreement with the occurrence date of earthquake (17 January 1995). The mechanism of radon anomaly before earthquakes is not fully understood. However, it might be possible to detect atmospheric radon anomaly as a precursor before a large earthquake, if (1) the measurement is conducted near the earthquake fault, (2) the monitoring station is located on granite (radon-rich) areas, and (3) the measurement is conducted for more than several years before the earthquake to obtain background data. (author)

  20. Detecting data anomalies methods in distributed systems

    Science.gov (United States)

    Mosiej, Lukasz

    2009-06-01

    Distributed systems became most popular systems in big companies. Nowadays many telecommunications companies want to hold large volumes of data about all customers. Obviously, those data cannot be stored in single database because of many technical difficulties, such as data access efficiency, security reasons, etc. On the other hand there is no need to hold all data in one place, because companies already have dedicated systems to perform specific tasks. In the distributed systems there is a redundancy of data and each system holds only interesting data in appropriate form. Data updated in one system should be also updated in the rest of systems, which hold that data. There are technical problems to update those data in all systems in transactional way. This article is about data anomalies in distributed systems. Avail data anomalies detection methods are shown. Furthermore, a new initial concept of new data anomalies detection methods is described on the last section.

  1. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

    Full Text Available The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.

  2. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

    Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  3. Detection of cardiovascular anomalies: Hybrid systems approach

    KAUST Repository

    Ledezma, Fernando

    2012-06-06

    In this paper, we propose a hybrid interpretation of the cardiovascular system. Based on a model proposed by Simaan et al. (2009), we study the problem of detecting cardiovascular anomalies that can be caused by variations in some physiological parameters, using an observerbased approach. We present the first numerical results obtained. © 2012 IFAC.

  4. Outlier Detection Method Use for the Network Flow Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Rimas Ciplinskas

    2016-06-01

    Full Text Available New and existing methods of cyber-attack detection are constantly being developed and improved because there is a great number of attacks and the demand to protect from them. In prac-tice, current methods of attack detection operates like antivirus programs, i. e. known attacks signatures are created and attacks are detected by using them. These methods have a drawback – they cannot detect new attacks. As a solution, anomaly detection methods are used. They allow to detect deviations from normal network behaviour that may show a new type of attack. This article introduces a new method that allows to detect network flow anomalies by using local outlier factor algorithm. Accom-plished research allowed to identify groups of features which showed the best results of anomaly flow detection according the highest values of precision, recall and F-measure.

  5. Anomaly Detection using the "Isolation Forest" algorithm

    CERN Document Server

    CERN. Geneva

    2015-01-01

    Anomaly detection can provide clues about an outlying minority class in your data: hackers in a set of network events, fraudsters in a set of credit card transactions, or exotic particles in a set of high-energy collisions. In this talk, we analyze a real dataset of breast tissue biopsies, with malignant results forming the minority class. The "Isolation Forest" algorithm finds anomalies by deliberately “overfitting” models that memorize each data point. Since outliers have more empty space around them, they take fewer steps to memorize. Intuitively, a house in the country can be identified simply as “that house out by the farm”, while a house in the city needs a longer description like “that house in Brooklyn, near Prospect Park, on Union Street, between the firehouse and the library, not far from the French restaurant”. We first use anomaly detection to find outliers in the biopsy data, then apply traditional predictive modeling to discover rules that separate anomalies from normal data...

  6. Adaptive Anomaly Detection using Isolation Forest

    Science.gov (United States)

    2009-12-20

    5) Personnel Supported The grant is used to support a research assistant James Tan Swee Chuan, part-time for a period of 10 months. (6...Information Technology Faculty: Information Technology 1 Mass: A New Ranking Measure for Anomaly Detection Kai Ming Ting, James Tan Swee Chuan...processing and computer vision, Whistler (2002). [6] P. Domingos and G. Hulten, Mining high-speed data streams, Proceedings of the Sixth ACM SIGKDD

  7. Anomaly Detection and Visualization of School Electricity Consumption Data

    OpenAIRE

    Cui, Wenqiang; Wang, Hao

    2017-01-01

    Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building energy data. We have investigated five models to detect anomalies in the school el...

  8. Residual generator for cardiovascular anomalies detection

    KAUST Repository

    Belkhatir, Zehor

    2014-06-01

    This paper discusses the possibility of using observer-based approaches for cardiovascular anomalies detection and isolation. We consider a lumped parameter model of the cardiovascular system that can be written in a form of nonlinear state-space representation. We show that residuals that are sensitive to variations in some cardiovascular parameters and to abnormal opening and closure of the valves, can be generated. Since the whole state is not easily available for measurement, we propose to associate the residual generator to a robust extended kalman filter. Numerical results performed on synthetic data are provided.

  9. Anomaly detection based on zero appearances in subspaces

    OpenAIRE

    Pang, Guansong

    2017-01-01

    Anomaly detection is regarded as one of the most important tasks in data mining due to its wide application in various domains, such as finance, information security, healthcare and earth science. With advancements in data collection techniques, the volume and dimensionality of anomaly detection data sets increase explosively, and diverse attribute types occur within these data sets. Also, in many data sets, anomalies can be detected in some attributes only, while other attributes are irrelev...

  10. Clustering and Recurring Anomaly Identification: Recurring Anomaly Detection System (ReADS)

    Science.gov (United States)

    McIntosh, Dawn

    2006-01-01

    This viewgraph presentation reviews the Recurring Anomaly Detection System (ReADS). The Recurring Anomaly Detection System is a tool to analyze text reports, such as aviation reports and maintenance records: (1) Text clustering algorithms group large quantities of reports and documents; Reduces human error and fatigue (2) Identifies interconnected reports; Automates the discovery of possible recurring anomalies; (3) Provides a visualization of the clusters and recurring anomalies We have illustrated our techniques on data from Shuttle and ISS discrepancy reports, as well as ASRS data. ReADS has been integrated with a secure online search

  11. Unsupervised Anomaly Detection for Liquid-Fueled Rocket Prop...

    Data.gov (United States)

    National Aeronautics and Space Administration — Title: Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. Abstract: This article describes the results of applying four...

  12. Towards Reliable Evaluation of Anomaly-Based Intrusion Detection Performance

    Science.gov (United States)

    Viswanathan, Arun

    2012-01-01

    This report describes the results of research into the effects of environment-induced noise on the evaluation process for anomaly detectors in the cyber security domain. This research was conducted during a 10-week summer internship program from the 19th of August, 2012 to the 23rd of August, 2012 at the Jet Propulsion Laboratory in Pasadena, California. The research performed lies within the larger context of the Los Angeles Department of Water and Power (LADWP) Smart Grid cyber security project, a Department of Energy (DoE) funded effort involving the Jet Propulsion Laboratory, California Institute of Technology and the University of Southern California/ Information Sciences Institute. The results of the present effort constitute an important contribution towards building more rigorous evaluation paradigms for anomaly-based intrusion detectors in complex cyber physical systems such as the Smart Grid. Anomaly detection is a key strategy for cyber intrusion detection and operates by identifying deviations from profiles of nominal behavior and are thus conceptually appealing for detecting "novel" attacks. Evaluating the performance of such a detector requires assessing: (a) how well it captures the model of nominal behavior, and (b) how well it detects attacks (deviations from normality). Current evaluation methods produce results that give insufficient insight into the operation of a detector, inevitably resulting in a significantly poor characterization of a detectors performance. In this work, we first describe a preliminary taxonomy of key evaluation constructs that are necessary for establishing rigor in the evaluation regime of an anomaly detector. We then focus on clarifying the impact of the operational environment on the manifestation of attacks in monitored data. We show how dynamic and evolving environments can introduce high variability into the data stream perturbing detector performance. Prior research has focused on understanding the impact of this

  13. A New Anomaly Detection System for School Electricity Consumption Data

    OpenAIRE

    Cui, Wenqiang; Wang, Hao

    2017-01-01

    Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building electricity consumption data. We investigated five models within electricity consu...

  14. A model for anomaly classification in intrusion detection systems

    Science.gov (United States)

    Ferreira, V. O.; Galhardi, V. V.; Gonçalves, L. B. L.; Silva, R. C.; Cansian, A. M.

    2015-09-01

    Intrusion Detection Systems (IDS) are traditionally divided into two types according to the detection methods they employ, namely (i) misuse detection and (ii) anomaly detection. Anomaly detection has been widely used and its main advantage is the ability to detect new attacks. However, the analysis of anomalies generated can become expensive, since they often have no clear information about the malicious events they represent. In this context, this paper presents a model for automated classification of alerts generated by an anomaly based IDS. The main goal is either the classification of the detected anomalies in well-defined taxonomies of attacks or to identify whether it is a false positive misclassified by the IDS. Some common attacks to computer networks were considered and we achieved important results that can equip security analysts with best resources for their analyses.

  15. Statistical Anomaly Detection for Monitoring of Human Dynamics

    Science.gov (United States)

    Kamiya, K.; Fuse, T.

    2015-05-01

    Understanding of human dynamics has drawn attention to various areas. Due to the wide spread of positioning technologies that use GPS or public Wi-Fi, location information can be obtained with high spatial-temporal resolution as well as at low cost. By collecting set of individual location information in real time, monitoring of human dynamics is recently considered possible and is expected to lead to dynamic traffic control in the future. Although this monitoring focuses on detecting anomalous states of human dynamics, anomaly detection methods are developed ad hoc and not fully systematized. This research aims to define an anomaly detection problem of the human dynamics monitoring with gridded population data and develop an anomaly detection method based on the definition. According to the result of a review we have comprehensively conducted, we discussed the characteristics of the anomaly detection of human dynamics monitoring and categorized our problem to a semi-supervised anomaly detection problem that detects contextual anomalies behind time-series data. We developed an anomaly detection method based on a sticky HDP-HMM, which is able to estimate the number of hidden states according to input data. Results of the experiment with synthetic data showed that our proposed method has good fundamental performance with respect to the detection rate. Through the experiment with real gridded population data, an anomaly was detected when and where an actual social event had occurred.

  16. An Immunity-Based Anomaly Detection System with Sensor Agents

    Directory of Open Access Journals (Sweden)

    Yoshiteru Ishida

    2009-11-01

    Full Text Available This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user’s command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

  17. An immunity-based anomaly detection system with sensor agents.

    Science.gov (United States)

    Okamoto, Takeshi; Ishida, Yoshiteru

    2009-01-01

    This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user's command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

  18. Ionospheric Anomalies Observed by GPS TEC Prior to the Qinghai-Tibet Region Earthquakes

    Directory of Open Access Journals (Sweden)

    Chunliang Xia

    2011-01-01

    Full Text Available The precursory processes detected from unambiguous and repeatable instrumental observations that precede an earthquake remain elusive despite the multiple types of pre-earthquake signals gained from observations of geo-electricity, geomagnetism, and electromagnetism. Recently, much attention has been paid to associate abnormal behaviors of TEC (total electron content in ionosphere, with seismic forcing. In this paper, we examined ionospheric TEC variations 1 - 2 weeks preceding 20 moderate to great earthquakes (M = 5 - 8 in the Tibetan Plateau and its neighboring regions between 1999 to 2008, with the help of a nationwide continuously-tracking GPS network. The temporal and spatial TEC variations over the specific seismogenic zones were calculated, and the causal linkage between the identified TEC anomalies and these earthquakes was examined. We find that most of the earthquakes showed significant abnormalities with similar characteristics. The anomalies, either upper anomalies (85%, 17/20 or lower anomalies (65%, 13/20 occurred in the ionosphere with dimensions of 30¢X in latitude and 30¢X in longitude above the epicenters. It is noted that the ionospheric anomalies were more dependent on focal depths of earthquakes than their magnitudes. Our results suggest that these anomalies of TEC may be possible seismo-ionospheric signatures for the earthquakes in Tibet and its margins.

  19. Multicriteria Similarity-Based Anomaly Detection Using Pareto Depth Analysis.

    Science.gov (United States)

    Hsiao, Ko-Jen; Xu, Kevin S; Calder, Jeff; Hero, Alfred O

    2016-06-01

    We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based anomaly detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) anomaly detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets.

  20. Anomaly detection through information sharing under different topologies

    NARCIS (Netherlands)

    Gallos, Lazaros K.; Korczynski, M.T.; Fefferman, Nina H.

    2017-01-01

    Early detection of traffic anomalies in networks increases the probability of effective intervention/mitigation actions, thereby improving the stability of system function. Centralized methods of anomaly detection are subject to inherent constraints: (1) they create a communication burden on the

  1. Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

    Directory of Open Access Journals (Sweden)

    Shaonian Huang

    2018-01-01

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

  2. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

    Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

  3. Detection Range of Airborne Magnetometers in Magnetic Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Chengjing Li

    2015-11-01

    Full Text Available Airborne magnetometers are utilized for the small-range search, precise positioning, and identification of the ferromagnetic properties of underwater targets. As an important performance parameter of sensors, the detection range of airborne magnetometers is commonly set as a fixed value in references regardless of the influences of environment noise, target magnetic properties, and platform features in a classical model to detect airborne magnetic anomalies. As a consequence, deviation in detection ability analysis is observed. In this study, a novel detection range model is proposed on the basis of classic detection range models of airborne magnetometers. In this model, probability distribution is applied, and the magnetic properties of targets and the environment noise properties of a moving submarine are considered. The detection range model is also constructed by considering the distribution of the moving submarine during detection. A cell-averaging greatest-of-constant false alarm rate test method is also used to calculate the detection range of the model at a desired false alarm rate. The detection range model is then used to establish typical submarine search probabilistic models. Results show that the model can be used to evaluate not only the effects of ambient magnetic noise but also the moving and geomagnetic features of the target and airborne detection platform. The model can also be utilized to display the actual operating range of sensor systems.

  4. Anomaly detection ensemble fusion for buried explosive material detection in forward looking infrared imaging for addressing diurnal temperature variation

    Science.gov (United States)

    Anderson, Derek T.; Stone, Kevin; Keller, James M.; Rose, John

    2012-06-01

    In prior work, we describe multiple image space anomaly detection algorithms for the identification of buried explosive materials in forward looking long wave infrared imagery. That work is extended here and focus is placed on improved detection with respect to diurnal temperature variation. An ensemble of shape and size independent image space anomaly detection algorithms are investigated. Specifically, anomalies are identified according to change and blob detection. This anomaly evidence is aggregated and targets are found using an ensemble of trainable size-contrast filters and weighted mean shift clustering. In addition, the blob detector makes use of contrast-limited adaptive histogram equalization for image enhancement. Experimental results are shown based on field data measurements from a U.S. Army test site.

  5. A New Anomaly Detection System for School Electricity Consumption Data

    Directory of Open Access Journals (Sweden)

    Wenqiang Cui

    2017-11-01

    Full Text Available Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building electricity consumption data. We investigated five models within electricity consumption data from different schools to detect anomalies in the data. Furthermore, we proposed a hybrid model that combines polynomial regression and Gaussian distribution, which detects anomalies in the data with 0 false negative and an average precision higher than 91%. Based on the proposed model, we developed a data detection and visualization system for a facilities management company to detect and visualize anomalies in school electricity consumption data. The system is tested and evaluated by facilities managers. According to the evaluation, our system has improved the efficiency of facilities managers to identify anomalies in the data.

  6. Load characterization and anomaly detection for voice over IP traffic

    NARCIS (Netherlands)

    M.R.H. Mandjes (Michel); I. Saniee; A. Stolyar

    2005-01-01

    textabstractWe consider the problem of traffic anomaly detection in IP networks. Traffic anomalies typically arise when there is focused overload or when a network element fails and it is desired to infer these purely from the measured traffic. We derive new general formulae for the variance of the

  7. Online Anomaly Energy Consumption Detection Using Lambda Architecture

    DEFF Research Database (Denmark)

    Liu, Xiufeng; Iftikhar, Nadeem; Nielsen, Per Sieverts

    2016-01-01

    With the widely use of smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics...... problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark...

  8. Some isotopic and geochemical anomalies observed in Mexico prior to large scale earthquakes and volcanic eruptions

    Energy Technology Data Exchange (ETDEWEB)

    Cruz R, S. de la; Armienta, M.A.; Segovia A, N

    1992-05-15

    A brief account of some experiences obtained in Mexico, related with the identification of geochemical precursors of volcanic eruptions and isotopic precursors of earthquakes and volcanic activity is given. The cases of three recent events of volcanic activity and one large earthquake are discussed in the context of an active geological environment. The positive results in the identification of some geochemical precursors that helped to evaluate the eruptive potential during two volcanic crises (Tacana 1986 and Colima 1991), and the significant radon-in-soil anomalies observed during a volcanic catastrophic eruption (El Chichon, 1982) and prior to a major earthquake (Michoacan, 1985) are critically analysed. (Author)

  9. Some isotopic and geochemical anomalies observed in Mexico prior to large scale earthquakes and volcanic eruptions

    International Nuclear Information System (INIS)

    Cruz R, S. de la; Armienta, M.A.; Segovia A, N.

    1992-05-01

    A brief account of some experiences obtained in Mexico, related with the identification of geochemical precursors of volcanic eruptions and isotopic precursors of earthquakes and volcanic activity is given. The cases of three recent events of volcanic activity and one large earthquake are discussed in the context of an active geological environment. The positive results in the identification of some geochemical precursors that helped to evaluate the eruptive potential during two volcanic crises (Tacana 1986 and Colima 1991), and the significant radon-in-soil anomalies observed during a volcanic catastrophic eruption (El Chichon, 1982) and prior to a major earthquake (Michoacan, 1985) are critically analysed. (Author)

  10. Anomaly Detection from ASRS Databases of Textual Reports

    Data.gov (United States)

    National Aeronautics and Space Administration — Our primary goal is to automatically analyze textual reports from the Aviation Safety Reporting System (ASRS) database to detect/discover the anomaly categories...

  11. Anomaly Detection and Diagnosis Algorithms for Discrete Symbols

    Data.gov (United States)

    National Aeronautics and Space Administration — We present a set of novel algorithms which we call sequenceMiner that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise...

  12. Detecting Anomalies by Fusing Voice and Operations Data, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Our innovation will detect, in near real-time, NAS operational anomalies by uniquely combing with analytical methods our existing Microsoft Azure based TFMData...

  13. In-Flight Diagnosis and Anomaly Detection, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — In flight diagnosis and anomaly detection is a difficult challenge that requires sufficient observation and real-time processing of health information. Our approach...

  14. Comparative Analysis of Data-Driven Anomaly Detection Methods

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper provides a review of three different advanced machine learning algorithms for anomaly detection in continuous data streams from a ground-test firing of a...

  15. Detection of sinkholes or anomalies using full seismic wave fields.

    Science.gov (United States)

    2013-04-01

    This research presents an application of two-dimensional (2-D) time-domain waveform tomography for detection of embedded sinkholes and anomalies. The measured seismic surface wave fields were inverted using a full waveform inversion (FWI) technique, ...

  16. Solving a prisoner's dilemma in distributed anomaly detection

    Data.gov (United States)

    National Aeronautics and Space Administration — Anomaly detection has recently become an important problem in many industrial and financial applications. In several instances, the data to be analyzed for possible...

  17. In-Flight Diagnosis and Anomaly Detection Project

    Data.gov (United States)

    National Aeronautics and Space Administration — In flight diagnosis and anomaly detection is a difficult challenge that requires sufficient observation and real-time processing of health information. Our approach...

  18. Density Estimation and Anomaly Detection in Large Social Networks

    Science.gov (United States)

    2014-07-15

    Apr-2013 Approved for Public Release; Distribution Unlimited Final Report: Density Estimation and Anomaly Detection in Large Social Networks The...Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 Online learning, social networks , dynamical models, big data REPORT DOCUMENTATION PAGE 11...of Papers published in peer-reviewed journals: Final Report: Density Estimation and Anomaly Detection in Large Social Networks Report Title High

  19. Support vector machines for TEC seismo-ionospheric anomalies detection

    Directory of Open Access Journals (Sweden)

    M. Akhoondzadeh

    2013-02-01

    Full Text Available Using time series prediction methods, it is possible to pursue the behaviors of earthquake precursors in the future and to announce early warnings when the differences between the predicted value and the observed value exceed the predefined threshold value. Support Vector Machines (SVMs are widely used due to their many advantages for classification and regression tasks. This study is concerned with investigating the Total Electron Content (TEC time series by using a SVM to detect seismo-ionospheric anomalous variations induced by the three powerful earthquakes of Tohoku (11 March 2011, Haiti (12 January 2010 and Samoa (29 September 2009. The duration of TEC time series dataset is 49, 46 and 71 days, for Tohoku, Haiti and Samoa earthquakes, respectively, with each at time resolution of 2 h. In the case of Tohoku earthquake, the results show that the difference between the predicted value obtained from the SVM method and the observed value reaches the maximum value (i.e., 129.31 TECU at earthquake time in a period of high geomagnetic activities. The SVM method detected a considerable number of anomalous occurrences 1 and 2 days prior to the Haiti earthquake and also 1 and 5 days before the Samoa earthquake in a period of low geomagnetic activities. In order to show that the method is acting sensibly with regard to the results extracted during nonevent and event TEC data, i.e., to perform some null-hypothesis tests in which the methods would also be calibrated, the same period of data from the previous year of the Samoa earthquake date has been taken into the account. Further to this, in this study, the detected TEC anomalies using the SVM method were compared to the previous results (Akhoondzadeh and Saradjian, 2011; Akhoondzadeh, 2012 obtained from the mean, median, wavelet and Kalman filter methods. The SVM detected anomalies are similar to those detected using the previous methods. It can be concluded that SVM can be a suitable learning method

  20. Support vector machines for TEC seismo-ionospheric anomalies detection

    Science.gov (United States)

    Akhoondzadeh, M.

    2013-02-01

    Using time series prediction methods, it is possible to pursue the behaviors of earthquake precursors in the future and to announce early warnings when the differences between the predicted value and the observed value exceed the predefined threshold value. Support Vector Machines (SVMs) are widely used due to their many advantages for classification and regression tasks. This study is concerned with investigating the Total Electron Content (TEC) time series by using a SVM to detect seismo-ionospheric anomalous variations induced by the three powerful earthquakes of Tohoku (11 March 2011), Haiti (12 January 2010) and Samoa (29 September 2009). The duration of TEC time series dataset is 49, 46 and 71 days, for Tohoku, Haiti and Samoa earthquakes, respectively, with each at time resolution of 2 h. In the case of Tohoku earthquake, the results show that the difference between the predicted value obtained from the SVM method and the observed value reaches the maximum value (i.e., 129.31 TECU) at earthquake time in a period of high geomagnetic activities. The SVM method detected a considerable number of anomalous occurrences 1 and 2 days prior to the Haiti earthquake and also 1 and 5 days before the Samoa earthquake in a period of low geomagnetic activities. In order to show that the method is acting sensibly with regard to the results extracted during nonevent and event TEC data, i.e., to perform some null-hypothesis tests in which the methods would also be calibrated, the same period of data from the previous year of the Samoa earthquake date has been taken into the account. Further to this, in this study, the detected TEC anomalies using the SVM method were compared to the previous results (Akhoondzadeh and Saradjian, 2011; Akhoondzadeh, 2012) obtained from the mean, median, wavelet and Kalman filter methods. The SVM detected anomalies are similar to those detected using the previous methods. It can be concluded that SVM can be a suitable learning method to detect

  1. Lidar detection algorithm for time and range anomalies

    Science.gov (United States)

    Ben-David, Avishai; Davidson, Charles E.; Vanderbeek, Richard G.

    2007-10-01

    A new detection algorithm for lidar applications has been developed. The detection is based on hyperspectral anomaly detection that is implemented for time anomaly where the question "is a target (aerosol cloud) present at range R within time t1 to t2" is addressed, and for range anomaly where the question "is a target present at time t within ranges R1 and R2" is addressed. A detection score significantly different in magnitude from the detection scores for background measurements suggests that an anomaly (interpreted as the presence of a target signal in space/time) exists. The algorithm employs an option for a preprocessing stage where undesired oscillations and artifacts are filtered out with a low-rank orthogonal projection technique. The filtering technique adaptively removes the one over range-squared dependence of the background contribution of the lidar signal and also aids visualization of features in the data when the signal-to-noise ratio is low. A Gaussian-mixture probability model for two hypotheses (anomaly present or absent) is computed with an expectation-maximization algorithm to produce a detection threshold and probabilities of detection and false alarm. Results of the algorithm for CO2 lidar measurements of bioaerosol clouds Bacillus atrophaeus (formerly known as Bacillus subtilis niger, BG) and Pantoea agglomerans, Pa (formerly known as Erwinia herbicola, Eh) are shown and discussed.

  2. A New Method for Early Anomaly Detection of BWR Instabilities

    International Nuclear Information System (INIS)

    Ivanov, K.N.

    2005-01-01

    The objective of the performed research is to develop an early anomaly detection methodology so as to enhance safety, availability, and operational flexibility of Boiling Water Reactor (BWR) nuclear power plants. The technical approach relies on suppression of potential power oscillations in BWRs by detecting small anomalies at an early stage and taking appropriate prognostic actions based on an anticipated operation schedule. The research utilizes a model of coupled (two-phase) thermal-hydraulic and neutron flux dynamics, which is used as a generator of time series data for anomaly detection at an early stage. The model captures critical nonlinear features of coupled thermal-hydraulic and nuclear reactor dynamics and (slow time-scale) evolution of the anomalies as non-stationary parameters. The time series data derived from this nonlinear non-stationary model serves as the source of information for generating the symbolic dynamics for characterization of model parameter changes that quantitatively represent small anomalies. The major focus of the presented research activity was on developing and qualifying algorithms of pattern recognition for power instability based on anomaly detection from time series data, which later can be used to formulate real-time decision and control algorithms for suppression of power oscillations for a variety of anticipated operating conditions. The research being performed in the framework of this project is essential to make significant improvement in the capability of thermal instability analyses for enhancing safety, availability, and operational flexibility of currently operating and next generation BWRs.

  3. A New Methodology for Early Anomaly Detection of BWR Instabilities

    Energy Technology Data Exchange (ETDEWEB)

    Ivanov, K. N.

    2005-11-27

    The objective of the performed research is to develop an early anomaly detection methodology so as to enhance safety, availability, and operational flexibility of Boiling Water Reactor (BWR) nuclear power plants. The technical approach relies on suppression of potential power oscillations in BWRs by detecting small anomalies at an early stage and taking appropriate prognostic actions based on an anticipated operation schedule. The research utilizes a model of coupled (two-phase) thermal-hydraulic and neutron flux dynamics, which is used as a generator of time series data for anomaly detection at an early stage. The model captures critical nonlinear features of coupled thermal-hydraulic and nuclear reactor dynamics and (slow time-scale) evolution of the anomalies as non-stationary parameters. The time series data derived from this nonlinear non-stationary model serves as the source of information for generating the symbolic dynamics for characterization of model parameter changes that quantitatively represent small anomalies. The major focus of the presented research activity was on developing and qualifying algorithms of pattern recognition for power instability based on anomaly detection from time series data, which later can be used to formulate real-time decision and control algorithms for suppression of power oscillations for a variety of anticipated operating conditions. The research being performed in the framework of this project is essential to make significant improvement in the capability of thermal instability analyses for enhancing safety, availability, and operational flexibility of currently operating and next generation BWRs.

  4. Fast and sensitive methods for on-line anomaly detection

    International Nuclear Information System (INIS)

    Hoogenboom, J.E.; Hagen van der, T.H.J.J.; Ciftcioglu, O.

    1989-01-01

    This paper discusses methods for anomaly detection based on comparison of signal values in time domain with predictions from an autoregressive model of the signal. As the calculation of the signal prediction value needs only a small number of operations, these methods are suitable for on-line applications. Three different methods for tests on anomalies are introduced and their advantages and disadvantages are discussed. Their performance is demonstrated on an artificially generated signal containing two different types of anomalies and on signals of an actual nuclear reactor under different operating conditions. Anomalies resulting a 10-20% change in standard deviation of the residual noise signal could be detected within 10 seconds with a negligibly low probability of false alarms

  5. Amalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring

    KAUST Repository

    Harrou, Fouzi

    2016-01-29

    Accurate and effective anomaly detection and diagnosis of modern industrial systems are crucial for ensuring reliability and safety and for maintaining desired product quality. Anomaly detection based on principal component analysis (PCA) has been studied intensively and largely applied to multivariate processes with highly cross-correlated process variables; howver conventional PCA-based methods often fail to detect small or moderate anomalies. In this paper, the proposed approach integrates two popular process-monitoring detection tools, the conventional PCA-based monitoring indices Hotelling’s T2 and Q and the exponentially weighted moving average (EWMA). We develop two EWMA tools based on the Q and T2 statistics, T2-EWMA and Q-EWMA, to detect anomalies in the process mean. The performances of the proposed methods were compared with that of conventional PCA-based anomaly-detection methods by applying each method to two examples: a synthetic data set and experimental data collected from a flow heating system. The results clearly show the benefits and effectiveness of the proposed methods over conventional PCA-based methods.

  6. EUROCAT website data on prenatal detection rates of congenital anomalies

    DEFF Research Database (Denmark)

    Garne, Ester; Dolk, Helen; Loane, Maria

    2010-01-01

    The EUROCAT website www.eurocat-network.eu publishes prenatal detection rates for major congenital anomalies using data from European population-based congenital anomaly registers, covering 28% of the EU population as well as non-EU countries. Data are updated annually. This information can...... be useful for comparative purposes to clinicians and public health service managers involved in the antenatal care of pregnant women as well as those interested in perinatal epidemiology....

  7. Multiple-Instance Learning for Anomaly Detection in Digital Mammography.

    Science.gov (United States)

    Quellec, Gwenole; Lamard, Mathieu; Cozic, Michel; Coatrieux, Gouenou; Cazuguel, Guy

    2016-07-01

    This paper describes a computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, breasts are first partitioned adaptively into regions. Then, features derived from the detection of lesions (masses and microcalcifications) as well as textural features, are extracted from each region and combined in order to classify mammography examinations as "normal" or "abnormal". Whenever an abnormal examination record is detected, the regions that induced that automated diagnosis can be highlighted. Two strategies are evaluated to define this anomaly detector. In a first scenario, manual segmentations of lesions are used to train an SVM that assigns an anomaly index to each region; local anomaly indices are then combined into a global anomaly index. In a second scenario, the local and global anomaly detectors are trained simultaneously, without manual segmentations, using various MIL algorithms (DD, APR, mi-SVM, MI-SVM and MILBoost). Experiments on the DDSM dataset show that the second approach, which is only weakly-supervised, surprisingly outperforms the first approach, even though it is strongly-supervised. This suggests that anomaly detectors can be advantageously trained on large medical image archives, without the need for manual segmentation.

  8. 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

  9. Applications of TOPS Anomaly Detection Framework to Amazon Drought Analysis

    Science.gov (United States)

    Votava, P.; Nemani, R. R.; Ganguly, S.; Michaelis, A.; Hashimoto, H.

    2011-12-01

    Terrestrial Observation and Prediction System (TOPS) is a flexible modeling software system that integrates ecosystem models with frequent satellite and surface weather observations to produce ecosystem nowcasts (assessments of current conditions) and forecasts useful in natural resources management, public health and disaster management. We have been extending the Terrestrial Observation and Prediction System (TOPS) to include capability for automated anomaly detection and analysis of both on-line (streaming) and off-line data. While there are large numbers of anomaly detection algorithms for multivariate datasets, we are extending this capability beyond the anomaly detection itself and towards an automated analysis that would discover the possible causes of the anomalies. In order to best capture the knowledge about data hierarchies, Earth science models and implied dependencies between anomalies and occurrences of observable events such as urbanization, deforestation, or fires, we have developed an ontology to serve as a knowledge base. The knowledge is captured using OWL ontology language, where connections are defined in a schema that is later extended by including specific instances of datasets and models. We have integrated this knowledge base with a framework for deploying an ensemble of anomaly detection algorithms on large volumes of Earth science datasets and applied it to specific scientific applications that support research conducted by our group. In one early application, we were able to process large number of MODIS, TRMM, CERES data along with ground-based weather and river flow observations to detect the evolution of 2010 drought in the Amazon, identify the affected area, and publish the results in three weeks. A similar analysis of the 2005 drought using the same data sets took nearly 2 years, highlighting the potential contribution of our anomaly framework in accelerating scientific discoveries.

  10. Anomaly Detection In Additively Manufactured Parts Using Laser Doppler Vibrometery

    Energy Technology Data Exchange (ETDEWEB)

    Hernandez, Carlos A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-09-29

    Additively manufactured parts are susceptible to non-uniform structure caused by the unique manufacturing process. This can lead to structural weakness or catastrophic failure. Using laser Doppler vibrometry and frequency response analysis, non-contact detection of anomalies in additively manufactured parts may be possible. Preliminary tests show promise for small scale detection, but more future work is necessary.

  11. Probabilistic Anomaly Detection Based On System Calls Analysis

    Directory of Open Access Journals (Sweden)

    Przemysław Maciołek

    2007-01-01

    Full Text Available We present an application of probabilistic approach to the anomaly detection (PAD. Byanalyzing selected system calls (and their arguments, the chosen applications are monitoredin the Linux environment. This allows us to estimate “(abnormality” of their behavior (bycomparison to previously collected profiles. We’ve attached results of threat detection ina typical computer environment.

  12. Poseidon: A 2-tier Anomaly-based Intrusion Detection System

    NARCIS (Netherlands)

    Bolzoni, D.; Zambon, Emmanuele; Etalle, Sandro; Hartel, Pieter H.

    2005-01-01

    We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection

  13. A hybrid approach for efficient anomaly detection using metaheuristic methods.

    Science.gov (United States)

    Ghanem, Tamer F; Elkilani, Wail S; Abdul-Kader, Hatem M

    2015-07-01

    Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms.

  14. A hybrid approach for efficient anomaly detection using metaheuristic methods

    Directory of Open Access Journals (Sweden)

    Tamer F. Ghanem

    2015-07-01

    Full Text Available Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms.

  15. Anomalies.

    Science.gov (United States)

    Online-Offline, 1999

    1999-01-01

    This theme issue on anomalies includes Web sites, CD-ROMs and software, videos, books, and additional resources for elementary and junior high school students. Pertinent activities are suggested, and sidebars discuss UFOs, animal anomalies, and anomalies from nature; and resources covering unexplained phenonmenas like crop circles, Easter Island,…

  16. On-line intermittent connector anomaly detection

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper investigates a non-traditional use of differential current sensor and current sensor to detect intermittent disconnection problems in connectors. An...

  17. Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

    Science.gov (United States)

    Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra

    2015-01-01

    Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection. PMID:25633599

  18. Anomaly detection based on sensor data in petroleum industry applications.

    Science.gov (United States)

    Martí, Luis; Sanchez-Pi, Nayat; Molina, José Manuel; Garcia, Ana Cristina Bicharra

    2015-01-27

    Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.

  19. Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

    Directory of Open Access Journals (Sweden)

    Luis Martí

    2015-01-01

    Full Text Available Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA, a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.

  20. Thermal anomalies detection before strong earthquakes (M > 6.0 using interquartile, wavelet and Kalman filter methods

    Directory of Open Access Journals (Sweden)

    M. Akhoondzadeh

    2011-04-01

    Full Text Available Thermal anomaly is known as a significant precursor of strong earthquakes, therefore Land Surface Temperature (LST time series have been analyzed in this study to locate relevant anomalous variations prior to the Bam (26 December 2003, Zarand (22 February 2005 and Borujerd (31 March 2006 earthquakes. The duration of the three datasets which are comprised of MODIS LST images is 44, 28 and 46 days for the Bam, Zarand and Borujerd earthquakes, respectively. In order to exclude variations of LST from temperature seasonal effects, Air Temperature (AT data derived from the meteorological stations close to the earthquakes epicenters have been taken into account. The detection of thermal anomalies has been assessed using interquartile, wavelet transform and Kalman filter methods, each presenting its own independent property in anomaly detection. The interquartile method has been used to construct the higher and lower bounds in LST data to detect disturbed states outside the bounds which might be associated with impending earthquakes. The wavelet transform method has been used to locate local maxima within each time series of LST data for identifying earthquake anomalies by a predefined threshold. Also, the prediction property of the Kalman filter has been used in the detection process of prominent LST anomalies. The results concerning the methodology indicate that the interquartile method is capable of detecting the highest intensity anomaly values, the wavelet transform is sensitive to sudden changes, and the Kalman filter method significantly detects the highest unpredictable variations of LST. The three methods detected anomalous occurrences during 1 to 20 days prior to the earthquakes showing close agreement in results found between the different applied methods on LST data in the detection of pre-seismic anomalies. The proposed method for anomaly detection was also applied on regions irrelevant to earthquakes for which no anomaly was detected

  1. The use of Compton scattering in detecting anomaly in soil-possible use in pyromaterial detection

    Science.gov (United States)

    Abedin, Ahmad Firdaus Zainal; Ibrahim, Noorddin; Zabidi, Noriza Ahmad; Demon, Siti Zulaikha Ngah

    2016-01-01

    The Compton scattering is able to determine the signature of land mine detection based on dependency of density anomaly and energy change of scattered photons. In this study, 4.43 MeV gamma of the Am-Be source was used to perform Compton scattering. Two detectors were placed between source with distance of 8 cm and radius of 1.9 cm. Detectors of thallium-doped sodium iodide NaI(TI) was used for detecting gamma ray. There are 9 anomalies used in this simulation. The physical of anomaly is in cylinder form with radius of 10 cm and 8.9 cm height. The anomaly is buried 5 cm deep in the bed soil measured 80 cm radius and 53.5 cm height. Monte Carlo methods indicated the scattering of photons is directly proportional to density of anomalies. The difference between detector response with anomaly and without anomaly namely contrast ratio values are in a linear relationship with density of anomalies. Anomalies of air, wood and water give positive contrast ratio values whereas explosive, sand, concrete, graphite, limestone and polyethylene give negative contrast ratio values. Overall, the contrast ratio values are greater than 2 % for all anomalies. The strong contrast ratios result a good detection capability and distinction between anomalies.

  2. Detection of data taking anomalies for the ATLAS experiment

    CERN Document Server

    De Castro Vargas Fernandes, Julio; The ATLAS collaboration; Lehmann Miotto, Giovanna

    2015-01-01

    The physics signals produced by the ATLAS detector at the Large Hadron Collider (LHC) at CERN are acquired and selected by a distributed Trigger and Data AcQuistition (TDAQ) system, comprising a large number of hardware devices and software components. In this work, we focus on the problem of online detection of anomalies along the data taking period. Anomalies, in this context, are defined as an unexpected behaviour of the TDAQ system that result in a loss of data taking efficiency: the causes for those anomalies may come from the TDAQ itself or from external sources. While the TDAQ system operates, it publishes several useful information (trigger rates, dead times, memory usage…). Such information over time creates a set of time series that can be monitored in order to detect (and react to) problems (or anomalies). Here, we approach TDAQ operation monitoring through a data quality perspective, i.e, an anomaly is seen as a loss of quality (an outlier) and it is reported: this information can be used to rea...

  3. FLEAD: online frequency likelihood estimation anomaly detection for mobile sensing

    NARCIS (Netherlands)

    Le Viet Duc, L Duc; Scholten, Johan; Havinga, Paul J.M.

    With the rise of smartphone platforms, adaptive sensing becomes an predominant key to overcome intricate constraints such as smartphone's capabilities and dynamic data. One way to do this is estimating the event probability based on anomaly detection to invoke heavy processes, such as switching on

  4. Anomaly Detection in Nuclear Power Plants via Symbolic Dynamic Filtering

    Science.gov (United States)

    Jin, Xin; Guo, Yin; Sarkar, Soumik; Ray, Asok; Edwards, Robert M.

    2011-02-01

    Tools of sensor-data-driven anomaly detection facilitate condition monitoring of dynamical systems especially if the physics-based models are either inadequate or unavailable. Along this line, symbolic dynamic filtering (SDF) has been reported in literature as a real-time data-driven tool of feature extraction for pattern identification from sensor time series. However, an inherent difficulty for a data-driven tool is that the quality of detection may drastically suffer in the event of sensor degradation. This paper proposes an anomaly detection algorithm for condition monitoring of nuclear power plants, where symbolic feature extraction and the associated pattern classification are optimized by appropriate partitioning of (possibly noise-contaminated) sensor time series. In this process, the system anomaly signatures are identified by masking the sensor degradation signatures. The proposed anomaly detection methodology is validated on the International Reactor Innovative & Secure (IRIS) simulator of nuclear power plants, and its performance is evaluated by comparison with that of principal component analysis (PCA).

  5. Anomaly detection in real-time gross payment data

    NARCIS (Netherlands)

    Triepels, Ron; Daniels, Hennie; Heijmans, R.; Camp, Olivier; Filipe, Joaquim

    2017-01-01

    We discuss how an autoencoder can detect system-level anomalies in a real-time gross settlement system by reconstructing a set of liquidity vectors. A liquidity vector is an aggregated representation of the underlying payment network of a settlement system for a particular time interval.

  6. Anomaly detection in VoIP traffic with trends

    NARCIS (Netherlands)

    Mata, F.; Zuraniewski, P.W.; Mandjes, M.; Mellia, M.

    2012-01-01

    In this paper we present methodological advances in anomaly detection, which, among other purposes, can be used to discover abnormal traffic patterns under the presence of deterministic trends in data, given that specific assumptions about the traffic type and nature are met. A performance study of

  7. Fuzzy Kernel k-Medoids algorithm for anomaly detection problems

    Science.gov (United States)

    Rustam, Z.; Talita, A. S.

    2017-07-01

    Intrusion Detection System (IDS) is an essential part of security systems to strengthen the security of information systems. IDS can be used to detect the abuse by intruders who try to get into the network system in order to access and utilize the available data sources in the system. There are two approaches of IDS, Misuse Detection and Anomaly Detection (behavior-based intrusion detection). Fuzzy clustering-based methods have been widely used to solve Anomaly Detection problems. Other than using fuzzy membership concept to determine the object to a cluster, other approaches as in combining fuzzy and possibilistic membership or feature-weighted based methods are also used. We propose Fuzzy Kernel k-Medoids that combining fuzzy and possibilistic membership as a powerful method to solve anomaly detection problem since on numerical experiment it is able to classify IDS benchmark data into five different classes simultaneously. We classify IDS benchmark data KDDCup'99 data set into five different classes simultaneously with the best performance was achieved by using 30 % of training data with clustering accuracy reached 90.28 percent.

  8. 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.

  9. Limitations of Aneuploidy and Anomaly Detection in the Obese Patient

    Directory of Open Access Journals (Sweden)

    Paula Zozzaro-Smith

    2014-07-01

    Full Text Available Obesity is a worldwide epidemic and can have a profound effect on pregnancy risks. Obese patients tend to be older and are at increased risk for structural fetal anomalies and aneuploidy, making screening options critically important for these women. Failure rates for first-trimester nuchal translucency (NT screening increase with obesity, while the ability to detect soft-markers declines, limiting ultrasound-based screening options. Obesity also decreases the chances of completing the anatomy survey and increases the residual risk of undetected anomalies. Additionally, non-invasive prenatal testing (NIPT is less likely to provide an informative result in obese patients. Understanding the limitations and diagnostic accuracy of aneuploidy and anomaly screening in obese patients can help guide clinicians in counseling patients on the screening options.

  10. Tactile sensor of hardness recognition based on magnetic anomaly detection

    Science.gov (United States)

    Xue, Lingyun; Zhang, Dongfang; Chen, Qingguang; Rao, Huanle; Xu, Ping

    2018-03-01

    Hardness, as one kind of tactile sensing, plays an important role in the field of intelligent robot application such as gripping, agricultural harvesting, prosthetic hand and so on. Recently, with the rapid development of magnetic field sensing technology with high performance, a number of magnetic sensors have been developed for intelligent application. The tunnel Magnetoresistance(TMR) based on magnetoresistance principal works as the sensitive element to detect the magnetic field and it has proven its excellent ability of weak magnetic detection. In the paper, a new method based on magnetic anomaly detection was proposed to detect the hardness in the tactile way. The sensor is composed of elastic body, ferrous probe, TMR element, permanent magnet. When the elastic body embedded with ferrous probe touches the object under the certain size of force, deformation of elastic body will produce. Correspondingly, the ferrous probe will be forced to displace and the background magnetic field will be distorted. The distorted magnetic field was detected by TMR elements and the output signal at different time can be sampled. The slope of magnetic signal with the sampling time is different for object with different hardness. The result indicated that the magnetic anomaly sensor can recognize the hardness rapidly within 150ms after the tactile moment. The hardness sensor based on magnetic anomaly detection principal proposed in the paper has the advantages of simple structure, low cost, rapid response and it has shown great application potential in the field of intelligent robot.

  11. Implementation of anomaly detection algorithms for detecting transmission control protocol synchronized flooding attacks

    CSIR Research Space (South Africa)

    Mkuzangwe, NNP

    2015-08-01

    Full Text Available This work implements two anomaly detection algorithms for detecting Transmission Control Protocol Synchronized (TCP SYN) flooding attack. The two algorithms are an adaptive threshold algorithm and a cumulative sum (CUSUM) based algorithm...

  12. Sparsity-driven anomaly detection for ship detection and tracking in maritime video

    Science.gov (United States)

    Shafer, Scott; Harguess, Josh; Forero, Pedro A.

    2015-05-01

    This work examines joint anomaly detection and dictionary learning approaches for identifying anomalies in persistent surveillance applications that require data compression. We have developed a sparsity-driven anomaly detector that can be used for learning dictionaries to address these challenges. In our approach, each training datum is modeled as a sparse linear combination of dictionary atoms in the presence of noise. The noise term is modeled as additive Gaussian noise and a deterministic term models the anomalies. However, no model for the statistical distribution of the anomalies is made. An estimator is postulated for a dictionary that exploits the fact that since anomalies by definition are rare, only a few anomalies will be present when considering the entire dataset. From this vantage point, we endow the deterministic noise term (anomaly-related) with a group-sparsity property. A robust dictionary learning problem is postulated where a group-lasso penalty is used to encourage most anomaly-related noise components to be zero. The proposed estimator achieves robustness by both identifying the anomalies and removing their effect from the dictionary estimate. Our approach is applied to the problem of ship detection and tracking from full-motion video with promising results.

  13. Profile-based adaptive anomaly detection for network security.

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Pengchu C. (Sandia National Laboratories, Albuquerque, NM); Durgin, Nancy Ann

    2005-11-01

    As information systems become increasingly complex and pervasive, they become inextricably intertwined with the critical infrastructure of national, public, and private organizations. The problem of recognizing and evaluating threats against these complex, heterogeneous networks of cyber and physical components is a difficult one, yet a solution is vital to ensuring security. In this paper we investigate profile-based anomaly detection techniques that can be used to address this problem. We focus primarily on the area of network anomaly detection, but the approach could be extended to other problem domains. We investigate using several data analysis techniques to create profiles of network hosts and perform anomaly detection using those profiles. The ''profiles'' reduce multi-dimensional vectors representing ''normal behavior'' into fewer dimensions, thus allowing pattern and cluster discovery. New events are compared against the profiles, producing a quantitative measure of how ''anomalous'' the event is. Most network intrusion detection systems (IDSs) detect malicious behavior by searching for known patterns in the network traffic. This approach suffers from several weaknesses, including a lack of generalizability, an inability to detect stealthy or novel attacks, and lack of flexibility regarding alarm thresholds. Our research focuses on enhancing current IDS capabilities by addressing some of these shortcomings. We identify and evaluate promising techniques for data mining and machine-learning. The algorithms are ''trained'' by providing them with a series of data-points from ''normal'' network traffic. A successful algorithm can be trained automatically and efficiently, will have a low error rate (low false alarm and miss rates), and will be able to identify anomalies in ''pseudo real-time'' (i.e., while the intrusion is still in progress

  14. Limitations of Aneuploidy and Anomaly Detection in the Obese Patient

    OpenAIRE

    Zozzaro-Smith, Paula; Gray, Lisa M.; Bacak, Stephen J.; Thornburg, Loralei L.

    2014-01-01

    Obesity is a worldwide epidemic and can have a profound effect on pregnancy risks. Obese patients tend to be older and are at increased risk for structural fetal anomalies and aneuploidy, making screening options critically important for these women. Failure rates for first-trimester nuchal translucency (NT) screening increase with obesity, while the ability to detect soft-markers declines, limiting ultrasound-based screening options. Obesity also decreases the chances of completing the anato...

  15. Bio-Inspired Distributed Decision Algorithms for Anomaly Detection

    Science.gov (United States)

    2017-03-01

    which purposefully and maliciously masquerade as ‘normal network behavior.’ Social insects in the natural world routinely need to make classification...thereby reduce the collateral damage to minimum. 4.1.4 Minimal and Marginal Deployment Gain. Deployment of networked services across administrative ...BIO-INSPIRED DISTRIBUTED DECISION ALGORITHMS FOR ANOMALY DETECTION RUTGERS UNIVERSITY MARCH 2017 FINAL TECHNICAL REPORT APPROVED FOR PUBLIC

  16. Occurrence and Detectability of Thermal Anomalies on Europa

    Science.gov (United States)

    Hayne, Paul O.; Christensen, Philip R.; Spencer, John R.; Abramov, Oleg; Howett, Carly; Mellon, Michael; Nimmo, Francis; Piqueux, Sylvain; Rathbun, Julie A.

    2017-10-01

    Endogenic activity is likely on Europa, given its young surface age of and ongoing tidal heating by Jupiter. Temperature is a fundamental signature of activity, as witnessed on Enceladus, where plumes emanate from vents with strongly elevated temperatures. Recent observations suggest the presence of similar water plumes at Europa. Even if plumes are uncommon, resurfacing may produce elevated surface temperatures, perhaps due to near-surface liquid water. Detecting endogenic activity on Europa is one of the primary mission objectives of NASA’s planned Europa Clipper flyby mission.Here, we use a probabilistic model to assess the likelihood of detectable thermal anomalies on the surface of Europa. The Europa Thermal Emission Imaging System (E-THEMIS) investigation is designed to characterize Europa’s thermal behavior and identify any thermal anomalies due to recent or ongoing activity. We define “detectability” on the basis of expected E-THEMIS measurements, which include multi-spectral infrared emission, both day and night.Thermal anomalies on Europa may take a variety of forms, depending on the resurfacing style, frequency, and duration of events: 1) subsurface melting due to hot spots, 2) shear heating on faults, and 3) eruptions of liquid water or warm ice on the surface. We use numerical and analytical models to estimate temperatures for these features. Once activity ceases, lifetimes of thermal anomalies are estimated to be 100 - 1000 yr. On average, Europa’s 10 - 100 Myr surface age implies a resurfacing rate of ~3 - 30 km2/yr. The typical size of resurfacing features determines their frequency of occurrence. For example, if ~100 km2 chaos features dominate recent resurfacing, we expect one event every few years to decades. Smaller features, such as double-ridges, may be active much more frequently. We model each feature type as a statistically independent event, with probabilities weighted by their observed coverage of Europa’s surface. Our results

  17. Ensemble regression model-based anomaly detection for cyber-physical intrusion detection in smart grids

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena; Gehrke, Oliver

    2016-01-01

    on an ensemble of non-linear artificial neural network DER models which detect and evaluate anomalies in DER operation. The proposed method is validated against measurement data which yields a precision of 0.947 and an accuracy of 0.976. This improves the precision and accuracy of a classic model-based anomaly...

  18. Anomaly-based intrusion detection for SCADA systems

    International Nuclear Information System (INIS)

    Yang, D.; Usynin, A.; Hines, J. W.

    2006-01-01

    Most critical infrastructure such as chemical processing plants, electrical generation and distribution networks, and gas distribution is monitored and controlled by Supervisory Control and Data Acquisition Systems (SCADA. These systems have been the focus of increased security and there are concerns that they could be the target of international terrorists. With the constantly growing number of internet related computer attacks, there is evidence that our critical infrastructure may also be vulnerable. Researchers estimate that malicious online actions may cause $75 billion at 2007. One of the interesting countermeasures for enhancing information system security is called intrusion detection. This paper will briefly discuss the history of research in intrusion detection techniques and introduce the two basic detection approaches: signature detection and anomaly detection. Finally, it presents the application of techniques developed for monitoring critical process systems, such as nuclear power plants, to anomaly intrusion detection. The method uses an auto-associative kernel regression (AAKR) model coupled with the statistical probability ratio test (SPRT) and applied to a simulated SCADA system. The results show that these methods can be generally used to detect a variety of common attacks. (authors)

  19. Detecting Anomaly in Traffic Flow from Road Similarity Analysis

    KAUST Repository

    Liu, Xinran

    2016-06-02

    Taxies equipped with GPS devices are considered as 24-hour moving sensors widely distributed in urban road networks. Plenty of accurate and realtime trajectories of taxi are recorded by GPS devices and are commonly studied for understanding traffic dynamics. This paper focuses on anomaly detection in traffic volume, especially the non-recurrent traffic anomaly caused by unexpected or transient incidents, such as traffic accidents, celebrations and disasters. It is important to detect such sharp changes of traffic status for sensing abnormal events and planning their impact on the smooth volume of traffic. Unlike existing anomaly detection approaches that mainly monitor the derivation of current traffic status from history in the past, the proposed method in this paper evaluates the abnormal score of traffic on one road by comparing its current traffic volume with not only its historical data but also its neighbors. We define the neighbors as the roads that are close in sense of both geo-location and traffic patterns, which are extracted by matrix factorization. The evaluation results on trajectories data of 12,286 taxies over four weeks in Beijing show that our approach outperforms other baseline methods with higher precision and recall.

  20. Statistical methods for anomaly detection in the complex process

    International Nuclear Information System (INIS)

    Al Mouhamed, Mayez

    1977-09-01

    In a number of complex physical systems the accessible signals are often characterized by random fluctuations about a mean value. The fluctuations (signature) often transmit information about the state of the system that the mean value cannot predict. This study is undertaken to elaborate statistical methods of anomaly detection on the basis of signature analysis of the noise inherent in the process. The algorithm presented first learns the characteristics of normal operation of a complex process. Then it detects small deviations from the normal behavior. The algorithm can be implemented in a medium-sized computer for on line application. (author) [fr

  1. Steganography anomaly detection using simple one-class classification

    Science.gov (United States)

    Rodriguez, Benjamin M.; Peterson, Gilbert L.; Agaian, Sos S.

    2007-04-01

    There are several security issues tied to multimedia when implementing the various applications in the cellular phone and wireless industry. One primary concern is the potential ease of implementing a steganography system. Traditionally, the only mechanism to embed information into a media file has been with a desktop computer. However, as the cellular phone and wireless industry matures, it becomes much simpler for the same techniques to be performed using a cell phone. In this paper, two methods are compared that classify cell phone images as either an anomaly or clean, where a clean image is one in which no alterations have been made and an anomalous image is one in which information has been hidden within the image. An image in which information has been hidden is known as a stego image. The main concern in detecting steganographic content with machine learning using cell phone images is in training specific embedding procedures to determine if the method has been used to generate a stego image. This leads to a possible flaw in the system when the learned model of stego is faced with a new stego method which doesn't match the existing model. The proposed solution to this problem is to develop systems that detect steganography as anomalies, making the embedding method irrelevant in detection. Two applicable classification methods for solving the anomaly detection of steganographic content problem are single class support vector machines (SVM) and Parzen-window. Empirical comparison of the two approaches shows that Parzen-window outperforms the single class SVM most likely due to the fact that Parzen-window generalizes less.

  2. Detection of Cardiovascular Anomalies: An Observer-Based Approach

    KAUST Repository

    Ledezma, Fernando

    2012-07-01

    In this thesis, a methodology for the detection of anomalies in the cardiovascular system is presented. The cardiovascular system is one of the most fascinating and complex physiological systems. Nowadays, cardiovascular diseases constitute one of the most important causes of mortality in the world. For instance, an estimate of 17.3 million people died in 2008 from cardiovascular diseases. Therefore, many studies have been devoted to modeling the cardiovascular system in order to better understand its behavior and find new reliable diagnosis techniques. The lumped parameter model of the cardiovascular system proposed in [1] is restructured using a hybrid systems approach in order to include a discrete input vector that represents the influence of the mitral and aortic valves in the different phases of the cardiac cycle. Parting from this model, a Taylor expansion around the nominal values of a vector of parameters is conducted. This expansion serves as the foundation for a component fault detection process to detect changes in the physiological parameters of the cardiovascular system which could be associated with cardiovascular anomalies such as atherosclerosis, aneurysm, high blood pressure, etc. An Extended Kalman Filter is used in order to achieve a joint estimation of the state vector and the changes in the considered parameters. Finally, a bank of filters is, as in [2], used in order to detect the appearance of heart valve diseases, particularly stenosis and regurgitation. The first numerical results obtained are presented.

  3. Detecting Traffic Anomalies in Urban Areas Using Taxi GPS Data

    Directory of Open Access Journals (Sweden)

    Weiming Kuang

    2015-01-01

    Full Text Available Large-scale GPS data contain hidden information and provide us with the opportunity to discover knowledge that may be useful for transportation systems using advanced data mining techniques. In major metropolitan cities, many taxicabs are equipped with GPS devices. Because taxies operate continuously for nearly 24 hours per day, they can be used as reliable sensors for the perceived traffic state. In this paper, the entire city was divided into subregions by roads, and taxi GPS data were transformed into traffic flow data to build a traffic flow matrix. In addition, a highly efficient anomaly detection method was proposed based on wavelet transform and PCA (principal component analysis for detecting anomalous traffic events in urban regions. The traffic anomaly is considered to occur in a subregion when the values of the corresponding indicators deviate significantly from the expected values. This method was evaluated using a GPS dataset that was generated by more than 15,000 taxies over a period of half a year in Harbin, China. The results show that this detection method is effective and efficient.

  4. DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

    Directory of Open Access Journals (Sweden)

    Peter Christiansen

    2016-11-01

    Full Text Available Convolutional neural network (CNN-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN. In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m than RCNN. RCNN has a similar performance at a short range (0–30 m. However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms = a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit.

  5. DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field.

    Science.gov (United States)

    Christiansen, Peter; Nielsen, Lars N; Steen, Kim A; Jørgensen, Rasmus N; Karstoft, Henrik

    2016-11-11

    Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

  6. Hyperspectral anomaly detection based on stacked denoising autoencoders

    Science.gov (United States)

    Zhao, Chunhui; Li, Xueyuan; Zhu, Haifeng

    2017-10-01

    Hyperspectral anomaly detection (AD) is an important technique of unsupervised target detection and has significance in real situations. Due to the high dimensionality of hyperspectral data, AD will be influenced by noise, nonlinear correlation of band, or other factors that lead to the decline of detection accuracy. To overcome this problem, a method of hyperspectral AD based on stacked denoising autoencoders (AE) (HADSDA) is proposed. Simultaneously, two different feature detection models, spectral feature (SF) and fused feature by clustering (FFC), are constructed to verify the effectiveness of the proposed algorithm. The SF detection model uses the SF of each pixel. The FFC detection model uses a similar set of pixels constructed by clustering and then fuses the set of pixels by the stacked denoising autoencoders algorithm (SDA). The SDA is an algorithm that can automatically learn nonlinear deep features of the image. Compared with other linear or nonlinear feature extraction methods, the detection result of the proposed algorithm is greatly improved. Experiment results show that the proposed algorithm is an excellent feature learning method and can achieve higher detection performance.

  7. A spatial analysis of the ionospheric TEC anomalies prior to M7.0+ earthquakes during 2003-2014

    Science.gov (United States)

    Zhu, Fuying; Lin, Jian; Su, Fanfan; Zhou, Yiyan

    2016-11-01

    In the present paper, by using the global navigation satellite system total electron content (GNSS TEC), we conducted a statistical study on the spatial distribution of the seismo-ionospheric precursors (SIPs) before the occurrence of 133 shallow earthquakes of magnitude M ⩾ 7.0 in the global area during 2003-2014. To exclude the effect of space weather and geomagnetic disturbance, we considered the variations in the geomagnetic Dst indices, Kp indices, and the F10.7 indices; the GNSS TEC over the regions of ±10° near the epicenters is then investigated, and the spatial distribution of ionospheric TEC anomalies 0-15 days before the earthquakes is reported for the first time. We also statistically analyzed and compared the counts of the TEC anomalies over the epicenters in the eastern, southern, western, and northern directions 0-15 days prior to the earthquakes. Results show that the maximum occurrence number of ionospheric TEC negative anomalies specially appears over the epicenters and the anomalous behaviors of the ionospheric TEC attenuate slightly with the distance to the epicenters. However, the ionospheric TEC positive anomalies in the western direction have the biggest chance of occurring. Finally, the spatial distribution characteristics of the observed SIPs are explained by the electric-field-coupling model.

  8. Low Count Anomaly Detection at Large Standoff Distances

    Science.gov (United States)

    Pfund, David Michael; Jarman, Kenneth D.; Milbrath, Brian D.; Kiff, Scott D.; Sidor, Daniel E.

    2010-02-01

    Searching for hidden illicit sources of gamma radiation in an urban environment is difficult. Background radiation profiles are variable and cluttered with transient acquisitions from naturally occurring radioactive materials and medical isotopes. Potentially threatening sources likely will be nearly hidden in this noise and encountered at high standoff distances and low threat count rates. We discuss an anomaly detection algorithm that characterizes low count sources as threatening or non-threatening and operates well in the presence of high benign source variability. We discuss the algorithm parameters needed to reliably find sources both close to the detector and far away from it. These parameters include the cutoff frequencies of background tracking filters and the integration time of the spectrometer. This work is part of the development of the Standoff Radiation Imaging System (SORIS) as part of DNDO's Standoff Radiation Detection System Advanced Technology Demonstration (SORDS-ATD) program.

  9. Anomaly detection of microstructural defects in continuous fiber reinforced composites

    Science.gov (United States)

    Bricker, Stephen; Simmons, J. P.; Przybyla, Craig; Hardie, Russell

    2015-03-01

    Ceramic matrix composites (CMC) with continuous fiber reinforcements have the potential to enable the next generation of high speed hypersonic vehicles and/or significant improvements in gas turbine engine performance due to their exhibited toughness when subjected to high mechanical loads at extreme temperatures (2200F+). Reinforced fiber composites (RFC) provide increased fracture toughness, crack growth resistance, and strength, though little is known about how stochastic variation and imperfections in the material effect material properties. In this work, tools are developed for quantifying anomalies within the microstructure at several scales. The detection and characterization of anomalous microstructure is a critical step in linking production techniques to properties, as well as in accurate material simulation and property prediction for the integrated computation materials engineering (ICME) of RFC based components. It is desired to find statistical outliers for any number of material characteristics such as fibers, fiber coatings, and pores. Here, fiber orientation, or `velocity', and `velocity' gradient are developed and examined for anomalous behavior. Categorizing anomalous behavior in the CMC is approached by multivariate Gaussian mixture modeling. A Gaussian mixture is employed to estimate the probability density function (PDF) of the features in question, and anomalies are classified by their likelihood of belonging to the statistical normal behavior for that feature.

  10. Diabetic rats exercised prior to and during pregnancy: maternal reproductive outcome, biochemical profile, and frequency of fetal anomalies.

    Science.gov (United States)

    Damasceno, Débora Cristina; Silva, Hellen Pontes; Vaz, Geizi Fátima; Vasques-Silva, Francine Aparecida; Calderon, Iracema Mattos Paranhos; Rudge, Marilza Vieira Cunha; Campos, Kleber Eduardo; Volpato, Gustavo Tadeu

    2013-07-01

    The aim of this study was to evaluate the effects of exercise prior to or during pregnancy on maternal reproductive outcome, biochemical profile, and on fetal anomaly frequency in a rat pregnancy model utilizing chemically induced diabetes. Wistar rats (minimum n = 11 animals/group) were randomly assigned the following groups: group 1 (G1), sedentary, nondiabetic; G2, nondiabetic, exercised during pregnancy; G3, nondiabetic, exercised prior to and during pregnancy; G4, sedentary, diabetic; G5, diabetic, exercised during pregnancy; and G6, diabetic, exercised prior to and during pregnancy. A swimming program was utilized for moderate exercise. On day 21 of pregnancy, all rats were anesthetized to obtain blood for biochemical measurements. The gravid uterus was weighed with its contents, and the fetuses were analyzed. The nondiabetic rats exercised prior to pregnancy presented a reduced maternal weight gain. Besides, G2 and G3 groups showed decreased fetal weights at term pregnancy, indicating slight intrauterine growth restriction (IUGR). In the diabetic dams, the swimming program did not have antihyperglycemic effects. The exercise applied only during pregnancy caused severe IUGR, as confirmed by reduced fetal weight mean, fetal weight classification, and ossification sites. Nevertheless, exercise was not a teratogenic factor and improved the rats' lipid profiles, demonstrating that the exercise presented possible benefits, but there are also risks prior and during pregnancy, especially in diabetic pregnant women.

  11. Coupling mechanism between geoacoustic emission and electromagnetic anomalies prior to earthquakes

    Directory of Open Access Journals (Sweden)

    Viacheslav Pilipenko

    2014-11-01

    Full Text Available Micro-cracking in the earthquake preparation zone is accompanied by the generation of acoustic emission (AE. Even low-intensity AE can essentially modify the underground fluid dynamics owing to the influence of high-frequency acoustic field on filtration process. Laboratory experiments show that acoustic impact on pour sample destroys a film with bounded water and results in a steep increase of its permeability up to 2 orders of magnitude. Impulsive acoustic fields also decrease the effective viscosity of the fluid. The occurrence in the crust under pressure of a region with distinct hydrodynamic and electrokinetic parameters will result in an appearance of anomalous telluric and magnetic fields on the surface above. This effect is estimated analytically using a simple model with an ellipticshaped inhomogeneity. The suggested hypothesis about possible coupling between AE and geoelectrical anomalies needs observational verification.

  12. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  13. Anomaly detection using clustering for ad hoc networks -behavioral approach-

    Directory of Open Access Journals (Sweden)

    Belacel Madani

    2012-06-01

    Full Text Available Mobile   ad   hoc   networks   (MANETs   are   multi-hop   wireless   networks   ofautonomous  mobile  nodes  without  any  fixed  infrastructure.  In  MANETs,  it  isdifficult to detect malicious nodes because the network topology constantly changesdue  to  node  mobility.  Intrusion  detection  is  the  means  to  identify  the  intrusivebehaviors and provide useful information to intruded systems to respond fast and toavoid  or  reduce  damages.  The  anomaly  detection  algorithms  have  the  advantagebecause  they  can  detect  new  types  of  attacks  (zero-day  attacks.In  this  paper,  wepresent  a  Intrusion  Detection  System  clustering-based  (ID-Cluster  that  fits  therequirement of MANET. This dissertation addresses both routing layer misbehaviorsissues,  with  main  focuses  on  thwarting  routing  disruption  attack  Dynamic  SourceRouting  (DSR.  To  validate  the  research,  a  case  study  is  presented  using  thesimulation with GloMoSum at different mobility levels. Simulation results show thatour  proposed  system  can  achieve  desirable  performance  and  meet  the  securityrequirement of MANET.

  14. FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection.

    Science.gov (United States)

    Noto, Keith; Brodley, Carla; Slonim, Donna

    2012-01-01

    Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called "normal" instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach.

  15. Precursor-Like Anomalies prior to the 2008 Wenchuan Earthquake: A Critical-but-Constructive Review

    Directory of Open Access Journals (Sweden)

    Tengfei Ma

    2012-01-01

    Full Text Available Results published since the last three years on the observations of the precursor-like anomalies before the May 12, 2008, Wenchuan, Ms8.0 earthquake are collected and analyzed. These retrospective case studies would have provided heuristic clues about the preparation process of this inland great earthquake and the predictability of this destructive event if the standards for the rigorous test of earthquake forecast schemes were strictly observed. At least in some of these studies, however, several issues still need to be further examined to confirm or falsify the connection of the reported observations with the Wenchuan earthquake. Some of the problems are due to the inevitable limitation of observational infrastructure at the recent time, but some of the problems are due to the lack of communication about the test of earthquake forecast schemes. For the interdisciplinary studies on earthquake forecast, reminding of the latter issue seems of special importance for promoting the works and cooperation in this field.

  16. Anomaly based Intrusion Detection using Modified Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    B.S. Harish

    2017-12-01

    Full Text Available This paper presents a network anomaly detection method based on fuzzy clustering. Computer security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. As a result, Intrusion Detection System has become an indispensable component of computer security. The proposed method consists of three steps: Pre-Processing, Feature Selection and Clustering. In pre-processing step, the duplicate samples are eliminated from the sample set. Next, principal component analysis is adopted to select the most discriminative features. In clustering step, the network samples are clustered using Robust Spatial Kernel Fuzzy C-Means (RSKFCM algorithm. RSKFCM is a variant of traditional Fuzzy C-Means which considers the neighbourhood membership information and uses kernel distance metric. To evaluate the proposed method, we conducted experiments on standard dataset and compared the results with state-of-the-art methods. We used cluster validity indices, accuracy and false positive rate as performance metrics. Experimental results inferred that, the proposed method achieves better results compared to other methods.

  17. Radon anomalies: When are they possible to be detected?

    Science.gov (United States)

    Passarelli, Luigi; Woith, Heiko; Seyis, Cemil; Nikkhoo, Mehdi; Donner, Reik

    2017-04-01

    Records of the Radon noble gas in different environments like soil, air, groundwater, rock, caves, and tunnels, typically display cyclic variations including diurnal (S1), semidiurnal (S2) and seasonal components. But there are also cases where theses cycles are absent. Interestingly, radon emission can also be affected by transient processes, which inhibit or enhance the radon carrying process at the surface. This results in transient changes in the radon emission rate, which are superimposed on the low and high frequency cycles. The complexity in the spectral contents of the radon time-series makes any statistical analysis aiming at understanding the physical driving processes a challenging task. In the past decades there have been several attempts to relate changes in radon emission rate with physical triggering processes such as earthquake occurrence. One of the problems in this type of investigation is to objectively detect anomalies in the radon time-series. In the present work, we propose a simple and objective statistical method for detecting changes in the radon emission rate time-series. The method uses non-parametric statistical tests (e.g., Kolmogorov-Smirnov) to compare empirical distributions of radon emission rate by sequentially applying various time window to the time-series. The statistical test indicates whether two empirical distributions of data originate from the same distribution at a desired significance level. We test the algorithm on synthetic data in order to explore the sensitivity of the statistical test to the sample size. We successively apply the test to six radon emission rate recordings from stations located around the Marmara Sea obtained within the MARsite project (MARsite has received funding from the European Union's Seventh Programme for research, technological development and demonstration under grant agreement No 308417). We conclude that the test performs relatively well on identify transient changes in the radon emission

  18. Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks

    KAUST Repository

    Wang, Wei

    2014-06-22

    In this work, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-managing: self-labeling, self-updating and self-adapting. Our framework employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies. Two large real HTTP traffic streams collected in our institute as well as a set of benchmark KDD’99 data are used to validate the framework and the method. The test results show that the autonomic model achieves better results in terms of effectiveness and efficiency compared to adaptive Sequential Karhunen–Loeve method and static AP as well as three other static anomaly detection methods, namely, k-NN, PCA and SVM.

  19. Learning patterns of human activity for anomaly detection

    Science.gov (United States)

    Gutchess, Daniel; Checka, Neal; Snorrason, Magnús S.

    2007-04-01

    Commercial security and surveillance systems offer advanced sensors, optics, and display capabilities but lack intelligent processing. This necessitates human operators who must closely monitor video for situational awareness and threat assessment. For instance, urban environments are typically in a state of constant activity, which generates numerous visual cues, each of which must be examined so that potential security breaches do not go unnoticed. We are building a prototype system called BALDUR (Behavior Adaptive Learning during Urban Reconnaissance) that learns probabilistic models of activity for a given site using online and unsupervised training techniques. Once a camera system is set up, no operator intervention is required for the system to begin learning patterns of activity. Anomalies corresponding to unusual or suspicious behavior are automatically detected in real time. All moving object tracks (pedestrians, vehicles, etc.) are efficiently stored in a relational database for use in training. The database is also well suited for answering human- initiated queries. An example of such a query is, "Display all pedestrians who approached the door of the building between the hours of 9:00pm and 11:00pm." This forensic analysis tool complements the system's real-time situational awareness capabilities. Several large datasets have been collected for the evaluation of the system, including one database containing an entire month of activity from a commercial parking lot.

  20. Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools

    Directory of Open Access Journals (Sweden)

    Yuejun Guo

    2017-06-01

    Full Text Available In this paper, we propose to improve trajectory shape analysis by explicitly considering the speed attribute of trajectory data, and to successfully achieve anomaly detection. The shape of object motion trajectory is modeled using Kernel Density Estimation (KDE, making use of both the angle attribute of the trajectory and the speed of the moving object. An unsupervised clustering algorithm, based on the Information Bottleneck (IB method, is employed for trajectory learning to obtain an adaptive number of trajectory clusters through maximizing the Mutual Information (MI between the clustering result and a feature set of the trajectory data. Furthermore, we propose to effectively enhance the performance of IB by taking into account the clustering quality in each iteration of the clustering procedure. The trajectories are determined as either abnormal (infrequently observed or normal by a measure based on Shannon entropy. Extensive tests on real-world and synthetic data show that the proposed technique behaves very well and outperforms the state-of-the-art methods.

  1. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

    Science.gov (United States)

    Goldstein, Markus; Uchida, Seiichi

    2016-01-01

    Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks. PMID:27093601

  2. A first approach on fault detection and isolation for cardiovascular anomalies detection

    KAUST Repository

    Ledezma, Fernando

    2015-07-01

    In this paper, we use an extended version of the cardiovascular system\\'s state space model presented by [1] and propose a fault detection and isolation methodology to study the problem of detecting cardiovascular anomalies that can originate from variations in physiological parameters and deviations in the performance of the heart\\'s mitral and aortic valves. An observer-based approach is discussed as the basis of the method. The approach contemplates a bank of Extended Kalman Filters to achieve joint estimation of the model\\'s states and parameters and to detect malfunctions in the valves\\' performance. © 2015 American Automatic Control Council.

  3. Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method

    Directory of Open Access Journals (Sweden)

    Fei Li

    2017-05-01

    Full Text Available Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering the collective anomalous data and both sensitivity and robustness of the anomaly detection model, a sequential symbolic anomaly detection method is proposed and applied to the gas turbine fuel system. A structural Finite State Machine is used to evaluate posterior probabilities of observing symbolic sequences and the most probable state sequences they may locate. Hence an estimation-based model and a decoding-based model are used to identify anomalies in two different ways. Experimental results indicate that both models have both ideal performance overall, but the estimation-based model has a strong robustness ability, whereas the decoding-based model has a strong accuracy ability, particularly in a certain range of sequence lengths. Therefore, the proposed method can facilitate well existing symbolic dynamic analysis- based anomaly detection methods, especially in the gas turbine domain.

  4. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Science.gov (United States)

    Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang

    2008-12-01

    Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation). The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  5. Detection of short-term anomaly using parasitic discrete wavelet transform

    International Nuclear Information System (INIS)

    Nagamatsu, Takashi; Gofuku, Akio

    2013-01-01

    A parasitic discrete wavelet transform (P-DWT) that has a large flexibility in design of the mother wavelet (MW) and a high processing speed was applied for simulation and measured anomalies. First, we applied the P-DWT to detection of the short-term anomalies. Second, we applied the P-DWT to detection of the collision of pump using the pump diagnostic experiment equipment that was designed taking into consideration the structure of the pump used for the water-steam system of the fast breeder reactor 'Monju'. The vibration signals were measured by the vibration sensor attached to the pump when injecting four types of small objects (sphere, small sphere, cube, and rectangular parallelepiped). Anomaly detection was performed by calculating the fast wavelet instantaneous correlation using the parasitic filter that was constructed on the basis of the measured signals. The results suggested that the anomalies could be detected for all types of the supposed anomalies. (author)

  6. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Yang Dan

    2008-12-01

    Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  7. A robust background regression based score estimation algorithm for hyperspectral anomaly detection

    Science.gov (United States)

    Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

    2016-12-01

    Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement

  8. Design of Hybrid Network Anomalies Detection System (H-NADS Using IP Gray Space Analysis

    Directory of Open Access Journals (Sweden)

    Yogendra Kumar JAIN

    2009-01-01

    Full Text Available In Network Security, there is a major issue to secure the public or private network from abnormal users. It is because each network is made up of users, services and computers with a specific behavior that is also called as heterogeneous system. To detect abnormal users, anomaly detection system (ADS is used. In this paper, we present a novel and hybrid Anomaly Detection System with the uses of IP gray space analysis and dominant scanning port identification heuristics used to detect various anomalous users with their potential behaviors. This methodology is the combination of both statistical and rule based anomaly detection which detects five types of anomalies with their three types of potential behaviors and generates respective alarm messages to GUI.

  9. Multi-scale anomaly detection algorithm based on infrequent pattern of time series

    Science.gov (United States)

    Chen, Xiao-Yun; Zhan, Yan-Yan

    2008-04-01

    In this paper, we propose two anomaly detection algorithms PAV and MPAV on time series. The first basic idea of this paper defines that the anomaly pattern is the most infrequent time series pattern, which is the lowest support pattern. The second basic idea of this paper is that PAV detects directly anomalies in the original time series, and MPAV algorithm extraction anomaly in the wavelet approximation coefficient of the time series. For complexity analyses, as the wavelet transform have the functions to compress data, filter noise, and maintain the basic form of time series, the MPAV algorithm, while maintaining the accuracy of the algorithm improves the efficiency. As PAV and MPAV algorithms are simple and easy to realize without training, this proposed multi-scale anomaly detection algorithm based on infrequent pattern of time series can therefore be proved to be very useful for computer science applications.

  10. Load characterization, overload prediction, and anomaly detection for voice over IP traffic

    NARCIS (Netherlands)

    Mandjes, Michel; Saniee, Iraj; Stolyar, Alexander; Heidelberger, P.

    2001-01-01

    We consider the problem of traffic anomaly detection in IP networks. Traffic anomalies arise when there is overload due to failures in a network. We present general formulae for the variance of the cumulative traffic over a fixed time interval and show how the derived analytical expression

  11. Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

    Science.gov (United States)

    Gorinevsky, Dimitry; Matthews, Bryan L.; Martin, Rodney

    2012-01-01

    This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

  12. Network-Wide Traffic Anomaly Detection and Localization Based on Robust Multivariate Probabilistic Calibration Model

    Directory of Open Access Journals (Sweden)

    Yuchong Li

    2015-01-01

    Full Text Available Network anomaly detection and localization are of great significance to network security. Compared with the traditional methods of host computer, single link and single path, the network-wide anomaly detection approaches have distinctive advantages with respect to detection precision and range. However, when facing the actual problems of noise interference or data loss, the network-wide anomaly detection approaches also suffer significant performance reduction or may even become unavailable. Besides, researches on anomaly localization are rare. In order to solve the mentioned problems, this paper presents a robust multivariate probabilistic calibration model for network-wide anomaly detection and localization. It applies the latent variable probability theory with multivariate t-distribution to establish the normal traffic model. Not only does the algorithm implement network anomaly detection by judging whether the sample’s Mahalanobis distance exceeds the threshold, but also it locates anomalies by contribution analysis. Both theoretical analysis and experimental results demonstrate its robustness and wider use. The algorithm is applicable when dealing with both data integrity and loss. It also has a stronger resistance over noise interference and lower sensitivity to the change of parameters, all of which indicate its performance stability.

  13. Methods for computational disease surveillance in infection prevention and control: Statistical process control versus Twitter's anomaly and breakout detection algorithms.

    Science.gov (United States)

    Wiemken, Timothy L; Furmanek, Stephen P; Mattingly, William A; Wright, Marc-Oliver; Persaud, Annuradha K; Guinn, Brian E; Carrico, Ruth M; Arnold, Forest W; Ramirez, Julio A

    2018-02-01

    Although not all health care-associated infections (HAIs) are preventable, reducing HAIs through targeted intervention is key to a successful infection prevention program. To identify areas in need of targeted intervention, robust statistical methods must be used when analyzing surveillance data. The objective of this study was to compare and contrast statistical process control (SPC) charts with Twitter's anomaly and breakout detection algorithms. SPC and anomaly/breakout detection (ABD) charts were created for vancomycin-resistant Enterococcus, Acinetobacter baumannii, catheter-associated urinary tract infection, and central line-associated bloodstream infection data. Both SPC and ABD charts detected similar data points as anomalous/out of control on most charts. The vancomycin-resistant Enterococcus ABD chart detected an extra anomalous point that appeared to be higher than the same time period in prior years. Using a small subset of the central line-associated bloodstream infection data, the ABD chart was able to detect anomalies where the SPC chart was not. SPC charts and ABD charts both performed well, although ABD charts appeared to work better in the context of seasonal variation and autocorrelation. Because they account for common statistical issues in HAI data, ABD charts may be useful for practitioners for analysis of HAI surveillance data. Copyright © 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

  14. OceanXtremes: Oceanographic Data-Intensive Anomaly Detection and Analysis Portal

    Data.gov (United States)

    National Aeronautics and Space Administration — Anomaly detection is a process of identifying items, events or observations, which do not conform to an expected pattern in a dataset or time series. Current and...

  15. nu-Anomica: A Fast Support Vector Based Anomaly Detection Technique

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper we propose $nu$-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the...

  16. Visibility Video Detection with Dark Channel Prior on Highway

    Directory of Open Access Journals (Sweden)

    Jiandong Zhao

    2016-01-01

    Full Text Available Dark channel prior (DCP has advantages in image enhancement and image haze removal and is explored to detect highway visibility according to the physical relationship between transmittance and extinction coefficient. However, there are three major error sources in calculating transmittance. The first is that sky regions do not satisfy the assumptions of DCP algorithm. So the optimization algorithms combined with region growing and coefficient correction method are proposed. When extracting atmospheric brightness, different values lead to the second error. Therefore, according to different visibility conditions, a multimode classification method is designed. Image blocky effect causes the third error. Then guided image filtering is introduced to obtain accurate transmittance of each pixel of image. Next, according to the definition meteorological optical visual range and the relationship between transmittance and extinction coefficient of Lambert-Beer’s Law, accurate visibility value can be calculated. A comparative experimental system including visibility detector and video camera was set up to verify the accuracy of these optimization algorithms. Finally, a large number of highway section videos were selected to test the validity of DCP method in different models. The results indicate that these detection visibility methods are feasible and reliable for the smooth operation of highways.

  17. Anomaly detection in OECD Benchmark data using co-variance methods

    International Nuclear Information System (INIS)

    Srinivasan, G.S.; Krinizs, K.; Por, G.

    1993-02-01

    OECD Benchmark data distributed for the SMORN VI Specialists Meeting in Reactor Noise were investigated for anomaly detection in artificially generated reactor noise benchmark analysis. It was observed that statistical features extracted from covariance matrix of frequency components are very sensitive in terms of the anomaly detection level. It is possible to create well defined alarm levels. (R.P.) 5 refs.; 23 figs.; 1 tab

  18. Anomaly Detection in Host Signaling Pathways for the Early Prognosis of Acute Infection.

    Directory of Open Access Journals (Sweden)

    Kun Wang

    Full Text Available Clinical diagnosis of acute infectious diseases during the early stages of infection is critical to administering the appropriate treatment to improve the disease outcome. We present a data driven analysis of the human cellular response to respiratory viruses including influenza, respiratory syncytia virus, and human rhinovirus, and compared this with the response to the bacterial endotoxin, Lipopolysaccharides (LPS. Using an anomaly detection framework we identified pathways that clearly distinguish between asymptomatic and symptomatic patients infected with the four different respiratory viruses and that accurately diagnosed patients exposed to a bacterial infection. Connectivity pathway analysis comparing the viral and bacterial diagnostic signatures identified host cellular pathways that were unique to patients exposed to LPS endotoxin indicating this type of analysis could be used to identify host biomarkers that can differentiate clinical etiologies of acute infection. We applied the Multivariate State Estimation Technique (MSET on two human influenza (H1N1 and H3N2 gene expression data sets to define host networks perturbed in the asymptomatic phase of infection. Our analysis identified pathways in the respiratory virus diagnostic signature as prognostic biomarkers that triggered prior to clinical presentation of acute symptoms. These early warning pathways correctly predicted that almost half of the subjects would become symptomatic in less than forty hours post-infection and that three of the 18 subjects would become symptomatic after only 8 hours. These results provide a proof-of-concept for utility of anomaly detection algorithms to classify host pathway signatures that can identify presymptomatic signatures of acute diseases and differentiate between etiologies of infection. On a global scale, acute respiratory infections cause a significant proportion of human co-morbidities and account for 4.25 million deaths annually. The

  19. [Detecting human chromosome anomalies with primed in situ labeling (PRINS)].

    Science.gov (United States)

    Zhu, Yi-Jian; Liu, Di-Shi; Ding, Xian-Ping

    2008-08-01

    Numerical chromosome anomaly was one of the most important kinds of human chromosome diseases by inducing pregnancy loss, miscarriage, infant death, congenital malformations and nerve damage. The present study was to establish a rapid, reliable and reasonable multicolor primed in situ labeling (PRINS) protocol for diagnosing numerical anomaly in human chromosome. First, nuclei of cultured lymphocytes and sperms were labeled with the method of PRINS, and then nuclei of cultured lymphocytes, sperms and other specimen were labeled with the method of updated non-ddNTP-blocking multicolor PRINS technique. The labeling effect of different target sequences and the feature of different fluorochromes were evaluated by experiment. Meanwhile, several parameters of PRINS were optimized to obtain more homogeneous and stable labeling effect. At last, the applicative value of PRINS was evaluated by comparing the clinical effect and labeling characteristics between FISH probe and PRINS. In the present study, several chromosomes were simultaneously marked successfully in the same sperm nucleus within 2.5 hours. And the frequency of one-color-labeling reached 99%. The many advantages, compared with FISH, make PRINS become the first choice in diagnosing diseases related to numerical anomaly in human chromosome.

  20. Improved Principal Component Analysis for Anomaly Detection: Application to an Emergency Department

    KAUST Repository

    Harrou, Fouzi

    2015-07-03

    Monitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotelling’s T2T2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.

  1. WE-H-BRC-06: A Unified Machine-Learning Based Probabilistic Model for Automated Anomaly Detection in the Treatment Plan Data

    International Nuclear Information System (INIS)

    Chang, X; Liu, S; Kalet, A; Yang, D

    2016-01-01

    Purpose: The purpose of this work was to investigate the ability of a machine-learning based probabilistic approach to detect radiotherapy treatment plan anomalies given initial disease classes information. Methods In total we obtained 1112 unique treatment plans with five plan parameters and disease information from a Mosaiq treatment management system database for use in the study. The plan parameters include prescription dose, fractions, fields, modality and techniques. The disease information includes disease site, and T, M and N disease stages. A Bayesian network method was employed to model the probabilistic relationships between tumor disease information, plan parameters and an anomaly flag. A Bayesian learning method with Dirichlet prior was useed to learn the joint probabilities between dependent variables in error-free plan data and data with artificially induced anomalies. In the study, we randomly sampled data with anomaly in a specified anomaly space.We tested the approach with three groups of plan anomalies – improper concurrence of values of all five plan parameters and values of any two out of five parameters, and all single plan parameter value anomalies. Totally, 16 types of plan anomalies were covered by the study. For each type, we trained an individual Bayesian network. Results: We found that the true positive rate (recall) and positive predictive value (precision) to detect concurrence anomalies of five plan parameters in new patient cases were 94.45±0.26% and 93.76±0.39% respectively. To detect other 15 types of plan anomalies, the average recall and precision were 93.61±2.57% and 93.78±3.54% respectively. The computation time to detect the plan anomaly of each type in a new plan is ∼0.08 seconds. Conclusion: The proposed method for treatment plan anomaly detection was found effective in the initial tests. The results suggest that this type of models could be applied to develop plan anomaly detection tools to assist manual and

  2. Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

    Science.gov (United States)

    Flach, Milan; Gans, Fabian; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus; Rodner, Erik; Bathiany, Sebastian; Bodesheim, Paul; Guanche, Yanira; Sippel, Sebastian; Mahecha, Miguel D.

    2017-08-01

    Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies

  3. An Optimized Method to Detect BDS Satellites' Orbit Maneuvering and Anomalies in Real-Time.

    Science.gov (United States)

    Huang, Guanwen; Qin, Zhiwei; Zhang, Qin; Wang, Le; Yan, Xingyuan; Wang, Xiaolei

    2018-02-28

    The orbital maneuvers of Global Navigation Satellite System (GNSS) Constellations will decrease the performance and accuracy of positioning, navigation, and timing (PNT). Because satellites in the Chinese BeiDou Navigation Satellite System (BDS) are in Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO), maneuvers occur more frequently. Also, the precise start moment of the BDS satellites' orbit maneuvering cannot be obtained by common users. This paper presented an improved real-time detecting method for BDS satellites' orbit maneuvering and anomalies with higher timeliness and higher accuracy. The main contributions to this improvement are as follows: (1) instead of the previous two-steps method, a new one-step method with higher accuracy is proposed to determine the start moment and the pseudo random noise code (PRN) of the satellite orbit maneuvering in that time; (2) BDS Medium Earth Orbit (MEO) orbital maneuvers are firstly detected according to the proposed selection strategy for the stations; and (3) the classified non-maneuvering anomalies are detected by a new median robust method using the weak anomaly detection factor and the strong anomaly detection factor. The data from the Multi-GNSS Experiment (MGEX) in 2017 was used for experimental analysis. The experimental results and analysis showed that the start moment of orbital maneuvers and the period of non-maneuver anomalies can be determined more accurately in real-time. When orbital maneuvers and anomalies occur, the proposed method improved the data utilization for 91 and 95 min in 2017.

  4. Multi-Level Anomaly Detection on Time-Varying Graph Data

    Energy Technology Data Exchange (ETDEWEB)

    Bridges, Robert A [ORNL; Collins, John P [ORNL; Ferragut, Erik M [ORNL; Laska, Jason A [ORNL; Sullivan, Blair D [ORNL

    2015-01-01

    This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

  5. A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

    Directory of Open Access Journals (Sweden)

    Hongchao Song

    2017-01-01

    Full Text Available Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE and an ensemble k-nearest neighbor graphs- (K-NNG- based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

  6. Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines

    Directory of Open Access Journals (Sweden)

    Liansheng Liu

    2016-04-01

    Full Text Available In a complex system, condition monitoring (CM can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR. The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA Ames Research Center and have been used as Prognostics and Health Management (PHM challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved.

  7. Theoretical and numerical investigations into the SPRT method for anomaly detection

    International Nuclear Information System (INIS)

    Schoonewelle, H.; Hagen, T.H.J.J. van der; Hoogenboom, J.E.

    1995-01-01

    The sequential probability ratio test developed by Wald is a powerful method of testing an alternative hypothesis against a null hypothesis. This makes the method applicable for anomaly detection. In this paper the method is used to detect a change of the standard deviation of a Gaussian distributed white noise signal. The false alarm probability, the alarm failure probability and the average time to alarm of the method, which are important parameters for anomaly detection, are determined by simulation and compared with theoretical results. Each of the three parameters is presented in dependence of the other two and the ratio of the standard deviation of the anomalous signal and that of the normal signal. Results show that the method is very well suited for anomaly detection. It can detect for example a 50% change in standard deviation within 1 second with a false alarm and alarm failure rate of less than once per month. (author)

  8. Extending TOPS: A Prototype MODIS Anomaly Detection Architecture

    Science.gov (United States)

    Votava, P.; Nemani, R. R.; Srivastava, A. N.

    2008-12-01

    The management and processing of Earth science data has been gaining importance over the last decade due to higher data volumes generated by a larger number of instruments, and due to the increase in complexity of Earth science models that use this data. The volume of data itself is often a limiting factor in obtaining the information needed by the scientists; without more sophisticated data volume reduction technologies, possible key information may not be discovered. We are especially interested in automatic identification of disturbances within the ecosystems (e,g, wildfires, droughts, floods, insect/pest damage, wind damage, logging), and focusing our analysis efforts on the identified areas. There are dozens of variables that define the health of our ecosystem and both long-term and short-term changes in these variables can serve as early indicators of natural disasters and shifts in climate and ecosystem health. These changes can have profound socio-economic impacts and we need to develop capabilities for identification, analysis and response to these changes in a timely manner. Because the ecosystem consists of a large number of variables, there can be a disturbance that is only apparent when we examine relationships among multiple variables despite the fact that none of them is by itself alarming. We have to be able to extract information from multiple sensors and observations and discover these underlying relationships. As the data volumes increase, there is also potential for large number of anomalies to "flood" the system, so we need to provide ability to automatically select the most likely ones and the most important ones and the ability to analyze the anomaly with minimal involvement of scientists. We describe a prototype architecture for anomaly driven data reduction for both near-real-time and archived surface reflectance data from the MODIS instrument collected over Central California and test it using Orca and One-Class Support Vector Machines

  9. An Economic Analysis of Prenatal Cytogenetic Technologies for Sonographically-Detected Fetal Anomalies

    OpenAIRE

    Harper, Lorie M.; Sutton, Amelia L.M.; Longman, Ryan E.; Odibo, Anthony O.

    2014-01-01

    When congenital anomalies are diagnosed on prenatal ultrasound, the current standard of care is to perform G-banded karyotyping on cultured amniotic cells. Chromosomal microarray (CMA) can detect smaller genomic deletions and duplications than traditional karyotype analysis. CMA is the first-tier test in postnatal evaluation of children with multiple congenital anomalies. Recent studies have demonstrated the utility of CMA in the prenatal setting and have advocated for widespread implementati...

  10. Improving Cyber-Security of Smart Grid Systems via Anomaly Detection and Linguistic Domain Knowledge

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Todd Vollmer; Milos Manic

    2012-08-01

    The planned large scale deployment of smart grid network devices will generate a large amount of information exchanged over various types of communication networks. The implementation of these critical systems will require appropriate cyber-security measures. A network anomaly detection solution is considered in this work. In common network architectures multiple communications streams are simultaneously present, making it difficult to build an anomaly detection solution for the entire system. In addition, common anomaly detection algorithms require specification of a sensitivity threshold, which inevitably leads to a tradeoff between false positives and false negatives rates. In order to alleviate these issues, this paper proposes a novel anomaly detection architecture. The designed system applies the previously developed network security cyber-sensor method to individual selected communication streams allowing for learning accurate normal network behavior models. Furthermore, the developed system dynamically adjusts the sensitivity threshold of each anomaly detection algorithm based on domain knowledge about the specific network system. It is proposed to model this domain knowledge using Interval Type-2 Fuzzy Logic rules, which linguistically describe the relationship between various features of the network communication and the possibility of a cyber attack. The proposed method was tested on experimental smart grid system demonstrating enhanced cyber-security.

  11. Panacea: Automating Attack Classification for Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, Sandro; Hartel, Pieter H.; Kirda, E.; Jha, S.; Balzarotti, D.

    Anomaly-based intrusion detection systems are usually criticized because they lack a classication of attack, thus security teams have to manually inspect any raised alert to classify it. We present a new approach, Panacea, to automatically and systematically classify attacks detected by an

  12. Panacea: Automating Attack Classification for Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, Sandro; Hartel, Pieter H.

    2009-01-01

    Anomaly-based intrusion detection systems are usually criticized because they lack a classication of attack, thus security teams have to manually inspect any raised alert to classify it. We present a new approach, Panacea, to automatically and systematically classify attacks detected by an

  13. Poseidon: a 2-tier Anomaly-based Network Intrusion Detection System

    NARCIS (Netherlands)

    Bolzoni, D.; Zambon, Emmanuele; Etalle, Sandro; Hartel, Pieter H.; Cole, Jack; Wolthusen, Stephen D.

    We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection

  14. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi

    2015-12-07

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  15. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery

    Science.gov (United States)

    Sun, Weiwei; Liu, Chun; Li, Jialin; Lai, Yenming Mark; Li, Weiyue

    2014-01-01

    A low-rank and sparse matrix decomposition (LRaSMD) detector has been proposed to detect anomalies in hyperspectral imagery (HSI). The detector assumes background images are low-rank while anomalies are gross errors that are sparsely distributed throughout the image scene. By solving a constrained convex optimization problem, the LRaSMD detector separates the anomalies from the background. This protects the background model from corruption. An anomaly value for each pixel is calculated using the Euclidean distance, and anomalies are determined by thresholding the anomaly value. Four groups of experiments on three widely used HSI datasets are designed to completely analyze the performances of the new detector. Experimental results show that the LRaSMD detector outperforms the global Reed-Xiaoli (GRX), the orthogonal subspace projection-GRX, and the cluster-based detectors. Moreover, the results show that LRaSMD achieves equal or better detection performance than the local support vector data description detector within a shorter computational time.

  16. Revisiting Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.

    2009-01-01

    Intrusion detection systems (IDSs) are well-known and widely-deployed security tools to detect cyber-attacks and malicious activities in computer systems and networks. A signature-based IDS works similar to anti-virus software. It employs a signature database of known attacks, and a successful match

  17. Hyperspectral Anomaly Detection Based on Low-Rank Representation and Learned Dictionary

    Directory of Open Access Journals (Sweden)

    Yubin Niu

    2016-03-01

    Full Text Available In this paper, a novel hyperspectral anomaly detector based on low-rank representation (LRR and learned dictionary (LD has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low rank matrix representing the background and a sparse matrix standing for the anomalies. The direct application of LRR model is sensitive to a tradeoff parameter that balances the two parts. To mitigate this problem, a learned dictionary is introduced into the decomposition process. The dictionary is learned from the whole image with a random selection process and therefore can be viewed as the spectra of the background only. It also requires a less computational cost with the learned dictionary. The statistic characteristic of the sparse matrix allows the application of basic anomaly detection method to obtain detection results. Experimental results demonstrate that, compared to other anomaly detection methods, the proposed method based on LRR and LD shows its robustness and has a satisfactory anomaly detection result.

  18. PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

    Directory of Open Access Journals (Sweden)

    Arup Ghosh

    2016-01-01

    Full Text Available Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

  19. Anomaly detection in random heterogeneous media Feynman-Kac formulae, stochastic homogenization and statistical inversion

    CERN Document Server

    Simon, Martin

    2015-01-01

    This monograph is concerned with the analysis and numerical solution of a stochastic inverse anomaly detection problem in electrical impedance tomography (EIT). Martin Simon studies the problem of detecting a parameterized anomaly in an isotropic, stationary and ergodic conductivity random field whose realizations are rapidly oscillating. For this purpose, he derives Feynman-Kac formulae to rigorously justify stochastic homogenization in the case of the underlying stochastic boundary value problem. The author combines techniques from the theory of partial differential equations and functional analysis with probabilistic ideas, paving the way to new mathematical theorems which may be fruitfully used in the treatment of the problem at hand. Moreover, the author proposes an efficient numerical method in the framework of Bayesian inversion for the practical solution of the stochastic inverse anomaly detection problem.   Contents Feynman-Kac formulae Stochastic homogenization Statistical inverse problems  Targe...

  20. Anomaly detection in homogenous populations: A sparse multiple kernel-based regularization method

    DEFF Research Database (Denmark)

    Chen, Tianshi; Andersen, Martin S.; Chiuso, Alessandro

    2014-01-01

    A problem of anomaly detection in homogenous populations consisting of linear stable systems is studied. The recently introduced sparse multiple kernel based regularization method is applied to solve the problem. A common problem with the existing regularization methods is that there lacks......, both the parameter and hyper-parameter estimation problems can be cast as convex and sequential convex optimization problems. It is possible to derive scalable solutions to both the parameter and hyper-parameter estimation problems and thus provide a scalable solution to the anomaly detection....

  1. Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

    Science.gov (United States)

    Sivaraks, Haemwaan

    2015-01-01

    Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284

  2. Multi-temporal mesoscale hyperspectral data of mixed agricultural and grassland regions for anomaly detection

    Science.gov (United States)

    McCann, Cooper; Repasky, Kevin S.; Lawrence, Rick; Powell, Scott

    2017-09-01

    Flight-based hyperspectral imaging systems have the potential to provide valuable information for ecosystem and environmental studies, as well as aid in land management and land health monitoring. This paper examines a series of images taken over the course of three years that were radiometrically referenced allowing for quantitative comparisons of changes in vegetation health and land usage. The study area is part of a geologic carbon sequestration project located in north-central Montana, approximately 580 ha in extent, at a site requiring permission from multiple land owners to access, making ground based validation difficult. Classification based on histogram splitting of the biophysically based parameters utilizing the entire three years of data is done to determine the major classes present in the data set in order to show the constancy between data sets taken over multiple years. Additionally, a method of anomaly detection for both single and multiple data sets, using Median Absolute Deviations (MADs), is presented along with a method of determining the appropriate size of area for a particular ecological system. Detection of local anomalies within a single data set is examined to determine, on a local scale, areas that are different from the surrounding area and depending on the specific MAD cutoff between 50-70% of the anomalies were located. Additionally, the detection and identification of persistent (anomalies that occur in the same location over multiple data sets) and non-persistent anomalies was qualitatively investigated.

  3. Kullback-Leibler distance-based enhanced detection of incipient anomalies

    KAUST Repository

    Harrou, Fouzi

    2016-09-09

    Accurate and effective anomaly detection and diagnosis of modern engineering systems by monitoring processes ensure reliability and safety of a product while maintaining desired quality. In this paper, an innovative method based on Kullback-Leibler divergence for detecting incipient anomalies in highly correlated multivariate data is presented. We use a partial least square (PLS) method as a modeling framework and a symmetrized Kullback-Leibler distance (KLD) as an anomaly indicator, where it is used to quantify the dissimilarity between current PLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, this paper reports the development of two monitoring charts based on the KLD. The first approach is a KLD-Shewhart chart, where the Shewhart monitoring chart with a three sigma rule is used to monitor the KLD of the response variables residuals from the PLS model. The second approach integrates the KLD statistic into the exponentially weighted moving average monitoring chart. The performance of the PLS-based KLD anomaly-detection methods is illustrated and compared to that of conventional PLS-based anomaly detection methods. Using synthetic data and simulated distillation column data, we demonstrate the greater sensitivity and effectiveness of the developed method over the conventional PLS-based methods, especially when data are highly correlated and small anomalies are of interest. Results indicate that the proposed chart is a very promising KLD-based method because KLD-based charts are, in practice, designed to detect small shifts in process parameters. © 2016 Elsevier Ltd

  4. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi

    2016-06-13

    Fault detection is essential for safe operation of various engineering systems. Principal component analysis (PCA) has been widely used in monitoring highly correlated process variables. Conventional PCA-based methods, nevertheless, often fail to detect small or incipient faults. In this paper, we develop new PCA-based monitoring charts, combining PCA with multivariate memory control charts, such as the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes. The multivariate control charts with memory are sensitive to small and moderate faults in the process mean, which significantly improves the performance of PCA methods and widen their applicability in practice. Using simulated data, we demonstrate that the proposed PCA-based MEWMA and MCUSUM control charts are more effective in detecting small shifts in the mean of the multivariate process variables, and outperform the conventional PCA-based monitoring charts. © 2015 IEEE.

  5. Low frequency of Y anomaly detected in Australian Brahman cow-herds.

    Science.gov (United States)

    de Camargo, Gregório M F; Porto-Neto, Laercio R; Fortes, Marina R S; Bunch, Rowan J; Tonhati, Humberto; Reverter, Antonio; Moore, Stephen S; Lehnert, Sigrid A

    2015-02-01

    Indicine cattle have lower reproductive performance in comparison to taurine. A chromosomal anomaly characterized by the presence Y markers in females was reported and associated with infertility in cattle. The aim of this study was to investigate the occurrence of the anomaly in Brahman cows. Brahman cows (n = 929) were genotyped for a Y chromosome specific region using real time-PCR. Only six out of 929 cows had the anomaly (0.6%). The anomaly frequency was much lower in Brahman cows than in the crossbred population, in which it was first detected. It also seems that the anomaly doesn't affect pregnancy in the population. Due to the low frequency, association analyses couldn't be executed. Further, SNP signal of the pseudoautosomal boundary region of the Y chromosome was investigated using HD SNP chip. Pooled DNA of "non-pregnant" and "pregnant" cows were compared and no difference in SNP allele frequency was observed. Results suggest that the anomaly had a very low frequency in this Australian Brahman population and had no effect on reproduction. Further studies comparing pregnant cows and cows that failed to conceive should be executed after better assembly and annotation of the Y chromosome in cattle.

  6. Signal analysis and anomaly detection for flood early warning systems

    NARCIS (Netherlands)

    Pyayt, A.L.; Kozionov, A.P.; Kusherbaeva, V.T.; Mokhov, I.I.; Krzhizhanovskaya, V.V.; Broekhuijsen, B.J.; Meijer, R.J.; Sloot, P.M.A.

    2014-01-01

    We describe the detection methods and the results of anomalous conditions in dikes (earthen dams/levees) based on a simultaneous processing of several data streams originating from sensors installed in these dikes. Applied methods are especially valuable in cases where lack of information or

  7. Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity

    Directory of Open Access Journals (Sweden)

    Paolo Napoletano

    2018-01-01

    Full Text Available Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.

  8. [A Hyperspectral Imagery Anomaly Detection Algorithm Based on Gauss-Markov Model].

    Science.gov (United States)

    Gao, Kun; Liu, Ying; Wang, Li-jing; Zhu, Zhen-yu; Cheng, Hao-bo

    2015-10-01

    With the development of spectral imaging technology, hyperspectral anomaly detection is getting more and more widely used in remote sensing imagery processing. The traditional RX anomaly detection algorithm neglects spatial correlation of images. Besides, it does not validly reduce the data dimension, which costs too much processing time and shows low validity on hyperspectral data. The hyperspectral images follow Gauss-Markov Random Field (GMRF) in space and spectral dimensions. The inverse matrix of covariance matrix is able to be directly calculated by building the Gauss-Markov parameters, which avoids the huge calculation of hyperspectral data. This paper proposes an improved RX anomaly detection algorithm based on three-dimensional GMRF. The hyperspectral imagery data is simulated with GMRF model, and the GMRF parameters are estimated with the Approximated Maximum Likelihood method. The detection operator is constructed with GMRF estimation parameters. The detecting pixel is considered as the centre in a local optimization window, which calls GMRF detecting window. The abnormal degree is calculated with mean vector and covariance inverse matrix, and the mean vector and covariance inverse matrix are calculated within the window. The image is detected pixel by pixel with the moving of GMRF window. The traditional RX detection algorithm, the regional hypothesis detection algorithm based on GMRF and the algorithm proposed in this paper are simulated with AVIRIS hyperspectral data. Simulation results show that the proposed anomaly detection method is able to improve the detection efficiency and reduce false alarm rate. We get the operation time statistics of the three algorithms in the same computer environment. The results show that the proposed algorithm improves the operation time by 45.2%, which shows good computing efficiency.

  9. Combining Priors, Appearance, and Context for Road Detection

    NARCIS (Netherlands)

    Álvarez, J.M.; López, A.M.; Gevers, T.; Lumbreras, F.

    2014-01-01

    Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally

  10. Anomaly Detection for Internet of Vehicles: A Trust Management Scheme with Affinity Propagation

    Directory of Open Access Journals (Sweden)

    Shu Yang

    2016-01-01

    Full Text Available Anomaly detection is critical for intelligent vehicle (IV collaboration. Forming clusters/platoons, IVs can work together to accomplish complex jobs that they are unable to perform individually. To improve security and efficiency of Internet of Vehicles, IVs’ anomaly detection has been extensively studied and a number of trust-based approaches have been proposed. However, most of these proposals either pay little attention to leader-based detection algorithm or ignore the utility of networked Roadside-Units (RSUs. In this paper, we introduce a trust-based anomaly detection scheme for IVs, where some malicious or incapable vehicles are existing on roads. The proposed scheme works by allowing IVs to detect abnormal vehicles, communicate with each other, and finally converge to some trustworthy cluster heads (CHs. Periodically, the CHs take responsibility for intracluster trust management. Moreover, the scheme is enhanced with a distributed supervising mechanism and a central reputation arbitrator to assure robustness and fairness in detecting process. The simulation results show that our scheme can achieve a low detection failure rate below 1%, demonstrating its ability to detect and filter the abnormal vehicles.

  11. Dynamic analysis methods for detecting anomalies in asynchronously interacting systems

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Akshat; Solis, John Hector; Matschke, Benjamin

    2014-01-01

    Detecting modifications to digital system designs, whether malicious or benign, is problematic due to the complexity of the systems being analyzed. Moreover, static analysis techniques and tools can only be used during the initial design and implementation phases to verify safety and liveness properties. It is computationally intractable to guarantee that any previously verified properties still hold after a system, or even a single component, has been produced by a third-party manufacturer. In this paper we explore new approaches for creating a robust system design by investigating highly-structured computational models that simplify verification and analysis. Our approach avoids the need to fully reconstruct the implemented system by incorporating a small verification component that dynamically detects for deviations from the design specification at run-time. The first approach encodes information extracted from the original system design algebraically into a verification component. During run-time this component randomly queries the implementation for trace information and verifies that no design-level properties have been violated. If any deviation is detected then a pre-specified fail-safe or notification behavior is triggered. Our second approach utilizes a partitioning methodology to view liveness and safety properties as a distributed decision task and the implementation as a proposed protocol that solves this task. Thus the problem of verifying safety and liveness properties is translated to that of verifying that the implementation solves the associated decision task. We develop upon results from distributed systems and algebraic topology to construct a learning mechanism for verifying safety and liveness properties from samples of run-time executions.

  12. 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

  13. 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

  14. Multi-level anomaly detection: Relevance of big data analytics in ...

    Indian Academy of Sciences (India)

    classify audit data based on a set of rules obtained from training data. .... hours. A more accurate way would be to compute the measure. ϕu = bu tot. N. ,. (1) where N is the number of Internet users during the day, bu is the bytes ... Two known techniques for intrusion detection are signature-based and anomaly-based. While.

  15. Dual Use Corrosion Inhibitor and Penetrant for Anomaly Detection in Neutron/X Radiography

    Science.gov (United States)

    Hall, Phillip B. (Inventor); Novak, Howard L. (Inventor)

    2004-01-01

    A dual purpose corrosion inhibitor and penetrant composition sensitive to radiography interrogation is provided. The corrosion inhibitor mitigates or eliminates corrosion on the surface of a substrate upon which the corrosion inhibitor is applied. In addition, the corrosion inhibitor provides for the attenuation of a signal used during radiography interrogation thereby providing for detection of anomalies on the surface of the substrate.

  16. Anomaly Detection Using Power Signature of Consumer Electrical Devices

    Directory of Open Access Journals (Sweden)

    CERNAZANU-GLAVAN, C.

    2015-02-01

    Full Text Available The use of the smart grid for developing intelligent applications is a current trend of great importance. One advantage lies in the possibility of direct monitoring of all devices connected to the electrical network in order to prevent possible malfunctions. Therefore, this paper proposes a method for an automatic detection of the malfunctioning of low-intelligence consumer electrical devices. Malfunctioning means any deviation of a household device from its normal operating schedule. The method is based on a comparison technique, consisting in the correlation between the current power signature of a device and an ideal signature (the standard signature provided by the manufacturer. The first step of this method is to achieve a simplified form of power signature which keeps all the original features. Further, the signal is segmented based on the data provided by an event detection algorithm (values of the first derivatives and each resulting component is approximated using a regression function. The final step consists of an analysis based on the correlation between the computed regression coefficients and the coefficients of the standard signal. Following this analysis all the differences are classified as a malfunctioning of the analyzed device.

  17. Using Machine Learning for Advanced Anomaly Detection and Classification

    Science.gov (United States)

    Lane, B.; Poole, M.; Camp, M.; Murray-Krezan, J.

    2016-09-01

    Machine Learning (ML) techniques have successfully been used in a wide variety of applications to automatically detect and potentially classify changes in activity, or a series of activities by utilizing large amounts data, sometimes even seemingly-unrelated data. The amount of data being collected, processed, and stored in the Space Situational Awareness (SSA) domain has grown at an exponential rate and is now better suited for ML. This paper describes development of advanced algorithms to deliver significant improvements in characterization of deep space objects and indication and warning (I&W) using a global network of telescopes that are collecting photometric data on a multitude of space-based objects. The Phase II Air Force Research Laboratory (AFRL) Small Business Innovative Research (SBIR) project Autonomous Characterization Algorithms for Change Detection and Characterization (ACDC), contracted to ExoAnalytic Solutions Inc. is providing the ability to detect and identify photometric signature changes due to potential space object changes (e.g. stability, tumble rate, aspect ratio), and correlate observed changes to potential behavioral changes using a variety of techniques, including supervised learning. Furthermore, these algorithms run in real-time on data being collected and processed by the ExoAnalytic Space Operations Center (EspOC), providing timely alerts and warnings while dynamically creating collection requirements to the EspOC for the algorithms that generate higher fidelity I&W. This paper will discuss the recently implemented ACDC algorithms, including the general design approach and results to date. The usage of supervised algorithms, such as Support Vector Machines, Neural Networks, k-Nearest Neighbors, etc., and unsupervised algorithms, for example k-means, Principle Component Analysis, Hierarchical Clustering, etc., and the implementations of these algorithms is explored. Results of applying these algorithms to EspOC data both in an off

  18. USBeSafe: Applying One Class SVM for Effective USB Event Anomaly Detection

    Science.gov (United States)

    2016-04-25

    a seemingly benign TD hijacked the bootloader process of a host and planted a rootkit to gain persistence [67], the novelty of the first two attack...Multimedia. ACM. 2001, pp. 107–118. [21] Adam Coates et al. “Text detection and character recognition in scene images with unsupervised feature...NORTHEASTERN UNIVERSITY MASTERS THESIS USBeSafe: Applying One-Class SVM for Effective USB Event Anomaly Detection Author: Brandon L. DALEY Supervisor

  19. HPNAIDM: The High-Performance Network Anomaly/Intrusion Detection and Mitigation System

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Yan [Northwesten University

    2013-12-05

    Identifying traffic anomalies and attacks rapidly and accurately is critical for large network operators. With the rapid growth of network bandwidth, such as the next generation DOE UltraScience Network, and fast emergence of new attacks/virus/worms, existing network intrusion detection systems (IDS) are insufficient because they: • Are mostly host-based and not scalable to high-performance networks; • Are mostly signature-based and unable to adaptively recognize flow-level unknown attacks; • Cannot differentiate malicious events from the unintentional anomalies. To address these challenges, we proposed and developed a new paradigm called high-performance network anomaly/intrustion detection and mitigation (HPNAIDM) system. The new paradigm is significantly different from existing IDSes with the following features (research thrusts). • Online traffic recording and analysis on high-speed networks; • Online adaptive flow-level anomaly/intrusion detection and mitigation; • Integrated approach for false positive reduction. Our research prototype and evaluation demonstrate that the HPNAIDM system is highly effective and economically feasible. Beyond satisfying the pre-set goals, we even exceed that significantly (see more details in the next section). Overall, our project harvested 23 publications (2 book chapters, 6 journal papers and 15 peer-reviewed conference/workshop papers). Besides, we built a website for technique dissemination, which hosts two system prototype release to the research community. We also filed a patent application and developed strong international and domestic collaborations which span both academia and industry.

  20. Structural Anomaly Detection Using Fiber Optic Sensors and Inverse Finite Element Method

    Science.gov (United States)

    Quach, Cuong C.; Vazquez, Sixto L.; Tessler, Alex; Moore, Jason P.; Cooper, Eric G.; Spangler, Jan. L.

    2005-01-01

    NASA Langley Research Center is investigating a variety of techniques for mitigating aircraft accidents due to structural component failure. One technique under consideration combines distributed fiber optic strain sensing with an inverse finite element method for detecting and characterizing structural anomalies anomalies that may provide early indication of airframe structure degradation. The technique identifies structural anomalies that result in observable changes in localized strain but do not impact the overall surface shape. Surface shape information is provided by an Inverse Finite Element Method that computes full-field displacements and internal loads using strain data from in-situ fiberoptic sensors. This paper describes a prototype of such a system and reports results from a series of laboratory tests conducted on a test coupon subjected to increasing levels of damage.

  1. Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificial neural network model

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

    This paper presents a contextual anomaly detection method and its use in the discovery of malicious voltage control actions in the low voltage distribution grid. The model-based anomaly detection uses an artificial neural network model to identify a distributed energy resource’s behaviour under...

  2. Detecting ship targets in spaceborne infrared image based on modeling radiation anomalies

    Science.gov (United States)

    Wang, Haibo; Zou, Zhengxia; Shi, Zhenwei; Li, Bo

    2017-09-01

    Using infrared imaging sensors to detect ship target in the ocean environment has many advantages compared to other sensor modalities, such as better thermal sensitivity and all-weather detection capability. We propose a new ship detection method by modeling radiation anomalies for spaceborne infrared image. The proposed method can be decomposed into two stages, where in the first stage, a test infrared image is densely divided into a set of image patches and the radiation anomaly of each patch is estimated by a Gaussian Mixture Model (GMM), and thereby target candidates are obtained from anomaly image patches. In the second stage, target candidates are further checked by a more discriminative criterion to obtain the final detection result. The main innovation of the proposed method is inspired by the biological mechanism that human eyes are sensitive to the unusual and anomalous patches among complex background. The experimental result on short wavelength infrared band (1.560 - 2.300 μm) and long wavelength infrared band (10.30 - 12.50 μm) of Landsat-8 satellite shows the proposed method achieves a desired ship detection accuracy with higher recall than other classical ship detection methods.

  3. Unsupervised Anomaly Detection Based on Clustering and Multiple One-Class SVM

    Science.gov (United States)

    Song, Jungsuk; Takakura, Hiroki; Okabe, Yasuo; Kwon, Yongjin

    Intrusion detection system (IDS) has played an important role as a device to defend our networks from cyber attacks. However, since it is unable to detect unknown attacks, i.e., 0-day attacks, the ultimate challenge in intrusion detection field is how we can exactly identify such an attack by an automated manner. Over the past few years, several studies on solving these problems have been made on anomaly detection using unsupervised learning techniques such as clustering, one-class support vector machine (SVM), etc. Although they enable one to construct intrusion detection models at low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: a low detection rate and a high false positive rate. In this paper, we propose a new anomaly detection method based on clustering and multiple one-class SVM in order to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that our approach outperforms the existing algorithms reported in the literature; especially in detection of unknown attacks.

  4. Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test

    KAUST Repository

    Madakyaru, Muddu

    2017-02-16

    Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale nature of process dynamics, a MSPLS algorithm combining PLS and wavelet analysis is used as modeling framework. Then, GLR hypothesis testing is applied using the uncorrelated residuals obtained from MSPLS model to improve the anomaly detection abilities of these latent variable based fault detection methods even further. Applications to a simulated distillation column data are used to evaluate the proposed MSPLS-GLR algorithm.

  5. Detection of a weak meddy-like anomaly from high-resolution satellite SST maps

    Directory of Open Access Journals (Sweden)

    Mikhail Emelianov

    2012-09-01

    Full Text Available Despite the considerable impact of meddies on climate through the long-distance transport of properties, a consistent observation of meddy generation and propagation in the ocean is rather elusive. Meddies propagate at about 1000 m below the ocean surface, so satellite sensors are not able to detect them directly and finding them in the open ocean is more fortuitous than intentional. However, a consistent census of meddies and their paths is required in order to gain knowledge about their role in transporting properties such as heat and salt. In this paper we propose a new methodology for processing high-resolution sea surface temperature maps in order to detect meddy-like anomalies in the open ocean on a near-real-time basis. We present an example of detection, involving an atypical meddy-like anomaly that was confirmed as such by in situ measurements.

  6. JACoW Model learning algorithms for anomaly detection in CERN control systems

    CERN Document Server

    Tilaro, Filippo; Gonzalez-Berges, Manuel; Roshchin, Mikhail; Varela, Fernando

    2018-01-01

    The CERN automation infrastructure consists of over 600 heterogeneous industrial control systems with around 45 million deployed sensors, actuators and control objects. Therefore, it is evident that the monitoring of such huge system represents a challenging and complex task. This paper describes three different mathematical approaches that have been designed and developed to detect anomalies in any of the CERN control systems. Specifically, one of these algorithms is purely based on expert knowledge; the other two mine the historical generated data to create a simple model of the system; this model is then used to detect faulty sensors measurements. The presented methods can be categorized as dynamic unsupervised anomaly detection; “dynamic” since the behaviour of the system and the evolution of its attributes are observed and changing in time. They are “unsupervised” because we are trying to predict faulty events without examples in the data history. So, the described strategies involve monitoring t...

  7. Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms

    National Research Council Canada - National Science Library

    Wei, Jun

    2006-01-01

    The goal of this project is to develop a computer-aided diagnosis (CAD) system for mass detection using advanced computer vision techniques that will be trained with retrospectively detected cancers on prior mammograms...

  8. Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms

    National Research Council Canada - National Science Library

    Wei, Jun

    2007-01-01

    The goal of this project is to develop a computer-aided diagnosis (CAD) system for mass detection using advanced computer vision techniques that will be trained with retrospectively detected cancers on prior mammograms...

  9. A new approach for structural health monitoring by applying anomaly detection on strain sensor data

    Science.gov (United States)

    Trichias, Konstantinos; Pijpers, Richard; Meeuwissen, Erik

    2014-03-01

    Structural Health Monitoring (SHM) systems help to monitor critical infrastructures (bridges, tunnels, etc.) remotely and provide up-to-date information about their physical condition. In addition, it helps to predict the structure's life and required maintenance in a cost-efficient way. Typically, inspection data gives insight in the structural health. The global structural behavior, and predominantly the structural loading, is generally measured with vibration and strain sensors. Acoustic emission sensors are more and more used for measuring global crack activity near critical locations. In this paper, we present a procedure for local structural health monitoring by applying Anomaly Detection (AD) on strain sensor data for sensors that are applied in expected crack path. Sensor data is analyzed by automatic anomaly detection in order to find crack activity at an early stage. This approach targets the monitoring of critical structural locations, such as welds, near which strain sensors can be applied during construction and/or locations with limited inspection possibilities during structural operation. We investigate several anomaly detection techniques to detect changes in statistical properties, indicating structural degradation. The most effective one is a novel polynomial fitting technique, which tracks slow changes in sensor data. Our approach has been tested on a representative test structure (bridge deck) in a lab environment, under constant and variable amplitude fatigue loading. In both cases, the evolving cracks at the monitored locations were successfully detected, autonomously, by our AD monitoring tool.

  10. Detecting errors and anomalies in computerized materials control and accountability databases

    International Nuclear Information System (INIS)

    Whiteson, R.; Hench, K.; Yarbro, T.; Baumgart, C.

    1998-01-01

    The Automated MC and A Database Assessment project is aimed at improving anomaly and error detection in materials control and accountability (MC and A) databases and increasing confidence in the data that they contain. Anomalous data resulting in poor categorization of nuclear material inventories greatly reduces the value of the database information to users. Therefore it is essential that MC and A data be assessed periodically for anomalies or errors. Anomaly detection can identify errors in databases and thus provide assurance of the integrity of data. An expert system has been developed at Los Alamos National Laboratory that examines these large databases for anomalous or erroneous data. For several years, MC and A subject matter experts at Los Alamos have been using this automated system to examine the large amounts of accountability data that the Los Alamos Plutonium Facility generates. These data are collected and managed by the Material Accountability and Safeguards System, a near-real-time computerized nuclear material accountability and safeguards system. This year they have expanded the user base, customizing the anomaly detector for the varying requirements of different groups of users. This paper describes the progress in customizing the expert systems to the needs of the users of the data and reports on their results

  11. Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets

    Science.gov (United States)

    Wang, Hongtao; Wen, Hui; Yi, Feng; Zhu, Hongsong; Sun, Limin

    2017-01-01

    Road traffic anomaly denotes a road segment that is anomalous in terms of traffic flow of vehicles. Detecting road traffic anomalies from GPS (Global Position System) snippets data is becoming critical in urban computing since they often suggest underlying events. However, the noisy and sparse nature of GPS snippets data have ushered multiple problems, which have prompted the detection of road traffic anomalies to be very challenging. To address these issues, we propose a two-stage solution which consists of two components: a Collaborative Path Inference (CPI) model and a Road Anomaly Test (RAT) model. CPI model performs path inference incorporating both static and dynamic features into a Conditional Random Field (CRF). Dynamic context features are learned collaboratively from large GPS snippets via a tensor decomposition technique. Then RAT calculates the anomalous degree for each road segment from the inferred fine-grained trajectories in given time intervals. We evaluated our method using a large scale real world dataset, which includes one-month GPS location data from more than eight thousand taxicabs in Beijing. The evaluation results show the advantages of our method beyond other baseline techniques. PMID:28282948

  12. Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets

    Directory of Open Access Journals (Sweden)

    Hongtao Wang

    2017-03-01

    Full Text Available Road traffic anomaly denotes a road segment that is anomalous in terms of traffic flow of vehicles. Detecting road traffic anomalies from GPS (Global Position System snippets data is becoming critical in urban computing since they often suggest underlying events. However, the noisy ands parse nature of GPS snippets data have ushered multiple problems, which have prompted the detection of road traffic anomalies to be very challenging. To address these issues, we propose a two-stage solution which consists of two components: a Collaborative Path Inference (CPI model and a Road Anomaly Test (RAT model. CPI model performs path inference incorporating both static and dynamic features into a Conditional Random Field (CRF. Dynamic context features are learned collaboratively from large GPS snippets via a tensor decomposition technique. Then RAT calculates the anomalous degree for each road segment from the inferred fine-grained trajectories in given time intervals. We evaluated our method using a large scale real world dataset, which includes one-month GPS location data from more than eight thousand taxi cabs in Beijing. The evaluation results show the advantages of our method beyond other baseline techniques.

  13. Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets.

    Science.gov (United States)

    Wang, Hongtao; Wen, Hui; Yi, Feng; Zhu, Hongsong; Sun, Limin

    2017-03-09

    Road traffic anomaly denotes a road segment that is anomalous in terms of traffic flow of vehicles. Detecting road traffic anomalies from GPS (Global Position System) snippets data is becoming critical in urban computing since they often suggest underlying events. However, the noisy ands parse nature of GPS snippets data have ushered multiple problems, which have prompted the detection of road traffic anomalies to be very challenging. To address these issues, we propose a two-stage solution which consists of two components: a Collaborative Path Inference (CPI) model and a Road Anomaly Test (RAT) model. CPI model performs path inference incorporating both static and dynamic features into a Conditional Random Field (CRF). Dynamic context features are learned collaboratively from large GPS snippets via a tensor decomposition technique. Then RAT calculates the anomalous degree for each road segment from the inferred fine-grained trajectories in given time intervals. We evaluated our method using a large scale real world dataset, which includes one-month GPS location data from more than eight thousand taxi cabs in Beijing. The evaluation results show the advantages of our method beyond other baseline techniques.

  14. Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms

    Science.gov (United States)

    Benmoussat, M. S.; Guillaume, M.; Caulier, Y.; Spinnler, K.

    2013-11-01

    A fully-automatic approach based on the use of induction thermography and detection algorithms is proposed to inspect industrial metallic parts containing different surface and sub-surface anomalies such as open cracks, open and closed notches with different sizes and depths. A practical experimental setup is developed, where lock-in and pulsed thermography (LT and PT, respectively) techniques are used to establish a dataset of thermal images for three different mockups. Data cubes are constructed by stacking up the temporal sequence of thermogram images. After the reduction of the data space dimension by means of denoising and dimensionality reduction methods; anomaly detection algorithms are applied on the reduced data cubes. The dimensions of the reduced data spaces are automatically calculated with arbitrary criterion. The results show that, when reduced data cubes are used, the anomaly detection algorithms originally developed for hyperspectral data, the well-known Reed and Xiaoli Yu detector (RX) and the regularized adaptive RX (RARX), give good detection performances for both surface and sub-surface defects in a non-supervised way.

  15. [Multi-DSP parallel processing technique of hyperspectral RX anomaly detection].

    Science.gov (United States)

    Guo, Wen-Ji; Zeng, Xiao-Ru; Zhao, Bao-Wei; Ming, Xing; Zhang, Gui-Feng; Lü, Qun-Bo

    2014-05-01

    To satisfy the requirement of high speed, real-time and mass data storage etc. for RX anomaly detection of hyperspectral image data, the present paper proposes a solution of multi-DSP parallel processing system for hyperspectral image based on CPCI Express standard bus architecture. Hardware topological architecture of the system combines the tight coupling of four DSPs sharing data bus and memory unit with the interconnection of Link ports. On this hardware platform, by assigning parallel processing task for each DSP in consideration of the spectrum RX anomaly detection algorithm and the feature of 3D data in the spectral image, a 4DSP parallel processing technique which computes and solves the mean matrix and covariance matrix of the whole image by spatially partitioning the image is proposed. The experiment result shows that, in the case of equivalent detective effect, it can reach the time efficiency 4 times higher than single DSP process with the 4-DSP parallel processing technique of RX anomaly detection algorithm proposed by this paper, which makes a breakthrough in the constraints to the huge data image processing of DSP's internal storage capacity, meanwhile well meeting the demands of the spectral data in real-time processing.

  16. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  17. Electrical Resistivity Tomography Using Wenner β - Schlumberger Configuration for Anomaly Detection in The Soil

    Science.gov (United States)

    Pebriyanto, Y.; Dahlan, K.; Sari, Y. W.

    2017-03-01

    In the subsurface exploration investigations there are many methods used, one of them is Electrical Resistivity Tomography (ERT). ERT method is able to measure the electrical properties of the material below the earth surface based on the value of the resistivity of the material by injecting electric current and measure the potential at the surface. Based on the data obtained then will be inputted into RES2DINV software for final processing of 2D image. This research has been created by testing 2 configurations Wenner-Schlumberger and Wenner β - Schlumberger for detecting anomalies in homogeneous soil. A wooden box containing homogeneous soil is used for the test. Three anomalies (wood, stone, and wet soil) were placed in different positions and the variation of resistivity was detected. We found that the Wenner β - Schlumberger configuration results in a smaller resistivity value error than the Wenner-Schlumberger configurations.

  18. Capacitance probe for detection of anomalies in non-metallic plastic pipe

    Science.gov (United States)

    Mathur, Mahendra P.; Spenik, James L.; Condon, Christopher M.; Anderson, Rodney; Driscoll, Daniel J.; Fincham, Jr., William L.; Monazam, Esmail R.

    2010-11-23

    The disclosure relates to analysis of materials using a capacitive sensor to detect anomalies through comparison of measured capacitances. The capacitive sensor is used in conjunction with a capacitance measurement device, a location device, and a processor in order to generate a capacitance versus location output which may be inspected for the detection and localization of anomalies within the material under test. The components may be carried as payload on an inspection vehicle which may traverse through a pipe interior, allowing evaluation of nonmetallic or plastic pipes when the piping exterior is not accessible. In an embodiment, supporting components are solid-state devices powered by a low voltage on-board power supply, providing for use in environments where voltage levels may be restricted.

  19. Application of Kalman filter in detecting pre-earthquake ionospheric TEC anomaly

    Directory of Open Access Journals (Sweden)

    Zhu Fuying

    2011-05-01

    Full Text Available : As an attempt, the Kalman filter was used to study the anomalous variations of ionospheric Total Electron Content (TEC before and after Wenchuan Ms8.0 earthquake, these TEC data were calculated from the GPS data observed by the Crustal Movement Observation Network of China. The result indicates that this method is reasonable and reliable in detecting TEC anomalies associated with large earthquakes.

  20. Improvement of statistical methods for detecting anomalies in climate and environmental monitoring systems

    Science.gov (United States)

    Yakunin, A. G.; Hussein, H. M.

    2018-01-01

    The article shows how the known statistical methods, which are widely used in solving financial problems and a number of other fields of science and technology, can be effectively applied after minor modification for solving such problems in climate and environment monitoring systems, as the detection of anomalies in the form of abrupt changes in signal levels, the occurrence of positive and negative outliers and the violation of the cycle form in periodic processes.

  1. A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

    Science.gov (United States)

    Simon, Donald L.; Rinehart, Aidan Walker

    2015-01-01

    This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

  2. Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Antonio Plaza

    2010-01-01

    Full Text Available Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS system over theWorld Trade Center (WTC in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

  3. Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

    Directory of Open Access Journals (Sweden)

    Paz Abel

    2010-01-01

    Full Text Available Abstract Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS system over theWorld Trade Center (WTC in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.

  4. Novel ST-MUSIC-based spectral analysis for detection of ULF geomagnetic signals anomalies associated with seismic events in Mexico

    OpenAIRE

    Omar Chavez; Juan Pablo Amezquita-Sanchez; Martin Valtierra-Rodriguez; Jose Antonio Cruz-Abeyro; Anatoliy Kotsarenko; Jesus Roberto Millan-Almaraz; Aurelio Dominguez-Gonzalez; Eduardo Rojas

    2016-01-01

    Recently, the analysis of ultra-low-frequency (ULF) geomagnetic signals in order to detect seismic anomalies has been reported in several works. Yet, they, although having promising results, present problems for their detection since these anomalies are generally too much weak and embedded in high noise levels. In this work, a short-time multiple signal classification (ST-MUSIC), which is a technique with high-frequency resolution and noise immunity, is proposed for the detection of seismic a...

  5. An anomaly detection and isolation scheme with instance-based learning and sequential analysis

    International Nuclear Information System (INIS)

    Yoo, T. S.; Garcia, H. E.

    2006-01-01

    This paper presents an online anomaly detection and isolation (FDI) technique using an instance-based learning method combined with a sequential change detection and isolation algorithm. The proposed method uses kernel density estimation techniques to build statistical models of the given empirical data (null hypothesis). The null hypothesis is associated with the set of alternative hypotheses modeling the abnormalities of the systems. A decision procedure involves a sequential change detection and isolation algorithm. Notably, the proposed method enjoys asymptotic optimality as the applied change detection and isolation algorithm is optimal in minimizing the worst mean detection/isolation delay for a given mean time before a false alarm or a false isolation. Applicability of this methodology is illustrated with redundant sensor data set and its performance. (authors)

  6. Kernel wavelet-Reed-Xiaoli: an anomaly detection for forward-looking infrared imagery.

    Science.gov (United States)

    Mehmood, Asif; Nasrabadi, Nasser M

    2011-06-10

    This paper describes a new kernel wavelet-based anomaly detection technique for long-wave (LW) forward-looking infrared imagery. The proposed approach called kernel wavelet-Reed-Xiaoli (wavelet-RX) algorithm is essentially an extension of the wavelet-RX algorithm (combination of wavelet transform and RX anomaly detector) to a high-dimensional feature space (possibly infinite) via a certain nonlinear mapping function of the input data. The wavelet-RX algorithm in this high-dimensional feature space can easily be implemented in terms of kernels that implicitly compute dot products in the feature space (kernelizing the wavelet-RX algorithm). In the proposed kernel wavelet-RX algorithm, a two-dimensional wavelet transform is first applied to decompose the input image into uniform subbands. A number of significant subbands (high-energy subbands) are concatenated together to form a subband-image cube. The kernel RX algorithm is then applied to this subband-image cube. Experimental results are presented for the proposed kernel wavelet-RX, wavelet-RX, and the classical constant false alarm rate (CFAR) algorithm for detecting anomalies (targets) in a large database of LW imagery. The receiver operating characteristic plots show that the proposed kernel wavelet-RX algorithm outperforms the wavelet-RX as well as the classical CFAR detector.

  7. Wavelet-RX anomaly detection for dual-band forward-looking infrared imagery.

    Science.gov (United States)

    Mehmood, Asif; Nasrabadi, Nasser M

    2010-08-20

    This paper describes a new wavelet-based anomaly detection technique for a dual-band forward-looking infrared (FLIR) sensor consisting of a coregistered longwave (LW) with a midwave (MW) sensor. The proposed approach, called the wavelet-RX (Reed-Xiaoli) algorithm, consists of a combination of a two-dimensional (2D) wavelet transform and a well-known multivariate anomaly detector called the RX algorithm. In our wavelet-RX algorithm, a 2D wavelet transform is first applied to decompose the input image into uniform subbands. A subband-image cube is formed by concatenating together a number of significant subbands (high-energy subbands). The RX algorithm is then applied to the subband-image cube obtained from a wavelet decomposition of the LW or MW sensor data. In the case of the dual band, the RX algorithm is applied to a subband-image cube constructed by concatenating together the high-energy subbands of the LW and MW subband-image cubes. Experimental results are presented for the proposed wavelet-RX and the classical constant false alarm rate (CFAR) algorithm for detecting anomalies (targets) in a single broadband FLIR (LW or MW) or in a coregistered dual-band FLIR sensor. The results show that the proposed wavelet-RX algorithm outperforms the classical CFAR detector for both single-band and dual-band FLIR sensors.

  8. Illustration, detection and prevention of sleep deprivation anomaly in mobile ad hoc networks

    International Nuclear Information System (INIS)

    Nadeem, A.; Ahsan, K.; Sarim, M.

    2017-01-01

    MANETs (Mobile Ad Hoc Networks) have applications in various walks of life from rescue operations to battle field operations, personal and commercial. However, routing operations in MANETs are still vulnerable to anomalies and DoS (Denial of Service) attacks such as sleep deprivation. In SD (Sleep Deprivation) attack malicious node exploits the vulnerability in the route discovery function of the reactive routing protocol for example AODV (Ad Hoc On-Demand Distance Vector). In this paper, we first illustrate the SD anomaly in MANETs and then propose a SD detection and prevention algorithm which efficiently deals with this attack. We assess the performance of our proposed approach through simulation, evaluating its successfulness using different network scenarios. (author)

  9. Small-scale anomaly detection in panoramic imaging using neural models of low-level vision

    Science.gov (United States)

    Casey, Matthew C.; Hickman, Duncan L.; Pavlou, Athanasios; Sadler, James R. E.

    2011-06-01

    Our understanding of sensory processing in animals has reached the stage where we can exploit neurobiological principles in commercial systems. In human vision, one brain structure that offers insight into how we might detect anomalies in real-time imaging is the superior colliculus (SC). The SC is a small structure that rapidly orients our eyes to a movement, sound or touch that it detects, even when the stimulus may be on a small-scale; think of a camouflaged movement or the rustle of leaves. This automatic orientation allows us to prioritize the use of our eyes to raise awareness of a potential threat, such as a predator approaching stealthily. In this paper we describe the application of a neural network model of the SC to the detection of anomalies in panoramic imaging. The neural approach consists of a mosaic of topographic maps that are each trained using competitive Hebbian learning to rapidly detect image features of a pre-defined shape and scale. What makes this approach interesting is the ability of the competition between neurons to automatically filter noise, yet with the capability of generalizing the desired shape and scale. We will present the results of this technique applied to the real-time detection of obscured targets in visible-band panoramic CCTV images. Using background subtraction to highlight potential movement, the technique is able to correctly identify targets which span as little as 3 pixels wide while filtering small-scale noise.

  10. Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems.

    Science.gov (United States)

    Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

    2015-01-01

    In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes.

  11. Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype

    Science.gov (United States)

    Martin, Rodney A.; Schwabacher, Mark A.; Matthews, Bryan L.

    2010-01-01

    In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the Inductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investigate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were previously unknown and not previously simulated will be studied in earnest, as well as apparent deficiencies or misapplications that result from using the data-driven paradigm. Our conclusions indicate that robust detection performance of simulated failures using IMS is not appreciably affected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.

  12. Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems

    Science.gov (United States)

    Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

    2015-01-01

    In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes. PMID:26267477

  13. Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

    KAUST Repository

    Kadri, Farid

    2015-10-22

    Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.

  14. PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION

    Data.gov (United States)

    National Aeronautics and Space Administration — PROBABILITY CALIBRATION BY THE MINIMUM AND MAXIMUM PROBABILITY SCORES IN ONE-CLASS BAYES LEARNING FOR ANOMALY DETECTION GUICHONG LI, NATHALIE JAPKOWICZ, IAN HOFFMAN,...

  15. High-resolution microarray in the assessment of fetal anomalies detected by ultrasound.

    Science.gov (United States)

    Charan, Poonam; Woodrow, Nicole; Walker, Sue P; Ganesamoorthy, Devika; McGillivray, George; Palma-Dias, Ricardo

    2014-02-01

    The main aim of this study was to determine the feasibility of using high-resolution microarray to assist with prenatal diagnosis of ultrasound-detected fetal abnormality and to describe the frequency of abnormal results in different categories of fetal anomalies. Prospective cross-sectional study was conducted on women diagnosed with a fetal anomaly (ies) between February 2009 and December 2011 who were offered testing by microarray analysis (Affymetrix 2.7M SNP) and fluorescent in situ hybridisation (FISH) instead of standard karyotyping. Fetal anomalies were categorised according to organ system involvement. One hundred and eighteen women consented to testing with microarray. Eleven of one hundred eighteen (9.3%) cases had aneuploidy detected by FISH. Of the remaining 107, 23 (21.5%) had an abnormality detected on microarray, only three of which would have been detected using the combination of six-probe FISH and banded karyotype. The maximum expected yield for six-probe FISH and karyotype was thus 14/118 (11.8%), compared to 34/118 (28.8%), P microarray, 10 (43%) were pathogenic, six (26%) were long continuous stretches of homozygosity and seven (30%) were of uncertain significance. The maximum yield was in cases with cardiovascular (100%); multiple (40%); central nervous system (CNS) (25%) and skeletal (9%) abnormalities. This study has confirmed the feasibility of translation of microarray into clinical practice. 11.8% (14/118) of the cases would have a genetic basis of an abnormality with a FISH and banded karyotype. This figure is approximately tripled to 28.8% (34/118) if we offer FISH and microarray. High yield for imbalances are multiple, cardiovascular, CNS and skeletal abnormalities. © 2014 The Royal Australian and New Zealand College of Obstetricians and Gynaecologists.

  16. A robust anomaly based change detection method for time-series remote sensing images

    Science.gov (United States)

    Shoujing, Yin; Qiao, Wang; Chuanqing, Wu; Xiaoling, Chen; Wandong, Ma; Huiqin, Mao

    2014-03-01

    Time-series remote sensing images record changes happening on the earth surface, which include not only abnormal changes like human activities and emergencies (e.g. fire, drought, insect pest etc.), but also changes caused by vegetation phenology and climate changes. Yet, challenges occur in analyzing global environment changes and even the internal forces. This paper proposes a robust Anomaly Based Change Detection method (ABCD) for time-series images analysis by detecting abnormal points in data sets, which do not need to follow a normal distribution. With ABCD we can detect when and where changes occur, which is the prerequisite condition of global change studies. ABCD was tested initially with 10-day SPOT VGT NDVI (Normalized Difference Vegetation Index) times series tracking land cover type changes, seasonality and noise, then validated to real data in a large area in Jiangxi, south of China. Initial results show that ABCD can precisely detect spatial and temporal changes from long time series images rapidly.

  17. A robust anomaly based change detection method for time-series remote sensing images

    International Nuclear Information System (INIS)

    Shoujing, Yin; Qiao, Wang; Chuanqing, Wu; Wandong, Ma; Huiqin, Mao; Xiaoling, Chen

    2014-01-01

    Time-series remote sensing images record changes happening on the earth surface, which include not only abnormal changes like human activities and emergencies (e.g. fire, drought, insect pest etc.), but also changes caused by vegetation phenology and climate changes. Yet, challenges occur in analyzing global environment changes and even the internal forces. This paper proposes a robust Anomaly Based Change Detection method (ABCD) for time-series images analysis by detecting abnormal points in data sets, which do not need to follow a normal distribution. With ABCD we can detect when and where changes occur, which is the prerequisite condition of global change studies. ABCD was tested initially with 10-day SPOT VGT NDVI (Normalized Difference Vegetation Index) times series tracking land cover type changes, seasonality and noise, then validated to real data in a large area in Jiangxi, south of China. Initial results show that ABCD can precisely detect spatial and temporal changes from long time series images rapidly

  18. Adaptive cancellation of geomagnetic background noise for magnetic anomaly detection using coherence

    International Nuclear Information System (INIS)

    Liu, Dunge; Xu, Xin; Huang, Chao; Zhu, Wanhua; Liu, Xiaojun; Fang, Guangyou; Yu, Gang

    2015-01-01

    Magnetic anomaly detection (MAD) is an effective method for the detection of ferromagnetic targets against background magnetic fields. Currently, the performance of MAD systems is mainly limited by the background geomagnetic noise. Several techniques have been developed to detect target signatures, such as the synchronous reference subtraction (SRS) method. In this paper, we propose an adaptive coherent noise suppression (ACNS) method. The proposed method is capable of evaluating and detecting weak anomaly signals buried in background geomagnetic noise. Tests with real-world recorded magnetic signals show that the ACNS method can excellently remove the background geomagnetic noise by about 21 dB or more in high background geomagnetic field environments. Additionally, as a general form of the SRS method, the ACNS method offers appreciable advantages over the existing algorithms. Compared to the SRS method, the ACNS algorithm can eliminate the false target signals and represents a noise suppressing capability improvement of 6.4 dB. The positive outcomes in terms of intelligibility make this method a potential candidate for application in MAD systems. (paper)

  19. Detecting primary precursors of January surface air temperature anomalies in China

    Science.gov (United States)

    Tan, Guirong; Ren, Hong-Li; Chen, Haishan; You, Qinglong

    2017-12-01

    This study aims to detect the primary precursors and impact mechanisms for January surface temperature anomaly (JSTA) events in China against the background of global warming, by comparing the causes of two extreme JSTA events occurring in 2008 and 2011 with the common mechanisms inferred from all typical episodes during 1979-2008. The results show that these two extreme events exhibit atmospheric circulation patterns in the mid-high latitudes of Eurasia, with a positive anomaly center over the Ural Mountains and a negative one to the south of Lake Baikal (UMLB), which is a pattern quite similar to that for all the typical events. However, the Eurasian teleconnection patterns in the 2011 event, which are accompanied by a negative phase of the North Atlantic Oscillation, are different to those of the typical events and the 2008 event. We further find that a common anomalous signal appearing in early summer over the tropical Indian Ocean may be responsible for the following late-winter Eurasian teleconnections and the associated JSTA events in China. We show that sea surface temperature anomalies (SSTAs) in the preceding summer over the western Indian Ocean (WIO) are intimately related to the UMLB-like circulation pattern in the following January. Positive WIOSSTAs in early summer tend to induce strong UMLB-like circulation anomalies in January, which may result in anomalously or extremely cold events in China, which can also be successfully reproduced in model experiments. Our results suggest that the WIOSSTAs may be a useful precursor for predicting JSTA events in China.

  20. Validity and efficiency of conformal anomaly detection on big distributed data

    Directory of Open Access Journals (Sweden)

    Ilia Nouretdinov

    2017-05-01

    Full Text Available Conformal Prediction is a recently developed framework for reliable confident predictions. In this work we discuss its possible application to big data coming from different, possibly heterogeneous data sources. On example of anomaly detection problem, we study the question of saving validity of Conformal Prediction in this case. We show that the straight forward averaging approach is invalid, while its easy alternative of maximizing is not very efficient because of its conservativeness. We propose the third compromised approach that is valid, but much less conservative. It is supported by both theoretical justification and experimental results in the area of energy engineering.

  1. Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

    Directory of Open Access Journals (Sweden)

    Xing Hu

    2014-01-01

    Full Text Available We propose a novel local nearest neighbor distance (LNND descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.

  2. Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

    Science.gov (United States)

    Das, Santanu; Srivastava, Ashok N.; Matthews, Bryan L.; Oza, Nikunj C.

    2010-01-01

    The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

  3. Fiber Optic Bragg Grating Sensors for Thermographic Detection of Subsurface Anomalies

    Science.gov (United States)

    Allison, Sidney G.; Winfree, William P.; Wu, Meng-Chou

    2009-01-01

    Conventional thermography with an infrared imager has been shown to be an extremely viable technique for nondestructively detecting subsurface anomalies such as thickness variations due to corrosion. A recently developed technique using fiber optic sensors to measure temperature holds potential for performing similar inspections without requiring an infrared imager. The structure is heated using a heat source such as a quartz lamp with fiber Bragg grating (FBG) sensors at the surface of the structure to detect temperature. Investigated structures include a stainless steel plate with thickness variations simulated by small platelets attached to the back side using thermal grease. A relationship is shown between the FBG sensor thermal response and variations in material thickness. For comparison, finite element modeling was performed and found to agree closely with the fiber optic thermography results. This technique shows potential for applications where FBG sensors are already bonded to structures for Integrated Vehicle Health Monitoring (IVHM) strain measurements and can serve dual-use by also performing thermographic detection of subsurface anomalies.

  4. Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

    Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.

  5. Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

    Science.gov (United States)

    Solano, Wanda; Banerjee, Bikramjit; Kraemer, Landon

    2012-01-01

    A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The

  6. Improving Accuracy of Dempster-Shafer Theory Based Anomaly Detection Systems

    Directory of Open Access Journals (Sweden)

    Ling Zou

    2014-07-01

    Full Text Available While the Dempster-Shafer theory of evidence has been widely used in anomaly detection, there are some issues with them. Dempster-Shafer theory of evidence trusts evidences equally which does not hold in distributed-sensor ADS. Moreover, evidences are dependent with each other sometimes which will lead to false alert. We propose improving by incorporating two algorithms. Features selection algorithm employs Gaussian Graphical Models to discover correlation between some candidate features. A group of suitable ADS were selected to detect and detection result were send to the fusion engine. Information gain is applied to set weight for every feature on Weights estimated algorithm. A weighted Dempster-Shafer theory of evidence combined the detection results to achieve a better accuracy. We evaluate our detection prototype through a set of experiments that were conducted with standard benchmark Wisconsin Breast Cancer Dataset and real Internet traffic. Evaluations on the Wisconsin Breast Cancer Dataset show that our prototype can find the correlation in nine features and improve the detection rate without affecting the false positive rate. Evaluations on Internet traffic show that Weights estimated algorithm can improve the detection performance significantly.

  7. Anomaly detection using temporal data mining in a smart home environment.

    Science.gov (United States)

    Jakkula, V; Cook, D J

    2008-01-01

    To many people, home is a sanctuary. With the maturing of smart home technologies, many people with cognitive and physical disabilities can lead independent lives in their own homes for extended periods of time. In this paper, we investigate the design of machine learning algorithms that support this goal. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and that the results can be used to perform automated health monitoring and to detect anomalies. Specifically, our algorithms draw upon the temporal nature of sensor data collected in a smart home to build a model of expected activities and to detect unexpected, and possibly health-critical, events in the home. We validate our algorithms using synthetic data and real activity data collected from volunteers in an automated smart environment. The results from our experiments support our hypothesis that a model can be learned from observed smart home data and used to report anomalies, as they occur, in a smart home.

  8. Based on Wide Area Environment Abnormal Behavior Analysis and Anomaly Detection Research

    Directory of Open Access Journals (Sweden)

    Zhang Lin

    2016-01-01

    Full Text Available Group anomaly identification and location is an important issue in the field of artificial intelligence. Capture of the accident source and rapid prediction of mass incidents in public places are difficult problems in intelligent video identification and processing, but the traditional group anomaly detection research has many limitations when it comes to accident source detection and intelligent recognition. We are to research on the algorithms of accident source location and abnormal group identification based on behavior analysis in the condition of dramatically changing group geometry appearance, including: 1 to propose a logic model of image density based on the social force model, and to build the crowd density trend prediction model integrating “fast and fuzzy matching at front-end” and “accurate and classified training at back-end”; 2 to design a fast abnormal source flagging algorithm based on support vector machine, and to realize intelligent and automatic marking of abnormal source point; 3 to construct a multi-view human body skeleton invariant moment model and a motion trajectory model based on linear parametric equations. The expected results of the research will help prevent abnormal events effectively, capture the first scene of incidents and the abnormal source point quickly, and play a decision support role in the proactive national security strategy.

  9. Experience with a category alters hemispheric asymmetries for the detection of anomalies.

    Science.gov (United States)

    Smith, Stephen D; Dixon, Michael J; Bulman-Fleming, M Barbara; Birch, Corey; Laudi, Nadine; Wagar, Brandon

    2005-01-01

    Previous research with both brain-damaged and neurologically intact individuals suggests that the right cerebral hemisphere (RH) is superior to the left cerebral hemisphere (LH) at detecting anomalies in objects. The current research assesses whether experience with a category is necessary for this RH advantage to emerge. Participants were taught the diagnostic criteria necessary to categorize two fictitious species of animals ("Dleebs" and "Tazes"). After training, participants were given a test in which half of the items were congruent with the diagnostic rules and half of the items were incongruent. Participants were tested on two occasions-once after the initial training session and once after five training sessions. The results demonstrated that experience is required for the RH advantage for anomaly detection to occur. On the first test, reaction times were faster when items were presented to the LH. After 5 days of training, reaction times were faster when items were presented to the RH. This interaction could be due to the fact that participants reported analyzing the items in terms of a series of features during the initial test, but analyzed the items as a configural whole as experience with the category increased.

  10. Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction

    Directory of Open Access Journals (Sweden)

    Karna Bryan

    2013-06-01

    Full Text Available Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection was developed for different levels of intermittency (i.e., sensor coverage and performance, persistence (i.e., time lag between subsequent observations and data sources (i.e., ground-based and space-based receivers.

  11. Semi-supervised anomaly detection - towards model-independent searches of new physics

    International Nuclear Information System (INIS)

    Kuusela, Mikael; Vatanen, Tommi; Malmi, Eric; Aaltonen, Timo; Raiko, Tapani; Nagai, Yoshikazu

    2012-01-01

    Most classification algorithms used in high energy physics fall under the category of supervised machine learning. Such methods require a training set containing both signal and background events and are prone to classification errors should this training data be systematically inaccurate for example due to the assumed MC model. To complement such model-dependent searches, we propose an algorithm based on semi-supervised anomaly detection techniques, which does not require a MC training sample for the signal data. We first model the background using a multivariate Gaussian mixture model. We then search for deviations from this model by fitting to the observations a mixture of the background model and a number of additional Gaussians. This allows us to perform pattern recognition of any anomalous excess over the background. We show by a comparison to neural network classifiers that such an approach is a lot more robust against misspecification of the signal MC than supervised classification. In cases where there is an unexpected signal, a neural network might fail to correctly identify it, while anomaly detection does not suffer from such a limitation. On the other hand, when there are no systematic errors in the training data, both methods perform comparably.

  12. Implementing Operational Analytics using Big Data Technologies to Detect and Predict Sensor Anomalies

    Science.gov (United States)

    Coughlin, J.; Mital, R.; Nittur, S.; SanNicolas, B.; Wolf, C.; Jusufi, R.

    2016-09-01

    Operational analytics when combined with Big Data technologies and predictive techniques have been shown to be valuable in detecting mission critical sensor anomalies that might be missed by conventional analytical techniques. Our approach helps analysts and leaders make informed and rapid decisions by analyzing large volumes of complex data in near real-time and presenting it in a manner that facilitates decision making. It provides cost savings by being able to alert and predict when sensor degradations pass a critical threshold and impact mission operations. Operational analytics, which uses Big Data tools and technologies, can process very large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, and other relevant information. When combined with predictive techniques, it provides a mechanism to monitor and visualize these data sets and provide insight into degradations encountered in large sensor systems such as the space surveillance network. In this study, data from a notional sensor is simulated and we use big data technologies, predictive algorithms and operational analytics to process the data and predict sensor degradations. This study uses data products that would commonly be analyzed at a site. This study builds on a big data architecture that has previously been proven valuable in detecting anomalies. This paper outlines our methodology of implementing an operational analytic solution through data discovery, learning and training of data modeling and predictive techniques, and deployment. Through this methodology, we implement a functional architecture focused on exploring available big data sets and determine practical analytic, visualization, and predictive technologies.

  13. Model-based temperature noise monitoring methods for LMFBR core anomaly detection

    International Nuclear Information System (INIS)

    Tamaoki, Tetsuo; Sonoda, Yukio; Sato, Masuo; Takahashi, Ryoichi.

    1994-01-01

    Temperature noise, measured by thermocouples mounted at each core fuel subassembly, is considered to be the most useful signal for detecting and locating local cooling anomalies in an LMFBR core. However, the core outlet temperature noise contains background noise due to fluctuations in the operating parameters including reactor power. It is therefore necessary to reduce this background noise for highly sensitive anomaly detection by subtracting predictable components from the measured signal. In the present study, both a physical model and an autoregressive model were applied to noise data measured in the experimental fast reactor JOYO. The results indicate that the autoregressive model has a higher precision than the physical model in background noise prediction. Based on these results, an 'autoregressive model modification method' is proposed, in which a temporary autoregressive model is generated by interpolation or extrapolation of reference models identified under a small number of different operating conditions. The generated autoregressive model has shown sufficient precision over a wide range of reactor power in applications to artificial noise data produced by an LMFBR noise simulator even when the coolant flow rate was changed to keep a constant power-to-flow ratio. (author)

  14. MedMon: securing medical devices through wireless monitoring and anomaly detection.

    Science.gov (United States)

    Zhang, Meng; Raghunathan, Anand; Jha, Niraj K

    2013-12-01

    Rapid advances in personal healthcare systems based on implantable and wearable medical devices promise to greatly improve the quality of diagnosis and treatment for a range of medical conditions. However, the increasing programmability and wireless connectivity of medical devices also open up opportunities for malicious attackers. Unfortunately, implantable/wearable medical devices come with extreme size and power constraints, and unique usage models, making it infeasible to simply borrow conventional security solutions such as cryptography. We propose a general framework for securing medical devices based on wireless channel monitoring and anomaly detection. Our proposal is based on a medical security monitor (MedMon) that snoops on all the radio-frequency wireless communications to/from medical devices and uses multi-layered anomaly detection to identify potentially malicious transactions. Upon detection of a malicious transaction, MedMon takes appropriate response actions, which could range from passive (notifying the user) to active (jamming the packets so that they do not reach the medical device). A key benefit of MedMon is that it is applicable to existing medical devices that are in use by patients, with no hardware or software modifications to them. Consequently, it also leads to zero power overheads on these devices. We demonstrate the feasibility of our proposal by developing a prototype implementation for an insulin delivery system using off-the-shelf components (USRP software-defined radio). We evaluate its effectiveness under several attack scenarios. Our results show that MedMon can detect virtually all naive attacks and a large fraction of more sophisticated attacks, suggesting that it is an effective approach to enhancing the security of medical devices.

  15. An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

    Directory of Open Access Journals (Sweden)

    B. Ravi Kiran

    2018-02-01

    Full Text Available Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.

  16. SU-G-JeP4-03: Anomaly Detection of Respiratory Motion by Use of Singular Spectrum Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kotoku, J; Kumagai, S; Nakabayashi, S; Kobayashi, T [Teikyo University, Tokyo (Japan); Haga, A [University of Tokyo Hospital, Tokyo (Japan)

    2016-06-15

    Purpose: The implementation and realization of automatic anomaly detection of respiratory motion is a very important technique to prevent accidental damage during radiation therapy. Here, we propose an automatic anomaly detection method using singular value decomposition analysis. Methods: The anomaly detection procedure consists of four parts:1) measurement of normal respiratory motion data of a patient2) calculation of a trajectory matrix representing normal time-series feature3) real-time monitoring and calculation of a trajectory matrix of real-time data.4) calculation of an anomaly score from the similarity of the two feature matrices. Patient motion was observed by a marker-less tracking system using a depth camera. Results: Two types of motion e.g. cough and sudden stop of breathing were successfully detected in our real-time application. Conclusion: Automatic anomaly detection of respiratory motion using singular spectrum analysis was successful in the cough and sudden stop of breathing. The clinical use of this algorithm will be very hopeful. This work was supported by JSPS KAKENHI Grant Number 15K08703.

  17. A New Unified Intrusion Anomaly Detection in Identifying Unseen Web Attacks

    Directory of Open Access Journals (Sweden)

    Muhammad Hilmi Kamarudin

    2017-01-01

    Full Text Available The global usage of more sophisticated web-based application systems is obviously growing very rapidly. Major usage includes the storing and transporting of sensitive data over the Internet. The growth has consequently opened up a serious need for more secured network and application security protection devices. Security experts normally equip their databases with a large number of signatures to help in the detection of known web-based threats. In reality, it is almost impossible to keep updating the database with the newly identified web vulnerabilities. As such, new attacks are invisible. This research presents a novel approach of Intrusion Detection System (IDS in detecting unknown attacks on web servers using the Unified Intrusion Anomaly Detection (UIAD approach. The unified approach consists of three components (preprocessing, statistical analysis, and classification. Initially, the process starts with the removal of irrelevant and redundant features using a novel hybrid feature selection method. Thereafter, the process continues with the application of a statistical approach to identifying traffic abnormality. We performed Relative Percentage Ratio (RPR coupled with Euclidean Distance Analysis (EDA and the Chebyshev Inequality Theorem (CIT to calculate the normality score and generate a finest threshold. Finally, Logitboost (LB is employed alongside Random Forest (RF as a weak classifier, with the aim of minimising the final false alarm rate. The experiment has demonstrated that our approach has successfully identified unknown attacks with greater than a 95% detection rate and less than a 1% false alarm rate for both the DARPA 1999 and the ISCX 2012 datasets.

  18. Spatio-temporal anomaly detection for environmental impact assessment: a case of an abandoned coal mine site in Turkey

    Science.gov (United States)

    Soydan, Hilal; Koz, Alper; Düzgün, H. Şebnem

    2017-09-01

    The main purpose of this research is to determine the anomalies regarding with the coal mining operations in an abandoned coal mine site in central Anatolia by multi-temporal image analysis of Landsat 4-5 surface reflectance data. A well-known anomaly detection algorithm, Reed-Xioli (RX), which calculates square of Mahalanobis metrics to calculate the likelihood ratios by normalizing the difference between the test pixel and the background to allocate anomaly pixels, is implemented across the time series. The experimental results reveal especially the profound land use - land cover change in time series, pointing out critically abandoned regions that need immediate rehabilitation action. The rate of anomaly scores together with their relation to mine development over the focused time spectrum discloses a linearity trend as of the operations are ceased at the end of 1990s, which is indicative of the capacity of the applied method. The performance of the algorithm is also quantified with Receiver Operating Characteristics (ROC) curves and precisionrecall graphs to quantify its capability on Landsat Thematic Mapper (TM) multispectral image series. The resulting plots show the increasing capability of the hyperspectral anomaly detection technique in multi-temporal data set, with a steady and slight increase in performance between 2000 and 2012 after the end of the mining activities, which substantiates the success of global RX algorithm to identify the mining-induced land use and land cover anomalies.

  19. Improved detection of incipient anomalies via multivariate memory monitoring charts: Application to an air flow heating system

    KAUST Repository

    Harrou, Fouzi

    2016-08-11

    Detecting anomalies is important for reliable operation of several engineering systems. Multivariate statistical monitoring charts are an efficient tool for checking the quality of a process by identifying abnormalities. Principal component analysis (PCA) was shown effective in monitoring processes with highly correlated data. Traditional PCA-based methods, nevertheless, often are relatively inefficient at detecting incipient anomalies. Here, we propose a statistical approach that exploits the advantages of PCA and those of multivariate memory monitoring schemes, like the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes to better detect incipient anomalies. Memory monitoring charts are sensitive to incipient anomalies in process mean, which significantly improve the performance of PCA method and enlarge its profitability, and to utilize these improvements in various applications. The performance of PCA-based MEWMA and MCUSUM control techniques are demonstrated and compared with traditional PCA-based monitoring methods. Using practical data gathered from a heating air-flow system, we demonstrate the greater sensitivity and efficiency of the developed method over the traditional PCA-based methods. Results indicate that the proposed techniques have potential for detecting incipient anomalies in multivariate data. © 2016 Elsevier Ltd

  20. Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

    Science.gov (United States)

    Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

    2016-04-01

    /index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

  1. Single and multi-subject clustering of flow cytometry data for cell-type identification and anomaly detection.

    Science.gov (United States)

    Pouyan, Maziyar Baran; Jindal, Vasu; Birjandtalab, Javad; Nourani, Mehrdad

    2016-08-10

    Measurement of various markers of single cells using flow cytometry has several biological applications. These applications include improving our understanding of behavior of cellular systems, identifying rare cell populations and personalized medication. A common critical issue in the existing methods is identification of the number of cellular populations which heavily affects the accuracy of results. Furthermore, anomaly detection is crucial in flow cytometry experiments. In this work, we propose a two-stage clustering technique for cell type identification in single subject flow cytometry data and extend it for anomaly detection among multiple subjects. Our experimentation on 42 flow cytometry datasets indicates high performance and accurate clustering (F-measure > 91 %) in identifying main cellular populations. Furthermore, our anomaly detection technique evaluated on Acute Myeloid Leukemia dataset results in only <2 % false positives.

  2. Cfetool: A General Purpose Tool for Anomaly Detection in Periodic Data

    Energy Technology Data Exchange (ETDEWEB)

    Wachsmann, Alf; /SLAC; Cassell, Elizabeth; /UC, Santa Barbara

    2007-03-06

    Cfengine's environment daemon ''cfenv'' has a limited and fixed set of metrics it measures on a computer. The data is assumed to be periodic in nature and cfenvd reports any data points that fall too far out of the pattern it has learned from past measurements. This is used to detect ''anomalies'' on computers. We introduce a new standalone tool, ''cfetool'', that allows arbitrary periodic data to be stored and evaluated. The user interface is modeled after rrdtool, another widely used tool to store measured data. Because a standalone tool can be used not only for computer related data, we have extended the built-in mathematics to apply to yearly data as well.

  3. Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Antonio Candelieri

    2017-03-01

    Full Text Available This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data and individual customer water consumption (AMR data. In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan.

  4. Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

    Science.gov (United States)

    Kumar, Sricharan; Srivistava, Ashok N.

    2012-01-01

    Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

  5. Real-time progressive hyperspectral image processing endmember finding and anomaly detection

    CERN Document Server

    Chang, Chein-I

    2016-01-01

    The book covers the most crucial parts of real-time hyperspectral image processing: causality and real-time capability. Recently, two new concepts of real time hyperspectral image processing, Progressive Hyperspectral Imaging (PHSI) and Recursive Hyperspectral Imaging (RHSI). Both of these can be used to design algorithms and also form an integral part of real time hyperpsectral image processing. This book focuses on progressive nature in algorithms on their real-time and causal processing implementation in two major applications, endmember finding and anomaly detection, both of which are fundamental tasks in hyperspectral imaging but generally not encountered in multispectral imaging. This book is written to particularly address PHSI in real time processing, while a book, Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation (Springer 2016) can be considered as its companion book. Includes preliminary background which is essential to those who work in hyperspectral ima...

  6. Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

    Energy Technology Data Exchange (ETDEWEB)

    Dumidu Wijayasekara; Ondrej Linda; Milos Manic; Craig Rieger

    2014-08-01

    Building Energy Management Systems (BEMSs) are essential components of modern buildings that utilize digital control technologies to minimize energy consumption while maintaining high levels of occupant comfort. However, BEMSs can only achieve these energy savings when properly tuned and controlled. Since indoor environment is dependent on uncertain criteria such as weather, occupancy, and thermal state, performance of BEMS can be sub-optimal at times. Unfortunately, the complexity of BEMS control mechanism, the large amount of data available and inter-relations between the data can make identifying these sub-optimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD) based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm based BEMS. In six different scenarios, the Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more), that the alarm based BEMS. In addition, the Fuzzy-ADLD method identified cases that were missed by the alarm based system, demonstrating potential for increased state-awareness of abnormal building behavior.

  7. Temporal distribution characteristics of GNSS ionospheric occultation data and its effects in earthquake-ionosphere anomaly detection

    Directory of Open Access Journals (Sweden)

    Zhao Ying

    2013-01-01

    Full Text Available The temporal distribution characteristics of COSMIC occultation data are analyzed in detail, and the limitations in earthquake-ionosphere anomaly detection caused by the temporal distribution characteristics of COSMIC occultation data are discussed using the example of the Wenchuan earthquake. The results demonstrate that there is no fixed temporal resolution for COSMIC occultation data when compared with other ionospheric observation techniques. Therefore, occultation data cannot currently be independently utilized in research studies but can only be used as a complement to other ionospheric observation techniques for applications with high temporal resolution demands, such as earthquake-ionosphere anomaly detection.

  8. Detecting and modeling persistent self-potential anomalies from underground nuclear explosions at the Nevada Test Site

    International Nuclear Information System (INIS)

    McKague, H.L.; Kansa, E.; Kasameyer, P.W.

    1992-01-01

    Self-potential anomalies are naturally occurring, nearly stationary electric fields that are detected by measuring the potential difference between two points on (or in) the ground. SP anomalies arise from a number of causes: principally electrochemical reactions, and heat and fluid flows. SP is routinely used to locate mineral deposits, geothermal systems, and zones of seepage. This paper is a progress report on our work toward detecting explosion-related SP signals at the Nevada Test Site (NTS) and in understanding the physics of these anomalies that persist and continue changing over periods of time that range from months to years. As background, we also include a brief description of how SP signals arise, and we mention their use in other areas such as exploring for geothermal resources and locating seepage through dams. Between the years 1988 and 1991, we surveyed the areas around seven underground nuclear tests for persistent SP anomalies. We not only detected anomalies, but we also found that various phenomena could be contributing to them and that we did not know which of these were actually occurring. We analyzed our new data with existing steady state codes and with a newly developed time-dependent thermal modeling code. Our results with the new code showed that the conductive decay of the thermal pulse from an underground nuclear test could produce many of the observed signals, and that others are probably caused by movement of fluid induced by the explosion. 25 refs

  9. Detection of Local Anomalies in High Resolution Hyperspectral Imagery Using Geostatistical Filtering and Local Spatial Statistics

    Science.gov (United States)

    Goovaerts, P.; Jacquez, G. M.; Marcus, A. W.

    2004-12-01

    finally the computation of a local indicator of spatial autocorrelation to detect local clusters of high or low reflectance values as well as anomalies. The approach is illustrated using one meter resolution data collected in Yellowstone National Park. Ground validation data demonstrate the ability of the filtering procedure to reduce the proportion of false alarms, and its robustness under low signal to noise ratios. In almost all scenarios, the proposed approach outperforms traditional anomaly detectors (i.e. RXD) and fewer false alarms were obtained when using statistic S2 (average absolute deviation of p-values from 0.5 through all spectral bands) to summarize information across bands. Image degradation through addition of noise or reduction of spectral resolution tends to blur the detection of anomalies, leading to more false alarms, in particular for the identification of the least pure pixels. Results from the tailings site demonstrated that the approach still performs reasonably well for highly complex landscape with multiple targets of various sizes and shapes. By leveraging both spectral and spatial information, the technique requires little or no input from the user, and hence can be readily automated.

  10. 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.

  11. The Use of Hidden Markov Models for Anomaly Detection in Nuclear Core Condition Monitoring

    Science.gov (United States)

    Stephen, Bruce; West, Graeme M.; Galloway, Stuart; McArthur, Stephen D. J.; McDonald, James R.; Towle, Dave

    2009-04-01

    Unplanned outages can be especially costly for generation companies operating nuclear facilities. Early detection of deviations from expected performance through condition monitoring can allow a more proactive and managed approach to dealing with ageing plant. This paper proposes an anomaly detection framework incorporating the use of the Hidden Markov Model (HMM) to support the analysis of nuclear reactor core condition monitoring data. Fuel Grab Load Trace (FGLT) data gathered within the UK during routine refueling operations has been seen to provide information relating to the condition of the graphite bricks that comprise the core. Although manual analysis of this data is time consuming and requires considerable expertise, this paper demonstrates how techniques such as the HMM can provide analysis support by providing a benchmark model of expected behavior against which future refueling events may be compared. The presence of anomalous behavior in candidate traces is inferred through the underlying statistical foundation of the HMM which gives an observation likelihood averaged along the length of the input sequence. Using this likelihood measure, the engineer can be alerted to anomalous behaviour, indicating data which might require further detailed examination. It is proposed that this data analysis technique is used in conjunction with other intelligent analysis techniques currently employed to analyse FGLT to provide a greater confidence measure in detecting anomalous behaviour from FGLT data.

  12. Value of Ultrasound in Detecting Urinary Tract Anomalies After First Febrile Urinary Tract Infection in Children.

    Science.gov (United States)

    Ghobrial, Emad E; Abdelaziz, Doaa M; Sheba, Maha F; Abdel-Azeem, Yasser S

    2016-05-01

    Background Urinary tract infection (UTI) is an infection that affects part of the urinary tract. Ultrasound is a noninvasive test that can demonstrate the size and shape of kidneys, presence of dilatation of the ureters, and the existence of anatomic abnormalities. The aim of the study is to estimate the value of ultrasound in detecting urinary tract anomalies after first attack of UTI. Methods This study was conducted at the Nephrology Clinic, New Children's Hospital, Faculty of Medicine, Cairo University, from August 2012 to March 2013, and included 30 children who presented with first attack of acute febrile UTI. All patients were subjected to urine analysis, urine culture and sensitivity, serum creatinine, complete blood count, and imaging in the form of renal ultrasound, voiding cysto-urethrography, and renal scan. Results All the patients had fever with a mean of 38.96°C ± 0.44°C and the mean duration of illness was 6.23 ± 5.64 days. Nineteen patients (63.3%) had an ultrasound abnormality. The commonest abnormalities were kidney stones (15.8%). Only 2 patients who had abnormal ultrasound had also vesicoureteric reflux on cystourethrography. Sensitivity of ultrasound was 66.7%, specificity was 37.5%, positive predictive value was 21.1%, negative predictive value was 81.8%, and total accuracy was 43.33%. Conclusion We concluded that ultrasound alone was not of much value in diagnosing and putting a plan of first attack of febrile UTI. It is recommended that combined investigations are the best way to confirm diagnosis of urinary tract anomalies. © The Author(s) 2015.

  13. Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest

    International Nuclear Information System (INIS)

    Liu, Jiamin; Hoffman, Joanne; Zhao, Jocelyn; Yao, Jianhua; Lu, Le; Kim, Lauren; Turkbey, Evrim B.; Summers, Ronald M.

    2016-01-01

    Purpose: To develop an automated system for mediastinal lymph node detection and station mapping for chest CT. Methods: The contextual organs, trachea, lungs, and spine are first automatically identified to locate the region of interest (ROI) (mediastinum). The authors employ shape features derived from Hessian analysis, local object scale, and circular transformation that are computed per voxel in the ROI. Eight more anatomical structures are simultaneously segmented by multiatlas label fusion. Spatial priors are defined as the relative multidimensional distance vectors corresponding to each structure. Intensity, shape, and spatial prior features are integrated and parsed by a random forest classifier for lymph node detection. The detected candidates are then segmented by the following curve evolution process. Texture features are computed on the segmented lymph nodes and a support vector machine committee is used for final classification. For lymph node station labeling, based on the segmentation results of the above anatomical structures, the textual definitions of mediastinal lymph node map according to the International Association for the Study of Lung Cancer are converted into patient-specific color-coded CT image, where the lymph node station can be automatically assigned for each detected node. Results: The chest CT volumes from 70 patients with 316 enlarged mediastinal lymph nodes are used for validation. For lymph node detection, their system achieves 88% sensitivity at eight false positives per patient. For lymph node station labeling, 84.5% of lymph nodes are correctly assigned to their stations. Conclusions: Multiple-channel shape, intensity, and spatial prior features aggregated by a random forest classifier improve mediastinal lymph node detection on chest CT. Using the location information of segmented anatomic structures from the multiatlas formulation enables accurate identification of lymph node stations.

  14. An Analysis of Mechanical Constraints when Using Superconducting Gravimeters for Far-Field Pre-Seismic Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Shyh-Chin Lan

    2011-01-01

    Full Text Available Pre-seismic gravity anomalies from records obtained at a 1 Hz sampling rate from superconducting gravimeters (SG around East Asia are analyzed. A comparison of gravity anomalies to the source parameters of associated earthquakes shows that the detection of pre-seismic gravity anomalies is constrained by several mechanical conditions of the seismic fault plane. The constraints of the far-field pre-seismic gravity amplitude perturbation were examined and the critical spatial relationship between the SG station and the epicenter precursory signal for detection was determined. The results show that: (1 the pre-seismic amplitude perturbation of gravity is inversely proportional to distance; (2 the transfer path from the epicenter to the SG station that crosses a tectonic boundary has a relatively low pre-seismic gravity anomaly amplitude; (3 the pre-seismic gravity perturbation amplitude is also affected by the attitude between the location of an SG station and the strike of the ruptured fault plane. The removal of typhoon effects and the selection of SG stations within a certain intersection angle to the strike of the fault plane are essential for obtaining reliable pre-seismic gravity anomaly results.

  15. A Fast Independent Component Analysis Algorithm for Geochemical Anomaly Detection and Its Application to Soil Geochemistry Data Processing

    Directory of Open Access Journals (Sweden)

    Bin Liu

    2014-01-01

    Full Text Available A fast independent component analysis algorithm (FICAA is introduced to process geochemical data for anomaly detection. In geochemical data processing, the geological significance of separated geochemical elements must be explicit. This requires that correlation coefficients be used to overcome the limitation of indeterminacy for the sequences of decomposed signals by the FICAA, so that the sequences of the decomposed signals can be correctly reflected. Meanwhile, the problem of indeterminacy in the scaling of the decomposed signals by the FICAA can be solved by the cumulative frequency method (CFM. To classify surface geochemical samples into true anomalies and false anomalies, assays of the 1 : 10 000 soil geochemical data in the area of Dachaidan in the Qinghai province of China are processed. The CFM and FICAA are used to detect the anomalies of Cu and Au. The results of this research demonstrate that the FICAA can demultiplex the mixed signals and achieve results similar to actual mineralization when 85%, 95%, and 98% are chosen as three levels of anomaly delineation. However, the traditional CFM failed to produce realistic results and has no significant use for prospecting indication. It is shown that application of the FICAA to geochemical data processing is effective.

  16. Hypergraph-based anomaly detection of high-dimensional co-occurrences.

    Science.gov (United States)

    Silva, Jorge; Willett, Rebecca

    2009-03-01

    This paper addresses the problem of detecting anomalous multivariate co-occurrences using a limited number of unlabeled training observations. A novel method based on using a hypergraph representation of the data is proposed to deal with this very high-dimensional problem. Hypergraphs constitute an important extension of graphs which allow edges to connect more than two vertices simultaneously. A variational Expectation-Maximization algorithm for detecting anomalies directly on the hypergraph domain without any feature selection or dimensionality reduction is presented. The resulting estimate can be used to calculate a measure of anomalousness based on the False Discovery Rate. The algorithm has O(np) computational complexity, where n is the number of training observations and p is the number of potential participants in each co-occurrence event. This efficiency makes the method ideally suited for very high-dimensional settings, and requires no tuning, bandwidth or regularization parameters. The proposed approach is validated on both high-dimensional synthetic data and the Enron email database, where p > 75,000, and it is shown that it can outperform other state-of-the-art methods.

  17. Behavior Drift Detection Based on Anomalies Identification in Home Living Quantitative Indicators

    Directory of Open Access Journals (Sweden)

    Fabio Veronese

    2018-01-01

    Full Text Available Home Automation and Smart Homes diffusion are providing an interesting opportunity to implement elderly monitoring. This is a new valid technological support to allow in-place aging of seniors by means of a detection system to notify potential anomalies. Monitoring has been implemented by means of Complex Event Processing on live streams of home automation data: this allows the analysis of the behavior of the house inhabitant through quantitative indicators. Different kinds of quantitative indicators for monitoring and behavior drift detection have been identified and implemented using the Esper complex event processing engine. The chosen solution permits us not only to exploit the queries when run “online”, but enables also “offline” (re-execution for testing and a posteriori analysis. Indicators were developed on both real world data and on realistic simulations. Tests were made on a dataset of 180 days: the obtained results prove that it is possible to evidence behavior changes for an evaluation of a person’s condition.

  18. Design of a Fuzzy Logic based Framework for Comprehensive Anomaly Detection in Real-World Energy Consumption Data

    NARCIS (Netherlands)

    Hol, M.; Bilgin, A.; Bosse, T.; Bredeweg, B.

    2017-01-01

    Due to the rapid growth of energy consumption worldwide, it has become a necessity that the energy waste caused by buildings is explicated by the aid of automated systems that can identify anomalous behaviour. Comprehensible anomaly detection, however, is a challenging task considering the lack of

  19. Anomaly Detection of IGS Predicted Orbits for Improvement of Near-Real-Time Positioning Accuracy Using GPS

    Science.gov (United States)

    Ha, Jihyun; Kang, Sang-Gu; Jeong, Wan-Seok; Lee, Jong-Min; Heo, Moon-Beom

    2013-04-01

    IGS ultra-rapid orbits consist of observed half and predicted half. The predicted orbits are suitable for real-time or near-real-time positioning. In this paper, we detected anomalies of the IGS predicted orbits using NANUs (Current Notice Advisories to NAVSTAR Users) messages and IGS BRDCs (Broadcast Ephemerides). IGS predicted orbits were used for anomalies detection. As a result, in case of using NANU-only, we can get detection performance of 88%. And we can achieve detection performance of 95% when both of NANUs and BRDCs were used. And also, we analyzed near-real-time positioning accuracies of precise point positioning technique using IGS predicted orbits. As a result, we could get the mean errors of 1~1.6 cm, standard deviation of 1~1.3cm. These results were similar level to positioning accuracy using the IGS rapid orbits. Positioning errors of >10 cm were, however, showed 44% of observed days of orbital anomalies. When the orbital anomalies of the predicted orbits were shown, maximum error was 1.7 km. From this study, we conclude that check and consideration were necessary before using the IGS predicted orbits.

  20. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

    NARCIS (Netherlands)

    Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F. D.

    2017-01-01

    This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of

  1. Estimation of the Potential Detection of Diatom Assemblages Based on Ocean Color Radiance Anomalies in the North Sea

    Directory of Open Access Journals (Sweden)

    Anne-Hélène Rêve-Lamarche

    2017-12-01

    Full Text Available Over the past years, a large number of new approaches in the domain of ocean-color have been developed, leading to a variety of innovative descriptors for phytoplankton communities. One of these methods, named PHYSAT, currently allows for the qualitative detection of five main phytoplankton groups from ocean-color measurements. Even though PHYSAT products are widely used in various applications and projects, the approach is limited by the fact it identifies only dominant phytoplankton groups. This current limitation is due to the use of biomarker pigment ratios for establishing empirical relationships between in-situ information and specific ocean-color radiance anomalies in open ocean waters. However, theoretical explanations of PHYSAT suggests that it could be possible to detect more than dominance cases but move more toward phytoplanktonic assemblage detection. Thus, to evaluate the potential of PHYSAT for the detection of phytoplankton assemblages, we took advantage of the Continuous Plankton Recorder (CPR survey, collected in both the English Channel and the North Sea. The available CPR dataset contains information on diatom abundance in two large areas of the North Sea for the period 1998-2010. Using this unique dataset, recurrent diatom assemblages were retrieved based on classification of CPR samples. Six diatom assemblages were identified in-situ, each having indicators taxa or species. Once this first step was completed, the in-situ analysis was used to empirically associate the diatom assemblages with specific PHYSAT spectral anomalies. This step was facilitated by the use of previous classifications of regional radiance anomalies in terms of shape and amplitude, coupled with phenological tools. Through a matchup exercise, three CPR assemblages were associated with specific radiance anomalies. The maps of detection of these specific radiances anomalies are in close agreement with current in-situ ecological knowledge.

  2. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Oscar Rojas

    2013-04-01

    Full Text Available Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today.

  3. Towards Large-Scale, Heterogeneous Anomaly Detection Systems in Industrial Networks: A Survey of Current Trends

    Directory of Open Access Journals (Sweden)

    Mikel Iturbe

    2017-01-01

    Full Text Available Industrial Networks (INs are widespread environments where heterogeneous devices collaborate to control and monitor physical processes. Some of the controlled processes belong to Critical Infrastructures (CIs, and, as such, IN protection is an active research field. Among different types of security solutions, IN Anomaly Detection Systems (ADSs have received wide attention from the scientific community. While INs have grown in size and in complexity, requiring the development of novel, Big Data solutions for data processing, IN ADSs have not evolved at the same pace. In parallel, the development of Big Data frameworks such as Hadoop or Spark has led the way for applying Big Data Analytics to the field of cyber-security, mainly focusing on the Information Technology (IT domain. However, due to the particularities of INs, it is not feasible to directly apply IT security mechanisms in INs, as IN ADSs face unique characteristics. In this work we introduce three main contributions. First, we survey the area of Big Data ADSs that could be applicable to INs and compare the surveyed works. Second, we develop a novel taxonomy to classify existing IN-based ADSs. And, finally, we present a discussion of open problems in the field of Big Data ADSs for INs that can lead to further development.

  4. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks.

    Science.gov (United States)

    Garcia-Font, Victor; Garrigues, Carles; Rifà-Pous, Helena

    2016-06-13

    In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens' quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.

  5. ANOMALY IDENTIFICATION FROM SUPER-LOW FREQUENCY ELECTROMAGNETIC DATA FOR THE COALBED METHANE DETECTION

    Directory of Open Access Journals (Sweden)

    S. S. Zhao

    2016-06-01

    Full Text Available Natural source Super Low Frequency(SLF electromagnetic prospecting methods have become an increasingly promising way in the resource detection. The capacity estimation of the reservoirs is of great importance to evaluate their exploitation potency. In this paper, we built a signal-estimate model for SLF electromagnetic signal and processed the monitored data with adaptive filter. The non-normal distribution test showed that the distribution of the signal was obviously different from Gaussian probability distribution, and Class B instantaneous amplitude probability model can well describe the statistical properties of SLF electromagnetic data. The Class B model parameter estimation is very complicated because its kernel function is confluent hypergeometric function. The parameters of the model were estimated based on property spectral function using Least Square Gradient Method(LSGM. The simulation of this estimation method was carried out, and the results of simulation demonstrated that the LGSM estimation method can reflect important information of the Class B signal model, of which the Gaussian component was considered to be the systematic noise and random noise, and the Intermediate Event Component was considered to be the background ground and human activity noise. Then the observation data was processed using adaptive noise cancellation filter. With the noise components subtracted out adaptively, the remaining part is the signal of interest, i.e., the anomaly information. It was considered to be relevant to the reservoir position of the coalbed methane stratum.

  6. Calibration of magnetic gradient tensor measurement array in magnetic anomaly detection

    Science.gov (United States)

    Chen, Jinfei; Zhang, Qi; Pan, Mengchun; Weng, Feibing; Chen, Dixiang; Pang, Hongfeng

    2013-01-01

    Magnetic anomaly detection based on magnetic gradient tensor has become more and more important in civil and military applications. Compared with methods based on magnetic total field or components measurement, magnetic gradient tensor has some unique advantages. Usually, a magnetic gradient tensor measurement array is constituted by four three-axis magnetometers. The prominent problem of magnetic gradient tensor measurement array is the misalignment of sensors. In order to measure the magnetic gradient tensor accurately, it is quite essential to calibrate the measurement array. The calibration method, which is proposed in this paper, is divided into two steps. In the first step, each sensor of the measurement array should be calibrated, whose error is mainly caused by constant biases, scale factor deviations and nonorthogonality of sensor axes. The error of measurement array is mainly caused by the misalignment of sensors, so that triplets' deviation in sensors array coordinates is calibrated in the second step. In order to verify the effectiveness of the proposed method, simulation was taken and the result shows that the proposed method improves the measurement accuracy of magnetic gradient tensor greatly.

  7. Novel ST-MUSIC-based spectral analysis for detection of ULF geomagnetic signals anomalies associated with seismic events in Mexico

    Directory of Open Access Journals (Sweden)

    Omar Chavez

    2016-05-01

    Full Text Available Recently, the analysis of ultra-low-frequency (ULF geomagnetic signals in order to detect seismic anomalies has been reported in several works. Yet, they, although having promising results, present problems for their detection since these anomalies are generally too much weak and embedded in high noise levels. In this work, a short-time multiple signal classification (ST-MUSIC, which is a technique with high-frequency resolution and noise immunity, is proposed for the detection of seismic anomalies in the ULF geomagnetic signals. Besides, the energy (E of geomagnetic signals processed by ST-MUSIC is also presented as a complementary parameter to measure the fluctuations between seismic activity and seismic calm period. The usefulness and effectiveness of the proposal are demonstrated through the analysis of a synthetic signal and five real signals with earthquakes. The analysed ULF geomagnetic signals have been obtained using a tri-axial fluxgate magnetometer at the Juriquilla station, which is localized in Queretaro, Mexico (geographic coordinates: longitude 100.45° E and latitude 20.70° N. The results obtained show the detection of seismic perturbations before, during, and after the main shock, making the proposal a suitable tool for detecting seismic precursors.

  8. High Order Non-Stationary Markov Models and Anomaly Propagation Analysis in Intrusion Detection System (IDS)

    National Research Council Canada - National Science Library

    Skormin, Victor A

    2007-01-01

    .... Unless anomaly propagation is observed, alarms are to be treated as false positives. The rationale behind the concept lies in the fact that the most common feature of worms and viruses is self-replication...

  9. Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

    Directory of Open Access Journals (Sweden)

    Kamran Siddique

    2017-09-01

    Full Text Available Anomaly detection systems, also known as intrusion detection systems (IDSs, continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii employs logistic regression and extreme gradient boosting techniques for classification; (iii introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

  10. Design of a model observer to evaluate calcification detectability in breast tomosynthesis and application to smoothing prior optimization.

    Science.gov (United States)

    Michielsen, Koen; Nuyts, Johan; Cockmartin, Lesley; Marshall, Nicholas; Bosmans, Hilde

    2016-12-01

    In this work, the authors design and validate a model observer that can detect groups of microcalcifications in a four-alternative forced choice experiment and use it to optimize a smoothing prior for detectability of microcalcifications. A channelized Hotelling observer (CHO) with eight Laguerre-Gauss channels was designed to detect groups of five microcalcifications in a background of acrylic spheres by adding the CHO log-likelihood ratios calculated at the expected locations of the five calcifications. This model observer is then applied to optimize the detectability of the microcalcifications as a function of the smoothing prior. The authors examine the quadratic and total variation (TV) priors, and a combination of both. A selection of these reconstructions was then evaluated by human observers to validate the correct working of the model observer. The authors found a clear maximum for the detectability of microcalcification when using the total variation prior with weight β TV = 35. Detectability only varied over a small range for the quadratic and combined quadratic-TV priors when weight β Q of the quadratic prior was changed by two orders of magnitude. Spearman correlation with human observers was good except for the highest value of β for the quadratic and TV priors. Excluding those, the authors found ρ = 0.93 when comparing detection fractions, and ρ = 0.86 for the fitted detection threshold diameter. The authors successfully designed a model observer that was able to predict human performance over a large range of settings of the smoothing prior, except for the highest values of β which were outside the useful range for good image quality. Since detectability only depends weakly on the strength of the combined prior, it is not possible to pick an optimal smoothness based only on this criterion. On the other hand, such choice can now be made based on other criteria without worrying about calcification detectability.

  11. Using an autonomous Wave Glider to detect seawater anomalies related to submarine groundwater discharge - engineering challenge

    Science.gov (United States)

    Leibold, P.; Brueckmann, W.; Schmidt, M.; Balushi, H. A.; Abri, O. A.

    2017-12-01

    Coastal aquifer systems are amongst the most precious and vulnerable water resources worldwide. While differing in lateral and vertical extent they commonly show a complex interaction with the marine realm. Excessive groundwater extraction can cause saltwater intrusion from the sea into the aquifers, having a strongly negative impact on the groundwater quality. While the reverse pathway, the discharge of groundwater into the sea is well understood in principle, it's mechanisms and quantities not well constrained. We will present a project that combines onshore monitoring and modeling of groundwater in the coastal plain of Salalah, Oman with an offshore autonomous robotic monitoring system, the Liquid Robotics Wave Glider. Eventually, fluxes detected by the Wave Glider system and the onshore monitoring of groundwater will be combined into a 3-D flow model of the coastal and deeper aquifers. The main tool for offshore SGD investigation project is a Wave Glider, an autonomous vehicle based on a new propulsion technology. The Wave Glider is a low-cost satellite-connected marine craft, consisting of a combination of a sea-surface and an underwater component which is propelled by the conversion of ocean wave energy into forward thrust. While the wave energy propulsion system is purely mechanical, electrical energy for onboard computers, communication and sensors is provided by photovoltaic cells. For the project the SGD Wave Glider is being equipped with dedicated sensors to measure temperature, conductivity, Radon isotope (222Rn, 220Rn) activity concentration as well as other tracers of groundwater discharge. Dedicated software using this data input will eventually allow the Wave Glider to autonomously collect information and actively adapt its search pattern to hunt for spatial and temporal anomalies. Our presentation will focus on the engineering and operational challenges ofdetecting submarine groundwater discharges with the Wave Glider system in the Bay of Salalah

  12. Discrete shearlet transform on GPU with applications in anomaly detection and denoising

    Science.gov (United States)

    Gibert, Xavier; Patel, Vishal M.; Labate, Demetrio; Chellappa, Rama

    2014-12-01

    Shearlets have emerged in recent years as one of the most successful methods for the multiscale analysis of multidimensional signals. Unlike wavelets, shearlets form a pyramid of well-localized functions defined not only over a range of scales and locations, but also over a range of orientations and with highly anisotropic supports. As a result, shearlets are much more effective than traditional wavelets in handling the geometry of multidimensional data, and this was exploited in a wide range of applications from image and signal processing. However, despite their desirable properties, the wider applicability of shearlets is limited by the computational complexity of current software implementations. For example, denoising a single 512 × 512 image using a current implementation of the shearlet-based shrinkage algorithm can take between 10 s and 2 min, depending on the number of CPU cores, and much longer processing times are required for video denoising. On the other hand, due to the parallel nature of the shearlet transform, it is possible to use graphics processing units (GPU) to accelerate its implementation. In this paper, we present an open source stand-alone implementation of the 2D discrete shearlet transform using CUDA C++ as well as GPU-accelerated MATLAB implementations of the 2D and 3D shearlet transforms. We have instrumented the code so that we can analyze the running time of each kernel under different GPU hardware. In addition to denoising, we describe a novel application of shearlets for detecting anomalies in textured images. In this application, computation times can be reduced by a factor of 50 or more, compared to multicore CPU implementations.

  13. Brain Stroke Detection by Microwaves Using Prior Information from Clinical Databases

    Directory of Open Access Journals (Sweden)

    Natalia Irishina

    2013-01-01

    Full Text Available Microwave tomographic imaging is an inexpensive, noninvasive modality of media dielectric properties reconstruction which can be utilized as a screening method in clinical applications such as breast cancer and brain stroke detection. For breast cancer detection, the iterative algorithm of structural inversion with level sets provides well-defined boundaries and incorporates an intrinsic regularization, which permits to discover small lesions. However, in case of brain lesion, the inverse problem is much more difficult due to the skull, which causes low microwave penetration and highly noisy data. In addition, cerebral liquid has dielectric properties similar to those of blood, which makes the inversion more complicated. Nevertheless, the contrast in the conductivity and permittivity values in this situation is significant due to blood high dielectric values compared to those of surrounding grey and white matter tissues. We show that using brain MRI images as prior information about brain's configuration, along with known brain dielectric properties, and the intrinsic regularization by structural inversion, allows successful and rapid stroke detection even in difficult cases. The method has been applied to 2D slices created from a database of 3D real MRI phantom images to effectively detect lesions larger than 2.5 × 10−2 m diameter.

  14. Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

    Directory of Open Access Journals (Sweden)

    Kai Wang

    2016-01-01

    Full Text Available Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.

  15. Ionospheric anomalies detected by ionosonde and possibly related to crustal earthquakes in Greece

    Science.gov (United States)

    Perrone, Loredana; De Santis, Angelo; Abbattista, Cristoforo; Alfonsi, Lucilla; Amoruso, Leonardo; Carbone, Marianna; Cesaroni, Claudio; Cianchini, Gianfranco; De Franceschi, Giorgiana; De Santis, Anna; Di Giovambattista, Rita; Marchetti, Dedalo; Pavòn-Carrasco, Francisco J.; Piscini, Alessandro; Spogli, Luca; Santoro, Francesca

    2018-03-01

    Ionosonde data and crustal earthquakes with magnitude M ≥ 6.0 observed in Greece during the 2003-2015 period were examined to check if the relationships obtained earlier between precursory ionospheric anomalies and earthquakes in Japan and central Italy are also valid for Greek earthquakes. The ionospheric anomalies are identified on the observed variations of the sporadic E-layer parameters (h'Es, foEs) and foF2 at the ionospheric station of Athens. The corresponding empirical relationships between the seismo-ionospheric disturbances and the earthquake magnitude and the epicentral distance are obtained and found to be similar to those previously published for other case studies. The large lead times found for the ionospheric anomalies occurrence may confirm a rather long earthquake preparation period. The possibility of using the relationships obtained for earthquake prediction is finally discussed.

  16. Ionospheric anomalies detected by ionosonde and possibly related to crustal earthquakes in Greece

    Directory of Open Access Journals (Sweden)

    L. Perrone

    2018-03-01

    Full Text Available Ionosonde data and crustal earthquakes with magnitude M ≥ 6.0 observed in Greece during the 2003–2015 period were examined to check if the relationships obtained earlier between precursory ionospheric anomalies and earthquakes in Japan and central Italy are also valid for Greek earthquakes. The ionospheric anomalies are identified on the observed variations of the sporadic E-layer parameters (h′Es, foEs and foF2 at the ionospheric station of Athens. The corresponding empirical relationships between the seismo-ionospheric disturbances and the earthquake magnitude and the epicentral distance are obtained and found to be similar to those previously published for other case studies. The large lead times found for the ionospheric anomalies occurrence may confirm a rather long earthquake preparation period. The possibility of using the relationships obtained for earthquake prediction is finally discussed.

  17. Development of references of anomalies detection on P91 material using Self-Magnetic Leakage Field (SMLF) technique

    Science.gov (United States)

    Husin, Shuib; Afiq Pauzi, Ahmad; Yunus, Salmi Mohd; Ghafar, Mohd Hafiz Abdul; Adilin Sekari, Saiful

    2017-10-01

    This technical paper demonstrates the successful of the application of self-magnetic leakage field (SMLF) technique in detecting anomalies in weldment of a thick P91 materials joint (1 inch thickness). Boiler components such as boiler tubes, stub boiler at penthouse and energy piping such as hot reheat pipe (HRP) and H-balance energy piping to turbine are made of P91 material. P91 is ferromagnetic material, therefore the technique of self-magnetic leakage field (SMLF) is applicable for P91 in detecting anomalies within material (internal defects). The technique is categorized under non-destructive technique (NDT). It is the second passive method after acoustic emission (AE), at which the information on structures radiation (magnetic field and energy waves) is used. The measured magnetic leakage field of a product or component is a magnetic leakage field occurring on the component’s surface in the zone of dislocation stable slipbands under the influence of operational (in-service) or residual stresses or in zones of maximum inhomogeneity of metal structure in new products or components. Inter-granular and trans-granular cracks, inclusion, void, cavity and corrosion are considered types of inhomogeneity and discontinuity in material where obviously the output of magnetic leakage field will be shown when using this technique. The technique does not required surface preparation for the component to be inspected. This technique is contact-type inspection, which means the sensor has to touch or in-contact to the component’s surface during inspection. The results of application of SMLF technique on the developed P91 reference blocks have demonstrated that the technique is practical to be used for anomaly inspection and detection as well as identification of anomalies’ location. The evaluation of this passive self-magnetic leakage field (SMLF) technique has been verified by other conventional non-destructive tests (NDTs) on the reference blocks where simulated

  18. Temporal anomaly detection: an artificial immune approach based on T cell activation, clonal size regulation and homeostasis.

    Science.gov (United States)

    Antunes, Mário J; Correia, Manuel E

    2010-01-01

    This paper presents an artificial immune system (AIS) based on Grossman's tunable activation threshold (TAT) for temporal anomaly detection. We describe the generic AIS framework and the TAT model adopted for simulating T Cells behaviour, emphasizing two novel important features: the temporal dynamic adjustment of T Cells clonal size and its associated homeostasis mechanism. We also present some promising results obtained with artificially generated data sets, aiming to test the appropriateness of using TAT in dynamic changing environments, to distinguish new unseen patterns as part of what should be detected as normal or as anomalous. We conclude by discussing results obtained thus far with artificially generated data sets.

  19. Detection of malignant right coronary artery anomaly by multi-slice CT coronary angiography

    NARCIS (Netherlands)

    Dirksen, M. S.; Bax, J. J.; Blom, N. A.; Schalij, M. J.; Jukema, W. J.; Vliegen, H. W.; van der Wall, E. E.; de Roos, A.; Lamb, H. J.

    2002-01-01

    Coronary artery anomalies occur in 0.3-0.8% of the population and infer a high risk for sudden cardiac death in young adults. Diagnosis is usually established during coronary angiography, which is hampered by poor spatial visualization. Magnetic resonance imaging is an alternative, but it is not

  20. Multi-level anomaly detection: Relevance of big data analytics in ...

    Indian Academy of Sciences (India)

    Proxy server logs of a campus LAN and edge router traces have been used for anomalies like abusive Internet access, systematic downloading (internal threats) and DDoS attacks (external threat); our techniques involve machine learning and time series analysis applied at different layers in TCP/IP stack. Accuracy of our ...

  1. Temporal subtraction technique for detection of subtle anomalies on temporally sequential bone-subtracted chest radiographs by energy subtraction

    International Nuclear Information System (INIS)

    Sanada, Shigeru; Kobayashi, Takeshi; Yoshida, Megumi; Takashima, Tsutomu; Matsui, Takeshi

    2000-01-01

    We developed a temporal subtraction technique for the detection of subtle anomalies on temporally sequential bone-subtracted chest radiographs (soft tissue images) by energy subtraction. To recognize the temporal changes in a current soft tissue image in comparison with those in a previous soft tissue image, we attempted to enhance the changes by a difference image processing technique. The lung markings were enhanced by the first derivative filter. The image registration for the lung markings on both images by the sequential similarity detection algorithm (SSDA) method was then employed. The soft tissue image provided by the energy subtraction technique was excellent in its detection of subtle abnormalities in the lung, and this method was able to detect subtle abnormalities such as infiltrates and nodules missed in screening. It was suggested that this temporal subtraction technique improves accuracy when radiologists diagnose soft tissue chest images by x-ray energy subtraction. (author)

  2. OGLE‐2008‐BLG‐510: first automated real‐time detection of a weak microlensing anomaly – brown dwarf or stellar binary?★

    DEFF Research Database (Denmark)

    Bozza, V.; Dominik, M.; Rattenbury, N. J.

    2012-01-01

    ‐lens and binary‐source models, including the possibility that the lens system consists of an M dwarf orbited by a brown dwarf. The detection of this microlensing anomaly and our analysis demonstrate that: (1) automated real‐time detection of weak microlensing anomalies with immediate feedback is feasible...... of both brown‐dwarf companions and binary‐source microlensing events might hide here....

  3. A Feasibility Study on the Application of the ScriptGenE Framework as an Anomaly Detection System in Industrial Control Systems

    Science.gov (United States)

    2015-09-17

    zero-day exploits, a Windows rootkit, antivirus evasion techniques, complex process injection and hooking code, network infection routines, and peer-to...Jiang and L. Yasakethu. Anomaly detection via one class SVM for pro- tection of SCADA systems. In 2013 International Conference on Cyber- enabled...sem284/ cse598e-f11/ papers/ zhu.pdf . 65 ZZLJ08. R. Zhang, S. Zhang, Y. Lan, and J. Jiang. Network anomaly detection using one class support vector

  4. Detection and analysis of anomalies in the brightness temperature difference field using MSG rapid scan data

    Czech Academy of Sciences Publication Activity Database

    Šťástka, J.; Radová, Michaela

    2013-01-01

    Roč. 123, SI (2013), s. 354-359 ISSN 0169-8095 R&D Projects: GA ČR GA205/07/0905 Institutional support: RVO:68378289 Keywords : brightness temperature difference (BTD) * BTD anomaly * cloud-top brightness temperature (BT) * convective storm * MSG Subject RIV: DG - Athmosphere Sciences, Meteorology OBOR OECD: Meteorology and atmospheric sciences Impact factor: 2.421, year: 2013 https://www.sciencedirect.com/science/article/pii/S0169809512001548

  5. Setup Instructions for the Applied Anomaly Detection Tool (AADT) Web Server

    Science.gov (United States)

    2016-09-01

    Introduction 1 2. Requirements 2 3. Install IIS 2 4. Install SQL Express 4 5. Install SQL Server Management Studio 5 6. Install Visual C++ Redistributable...through visual cues of anomalies through imagery. Two versions of the software have been developed by the US Army Research Laboratory (ARL) that deal with...4 Fig. 4 SQL Server Express installation types ...................................................5 Fig. 5 Downloading the Visual C

  6. Musical experts recruit action-related neural structures in harmonic anomaly detection: evidence for embodied cognition in expertise.

    Science.gov (United States)

    Sherwin, Jason; Sajda, Paul

    2013-11-01

    Humans are extremely good at detecting anomalies in sensory input. For example, while listening to a piece of Western-style music, an anomalous key change or an out-of-key pitch is readily apparent, even to the non-musician. In this paper we investigate differences between musical experts and non-experts during musical anomaly detection. Specifically, we analyzed the electroencephalograms (EEG) of five expert cello players and five non-musicians while they listened to excerpts of J.S. Bach's Prelude from Cello Suite No. 1. All subjects were familiar with the piece, though experts also had extensive experience playing the piece. Subjects were told that anomalous musical events (AMEs) could occur at random within the excerpts of the piece and were told to report the number of AMEs after each excerpt. Furthermore, subjects were instructed to remain still while listening to the excerpts and their lack of movement was verified via visual and EEG monitoring. Experts had significantly better behavioral performance (i.e. correctly reporting AME counts) than non-experts, though both groups had mean accuracies greater than 80%. These group differences were also reflected in the EEG correlates of key-change detection post-stimulus, with experts showing more significant, greater magnitude, longer periods of, and earlier peaks in condition-discriminating EEG activity than novices. Using the timing of the maximum discriminating neural correlates, we performed source reconstruction and compared significant differences between cellists and non-musicians. We found significant differences that included a slightly right lateralized motor and frontal source distribution. The right lateralized motor activation is consistent with the cortical representation of the left hand - i.e. the hand a cellist would use, while playing, to generate the anomalous key-changes. In general, these results suggest that sensory anomalies detected by experts may in fact be partially a result of an embodied

  7. Towards real-time topical detection and characterization of FDG dose infiltration prior to PET imaging

    Energy Technology Data Exchange (ETDEWEB)

    Williams, Jason M.; Arlinghaus, Lori R. [Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN (United States); Rani, Sudheer D. [Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN (United States); Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, TN (United States); Shone, Martha D. [Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, TN (United States); Abramson, Vandana G. [Vanderbilt University Medical Center, Department of Medicine, Nashville, TN (United States); Vanderbilt-Ingram Cancer Center, Nashville, TN (United States); Pendyala, Praveen [Vanderbilt University Medical Center, Department of Radiation Oncology, Nashville, TN (United States); Chakravarthy, A.B. [Vanderbilt-Ingram Cancer Center, Nashville, TN (United States); Vanderbilt University Medical Center, Department of Radiation Oncology, Nashville, TN (United States); Gorge, William J.; Knowland, Joshua G.; Lattanze, Ronald K.; Perrin, Steven R. [Lucerno Dynamics, LLC, Morrisville, NC (United States); Scarantino, Charles W. [Lucerno Dynamics, LLC, Morrisville, NC (United States); University of North Carolina, Department of Radiation Oncology, Chapel Hill, NC (United States); Townsend, David W. [Lucerno Dynamics, LLC, Morrisville, NC (United States); Technology and Research-National University of Singapore, Clinical Imaging Research Centre, Agency for Science, Singapore (Singapore); Abramson, Richard G. [Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN (United States); Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, TN (United States); Vanderbilt-Ingram Cancer Center, Nashville, TN (United States); Yankeelov, Thomas E. [The University of Texas at Austin, Institute for Computational and Engineering Sciences, and Departments of Biomedical Engineering and Internal Medicine, Austin, TX (United States)

    2016-12-15

    To dynamically detect and characterize {sup 18}F-fluorodeoxyglucose (FDG) dose infiltrations and evaluate their effects on positron emission tomography (PET) standardized uptake values (SUV) at the injection site and in control tissue. Investigational gamma scintillation sensors were topically applied to patients with locally advanced breast cancer scheduled to undergo limited whole-body FDG-PET as part of an ongoing clinical study. Relative to the affected breast, sensors were placed on the contralateral injection arm and ipsilateral control arm during the resting uptake phase prior to each patient's PET scan. Time-activity curves (TACs) from the sensors were integrated at varying intervals (0-10, 0-20, 0-30, 0-40, and 30-40 min) post-FDG and the resulting areas under the curve (AUCs) were compared to SUVs obtained from PET. In cases of infiltration, observed in three sensor recordings (30 %), the injection arm TAC shape varied depending on the extent and severity of infiltration. In two of these cases, TAC characteristics suggested the infiltration was partially resolving prior to image acquisition, although it was still apparent on subsequent PET. Areas under the TAC 0-10 and 0-20 min post-FDG were significantly different in infiltrated versus non-infiltrated cases (Mann-Whitney, p < 0.05). When normalized to control, all TAC integration intervals from the injection arm were significantly correlated with SUV{sub peak} and SUV{sub max} measured over the infiltration site (Spearman ρ ≥ 0.77, p < 0.05). Receiver operating characteristic (ROC) analyses, testing the ability of the first 10 min of post-FDG sensor data to predict infiltration visibility on the ensuing PET, yielded an area under the ROC curve of 0.92. Topical sensors applied near the injection site provide dynamic information from the time of FDG administration through the uptake period and may be useful in detecting infiltrations regardless of PET image field of view. This dynamic information

  8. Millimeter Wave Detection of Localized Anomalies in the Space Shuttle External Fuel Tank Insulating Foam and Acreage Heat Tiles

    Science.gov (United States)

    Kharkovsky, S.; Case, J. T.; Zoughi, R.; Hepburn, F.

    2005-01-01

    The Space Shuttle Columbia's catastrophic accident emphasizes the growing need for developing and applying effective, robust and life-cycle oriented nondestructive testing (NDT) methods for inspecting the shuttle external fuel tank spray on foam insulation (SOFI) and its protective acreage heat tiles. Millimeter wave NDT techniques were one of the methods chosen for evaluating their potential for inspecting these structures. Several panels with embedded anomalies (mainly voids) were produced and tested for this purpose. Near-field and far-field millimeter wave NDT methods were used for producing millimeter wave images of the anomalies in SOFI panel and heat tiles. This paper presents the results of an investigation for the purpose of detecting localized anomalies in two SOFI panels and a set of heat tiles. To this end, reflectometers at a relatively wide range of frequencies (Ka-band (26.5 - 40 GHz) to W-band (75 - 110 GHz)) and utilizing different types of radiators were employed. The results clearly illustrate the utility of these methods for this purpose.

  9. Detection of Anomalies and Changes of Rainfall in the Yellow River Basin, China, through Two Graphical Methods

    Directory of Open Access Journals (Sweden)

    Hao Wu

    2017-12-01

    Full Text Available This study aims to reveal rainfall anomalies and changes over the Yellow River Basin due to the fragile ecosystem and rainfall-related disasters. Common trend analyses relate to overall trends in mean values. Therefore, we used two graphical methods: the quantile perturbation method (QPM was used to investigate anomalies over time in extreme rainfall, and the partial trend method (PTM was used to analyze rainfall changes at different intensities. A nonparametric bootstrap procedure is proposed in order to identify significant PTM indices. The QPM indicated prevailing positive anomalies in extreme daily rainfall 50 years ago and in the middle reaches during the 1970s and 1980s. The PTM detected significant decreases in annual rainfall mainly in the latter half of the middle reaches, two-thirds of which occurred in high and heavy rainfall. Most stations in the middle and lower reaches showed significant decreases in rainy days. Daily rainfall intensity had a significant increase at 13 stations, where rainy days were generally decreasing. The combined effect of these opposing changes explains the prevailing absence of change in annual rainfall, and the observed decreases in annual rainfall can be attributed to the decreasing number of rainy days. The changes in rainy days and rainfall intensity were dominated by the wet season and dry season, respectively.

  10. Validity and reliability of an IMU-based method to detect APAs prior to gait initiation.

    Science.gov (United States)

    Mancini, Martina; Chiari, Lorenzo; Holmstrom, Lars; Salarian, Arash; Horak, Fay B

    2016-01-01

    Anticipatory postural adjustments (APAs) prior to gait initiation have been largely studied in traditional, laboratory settings using force plates under the feet to characterize the displacement of the center of pressure. However clinical trials and clinical practice would benefit from a portable, inexpensive method for characterizing APAs. Therefore, the main objectives of this study were (1) to develop a novel, automatic IMU-based method to detect and characterize APAs during gait initiation and (2) to measure its test-retest reliability. Experiment I was carried out in the laboratory to determine the validity of the IMU-based method in 10 subjects with PD (OFF medication) and 12 control subjects. Experiment II was carried out in the clinic, to determine test-retest reliability of the IMU-based method in a different set of 17 early-to-moderate, treated subjects with PD (tested ON medication) and 17 age-matched control subjects. Results showed that gait initiation characteristics (both APAs and 1st step) detected with our novel method were significantly correlated to the characteristics calculated with a force plate and motion analysis system. The size of APAs measured with either inertial sensors or force plate was significantly smaller in subjects with PD than in control subjects (p<0.05). Test-retest reliability for the gait initiation characteristics measured with inertial sensors was moderate-to-excellent (0.56

  11. Detection of malignant right coronary artery anomaly by multi-slice CT coronary angiography

    Energy Technology Data Exchange (ETDEWEB)

    Dirksen, M.S.; Roos, A. de; Lamb, H.J. [Department of Radiology, C2-S, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden (Netherlands); Bax, J.J.; Schalij, M.J.; Jukema, W.J.; Vliegen, H.W.; Wall, E.E. van der [Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden (Netherlands); Blom, N.A. [Department of Pediatrics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden (Netherlands)

    2002-07-01

    Coronary artery anomalies occur in 0.3-0.8% of the population and infer a high risk for sudden cardiac death in young adults. Diagnosis is usually established during coronary angiography, which is hampered by poor spatial visualization. Magnetic resonance imaging is an alternative, but it is not feasible in the presence of metal objects or claustrophobia. In this report, a 15-year-old boy experienced ventricular fibrillation and was successfully resuscitated. Cardiac catheterization was inconclusive, and pacemaker implantation prohibited the use of MR imaging. Multi-slice CT coronary angiography revealed a malignant anomalous right coronary artery. (orig.)

  12. Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge

    Directory of Open Access Journals (Sweden)

    Huang Chia-Ling

    2012-03-01

    Full Text Available Abstract Background Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators. Results We present a simple data-driven method, Correlation Set Analysis (CSA, for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships. Conclusions CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression.

  13. The performance of computer-aided detection when analyzing prior mammograms of newly detected breast cancers with special focus on the time interval from initial imaging to detection

    Energy Technology Data Exchange (ETDEWEB)

    Malich, Ansgar [Institute of Diagnostic Radiology, Suedharz-Hospital Nordhausen, Dr.-R.-Koch-Street 38, 99734 Nordhausen (Germany)], E-mail: ansgar.malich@gmx.de; Schmidt, Sabine [Institute of Diagnostic and Interventional Radiology, Friedrich-Schiller-University Jena, Bachstrasse 18, 07740 Jena (Germany); Fischer, Dorothee R. [Institute of diagnostic, interventional and pediatric Radiology, CH-3010 Bern (Switzerland); Facius, Mirjam; Kaiser, Werner A. [Institute of Diagnostic and Interventional Radiology, Friedrich-Schiller-University Jena, Bachstrasse 18, 07740 Jena (Germany)

    2009-03-15

    Purpose: The clinical role of CAD systems to detect breast cancer, which have not been on cancer containing mammograms not detected by the radiologist was proven retrospectively. Methods: All patients from 1992 to 2005 with a histologically verified malignant breast lesion and a mammogram at our department, were analyzed in retrospect focussing on the time of detection of the malignant lesion. All prior mammograms were analyzed by CAD (CADx, USA). The resulting CAD printout was matched with the cancer containing images yielding to the radiological diagnosis of breast cancer. CAD performance, sensitivity as well as the association of CAD and radiological features were analyzed. Results: 278 mammograms fulfilled the inclusion criteria. 111 cases showed a retrospectively visible lesion (71 masses, 23 single microcalcification clusters, 16 masses with microcalcifications, in one case two microcalcification clusters). 54/87 masses and 34/41 microcalcifications were detected by CAD. Detection rates varied from 9/20 (ACR 1) to 5/7 (ACR 4) (45% vs. 71%). The detection of microcalcifications was not influenced by breast tissue density. Conclusion: CAD might be useful in an earlier detection of subtle breast cancer cases, which might remain otherwise undetected.

  14. Seismic b-value anomalies prior to the 3rd January 2016, Mw = 6.7 Manipur earthquake of northeast India

    Science.gov (United States)

    Borgohain, Jayanta Madhab; Borah, Kajaljyoti; Biswas, Rajib; Bora, Dipok K.

    2018-04-01

    Spatial variation of seismic b-value is estimated in the Indo-Myanmar subduction zone of northeast (NE) India using the homogeneous part of earthquake catalogue (1996-2015), recorded by International Seismological Center (ISC), consisting of 895 events of magnitude MW ≥ 3.9. The study region is divided into 1° × 1° square grids and b-values are estimated at each grid by maximum likelihood method. In this study, the b-value varies from 0.75 to 1.54 in the region. Significant variation of low b-value in the respective location may indicate high stress accumulation in that region. Spatial variation reveals intermediate b-value anomalies around the epicenter of the Mw = 6.7 Manipur earthquake which occurred on 3rd January at 23:05 UTC (4 January 2016 at 04:35 IST). The variations of b-values are also estimated with respect to depth. The low b-value associated with the depth range ∼15-55 km, which may imply crustal homogeneity and high stress accumulation in the crust. Since, NE India lies in the seismic zone V of the country; this study can be helpful to understand seismotectonics in the region.

  15. Detection of architectural distortion in prior screening mammograms using Gabor filters, phase portraits, fractal dimension, and texture analysis

    International Nuclear Information System (INIS)

    Rangayyan, Rangaraj M.; Prajna, Shormistha; Ayres, Fabio J.; Desautels, J.E.L.

    2008-01-01

    Mammography is a widely used screening tool for the early detection of breast cancer. One of the commonly missed signs of breast cancer is architectural distortion. The purpose of this study is to explore the application of fractal analysis and texture measures for the detection of architectural distortion in screening mammograms taken prior to the detection of breast cancer. A method based on Gabor filters and phase portrait analysis was used to detect initial candidates for sites of architectural distortion. A total of 386 regions of interest (ROIs) were automatically obtained from 14 ''prior mammograms'', including 21 ROIs related to architectural distortion. From the corresponding set of 14 ''detection mammograms'', 398 ROIs were obtained, including 18 related to breast cancer. For each ROI, the fractal dimension and Haralick's texture features were computed. The fractal dimension of the ROIs was calculated using the circular average power spectrum technique. The average fractal dimension of the normal (false-positive) ROIs was significantly higher than that of the ROIs with architectural distortion (p = 0.006). For the ''prior mammograms'', the best receiver operating characteristics (ROC) performance achieved, in terms of the area under the ROC curve, was 0.80 with a Bayesian classifier using four features including fractal dimension, entropy, sum entropy, and inverse difference moment. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.79 at 8.4 false positives per image in the detection of sites of architectural distortion in the ''prior mammograms''. Fractal dimension offers a promising way to detect the presence of architectural distortion in prior mammograms. (orig.)

  16. Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Todd Vollmer; Jason Wright; Milos Manic

    2011-04-01

    Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.

  17. Data Integration and Anomaly Detection for Decision Support in Protected Area Management

    Science.gov (United States)

    Melton, F.; Votava, P.; Michaelis, A.; Kuhn, B.; Milesi, C.; Tague, C.; Nemani, R.

    2006-12-01

    We present a case study in the use of cyberinfrastructure to identify anomalies in ecosystem conditions to support decision making for protected area management. U.S. National Parks and other protected areas internationally are subject to increasing pressure from environmental change within and adjacent to park boundaries. Despite great interest in these areas and the fact that some U.S. parks receive as many as 3.5 million visitors per year, protected areas are often sparsely instrumented, making it difficult for resource managers to quickly identify trends and changes in park conditions. Remote sensing and ecosystem models offer protected area managers important tools for comprehensive monitoring of ecosystem conditions and scientifically based decision-making. These tools, however, can generate tremendous data volumes. New techniques are required to identify and present key data features to decision makers. The Terrestrial Observation and Prediction System (TOPS) is currently being applied to automate the production, analysis, and delivery of a suite of data products from NASA satellites and ecosystem models to assist managers of U.S. and international protected areas. TOPS uses ecosystem models to combine satellite data with ground-based observations to produce nowcasts and forecasts of ecosystem conditions. We are utilizing TOPS to deliver data products to NPS resource managers in near-real-time for use in operational decision-making. Current products include estimates of vegetation condition, ecosystem productivity, soil moisture, snow cover, fire occurrence, and others. In addition, the use of TOPS to automate the identification and display of trends and anomalies in ecosystem conditions enables protected area managers to track park- wide conditions daily, identify changes, focus monitoring efforts, and improve decision making through infusion of NASA data.

  18. Forward looking anomaly detection via fusion of infrared and color imagery

    Science.gov (United States)

    Stone, K.; Keller, J. M.; Popescu, M.; Havens, T. C.; Ho, K. C.

    2010-04-01

    This paper develops algorithms for the detection of interesting and abnormal objects in color and infrared imagery taken from cameras mounted on a moving vehicle, observing a fixed scene. The primary purpose of detection is to cue a human-in-the-loop detection system. Algorithms for direct detection and change detection are investigated, as well as fusion of the two. Both methods use temporal information to reduce the number of false alarms. The direct detection algorithm uses image self-similarity computed between local neighborhoods to determine interesting, or unique, parts of an image. Neighborhood similarity is computed using Euclidean distance in CIELAB color space for the color imagery, and Euclidean distance between grey levels in the infrared imagery. The change detection algorithm uses the affine scale-invariant feature transform (ASIFT) to transform multiple background frames into the current image space. Each transformed image is then compared to the current image, and the multiple outputs are fused to produce a single difference image. Changes in lighting and contrast between the background run and the current run are adjusted for in both color and infrared imagery. Frame-to-frame motion is modeled using a perspective transformation, the parameters of which are computed using scale-invariant feature transform (SIFT) keypoint correspondences. This information is used to perform temporal accumulation of single frame detections for both the direct detection and change detection algorithms. Performance of the proposed algorithms is evaluated on multiple lanes from a data collection at a US Army test site.

  19. ANOMALY DETECTION AND COMPARATIVE ANALYSIS OF HYDROTHERMAL ALTERATION MATERIALS TROUGH HYPERSPECTRAL MULTISENSOR DATA IN THE TURRIALBA VOLCANO

    Directory of Open Access Journals (Sweden)

    J. G. Rejas

    2012-07-01

    Full Text Available The aim of this work is the comparative study of the presence of hydrothermal alteration materials in the Turrialba volcano (Costa Rica in relation with computed spectral anomalies from multitemporal and multisensor data adquired in spectral ranges of the visible (VIS, short wave infrared (SWIR and thermal infrared (TIR. We used for this purposes hyperspectral and multispectral images from the HyMAP and MASTER airborne sensors, and ASTER and Hyperion scenes in a period between 2002 and 2010. Field radiometry was applied in order to remove the atmospheric contribution in an empirical line method. HyMAP and MASTER images were georeferenced directly thanks to positioning and orientation data that were measured at the same time in the acquisition campaign from an inertial system based on GPS/IMU. These two important steps were allowed the identification of spectral diagnostic bands of hydrothermal alteration minerals and the accuracy spatial correlation. Enviromental impact of the volcano activity has been studied through different vegetation indexes and soil patterns. Have been mapped hydrothermal materials in the crater of the volcano, in fact currently active, and their surrounding carrying out a principal components analysis differentiated for a high and low absorption bands to characterize accumulations of kaolinite, illite, alunite and kaolinite+smectite, delimitating zones with the presence of these minerals. Spectral anomalies have been calculated on a comparative study of methods pixel and subpixel focused in thermal bands fused with high-resolution images. Results are presented as an approach based on expert whose main interest lies in the automated identification of patterns of hydrothermal altered materials without prior knowledge or poor information on the area.

  20. Anomaly Detection and Comparative Analysis of Hydrothermal Alteration Materials Trough Hyperspectral Multisensor Data in the Turrialba Volcano

    Science.gov (United States)

    Rejas, J. G.; Martínez-Frías, J.; Bonatti, J.; Martínez, R.; Marchamalo, M.

    2012-07-01

    The aim of this work is the comparative study of the presence of hydrothermal alteration materials in the Turrialba volcano (Costa Rica) in relation with computed spectral anomalies from multitemporal and multisensor data adquired in spectral ranges of the visible (VIS), short wave infrared (SWIR) and thermal infrared (TIR). We used for this purposes hyperspectral and multispectral images from the HyMAP and MASTER airborne sensors, and ASTER and Hyperion scenes in a period between 2002 and 2010. Field radiometry was applied in order to remove the atmospheric contribution in an empirical line method. HyMAP and MASTER images were georeferenced directly thanks to positioning and orientation data that were measured at the same time in the acquisition campaign from an inertial system based on GPS/IMU. These two important steps were allowed the identification of spectral diagnostic bands of hydrothermal alteration minerals and the accuracy spatial correlation. Enviromental impact of the volcano activity has been studied through different vegetation indexes and soil patterns. Have been mapped hydrothermal materials in the crater of the volcano, in fact currently active, and their surrounding carrying out a principal components analysis differentiated for a high and low absorption bands to characterize accumulations of kaolinite, illite, alunite and kaolinite+smectite, delimitating zones with the presence of these minerals. Spectral anomalies have been calculated on a comparative study of methods pixel and subpixel focused in thermal bands fused with high-resolution images. Results are presented as an approach based on expert whose main interest lies in the automated identification of patterns of hydrothermal altered materials without prior knowledge or poor information on the area.

  1. The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

    Directory of Open Access Journals (Sweden)

    R. M. Parinussa

    2011-10-01

    MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7% of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7% when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (R2 = 0.95 and consistency between the two evaluation techniques lends further credibility to the obtained results.

  2. Automatic Detection and Classification of Pole-Like Objects in Urban Point Cloud Data Using an Anomaly Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Borja Rodríguez-Cuenca

    2015-09-01

    Full Text Available Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS or terrestrial laser scanner (TLS point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical objects, by means of a geometric index based on features of the point cloud. Vertical object detection is carried out through the Reed and Xiaoli (RX anomaly detection algorithm applied to a pillar structure in which the point cloud was previously organized. A clustering algorithm is then used to classify the detected vertical elements as man-made poles or trees. The effectiveness of the proposed method was tested in two point clouds from heterogeneous street scenarios and measured by two different sensors. The results for the two test sites achieved detection rates higher than 96%; the classification accuracy was around 95%, and the completion quality of both procedures was 90%. Non-detected poles come from occlusions in the point cloud and low-height traffic signs; most misclassifications occurred in man-made poles adjacent to trees.

  3. Detection of oxygen isotopic anomaly in terrestrial atmospheric carbonates and its implications to Mars.

    Science.gov (United States)

    Shaheen, R; Abramian, A; Horn, J; Dominguez, G; Sullivan, R; Thiemens, Mark H

    2010-11-23

    The debate of life on Mars centers around the source of the globular, micrometer-sized mineral carbonates in the ALH84001 meteorite; consequently, the identification of Martian processes that form carbonates is critical. This paper reports a previously undescribed carbonate formation process that occurs on Earth and, likely, on Mars. We identified micrometer-sized carbonates in terrestrial aerosols that possess excess (17)O (0.4-3.9‰). The unique O-isotopic composition mechanistically describes the atmospheric heterogeneous chemical reaction on aerosol surfaces. Concomitant laboratory experiments define the transfer of ozone isotopic anomaly to carbonates via hydrogen peroxide formation when O(3) reacts with surface adsorbed water. This previously unidentified chemical reaction scenario provides an explanation for production of the isotopically anomalous carbonates found in the SNC (shergottites, nakhlaites, chassignites) Martian meteorites and terrestrial atmospheric carbonates. The anomalous hydrogen peroxide formed on the aerosol surfaces may transfer its O-isotopic signature to the water reservoir, thus producing mass independently fractionated secondary mineral evaporites. The formation of peroxide via heterogeneous chemistry on aerosol surfaces also reveals a previously undescribed oxidative process of utility in understanding ozone and oxygen chemistry, both on Mars and Earth.

  4. Outcome of fetuses with short femur length detected at second-trimester anomaly scan

    DEFF Research Database (Denmark)

    Mathiesen, J. M.; Aksglaede, L.; Skibsted, L.

    2014-01-01

    was identified in 2718 (1.8%) of 147 766 fetuses and was present in 11 (16.2%) of the 68 fetuses affected by trisomy 21 (positive likelihood ratio (LR+) 8.8 (95% CI, 5.1–15.2)). Trisomy 13/18 and unbalanced autosomal structural abnormalities were also associated with a short FL in three (12.0%, LR+ 6.5 (95% CI......, 2.3–18.9)) and eight (32.0%, LR+ 17.4 (95% CI, 9.8–30.9)) of the cases, respectively. The risk of a fetus having trisomy 21, trisomy 18, trisomy 13 or an unbalanced autosomal structural abnormality was 1 : 123 (95% CI, 79–192), given a short FL. Pregnancies with a fetus with short FL were more often...... affected by early preterm delivery (before 34 weeks) (5.6%; odds ratio (OR) = 4.2 (95% CI, 3.5–4.9)) and small-for-gestational-age (SGA) infants (13.9%; OR = 4.3 (95% CI, 3.8–4.8)). Conclusion Short FL at the second-trimester anomaly scan is associated with a significantly higher relative risk...

  5. DEVELOPMENT AND TESTING OF PROCEDURES FOR CARRYING OUT EMERGENCY PHYSICAL INVENTORY TAKING AFTER DETECTING ANOMALY EVENTS CONCERNING NM SECURITY

    International Nuclear Information System (INIS)

    VALENTE, J.; FISHBONE, L.

    2003-01-01

    In the State Scientific Center of Russian Federation - Institute of Physics and Power Engineering (SSC RF-IPPE, Obninsk), which is under Minatom jurisdiction, the procedures for carrying out emergency physical inventory taking (EPIT) were developed and tested in cooperation with the Brookhaven National Laboratory (USA). Here the emergency physical inventory taking means the PIT, which is carried out in case of symptoms indicating a possibility of NM loss (theft). Such PIT often requires a verification of attributes and quantitative characteristics for all the NM items located in a specific Material Balance Area (MBA). In order to carry out the exercise, an MBA was selected where many thousands of NM items containing highly enriched uranium are used. Three clients of the computerized material accounting system (CMAS) are installed in this MBA. Labels with unique (within IPPE site) identification numbers in the form of digit combinations and an appropriate bar code have been applied on the NM items, containers and authorized locations. All the data to be checked during the EPIT are stored in the CMAS database. Five variants of anomalies initiating EPIT and requiring different types of activities on EPIT organization are considered. Automatic working places (AWP) were created on the basis of the client computers in order to carry out a large number of measurements within a reasonable time. In addition to a CMAS client computer, the main components of an AWP include a bar-code reader, an electronic scale and an enrichment meter with NaI--detector--the lMCA Inspector (manufactured by the Canberra Company). All these devices work together with a client computer in the on-line mode. Special computer code (Emergency Inventory Software-EIS) was developed. All the algorithms of interaction between the operator and the system, as well as algorithms of data exchange during the measurements and data comparison, are implemented in this software. Registration of detected

  6. Enhanced Anomaly Detection Via PLS Regression Models and Information Entropy Theory

    KAUST Repository

    Harrou, Fouzi

    2015-12-07

    Accurate and effective fault detection and diagnosis of modern engineering systems is crucial for ensuring reliability, safety and maintaining the desired product quality. In this work, we propose an innovative method for detecting small faults in the highly correlated multivariate data. The developed method utilizes partial least square (PLS) method as a modelling framework, and the symmetrized Kullback-Leibler divergence (KLD) as a monitoring index, where it is used to quantify the dissimilarity between probability distributions of current PLS-based residual and reference one obtained using fault-free data. The performance of the PLS-based KLD fault detection algorithm is illustrated and compared to the conventional PLS-based fault detection methods. Using synthetic data, we have demonstrated the greater sensitivity and effectiveness of the developed method over the conventional methods, especially when data are highly correlated and small faults are of interest.

  7. Multi-level anomaly detection: Relevance of big data analytics in ...

    Indian Academy of Sciences (India)

    -actively detect systematic ... all the more important in the context of such attacks, that are shown to affect the dynamics of the system at fine scales ... accessing useful (academic) information that is available through these channels. The idea is.

  8. Behaviour-based anomaly detection of cyber-physical attacks on a robotic vehicle

    OpenAIRE

    Bezemskij, Anatolij; Loukas, George; Anthony, Richard J.; Gan, Diane

    2017-01-01

    Security is one of the key challenges in cyber-physical systems, because by their nature, any cyber attack against them can have physical repercussions. This is a critical issue for autonomous vehicles; if compromised in terms of their communications or computation they can cause considerable physical damage due to their mobility. Our aim here is to facilitate the automatic detection of cyber attacks on a robotic vehicle. For this purpose, we have developed a detection mechanism, which monito...

  9. Risk of developing palatally displaced canines in patients with early detectable dental anomalies: a retrospective cohort study.

    Science.gov (United States)

    Garib, Daniela Gamba; Lancia, Melissa; Kato, Renata Mayumi; Oliveira, Thais Marchini; Neves, Lucimara Teixeira das

    2016-01-01

    To estimate the risk of PDC occurrence in children with dental anomalies identified early during mixed dentition. The sample comprised 730 longitudinal orthodontic records from children (448 females and 282 males) with an initial mean age of 8.3 years (SD=1.36). The dental anomaly group (DA) included 263 records of patients with at least one dental anomaly identified in the initial or middle mixed dentition. The non-dental anomaly group (NDA) was composed of 467 records of patients with no dental anomalies. The occurrence of PDC in both groups was diagnosed using panoramic and periapical radiographs taken in the late mixed dentition or early permanent dentition. The prevalence of PDC in patients with and without early diagnosed dental anomalies was compared using the chi-square test (panomalies diagnosed during early mixed dentition have an approximately two and a half fold increased risk of developing PDC during late mixed dentition compared with children without dental anomalies.

  10. MEASURE/ANOMTEST. Anomaly detection software package for the Dodewaard power plant facility. Supplement 1. Extension of measurement analysis part, addition of plot package

    International Nuclear Information System (INIS)

    Schoonewelle, H.

    1995-01-01

    The anomaly detection software package installed at the Dodewaard nuclear power plant has been revised with respect to the part of the measurement analysis. A plot package has been added to the package. Signals in which an anomaly has been detected are automatically plotted including the uncertainty margins of the signals. This report gives a description of the revised measurement analysis part and the plot package. Each new routine of the plot package is described briefly and the new input and output files are given. (orig.)

  11. Behavioral Anomaly Detection: A Socio-Technical Study of Trustworthiness in Virtual Organizations

    Science.gov (United States)

    Ho, Shuyuan Mary

    2009-01-01

    This study examines perceptions of human "trustworthiness" as a key component in countering insider threats. The term "insider threat" refers to situations where a critical member of an organization behaves against the interests of the organization, in an illegal and/or unethical manner. Identifying and detecting how an individual's behavior…

  12. Anomaly based intrusion detection for a biometric identification system using neural networks

    CSIR Research Space (South Africa)

    Mgabile, T

    2012-10-01

    Full Text Available detection technique that analyses the fingerprint biometric network traffic for evidence of intrusion. The neural network algorithm that imitates the way a human brain works is used in this study to classify normal traffic and learn the correct traffic...

  13. An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots

    CSIR Research Space (South Africa)

    Machaka, P

    2015-01-01

    Full Text Available . The Neural Networks, Bayesian Networks and Artificial Immune Systems were used for this experiment. Using a set of data extracted from a live network of Wi-Fi hotspots managed by an ISP; we integrated algorithms into a data collection system to detect...

  14. A Survey of Visualization Tools Assessed for Anomaly-Based Intrusion Detection Analysis

    Science.gov (United States)

    2014-04-01

    known set behaviors and detected intrusions (5). Host-based was the first IDS ever designed to audit information provided by a mainframe (6). It...performed its audit locally or on separate machines (6). A shift in computing from mainframe environments to distributed workstation networks was the

  15. Host-Based Multivariate Statistical Computer Operating Process Anomaly Intrusion Detection System (PAIDS)

    Science.gov (United States)

    2009-03-01

    course, network-based IDSs also have disadvantages. “Network agents can monitor and detect network attacks (e.g. SYN flood and packet storm attacks...destination Transport Control Protocol/Internet Protocol ( TCP /IP) addresses. Although parsing network traffic is highly effective for identifying...but these datasets, though they have faults and benefits, only provide TCP dumps and other characteristics of network traffic with no information

  16. Pre-seismic anomalies from optical satellite observations: a review

    Science.gov (United States)

    Jiao, Zhong-Hu; Zhao, Jing; Shan, Xinjian

    2018-04-01

    Detecting various anomalies using optical satellite data prior to strong earthquakes is key to understanding and forecasting earthquake activities because of its recognition of thermal-radiation-related phenomena in seismic preparation phases. Data from satellite observations serve as a powerful tool in monitoring earthquake preparation areas at a global scale and in a nearly real-time manner. Over the past several decades, many new different data sources have been utilized in this field, and progressive anomaly detection approaches have been developed. This paper reviews the progress and development of pre-seismic anomaly detection technology in this decade. First, precursor parameters, including parameters from the top of the atmosphere, in the atmosphere, and on the Earth's surface, are stated and discussed. Second, different anomaly detection methods, which are used to extract anomalous signals that probably indicate future seismic events, are presented. Finally, certain critical problems with the current research are highlighted, and new developing trends and perspectives for future work are discussed. The development of Earth observation satellites and anomaly detection algorithms can enrich available information sources, provide advanced tools for multilevel earthquake monitoring, and improve short- and medium-term forecasting, which play a large and growing role in pre-seismic anomaly detection research.

  17. Using Information From Prior Satellite Scans to Improve Cloud Detection Near the Day-Night Terminator

    Science.gov (United States)

    Yost, Christopher R.; Minnis, Patrick; Trepte, Qing Z.; Palikonda, Rabindra; Ayers, Jeffrey K.; Spangenberg, Doulas A.

    2012-01-01

    With geostationary satellite data it is possible to have a continuous record of diurnal cycles of cloud properties for a large portion of the globe. Daytime cloud property retrieval algorithms are typically superior to nighttime algorithms because daytime methods utilize measurements of reflected solar radiation. However, reflected solar radiation is difficult to accurately model for high solar zenith angles where the amount of incident radiation is small. Clear and cloudy scenes can exhibit very small differences in reflected radiation and threshold-based cloud detection methods have more difficulty setting the proper thresholds for accurate cloud detection. Because top-of-atmosphere radiances are typically more accurately modeled outside the terminator region, information from previous scans can help guide cloud detection near the terminator. This paper presents an algorithm that uses cloud fraction and clear and cloudy infrared brightness temperatures from previous satellite scan times to improve the performance of a threshold-based cloud mask near the terminator. Comparisons of daytime, nighttime, and terminator cloud fraction derived from Geostationary Operational Environmental Satellite (GOES) radiance measurements show that the algorithm greatly reduces the number of false cloud detections and smoothes the transition from the daytime to the nighttime clod detection algorithm. Comparisons with the Geoscience Laser Altimeter System (GLAS) data show that using this algorithm decreases the number of false detections by approximately 20 percentage points.

  18. Rapid Anomaly Detection and Tracking via Compressive Time-Spectra Measurement

    Science.gov (United States)

    2016-02-12

    to digital conversion (ADC) electronics integrated as a whole system. The InView camera uses a Texas Instrument DMD chip (DLP7000) with a...nm to near Infrared (NIR) of 2000 nm. The micromirrors are 13.6 μm on the diagonal and rotate on an axis to two angles. The DMD is put at the...in two complementary left and right directions from the micromirror surface normal. Throughout this project we employed our detection on only one of

  19. EEGgui: a program used to detect electroencephalogram anomalies after traumatic brain injury.

    Science.gov (United States)

    Sick, Justin; Bray, Eric; Bregy, Amade; Dietrich, W Dalton; Bramlett, Helen M; Sick, Thomas

    2013-05-21

    Identifying and quantifying pathological changes in brain electrical activity is important for investigations of brain injury and neurological disease. An example is the development of epilepsy, a secondary consequence of traumatic brain injury. While certain epileptiform events can be identified visually from electroencephalographic (EEG) or electrocorticographic (ECoG) records, quantification of these pathological events has proved to be more difficult. In this study we developed MATLAB-based software that would assist detection of pathological brain electrical activity following traumatic brain injury (TBI) and present our MATLAB code used for the analysis of the ECoG. Software was developed using MATLAB(™) and features of the open access EEGLAB. EEGgui is a graphical user interface in the MATLAB programming platform that allows scientists who are not proficient in computer programming to perform a number of elaborate analyses on ECoG signals. The different analyses include Power Spectral Density (PSD), Short Time Fourier analysis and Spectral Entropy (SE). ECoG records used for demonstration of this software were derived from rats that had undergone traumatic brain injury one year earlier. The software provided in this report provides a graphical user interface for displaying ECoG activity and calculating normalized power density using fast fourier transform of the major brain wave frequencies (Delta, Theta, Alpha, Beta1, Beta2 and Gamma). The software further detects events in which power density for these frequency bands exceeds normal ECoG by more than 4 standard deviations. We found that epileptic events could be identified and distinguished from a variety of ECoG phenomena associated with normal changes in behavior. We further found that analysis of spectral entropy was less effective in distinguishing epileptic from normal changes in ECoG activity. The software presented here was a successful modification of EEGLAB in the Matlab environment that allows

  20. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

    Science.gov (United States)

    Romaguera, Mireia; Vaughan, R. Greg; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F.D.

    2018-01-01

    This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of LST, the LSTgt. We hypothesize that in geothermal areas the LSM time series will underestimate the LST as compared to the remote sensing data, since the former does not account for the geothermal component in its model.In order to extract LSTgt, two approaches of different nature (physical based and data mining) were developed and tested in an area of about 560 × 560 km2 centered at the Kenyan Rift. Pre-dawn data in the study area during the first 45 days of 2012 were analyzed.The results show consistent spatial and temporal LSTgt patterns between the two approaches, and systematic differences of about 2 K. A geothermal area map from surface studies was used to assess LSTgt inside and outside the geothermal boundaries. Spatial means were found to be higher inside the geothermal limits, as well as the relative frequency of occurrence of high LSTgt. Results further show that areas with strong topography can result in anomalously high LSTgt values (false positives), which suggests the need for a slope and aspect correction in the inputs to achieve realistic results in those areas. The uncertainty analysis indicates that large uncertainties of the input parameters may limit detection of LSTgt anomalies. To validate the approaches, higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over the Olkaria geothermal field were used. An established method to estimate radiant geothermal flux was applied providing values between 9 and 24 W/m2 in the geothermal area, which coincides with the LSTgt flux rates obtained with the proposed approaches.The proposed approaches are a first step in estimating LSTgt

  1. Sensitivity of susceptibility-weighted imaging in detecting developmental venous anomalies and associated cavernomas and microhemorrhages in children

    International Nuclear Information System (INIS)

    Young, Allen; Bosemani, Thangamadhan; Goel, Reema; Huisman, Thierry A.G.M.; Poretti, Andrea

    2017-01-01

    Developmental venous anomalies (DVA) are common neuroimaging abnormalities that are traditionally diagnosed by contrast-enhanced T1-weighted images as the gold standard. We aimed to evaluate the sensitivity of SWI in detecting DVA and associated cavernous malformations (CM) and microhemorrhages in children in order to determine if SWI may replace contrast-enhanced MRI sequences. Contrast-enhanced T1-weighted images were used as diagnostic gold standard for DVA. The presence of DVA was qualitatively assessed on axial SWI and T2-weighted images by an experienced pediatric neuroradiologist. In addition, the presence of CM and microhemorrhages was evaluated on SWI and contrast-enhanced T1-weighted images. Fifty-seven children with DVA (34 males, mean age at neuroimaging 11.2 years, range 1 month to 17.9 years) were included in this study. Forty-nine out of 57 DVA were identified on SWI (sensitivity of 86%) and 16 out of 57 DVA were detected on T2-weighted images (sensitivity of 28.1%). General anesthesia-related changes in brain hemodynamics and oxygenation were most likely responsible for the majority of SWI false negative. CM were detected in 12 patients on axial SWI, but only in six on contrast-enhanced T1-weighted images. Associated microhemorrhages could be identified in four patients on both axial SWI and contrast-enhanced T1-weighted images, although more numerous and conspicuous on SWI. SWI can identify DVA and associated cavernous malformations and microhemorrhages with high sensitivity, obviating the need for contrast-enhanced MRI sequences. (orig.)

  2. Sensitivity of susceptibility-weighted imaging in detecting developmental venous anomalies and associated cavernomas and microhemorrhages in children.

    Science.gov (United States)

    Young, Allen; Poretti, Andrea; Bosemani, Thangamadhan; Goel, Reema; Huisman, Thierry A G M

    2017-08-01

    Developmental venous anomalies (DVA) are common neuroimaging abnormalities that are traditionally diagnosed by contrast-enhanced T1-weighted images as the gold standard. We aimed to evaluate the sensitivity of SWI in detecting DVA and associated cavernous malformations (CM) and microhemorrhages in children in order to determine if SWI may replace contrast-enhanced MRI sequences. Contrast-enhanced T1-weighted images were used as diagnostic gold standard for DVA. The presence of DVA was qualitatively assessed on axial SWI and T2-weighted images by an experienced pediatric neuroradiologist. In addition, the presence of CM and microhemorrhages was evaluated on SWI and contrast-enhanced T1-weighted images. Fifty-seven children with DVA (34 males, mean age at neuroimaging 11.2 years, range 1 month to 17.9 years) were included in this study. Forty-nine out of 57 DVA were identified on SWI (sensitivity of 86%) and 16 out of 57 DVA were detected on T2-weighted images (sensitivity of 28.1%). General anesthesia-related changes in brain hemodynamics and oxygenation were most likely responsible for the majority of SWI false negative. CM were detected in 12 patients on axial SWI, but only in six on contrast-enhanced T1-weighted images. Associated microhemorrhages could be identified in four patients on both axial SWI and contrast-enhanced T1-weighted images, although more numerous and conspicuous on SWI. SWI can identify DVA and associated cavernous malformations and microhemorrhages with high sensitivity, obviating the need for contrast-enhanced MRI sequences.

  3. Sensitivity of susceptibility-weighted imaging in detecting developmental venous anomalies and associated cavernomas and microhemorrhages in children

    Energy Technology Data Exchange (ETDEWEB)

    Young, Allen; Bosemani, Thangamadhan; Goel, Reema; Huisman, Thierry A.G.M. [The Johns Hopkins School of Medicine, Charlotte R. Bloomberg Children' s Center, Division of Pediatric Radiology and Pediatric Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, MD (United States); Poretti, Andrea [The Johns Hopkins School of Medicine, Charlotte R. Bloomberg Children' s Center, Division of Pediatric Radiology and Pediatric Neuroradiology, The Russell H. Morgan Department of Radiology and Radiological Science, Baltimore, MD (United States); Kennedy Krieger Institute, Department of Neurogenetics, Baltimore, MD (United States)

    2017-08-15

    Developmental venous anomalies (DVA) are common neuroimaging abnormalities that are traditionally diagnosed by contrast-enhanced T1-weighted images as the gold standard. We aimed to evaluate the sensitivity of SWI in detecting DVA and associated cavernous malformations (CM) and microhemorrhages in children in order to determine if SWI may replace contrast-enhanced MRI sequences. Contrast-enhanced T1-weighted images were used as diagnostic gold standard for DVA. The presence of DVA was qualitatively assessed on axial SWI and T2-weighted images by an experienced pediatric neuroradiologist. In addition, the presence of CM and microhemorrhages was evaluated on SWI and contrast-enhanced T1-weighted images. Fifty-seven children with DVA (34 males, mean age at neuroimaging 11.2 years, range 1 month to 17.9 years) were included in this study. Forty-nine out of 57 DVA were identified on SWI (sensitivity of 86%) and 16 out of 57 DVA were detected on T2-weighted images (sensitivity of 28.1%). General anesthesia-related changes in brain hemodynamics and oxygenation were most likely responsible for the majority of SWI false negative. CM were detected in 12 patients on axial SWI, but only in six on contrast-enhanced T1-weighted images. Associated microhemorrhages could be identified in four patients on both axial SWI and contrast-enhanced T1-weighted images, although more numerous and conspicuous on SWI. SWI can identify DVA and associated cavernous malformations and microhemorrhages with high sensitivity, obviating the need for contrast-enhanced MRI sequences. (orig.)

  4. InSAR-detected Local Ground Inflation Prior to Small Phreatic Eruption

    Science.gov (United States)

    Kobayashi, T.; Morishita, Y.

    2017-12-01

    Phreatic eruptions may be related to transient pressure changes in subsurface regions of hydrothermal systems. It means that crustal deformation presumably proceeds with the pressure increase under the ground, which can be a kind of precursor. In this context, Mt. Hakone volcano is a good study target. This is because the crustal deformation has been successfully detected two months before small phreatic eruptions at an active geothermal area, called Owaku-dani. The anomalous activity such as an increase of seismicity started in the end of April, 2015. With this anomalous activity, SAR (ALOS-2) observations was conducted, and small but significant crustal deformation was detected in a local area with a diameter of 200 m with a displacement of 5 cm. The amount of deformation has increased with time although the spatial size has not changed, and resultantly the amount reached up to 60 cm. Finally, in the end of June, eruptions occurred just at the local crustal deformation area. It should be noted that the eruption started from the InSAR-detected inflational area. This is an excellent case that we were able to identify the location of small phreatic eruption in advance by detecting anomalous ground inflation. Further, we investigated whether or not the inflational deformation preceded the anomalous activity observed since the end of April. Applying InSAR time series analysis incorporating the phase linking method to C-band SAR data of RADARSAT-2 (RS2) and Sentinel-1A (S1), we successfully detected small but significant inflational ground deformation that has already proceeded since the end of 2014 at the latest. The amount of deformation reaches up to 3 cm during 4 months. The most striking point is that the spatial distribution is quite similar to the deformation detected by ALOS-2. It strongly suggests that the pressure increase in subsurface have already started before the anomalous activities such as seismic swarm and widely-distributed deformation have been

  5. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition

    Science.gov (United States)

    Hébert-Dufresne, Laurent; Grochow, Joshua A.; Allard, Antoine

    2016-08-01

    We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.

  6. Detection and correction of underassigned rotational symmetry prior to structure deposition

    International Nuclear Information System (INIS)

    Poon, Billy K.; Grosse-Kunstleve, Ralf W.; Zwart, Peter H.; Sauter, Nicholas K.

    2010-01-01

    An X-ray structural model can be reassigned to a higher symmetry space group using the presented framework if its noncrystallographic symmetry operators are close to being exact crystallographic relationships. About 2% of structures in the Protein Data Bank can be reclassified in this way. Up to 2% of X-ray structures in the Protein Data Bank (PDB) potentially fit into a higher symmetry space group. Redundant protein chains in these structures can be made compatible with exact crystallographic symmetry with minimal atomic movements that are smaller than the expected range of coordinate uncertainty. The incidence of problem cases is somewhat difficult to define precisely, as there is no clear line between underassigned symmetry, in which the subunit differences are unsupported by the data, and pseudosymmetry, in which the subunit differences rest on small but significant intensity differences in the diffraction pattern. To help catch symmetry-assignment problems in the future, it is useful to add a validation step that operates on the refined coordinates just prior to structure deposition. If redundant symmetry-related chains can be removed at this stage, the resulting model (in a higher symmetry space group) can readily serve as an isomorphous replacement starting point for re-refinement using re-indexed and re-integrated raw data. These ideas are implemented in new software tools available at http://cci.lbl.gov/labelit

  7. Creating an experimental testbed for information-theoretic analysis of architectures for x-ray anomaly detection

    Science.gov (United States)

    Coccarelli, David; Greenberg, Joel A.; Mandava, Sagar; Gong, Qian; Huang, Liang-Chih; Ashok, Amit; Gehm, Michael E.

    2017-05-01

    Anomaly detection requires a system that can reliably convert measurements of an object into knowledge about that object. Previously, we have shown that an information-theoretic approach to the design and analysis of such systems provides insight into system performance as it pertains to architectural variations in source fluence, view number/angle, spectral resolution, and spatial resolution.1 However, this work was based on simulated measurements which, in turn, relied on assumptions made in our simulation models and virtual objects. In this work, we describe our experimental testbed capable of making transmission x-ray measurements. The spatial, spectral, and temporal resolution is sufficient to validate aspects of the simulation-based framework, including the forward models, bag packing techniques, and performance analysis. In our experimental CT system, designed baggage is placed on a rotation stage located between a tungsten-anode source and a spectroscopic detector array. The setup is able to measure a full 360° rotation with 18,000 views, each of which defines a 10 ms exposure of 1,536 detector elements, each with 64 spectral channels. Measurements were made of 1,000 bags that comprise 100 clutter instantiations each with 10 different target materials. Moreover, we develop a systematic way to generate bags representative of our desired clutter and target distributions. This gives the dataset a statistical significance valuable in future investigations.

  8. Risk of developing palatally displaced canines in patients with early detectable dental anomalies: a retrospective cohort study

    Science.gov (United States)

    GARIB, Daniela Gamba; LANCIA, Melissa; KATO, Renata Mayumi; OLIVEIRA, Thais Marchini; NEVES, Lucimara Teixeira das

    2016-01-01

    ABSTRACT The early recognition of risk factors for the occurrence of palatally displaced canines (PDC) can increase the possibility of impaction prevention. Objective To estimate the risk of PDC occurrence in children with dental anomalies identified early during mixed dentition. Material and Methods The sample comprised 730 longitudinal orthodontic records from children (448 females and 282 males) with an initial mean age of 8.3 years (SD=1.36). The dental anomaly group (DA) included 263 records of patients with at least one dental anomaly identified in the initial or middle mixed dentition. The non-dental anomaly group (NDA) was composed of 467 records of patients with no dental anomalies. The occurrence of PDC in both groups was diagnosed using panoramic and periapical radiographs taken in the late mixed dentition or early permanent dentition. The prevalence of PDC in patients with and without early diagnosed dental anomalies was compared using the chi-square test (p<0.01), relative risk assessments (RR), and positive and negative predictive values (PPV and NPV). Results PDC frequency was 16.35% and 6.2% in DA and NDA groups, respectively. A statistically significant difference was observed between groups (p<0.01), with greater risk of PDC development in the DA group (RR=2.63). The PPV and NPV was 16% and 93%, respectively. Small maxillary lateral incisors, deciduous molar infraocclusion, and mandibular second premolar distoangulation were associated with PDC. Conclusion Children with dental anomalies diagnosed during early mixed dentition have an approximately two and a half fold increased risk of developing PDC during late mixed dentition compared with children without dental anomalies. PMID:28076458

  9. Risk of developing palatally displaced canines in patients with early detectable dental anomalies: a retrospective cohort study

    Directory of Open Access Journals (Sweden)

    Daniela Gamba GARIB

    Full Text Available ABSTRACT The early recognition of risk factors for the occurrence of palatally displaced canines (PDC can increase the possibility of impaction prevention. Objective To estimate the risk of PDC occurrence in children with dental anomalies identified early during mixed dentition. Material and Methods The sample comprised 730 longitudinal orthodontic records from children (448 females and 282 males with an initial mean age of 8.3 years (SD=1.36. The dental anomaly group (DA included 263 records of patients with at least one dental anomaly identified in the initial or middle mixed dentition. The non-dental anomaly group (NDA was composed of 467 records of patients with no dental anomalies. The occurrence of PDC in both groups was diagnosed using panoramic and periapical radiographs taken in the late mixed dentition or early permanent dentition. The prevalence of PDC in patients with and without early diagnosed dental anomalies was compared using the chi-square test (p<0.01, relative risk assessments (RR, and positive and negative predictive values (PPV and NPV. Results PDC frequency was 16.35% and 6.2% in DA and NDA groups, respectively. A statistically significant difference was observed between groups (p<0.01, with greater risk of PDC development in the DA group (RR=2.63. The PPV and NPV was 16% and 93%, respectively. Small maxillary lateral incisors, deciduous molar infraocclusion, and mandibular second premolar distoangulation were associated with PDC. Conclusion Children with dental anomalies diagnosed during early mixed dentition have an approximately two and a half fold increased risk of developing PDC during late mixed dentition compared with children without dental anomalies.

  10. Risk of developing palatally displaced canines in patients with early detectable dental anomalies: a retrospective cohort study

    OpenAIRE

    GARIB, Daniela Gamba; LANCIA, Melissa; KATO, Renata Mayumi; OLIVEIRA, Thais Marchini; NEVES, Lucimara Teixeira das

    2016-01-01

    ABSTRACT The early recognition of risk factors for the occurrence of palatally displaced canines (PDC) can increase the possibility of impaction prevention. Objective To estimate the risk of PDC occurrence in children with dental anomalies identified early during mixed dentition. Material and Methods The sample comprised 730 longitudinal orthodontic records from children (448 females and 282 males) with an initial mean age of 8.3 years (SD=1.36). The dental anomaly group (DA) included 263...

  11. Screen-detected breast cancer: Does presence of minimal signs on prior mammograms predict staging or grading of cancer?

    Energy Technology Data Exchange (ETDEWEB)

    Bansal, G.J., E-mail: gjbansal@gmail.com [Department of Radiology, University Hospital of Wales, Heath Park, Cardiff, CF14 4XW (United Kingdom); Thomas, K.G. [Department of Radiology, Breast Test Wales, Cathedral Road, Cardiff (United Kingdom)

    2011-07-15

    Aim: To investigate whether the presence of minimal signs on prior mammograms predict staging or grading of cancer. Materials and methods: The previous mammograms of 148 consecutive patients with screen-detected breast cancer were examined. Women with an abnormality visible (minimal signs) on both current and prior mammograms formed the study group; the remaining patients formed the control group. Age, average size of tumour, tumour characteristic, histopathology, grade, and lymph node status were compared between the two groups, using Fisher's exact test. Cases in which earlier diagnosis would have made a significant prognostic difference were also evaluated. Results: Eighteen percent of patients showed an abnormality at the site of the tumour on previous mammograms. There was no statistically significant difference between the two groups with respect to age, average size of tumour, histopathology, grade or lymph node status with p-values being 0.609, 0.781, 0.938, and 0.444, respectively. The only statistically significant difference between the two groups was tumour characteristics with more microcalcifications associated with either mass or asymmetrical density seen in the study group (p = 0.003). Five patients in the study group showed lymph node positivity and were grade 3, and therefore, may have had possible gain from earlier diagnosis. Conclusion: The present study did not demonstrate a statistical difference in grading or staging between the group that showed 'minimal signs' on prior mammograms versus normal prior mammograms. Microcalcification seems to be the most common characteristic seen in the missed cancer and a more aggressive management approach is suggested for breast microcalcifications.

  12. Anomalie de developpement sexuel : Un cas de ...

    African Journals Online (AJOL)

    Mots clés : Pseudohermaphrodisme masculin, anomalie de développement sexuel XY, caryotype, sexe social. Anomaly of sexual development: a case of masculine pseudohermaphrodism or anomaly of development sexual XY. The anomalies of the sexual development must be detected to the birth where they constitute ...

  13. Independent component analysis using prior information for signal detection in a functional imaging system of the retina.

    Science.gov (United States)

    Barriga, E Simon; Pattichis, Marios; Ts'o, Dan; Abramoff, Michael; Kardon, Randy; Kwon, Young; Soliz, Peter

    2011-02-01

    Independent component analysis (ICA) is a statistical technique that estimates a set of sources mixed by an unknown mixing matrix using only a set of observations. For this purpose, the only assumption is that the sources are statistically independent. In many applications, some information about the nature of the unknown signals is available. In this paper we show a method for incorporating prior information about the mixing matrix to increase the levels of detection of responses to visual stimuli. Experimentally, our method matches the performance of known ICA algorithms for high SNR and can greatly improve the performance for low levels of SNR or low levels of signal-to-background ratio (SBR). For the problem of signal extraction, we have achieved detection for signals as small as 0.01% (-40 dB SBR) in hybrid live/synthetic data simulations. In experiments using a functional imager of the retina, measured changes in reflectance in response to visual stimulus are in the order of 0.1-1% of the total pixel intensity value, which makes the functional signal difficult to detect by standard methods. The results of the analysis show that using ICA-P signal levels of 0.1% can be detected. The approach also generalizes the standard Infomax algorithm which can be thought of as a special case of ICA-P when the confidence parameter or a tolerance value is zero. For in vivo animal experiments, we show that signal detection agreement over a range of confidence values parameters can be used to establish reflectance changes in response to the visual stimulus. Copyright © 2010 Elsevier B.V. All rights reserved.

  14. Chromosomal differences between acute nonlymphocytic leukemia in patients with prior solid tumors and prior hematologic malignancies. A study of 14 cases with prior breast cancer

    International Nuclear Information System (INIS)

    Mamuris, Z.; Dumont, J.; Dutrillaux, B.; Aurias, A.

    1989-01-01

    A cytogenetic study of 14 patients with secondary acute nonlymphocytic leukemia (S-ANLL) with prior treatment for breast cancer is reported. The chromosomes recurrently involved in numerical or structural anomalies are chromosomes 7, 5, 17, and 11, in decreasing order of frequency. The distribution of the anomalies detected in this sample of patients is similar to that observed in published cases with prior breast or other solid tumors, though anomalies of chromosome 11 were not pointed out, but it significantly differs from that of the S-ANLL with prior hematologic malignancies. This difference is principally due to a higher involvement of chromosome 7 in patients with prior hematologic malignancies and of chromosomes 11 and 17 in patients with prior solid tumors. A genetic determinism involving abnormal recessive alleles located on chromosomes 5, 7, 11, and 17 uncovered by deletions of the normal homologs may be a cause of S-ANLL. The difference between patients with prior hematologic malignancies or solid tumors may be explained by different constitutional mutations of recessive genes in the two groups of patients

  15. A comparison of classical and intelligent methods to detect potential thermal anomalies before the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4

    Directory of Open Access Journals (Sweden)

    M. Akhoondzadeh

    2013-04-01

    Full Text Available In this paper, a number of classical and intelligent methods, including interquartile, autoregressive integrated moving average (ARIMA, artificial neural network (ANN and support vector machine (SVM, have been proposed to quantify potential thermal anomalies around the time of the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4. The duration of the data set, which is comprised of Aqua-MODIS land surface temperature (LST night-time snapshot images, is 62 days. In order to quantify variations of LST data obtained from satellite images, the air temperature (AT data derived from the meteorological station close to the earthquake epicenter has been taken into account. For the models examined here, results indicate the following: (i ARIMA models, which are the most widely used in the time series community for short-term forecasting, are quickly and easily implemented, and can efficiently act through linear solutions. (ii A multilayer perceptron (MLP feed-forward neural network can be a suitable non-parametric method to detect the anomalous changes of a non-linear time series such as variations of LST. (iii Since SVMs are often used due to their many advantages for classification and regression tasks, it can be shown that, if the difference between the predicted value using the SVM method and the observed value exceeds the pre-defined threshold value, then the observed value could be regarded as an anomaly. (iv ANN and SVM methods could be powerful tools in modeling complex phenomena such as earthquake precursor time series where we may not know what the underlying data generating process is. There is good agreement in the results obtained from the different methods for quantifying potential anomalies in a given LST time series. This paper indicates that the detection of the potential thermal anomalies derive credibility from the overall efficiencies and potentialities of the four integrated methods.

  16. Potential tank waste material anomalies located near the liquid observation wells: Model predicted responses of a neutron moisture detection system

    International Nuclear Information System (INIS)

    Finfrock, S.H.; Toffer, H.; Watson, W.T.

    1994-09-01

    Extensive analyses have been completed to demonstrate that a neutron moisture probe can be used to recognize anomalies in materials and geometry surrounding the liquid observation wells (LOWs). Furthermore, techniques can be developed that will permit the interpretation of detector readings, perturbed by the presence of anomalies, as more accurate moisture concentrations. This analysis effort extends the usefulness of a neutron moisture probe system significantly, especially in the complicated geometries and material conditions that may be encountered in the waste tanks. Both static-source and pulsed-source neutron probes were considered in the analyses. Four different detector configurations were investigated: Thermal and epithermal neutron detectors located in both the near and far field

  17. Enzyme leaching of surficial geochemical samples for detecting hydromorphic trace-element anomalies associated with precious-metal mineralized bedrock buried beneath glacial overburden in northern Minnesota

    Science.gov (United States)

    Clark, Robert J.; Meier, A.L.; Riddle, G.; ,

    1990-01-01

    One objective of the International Falls and Roseau, Minnesota, CUSMAP projects was to develop a means of conducting regional-scale geochemical surveys in areas where bedrock is buried beneath complex glacially derived overburden. Partial analysis of B-horizon soils offered hope for detecting subtle hydromorphic trace-element dispersion patterns. An enzyme-based partial leach selectively removes metals from oxide coatings on the surfaces of soil materials without attacking their matrix. Most trace-element concentrations in the resulting solutions are in the part-per-trillion to low part-per-billion range, necessitating determinations by inductively coupled plasma/mass spectrometry. The resulting data show greater contrasts for many trace elements than with other techniques tested. Spatially, many trace metal anomalies are locally discontinuous, but anomalous trends within larger areas are apparent. In many instances, the source for an anomaly seems to be either basal till or bedrock. Ground water flow is probably the most important mechanism for transporting metals toward the surface, although ionic diffusion, electrochemical gradients, and capillary action may play a role in anomaly dispersal. Sample sites near the Rainy Lake-Seine River fault zone, a regional shear zone, often have anomalous concentrations of a variety of metals, commonly including Zn and/or one or more metals which substitute for Zn in sphalerite (Cd, Ge, Ga, and Sn). Shifts in background concentrations of Bi, Sb, and As show a trend across the area indicating a possible regional zoning of lode-Au mineralization. Soil anomalies of Ag, Co, and Tl parallel basement structures, suggesting areas that may have potential for Cobalt/Thunder Baytype silver viens. An area around Baudette, Minnesota, which is underlain by quartz-chlorite-carbonate-altered shear zones, is anomalous in Ag, As, Bi, Co, Mo, Te, Tl, and W. Anomalies of Ag, As, Bi, Te, and W tend to follow the fault zones, suggesting potential

  18. Time series analysis of precipitation and vegetation to detect food production anomalies in the Horn of Africa. The case of Lower Shabelle (Somalia

    Directory of Open Access Journals (Sweden)

    M. A. Belenguer-Plomer

    2016-12-01

    Full Text Available The Horn of Africa is one of the most food-insecure locations around the world due to the continuous increase of its population and the practice of the subsistence agriculture. This causes that much of the population cannot take the minimum nutritional needs for a healthy life. Moreover, this situation of food vulnerability may be seriously affected in the coming years due to the effects of climate change. The aim of this work is combine the information about the state of the vegetation that offers the NDVI with rainfall data to detect negative anomalies in food production. This work has been used the monthly products of NDVI MOD13A3 of MODIS and the rainfall estimation product TAMSAT, both during the period 2001-2015. With these products we have calculated the average of the entire time period selected and we have detected the years whose NDVI values were further away from the average, being these 2010, 2011 and 2014. Once detected the years with major anomalies in NDVI, there has been an exclusive monthly analysis of those years, where we have analysed the relationships between the value of NDVI and monthly rainfall, obtaining a direct relationship between the two values. It also has been used crop calendar to focus the analysis in the months of agricultural production and finding that the main cause of anomalies in vegetation is a decrease in the registration of rainfall during the months of agricultural production. This reason explains the origin of the food shortages that occurred in 2010 and 2011 that generated an enormous humanitarian crisis in this area.

  19. DOWN'S ANOMALY.

    Science.gov (United States)

    PENROSE, L.S.; SMITH, G.F.

    BOTH CLINICAL AND PATHOLOGICAL ASPECTS AND MATHEMATICAL ELABORATIONS OF DOWN'S ANOMALY, KNOWN ALSO AS MONGOLISM, ARE PRESENTED IN THIS REFERENCE MANUAL FOR PROFESSIONAL PERSONNEL. INFORMATION PROVIDED CONCERNS (1) HISTORICAL STUDIES, (2) PHYSICAL SIGNS, (3) BONES AND MUSCLES, (4) MENTAL DEVELOPMENT, (5) DERMATOGLYPHS, (6) HEMATOLOGY, (7)…

  20. Intracardiac Eustachian Valve Cyst in an Adult Detected with Other Cardiac Anomalies: Usefulness of Multidetector CT in Diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Hyung Ji; Jung, Jung Im; Kim, Hwan Wook; Lee, Kyo Young [Seoul St. Mary' s Hospital, College of Medicine, The Catholic University of Korea, Seoul (Korea, Republic of)

    2012-07-15

    We present an unusual case of an intracardiac Eustachian valve cyst observed concurrently with atresia of the coronary sinus ostium, a persistent left superior vena cava (LSVC) and a bicuspid aortic valve. There have been several echocardiographic reports of Eustachian valve cysts; however, there is no report of multidetector computed tomography (MDCT) findings related to a Eustachian valve cyst. Recently, we observed a Eustachian valve cyst diagnosed on MDCT showing a hypodense cyst at the characteristic location of the Eustachian valve (the junction of the right atrium and inferior vena cava). MDCT also demonstrated additional cardiovascular anomalies including atresia of the coronary sinus ostium and a persistent LSVC and bicuspid aortic valve.

  1. Global navigation satellite system detection of preseismic ionospheric total electron content anomalies for strong magnitude (Mw>6) Himalayan earthquakes

    Science.gov (United States)

    Sharma, Gopal; Champati ray, Prashant Kumar; Mohanty, Sarada; Gautam, Param Kirti Rao; Kannaujiya, Suresh

    2017-10-01

    Electron content in the ionosphere is very sensitive to temporary disturbances of the Earth's magnetosphere (geomagnetic storm), solar flares, and seismic activities. The Global Navigation Satellite System (GNSS)-based total electron content (TEC) measurement has emerged as an important technique for computations of earthquake precursor signals. We examined the pre-earthquake signatures for eight strong magnitude (Mw>6: 6.1 to 7.8) earthquakes with the aid of GNSS-based TEC measurement in the tectonically active Himalayan region using International GNSS Service (IGS) stations as well as local GNSS-based continuously operating reference stations (CORS). The results indicate very significant ionospheric anomalies in the vertical total electron content (vTEC) a few days before the main shock for all of the events. Geomagnetic activities were also studied during the TEC observation window to ascertain their role in ionospheric perturbations. It was also inferred that TEC variation due to low magnitude events could also be monitored if the epicenter lies closer to the GNSS or IGS station. Therefore, the study has confirmed TEC anomalies before major Himalayan earthquakes, thereby making it imperative to set up a much denser network of IGS/CORS for real-time data analysis and forewarning.

  2. Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

    Science.gov (United States)

    Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

    2017-12-01

    We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

  3. Fetal renal anomalies : diagnosis, management, and outcome

    NARCIS (Netherlands)

    Damen-Elias, Henrica Antonia Maria

    2004-01-01

    In two to three percent of fetuses structural anomalies can be found with prenatal ultrasound investigation. Anomalies of the urinary tract account for 15 to 20% of these anomalies with a detection rate of approximately of 90%. In Chapter 2, 3 and 4 we present reference curves for size and growth

  4. Impact of low signal intensity assessed by cine magnetic resonance imaging on detection of poorly viable myocardium in patients with prior myocardial infarction.

    Science.gov (United States)

    Ota, Shingo; Tanimoto, Takashi; Orii, Makoto; Hirata, Kumiko; Shiono, Yasutsugu; Shimamura, Kunihiro; Matsuo, Yoshiki; Yamano, Takashi; Ino, Yasushi; Kitabata, Hironori; Yamaguchi, Tomoyuki; Kubo, Takashi; Tanaka, Atsushi; Imanishi, Toshio; Akasaka, Takashi

    2015-05-13

    Late gadolinium enhancement magnetic resonance imaging (LGE-MRI) has been established as a modality to detect myocardial infarction (MI). However, the use of gadolinium contrast is limited in patients with advanced renal dysfunction. Although the signal intensity (SI) of infarct area assessed by cine MRI is low in some patients with prior MI, the prevalence and clinical significance of low SI has not been evaluated. The aim of this study was to evaluate how low SI assessed by cine MRI may relate to the myocardial viability in patients with prior MI. Fifty patients with prior MI underwent both cine MRI and LGE-MRI. The left ventricle was divided into 17 segments. The presence of low SI and the wall motion score (WMS) of each segment were assessed by cine MRI. The transmural extent of infarction was evaluated by LGE-MRI. LGE was detected in 329 of all 850 segments (39%). The low SI assessed by cine MRI was detected in 105 of 329 segments with LGE (32%). All segments with low SI had LGE. Of all 329 segments with LGE, the segments with low SI showed greater transmural extent of infarction (78 [72 - 84] % versus 53 [38 - 72] %, P cine MRI may be effective for detecting poorly viable myocardium in patients with prior MI.

  5. When ultrasound anomalies are present: An estimation of the frequency of chromosome abnormalities not detected by cell-free DNA aneuploidy screens.

    Science.gov (United States)

    Reimers, Rebecca M; Mason-Suares, Heather; Little, Sarah E; Bromley, Bryann; Reiff, Emily S; Dobson, Lori J; Wilkins-Haug, Louise

    2018-03-01

    This study characterizes cytogenetic abnormalities with ultrasound findings to refine counseling following negative cell-free DNA (cfDNA). A retrospective cohort of pregnancies with chromosome abnormalities and ultrasound findings was examined to determine the residual risk following negative cfDNA. Cytogenetic data was categorized as cfDNA detectable for aneuploidies of chromosomes 13, 18, 21, X, or Y or non-cfDNA detectable for other chromosome abnormalities. Ultrasound reports were categorized as structural anomaly, nuchal translucency (NT) ≥3.0 mm, or other "soft markers". Results were compared using chi squared and Fishers exact tests. Of the 498 fetuses with cytogenetic abnormalities and ultrasound findings, 16.3% (81/498) had non-cfDNA detectable results. In the first, second, and third trimesters, 12.4% (32/259), 19.5% (42/215), and 29.2% (7/24) had non-cfDNA detectable results respectively. The first trimester non-cfDNA detectable results reduced to 7.7% (19/246) if triploidy was detectable by cfDNA testing. For isolated first trimester NT of 3.0-3.49 mm, 15.8% (6/38) had non-cfDNA detectable results, while for NT ≥3.5 mm, it was 12.3% (20/162). For cystic hygroma, 4.3% (4/94) had non-cfDNA detectable results. Counseling for residual risk following cfDNA in the presence of an ultrasound finding is impacted by gestational age, ultrasound finding, and cfDNA detection of triploidy. © 2018 John Wiley & Sons, Ltd.

  6. Concept for Inclusion of Analytical and Computational Capability in Optical Plume Anomaly Detection (OPAD) for Measurement of Neutron Flux

    Science.gov (United States)

    Patrick, Marshall Clint; Cooper, Anita E.; Powers, W. T.

    2004-01-01

    Researchers are working on many fronts to make possible high-speed, automated classification and quantification of constituent materials in numerous environments. NASA's Marshall Space Flight Center has implemented a system for rocket engine flowfields/plumes. The Optical Plume Anomaly Detector (OPAD) system was designed to utilize emission and absorption spectroscopy for monitoring molecular and atomic particulates in gas plasma. An accompanying suite of tools and analytical package designed to utilize information collected by OPAD is known as the Engine Diagnostic Filtering System (EDiFiS). The current combination of these systems identifies atomic and molecular species and quantifies mass loss rates in H2/O2 rocket plumes. Capabilities for real-time processing are being advanced on several fronts, including an effort to hardware encode components of the EDiFiS for health monitoring and management. This paper addresses the OPAD with its tool suites, and discusses what is considered a natural progression: a concept for taking OPAD to the next logical level of high energy physics, incorporating fermion and boson particle analyses in measurement of neutron flux.

  7. Dyonic anomalies

    International Nuclear Information System (INIS)

    Henningson, Mans; Johansson, Erik P.G.

    2005-01-01

    We consider the problem of coupling a dyonic p-brane in d=2p+4 space-time dimensions to a prescribed (p+2)-form field strength. This is particularly subtle when p is odd. For the case p=1, we explicitly construct a coupling functional, which is a sum of two terms: one which is linear in the prescribed field strength, and one which describes the coupling of the brane to its self-field and takes the form of a Wess-Zumino term depending only on the embedding of the brane world-volume into space-time. We then show that this functional is well-defined only modulo a certain anomaly, related to the Euler class of the normal bundle of the brane world-volume

  8. The Pioneer Anomaly

    Directory of Open Access Journals (Sweden)

    Viktor T. Toth

    2010-09-01

    Full Text Available Radio-metric Doppler tracking data received from the Pioneer 10 and 11 spacecraft from heliocentric distances of 20-70 AU has consistently indicated the presence of a small, anomalous, blue-shifted frequency drift uniformly changing with a rate of ~6 × 10–9 Hz/s. Ultimately, the drift was interpreted as a constant sunward deceleration of each particular spacecraft at the level of aP = (8.74 ± 1.33 × 10–10 m/s2. This apparent violation of the Newton's gravitational inverse square law has become known as the Pioneer anomaly; the nature of this anomaly remains unexplained. In this review, we summarize the current knowledge of the physical properties of the anomaly and the conditions that led to its detection and characterization. We review various mechanisms proposed to explain the anomaly and discuss the current state of efforts to determine its nature. A comprehensive new investigation of the anomalous behavior of the two Pioneers has begun recently. The new efforts rely on the much-extended set of radio-metric Doppler data for both spacecraft in conjunction with the newly available complete record of their telemetry files and a large archive of original project documentation. As the new study is yet to report its findings, this review provides the necessary background for the new results to appear in the near future. In particular, we provide a significant amount of information on the design, operations and behavior of the two Pioneers during their entire missions, including descriptions of various data formats and techniques used for their navigation and radio-science data analysis. As most of this information was recovered relatively recently, it was not used in the previous studies of the Pioneer anomaly, but it is critical for the new investigation.

  9. Angelman syndrome without detectable chromosome 15q11-13 anomaly: clinical study of familial and isolated cases

    NARCIS (Netherlands)

    Laan, L. A.; Halley, D. J.; den Boer, A. T.; Hennekam, R. C.; Renier, W. O.; Brouwer, O. F.

    1998-01-01

    The clinical findings in 12 Angelman syndrome (AS) patients (4 sib pairs and 4 sporadic cases, aged 12-55 years) without a cytogenetic or molecular detectable defect at the AS locus were compared to those of 28 AS patients (aged 11-50 years) with a deletion, in order to determine whether the

  10. Minor Physical Anomalies, Footprints, and Behavior: Was the Buddha Right?

    Science.gov (United States)

    Draper, Thomas W.; Munoz, Milagros M.

    1982-01-01

    A relationship between an anomaly of the footprint suggested by ancient Abhidhamma meditations and Minor Physical Anomalies Scale was observed in children. The footprint anomalies correlated with the activity levels of children in the same way as the scores on the scale and consistently with prior research using the scale. (Author/RD)

  11. Prior HIV testing among STD patients in Guangdong Province, China: opportunities for expanding detection of sexually transmitted HIV infection.

    Science.gov (United States)

    Tucker, Joseph D; Yang, Li-Gang; Yang, Bin; Young, Darwin; Henderson, Gail E; Huang, Shu-Jie; Lu, He-Kun; Chen, Xiang-Sheng; Cohen, Myron S

    2012-03-01

    Expanding HIV testing is important among individuals at increased risk for sexual HIV transmission in China, but little is known about prior HIV testing experiences among sexually transmitted disease (STD) patients. This cross-sectional study of 1792 outpatients from 6 public STD clinics in Guangdong Province recorded detailed information about ever having been tested for HIV infection in addition to sociodemographic variables, health seeking, clinical STD history, and HIV stigma using a validated survey instrument. A total of 456 (25.4%) of the STD patients in this sample had ever been tested for HIV infection. STD patients who were male, had higher income, more education, were at City A and City C, received STD services at public facilities, had used intravenous drugs, and had a history of an STD were more likely to ever receive an HIV test in multivariate analysis. Low perceived HIV risk was the most common reason for not receiving an HIV test. Only 7.7% of the sample reported fear of discrimination or loss of face as influencing their lack of HIV testing. Incomplete prior HIV screening among STD patients in China suggests the need for broadening HIV testing opportunities at STD clinics and similar clinical settings attended by those with increased sexual risk.

  12. ISHM Anomaly Lexicon for Rocket Test

    Science.gov (United States)

    Schmalzel, John L.; Buchanan, Aubri; Hensarling, Paula L.; Morris, Jonathan; Turowski, Mark; Figueroa, Jorge F.

    2007-01-01

    Integrated Systems Health Management (ISHM) is a comprehensive capability. An ISHM system must detect anomalies, identify causes of such anomalies, predict future anomalies, help identify consequences of anomalies for example, suggested mitigation steps. The system should also provide users with appropriate navigation tools to facilitate the flow of information into and out of the ISHM system. Central to the ability of the ISHM to detect anomalies is a clearly defined catalog of anomalies. Further, this lexicon of anomalies must be organized in ways that make it accessible to a suite of tools used to manage the data, information and knowledge (DIaK) associated with a system. In particular, it is critical to ensure that there is optimal mapping between target anomalies and the algorithms associated with their detection. During the early development of our ISHM architecture and approach, it became clear that a lexicon of anomalies would be important to the development of critical anomaly detection algorithms. In our work in the rocket engine test environment at John C. Stennis Space Center, we have access to a repository of discrepancy reports (DRs) that are generated in response to squawks identified during post-test data analysis. The DR is the tool used to document anomalies and the methods used to resolve the issue. These DRs have been generated for many different tests and for all test stands. The result is that they represent a comprehensive summary of the anomalies associated with rocket engine testing. Fig. 1 illustrates some of the data that can be extracted from a DR. Such information includes affected transducer channels, narrative description of the observed anomaly, and the steps used to correct the problem. The primary goal of the anomaly lexicon development efforts we have undertaken is to create a lexicon that could be used in support of an associated health assessment database system (HADS) co-development effort. There are a number of significant

  13. Mobile gamma-ray scanning system for detecting radiation anomalies associated with 226Ra-bearing materials

    International Nuclear Information System (INIS)

    Myrick, T.E.; Blair, M.S.; Doane, R.W.; Goldsmith, W.A.

    1982-11-01

    A mobile gamma-ray scanning system has been developed by Oak Ridge National Laboratory for use in the Department of Energy's remedial action survey programs. The unit consists of a NaI(T1) detection system housed in a specially-equipped van. The system is operator controlled through an on-board mini-computer, with data output provided on the computer video screen, strip chart recorders, and an on-line printer. Data storage is provided by a floppy disk system. Multichannel analysis capabilities are included for qualitative radionuclide identification. A 226 Ra-specific algorithm is employed to identify locations containing residual radium-bearing materials. This report presents the details of the system description, software development, and scanning methods utilized with the ORNL system. Laboratory calibration and field testing have established the system sensitivity, field of view, and other performance characteristics, the results of which are also presented. Documentation of the instrumentation and computer programs are included

  14. Coronary anomalies: what the radiologist should know

    Directory of Open Access Journals (Sweden)

    Priscilla Ornellas Neves

    2015-08-01

    Full Text Available AbstractCoronary anomalies comprise a diverse group of malformations, some of them asymptomatic with a benign course, and the others related to symptoms as chest pain and sudden death. Such anomalies may be classified as follows: 1 anomalies of origination and course; 2 anomalies of intrinsic coronary arterial anatomy; 3 anomalies of coronary termination. The origin and the proximal course of anomalous coronary arteries are the main prognostic factors, and interarterial course or a coronary artery is considered to be malignant due its association with increased risk of sudden death. Coronary computed tomography angiography has become the reference method for such an assessment as it detects not only anomalies in origination of these arteries, but also its course in relation to other mediastinal structures, which plays a relevant role in the definition of the therapeutic management. Finally, it is essential for radiologists to recognize and characterize such anomalies.

  15. Coronary anomalies: what the radiologist should know*

    Science.gov (United States)

    Neves, Priscilla Ornellas; Andrade, Joalbo; Monção, Henry

    2015-01-01

    Coronary anomalies comprise a diverse group of malformations, some of them asymptomatic with a benign course, and the others related to symptoms as chest pain and sudden death. Such anomalies may be classified as follows: 1) anomalies of origination and course; 2) anomalies of intrinsic coronary arterial anatomy; 3) anomalies of coronary termination. The origin and the proximal course of anomalous coronary arteries are the main prognostic factors, and interarterial course or a coronary artery is considered to be malignant due its association with increased risk of sudden death. Coronary computed tomography angiography has become the reference method for such an assessment as it detects not only anomalies in origination of these arteries, but also its course in relation to other mediastinal structures, which plays a relevant role in the definition of the therapeutic management. Finally, it is essential for radiologists to recognize and characterize such anomalies. PMID:26379322

  16. A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Wen Chen

    2011-08-01

    Full Text Available A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance. First, the self data set is replaced by the self cluster centers to compare with the detector candidates in each cluster level. As the number of self clusters is much less than the self set size, the detector generation efficiency is improved. Second, during the detector generation process, the detector candidates are restricted to the lower coverage space to reduce detector redundancy. In the article, the problem that the distances between antigens coverage to a constant value in the high dimensional space is analyzed, accordingly the Principle Component Analysis (PCA method is used to reduce the data dimension, and the fractional distance function is employed to enhance the distinctiveness between the self and non-self antigens. The detector generation procedure is terminated when the expected non-self coverage is reached. The theory analysis and experimental results demonstrate that the detection rate of HC-RNSA is higher than that of the traditional negative selection algorithms while the false alarm rate and time cost are reduced.

  17. [Developmental venous anomaly (DVA)].

    Science.gov (United States)

    Zimmer, A; Hagen, T; Ahlhelm, F; Viera, J; Reith, W; Schulte-Altedorneburg, G

    2007-10-01

    As congenital anatomic variants of venous drainage, developmental venous anomalies (DVA) represent up to 60% of all cerebral vascular malformations. The prior term "venous angioma" is a misnomer implicating an abnormal vascular structure with an increased bleeding risk. They are often found incidentally and are hardly ever symptomatic. Their morphologic characteristics are dilated vessels in the white matter, which converge on a greater collector vein, forming the typical caput medusae. They drain into the superficial or deep venous system. The frequent association with other, potentially bleeding-prone vascular malformations is clinically relevant, in particular cavernous angioma, which might require therapeutic action. Therefore, coincident vascular lesions need to be actively sought by appropriate additional imaging techniques.

  18. Operating experiences with an on-line, computer based nuclear plant surveillance and anomaly detection system based on pattern recognition and artificial intelligence

    International Nuclear Information System (INIS)

    Kemeny, L.G.

    1988-01-01

    The control room of a nuclear power plant can represent a hostile work environment for all but a highly trained operating team. Whilst disciplined training and long professional experience will guarantee assurance of safety and reliability in nuclear plant operation, any surveillance system which has the ability to minimise human error and provide additional safeguards is a desirable asset. This paper proposes a scheme whereby some key parameters of a nuclear power plant, precisely known through detailed calculation and accurate measurement are stored as a data base in an on-line computer. Through the systematic statistical analysis of key stochastic variates a comparison is made by the on-line system with the data base at regular intervals. These time intervals may be as short as seconds during periods of reactor transients such as at start-up or shut down. Alternatively, during steady state operation, the parameters are calculated and displayed at intervals of an hour or greater. An anomaly, or an indication of unusual operational behaviour is indicated both numerically and graphically by the computer if it detects a variance greater than a few percent from the mean value of the reference data base. (author)

  19. Chiral anomalies and differential geometry

    Energy Technology Data Exchange (ETDEWEB)

    Zumino, B.

    1983-10-01

    Some properties of chiral anomalies are described from a geometric point of view. Topics include chiral anomalies and differential forms, transformation properties of the anomalies, identification and use of the anomalies, and normalization of the anomalies. 22 references. (WHK)

  20. Graph anomalies in cyber communications

    Energy Technology Data Exchange (ETDEWEB)

    Vander Wiel, Scott A [Los Alamos National Laboratory; Storlie, Curtis B [Los Alamos National Laboratory; Sandine, Gary [Los Alamos National Laboratory; Hagberg, Aric A [Los Alamos National Laboratory; Fisk, Michael [Los Alamos National Laboratory

    2011-01-11

    Enterprises monitor cyber traffic for viruses, intruders and stolen information. Detection methods look for known signatures of malicious traffic or search for anomalies with respect to a nominal reference model. Traditional anomaly detection focuses on aggregate traffic at central nodes or on user-level monitoring. More recently, however, traffic is being viewed more holistically as a dynamic communication graph. Attention to the graph nature of the traffic has expanded the types of anomalies that are being sought. We give an overview of several cyber data streams collected at Los Alamos National Laboratory and discuss current work in modeling the graph dynamics of traffic over the network. We consider global properties and local properties within the communication graph. A method for monitoring relative entropy on multiple correlated properties is discussed in detail.

  1. Ready for a fight? The physiological effects of detecting an opponent's pheromone cues prior to a contest.

    Science.gov (United States)

    Garcia, Mark J; Williams, John; Sinderman, Benjamin; Earley, Ryan L

    2015-10-01

    Reception of pheromone cues can elicit significant physiological (e.g. steroid hormone levels) changes in the recipient. These pheromone-induced physiological changes have been well documented for male-female interactions, but scarcely in same-sex interactions (male-male and female-female). We sought to address this dearth in the current literature and examine whether mangrove rivulus fish (Kryptolebias marmoratus) could detect and, ultimately, mount a physiological response to the pheromone signature of a potential, same-sex competitor. We examined steroid hormone levels in mangrove rivulus exposed to one of three treatments: 1) isolation, 2) exposure to pheromones of a size-matched partner, and 3) pheromone exposure to a size-matched opponent followed by a physical encounter with the opponent. We found that exposure to a competitor's pheromone cues elicited a significant increase in testosterone levels. Increases in testosterone were similar across genetically distinct lineages derived from geographically distinct populations. Further, testosterone levels were similar between individuals only exposed to pheromone cues and individuals exposed to both pheromone cues and a subsequent physical encounter. Our findings led us to generate a number of testable predictions regarding how mangrove rivulus utilize pheromone signals in social interactions, the molecular mechanisms linking social stimuli and hormonal responses, and the possible adaptive benefits of hormonal responsiveness to receiving a potential competitor's pheromone cues. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Surveillance of human enteric viruses in coastal waters using concentration with methacrylate monolithic supports prior to detection by RT-qPCR.

    Science.gov (United States)

    Gonçalves, José; Gutiérrez-Aguirre, Ion; Balasubramanian, Mukundh N; Zagorščak, Maja; Ravnikar, Maja; Turk, Valentina

    2018-03-01

    This is the first surveillance study using methacrylate monolithic supports to concentrate environmental coastal water samples, prior to molecular target detection by RT-qPCR. Rotaviruses (RoV) and Noroviruses (NoV) were monitored in a polluted area at the Bay of Koper (Gulf of Trieste, Northern Adriatic Sea) and at a nearby bathing area and mussel farm areas. RoV and NoV are released into the Bay of Koper, with higher rates close to the discharge of the wastewater treatment plant, however, they can be detected at recreational and mussel farming areas. Our results showed that water bodies considered safe based on FC concentrations, can still have low, yet potentially infective, concentrations of human viruses. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  3. Anomaly Detection with Text Mining

    Data.gov (United States)

    National Aeronautics and Space Administration — Many existing complex space systems have a significant amount of historical maintenance and problem data bases that are stored in unstructured text forms. The...

  4. Anomaly Detection for Complex Systems

    Data.gov (United States)

    National Aeronautics and Space Administration — In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about...

  5. MR imaging of paediatric uterovaginal anomalies

    International Nuclear Information System (INIS)

    Lang, I.M.; Babyn, P.; Oliver, G.D.

    1999-01-01

    Background. Transabdominal ultrasound (US) has not proved completely reliable in Muellerian duct anomalies. One study has shown it useful in obstructed uterovaginal anomalies. We are unaware of a study that has used endovaginal ultrasound in children to investigate uterovaginal anomalies. Magnetic resonance imaging (MRI) is now gaining wide acceptance in imaging congenital abnormalities of the genital tract. Objective. To identify the problems and potential pitfalls of using MRI to evaluate the female genital tract in paediatric patients. Materials and methods. A retrospective review of the MRI scans of 19 patients, aged 3 months to 19 years (mean 14 years), with uterovaginal anomalies. Results. The uterovaginal anomalies were categorised into three groups: (1) congenital absence of the Muellerian ducts, or the Mayer-Rokitansky-Kuster-Hauser syndrome (n = 7), (2) disorders of vertical fusion (n = 2) and (3) disorders of lateral fusion (n = 10). Conclusions. MRI is a reliable method for evaluating paediatric uterovaginal anomalies, but should be analysed in conjunction with other imaging modalities (US and genitography). Previous surgery makes interpretation more difficult and, if possible, MRI should be carried out prior to any surgery. An accurate MRI examination can be extremely helpful prior to surgery and it is important for the radiologist to have knowledge of how these complex anomalies are managed and what pitfalls to avoid. (orig.)

  6. Impending ionospheric anomaly preceding the Iquique Mw8.2 earthquake in Chile on 2014 April 1

    Science.gov (United States)

    Guo, Jinyun; Li, Wang; Yu, Hongjuan; Liu, Zhimin; Zhao, Chunmei; Kong, Qiaoli

    2015-12-01

    To investigate the coupling relationship between great earthquake and ionosphere, the GPS-derived total electron contents (TECs) by the Center for Orbit Determination in Europe and the foF2 data from the Space Weather Prediction Center were used to analyse the impending ionospheric anomalies before the Iquique Mw8.2 earthquake in Chile on 2014 April 1. Eliminating effects of the solar and geomagnetic activities on ionosphere by the sliding interquartile range with the 27-day window, the TEC analysis results represent that there were negative anomalies occurred on 15th day prior to the earthquake, and positive anomalies appeared in 5th day before the earthquake. The foF2 analysis results of ionosonde stations Jicamarca, Concepcion and Ramey show that the foF2 increased by 40, 50 and 45 per cent, respectively, on 5th day before the earthquake. The TEC anomalous distribution indicates that there was a widely TEC decrement over the epicentre with the duration of 6 hr on 15th day before the earthquake. On 5th day before the earthquake, the TEC over the epicentre increased with the amplitude of 15 TECu, and the duration exceeded 6 hr. The anomalies occurred on the side away from the equator. All TEC anomalies in these days were within the bounds of equatorial anomaly zone where should be the focal area to monitor ionospheric anomaly before strong earthquakes. The relationship between ionospheric anomalies and geomagnetic activity was detected by the cross wavelet analysis, which implied that the foF2 was not affected by the magnetic activities on 15th day and 5th day prior to the earthquake, but the TECs were partially affected by anomalous magnetic activity during some periods of 5th day prior to the earthquake.

  7. Learning about Poland Anomaly

    Science.gov (United States)

    ... these symptoms occur on one side of the body (unilateral). Also, it is important to note that Poland anomaly does not typically affect intelligence. Top of page What causes Poland anomaly? The ...

  8. Vascular Anomalies in Pediatrics.

    Science.gov (United States)

    Foley, Lisa S; Kulungowski, Ann M

    2015-08-01

    A standardized classification system allows improvements in diagnostic accuracy. Multidisciplinary vascular anomaly centers combine medical, surgical, radiologic, and pathologic expertise. This collaborative approach tailors treatment and management of vascular anomalies for affected individuals.

  9. Magnetic hyperfine anomalies

    International Nuclear Information System (INIS)

    Buettgenbach, S.

    1984-01-01

    This study is concerned with the measurement and interpretation of magnetic hyperfine anomalies in electronic and muonic atoms, i.e. effects of the distribution of nuclear magnetization on the magnetic dipole hyperfine interaction. After a summary of the relevant theory and a review of experimental techniques, hyperfine anomaly results are discussed in terms of various nuclear models. The use of the anomaly for yielding information about the origin of magnetic hyperfine interactions is outlined. Experimental and theoretical hyperfine anomalies are tabulated. (Auth.)

  10. Tracheobronchial Branching Anomalies

    International Nuclear Information System (INIS)

    Hong, Min Ji; Kim, Young Tong; Jou, Sung Shick; Park, A Young

    2010-01-01

    There are various congenital anomalies with respect to the number, length, diameter, and location of tracheobronchial branching patterns. The tracheobronchial anomalies are classified into two groups. The first one, anomalies of division, includes tracheal bronchus, cardiac bronchus, tracheal diverticulum, pulmonary isomerism, and minor variations. The second one, dysmorphic lung, includes lung agenesis-hypoplasia complex and lobar agenesis-aplasia complex

  11. Tracheobronchial Branching Anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Min Ji; Kim, Young Tong; Jou, Sung Shick [Soonchunhyang University, Cheonan Hospital, Cheonan (Korea, Republic of); Park, A Young [Soonchunhyang University College of Medicine, Asan (Korea, Republic of)

    2010-04-15

    There are various congenital anomalies with respect to the number, length, diameter, and location of tracheobronchial branching patterns. The tracheobronchial anomalies are classified into two groups. The first one, anomalies of division, includes tracheal bronchus, cardiac bronchus, tracheal diverticulum, pulmonary isomerism, and minor variations. The second one, dysmorphic lung, includes lung agenesis-hypoplasia complex and lobar agenesis-aplasia complex

  12. Anomalies of nuclear criticality

    Energy Technology Data Exchange (ETDEWEB)

    Clayton, E.D.

    1979-06-01

    During the development of nuclear energy, a number of apparent anomalies have become evident in nuclear criticality. Some of these have appeared in the open literature and some have not. Yet, a naive extrapolation or application of existing data, without knowledge of the anomalies, could lead to potentially serious consequences. This report discusses several of these anomalies.

  13. Automated fast extraction of nitrated polycyclic aromatic hydrocarbons from soil by focused microwave-assisted Soxhlet extraction prior to gas chromatography--electron-capture detection.

    Science.gov (United States)

    Priego-Capote, F; Luque-García, J L; Luque de Castro, M D

    2003-04-25

    An approach for the automated fast extraction of nitrated polycyclic aromatic hydrocarbons (nitroPAHs) from soil, using a focused microwave-assisted Soxhlet extractor, is proposed. The main factors affecting the extraction efficiency (namely: irradiation power, irradiation time, number of cycles and extractant volume) were optimised by using experimental design methodology. The reduction of the nitro-PAHs to amino-PAHs and the derivatisation of the reduced analytes with heptafluorobutyric anhydride was mandatory prior to the separation-determination step by gas chromatography--electron-capture detection. The proposed approach has allowed the extraction of these pollutants from spiked and "real" contaminated soils with extraction efficiencies similar to those provided by the US Environmental Protection Agency methods 3540-8091, but with a drastic reduction in both the extraction time and sample handling, and using less organic solvent, as 75-85% of it was recycled.

  14. Peripheral leukocyte anomaly detected with routine automated hematology analyzer sensitive to adipose triglyceride lipase deficiency manifesting neutral lipid storage disease with myopathy/triglyceride deposit cardiomyovasculopathy

    Directory of Open Access Journals (Sweden)

    Akira Suzuki

    2014-01-01

    Full Text Available Adipose triglyceride lipase (ATGL deficiency manifesting neutral lipid storage disease with myopathy/triglyceride deposit cardiomyovasculopathy presents distinct fat-containing vacuoles known as Jordans' anomaly in peripheral leucocytes. To develop an automatic notification system for Jordans' anomaly in ATGL-deficient patients, we analyzed circulatory leukocyte scattergrams on automated hematology analyzer XE-5000. The BASO-WX and BASO-WY values were found to be significantly higher in patients than those in non-affected subjects. The two parameters measured by automated hematology analyzer may be expected to provide an important diagnostic clue for homozygous ATGL deficiency.

  15. A review on remotely sensed land surface temperature anomaly as an earthquake precursor

    Science.gov (United States)

    Bhardwaj, Anshuman; Singh, Shaktiman; Sam, Lydia; Joshi, P. K.; Bhardwaj, Akanksha; Martín-Torres, F. Javier; Kumar, Rajesh

    2017-12-01

    The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors.

  16. Combining Prostate Health Index density, magnetic resonance imaging and prior negative biopsy status to improve the detection of clinically significant prostate cancer.

    Science.gov (United States)

    Druskin, Sasha C; Tosoian, Jeffrey J; Young, Allen; Collica, Sarah; Srivastava, Arnav; Ghabili, Kamyar; Macura, Katarzyna J; Carter, H Ballentine; Partin, Alan W; Sokoll, Lori J; Ross, Ashley E; Pavlovich, Christian P

    2017-12-12

    To determine the performance of Prostate Health Index (PHI) density (PHID) combined with MRI and prior negative biopsy (PNB) status for the diagnosis of clinically significant prostate cancer (PCa). Patients without a prior diagnosis of PCa, with elevated prostate-specific antigen and a normal digital rectal examination who underwent PHI testing prospectively prior to prostate biopsy were included in this study. PHID was calculated retrospectively using prostate volume derived from transrectal ultrasonography at biopsy. Univariable and multivariable logistic regression modelling, along with receiver-operating characteristic (ROC) curve analysis, was used to determine the ability of serum biomarkers to predict clinically significant PCa (defined as either grade group [GG] ≥2 disease or GG1 PCa detected in >2 cores or >50% of any one core) on biopsy. Age, PNB status and Prostate Imaging Reporting and Data System (PI-RADS) score were incorporated into the regression models. Of the 241 men who qualified for the study, 91 (37.8%) had clinically significant PCa on biopsy. The median (interquartile range) PHID was 0.74 (0.44-1.24); it was 1.18 (0.77-1.83) and 0.55 (0.38-0.89) in those with and without clinically significant PCa on biopsy, respectively (P PI-RADS score was complementary to PHID, with a PI-RADS score ≥3 or, if PI-RADS score ≤2, a PHID ≥0.44, detecting 100% of clinically significant disease. For that subgroup, of the biomarkers tested, PHID (AUC 0.90) demonstrated the highest discriminative ability for clinically significant disease on multivariable logistic regression incorporating age, PNB status and PI-RADS score. In this contemporary cohort of men undergoing prostate biopsy for the diagnosis of PCa, PHID outperformed PHI and other PSA derivatives in the diagnosis of clinically significant cancer. Incorporating age, PNB status and PI-RADS score led to even further gains in the diagnostic performance of PHID. Furthermore, PI-RADS score was found to

  17. Generation of volatile copper species after in situ ionic liquid formation dispersive liquid-liquid microextraction prior to atomic absorption spectrometric detection.

    Science.gov (United States)

    Stanisz, Ewa; Zgoła-Grześkowiak, Agnieszka; Matusiewicz, Henryk

    2014-11-01

    The new procedure using in situ synthesis of ionic liquid extractant for dispersive liquid-liquid microextraction (in situ IL DLLME) combined with generation of volatile species prior to electrothermal atomic absorption spectrometry (ET AAS) for the determination of copper in soil samples was developed. Analytical signals were obtained without the back-extraction of copper from the IL phase prior to its determination. Under optimal conditions, the extraction in 10 mL of sample solution employing 8 μL of 1-hexyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide (HmimNTf2) (as the extraction solvent) was conducted. The ionic liquid served as two-task reagent: the efficient extractant and enhancement substance for generation step. The chemical generation of volatile species was performed by reduction of acidified copper solution (HCl 0.8 mol L(-1)) with NaBH4 (1.5%). Some essential parameters of the chemical generation such as NaBH4 and HCl concentrations, the kind and concentration of ionic liquid, carrier gas (Ar) flow rate, reaction and trapping time as well as pyrolysis and atomization temperatures were studied. For photogeneration the effect of the parameters such as the kind and concentration of low molecular weight organic acids and ionic liquid, carrier gas (Ar) flow rate, UV irradiation and ultrasonication time on the analytical signals were studied. The detection limit was found as 1.8 ng mL(-1) and the relative standard deviation (RSD) for seven replicate measurements of 100 µg mL(-1) in sample solution was 7%. The accuracy of the proposed method was evaluated by analysis of the certified reference materials. The measured copper contents in the reference materials were in satisfactory agreement with the certified values. The method was successfully applied to analysis of the soil and sediment samples. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies

    Science.gov (United States)

    Chemura, Abel; Mutanga, Onisimo; Dube, Timothy

    2017-05-01

    The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α ≤ 0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy = 80.9% and 68.1% respectively) and for LSWI (Overall accuracy = 68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and

  19. Congenital optic nerve anomalies.

    Science.gov (United States)

    Martín-Begué, N; Saint-Gerons, M

    2016-12-01

    To update the current knowledge about congenital optic disc anomalies. A comprehensive literature search was performed in the major biomedical databases. Patients with these anomalies usually have poor vision in infancy. Refractive errors are common, and serous retinal detachment may develop in some of these anomalies. It is critically important to clinically differentiate between these congenital optic disc anomalies, as central nervous system malformations are common in some, whereas others may be associated with systemic anomalies. Congenital optic disc anomalies are a heterogeneous group of pathologies with characteristic fundus appearance and systemic associations. We should always try to make a correct diagnosis, in order to ask for specific tests, as well as to provide an adequate follow-up. Copyright © 2016 Sociedad Española de Oftalmología. Publicado por Elsevier España, S.L.U. All rights reserved.

  20. Anomaly-free models for flavour anomalies

    Science.gov (United States)

    Ellis, John; Fairbairn, Malcolm; Tunney, Patrick

    2018-03-01

    We explore the constraints imposed by the cancellation of triangle anomalies on models in which the flavour anomalies reported by LHCb and other experiments are due to an extra U(1)^' gauge boson Z^' . We assume universal and rational U(1)^' charges for the first two generations of left-handed quarks and of right-handed up-type quarks but allow different charges for their third-generation counterparts. If the right-handed charges vanish, cancellation of the triangle anomalies requires all the quark U(1)^' charges to vanish, if there are either no exotic fermions or there is only one Standard Model singlet dark matter (DM) fermion. There are non-trivial anomaly-free models with more than one such `dark' fermion, or with a single DM fermion if right-handed up-type quarks have non-zero U(1)^' charges. In some of the latter models the U(1)^' couplings of the first- and second-generation quarks all vanish, weakening the LHC Z^' constraint, and in some other models the DM particle has purely axial couplings, weakening the direct DM scattering constraint. We also consider models in which anomalies are cancelled via extra vector-like leptons, showing how the prospective LHC Z^' constraint may be weakened because the Z^' → μ ^+ μ ^- branching ratio is suppressed relative to other decay modes.

  1. Cryo-electron microscopy and single molecule fluorescent microscopy detect CD4 receptor induced HIV size expansion prior to cell entry

    Energy Technology Data Exchange (ETDEWEB)

    Pham, Son [Deakin University, Victoria 3216 (Australia); CSIRO Australian Animal Health Laboratory, Victoria 3220 (Australia); Tabarin, Thibault [ARC Centre of Excellence in Advanced Molecular Imaging, University of New South Wales, New South Wales 3220 (Australia); Garvey, Megan; Pade, Corinna [Deakin University, Victoria 3216 (Australia); CSIRO Australian Animal Health Laboratory, Victoria 3220 (Australia); Rossy, Jérémie [ARC Centre of Excellence in Advanced Molecular Imaging, University of New South Wales, New South Wales 3220 (Australia); Monaghan, Paul; Hyatt, Alex [CSIRO Australian Animal Health Laboratory, Victoria 3220 (Australia); Böcking, Till [ARC Centre of Excellence in Advanced Molecular Imaging, University of New South Wales, New South Wales 3220 (Australia); Leis, Andrew [CSIRO Australian Animal Health Laboratory, Victoria 3220 (Australia); Gaus, Katharina, E-mail: k.gaus@unsw.edu.au [ARC Centre of Excellence in Advanced Molecular Imaging, University of New South Wales, New South Wales 3220 (Australia); Mak, Johnson, E-mail: j.mak@deakin.edu.au [Deakin University, Victoria 3216 (Australia); CSIRO Australian Animal Health Laboratory, Victoria 3220 (Australia)

    2015-12-15

    Viruses are often thought to have static structure, and they only remodel after the viruses have entered target cells. Here, we detected a size expansion of virus particles prior to viral entry using cryo-electron microscopy (cryo-EM) and single molecule fluorescence imaging. HIV expanded both under cell-free conditions with soluble receptor CD4 (sCD4) targeting the CD4 binding site on the HIV-1 envelope protein (Env) and when HIV binds to receptor on cellular membrane. We have shown that the HIV Env is needed to facilitate receptor induced virus size expansions, showing that the ‘lynchpin’ for size expansion is highly specific. We demonstrate that the size expansion required maturation of HIV and an internal capsid core with wild type stability, suggesting that different HIV compartments are linked and are involved in remodelling. Our work reveals a previously unknown event in HIV entry, and we propose that this pre-entry priming process enables HIV particles to facilitate the subsequent steps in infection. - Highlights: • Cell free viruses are able to receive external trigger that leads to apparent size expansion. • Virus envelope and CD4 receptor engagement is the lynchpin of virus size expansion. • Internal capsid organisation can influence receptor mediated virus size expansion. • Pre-existing virus-associated lipid membrane in cell free virus can accommodate the receptor mediated virus size expansion.

  2. Cryo-electron microscopy and single molecule fluorescent microscopy detect CD4 receptor induced HIV size expansion prior to cell entry

    International Nuclear Information System (INIS)

    Pham, Son; Tabarin, Thibault; Garvey, Megan; Pade, Corinna; Rossy, Jérémie; Monaghan, Paul; Hyatt, Alex; Böcking, Till; Leis, Andrew; Gaus, Katharina; Mak, Johnson

    2015-01-01

    Viruses are often thought to have static structure, and they only remodel after the viruses have entered target cells. Here, we detected a size expansion of virus particles prior to viral entry using cryo-electron microscopy (cryo-EM) and single molecule fluorescence imaging. HIV expanded both under cell-free conditions with soluble receptor CD4 (sCD4) targeting the CD4 binding site on the HIV-1 envelope protein (Env) and when HIV binds to receptor on cellular membrane. We have shown that the HIV Env is needed to facilitate receptor induced virus size expansions, showing that the ‘lynchpin’ for size expansion is highly specific. We demonstrate that the size expansion required maturation of HIV and an internal capsid core with wild type stability, suggesting that different HIV compartments are linked and are involved in remodelling. Our work reveals a previously unknown event in HIV entry, and we propose that this pre-entry priming process enables HIV particles to facilitate the subsequent steps in infection. - Highlights: • Cell free viruses are able to receive external trigger that leads to apparent size expansion. • Virus envelope and CD4 receptor engagement is the lynchpin of virus size expansion. • Internal capsid organisation can influence receptor mediated virus size expansion. • Pre-existing virus-associated lipid membrane in cell free virus can accommodate the receptor mediated virus size expansion.

  3. CONGENITAL ANOMALIES OF THE KIDNEYS AND URINARY TRACT IN CHILDREN

    Directory of Open Access Journals (Sweden)

    Matjaž Kopač

    2015-03-01

    Full Text Available Congenital anomalies of the kidney and urinary tract are the commonest congenital anomalies in children, often detected prenatally with ultrasound. This method is useful for assesing the degree of dilatation of the collecting system, structure of the kidney parenchyma, amount of amniotic fluid and urinary bladder. Hydronephrosis is the most common among them. Anomalies can be bilateral or unilateral and different defects may coexist in an individual child. Anomalies of other organs and organ systems are often associated with anomalies of the kidneys and urinary tract, described in numerous syndromes. Congenital anomalies of the kidney and urinary tract can be divided in anomalies of the renal parencyma development, renal embryonic migration and position, cystic kidney diseases and anomalies of the urinary tract (collecting system of the kidneys, ureters, urinary bladder and urethra. They are the commonest cause of end-stage renal disease in children.

  4. An exceptional combination of congenital coronary anomalies.

    Science.gov (United States)

    Kharrat, Ilyes; El-Fassy, Eric; Amabile, Nicolas

    2012-01-01

    We present a case of congenital coronary artery anomalies combining the absence of the circumflex artery, ectopic origins of left anterior descending and diagonal arteries and abnormal courses of these vessels. These rare anomalies were detected during an elective coronary angiography in a patient with stable angina that was related to significant stenosis of the posterolateral and middle right coronary artery. A computed tomography scanner with three-dimensional reconstructions confirmed the anatomy. Copyright © 2011 Wiley Periodicals, Inc.

  5. Dental Anomalies: An Update

    Directory of Open Access Journals (Sweden)

    Fatemeh Jahanimoghadam

    2016-01-01

    Full Text Available Dental anomalies are usual congenital malformation that can happen either as isolated findings or as a part of a syndrome. Developmental anomalies influencing the morphology exists in both deciduous and permanent dentition and shows different forms such as gemination, fusion, concrescence, dilaceration, dens evaginatus (DE, enamel pearls, taurodontism or peg-shaped laterals. All These anomalies have clinical significance concerning aesthetics, malocclusion and more necessary preparing of the development of dental decays and oral diseases. Through a search in PubMed, Google, Scopus and Medline, a total of eighty original research papers during 1928-2016 were found with the keywords such as dental anomaly, syndrome, tooth and hypodontia. One hundred review titles were identified, eighty reviews were retrieved that were finally included as being relevant and of sufficient quality. In this review, dental anomalies including gemination, fusion, concrescence, dilaceration, dens invaginatus, DE, taurodontism, enamel pearls, fluorosis, peg-shaped laterals, dentinal dysplasia, regional odontodysplasia and hypodontia are discussed. Diagnosing dental abnormality needs a thorough evaluation of the patient, involving a medical, dental, familial and clinical history. Clinical examination and radiographic evaluation and in some of the cases, specific laboratory tests are also needed. Developmental dental anomalies require careful examination and treatment planning. Where one anomaly is present, clinicians should suspect that other anomalies may also be present. Moreover, careful clinical and radiographical examination is required. Furthermore, more complex cases need multidisciplinary planning and treatment.

  6. Detecting Ecosystem Performance Anomalies for Land Management in the Upper Colorado River Basin Using Satellite Observations, Climate Data, and Ecosystem Models

    Directory of Open Access Journals (Sweden)

    Bruce K. Wylie

    2010-07-01

    Full Text Available This study identifies areas with ecosystem performance anomalies (EPA within the Upper Colorado River Basin (UCRB during 2005–2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions at the 90% level of confidence. The EPA maps were validated using “percentage of bare soil” ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming tended to have a higher (lower percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005–2007 persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions.

  7. Congenital basis of posterior fossa anomalies

    Science.gov (United States)

    Cotes, Claudia; Bonfante, Eliana; Lazor, Jillian; Jadhav, Siddharth; Caldas, Maria; Swischuk, Leonard

    2015-01-01

    The classification of posterior fossa congenital anomalies has been a controversial topic. Advances in genetics and imaging have allowed a better understanding of the embryologic development of these abnormalities. A new classification schema correlates the embryologic, morphologic, and genetic bases of these anomalies in order to better distinguish and describe them. Although they provide a better understanding of the clinical aspects and genetics of these disorders, it is crucial for the radiologist to be able to diagnose the congenital posterior fossa anomalies based on their morphology, since neuroimaging is usually the initial step when these disorders are suspected. We divide the most common posterior fossa congenital anomalies into two groups: 1) hindbrain malformations, including diseases with cerebellar or vermian agenesis, aplasia or hypoplasia and cystic posterior fossa anomalies; and 2) cranial vault malformations. In addition, we will review the embryologic development of the posterior fossa and, from the perspective of embryonic development, will describe the imaging appearance of congenital posterior fossa anomalies. Knowledge of the developmental bases of these malformations facilitates detection of the morphological changes identified on imaging, allowing accurate differentiation and diagnosis of congenital posterior fossa anomalies. PMID:26246090

  8. Anomalies on orbifolds

    Energy Technology Data Exchange (ETDEWEB)

    Arkani-Hamed, Nima; Cohen, Andrew G.; Georgi, Howard

    2001-03-16

    We discuss the form of the chiral anomaly on an S1/Z2 orbifold with chiral boundary conditions. We find that the 4-divergence of the higher-dimensional current evaluated at a given point in the extra dimension is proportional to the probability of finding the chiral zero mode there. Nevertheless the anomaly, appropriately defined as the five dimensional divergence of the current, lives entirely on the orbifold fixed planes and is independent of the shape of the zero mode. Therefore long distance four dimensional anomaly cancellation ensures the consistency of the higher dimensional orbifold theory.

  9. Prior data for non-normal priors.

    Science.gov (United States)

    Greenland, Sander

    2007-08-30

    Data augmentation priors facilitate contextual evaluation of prior distributions and the generation of Bayesian outputs from frequentist software. Previous papers have presented approximate Bayesian methods using 2x2 tables of 'prior data' to represent lognormal relative-risk priors in stratified and regression analyses. The present paper describes extensions that use the tables to represent generalized-F prior distributions for relative risks, which subsume lognormal priors as a limiting case. The method provides a means to increase tail-weight or skew the prior distribution for the log relative risk away from normality, while retaining the simple 2x2 table form of the prior data. When prior normality is preferred, it also provides a more accurate lognormal relative-risk prior in for the 2x2 table format. For more compact representation in regression analyses, the prior data can be compressed into a single data record. The method is illustrated with historical data from a study of electronic foetal monitoring and neonatal death.

  10. Skyrmions and anomalies

    International Nuclear Information System (INIS)

    Rho, M.

    1987-02-01

    The author summarizes the works presented at the meeting on skyrmions and anomalies. He divides the principal issues of this workshop into five categories: QCD effective lagrangians, chiral bags and the Cheshire cat principle, strangeness problem, phenomenology, mathematical structure

  11. Ionic liquid-based zinc oxide nanofluid for vortex assisted liquid liquid microextraction of inorganic mercury in environmental waters prior to cold vapor atomic fluorescence spectroscopic detection.

    Science.gov (United States)

    Amde, Meseret; Liu, Jing-Fu; Tan, Zhi-Qiang; Bekana, Deribachew

    2016-01-01

    Zinc oxide nanofluid (ZnO-NF) based vortex assisted liquid liquid microextraction (ZnO-NF VA-LLME) was developed and employed in extraction of inorganic mercury (Hg(2+)) in environmental water samples, followed by cold vapor atomic fluorescence spectrometry (CV-AFS). Unlike other dispersive liquid liquid microextraction techniques, ZnO-NF VA-LLME is free of volatile organic solvents and dispersive solvent consumption. Analytical signals were obtained without back-extraction from the ZnO-NF phase prior to CV-AFS determination. Some essential parameters of the ZnO-NF VA-LLME and cold vapor generation such as composition and volume of the nanofluid, vortexing time, pH of the sample solution, amount of the chelating agent, ionic strength and matrix interferences have been studied. Under optimal conditions, efficient extraction of 1ng/mL of Hg(2+) in 10mL of sample solution was achieved using 50μL of ZnO-NF. The enrichment factor before dilution, detection limits and limits of quantification of the method were about 190, 0.019 and 0.064ng/mL, respectively. The intra and inter days relative standard deviations (n=8) were found to be 4.6% and 7.8%, respectively, at 1ng/mL spiking level. The accuracy of the current method was also evaluated by the analysis of certified reference materials, and the measured Hg(2+) concentration of GBW08603 (9.6ng/mL) and GBW(E)080392 (8.9ng/mL) agreed well with their certified value (10ng/mL). The method was applied to the analysis of Hg(2+) in effluent, influent, lake and river water samples, with recoveries in the range of 79.8-92.8% and 83.6-106.1% at 1ng/mL and 5ng/mL spiking levels, respectively. Overall, ZnO-NF VA-LLME is fast, simple, cost-effective and environmentally friendly and it can be employed for efficient enrichment of the analyte from various water samples. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Congenital laryngeal anomalies,

    OpenAIRE

    Rutter, Michael J.

    2014-01-01

    Introduction: It is essential for clinicians to understand issues relevant to the airway management of infants and to be cognizant of the fact that infants with congenital laryngeal anomalies are at particular risk for an unstable airway. Objectives: To familiarize clinicians with issues relevant to the airway management of infants and to present a succinct description of the diagnosis and management of an array of congenital laryngeal anomalies. Methods: Revision article, in which the ma...

  13. Fivebrane gravitational anomalies

    International Nuclear Information System (INIS)

    Becker, Katrin; Becker, Melanie

    2000-01-01

    Freed, Harvey, Minasian and Moore (FHMM) have proposed a mechanism to cancel the gravitational anomaly of the M-theory fivebrane coming from diffeomorphisms acting on the normal bundle. This procedure is based on a modification of the conventional M-theory Chern-Simons term. We apply the FHMM mechanism in the ten-dimensional type IIA theory. We then analyze the relation to the anomaly cancellation mechanism for the type IIA fivebrane proposed by Witten

  14. The Holographic Weyl anomaly

    CERN Document Server

    Henningson, M; Henningson, Mans; Skenderis, Kostas

    1998-01-01

    We calculate the Weyl anomaly for conformal field theories that can be described via the adS/CFT correspondence. This entails regularizing the gravitational part of the corresponding supergravity action in a manner consistent with general covariance. Up to a constant, the anomaly only depends on the dimension d of the manifold on which the conformal field theory is defined. We present concrete expressions for the anomaly in the physically relevant cases d = 2, 4 and 6. In d = 2 we find for the central charge c = 3 l/ 2 G_N in agreement with considerations based on the asymptotic symmetry algebra of adS_3. In d = 4 the anomaly agrees precisely with that of the corresponding N = 4 superconformal SU(N) gauge theory. The result in d = 6 provides new information for the (0, 2) theory, since its Weyl anomaly has not been computed previously. The anomaly in this case grows as N^3, where N is the number of coincident M5 branes, and it vanishes for a Ricci-flat background.

  15. Radioactive anomaly discrimination from spectral ratios

    Science.gov (United States)

    Maniscalco, James; Sjoden, Glenn; Chapman, Mac Clements

    2013-08-20

    A method for discriminating a radioactive anomaly from naturally occurring radioactive materials includes detecting a first number of gamma photons having energies in a first range of energy values within a predetermined period of time and detecting a second number of gamma photons having energies in a second range of energy values within the predetermined period of time. The method further includes determining, in a controller, a ratio of the first number of gamma photons having energies in the first range and the second number of gamma photons having energies in the second range, and determining that a radioactive anomaly is present when the ratio exceeds a threshold value.

  16. FOETAL ULTRASOUND - NEUROECTODERMAL ANOMALIES IN RURAL PREGNANT WOMEN

    Directory of Open Access Journals (Sweden)

    Mala Venkata

    2016-06-01

    Full Text Available BACKGROUND A prospective clinical study to know the various types of congenital Neuroectodermal Anomalies on obstetric Ultrasound, in rural pregnant women. To reduce the maternal morbidity and mortality by early detection of these Congenital Neuroectodermal Anomalies. To calculate the incidence and prevalence of different types of Congenital Neuroectodermal Anomalies, in these rural pregnant women. To assist the obstetrician in taking decisions regarding the termination or continuation of the pregnancy in relation to the type of malformation and its prognosis. METHODS A prospective clinical study of Congenital Neuroectodermal Anomalies in 22,000 rural pregnant women coming to the Santhiram Medical College, Radiology Department for a routine obstetric scan. 44 cases of neuroectodermal anomalies were detected out of the 22000 cases, within an incidence of 2 per 1000 cases. Approximately 1 in every 500 cases showed an anomaly. RESULTS The most common lesions detected were hydrocephalus, and spina bifida followed by anencephaly. Association of these lesions with consanguinity, previous history of similar anomaly and intake of iron and folic acid tablets was noted. CONCLUSION Ultrasound is an excellent modality for the diagnosis and characterisation of the neuroectodermal anomalies. Its multiplanar imaging property along with real time image visualisation make it an excellent tool for the diagnosis and characterisation of these anomalies

  17. Mixed hemimicelles solid-phase extraction based on sodium dodecyl sulfate-coated nano-magnets for selective adsorption and enrichment of illegal cationic dyes in food matrices prior to high-performance liquid chromatography-diode array detection detection.

    Science.gov (United States)

    Qi, Ping; Liang, Zhi-An; Wang, Yu; Xiao, Jian; Liu, Jia; Zhou, Qing-Qiong; Zheng, Chun-Hao; Luo, Li-Ni; Lin, Zi-Hao; Zhu, Fang; Zhang, Xue-Wu

    2016-03-11

    In this study, mixed hemimicelles solid-phase extraction (MHSPE) based on sodium dodecyl sulfate (SDS) coated nano-magnets Fe3O4 was investigated as a novel method for the extraction and separation of four banned cationic dyes, Auramine O, Rhodamine B, Basic orange 21 and Basic orange 22, in condiments prior to HPLC detection. The main factors affecting the extraction of analysts, such as pH, surfactant and adsorbent concentrations and zeta potential were studied and optimized. Under optimized conditions, the proposed method was successful applied for the analysis of banned cationic dyes in food samples such as chili sauce, soybean paste and tomato sauce. Validation data showed the good recoveries in the range of 70.1-104.5%, with relative standard deviations less than 15%. The method limits of determination/quantification were in the range of 0.2-0.9 and 0.7-3μgkg(-1), respectively. The selective adsorption and enrichment of cationic dyes were achieved by the synergistic effects of hydrophobic interactions and electrostatic attraction between mixed hemimicelles and the cationic dyes, which also resulted in the removal of natural pigments interferences from sample extracts. When applied to real samples, RB was detected in several positive samples (chili powders) within the range from 0.042 to 0.177mgkg(-1). These results indicate that magnetic MHSPE is an efficient and selective sample preparation technique for the extraction of banned cationic dyes in a complex matrix. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Anomaly observed in the Pamir experiment

    International Nuclear Information System (INIS)

    Kempa, Ya.; Malinovski, Ya.

    2001-01-01

    Emulsion cloud chambers used in the Pamir experiment during many years differed both in area and in design. It enabled to analyze spatial nad angular parameters of the recorded particles in chambers of different designs. Paper contains the detected anomalies and their preliminary interpretation [ru

  19. Applying rule-base anomalies to KADS inference structures

    NARCIS (Netherlands)

    Van Harmelen, Frank

    1997-01-01

    The literature on validation and verification of knowledge-based systems contains a catalogue of anomalies for knowledge-based systems, such as redundant, contradictory or deficient knowledge. Detecting such anomalies is a method for verifying knowledge-based systems. Unfortunately, the traditional

  20. Radon anomalies in ground water before earthquakes in Tokyo

    International Nuclear Information System (INIS)

    Saito, Masaaki

    1992-01-01

    Radon contents in ground waters in Tokyo have been measured since 1976. The correlation between earthquake and radon anomaly will be evaluated easily, when both earthquakes and radon anomalies are a few. In addition, the high reliability of the correlation will be obtained, if an earthquake and an anomaly occur at almost same time. The six earthquakes occurred in 1976∼1990 were chosen based on the magnitude (≥6.0) and the epicentral distance (<100 km). Radon anomalies shortly before the six earthquakes were investigated at the stations where few anomalies have been detected. Anomalies which can be considered to relate with earthquakes appeared near around the dates of the Ibaraki-Chiba (1985) and the Yamanashi-Kanagawa (1983) earthquakes. The anomalies appeared in 6 d before ∼4 d after the earthquakes, and no other anomalies had appeared in over 600 d before the earthquakes. Then it is presumed that these anomalies would be earthquake precursors. The anomalies were found at the stations distributed in 50∼70 km epicentral distances and on the compress quadrants of the earthquake mechanism. (author)

  1. Low Risk Anomalies?

    DEFF Research Database (Denmark)

    Schneider, Paul; Wagner, Christian; Zechner, Josef

    This paper shows that stocks' CAPM alphas are negatively related to CAPM betas if investors demand compensation for negative skewness. Thus, high (low) beta stocks appear to underperform (outperform). This apparent anomaly merely reflects compensation for residual coskewness ignored by the CAPM...

  2. Venus - Ishtar gravity anomaly

    Science.gov (United States)

    Sjogren, W. L.; Bills, B. G.; Mottinger, N. A.

    1984-01-01

    The gravity anomaly associated with Ishtar Terra on Venus is characterized, comparing line-of-sight acceleration profiles derived by differentiating Pioneer Venus Orbiter Doppler residual profiles with an Airy-compensated topographic model. The results are presented in graphs and maps, confirming the preliminary findings of Phillips et al. (1979). The isostatic compensation depth is found to be 150 + or - 30 km.

  3. Bolivian Bouguer Anomaly Grid

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A 1 kilometer Bouguer anomaly grid for the country of Bolivia.Number of columns is 550 and number of rows is 900. The order of the data is from the lower left to the...

  4. Anomaly Busters II

    International Nuclear Information System (INIS)

    Anon.

    1985-01-01

    The anomaly busters had struck on the first day of the Kyoto meeting with Yoji Totsuka of Tokyo speaking on baryon number nonjjonservation and 'related topics'. The unstable proton is a vital test of grand unified pictures pulling together the electroweak and quark/gluon forces in a single field theory

  5. The reactor antineutrino anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Haser, Julia; Buck, Christian; Lindner, Manfred [Max-Planck-Institut fuer Kernphysik, Heidelberg (Germany)

    2016-07-01

    Major discoveries were made in the past few years in the field of neutrino flavour oscillation. Nuclear reactors produce a clean and intense flux of electron antineutrinos and are thus an essential neutrino source for the determination of oscillation parameters. Most currently the reactor antineutrino experiments Double Chooz, Daya Bay and RENO have accomplished to measure θ{sub 13}, the smallest of the three-flavour mixing angles. In the course of these experiments two anomalies emerged: (1) the reanalysis of the reactor predictions revealed a deficit in experimentally observed antineutrino flux, known as the ''reactor antineutrino anomaly''. (2) The high precision of the latest generation of neutrino experiments resolved a spectral shape distortion relative to the expected energy spectra. Both puzzles are yet to be solved and triggered new experimental as well as theoretical studies, with the search for light sterile neutrinos as most popular explanation for the flux anomaly. This talk outlines the two reactor antineutrino anomalies. Discussing possible explanations for their occurrence, recent and upcoming efforts to solve the reactor puzzles are highlighted.

  6. Echocardiography in Ebstein's anomaly

    NARCIS (Netherlands)

    W.J. Gussenhoven (Wilhelmina Johanna)

    1984-01-01

    textabstractIn this thesis the value of echocardiography is evaluated for the diagnosis of Ebstein's anomaly of the tricuspid valve. This congenital heart defect, first described in 1866 by Wilhelm Ebstein, is characterized by an apical displacement of the septal and inferior tricuspid valve

  7. Dealing with Ebstein's anomaly

    NARCIS (Netherlands)

    Geerdink, L.M.; Kapusta, L.

    2014-01-01

    Ebstein's anomaly is a complex congenital disorder of the tricuspid valve. Presentation in neonatal life and (early) childhood is common. Disease severity and clinical features vary widely and require a patient-tailored treatment. In this review, we describe the natural history of children and

  8. Assessing Asset Pricing Anomalies

    NARCIS (Netherlands)

    W.A. de Groot (Wilma)

    2017-01-01

    markdownabstractOne of the most important challenges in the field of asset pricing is to understand anomalies: empirical patterns in asset returns that cannot be explained by standard asset pricing models. Currently, there is no consensus in the academic literature on the underlying causes of

  9. Algebra of anomalies

    International Nuclear Information System (INIS)

    Talon, M.

    1987-01-01

    The algebraic set up for anomalies, a la Stora, is reviewed. Then a brief account is provided of the work of M. Dubois Violette, M. Talon, C. Viallet, in which the general algebraic solution to the consistency conditions is described. 34 references

  10. Major congenital anomalies in babies born with Down syndrome

    DEFF Research Database (Denmark)

    Morris, Joan K; Garne, Ester; Wellesley, Diana

    2014-01-01

    Previous studies have shown that over 40% of babies with Down syndrome have a major cardiac anomaly and are more likely to have other major congenital anomalies. Since 2000, many countries in Europe have introduced national antenatal screening programs for Down syndrome. This study aimed...... to determine if the introduction of these screening programs and the subsequent termination of prenatally detected pregnancies were associated with any decline in the prevalence of additional anomalies in babies born with Down syndrome. The study sample consisted of 7,044 live births and fetal deaths with Down...... syndrome registered in 28 European population-based congenital anomaly registries covering seven million births during 2000-2010. Overall, 43.6% (95% CI: 42.4-44.7%) of births with Down syndrome had a cardiac anomaly and 15.0% (14.2-15.8%) had a non-cardiac anomaly. Female babies with Down syndrome were...

  11. EUROmediCAT signal detection

    DEFF Research Database (Denmark)

    Given, Joanne E; Loane, Maria; Luteijn, Johannes Michiel

    2016-01-01

    AIMS: To evaluate congenital anomaly (CA)-medication exposure associations produced by the new EUROmediCAT signal detection system and determine which require further investigation. METHODS: Data from 15 EUROCAT registries (1995-2011) with medication exposures at the chemical substance (5th level...... persisted after data validation, a literature review was conducted for prior evidence of human teratogenicity. RESULTS: Thirteen out of 27 CA-medication exposure signals, based on 389 exposed cases, passed data validation. There was some prior evidence in the literature to support six signals (gastroschisis...

  12. Diagnostic value of biparametric magnetic resonance imaging (MRI) as an adjunct to prostate-specific antigen (PSA)-based detection of prostate cancer in men without prior biopsies.

    Science.gov (United States)

    Rais-Bahrami, Soroush; Siddiqui, M Minhaj; Vourganti, Srinivas; Turkbey, Baris; Rastinehad, Ardeshir R; Stamatakis, Lambros; Truong, Hong; Walton-Diaz, Annerleim; Hoang, Anthony N; Nix, Jeffrey W; Merino, Maria J; Wood, Bradford J; Simon, Richard M; Choyke, Peter L; Pinto, Peter A

    2015-03-01

    To determine the diagnostic yield of analysing biparametric (T2- and diffusion-weighted) magnetic resonance imaging (B-MRI) for prostate cancer detection compared with standard digital rectal examination (DRE) and prostate-specific antigen (PSA)-based screening. Review of patients who were enrolled in a trial to undergo multiparametric-prostate (MP)-MRI and MR/ultrasound fusion-guided prostate biopsy at our institution identified 143 men who underwent MP-MRI in addition to standard DRE and PSA-based prostate cancer screening before any prostate biopsy. Patient demographics, DRE staging, PSA level, PSA density (PSAD), and B-MRI findings were assessed for association with prostate cancer detection on biopsy. Men with detected prostate cancer tended to be older, with a higher PSA level, higher PSAD, and more screen-positive lesions (SPL) on B-MRI. B-MRI performed well for the detection of prostate cancer with an area under the curve (AUC) of 0.80 (compared with 0.66 and 0.74 for PSA level and PSAD, respectively). We derived combined PSA and MRI-based formulas for detection of prostate cancer with optimised thresholds. (i) for PSA and B-MRI: PSA level + 6 x (the number of SPL) > 14 and (ii) for PSAD and B-MRI: 14 × (PSAD) + (the number of SPL) >4.25. AUC for equations 1 and 2 were 0.83 and 0.87 and overall accuracy of prostate cancer detection was 79% in both models. The number of lesions positive on B-MRI outperforms PSA alone in detection of prostate cancer. Furthermore, this imaging criteria coupled as an adjunct with PSA level and PSAD, provides even more accuracy in detecting clinically significant prostate cancer. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

  13. INVESTIGATION OF NEURAL NETWORK ALGORITHM FOR DETECTION OF NETWORK HOST ANOMALIES IN THE AUTOMATED SEARCH FOR XSS VULNERABILITIES AND SQL INJECTIONS

    Directory of Open Access Journals (Sweden)

    Y. D. Shabalin

    2016-03-01

    Full Text Available A problem of aberrant behavior detection for network communicating computer is discussed. A novel approach based on dynamic response of computer is introduced. The computer is suggested as a multiple-input multiple-output (MIMO plant. To characterize dynamic response of the computer on incoming requests a correlation between input data rate and observed output response (outgoing data rate and performance metrics is used. To distinguish normal and aberrant behavior of the computer one-class neural network classifieris used. General idea of the algorithm is shortly described. Configuration of network testbed for experiments with real attacks and their detection is presented (the automated search for XSS and SQL injections. Real found-XSS and SQL injection attack software was used to model the intrusion scenario. It would be expectable that aberrant behavior of the server will reveal itself by some instantaneous correlation response which will be significantly different from any of normal ones. It is evident that correlation picture of attacks from different malware running, the site homepage overriding on the server (so called defacing, hardware and software failures will differ from correlation picture of normal functioning. Intrusion detection algorithm is investigated to estimate false positive and false negative rates in relation to algorithm parameters. The importance of correlation width value and threshold value selection was emphasized. False positive rate was estimated along the time series of experimental data. Some ideas about enhancement of the algorithm quality and robustness were mentioned.

  14. Genetics Home Reference: Peters anomaly

    Science.gov (United States)

    ... navigation Home Page Search Home Health Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Share: Email Facebook Twitter Home Health Conditions Peters anomaly Peters anomaly Printable PDF Open All Close All ...

  15. Survey of prenatal screening policies in Europe for structural malformations and chromosome anomalies, and their impact on detection and termination rates for neural tube defects and Down's syndrome

    DEFF Research Database (Denmark)

    Boyd, P A; Devigan, C; Khoshnood, B

    2008-01-01

    tube defects (NTDs) using the EUROCAT database. MAIN OUTCOME MEASURES: Existence of national prenatal screening policies, legal gestation limit for TOPFA, prenatal detection and termination rates for Down's syndrome and NTD. RESULTS: Ten of the 18 countries had a national country-wide policy for Down...... cases. Six of the 18 countries had a legal gestational age limit for TOPFA, and in two countries, termination of pregnancy was illegal at any gestation. CONCLUSIONS: There are large differences in screening policies between countries in Europe. These, as well as organisational and cultural factors...

  16. Accuracy of multiparametric MR imaging with PI-RADS V2 assessment in detecting infiltration of the neurovascular bundles prior to prostatectomy.

    Science.gov (United States)

    Sauer, Markus; Weinrich, Julius M; Fraune, Christoph; Salomon, Georg; Tennstedt, Pierre; Adam, Gerhard; Beyersdorff, Dirk

    2018-01-01

    To evaluate the accuracy of assessment of neurovascular bundle (NVB) infiltration using multiparametric magnetic resonance imaging (mpMRI) and PI-RADS V2 prior to prostatectomy. The ethics committee approved this retrospective study with waiver of informed consent. N=198 consecutive patients with biopsy proved cancer underwent standardized mpMRI at 3T prior to surgery. NVB infiltration was assessed for each side (a total of 396). Maximum PI-RADS V2 scores were determined for the posterolateral areas adjacent to the NVBs. Imaging results were correlated with postoperative pathology and standard descriptive statistics were calculated. Overall T-staging sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of mpMRI were 64.4%, 89.2%, 82.4%, 76.2% and 78.3%, respectively. In 396 cases NVB infiltration was predicted with 75.3%, 94.0%, 80.2%, 92.1 % and 89.4 % sensitivity, specificity, PPV, NPV and accuracy, respectively. Analyses of 396 NVB and their adjacent PI-RADS V2 scores with pathology revealed significantly more NVB-infiltrations in suspect scores of 5 and 4 vs. uncertain scores of 3-1 (81/264 vs. 16/132, p=0.0001). Considering scores higher than 3 as a criterion of infiltration demonstrated moderate sensitivity and poor specificity (83.5% and 38.8%, respectively). Interobserver agreement of a second reading of a random sample was good (κ=0.64) for NVB infiltrations and moderate (κ=0.59) for PI-RADS V2. Assessment of infiltration of the neurovascular bundles using mpMRI has valuable diagnostic performance, yet PI-RADS V2 Scores demonstrate limited eligibility. Combined findings offer crucial information for the planning of prostatectomy. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Is there a one-to-one correspondence between ionospheric anomalies and large earthquakes along Longmenshan faults?

    Directory of Open Access Journals (Sweden)

    L. M. He

    2014-02-01

    Full Text Available On 12 May 2008, a destructive M8.0 earthquake struck Wenchuan County (31.0° N, 103.4° E in the Longmenshan fault zone of southwestern China. Five years later, on 20 April 2013, another terrible M7.0 earthquake struck Lushan County (30.3° N, 103.0° E in the same fault area, only 87 km away from the epicenter of the Wenchuan earthquake. In this paper, an integrated wavelet analysis methodology is proposed to detect and diagnose ionospheric total electron content (TEC anomalies related to seismic activities. Analytic wavelet transform is used to detect ionospheric perturbations, and then cross-wavelet analysis is used to diagnose ionospheric anomalies by gaining further insights into the dynamic relationship between the anomaly variability of ionospheric TEC and geomagnetic indices for the same set of observations. The results show that a significant ionospheric disturbance occurred on 9 May 2008 above the forthcoming epicenter, 3 days prior to the Wenchuan earthquake. However, we did not observe an ionospheric anomaly over the epicenter of the Ya'an earthquake during the 1 month period before the shock. Finally, we discuss the possible interpretations of the different seismo-ionospheric effects for the two similar earthquakes.

  18. Detection of a Hobi-like virus in archival samples suggests circulation of this emerging pestivirus species in Europe prior to 2007

    Science.gov (United States)

    The first reported incidence of Hobi-like viruses in Europe dates to a 2010 outbreak of respiratory disease in cattle in Italy. In this study, a Hobi-like virus was detected in archival samples, collected in 2007 in Italy from a cattle herd displaying respiratory disease, during the validation of a...

  19. The radon anomaly of Porcheresse (Ardennes, Belgium). A case study

    International Nuclear Information System (INIS)

    Charlet, J.M.; Zhu, H.C.; Poffijn, A.

    1999-01-01

    From a very high radon concentration in a dwelling of the village of Porcheresse (Belgium), the paper discusses on of the significance of the numerous radon indoor anomalies detected in the Southern part of Belgium

  20. Penile Anomalies in Adolescence

    Directory of Open Access Journals (Sweden)

    Dan Wood

    2011-01-01

    Full Text Available This article considers the impact and outcomes of both treatment and underlying condition of penile anomalies in adolescent males. Major congenital anomalies (such as exstrophy/epispadias are discussed, including the psychological outcomes, common problems (such as corporal asymmetry, chordee, and scarring in this group, and surgical assessment for potential surgical candidates. The emergence of new surgical techniques continues to improve outcomes and potentially raises patient expectations. The importance of balanced discussion in conditions such as micropenis, including multidisciplinary support for patients, is important in order to achieve appropriate treatment decisions. Topical treatments may be of value, but in extreme cases, phalloplasty is a valuable option for patients to consider. In buried penis, the importance of careful assessment and, for the majority, a delay in surgery until puberty has completed is emphasised. In hypospadias patients, the variety of surgical procedures has complicated assessment of outcomes. It appears that true surgical success may be difficult to measure as many men who have had earlier operations are not reassessed in either puberty or adult life. There is also a brief discussion of acquired penile anomalies, including causation and treatment of lymphoedema, penile fracture/trauma, and priapism.

  1. On the electric field transient anomaly observed at the time of the Kythira M=6.9 earthquake on January 2006

    Directory of Open Access Journals (Sweden)

    M. R. Varley

    2007-11-01

    Full Text Available The study of the Earth's electromagnetic fields prior to the occurrence of strong seismic events has repeatedly revealed cases were transient anomalies, often deemed as possible earthquake precursors, were observed on electromagnetic field recordings of surface, atmosphere and near space carried out measurements. In an attempt to understand the nature of such signals several models have been proposed based upon the exhibited characteristics of the observed anomalies and different possible generation mechanisms, with electric earthquake precursors (EEP appearing to be the main candidates for short-term earthquake precursors. This paper discusses the detection of a ULF electric field transient anomaly and its identification as a possible electric earthquake precursor accompanying the Kythira M=6.9 earthquake occurred on the 8 January 2006.

  2. Detection of dwarf gourami iridovirus (Infectious spleen and kidney necrosis virus) in populations of ornamental fish prior to and after importation into Australia, with the first evidence of infection in domestically farmed Platy (Xiphophorus maculatus).

    Science.gov (United States)

    Rimmer, Anneke E; Becker, Joy A; Tweedie, Alison; Lintermans, Mark; Landos, Matthew; Stephens, Fran; Whittington, Richard J

    2015-11-01

    The movement of ornamental fish through international trade is a major factor for the transboundary spread of pathogens. In Australia, ornamental fish which may carry dwarf gourami iridovirus (DGIV), a strain of Infectious spleen and kidney necrosis virus (ISKNV), have been identified as a biosecurity risk despite relatively stringent import quarantine measures being applied. In order to gain knowledge of the potential for DGIV to enter Australia, imported ornamental fish were sampled prior to entering quarantine, during quarantine, and post quarantine from wholesalers and aquatic retail outlets in Australia. Samples were tested by quantitative polymerase chain reaction (qPCR) for the presence of megalocytivirus. Farmed and wild ornamental fish were also tested. Megalocytivirus was detected in ten of fourteen species or varieties of ornamental fish. Out of the 2086 imported gourami tested prior to entering quarantine, megalocytivirus was detected in 18.7% of fish and out of the 51 moribund/dead ornamental fish tested during the quarantine period, 68.6% were positive for megalocytivirus. Of fish from Australian wholesalers and aquatic retail outlets 14.5% and 21.9%, respectively, were positive. Out of 365 farmed ornamental fish, ISKNV-like megalocytivirus was detected in 1.1%; these were Platy (Xiphophorus maculatus). Megalocytivirus was not detected in free-living breeding populations of Blue gourami (Trichopodus trichopterus) caught in Queensland. This study showed that imported ornamental fish are vectors for DGIV and it was used to support an import risk analysis completed by the Australian Department of Agriculture. Subsequently, the national biosecurity policy was revised and from 1 March 2016, a health certification is required for susceptible families of fish to be free of this virus prior to importation. Crown Copyright © 2015. Published by Elsevier B.V. All rights reserved.

  3. Multiprobe in-situ measurement of magnetic field in a minefield via a distributed network of miniaturized low-power integrated sensor systems for detection of magnetic field anomalies

    Science.gov (United States)

    Javadi, Hamid H. S.; Bendrihem, David; Blaes, B.; Boykins, Kobe; Cardone, John; Cruzan, C.; Gibbs, J.; Goodman, W.; Lieneweg, U.; Michalik, H.; Narvaez, P.; Perrone, D.; Rademacher, Joel D.; Snare, R.; Spencer, Howard; Sue, Miles; Weese, J.

    1998-09-01

    Based on technologies developed for the Jet Propulsion Laboratory (JPL) Free-Flying-Magnetometer (FFM) concept, we propose to modify the present design of FFMs for detection of mines and arsenals with large magnetic signature. The result will be an integrated miniature sensor system capable of identifying local magnetic field anomaly caused by a magnetic dipole moment. Proposed integrated sensor system is in line with the JPL technology road-map for development of autonomous, intelligent, networked, integrated systems with a broad range of applications. In addition, advanced sensitive magnetic sensors (e.g., silicon micromachined magnetometer, laser pumped helium magnetometer) are being developed for future NASA space plasma probes. It is envisioned that a fleet of these Integrated Sensor Systems (ISS) units will be dispersed on a mine-field via an aerial vehicle (a low-flying airplane or helicopter). The number of such sensor systems in each fleet and the corresponding in-situ probe-grid cell size is based on the strength of magnetic anomaly of the target and ISS measurement resolution of magnetic field vector. After a specified time, ISS units will transmit the measured magnetic field and attitude data to an air-borne platform for further data processing. The cycle of data acquisition and transmission will be continued until batteries run out. Data analysis will allow a local deformation of the Earth's magnetic field vector by a magnetic dipole moment to be detected. Each ISS unit consists of miniaturized sensitive 3- axis magnetometer, high resolution analog-to-digital converter (ADC), Field Programmable Gate Array (FPGA)-based data subsystem, Li-batteries and power regulation circuitry, memory, S-band transmitter, single-patch antenna, and a sun angle sensor. ISS unit is packaged with non-magnetic components and the electronic design implements low-magnetic signature circuits. Care is undertaken to guarantee no corruption of magnetometer sensitivity as a result

  4. Magnetic anomalies in the Cosmonauts Sea, off East Antarctica

    Science.gov (United States)

    Nogi, Y.; Hanyu, T.; Fujii, M.

    2017-12-01

    Identification of magnetic anomaly lineations and fracture zone trends in the Southern Indian Ocean, are vital to understanding the breakup of Gondwana. However, the magnetic spreading anomalies and fracture zones are not clear in the Southern Indian Ocean. Magnetic anomaly lineations in the Cosmonauts Sea, off East Antarctica, are key to elucidation of separation between Sri Lanka/India and Antarctica. No obvious magnetic anomaly lineations are observed from a Japanese/German aerogeophysical survey in the Cosmonauts Sea, and this area is considered to be created by seafloor spreading during the Cretaceous Normal Superchron. Vector magnetic anomaly measurements have been conducted on board the Icebreaker Shirase mainly to understand the process of Gondwana fragmentation in the Indian Ocean. Magnetic boundary strikes are derived from vector magnetic anomalies obtained in the Cosmonauts Sea. NE-SW trending magnetic boundary strikes are mainly observed along the several NW-SE oriented observation lines with magnetic anomaly amplitudes of about 200 nT. These NE-SW trending magnetic boundary strikes possibly indicate M-series magnetic anomalies that can not be detected from the aerogeophysical survey with nearly N-S observation lines. We will discuss the magnetic spreading anomalies and breakup process between Sri Lanka/India and Antarctica in the Cosmonauts Sea.

  5. The reliability of the ball-tipped probe for detecting pedicle screw tract violations prior to instrumenting the thoracic and lumbar spine.

    Science.gov (United States)

    Sedory, David M; Crawford, John J; Topp, Raymond F

    2011-03-15

    Cadaveric. To determine the confidence with which surgeons should rely on a flexible ball-tipped probe to detect pedicle breeches in the thoracic and lumbar spine. The reliability of a ball-tipped probe for detecting cortical violations of the pedicle tract has not been studied among fellowship-trained surgeons. A total of 134 pedicles were randomized to have pedicle screw tracts with one of six possible options: no violation, anterior, superior, inferior, medial, or lateral violations. Five fellowship-trained spine surgeons examined each pedicle, using a standard flexible ball-tipped probe on three nonsequential occasions. The percentage of correctly identified violations, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the surgeons as a group and individually. The Cohen kappa coefficient was used to assess the accuracy of the observers and the interobserver and intraobserver agreement. Finally, we analyzed our results by spinal region to see whether this impacted the surgeons' ability to detect a pedicle violation. The surgeons were able to correctly identify 81% of intact pedicles, 39% of superior, 68% of medial, 74% of lateral, 62% of anterior, and 50% of inferior violations. The sensitivity varied considerably by breech location and surgeon with a range of 18% to 85%. Positive predictive value for each breech location ranged from 12% to 20%. The specificity was 81% and negative predictive value 98% overall. The intraobserver reliability was moderate and interobserver reliability was low in this series. The ability to detect a pedicle violation was significantly better in the lower thoracic region (T6-T12) than in other areas of the spine. The standard ball-tipped probe was much less reliable than expected. This technique can be used to confirm an intact pedicle but has an unacceptably high false-positive rate and should be used with caution. Our study suggests that overconfidence in pedicle probing might

  6. An Unsupervised Deep Hyperspectral Anomaly Detector.

    Science.gov (United States)

    Ma, Ning; Peng, Yu; Wang, Shaojun; Leong, Philip H W

    2018-02-26

    Hyperspectral image (HSI) based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN) based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption. To reduce contamination by local anomalies, adaptive weights are constructed from reconstruction errors and statistical information. By using the code image which is generated during the inference of DBN and modified by adaptively updated weights, a local Euclidean distance between under test pixels and their neighboring pixels is used to determine the anomaly targets. Experimental results on synthetic and recorded HSI datasets show the performance of proposed method outperforms the classic global Reed-Xiaoli detector (RXD), local RX detector (LRXD) and the-state-of-the-art Collaborative Representation detector (CRD).

  7. An Unsupervised Deep Hyperspectral Anomaly Detector

    Directory of Open Access Journals (Sweden)

    Ning Ma

    2018-02-01

    Full Text Available Hyperspectral image (HSI based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption. To reduce contamination by local anomalies, adaptive weights are constructed from reconstruction errors and statistical information. By using the code image which is generated during the inference of DBN and modified by adaptively updated weights, a local Euclidean distance between under test pixels and their neighboring pixels is used to determine the anomaly targets. Experimental results on synthetic and recorded HSI datasets show the performance of proposed method outperforms the classic global Reed-Xiaoli detector (RXD, local RX detector (LRXD and the-state-of-the-art Collaborative Representation detector (CRD.

  8. A Correlation between Renal Anomalies and Vesicoureteral Reflux

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Seung Soo; Kim, Young Tong; Kim, Il Young; Shin, Hyeong Cheol [Dept. of Radiology, Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan (Korea, Republic of)

    2011-12-15

    To investigate the frequency of vesicoureteral reflux (VUR) in children with renal anomalies a evaluate the correlation between renal anomalies and VUR. Eighty-one children (1 day-8 years) with renal anomalies underwent voiding cystourethrogram between 2006 and 2009 were reviewed. This study included ureteropelvic junction stenosis (n = 32), ureteropelvic duplication (n = 20), multicystic dysplastic kidney (n = 12), fusion anomaly (n = 11), renal agenesis (n = 3), unilateral renal hypoplasia (n = 2), and ectopic kidney (n = 1). The frequency, grade, and location of VUR were evaluated. The grade of VUR according to age and anomaly type was statistically analyzed, and the patients with VUR were followed. The VUR was present in 14 (17.3%); ipsilateral VUR was present in 8 (57.1%), bilateral VUR in 4 (28.6%), and contralateral VUR in 2 (14.2%). VUR was detected in 9 patients under the age of one. There was no statistical correlation between VUR grade and either age or anomaly type of the nine patients showed continuous VUR on up. The frequency of VUR in children with renal anomalies was 17.3%. VUR was most frequently detected in children under the age of one, and VUR grade was not related to age and anomaly type.

  9. A Correlation between Renal Anomalies and Vesicoureteral Reflux

    International Nuclear Information System (INIS)

    Kim, Seung Soo; Kim, Young Tong; Kim, Il Young; Shin, Hyeong Cheol

    2011-01-01

    To investigate the frequency of vesicoureteral reflux (VUR) in children with renal anomalies a evaluate the correlation between renal anomalies and VUR. Eighty-one children (1 day-8 years) with renal anomalies underwent voiding cystourethrogram between 2006 and 2009 were reviewed. This study included ureteropelvic junction stenosis (n = 32), ureteropelvic duplication (n = 20), multicystic dysplastic kidney (n = 12), fusion anomaly (n = 11), renal agenesis (n = 3), unilateral renal hypoplasia (n = 2), and ectopic kidney (n = 1). The frequency, grade, and location of VUR were evaluated. The grade of VUR according to age and anomaly type was statistically analyzed, and the patients with VUR were followed. The VUR was present in 14 (17.3%); ipsilateral VUR was present in 8 (57.1%), bilateral VUR in 4 (28.6%), and contralateral VUR in 2 (14.2%). VUR was detected in 9 patients under the age of one. There was no statistical correlation between VUR grade and either age or anomaly type of the nine patients showed continuous VUR on up. The frequency of VUR in children with renal anomalies was 17.3%. VUR was most frequently detected in children under the age of one, and VUR grade was not related to age and anomaly type.

  10. An anomaly of positron parameter appearing in the study of an electron-brush-plated material -- Is it possible to use positron to detect molecular hydrogen content

    Energy Technology Data Exchange (ETDEWEB)

    Wang Jingcheng; Qin Yunjie (Shanghai Iron and Steel Research Inst. (China). Applied Physics Dept.); Zhang Guilin; Zheng Wanhui (Shanghai Nuclear Research Inst. (China))

    1994-11-01

    On account of the ease with which positron annihilation experiments may be made and the well-known manner in which positrons behave much in the way that hydrogen protons do, the positron technique is thus commonly used to study hydrogen-charging and hydrogen-absorbed metals and alloys. Indeed, the positron technique has been an indispensable and powerful tool to investigate the influence of hydrogen on microstructures and properties of materials, thus a lot of the related papers have been published. However, papers regarding the use of positrons to detect molecular hydrogen content are few in the literature. This is because hydrogen-charging of metallic materials is a quite complicated process. Generally speaking, when hydrogen-charging, the content of hydrogen increases but occasionally it becomes lower instead of higher with increasing charging time. In addition, positrons, after entering a material, are affected not only by the old defects and the newly generated defects, but by hydrogen proton screening as well. Therefore, the existence of certain relationships between the measured positron parameter and hydrogen content in the material has not been found. Among the curves of positron parameter as a function of charging time which appeared in publications, the one which has a wavelike characteristic was frequently seen. In this letter, the author found that the measured Doppler lineshape parameter happens to be roughly directly proportional to the molecular hydrogen content involved in a particular material.

  11. Hyades CN anomaly

    Energy Technology Data Exchange (ETDEWEB)

    Brown, J.A.; Twarog, B.A.

    1983-05-01

    Recent uvby photometric work indicating possible CN variation among main-sequence stars in the Hyades is tested. Comparison of Reticon spectra of normal stars of similar temperature to five anomalous CN candidates in the Hyades demonstrates that there is no significant difference between the spectra of the program and comparison stars for four of the anomalous CN candidates in the wavelength region of CN 4216. The observed spectral discrepancy for the fifth program star appears to be the result of an incorrect temperature index as compared to previous observations of the same star. The source of the photometric anomaly remains unexplained.

  12. TIR time series satellite and field data for seismic anomalies monitoring

    Science.gov (United States)

    Zoran, M. A.; Savastru, R. S.; Savastru, D. M.

    2017-09-01

    During last decade, due to fast progress of thermal infrared (TIR) technology, all weather, high-resolution and highdynamic range of new developed sensors, a large time-series data base is available for seismic anomalies monitoring. As received satellite infrared information is influenced by many types of factors, the main problem for seismic anomalies recognition is to extract useful information associated with tectonic activities and to eliminate the effects of non-tectonic factors. Pre-earthquake spatio-temporal thermal anomalies are controlled by various factors like as earthquake moment magnitude and its focal depth, geological setting, topography and land covers. In this paper, changes before and after the Vrancea earthquakes in the atmospheric parameters have been investigated on the basis of time-series geospatial and field data analysis. The detected changes show a complementary behavior in terms of the various atmospheric parameters, further showing strong evidence of coupling between lithosphere-land surface-atmosphere associated with the Vrancea's earthquakes. Have been selected the atmospheric earthquake presignals detectable from space: surface latent heat flux (SLHF), and air (AT) surface temperature anomalies, provided by time-series satellite NOAA AVHRR and in-situ monitoring data. For some analyzed earthquakes, starting with ten days up to one week prior to a moderate or strong earthquake a transient thermal infrared rise appeared in SLHF (tens of W/m2) and AT (2-10°) values higher than the normal, function of the magnitude and focal depth, which disappeared after the main shock. The joint analysis of geospatial, geophysical, and geological information is revealing new insights for Vrancea zone seismicity understanding in Romania.

  13. A study of associated congenital anomalies with biliary atresia

    Science.gov (United States)

    Gupta, Lucky; Bhatnagar, Veereshwar

    2016-01-01

    Background/Purpose: This study aims to analyze the incidence and type of various associated anomalies among infants with extrahepatic biliary atresia (EHBA), compare their frequency with those quoted in the existing literature and assess their role in the overall management. Materials and Methods: A retrospective study was performed on 137 infants who underwent the Kasai procedure for EHBA during the past 12 years. The medical records were reviewed for the incidence and type of associated anomalies in addition to the details of the management of the EHBA. Results: Of the137 infants, 40 (29.2%) were diagnosed as having 58 anomalies. The majority of patients had presented in the 3rd month of life; mean age was 81 ± 33 days (range = 20-150 days). There were 32 males and 8 females; boys with EHBA had a higher incidence of associated anomalies. Of these 40 patients, 22 (37.9%) had vascular anomalies, 13 patients (22.4%) had hernias (umbilical-10, inguinal-3), 7 patients (12.1%) had intestinal malrotation, 4 patients (6.8%) had choledochal cyst, 1 patient (1.7%) had Meckel's diverticulum, 3 patients (5%) had undergone prior treatment for jejunoileal atresias (jejunal-2, ileal-1), 2 patients (3.4%) had undergone prior treatment for esophageal atresia and tracheoesophageal fistula, 2 patients (3.4%) had spleniculi, and 2 patients (3.4%) were diagnosed as having situs inversus. Conclusions: The most common associated anomalies in our study were related to the vascular variation at the porta hepatis and the digestive system. The existence of anomalies in distantly developing anatomic regions in patients with EHBA supports the possibility of a “generalized” insult during embryogenesis rather than a “localized” defect. In addition, male infants were observed to have significantly more associated anomalies as compared with the female infants in contrast to earlier reports. PMID:26862288

  14. Cognitive Temporal Document Priors

    NARCIS (Netherlands)

    Peetz, M.H.; de Rijke, M.

    2013-01-01

    Temporal information retrieval exploits temporal features of document collections and queries. Temporal document priors are used to adjust the score of a document based on its publication time. We consider a class of temporal document priors that is inspired by retention functions considered in

  15. Magnetic Solid-Phase Extraction of N,N-Diethyl-m-Toluamide From Baby Toilet Water Prior to its HPLC-UV Detection.

    Science.gov (United States)

    Ma, Xiaowei; Feng, Fan; Yang, Yang; Dang, Xueping; Huang, Jianlin; Chen, Huaixia

    2017-07-01

    Fe3O4@MIL-100 (MIL, Material Institut Lavoisier) core-shell magnetic microspheres were prepared and applied as the sorbent for the magnetic solid phase extraction (MSPE) of N,N-diethyl-m-toluamide (DEET) in baby toilet water for the first time. The synthesized magnetic metal-organic frameworks were characterized by transmission electron microscope, infrared spectroscopy and thermogravimetric analysis. The functionalized magnetic microparticles showed excellent dispersibility in aqueous solution. The MSPE conditions were investigated in detail. Under the optimized conditions, an MSPE-high performance liquid chromatography method for the determination of DEET was developed. The method was linear in the concentration range from 5 to 500 μg L-1 for DEET in baby toilet water and good linearity (r2 > 0.9998) was obtained for the calibration curve. The limit of detection was 1.5 μg L-1. Both the intra-day and inter-day precisions (relative standard deviations) were <3%. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  16. Analyzing Spatiotemporal Anomalies through Interactive Visualization

    Directory of Open Access Journals (Sweden)

    Tao Zhang

    2014-06-01

    Full Text Available As we move into the big data era, data grows not just in size, but also in complexity, containing a rich set of attributes, including location and time information, such as data from mobile devices (e.g., smart phones, natural disasters (e.g., earthquake and hurricane, epidemic spread, etc. We are motivated by the rising challenge and build a visualization tool for exploring generic spatiotemporal data, i.e., records containing time location information and numeric attribute values. Since the values often evolve over time and across geographic regions, we are particularly interested in detecting and analyzing the anomalous changes over time/space. Our analytic tool is based on geographic information system and is combined with spatiotemporal data mining algorithms, as well as various data visualization techniques, such as anomaly grids and anomaly bars superimposed on the map. We study how effective the tool may guide users to find potential anomalies through demonstrating and evaluating over publicly available spatiotemporal datasets. The tool for spatiotemporal anomaly analysis and visualization is useful in many domains, such as security investigation and monitoring, situation awareness, etc.

  17. Pregnancy outcome and Ebstein's anomaly.

    OpenAIRE

    Donnelly, J E; Brown, J M; Radford, D J

    1991-01-01

    BACKGROUND--Ebstein's anomaly is an uncommon congenital cardiac abnormality that may be associated with cyanosis and arrhythmias. For those female patients with the anomaly who survive to adult life there is little information available about pregnancy, maternal complications, and fetal outcome. This study was designed to address this issue so that these patients can receive appropriate advice and management. METHODS AND RESULTS--Forty two pregnancies in 12 women with Ebstein's anomaly were s...

  18. Anomalies in Economics and Finance

    OpenAIRE

    Christopher L. Gilbert

    2010-01-01

    The term “anomaly” played a crucial role in Thomas Kuhn’s characterization of scientific progress. For Kuhn, an anomaly is a puzzle which challenges an accepted paradigm. Puzzles only achieve anomalous status once an alternative paradigm becomes available which allows explanation of the puzzle. Anomalies were introduced into the finance literature by Michael Jensen but more as resolvable puzzles than Kuhnian anomalies. They entered economics via Richard Thaler who saw behavioural economics as...

  19. Congenital Anomalies among Live Births

    OpenAIRE

    Vivian Rosa Vázquez Martínez; Cristobal Jorge Torres González; Alina Luisa Díaz Dueñas; Grisel Torres Vázquez; Dariel Diaz Díaz; Rafael de la Rosa López

    2014-01-01

    Background: congenital anomalies contribute significantly to mortality during early stages of life; they are the leading cause of infant death in developed countries.Objective: to determine the characteristics of congenital anomalies among live births. Methods: a descriptive study was conducted in the province of Cienfuegos in 2012. Thirty-seven women who had live-born neonates with congenital anomalies were studied. The variables analyzed were: parental age, skin color, order of birth, famil...

  20. Turtle carapace anomalies: the roles of genetic diversity and environment.

    Directory of Open Access Journals (Sweden)

    Guillermo Velo-Antón

    2011-04-01

    Full Text Available Phenotypic anomalies are common in wild populations and multiple genetic, biotic and abiotic factors might contribute to their formation. Turtles are excellent models for the study of developmental instability because anomalies are easily detected in the form of malformations, additions, or reductions in the number of scutes or scales.In this study, we integrated field observations, manipulative experiments, and climatic and genetic approaches to investigate the origin of carapace scute anomalies across Iberian populations of the European pond turtle, Emys orbicularis. The proportion of anomalous individuals varied from 3% to 69% in local populations, with increasing frequency of anomalies in northern regions. We found no significant effect of climatic and soil moisture, or climatic temperature on the occurrence of anomalies. However, lower genetic diversity and inbreeding were good predictors of the prevalence of scute anomalies among populations. Both decreasing genetic diversity and increasing proportion of anomalous individuals in northern parts of the Iberian distribution may be linked to recolonization events from the Southern Pleistocene refugium.Overall, our results suggest that developmental instability in turtle carapace formation might be caused, at least in part, by genetic factors, although the influence of environmental factors affecting the developmental stability of turtle carapace cannot be ruled out. Further studies of the effects of environmental factors, pollutants and heritability of anomalies would be useful to better understand the complex origin of anomalies in natural populations.

  1. To detect anomalies in diaphragm walls

    NARCIS (Netherlands)

    Spruit, R.

    2015-01-01

    Diaphragm walls are potentially ideal retaining walls for deep excavations in densely built-up areas, as they cause no vibrations during their construction and provide structural elements with high strength and stiffness. In the recent past, however, several projects using diaphragm walls as soil

  2. Detection of Anomalies in Diaphragm Walls

    NARCIS (Netherlands)

    Spruit, R.; Van Tol, F.; Broere, W.

    2015-01-01

    If a calamity with a retaining wall occurs, the impact on surrounding buildings and infrastructure is at least an order of magnitude more severe than without the calamity. In 2005 and 2006 major leaks in the retaining walls of underground stations in Amsterdam and Rotterdam occurred. After these

  3. Anomaly Detection at Multiple Scales (ADAMS)

    Science.gov (United States)

    2011-11-09

    must resort to generating their own data that simulates insider attacks. The Schonlau dataset is the most widely used for academic study. It...measurements are estimated by well-known software plagiarism tools . 39 As explained above, there are many different techniques for code trans- formation

  4. Magnetic Anomaly Detection by Remote Means

    Science.gov (United States)

    2016-09-21

    methods that use atmospheric constituents to measure nano Tesla variations in the earth’s magnetic field from a remote platform. Our approach is to...designated 1S0. The hyperfine separation of energy levels associated with electron coupling to the nuclear spin and the isotopic shifts are too small to be...polarization of the excitation laser(s). Figure 2: Xenon energy levels accessed by two photon absorption at 224-226 nm Figure 3 shows the Radar REMPI

  5. Recurring Anomaly Detection System (ReADS)

    Data.gov (United States)

    National Aeronautics and Space Administration — Overview: ReADS can analyze text reports, such as aviation reports and problem or maintenance records. ReADS uses text clustering algorithms to group loosely related...

  6. Advanced Ground Systems Maintenance Anomaly Detection

    Data.gov (United States)

    National Aeronautics and Space Administration — The Inductive Monitoring System (IMS) software utilizes techniques from the fields of model-based reasoning, machine learning, and data mining to build system...

  7. Compressive Hyperspectral Imaging and Anomaly Detection

    Science.gov (United States)

    2013-03-01

    be solved. To solve argmin ( µ |u|1 + 1 2 ‖u− f‖ 2 ) (3) we have the following well known shrinkage formula ui = shrink(fi, µ) =    fi − µ if fi...between them. We see that the 5th and 6th are the closest endmembers, and they appear to be shady concrete and sunny concrete , respectively. Combining...quadratic regularization,” in IEEE International Conference on Image Processing – ICIP, 1, IEEE. [9] Y. Xu, W. Yin, Z. Wen, and Y. Zhang, “An alternating

  8. Anomaly Detection in a Fleet of Systems

    Data.gov (United States)

    National Aeronautics and Space Administration — A fleet is a group of systems (e.g., cars, aircraft) that are designed and manufactured the same way and are intended to be used the same way. For example, a fleet...

  9. Anomaly Detection of IP Header Threats

    OpenAIRE

    S.H.C. Haris, Ghossoon M. Waleed, R.B. Ahmad & M.A.H.A. Ghani

    2011-01-01

    Threats have become a big problem since the past few years as computerviruses are widely recognized as a significant computer threat. However, the roleof Information Technology security must be revisit again since it is too often. ITsecurity managers find themselves in the hopeless situation of trying to uphold amaximum of security as requested from management. At the same time they areconsidered an obstacle in the way of developing and introducing newapplications into business and government...

  10. Water radon anomaly fields

    Energy Technology Data Exchange (ETDEWEB)

    Yin, H.

    1980-01-01

    A striking aspect of water radon levels in relation to earthquakes is that before the Tangshan quake there was a remarkable synchronicity of behavior of many wells within 200 km of Tangshan. However, for many wells anomalous values persisted after the earthquake, particularly outside the immediate region of the quake. It is clear that radon may be produced by various processes; some candidates are pressure, shear, vibration, temperature and pressure, mixing of water-bearing strata, breakdown of mineral crystal structure, and the like, although it is not clear which of these are primary. It seems that a possible explanation of the persistence of the anomaly in the case of Tangshan may be that the earthquake released strain in the vicinity of Tangshan but increased it further along the geological structures involved, thus producing a continued radon buildup.

  11. Limb anomalies in DiGeorge and CHARGE syndromes

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, C.; Quackenbush, E.J.; Whiteman, D.; Korf, B. [Harvard Medical School, Boston, MA (United States)

    1997-01-20

    Limb anomalies are not common in the DiGeorge or CHARGE syndromes. We describe limb anomalies in two children, one with DiGeorge and the other with CHARGE syndrome. Our first patient had a bifid left thumb, Tetralogy of Fallot, absent thymus, right facial palsy, and a reduced number of T-cells. A deletion of 22q11 was detected by fluorescence in situ hybridization (FISH). The second patient, with CHARGE syndrome, had asymmetric findings that included right fifth finger clinodactyly, camptodactyly, tibial hemimelia and dimpling, and severe club-foot. The expanded spectrum of the DiGeorge and CHARGE syndromes includes limb anomalies. 14 refs., 4 figs.

  12. Arthur Prior and 'Now'

    DEFF Research Database (Denmark)

    Blackburn, Patrick Rowan; Jørgensen, Klaus Frovin

    2016-01-01

    On the 4th of December 1967, Hans Kamp sent his UCLA seminar notes on the logic of ‘now’ to Arthur N. Prior. Kamp’s two-dimensional analysis stimulated Prior to an intense burst of creativity in which he sought to integrate Kamp’s work into tense logic using a one-dimensional approach. Prior...... to a one-dimensional tense logic containing the ‘now’ operator J. Drawing on material from the Prior archive, and the paper “‘Now”’ that detailed Prior’s findings, we retell this story. We focus on Prior’s completeness conjecture for the hybrid system and the role played by temporal reference....

  13. Axial anomaly at finite temperature

    International Nuclear Information System (INIS)

    Chaturvedi, S.; Gupte, Neelima; Srinivasan, V.

    1985-01-01

    The Jackiw-Bardeen-Adler anomaly for QED 4 and QED 2 are calculated at finite temperature. It is found that the anomaly is independent of temperature. Ishikawa's method [1984, Phys. Rev. Lett. vol. 53 1615] for calculating the quantised Hall effect is extended to finite temperature. (author)

  14. Anomaly Structure of Regularized Supergravity

    Science.gov (United States)

    Butter, Daniel; Gaillard, Mary K.

    2015-01-01

    On-shell Pauli-Villars regularization of the one-loop divergences of supergravity theories is used to study the anomaly structure of supergravity and the cancellation of field theory anomalies under a U (1 ) gauge transformation and under the T -duality group of modular transformations in effective supergravity theories with three Kähler moduli Ti obtained from orbifold compactification of the weakly coupled heterotic string. This procedure requires constraints on the chiral matter representations of the gauge group that are consistent with known results from orbifold compactifications. Pauli-Villars (PV) regulator fields allow for the cancellation of all quadratic and logarithmic divergences, as well as most linear divergences. If all linear divergences were canceled, the theory would be anomaly free, with noninvariance of the action arising only from Pauli-Villars masses. However there are linear divergences associated with nonrenormalizable gravitino/gaugino interactions that cannot be canceled by PV fields. The resulting chiral anomaly forms a supermultiplet with the corresponding conformal anomaly, provided the ultraviolet cutoff has the appropriate field dependence, in which case total derivative terms, such as Gauss-Bonnet, do not drop out from the effective action. The anomalies can be partially canceled by the four-dimensional version of the Green-Schwarz mechanism, but additional counterterms, and/or a more elaborate set of Pauli-Villars fields and couplings, are needed to cancel the full anomaly, including D -term contributions to the conformal anomaly that are nonlinear in the parameters of the anomalous transformations.

  15. CRANIOVERTEBRAL JUNCTION ANOMALIES SEEN AT ...

    African Journals Online (AJOL)

    hi-tech

    2000-03-03

    Mar 3, 2000 ... anomalies that give rise to symptoms in this area are basilar impression, occipitalisation of the atlas, odontoid process abnormalities and atlanto-axial dislocation. Neuromeningeal anomalies in this region include Arnold-. Chiari malformation, syringomyelia and basal arachnoiditis. The clinical presentation ...

  16. What is a Timing Anomaly?

    DEFF Research Database (Denmark)

    Cassez, Franck; Hansen, Rene Rydhof; Olesen, Mads Chr.

    2012-01-01

    difficult. We examine previous definitions of timing anomalies, and identify examples where they do not align with common observations. We then provide a definition for consistently slower hardware traces that can be used to define timing anomalies and aligns with common observations....

  17. Fetal central nervous system anomalies: fast MRI vs ultrasonography

    International Nuclear Information System (INIS)

    Yang Wenzhong; Xia Liming; Yang Minjie; Feng Dingyi; Hu Junwu; Zou Mingli; Wang Chengyuan; Chen Xinlin; Yang Xiaohong

    2006-01-01

    Objective: To evaluate the ability of fast MRI to detect fetal central nervous system (CNS) anomalies and to compare its performance with that of prenatal ultrasonography (US). Methods Forty-eight pregnant women were detected by conventional prenatal US and MRI. Twenty-two fetuses with CNS anomalies were conformed by autopsy and follow-up. The MR and US appearances of fetal CNS structure were compared to each other and to that of autopsy. Results: A total of 26 CNS anomalies were identified by autopsy (n=17) and follow-up (n=9) including anencephaly (n=6), rachischisis (n=2), encephalocele (n=3), congenital hydrocephalus (n=7), alobar holoprosencephaly (n=1), porencephalia (n=3), arachnoid cyst (n=2) and choroids plexus cyst (n=2). US diagnosed 24 CNS anomalies, the correct diagnostic rate was 92.3%, the false-positive rate was 3.8%, the missed-diagnostic rate was 3.8%. MRI diagnosed 23 CNS anomalies, the correct-diagnostic rate was 88.5%, the false-positive rate was 3.8% ,the missed-diagnostic rate was 7.7%. There was no difference between US and MRI (P>0.05), but MRI have larger FOV, higher tissues resolution, and can demonstrate gray-white matter in detail. Conclusions: MR imaging has a similar sensitivity to that of US in the detection of fetal CNS anomalies. (authors)

  18. Enhanced detection sensitivity of prostate-specific antigen via PSA-conjugated gold nanoparticles based on localized surface plasmon resonance: GNP-coated anti-PSA/LSPR as a novel approach for the identification of prostate anomalies.

    Science.gov (United States)

    Jazayeri, M H; Amani, H; Pourfatollah, A A; Avan, A; Ferns, G A; Pazoki-Toroudi, H

    2016-10-01

    Prostate-specific antigen (PSA) is used to screen for prostate disease, although it has several limitations in its application as an organ-specific or cancer-specific marker. Furthermore, a highly specific/sensitive and/or label-free identification of PSA still remains a challenge in the diagnosis of prostate anomalies. We aimed to develop a gold nanoparticle (GNP)-conjugated anti-PSA antibody-based localized surface plasmon resonance (LSPR) as a novel approach to detect prostatic disease. A total of 25 nm colloidal gold particles were prepared followed by conjugation with anti-PSA pAb (GNPs-PSA pAb). LSPR was used to monitor the absorption changes of the aggregation of the particles. The size, shape and stability of the GNP-anti-PSA were evaluated by dynamic light scattering transmission electron microscopy (TEM) and zetasizer. The GNPs-conjugated PSA-pAb was successfully synthesized and subsequently characterized using ultraviolet absorption spectroscopy and TEM to determine the size distribution, crystallinity and stability of the particles (for example, stability of GNP: 443 mV). To increase the stability of the particles, we pegylated GNPs using an N-(3-dimethylaminopropyl)-N*-ethylcarbodiimide hydrochloride (EDC)/N-hydroxylsuccinimide (NHS) linker (for example, stability of GNP after pegylation: 272 mV). We found a significant increase in the absorbance and intensity of the particles with extinction peak at 545/2 nm, which was shifted by ~1 nm after conjugation. To illustrate the potential of the GNPs-PSA pAb to bind specifically to PSA, LSPR was used. We found that the extinction peak shifted 3 nm for a solution of 100 nM unlabeled antigen. In summary, we have established a novel approach for improving the efficacy/sensitivity of PSA in the assessment of prostate disease, supporting further investigation on the diagnostic value of GNP-conjugated anti-PSA/LSPR for the detection of prostate cancer.

  19. Identifying frauds and anomalies in Medicare-B dataset.

    Science.gov (United States)

    Jiwon Seo; Mendelevitch, Ofer

    2017-07-01

    Healthcare industry is growing at a rapid rate to reach a market value of $7 trillion dollars world wide. At the same time, fraud in healthcare is becoming a serious problem, amounting to 5% of the total healthcare spending, or $100 billion dollars each year in US. Manually detecting healthcare fraud requires much effort. Recently, machine learning and data mining techniques are applied to automatically detect healthcare frauds. This paper proposes a novel PageRank-based algorithm to detect healthcare frauds and anomalies. We apply the algorithm to Medicare-B dataset, a real-life data with 10 million healthcare insurance claims. The algorithm successfully identifies tens of previously unreported anomalies.

  20. Coronary Artery Anomalies in Animals.

    Science.gov (United States)

    Scansen, Brian A

    2017-04-12

    Coronary artery anomalies represent a disease spectrum from incidental to life-threatening. Anomalies of coronary artery origin and course are well-recognized in human medicine, but have received limited attention in veterinary medicine. Coronary artery anomalies are best described in the dog, hamster, and cow though reports also exist in the horse and pig. The most well-known anomaly in veterinary medicine is anomalous coronary artery origin with a prepulmonary course in dogs, which limits treatment of pulmonary valve stenosis. A categorization scheme for coronary artery anomalies in animals is suggested, dividing these anomalies into those of major or minor clinical significance. A review of coronary artery development, anatomy, and reported anomalies in domesticated species is provided and four novel canine examples of anomalous coronary artery origin are described: an English bulldog with single left coronary ostium and a retroaortic right coronary artery; an English bulldog with single right coronary ostium and transseptal left coronary artery; an English bulldog with single right coronary ostium and absent left coronary artery with a prepulmonary paraconal interventricular branch and an interarterial circumflex branch; and a mixed-breed dog with tetralogy of Fallot and anomalous origin of all coronary branches from the brachiocephalic trunk. Coronary arterial fistulae are also described including a coronary cameral fistula in a llama cria and an English bulldog with coronary artery aneurysm and anomalous shunting vessels from the right coronary artery to the pulmonary trunk. These examples are provided with the intent to raise awareness and improve understanding of such defects.

  1. Reliability of CHAMP Anomaly Continuations

    Science.gov (United States)

    vonFrese, Ralph R. B.; Kim, Hyung Rae; Taylor, Patrick T.; Asgharzadeh, Mohammad F.

    2003-01-01

    CHAMP is recording state-of-the-art magnetic and gravity field observations at altitudes ranging over roughly 300 - 550 km. However, anomaly continuation is severely limited by the non-uniqueness of the process and satellite anomaly errors. Indeed, our numerical anomaly simulations from satellite to airborne altitudes show that effective downward continuations of the CHAMP data are restricted to within approximately 50 km of the observation altitudes while upward continuations can be effective over a somewhat larger altitude range. The great unreliability of downward continuation requires that the satellite geopotential observations must be analyzed at satellite altitudes if the anomaly details are to be exploited most fully. Given current anomaly error levels, joint inversion of satellite and near- surface anomalies is the best approach for implementing satellite geopotential observations for subsurface studies. We demonstrate the power of this approach using a crustal model constrained by joint inversions of near-surface and satellite magnetic and gravity observations for Maude Rise, Antarctica, in the southwestern Indian Ocean. Our modeling suggests that the dominant satellite altitude magnetic anomalies are produced by crustal thickness variations and remanent magnetization of the normal polarity Cretaceous Quiet Zone.

  2. Methods and Systems for Characterization of an Anomaly Using Infrared Flash Thermography

    Science.gov (United States)

    Koshti, Ajay M. (Inventor)

    2013-01-01

    A method for characterizing an anomaly in a material comprises (a) extracting contrast data; (b) measuring a contrast evolution; (c) filtering the contrast evolution; (d) measuring a peak amplitude of the contrast evolution; (d) determining a diameter and a depth of the anomaly, and (e) repeating the step of determining the diameter and the depth of the anomaly until a change in the estimate of the depth is less than a set value. The step of determining the diameter and the depth of the anomaly comprises estimating the depth using a diameter constant C.sub.D equal to one for the first iteration of determining the diameter and the depth; estimating the diameter; and comparing the estimate of the depth of the anomaly after each iteration of estimating to the prior estimate of the depth to calculate the change in the estimate of the depth of the anomaly.

  3. Gravitational anomaly and transport phenomena.

    Science.gov (United States)

    Landsteiner, Karl; Megías, Eugenio; Pena-Benitez, Francisco

    2011-07-08

    Quantum anomalies give rise to new transport phenomena. In particular, a magnetic field can induce an anomalous current via the chiral magnetic effect and a vortex in the relativistic fluid can also induce a current via the chiral vortical effect. The related transport coefficients can be calculated via Kubo formulas. We evaluate the Kubo formula for the anomalous vortical conductivity at weak coupling and show that it receives contributions proportional to the gravitational anomaly coefficient. The gravitational anomaly gives rise to an anomalous vortical effect even for an uncharged fluid.

  4. Quantum topology and global anomalies

    CERN Document Server

    Baadhio, R A

    1996-01-01

    Anomalies are ubiquitous features in quantum field theories. They can ruin the consistency of such theories and put significant restrictions on their viability, especially in dimensions higher than four. Global gauge and gravitational anomalies are to date, one of the scant powerful and probing tools available to physicists in the pursuit of uniqueness.This monograph is one of the very few that specializes in the study of global anomalies in quantum field theories. A discussion of various issues associated to three dimensional physics - the Chern-Simons-Witten theories - widen the scope of thi

  5. Weather Satellite Thermal IR Responses Prior to Earthquakes

    Science.gov (United States)

    OConnor, Daniel P.

    2005-01-01

    A number of observers claim to have seen thermal anomalies prior to earthquakes, but subsequent analysis by others has failed to produce similar findings. What exactly are these anomalies? Might they be useful for earthquake prediction? It is the purpose of this study to determine if thermal anomalies can be found in association with known earthquakes by systematically co-registering weather satellite images at the sub-pixel level and then determining if statistically significant responses occurred prior to the earthquake event. A new set of automatic co-registration procedures was developed for this task to accommodate all properties particular to weather satellite observations taken at night, and it relies on the general condition that the ground cools after sunset. Using these procedures, we can produce a set of temperature-sensitive satellite images for each of five selected earthquakes (Algeria 2003; Bhuj, India 2001; Izmit, Turkey 2001; Kunlun Shan, Tibet 2001; Turkmenistan 2000) and thus more effectively investigate heating trends close to the epicenters a few hours prior to the earthquake events. This study will lay tracks for further work in earthquake prediction and provoke the question of the exact nature of the thermal anomalies.

  6. Brain anomalies in velo-cardio-facial syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Mitnick, R.J.; Bello, J.A.; Shprintzen, R.J. [Albert Einstein College of Medicine, Bronx, NY (United States)

    1994-06-15

    Magnetic resonance imaging of the brain in 11 consecutively referred patients with velo-cardio-facial syndrome (VCF) showed anomalies in nine cases including small vermis, cysts adjacent to the frontal horns, and small posterior fossa. Focal signal hyperintensities in the white matter on long TR images were also noted. The nine patients showed a variety of behavioral abnormalities including mild development delay, learning disabilities, and characteristic personality traits typical of this common multiple anomaly syndrome which has been related to a microdeletion at 22q11. Analysis of the behavorial findings showed no specific pattern related to the brain anomalies, and the patients with VCF who did not have detectable brain lesions also had behavioral abnormalities consistent with VCF. The significance of the lesions is not yet known, but the high prevalence of anomalies in this sample suggests that structural brain abnormalities are probably common in VCF. 25 refs.

  7. [Prevalence of genital anomalies in young football players].

    Science.gov (United States)

    Mónaco, M; Verdugo, F; Bodell, M; Avendaño, E; Til, L; Drobnic, F

    2015-01-01

    The purpose of genital examination (GE) during the Pre-participation Physical Examination (PPE) is to identify the state of maturity, and rule out any genital pathology. To describe genital anomalies (GA) and estimate the awareness of GE in young football players. A descriptive, cross-sectional study was conducted in 280 elite football players from the results of PPE over two seasons. There was a detection rate of 5.4% GA, with varicocele being 3.2%, and of which only 13% were aware of their condition. Although this study shows a low incidence of genital abnormality in the study population, only 13% were aware of the GE prior to assessment. These findings demonstrate a low incidence of GA in this population. While GE is recommended during PPE, it is not a routine practice performed by family doctors or sports medicine specialists. This article attempts to raise awareness of the importance of GE in PPE as a preventive health strategy. Copyright © 2014 Asociación Española de Pediatría. Published by Elsevier Espana. All rights reserved.

  8. Obstetric consequences of uterovaginal anomalies

    International Nuclear Information System (INIS)

    Rock, J.A.; Schlaff, W.D.

    1985-01-01

    This review discusses the diagnosis and classification of utero-vaginal anomalies as well as obstetric considerations in their management. Diagnosis is usually made by hysterosalpingography antepartum. Ultrasonography is also recommended. 40 references, 10 figures, 9 tables

  9. Unsuspected urological anomalies in asymptomatic cryptorchid boys

    Energy Technology Data Exchange (ETDEWEB)

    Pappis, C.H.; Argianas, S.A.; Bousgas, D.; Athanasiades, E.

    1988-01-01

    In a period of 6 years 144 asymptomatic boys with cryptorchidism, of mean age 7 +- SD 3.5 years, underwent orchiopexy. None of these boys referred to a history of a known urological anomaly, urinary tract infection haematuria, palpable mass in the renal region, bladder extrophy, epispadias, hypospadias or anorectal malformation. On the third day after orchiopexy an intravenous pyelography was done in every boy following testicular protection against irradiation. Ultrasonic investigation was not available at that time. There were minor urological abnormalities in 36 (25%) boys and major ones in 8 (5.5%) boys. A major anomaly is defined as one resulting in significant loss of renal substance (one case of single kidney and three cases of unilateral renal hypoplasia), or requiring surgical correction for conservation of the renal substance (one case of ureterocele, two cases of pelviureteric stenosis and one case of vesicoureteric stenosis with ipsilateral hydronephrosis). The unsuspected major urological abnormalities are usually ipsilateral to the more undescended testis. They may be associated with a hernia and are more frequent in bilateral cryptorchidism. In conclusion we encourage the routine use of IVP, or ultrasonic investigation or dynamic renal scanning (99/sup mTc/-DTPA), if it is possible, in all patients undergoing orchiopexy for the detection of an unsuspected major renal anomaly.

  10. Anomalies of abdominal organs in polysplenia syndrome: Multidetector computed tomography findings

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sung Won; Lee, Yong Seok; Jung, Jin Hee [Dept. of Radiology, Dongguk University Ilsan Hospital, Dongguk University School of Medicine, Goyang (Korea, Republic of)

    2016-02-15

    Polysplenia syndrome is a rare situs ambiguous anomaly associated with multiple spleens and anomalies of abdominal organs. Because most of the minor anomalies do not cause clinical symptoms, polysplenia syndrome is detected incidentally in the adults. Anomalies of abdominal organs may include multiple spleens of variable size or right-sided spleen, large midline or left-sided liver, midline gallbladder, biliary tract anomalies, short pancreas, right-sided stomach, intestinal malrotation, inferior vena cava interruption with azygos or hemiazygos continuation, and a preduodenal portal vein. As the multidetector computed tomography is increasingly used, situs anomalies will likely to be found with greater frequency in the adults. Therefore, radiologists should become familiar with these rare and peculiar anomalies of abdominal organs in polysplenia syndrome.

  11. Dimensional reduction in anomaly mediation

    Science.gov (United States)

    Boyda, Ed; Murayama, Hitoshi; Pierce, Aaron

    2002-04-01

    We offer a guide to dimensional reduction in theories with anomaly-mediated supersymmetry breaking. Evanescent operators proportional to ɛ arise in the bare Lagrangian when it is reduced from d=4 to d=4-2ɛ dimensions. In the course of a detailed diagrammatic calculation, we show that inclusion of these operators is crucial. The evanescent operators conspire to drive the supersymmetry-breaking parameters along anomaly-mediation trajectories across heavy particle thresholds, guaranteeing the ultraviolet insensitivity.

  12. Gravitational anomaly and transport phenomena

    OpenAIRE

    Landsteiner, Karl

    2011-01-01

    Quantum anomalies give rise to new transport phenomena. In particular, a magnetic field can induce an anomalous current via the chiral magnetic effect and a vortex in the relativistic fluid can also induce a current via the chiral vortical effect. The related transport coefficients can be calculated via Kubo formulas. We evaluate the Kubo formula for the anomalous vortical conductivity at weak coupling and show that it receives contributions proportional to the gravitational anomaly coefficie...

  13. Sharing AIS Related Anomalies (SARA)

    Science.gov (United States)

    2016-03-01

    misconfiguration and intentional misuse. These unintended behaviours generate an abundance of anomalies that the security community has an interest in monitoring...to end-user needs and thus be adopted by them. This section is organised as follows : • Section 2.1 describes the strategy used to identify potential...intrinsic and behavioural . Anomalies tagged and reported by TimeCaster can be found in the patent claim (see [13]) and can be augmented. 2.3.7 exactEarth

  14. MODIS/Aqua Thermal Anomalies/Fire 5-Min L2 Swath 1km V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  15. MODIS/Terra Thermal Anomalies/Fire 5-Min L2 Swath 1km V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  16. Unsupervised learning for robust bitcoin fraud detection

    CSIR Research Space (South Africa)

    Monamo, Patrick

    2016-08-01

    Full Text Available The rampant absorption of Bitcoin as a cryptographic currency, along with rising cybercrime activities, warrants utilization of anomaly detection to identify potential fraud. Anomaly detection plays a pivotal role in data mining since most outlying...

  17. Space weather and space anomalies

    Directory of Open Access Journals (Sweden)

    L. I. Dorman

    2005-11-01

    Full Text Available A large database of anomalies, registered by 220 satellites in different orbits over the period 1971-1994 has been compiled. For the first time, data from 49 Russian Kosmos satellites have been included in a statistical analysis. The database also contains a large set of daily and hourly space weather parameters. A series of statistical analyses made it possible to quantify, for different satellite orbits, space weather conditions on the days characterized by anomaly occurrences. In particular, very intense fluxes (>1000 pfu at energy >10 MeV of solar protons are linked to anomalies registered by satellites in high-altitude (>15000 km, near-polar (inclination >55° orbits typical for navigation satellites, such as those used in the GPS network, NAVSTAR, etc. (the rate of anomalies increases by a factor ~20, and to a much smaller extent to anomalies in geostationary orbits, (they increase by a factor ~4. Direct and indirect connections between anomaly occurrence and geomagnetic perturbations are also discussed.

  18. Imaging evaluation of fetal vascular anomalies

    International Nuclear Information System (INIS)

    Calvo-Garcia, Maria A.; Kline-Fath, Beth M.; Koch, Bernadette L.; Laor, Tal; Adams, Denise M.; Gupta, Anita; Lim, Foong-Yen

    2015-01-01

    Vascular anomalies can be detected in utero and should be considered in the setting of solid, mixed or cystic lesions in the fetus. Evaluation of the gray-scale and color Doppler US and MRI characteristics can guide diagnosis. We present a case-based pictorial essay to illustrate the prenatal imaging characteristics in 11 pregnancies with vascular malformations (5 lymphatic malformations, 2 Klippel-Trenaunay syndrome, 1 venous-lymphatic malformation, 1 Parkes-Weber syndrome) and vascular tumors (1 congenital hemangioma, 1 kaposiform hemangioendothelioma). Concordance between prenatal and postnatal diagnoses is analyzed, with further discussion regarding potential pitfalls in identification. (orig.)

  19. Imaging evaluation of fetal vascular anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Calvo-Garcia, Maria A.; Kline-Fath, Beth M.; Koch, Bernadette L.; Laor, Tal [MLC 5031 Cincinnati Children' s Hospital Medical Center, Department of Radiology, Cincinnati, OH (United States); Adams, Denise M. [Cincinnati Children' s Hospital Medical Center, Department of Pediatrics and Hemangioma and Vascular Malformation Center, Cincinnati, OH (United States); Gupta, Anita [Cincinnati Children' s Hospital Medical Center, Department of Pathology, Cincinnati, OH (United States); Lim, Foong-Yen [Cincinnati Children' s Hospital Medical Center, Pediatric Surgery and Fetal Center of Cincinnati, Cincinnati, OH (United States)

    2015-08-15

    Vascular anomalies can be detected in utero and should be considered in the setting of solid, mixed or cystic lesions in the fetus. Evaluation of the gray-scale and color Doppler US and MRI characteristics can guide diagnosis. We present a case-based pictorial essay to illustrate the prenatal imaging characteristics in 11 pregnancies with vascular malformations (5 lymphatic malformations, 2 Klippel-Trenaunay syndrome, 1 venous-lymphatic malformation, 1 Parkes-Weber syndrome) and vascular tumors (1 congenital hemangioma, 1 kaposiform hemangioendothelioma). Concordance between prenatal and postnatal diagnoses is analyzed, with further discussion regarding potential pitfalls in identification. (orig.)

  20. Euro-African MAGSAT anomaly-tectonic observations

    Science.gov (United States)

    Hinze, W. J.; Olivier, R.; Vonfrese, R. R. B.

    1985-01-01

    Preliminary satellite (MAGSAT) scalar magnetic anomaly data are compiled and differentially reduced to radial polarization by equivalent point source inversion for comparison with tectonic data of Africa, Europe and adjacent marine areas. A number of associations are evident to constrain analyses of the tectonic features and history of the region. The Precambrian shields of Africa and Europe exhibit varied magnetic signatures. All shields are not magnetic highs and, in fact, the Baltic shield is a marked minimum. The reduced-to-the-pole magnetic map shows a marked tendency for northeasterly striking anomalies in the eastern Atlantic and adjacent Africa, which is coincident to the track of several hot spots for the past 100 million years. However, there is little consistency in the sign of the magnetic anomalies and the track of the hot spots. Comparison of the radially polarized anomalies of Africa and Europe with other reduced-to-the-pole magnetic satellite anomaly maps of the Western Hemisphere support the reconstruction of the continents prior to the origin of the present-day Atlantic Ocean in the Mesozoic Era.

  1. Prenatal sonographic diagnosis of focal musculoskeletal anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Ryu, Jung Kyu; Cho, Jeong Yeon; Lee, Young Ho; Kim, Ei Jeong; Chun, Yi Kyeong [Samsung Cheil Hospital, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

    2002-09-15

    Focal musculoskeletal anomalies are various and may be an isolated finding or may be found in conjunction with numerous associations, including genetic syndromes, Karyotype abnormals, central nervous system anomalies and other general musculoskeletal disorders. Early prenatal diagnosis of these focal musculoskeletal anomalies nor only affects prenatal care and postnatal outcome but also helps in approaching other numerous associated anomalies.

  2. Prenatal sonographic diagnosis of focal musculoskeletal anomalies

    International Nuclear Information System (INIS)

    Ryu, Jung Kyu; Cho, Jeong Yeon; Lee, Young Ho; Kim, Ei Jeong; Chun, Yi Kyeong

    2002-01-01

    Focal musculoskeletal anomalies are various and may be an isolated finding or may be found in conjunction with numerous associations, including genetic syndromes, Karyotype abnormals, central nervous system anomalies and other general musculoskeletal disorders. Early prenatal diagnosis of these focal musculoskeletal anomalies nor only affects prenatal care and postnatal outcome but also helps in approaching other numerous associated anomalies.

  3. Coronary Artery Anomalies in Animals

    Directory of Open Access Journals (Sweden)

    Brian A. Scansen

    2017-04-01

    Full Text Available Coronary artery anomalies represent a disease spectrum from incidental to life-threatening. Anomalies of coronary artery origin and course are well-recognized in human medicine, but have received limited attention in veterinary medicine. Coronary artery anomalies are best described in the dog, hamster, and cow though reports also exist in the horse and pig. The most well-known anomaly in veterinary medicine is anomalous coronary artery origin with a prepulmonary course in dogs, which limits treatment of pulmonary valve stenosis. A categorization scheme for coronary artery anomalies in animals is suggested, dividing these anomalies into those of major or minor clinical significance. A review of coronary artery development, anatomy, and reported anomalies in domesticated species is provided and four novel canine examples of anomalous coronary artery origin are described: an English bulldog with single left coronary ostium and a retroaortic right coronary artery; an English bulldog with single right coronary ostium and transseptal left coronary artery; an English bulldog with single right coronary ostium and absent left coronary artery with a prepulmonary paraconal interventricular branch and an interarterial circumflex branch; and a mixed-breed dog with tetralogy of Fallot and anomalous origin of all coronary branches from the brachiocephalic trunk. Coronary arterial fistulae are also described including a coronary cameral fistula in a llama cria and an English bulldog with coronary artery aneurysm and anomalous shunting vessels from the right coronary artery to the pulmonary trunk. These examples are provided with the intent to raise awareness and improve understanding of such defects.

  4. Expanding the spectrum of human ganglionic eminence region anomalies on fetal magnetic resonance imaging

    Energy Technology Data Exchange (ETDEWEB)

    Righini, Andrea; Parazzini, Cecilia; Izzo, Giana [Children' s Hospital ' ' V. Buzzi' ' , Department of Radiology and Neuroradiology, Milan (Italy); Cesaretti, Claudia [Children' s Hospital ' ' V. Buzzi' ' , Department of Radiology and Neuroradiology, Milan (Italy); Ospedale Maggiore Policlinico, Medical Genetics Unit, Fondazione I.R.C.C.S. Ca' Granda, Milan (Italy); Conte, Giorgio [Children' s Hospital ' ' V. Buzzi' ' , Department of Radiology and Neuroradiology, Milan (Italy); University of Milan, Department of Health Sciences, Milan (Italy); Frassoni, Carolina; Inverardi, Francesca [Fondazione I.R.C.C.S. Istituto Neurologico ' ' C. Besta' ' , Clinical Epileptology and Experimental Neurophysiology Unit, Milan (Italy); Bulfamante, Gaetano; Avagliano, Laura [San Paolo Hospital, Division of Human Pathology, Milan (Italy); Rustico, Mariangela [Children' s Hospital ' ' V. Buzzi' ' , Department of Obstetrics and Gynaecology, Prenatal Diagnosis, Milan (Italy)

    2016-03-15

    Ganglionic eminence (GE) is a transient fetal brain structure that harvests a significant amount of precursors of cortical GABA-ergic interneurons. Prenatal magnetic resonance (MR) imaging features of GE anomalies (i.e., cavitations) have already been reported associated with severe micro-lissencephaly. The purpose of this report was to illustrate the MR imaging features of GE anomalies in conditions other than severe micro-lissencephalies. Among all the fetuses submitted to prenatal MR imaging at our center from 2005 to 2014, we collected eight cases with GE anomalies and only limited associated brain anomalies. The median gestational age at the time of MR imaging was 21 weeks ranging from 19 to 29 weeks. Two senior pediatric neuroradiologists categorized the anomalies of the GE region in two groups: group one showing cavitation in the GE region and group two showing enlarged GE region. For each fetal case, associated cranial anomalies were also reported. Five out of the eight cases were included in group one and three in group two. Besides the GE region abnormality, all eight cases had additional intracranial anomalies, such as mild partial callosal agenesis, vermian hypoplasia and rotation, cerebellar hypoplasia, ventriculomegaly, enlarged subarachnoid spaces, molar tooth malformation. Ultrasound generally detected most of the associated intracranial anomalies, prompting the MR investigation; on the contrary in none of the cases, GE anomalies had been detected by ultrasound. Our observation expands the spectrum of human GE anomalies, demonstrating that these may take place also without associated severe micro-lissencephalies. (orig.)

  5. MAGSAT anomaly map and continental drift

    Science.gov (United States)

    Lemouel, J. L. (Principal Investigator); Galdeano, A.; Ducruix, J.

    1981-01-01

    Anomaly maps of high quality are needed to display unambiguously the so called long wave length anomalies. The anomalies were analyzed in terms of continental drift and the nature of their sources is discussed. The map presented confirms the thinness of the oceanic magnetized layer. Continental magnetic anomalies are characterized by elongated structures generally of east-west trend. Paleomagnetic reconstruction shows that the anomalies found in India, Australia, and Antarctic exhibit a fair consistency with the African anomalies. It is also shown that anomalies are locked under the continents and have a fixed geometry.

  6. Adrenal glands in patients with cogenital renal anomalies: CT appearance

    Energy Technology Data Exchange (ETDEWEB)

    Kenney, P.J.; Robbins, G.L.; Ellis, D.A.; Spirt, B.A.

    1985-04-01

    The CT appearance of the adrenal glands was investigated in 30 patients with congenital renal anomalies. The ipsilateral adrenal was clearly identified in 83% of these patients; in all of them, the adrenal was a paraspinal disk-shaped organ, which appeared linear on CT. Conversely, the adrenals retained their normal shape in a control group of 20 patients with acquired renal atrophy or prior simple nephrectomy.

  7. Ant colony optimization-based firewall anomaly mitigation engine.

    Science.gov (United States)

    Penmatsa, Ravi Kiran Varma; Vatsavayi, Valli Kumari; Samayamantula, Srinivas Kumar

    2016-01-01

    A firewall is the most essential component of network perimeter security. Due to human error and the involvement of multiple administrators in configuring firewall rules, there exist common anomalies in firewall rulesets such as Shadowing, Generalization, Correlation, and Redundancy. There is a need for research on efficient ways of resolving such anomalies. The challenge is also to see that the reordered or resolved ruleset conforms to the organization's framed security policy. This study proposes an ant colony optimization (ACO)-based anomaly resolution and reordering of firewall rules called ACO-based firewall anomaly mitigation engine. Modified strategies are also introduced to automatically detect these anomalies and to minimize manual intervention of the administrator. Furthermore, an adaptive reordering strategy is proposed to aid faster reordering when a new rule is appended. The proposed approach was tested with different firewall policy sets. The results were found to be promising in terms of the number of conflicts resolved, with minimal availability loss and marginal security risk. This work demonstrated the application of a metaheuristic search technique, ACO, in improving the performance of a packet-filter firewall with respect to mitigating anomalies in the rules, and at the same time demonstrated conformance to the security policy.

  8. Determination of organophosphorus pesticides and metabolites in cereal-based baby foods and wheat flour by means of ultrasound-assisted extraction and hollow-fiber liquid-phase microextraction prior to gas chromatography with nitrogen phosphorus detection.

    Science.gov (United States)

    González-Curbelo, Miguel Ángel; Hernández-Borges, Javier; Borges-Miquel, Teresa María; Rodríguez-Delgado, Miguel Ángel

    2013-10-25

    A new method based on hollow-fiber liquid-phase microextraction (HF-LPME) has been developed for the determination of a group of organophosphorus pesticides, including some of their metabolites, in two commercial cereal-based baby foods and one wheat flour prior to gas chromatography-nitrogen phosphorus detection. Samples were first extracted by ultrasound-assisted extraction with acetonitrile (ACN) containing 1.25% (v/v) of formic acid. After evaporation and reconstitution in Milli-Q water, the HF-LPME procedure, using 1-octanol as extraction solvent, was applied followed by a desorption step in ACN, which clearly improved the performance of the technique. The effects of sample pH, ionic strength, stirring rate, extraction temperature and time as well as the desorption procedure were investigated. Under the optimum conditions that involved the extraction of the analytes from 10 mL of the water reconstituted extract at pH 7.0 containing 5% (w/v) of NaCl for 45 min at 960 rpm, the method was validated in terms of linearity, precision and accuracy. The limits of detection (LODs) were between 0.29 and 3.20 μg/kg. The extraction of Milli-Q water, as an example of the applicability of the procedure to aqueous samples, allowed achieving LODs in the range 0.01-0.04 μg/L. Such values, together with the ones achieved for the rest of the samples, are below or equal to the maximum residue limits specified by the European Union. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. Prior indigenous technological species

    Science.gov (United States)

    Wright, Jason T.

    2018-01-01

    One of the primary open questions of astrobiology is whether there is extant or extinct life elsewhere the solar system. Implicit in much of this work is that we are looking for microbial or, at best, unintelligent life, even though technological artefacts might be much easier to find. Search for Extraterrestrial Intelligence (SETI) work on searches for alien artefacts in the solar system typically presumes that such artefacts would be of extrasolar origin, even though life is known to have existed in the solar system, on Earth, for eons. But if a prior technological, perhaps spacefaring, species ever arose in the solar system, it might have produced artefacts or other technosignatures that have survived to present day, meaning solar system artefact SETI provides a potential path to resolving astrobiology's question. Here, I discuss the origins and possible locations for technosignatures of such a prior indigenous technological species, which might have arisen on ancient Earth or another body, such as a pre-greenhouse Venus or a wet Mars. In the case of Venus, the arrival of its global greenhouse and potential resurfacing might have erased all evidence of its existence on the Venusian surface. In the case of Earth, erosion and, ultimately, plate tectonics may have erased most such evidence if the species lived Gyr ago. Remaining indigenous technosignatures might be expected to be extremely old, limiting the places they might still be found to beneath the surfaces of Mars and the Moon, or in the outer solar system.

  10. Radiologic analysis of congenital limb anomalies

    International Nuclear Information System (INIS)

    Chung, Hong Jun; Kim, Ok Hwa; Shinn, Kyung Sub; Kim, Nam Ae

    1994-01-01

    Congenital limb anomalies are manifested in various degree of severity and complexity bearing conclusion for description and nomenclature of each anomaly. We retrospectively analyzed the roentgenograms of congenital limb anomalies for the purpose of further understanding of the radiologic manifestations based on the embryonal defect and also to find the incidence of each anomaly. Total number of the patients was 89 with 137 anomalies. Recently the uniform system of classification for congenital anomalies of the upper limb was adopted by International Federation of Societies for Surgery of the Hand (IFSSH), which were categorized as 7 classifications. We used the IFSSH classification with some modification as 5 classifications; failure of formation of parts, failure of differentiation of parts, duplications, overgrowth and undergrowth. The patients with upper limb anomalies were 65 out of 89(73%), lower limb were 21(24%), and both upper and lower limb anomalies were 3(4%). Failure of formation was seen in 18%, failure of differentiation 39%, duplications 39%, overgrowth 8%, and undergrowth in 12%. Thirty-five patients had more than one anomaly, and 14 patients had intergroup anomalies. The upper limb anomalies were more common than lower limb. Among the anomalies, failure of differentiation and duplications were the most common types of congenital limb anomalies. Patients with failure of formation, failure of differentiation, and undergrowth had intergroup association of anomalies, but duplication and overgrowth tended to be isolated anomalies

  11. WFC3 IR subarray anomaly

    Science.gov (United States)

    Bushouse, Howard

    2009-07-01

    Certain combinations of WFC3 IR subarray size and sample sequence yield images that show a sharp change in background level that exactly bi-sects each detector amplifier quadrant. The change in level has an amplitude of a few DN per pixel. The cause of this anomaly and its apparent correlation with subarray size and sample sequence is not understood. Given the 4 available subarray sizes and 11 available readout sample sequences, there are a total of 44 possible subarray mode readout combinations. To date, 14 of those combinations have been used on-orbit in either calibration and GO programs. Of those, 3 combinations show the anomaly. This program will obtain IR dark exposures in the remaining 30 readout combinations that have not yet been explored. This will add to our knowledge of which combinations show the anomaly and will therefore help us to understand its origin.

  12. Multiple Visceral and Peritoneal Anomalies

    Directory of Open Access Journals (Sweden)

    Gayathri Prabhu S

    2016-07-01

    Full Text Available Visceral and peritoneal anomalies are frequently encountered during cadaveric dissections and surgical procedures of abdomen. A thorough knowledge of the same is required for the success of diagnostic, surgical and radiological procedures of abdomen. We report multiple peritoneal and visceral anomalies noted during dissection classes for medical undergraduates. The anomalies were found in an adult male cadaver aged approximately 70 years. The right iliac fossa was empty due to the sub-hepatic position of caecum and appendix. The sigmoid colon formed an inverted “U” shaped loop above the sacral promontory in the median position. It entered the pelvis from the right side and descended along the lateral wall of the pelvis. The sigmoid mesocolon was attached obliquely to the posterior abdominal wall, just above the sacral promontory. Further there was a cysto-colic fold of peritoneum extending from the right colic flexure. We discuss the clinical significance of the variations.

  13. Incidence of Congenital Spinal Abnormalities Among Pediatric Patients and Their Association With Scoliosis and Systemic Anomalies.

    Science.gov (United States)

    Passias, Peter G; Poorman, Gregory W; Jalai, Cyrus M; Diebo, Bassel G; Vira, Shaleen; Horn, Samantha R; Baker, Joseph F; Shenoy, Kartik; Hasan, Saqib; Buza, John; Bronson, Wesley; Paul, Justin C; Kaye, Ian; Foster, Norah A; Cassilly, Ryan T; Oren, Jonathan H; Moskovich, Ronald; Line, Breton; Oh, Cheongeun; Bess, Shay; LaFage, Virginie; Errico, Thomas J

    2017-10-09

    Congenital abnormalities when present, according to VACTERL theory, occur nonrandomly with other congenital anomalies. This study estimates the prevalence of congenital spinal anomalies, and their concurrence with other systemic anomalies. A retrospective cohort analysis on Health care Cost and Utilization Project's Kids Inpatient Database (KID), years 2000, 2003, 2006, 2009 was performed. ICD-9 coding identified congenital anomalies of the spine and other body systems. Overall incidence of congenital spinal abnormalities in pediatric patients, and the concurrence of spinal anomaly diagnoses with other organ system anomalies. Frequencies of congenital spine anomalies were estimated using KID hospital-and-year-adjusted weights. Poisson distribution in contingency tables tabulated concurrence of other congenital anomalies, grouped by body system. Of 12,039,432 patients, rates per 100,000 cases were: 9.1 hemivertebra, 4.3 Klippel-Fiel, 56.3 Chiari malformation, 52.6 tethered cord, 83.4 spina bifida, 1.2 absence of vertebra, and 6.2 diastematomyelia. Diastematomyelia had the highest concurrence of other anomalies: 70.1% of diastematomyelia patients had at least one other congenital anomaly. Next, 63.2% of hemivertebra, and 35.2% of Klippel-Fiel patients had concurrent anomalies. Of the other systems deformities cooccuring, cardiac system had the highest concurrent incidence (6.5% overall). In light of VACTERL's definition of a patient being diagnosed with at least 3 VACTERL anomalies, hemivertebra patients had the highest cooccurrence of ≥3 anomalies (31.3%). With detailed analysis of hemivertebra patients, secundum ASD (14.49%), atresia of large intestine (10.2%), renal agenesis (7.43%) frequently cooccured. Congenital abnormalities of the spine are associated with serious systemic anomalies that may have delayed presentations. These patients continue to be at a very high, and maybe higher than previously thought, risk for comorbidities that can cause devastating

  14. Review on possible gravitational anomalies

    International Nuclear Information System (INIS)

    Amador, Xavier E

    2005-01-01

    This is an updated introductory review of 2 possible gravitational anomalies that has attracted part of the Scientific community: the Allais effect that occur during solar eclipses, and the Pioneer 10 spacecraft anomaly, experimented also by Pioneer 11 and Ulysses spacecrafts. It seems that, to date, no satisfactory conventional explanation exist to these phenomena, and this suggests that possible new physics will be needed to account for them. The main purpose of this review is to announce 3 other new measurements that will be carried on during the 2005 solar eclipses in Panama and Colombia (Apr. 8) and in Portugal (Oct.15)

  15. Sequential auctions and price anomalies

    Directory of Open Access Journals (Sweden)

    Trifunović Dejan

    2014-01-01

    Full Text Available In sequential auctions objects are sold one by one in separate auctions. These sequential auctions might be organized as sequential first-price, second-price, or English auctions. We will derive equilibrium bidding strategies for these auctions. Theoretical models suggest that prices in sequential auctions with private values or with randomly assigned heterogeneous objects should have no trend. However, empirical research contradicts this result and prices exhibit a declining or increasing trend, which is called declining and increasing price anomaly. We will present a review of these empirical results, as well as different theoretical explanations for these anomalies.

  16. arXiv Anomaly-Free Models for Flavour Anomalies

    CERN Document Server

    Ellis, John; Tunney, Patrick

    We explore the constraints imposed by the cancellation of triangle anomalies on models in which the flavour anomalies reported by LHCb and other experiments are due to an extra U(1)' gauge boson Z'. We assume universal and rational U(1)' charges for the first two generations of left-handed quarks and of right-handed up-type quarks but allow different charges for their third-generation counterparts. If the right-handed charges vanish, cancellation of the triangle anomalies requires all the quark U(1)' charges to vanish, if there are either no exotic fermions or there is only one Standard Model singlet dark matter (DM) fermion. There are non-trivial anomaly-free models with more than one such `dark' fermion, or with a single DM fermion if right-handed up-type quarks have non-zero U(1)' charges. In some of the latter models the U(1)' couplings of the first- and second-generation quarks all vanish, weakening the LHC Z' constraint, and in some other models the DM particle has purely axial couplings, weakening the ...

  17. Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

    Science.gov (United States)

    Sharma, Manali; Das, Kamalika; Bilgic, Mustafa; Matthews, Bryan; Nielsen, David Lynn; Oza, Nikunj C.

    2016-01-01

    A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

  18. Controversies and considerations regarding the termination of pregnancy for foetal anomalies in Islam.

    Science.gov (United States)

    Al-Matary, Abdulrahman; Ali, Jaffar

    2014-02-05

    Approximately one-fourth of all the inhabitants on earth are Muslims. Due to unprecedented migration, physicians are often confronted with cultures other than their own that adhere to different paradigms. In Islam, and most religions, abortion is forbidden. Islam is considerably liberal concerning abortion, which is dependent on (i) the threat of harm to mothers, (ii) the status of the pregnancy before or after ensoulment (on the 120th day of gestation), and (iii) the presence of foetal anomalies that are incompatible with life. Considerable variation in religious edicts exists, but most Islamic scholars agree that the termination of a pregnancy for foetal anomalies is allowed before ensoulment, after which abortion becomes totally forbidden, even in the presence of foetal abnormalities; the exception being a risk to the mother's life or confirmed intrauterine death. The authors urge Muslim law makers to also consider abortion post ensoulment if it is certain that the malformed foetus will decease soon after birth or will be severely malformed and physically and mentally incapacitated after birth to avoid substantial hardship that may continue for years for mothers and family members. The authors recommend that an institutional committee governed and monitored by a national committee make decisions pertaining to abortion to ensure that ethics are preserved and mistakes are prevented. Anomalous foetuses must be detected at the earliest possible time to enable an appropriate medical intervention prior to the 120th day.

  19. Using scan statistics for congenital anomalies surveillance: the EUROCAT methodology.

    Science.gov (United States)

    Teljeur, Conor; Kelly, Alan; Loane, Maria; Densem, James; Dolk, Helen

    2015-11-01

    Scan statistics have been used extensively to identify temporal clusters of health events. We describe the temporal cluster detection methodology adopted by the EUROCAT (European Surveillance of Congenital Anomalies) monitoring system. Since 2001, EUROCAT has implemented variable window width scan statistic for detecting unusual temporal aggregations of congenital anomaly cases. The scan windows are based on numbers of cases rather than being defined by time. The methodology is imbedded in the EUROCAT Central Database for annual application to centrally held registry data. The methodology was incrementally adapted to improve the utility and to address statistical issues. Simulation exercises were used to determine the power of the methodology to identify periods of raised risk (of 1-18 months). In order to operationalize the scan methodology, a number of adaptations were needed, including: estimating date of conception as unit of time; deciding the maximum length (in time) and recency of clusters of interest; reporting of multiple and overlapping significant clusters; replacing the Monte Carlo simulation with a lookup table to reduce computation time; and placing a threshold on underlying population change and estimating the false positive rate by simulation. Exploration of power found that raised risk periods lasting 1 month are unlikely to be detected except when the relative risk and case counts are high. The variable window width scan statistic is a useful tool for the surveillance of congenital anomalies. Numerous adaptations have improved the utility of the original methodology in the context of temporal cluster detection in congenital anomalies.

  20. Anomaly Monitoring Method for Key Components of Satellite

    Directory of Open Access Journals (Sweden)

    Jian Peng

    2014-01-01

    Full Text Available This paper presented a fault diagnosis method for key components of satellite, called Anomaly Monitoring Method (AMM, which is made up of state estimation based on Multivariate State Estimation Techniques (MSET and anomaly detection based on Sequential Probability Ratio Test (SPRT. On the basis of analysis failure of lithium-ion batteries (LIBs, we divided the failure of LIBs into internal failure, external failure, and thermal runaway and selected electrolyte resistance (Re and the charge transfer resistance (Rct as the key parameters of state estimation. Then, through the actual in-orbit telemetry data of the key parameters of LIBs, we obtained the actual residual value (RX and healthy residual value (RL of LIBs based on the state estimation of MSET, and then, through the residual values (RX and RL of LIBs, we detected the anomaly states based on the anomaly detection of SPRT. Lastly, we conducted an example of AMM for LIBs, and, according to the results of AMM, we validated the feasibility and effectiveness of AMM by comparing it with the results of threshold detective method (TDM.