WorldWideScience

Sample records for anomalies detected prior

  1. 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...

  2. 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...

  3. Road Anomalies Detection System Evaluation.

    Science.gov (United States)

    Silva, Nuno; Shah, Vaibhav; Soares, João; Rodrigues, Helena

    2018-06-21

    Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a “conditioned” and a real world setup, where the system performed worse compared to the “conditioned” setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities.

  4. 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

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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...

  10. 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...

  11. 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)

  12. 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.

  13. 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....

  14. 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.

  15. Detection of cardiovascular anomalies: Hybrid systems approach

    KAUST Repository

    Ledezma, Fernando; Laleg-Kirati, Taous-Meriem

    2012-01-01

    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

  16. Residual generator for cardiovascular anomalies detection

    KAUST Repository

    Belkhatir, Zehor; Laleg-Kirati, Taous-Meriem; Tadjine, Mohamed

    2014-01-01

    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

  17. 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)

  18. Reducing customer minutes lost by anomaly detection?

    NARCIS (Netherlands)

    Bakker, M.; Vreeburg, J.H.G.; Rietveld, L.C.; van der Roer, M.

    2012-01-01

    An method which compares measured and predicted water demands to detect anomalies, was developed and tested on three data sets of water demand of three years in which and 25 pipe bursts were reported. The method proved to be able to detect bursts where the water loss exceeds 30% of the average water

  19. 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.

  20. 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.

  1. Network Anomaly Detection Based on Wavelet Analysis

    Science.gov (United States)

    Lu, Wei; Ghorbani, Ali A.

    2008-12-01

    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.

  2. A Semiparametric Model for Hyperspectral Anomaly Detection

    Science.gov (United States)

    2012-01-01

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

  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. Hyperspectral anomaly detection using Sony PlayStation 3

    Science.gov (United States)

    Rosario, Dalton; Romano, João; Sepulveda, Rene

    2009-05-01

    We present a proof-of-principle demonstration using Sony's IBM Cell processor-based PlayStation 3 (PS3) to run-in near real-time-a hyperspectral anomaly detection algorithm (HADA) on real hyperspectral (HS) long-wave infrared imagery. The PS3 console proved to be ideal for doing precisely the kind of heavy computational lifting HS based algorithms require, and the fact that it is a relatively open platform makes programming scientific applications feasible. The PS3 HADA is a unique parallel-random sampling based anomaly detection approach that does not require prior spectra of the clutter background. The PS3 HADA is designed to handle known underlying difficulties (e.g., target shape/scale uncertainties) often ignored in the development of autonomous anomaly detection algorithms. The effort is part of an ongoing cooperative contribution between the Army Research Laboratory and the Army's Armament, Research, Development and Engineering Center, which aims at demonstrating performance of innovative algorithmic approaches for applications requiring autonomous anomaly detection using passive sensors.

  5. Anomaly Detection using the "Isolation Forest" algorithm

    CERN Multimedia

    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. System and method for anomaly detection

    Science.gov (United States)

    Scherrer, Chad

    2010-06-15

    A system and method for detecting one or more anomalies in a plurality of observations is provided. In one illustrative embodiment, the observations are real-time network observations collected from a stream of network traffic. The method includes performing a discrete decomposition of the observations, and introducing derived variables to increase storage and query efficiencies. A mathematical model, such as a conditional independence model, is then generated from the formatted data. The formatted data is also used to construct frequency tables which maintain an accurate count of specific variable occurrence as indicated by the model generation process. The formatted data is then applied to the mathematical model to generate scored data. The scored data is then analyzed to detect anomalies.

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

    KAUST Repository

    Harrou, Fouzi; Sun, Ying; Khadraoui, Sofiane

    2016-01-01

    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

  8. Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms

    OpenAIRE

    Zhang, James; Vukotic, Ilija; Gardner, Robert

    2018-01-01

    Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network performance data, gathered by perfSONAR, using two machine learning algorithms: Boosted Decision Trees (BDT...

  9. 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.

  10. Suboptimal processor for anomaly detection for system surveillance and diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Ciftcioglu, Oe.; Hoogenboom, J.E.; Dam, H. van

    1989-06-01

    Anomaly detection for nuclear reactor surveillance and diagnosis is described. The residual noise obtained as a result of autoregressive (AR) modelling is essential to obtain high sensitivity for anomaly detection. By means of the method of hypothesis testing a suboptimal anomaly detection processor is devised for system surveillance and diagnosis. Experiments are carried out to investigate the performance of the processor, which is in particular of interest for on-line and real-time applications.

  11. 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

  12. 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...

  13. 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

  14. Signal anomaly detection using modified CUSUM [cumulative sum] method

    International Nuclear Information System (INIS)

    Morgenstern, V.; Upadhyaya, B.R.; Benedetti, M.

    1988-01-01

    An important aspect of detection of anomalies in signals is the identification of changes in signal behavior caused by noise, jumps, changes in band-width, sudden pulses and signal bias. A methodology is developed to identify, isolate and characterize these anomalies using a modification of the cumulative sum (CUSUM) approach. The new algorithm performs anomaly detection at three levels and is implemented on a general purpose computer. 7 refs., 4 figs

  15. 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

  16. 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.

  17. Generative adversarial networks for anomaly detection in images

    OpenAIRE

    Batiste Ros, Guillem

    2018-01-01

    Anomaly detection is used to identify abnormal observations that don t follow a normal pattern. Inthis work, we use the power of Generative Adversarial Networks in sampling from image distributionsto perform anomaly detection with images and to identify local anomalous segments within thisimages. Also, we explore potential application of this method to support pathological analysis ofbiological tissues

  18. Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

    Directory of Open Access Journals (Sweden)

    Shaonian Huang

    2018-01-01

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

  19. 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.

  20. 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.

  1. 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.

  2. Anomaly Detection in the Bitcoin System - A Network Perspective

    OpenAIRE

    Pham, Thai; Lee, Steven

    2016-01-01

    The problem of anomaly detection has been studied for a long time, and many Network Analysis techniques have been proposed as solutions. Although some results appear to be quite promising, no method is clearly to be superior to the rest. In this paper, we particularly consider anomaly detection in the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use ...

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

    NARCIS (Netherlands)

    Garne, Ester; Dolk, Helen; Loane, Maria; Boyd, Patricia A.

    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

  4. 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 us...

  5. 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)

  6. 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)

  7. 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...

  8. 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, ...

  9. 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...

  10. Condition Parameter Modeling for Anomaly Detection in Wind Turbines

    Directory of Open Access Journals (Sweden)

    Yonglong Yan

    2014-05-01

    Full Text Available Data collected from the supervisory control and data acquisition (SCADA system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs, is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified.

  11. 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...

  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. ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM

    Directory of Open Access Journals (Sweden)

    D. Amutha Guka

    2012-01-01

    Full Text Available Especially in today’s network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA which is based on Artificial Immune Systems (AIS and Genetic Algorithm (GA. In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS. The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.

  14. Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods

    National Research Council Canada - National Science Library

    Smetek, Timothy E

    2007-01-01

    This research extends the field of hyperspectral target detection by developing autonomous anomaly detection and signature matching methodologies that reduce false alarms relative to existing benchmark detectors...

  15. Anomaly detection using magnetic flux leakage technology

    Energy Technology Data Exchange (ETDEWEB)

    Rempel, Raymond G. [BJ Pipeline Inspection Services, Alberta (Canada)

    2005-07-01

    There are many aspects to properly assessing the integrity of a pipeline. In-line-Inspection (ILI) tools, in particular those that employ the advanced use of Magnetic Flux Leakage (MFL) technology, provide a valuable means of achieving required up-to-date knowledge of a pipeline. More prevalent use of High Resolution MFL In-Line-Inspection tools is growing the knowledge base that leads to more reliable and accurate identification of anomalies in a pipeline, thus, minimizing the need for expensive verification excavations. Accurate assessment of pipeline anomalies can improve the decision making process within an Integrity Management Program and excavation programs can then focus on required repairs instead of calibration or exploratory digs. Utilizing the information from an MFL ILI inspection is not only cost effective but, as well, can also prove to be an extremely valuable building block of a Pipeline Integrity Management Program. (author)

  16. Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

    Science.gov (United States)

    Uchida, Masato; Nawata, Shuichi; Gu, Yu; Tsuru, Masato; Oie, Yuji

    We propose an anomaly detection method for finding patterns in network traffic that do not conform to legitimate (i.e., normal) behavior. The proposed method trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline model is used as the basis for comparison with the audit network traffic. This anomaly detection works in an unsupervised manner through the use of time-periodic packet sampling, which is used in a manner that differs from its intended purpose — the lossy nature of packet sampling is used to extract normal packets from the unlabeled original traffic data. Evaluation using actual traffic traces showed that the proposed method has false positive and false negative rates in the detection of anomalies regarding TCP SYN packets comparable to those of a conventional method that uses manually labeled traffic data to train the baseline model. Performance variation due to the probabilistic nature of sampled traffic data is mitigated by using ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Alarm sensitivity is adjusted for the intended use by using maximum- and minimum-based anomaly detection that effectively take advantage of the performance variations among the multiple baseline models. Testing using actual traffic traces showed that the proposed anomaly detection method performs as well as one using manually labeled traffic data and better than one using randomly sampled (unlabeled) traffic data.

  17. A Survey on Anomaly Based Host Intrusion Detection System

    Science.gov (United States)

    Jose, Shijoe; Malathi, D.; Reddy, Bharath; Jayaseeli, Dorathi

    2018-04-01

    An intrusion detection system (IDS) is hardware, software or a combination of two, for monitoring network or system activities to detect malicious signs. In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems. The primary function of system is detecting intrusion and gives alerts when user tries to intrusion on timely manner. In these techniques when IDS find out intrusion it will send alert massage to the system administrator. Anomaly detection is an important problem that has been researched within diverse research areas and application domains. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. From the existing anomaly detection techniques, each technique has relative strengths and weaknesses. The current state of the experiment practice in the field of anomaly-based intrusion detection is reviewed and survey recent studies in this. This survey provides a study of existing anomaly detection techniques, and how the techniques used in one area can be applied in another application domain.

  18. 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

  19. 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.

  20. Structural material anomaly detection system using water chemistry data

    International Nuclear Information System (INIS)

    Asakura, Yamato; Nagase, Makoto; Uchida, Shunsuke; Ohsumi, Katsumi.

    1992-01-01

    The concept of an advanced water chemistry diagnosis system for detection of anomalies and preventive maintenance of system components is proposed and put into a concrete form. Using the analogy to a medical inspection system, analyses of water chemistry change will make it possible to detect symptoms of anomalies in system components. Then, correlations between water chemistry change and anomaly occurrence in the components of the BWR primary cooling system are analyzed theoretically. These fragmentary correlations are organized and reduced to an algorithm for the on-line diagnosis system using on-line monitoring data, pH and conductivity. By using actual plant data, the on-line diagnosis model system is verified to be applicable for early and automatic finding of the anomaly cause and for timely supply of much diagnostic information to plant operators. (author)

  1. 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.

  2. 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

  3. An incremental anomaly detection model for virtual machines.

    Directory of Open Access Journals (Sweden)

    Hancui Zhang

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

  4. 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.

  5. Approaches in anomaly-based network intrusion detection systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, S.; Di Pietro, R.; Mancini, L.V.

    2008-01-01

    Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at

  6. Approaches in Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, Sandro

    Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at

  7. 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.

  8. Online Anomaly Energy Consumption Detection Using Lambda Architecture

    DEFF Research Database (Denmark)

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

    2016-01-01

    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...... of the lambda detection system....

  9. 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.

  10. Identifying Threats Using Graph-based Anomaly Detection

    Science.gov (United States)

    Eberle, William; Holder, Lawrence; Cook, Diane

    Much of the data collected during the monitoring of cyber and other infrastructures is structural in nature, consisting of various types of entities and relationships between them. The detection of threatening anomalies in such data is crucial to protecting these infrastructures. We present an approach to detecting anomalies in a graph-based representation of such data that explicitly represents these entities and relationships. The approach consists of first finding normative patterns in the data using graph-based data mining and then searching for small, unexpected deviations to these normative patterns, assuming illicit behavior tries to mimic legitimate, normative behavior. The approach is evaluated using several synthetic and real-world datasets. Results show that the approach has high truepositive rates, low false-positive rates, and is capable of detecting complex structural anomalies in real-world domains including email communications, cellphone calls and network traffic.

  11. Anomalies

    International Nuclear Information System (INIS)

    Bardeen, W.A.

    1985-08-01

    Anomalies have a diverse impact on many aspects of physical phenomena. The role of anomalies in determining physical structure from the amplitude for π 0 decay to the foundations of superstring theory will be reviewed. 36 refs

  12. 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.

  13. 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...

  14. 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

  15. Automatic detection of anomalies in screening mammograms

    Science.gov (United States)

    2013-01-01

    Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature

  16. Unsupervised topic discovery by anomaly detection

    OpenAIRE

    Cheng, Leon

    2013-01-01

    Approved for public release; distribution is unlimited With the vast amount of information and public comment available online, it is of increasing interest to understand what is being said and what topics are trending online. Government agencies, for example, want to know what policies concern the public without having to look through thousands of comments manually. Topic detection provides automatic identification of topics in documents based on the information content and enhances many ...

  17. 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...

  18. 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

  19. Monitoring water supply systems for anomaly detection and response

    NARCIS (Netherlands)

    Bakker, M.; Lapikas, T.; Tangena, B.H.; Vreeburg, J.H.G.

    2012-01-01

    Water supply systems are vulnerable to damage caused by unintended or intended human actions, or due to aging of the system. In order to minimize the damages and the inconvenience for the customers, a software tool was developed to detect anomalies at an early stage, and to support the responsible

  20. Searching for Complex Patterns Using Disjunctive Anomaly Detection

    OpenAIRE

    Sabhnani, Maheshkumar; Dubrawski, Artur; Schneider, Jeff

    2013-01-01

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

  1. 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.

  2. 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

  3. 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.

  4. Evaluation of Anomaly Detection Techniques for SCADA Communication Resilience

    OpenAIRE

    Shirazi, Syed Noor Ul Hassan; Gouglidis, Antonios; Syeda, Kanza Noor; Simpson, Steven; Mauthe, Andreas Ulrich; Stephanakis, Ioannis M.; Hutchison, David

    2016-01-01

    Attacks on critical infrastructures’ Supervisory Control and Data Acquisition (SCADA) systems are beginning to increase. They are often initiated by highly skilled attackers, who are capable of deploying sophisticated attacks to exfiltrate data or even to cause physical damage. In this paper, we rehearse the rationale for protecting against cyber attacks and evaluate a set of Anomaly Detection (AD) techniques in detecting attacks by analysing traffic captured in a SCADA network. For this purp...

  5. 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.

  6. Boiling anomaly detection by various signal characterization methods

    International Nuclear Information System (INIS)

    Sakuma, M.; Kozma, R.; Kitamura, M.; Schoonewelle, H.; Hoogenboom, J.E.

    1996-01-01

    In order to detect anomalies in the early stage for complex dynamical systems like nuclear power plants, it is important to characterize various statistical features of the data acquired in normal operating condition. In this paper, concept of hierarchical anomaly monitoring method is outlined, which is based on the diversification principle. In addition to usual time and frequency domain analysis (FFT, APDF, MAR-SPRT), other analysis (wavelet, fractal, etc.) are performed. As soon as any inconsistency arises in the results of the analysis on the upper level, a thorough analysis is initiated. A comparison among these methods is performed and the efficiency of the diversification approach has been demonstrated through simulated boiling anomalies in nuclear reactors. (authors)

  7. 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.

  8. 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...

  9. A comparison of three time-domain anomaly detection methods

    Energy Technology Data Exchange (ETDEWEB)

    Schoonewelle, H.; Hagen, T.H.J.J. van der; Hoogenboom, J.E. [Delft University of Technology (Netherlands). Interfaculty Reactor Institute

    1996-01-01

    Three anomaly detection methods based on a comparison of signal values with predictions from an autoregressive model are presented. These methods are: the extremes method, the {chi}{sup 2} method and the sequential probability ratio test. The methods are used to detect a change of the standard deviation of the residual noise obtained from applying an autoregressive model. They are fast and can be used in on-line applications. For each method some important anomaly detection parameters are determined by calculation or simulation. These parameters are: the false alarm rate, the average time to alarm and - being of minor importance -the alarm failure rate. Each method is optimized with respect to the average time to alarm for a given value of the false alarm rate. The methods are compared with each other, resulting in the sequential probability ratio test being clearly superior. (author).

  10. A comparison of three time-domain anomaly detection methods

    International Nuclear Information System (INIS)

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

    1996-01-01

    Three anomaly detection methods based on a comparison of signal values with predictions from an autoregressive model are presented. These methods are: the extremes method, the χ 2 method and the sequential probability ratio test. The methods are used to detect a change of the standard deviation of the residual noise obtained from applying an autoregressive model. They are fast and can be used in on-line applications. For each method some important anomaly detection parameters are determined by calculation or simulation. These parameters are: the false alarm rate, the average time to alarm and - being of minor importance -the alarm failure rate. Each method is optimized with respect to the average time to alarm for a given value of the false alarm rate. The methods are compared with each other, resulting in the sequential probability ratio test being clearly superior. (author)

  11. 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

  12. Spectral anomaly methods for aerial detection using KUT nuisance rejection

    International Nuclear Information System (INIS)

    Detwiler, R.S.; Pfund, D.M.; Myjak, M.J.; Kulisek, J.A.; Seifert, C.E.

    2015-01-01

    This work discusses the application and optimization of a spectral anomaly method for the real-time detection of gamma radiation sources from an aerial helicopter platform. Aerial detection presents several key challenges over ground-based detection. For one, larger and more rapid background fluctuations are typical due to higher speeds, larger field of view, and geographically induced background changes. As well, the possible large altitude or stand-off distance variations cause significant steps in background count rate as well as spectral changes due to increased gamma-ray scatter with detection at higher altitudes. The work here details the adaptation and optimization of the PNNL-developed algorithm Nuisance-Rejecting Spectral Comparison Ratios for Anomaly Detection (NSCRAD), a spectral anomaly method previously developed for ground-based applications, for an aerial platform. The algorithm has been optimized for two multi-detector systems; a NaI(Tl)-detector-based system and a CsI detector array. The optimization here details the adaptation of the spectral windows for a particular set of target sources to aerial detection and the tailoring for the specific detectors. As well, the methodology and results for background rejection methods optimized for the aerial gamma-ray detection using Potassium, Uranium and Thorium (KUT) nuisance rejection are shown. Results indicate that use of a realistic KUT nuisance rejection may eliminate metric rises due to background magnitude and spectral steps encountered in aerial detection due to altitude changes and geographically induced steps such as at land–water interfaces

  13. Anomaly Detection for Next-Generation Space Launch Ground Operations

    Science.gov (United States)

    Spirkovska, Lilly; Iverson, David L.; Hall, David R.; Taylor, William M.; Patterson-Hine, Ann; Brown, Barbara; Ferrell, Bob A.; Waterman, Robert D.

    2010-01-01

    NASA is developing new capabilities that will enable future human exploration missions while reducing mission risk and cost. The Fault Detection, Isolation, and Recovery (FDIR) project aims to demonstrate the utility of integrated vehicle health management (IVHM) tools in the domain of ground support equipment (GSE) to be used for the next generation launch vehicles. In addition to demonstrating the utility of IVHM tools for GSE, FDIR aims to mature promising tools for use on future missions and document the level of effort - and hence cost - required to implement an application with each selected tool. One of the FDIR capabilities is anomaly detection, i.e., detecting off-nominal behavior. The tool we selected for this task uses a data-driven approach. Unlike rule-based and model-based systems that require manual extraction of system knowledge, data-driven systems take a radically different approach to reasoning. At the basic level, they start with data that represent nominal functioning of the system and automatically learn expected system behavior. The behavior is encoded in a knowledge base that represents "in-family" system operations. During real-time system monitoring or during post-flight analysis, incoming data is compared to that nominal system operating behavior knowledge base; a distance representing deviation from nominal is computed, providing a measure of how far "out of family" current behavior is. We describe the selected tool for FDIR anomaly detection - Inductive Monitoring System (IMS), how it fits into the FDIR architecture, the operations concept for the GSE anomaly monitoring, and some preliminary results of applying IMS to a Space Shuttle GSE anomaly.

  14. 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

  15. 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)

  16. 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.

  17. Using Physical Models for Anomaly Detection in Control Systems

    Science.gov (United States)

    Svendsen, Nils; Wolthusen, Stephen

    Supervisory control and data acquisition (SCADA) systems are increasingly used to operate critical infrastructure assets. However, the inclusion of advanced information technology and communications components and elaborate control strategies in SCADA systems increase the threat surface for external and subversion-type attacks. The problems are exacerbated by site-specific properties of SCADA environments that make subversion detection impractical; and by sensor noise and feedback characteristics that degrade conventional anomaly detection systems. Moreover, potential attack mechanisms are ill-defined and may include both physical and logical aspects.

  18. Inflight and Preflight Detection of Pitot Tube Anomalies

    Science.gov (United States)

    Mitchell, Darrell W.

    2014-01-01

    The health and integrity of aircraft sensors play a critical role in aviation safety. Inaccurate or false readings from these sensors can lead to improper decision making, resulting in serious and sometimes fatal consequences. This project demonstrated the feasibility of using advanced data analysis techniques to identify anomalies in Pitot tubes resulting from blockage such as icing, moisture, or foreign objects. The core technology used in this project is referred to as noise analysis because it relates sensors' response time to the dynamic component (noise) found in the signal of these same sensors. This analysis technique has used existing electrical signals of Pitot tube sensors that result from measured processes during inflight conditions and/or induced signals in preflight conditions to detect anomalies in the sensor readings. Analysis and Measurement Services Corporation (AMS Corp.) has routinely used this technology to determine the health of pressure transmitters in nuclear power plants. The application of this technology for the detection of aircraft anomalies is innovative. Instead of determining the health of process monitoring at a steady-state condition, this technology will be used to quickly inform the pilot when an air-speed indication becomes faulty under any flight condition as well as during preflight preparation.

  19. Rule-based expert system for maritime anomaly detection

    Science.gov (United States)

    Roy, Jean

    2010-04-01

    Maritime domain operators/analysts have a mandate to be aware of all that is happening within their areas of responsibility. This mandate derives from the needs to defend sovereignty, protect infrastructures, counter terrorism, detect illegal activities, etc., and it has become more challenging in the past decade, as commercial shipping turned into a potential threat. In particular, a huge portion of the data and information made available to the operators/analysts is mundane, from maritime platforms going about normal, legitimate activities, and it is very challenging for them to detect and identify the non-mundane. To achieve such anomaly detection, they must establish numerous relevant situational facts from a variety of sensor data streams. Unfortunately, many of the facts of interest just cannot be observed; the operators/analysts thus use their knowledge of the maritime domain and their reasoning faculties to infer these facts. As they are often overwhelmed by the large amount of data and information, automated reasoning tools could be used to support them by inferring the necessary facts, ultimately providing indications and warning on a small number of anomalous events worthy of their attention. Along this line of thought, this paper describes a proof-of-concept prototype of a rule-based expert system implementing automated rule-based reasoning in support of maritime anomaly detection.

  20. 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.

  1. Detection of Anomalies in Hydrometric Data Using Artificial Intelligence Techniques

    Science.gov (United States)

    Lauzon, N.; Lence, B. J.

    2002-12-01

    This work focuses on the detection of anomalies in hydrometric data sequences, such as 1) outliers, which are individual data having statistical properties that differ from those of the overall population; 2) shifts, which are sudden changes over time in the statistical properties of the historical records of data; and 3) trends, which are systematic changes over time in the statistical properties. For the purpose of the design and management of water resources systems, it is important to be aware of these anomalies in hydrometric data, for they can induce a bias in the estimation of water quantity and quality parameters. These anomalies may be viewed as specific patterns affecting the data, and therefore pattern recognition techniques can be used for identifying them. However, the number of possible patterns is very large for each type of anomaly and consequently large computing capacities are required to account for all possibilities using the standard statistical techniques, such as cluster analysis. Artificial intelligence techniques, such as the Kohonen neural network and fuzzy c-means, are clustering techniques commonly used for pattern recognition in several areas of engineering and have recently begun to be used for the analysis of natural systems. They require much less computing capacity than the standard statistical techniques, and therefore are well suited for the identification of outliers, shifts and trends in hydrometric data. This work constitutes a preliminary study, using synthetic data representing hydrometric data that can be found in Canada. The analysis of the results obtained shows that the Kohonen neural network and fuzzy c-means are reasonably successful in identifying anomalies. This work also addresses the problem of uncertainties inherent to the calibration procedures that fit the clusters to the possible patterns for both the Kohonen neural network and fuzzy c-means. Indeed, for the same database, different sets of clusters can be

  2. 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.

  3. Anomaly depth detection in trans-admittance mammography: a formula independent of anomaly size or admittivity contrast

    International Nuclear Information System (INIS)

    Zhang, Tingting; Lee, Eunjung; Seo, Jin Keun

    2014-01-01

    Trans-admittance mammography (TAM) is a bioimpedance technique for breast cancer detection. It is based on the comparison of tissue conductivity: cancerous tissue is identified by its higher conductivity in comparison with the surrounding normal tissue. In TAM, the breast is compressed between two electrical plates (in a similar architecture to x-ray mammography). The bottom plate has many sensing point electrodes that provide two-dimensional images (trans-admittance maps) that are induced by voltage differences between the two plates. Multi-frequency admittance data (Neumann data) are measured over the range 50 Hz–500 kHz. TAM aims to determine the location and size of any anomaly from the multi-frequency admittance data. Various anomaly detection algorithms can be used to process TAM data to determine the transverse positions of anomalies. However, existing methods cannot reliably determine the depth or size of an anomaly. Breast cancer detection using TAM would be improved if the depth or size of an anomaly could also be estimated, properties that are independent of the admittivity contrast. A formula is proposed here that can estimate the depth of an anomaly independent of its size and the admittivity contrast. This depth estimation can also be used to derive an estimation of the size of the anomaly. The proposed estimations are verified rigorously under a simplified model. Numerical simulation shows that the proposed method also works well in general settings. (paper)

  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. Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series

    Directory of Open Access Journals (Sweden)

    Jingyue Pang

    2018-03-01

    Full Text Available Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR and relevance vector machine (RVM are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP, which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%. There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application.

  6. Mobile Anomaly Detection Based on Improved Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Chunyong Yin

    2017-01-01

    Full Text Available Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. Early Detection and Identification of Anomalies in Chemical Regime

    International Nuclear Information System (INIS)

    Figedy, Stefan; Smiesko, Ivan

    2011-01-01

    This paper provides a brief information about the basic features of a newly developed diagnostic system for early detection and identification of anomalies incoming in the water chemistry regime of the primary and secondary circuit of VVER-440 reactor. This system, called SACHER (System of Analysis of CHEmical Regime) is being installed within the major modernization project at the NPP-V2 Bohunice in the Slovak Republic. System SACHER has been developed fully in MATLAB environment. The availability of prompt information about the chemical conditions of the primary and secondary circuit is very important to prevent the undue corrosion and deposit build-up. The typical chemical information systems that exist and work at the NPPs give the user values of the measured quantities together with their time trends and other derived values. It is then the experienced user's role to recognize the situation the monitored process is in and make the subsequent decisions and take the measures. The SACHER system, based on the computational intelligence techniques, inserts the elements of intelligence into the overall chemical information system. It has the modular structure with the following most important modules: normality module- its aim is to recognize that the process starts to deviate from the normal one and serves as the early warning to the staff to take the adequate measures, fuzzy identification module- its aim is to identify the anomaly on the basis of a set of fuzzy rules, time-prediction module- its aim is to predict the behavior/trend of selected chemical quantities 8 hours ahead in 15 min step from the moment of request, validation module- its aim is to validate the measured quantities, trend module- this module serves for showing the trends of the acquired quantities

  12. Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

    Science.gov (United States)

    Haque, Shah Ahsanul; Rahman, Mustafizur; Aziz, Syed Mahfuzul

    2015-01-01

    Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). PMID:25884786

  13. 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

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

    KAUST Repository

    Wang, Wei; Guyet, Thomas; Quiniou, René ; Cordier, Marie-Odile; Masseglia, Florent; Zhang, Xiangliang

    2014-01-01

    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.

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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)

  1. 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.

  2. Anomaly Detection for Temporal Data using Long Short-Term Memory (LSTM)

    OpenAIRE

    Singh, Akash

    2017-01-01

    We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. We train recurrent neural networks (RNNs) with LSTM units to learn the normal time series patterns and predict future values. The resulting prediction errors are modeled to give anomaly scores. We investigate different ways of maintaining LSTM state, and the effect of using a fixed number of time steps on...

  3. 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

  4. A Multi-Agent Framework for Anomalies Detection on Distributed Firewalls Using Data Mining Techniques

    Science.gov (United States)

    Karoui, Kamel; Ftima, Fakher Ben; Ghezala, Henda Ben

    The Agents and Data Mining integration has emerged as a promising area for disributed problems solving. Applying this integration on distributed firewalls will facilitate the anomalies detection process. In this chapter, we present a set of algorithms and mining techniques to analyse, manage and detect anomalies on distributed firewalls' policy rules using the multi-agent approach; first, for each firewall, a static agent will execute a set of data mining techniques to generate a new set of efficient firewall policy rules. Then, a mobile agent will exploit these sets of optimized rules to detect eventual anomalies on a specific firewall (intra-firewalls anomalies) or between firewalls (inter-firewalls anomalies). An experimental case study will be presented to demonstrate the usefulness of our approach.

  5. 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

    The shift from centralised large production to distributed energy production has several consequences for current power system operation. The replacement of large power plants by growing numbers of distributed energy resources (DERs) increases the dependency of the power system on small scale......, distributed production. Many of these DERs can be accessed and controlled remotely, posing a cybersecurity risk. This paper investigates an intrusion detection system which evaluates the DER operation in order to discover unauthorized control actions. The proposed anomaly detection method is based...

  6. 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.

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

    Science.gov (United States)

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

    2018-05-11

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

  8. 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.

  9. Energy detection based on undecimated discrete wavelet transform and its application in magnetic anomaly detection.

    Directory of Open Access Journals (Sweden)

    Xinhua Nie

    Full Text Available Magnetic anomaly detection (MAD is a passive approach for detection of a ferromagnetic target, and its performance is often limited by external noises. In consideration of one major noise source is the fractal noise (or called 1/f noise with a power spectral density of 1/fa (0detection method based on undecimated discrete wavelet transform (UDWT is proposed in this paper. Firstly, the foundations of magnetic anomaly detection and UDWT are introduced in brief, while a possible detection system based on giant magneto-impedance (GMI magnetic sensor is also given out. Then our proposed energy detection based on UDWT is described in detail, and the probabilities of false alarm and detection for given the detection threshold in theory are presented. It is noticeable that no a priori assumptions regarding the ferromagnetic target or the magnetic noise probability are necessary for our method, and different from the discrete wavelet transform (DWT, the UDWT is shift invariant. Finally, some simulations are performed and the results show that the detection performance of our proposed detector is better than that of the conventional energy detector even utilized in the Gaussian white noise, especially when the spectral parameter α is less than 1.0. In addition, a real-world experiment was done to demonstrate the advantages of the proposed method.

  10. An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

    Directory of Open Access Journals (Sweden)

    Jun Liu

    2016-01-01

    Full Text Available Virtual machines (VM on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.

  11. 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...

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

    KAUST Repository

    Harrou, Fouzi; Sun, Ying; Madakyaru, Muddu

    2016-01-01

    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

  13. 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

  14. Bio-Inspired Distributed Decision Algorithms for Anomaly Detection

    Science.gov (United States)

    2017-03-01

    Generation Services (ETG) 3. Replay of Traffic Traces (RTT) BTG creates “ norm ” traffic background with pre-specified distribution, BTG takes in a...a cap on the IP counter to offset this artificial effect. For this reason, we also evaluated the dependence of DIAMoND performance on the IP counter... cap . 3.3.2.10 Performance Evaluation Metrics. Given the local anomaly detector is based on TCP session negotiation protocols, it is natural to

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

    KAUST Repository

    Harrou, Fouzi; Kadri, Farid; Chaabane, Sondé s; Tahon, Christian; Sun, Ying

    2015-01-01

    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.

  16. 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.

  17. Detection of Airway Anomalies in?Pediatric?Patients with Cardiovascular Anomalies with Low Dose Prospective ECG-Gated Dual-Source CT

    OpenAIRE

    Jiao, Hui; Xu, Zhuodong; Wu, Lebin; Cheng, Zhaoping; Ji, Xiaopeng; Zhong, Hai; Meng, Chen

    2013-01-01

    OBJECTIVES: To assess the feasibility of low-dose prospective ECG-gated dual-source CT (DSCT) in detecting airway anomalies in pediatric patients with cardiovascular anomalies compared with flexible tracheobronchoscopy (FTB). METHODS: 33 pediatrics with respiratory symptoms who had been revealed cardiovascular anomalies by transthoracic echocardiography underwent FTB and contrast material-enhanced prospective ECG-triggering CT were enrolled. The study was approved by our institution review bo...

  18. 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

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

    Energy Technology Data Exchange (ETDEWEB)

    Chang, X; Liu, S [Washington University in St. Louis, St. Louis, MO (United States); Kalet, A [University of Washington Medical Center, Seattle, WA (United States); Yang, D [Washington University in St Louis, St Louis, MO (United States)

    2016-06-15

    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

  20. Lunar magnetic anomalies detected by the Apollo substatellite magnetometers

    Science.gov (United States)

    Hood, L.L.; Coleman, P.J.; Russell, C.T.; Wilhelms, D.E.

    1979-01-01

    Properties of lunar crustal magnetization thus far deduced from Apollo subsatellite magnetometer data are reviewed using two of the most accurate presently available magnetic anomaly maps - one covering a portion of the lunar near side and the other a part of the far side. The largest single anomaly found within the region of coverage on the near-side map correlates exactly with a conspicuous, light-colored marking in western Oceanus Procellarum called Reiner Gamma. This feature is interpreted as an unusual deposit of ejecta from secondary craters of the large nearby primary impact crater Cavalerius. An age for Cavalerius (and, by implication, for Reiner Gamma) of 3.2 ?? 0.2 ?? 109 y is estimated. The main (30 ?? 60 km) Reiner Gamma deposit is nearly uniformly magnetized in a single direction, with a minimum mean magnetization intensity of ???7 ?? 10-2 G cm3/g (assuming a density of 3 g/cm3), or about 700 times the stable magnetization component of the most magnetic returned samples. Additional medium-amplitude anomalies exist over the Fra Mauro Formation (Imbrium basin ejecta emplaced ???3.9 ?? 109 y ago) where it has not been flooded by mare basalt flows, but are nearly absent over the maria and over the craters Copernicus, Kepler, and Reiner and their encircling ejecta mantles. The mean altitude of the far-side anomaly gap is much higher than that of the near-side map and the surface geology is more complex, so individual anomaly sources have not yet been identified. However, it is clear that a concentration of especially strong sources exists in the vicinity of the craters Van de Graaff and Aitken. Numerical modeling of the associated fields reveals that the source locations do not correspond with the larger primary impact craters of the region and, by analogy with Reiner Gamma, may be less conspicuous secondary crater ejecta deposits. The reason for a special concentration of strong sources in the Van de Graaff-Aitken region is unknown, but may be indirectly

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

    Directory of Open Access Journals (Sweden)

    M. Flach

    2017-08-01

    Full Text Available 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

  2. 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.

  3. Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

    Science.gov (United States)

    Dominguez, Jesus A.; Klinko, Steve; Ferrell, Bob; Steinrock, Todd (Technical Monitor)

    2001-01-01

    The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

  4. Detection of anomaly in human retina using Laplacian Eigenmaps and vectorized matched filtering

    Science.gov (United States)

    Yacoubou Djima, Karamatou A.; Simonelli, Lucia D.; Cunningham, Denise; Czaja, Wojciech

    2015-03-01

    We present a novel method for automated anomaly detection on auto fluorescent data provided by the National Institute of Health (NIH). This is motivated by the need for new tools to improve the capability of diagnosing macular degeneration in its early stages, track the progression over time, and test the effectiveness of new treatment methods. In previous work, macular anomalies have been detected automatically through multiscale analysis procedures such as wavelet analysis or dimensionality reduction algorithms followed by a classification algorithm, e.g., Support Vector Machine. The method that we propose is a Vectorized Matched Filtering (VMF) algorithm combined with Laplacian Eigenmaps (LE), a nonlinear dimensionality reduction algorithm with locality preserving properties. By applying LE, we are able to represent the data in the form of eigenimages, some of which accentuate the visibility of anomalies. We pick significant eigenimages and proceed with the VMF algorithm that classifies anomalies across all of these eigenimages simultaneously. To evaluate our performance, we compare our method to two other schemes: a matched filtering algorithm based on anomaly detection on single images and a combination of PCA and VMF. LE combined with VMF algorithm performs best, yielding a high rate of accurate anomaly detection. This shows the advantage of using a nonlinear approach to represent the data and the effectiveness of VMF, which operates on the images as a data cube rather than individual images.

  5. 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.

  6. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Schoonewelle, H.; Hagen, T.H.J.J. van der; Hoogenboom, J.E. [Interuniversitair Reactor Inst., Delft (Netherlands)

    1995-11-01

    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. 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)

  9. A new comparison of hyperspectral anomaly detection algorithms for real-time applications

    Science.gov (United States)

    Díaz, María.; López, Sebastián.; Sarmiento, Roberto

    2016-10-01

    Due to the high spectral resolution that remotely sensed hyperspectral images provide, there has been an increasing interest in anomaly detection. The aim of anomaly detection is to stand over pixels whose spectral signature differs significantly from the background spectra. Basically, anomaly detectors mark pixels with a certain score, considering as anomalies those whose scores are higher than a threshold. Receiver Operating Characteristic (ROC) curves have been widely used as an assessment measure in order to compare the performance of different algorithms. ROC curves are graphical plots which illustrate the trade- off between false positive and true positive rates. However, they are limited in order to make deep comparisons due to the fact that they discard relevant factors required in real-time applications such as run times, costs of misclassification and the competence to mark anomalies with high scores. This last fact is fundamental in anomaly detection in order to distinguish them easily from the background without any posterior processing. An extensive set of simulations have been made using different anomaly detection algorithms, comparing their performances and efficiencies using several extra metrics in order to complement ROC curves analysis. Results support our proposal and demonstrate that ROC curves do not provide a good visualization of detection performances for themselves. Moreover, a figure of merit has been proposed in this paper which encompasses in a single global metric all the measures yielded for the proposed additional metrics. Therefore, this figure, named Detection Efficiency (DE), takes into account several crucial types of performance assessment that ROC curves do not consider. Results demonstrate that algorithms with the best detection performances according to ROC curves do not have the highest DE values. Consequently, the recommendation of using extra measures to properly evaluate performances have been supported and justified by

  10. Global Anomaly Detection in Two-Dimensional Symmetry-Protected Topological Phases

    Science.gov (United States)

    Bultinck, Nick; Vanhove, Robijn; Haegeman, Jutho; Verstraete, Frank

    2018-04-01

    Edge theories of symmetry-protected topological phases are well known to possess global symmetry anomalies. In this Letter we focus on two-dimensional bosonic phases protected by an on-site symmetry and analyze the corresponding edge anomalies in more detail. Physical interpretations of the anomaly in terms of an obstruction to orbifolding and constructing symmetry-preserving boundaries are connected to the cohomology classification of symmetry-protected phases in two dimensions. Using the tensor network and matrix product state formalism we numerically illustrate our arguments and discuss computational detection schemes to identify symmetry-protected order in a ground state wave function.

  11. Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

    Directory of Open Access Journals (Sweden)

    Mohd Aizaini Maarof

    2013-08-01

    Full Text Available Wireless Sensor Networks (WSNs are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed.

  12. Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

    Science.gov (United States)

    Rassam, Murad A.; Zainal, Anazida; Maarof, Mohd Aizaini

    2013-01-01

    Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed. PMID:23966182

  13. Anomaly Detection and Life Pattern Estimation for the Elderly Based on Categorization of Accumulated Data

    Science.gov (United States)

    Mori, Taketoshi; Ishino, Takahito; Noguchi, Hiroshi; Shimosaka, Masamichi; Sato, Tomomasa

    2011-06-01

    We propose a life pattern estimation method and an anomaly detection method for elderly people living alone. In our observation system for such people, we deploy some pyroelectric sensors into the house and measure the person's activities all the time in order to grasp the person's life pattern. The data are transferred successively to the operation center and displayed to the nurses in the center in a precise way. Then, the nurses decide whether the data is the anomaly or not. In the system, the people whose features in their life resemble each other are categorized as the same group. Anomalies occurred in the past are shared in the group and utilized in the anomaly detection algorithm. This algorithm is based on "anomaly score." The "anomaly score" is figured out by utilizing the activeness of the person. This activeness is approximately proportional to the frequency of the sensor response in a minute. The "anomaly score" is calculated from the difference between the activeness in the present and the past one averaged in the long term. Thus, the score is positive if the activeness in the present is higher than the average in the past, and the score is negative if the value in the present is lower than the average. If the score exceeds a certain threshold, it means that an anomaly event occurs. Moreover, we developed an activity estimation algorithm. This algorithm estimates the residents' basic activities such as uprising, outing, and so on. The estimation is shown to the nurses with the "anomaly score" of the residents. The nurses can understand the residents' health conditions by combining these two information.

  14. Detection of admittivity anomaly on high-contrast heterogeneous backgrounds using frequency difference EIT.

    Science.gov (United States)

    Jang, J; Seo, J K

    2015-06-01

    This paper describes a multiple background subtraction method in frequency difference electrical impedance tomography (fdEIT) to detect an admittivity anomaly from a high-contrast background conductivity distribution. The proposed method expands the use of the conventional weighted frequency difference EIT method, which has been used limitedly to detect admittivity anomalies in a roughly homogeneous background. The proposed method can be viewed as multiple weighted difference imaging in fdEIT. Although the spatial resolutions of the output images by fdEIT are very low due to the inherent ill-posedness, numerical simulations and phantom experiments of the proposed method demonstrate its feasibility to detect anomalies. It has potential application in stroke detection in a head model, which is highly heterogeneous due to the skull.

  15. Statistical Traffic Anomaly Detection in Time-Varying Communication Networks

    Science.gov (United States)

    2015-02-01

    PLs can be generated using tad and (7). Otherwise, the network is periodic according to feature a, and a family of candidate PLs can be generated...using tad , t a p, and (8). In addition, in case that some prior knowledge of td and tp is available, the family of candidate PLs can include the PLs

  16. Statistical Traffic Anomaly Detection in Time Varying Communication Networks

    Science.gov (United States)

    2015-02-01

    PLs can be generated using tad and (7). Otherwise, the network is periodic according to feature a, and a family of candidate PLs can be generated...using tad , t a p, and (8). In addition, in case that some prior knowledge of td and tp is available, the family of candidate PLs can include the PLs

  17. 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

  18. Panacea : Automating attack classification for anomaly-based network intrusion detection systems

    NARCIS (Netherlands)

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

    2009-01-01

    Anomaly-based intrusion detection systems are usually criticized because they lack a classification of attacks, 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

  19. Panacea : Automating attack classification for anomaly-based network intrusion detection systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, S.; Hartel, P.H.

    2009-01-01

    Anomaly-based intrusion detection systems are usually criticized because they lack a classification 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

  20. 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

  1. 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.

  2. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2015-01-01

    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.

  3. 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.

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

    Science.gov (United States)

    Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun; Wang, Gi-Nam

    2016-01-01

    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.

  5. Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction.

    Science.gov (United States)

    Faust, Kevin; Xie, Quin; Han, Dominick; Goyle, Kartikay; Volynskaya, Zoya; Djuric, Ugljesa; Diamandis, Phedias

    2018-05-16

    There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce. Here, we leverage t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce dimensionality and depict how CNNs organize histomorphologic information. Unique to our workflow, we develop a quantitative and transparent approach to visualizing classification decisions prior to softmax compression. By discretizing the relationships between classes on the t-SNE plot, we show we can super-impose randomly sampled regions of test images and use their distribution to render statistically-driven classifications. Therefore, in addition to providing intuitive outputs for human review, this visual approach can carry out automated and objective multi-class classifications similar to more traditional and less-transparent categorical probability distribution scores. Importantly, this novel classification approach is driven by a priori statistically defined cutoffs. It therefore serves as a generalizable classification and anomaly detection tool less reliant on post-hoc tuning. Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered.

  6. 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

  7. Anomaly Detection and Mitigation at Internet Scale: A Survey

    NARCIS (Netherlands)

    Steinberger, Jessica; Schehlmann, Lisa; Abt, Sebastian; Baier, Harald; Doyen, Guillaume; Waldburger, Martin; Celeda, Pavel; Sperotto, Anna; Stiller, Burkhard

    Network-based attacks pose a strong threat to the Internet landscape. There are different possibilities to encounter these threats. On the one hand attack detection operated at the end-users' side, on the other hand attack detection implemented at network operators' infrastructures. An obvious

  8. 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...

  9. Apparatus and method for detecting a magnetic anomaly contiguous to remote location by SQUID gradiometer and magnetometer systems

    Science.gov (United States)

    Overton, W.C. Jr.; Steyert, W.A. Jr.

    1981-05-22

    A superconducting quantum interference device (SQUID) magnetic detection apparatus detects magnetic fields, signals, and anomalies at remote locations. Two remotely rotatable SQUID gradiometers may be housed in a cryogenic environment to search for and locate unambiguously magnetic anomalies. The SQUID magnetic detection apparatus can be used to determine the azimuth of a hydrofracture by first flooding the hydrofracture with a ferrofluid to create an artificial magnetic anomaly therein.

  10. Statistical Techniques For Real-time Anomaly Detection Using Spark Over Multi-source VMware Performance Data

    Energy Technology Data Exchange (ETDEWEB)

    Solaimani, Mohiuddin [Univ. of Texas-Dallas, Richardson, TX (United States); Iftekhar, Mohammed [Univ. of Texas-Dallas, Richardson, TX (United States); Khan, Latifur [Univ. of Texas-Dallas, Richardson, TX (United States); Thuraisingham, Bhavani [Univ. of Texas-Dallas, Richardson, TX (United States); Ingram, Joey Burton [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-09-01

    Anomaly detection refers to the identi cation of an irregular or unusual pat- tern which deviates from what is standard, normal, or expected. Such deviated patterns typically correspond to samples of interest and are assigned different labels in different domains, such as outliers, anomalies, exceptions, or malware. Detecting anomalies in fast, voluminous streams of data is a formidable chal- lenge. This paper presents a novel, generic, real-time distributed anomaly detection framework for heterogeneous streaming data where anomalies appear as a group. We have developed a distributed statistical approach to build a model and later use it to detect anomaly. As a case study, we investigate group anomaly de- tection for a VMware-based cloud data center, which maintains a large number of virtual machines (VMs). We have built our framework using Apache Spark to get higher throughput and lower data processing time on streaming data. We have developed a window-based statistical anomaly detection technique to detect anomalies that appear sporadically. We then relaxed this constraint with higher accuracy by implementing a cluster-based technique to detect sporadic and continuous anomalies. We conclude that our cluster-based technique out- performs other statistical techniques with higher accuracy and lower processing time.

  11. 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

  12. 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

  13. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2016-01-01

    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.

  14. 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.

  15. Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

    Science.gov (United States)

    Koeppen, W. C.; Pilger, E.; Wright, R.

    2011-07-01

    We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

  16. 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.

  17. Time series of GNSS-derived ionospheric maps to detect anomalies as possible precursors of high magnitude earthquakes

    Science.gov (United States)

    Barbarella, M.; De Giglio, M.; Galeandro, A.; Mancini, F.

    2012-04-01

    The modification of some atmospheric physical properties prior to a high magnitude earthquake has been recently debated within the Lithosphere-Atmosphere-Ionosphere (LAI) Coupling model. Among this variety of phenomena the ionization of air at the higher level of the atmosphere, called ionosphere, is investigated in this work. Such a ionization occurrences could be caused by possible leaking of gases from earth crust and their presence was detected around the time of high magnitude earthquakes by several authors. However, the spatial scale and temporal domain over which such a disturbances come into evidence is still a controversial item. Even thought the ionospheric activity could be investigated by different methodologies (satellite or terrestrial measurements), we selected the production of ionospheric maps by the analysis of GNSS (Global Navigation Satellite Data) data as possible way to detect anomalies prior of a seismic event over a wide area around the epicentre. It is well known that, in the GNSS sciences, the ionospheric activity could be probed by the analysis of refraction phenomena occurred on the dual frequency signals along the satellite to receiver path. The analysis of refraction phenomena affecting data acquired by the GNSS permanent trackers is able to produce daily to hourly maps representing the spatial distribution of the ionospheric Total Electron Content (TEC) as an index of the ionization degree in the upper atmosphere. The presence of large ionospheric anomalies could be therefore interpreted in the LAI Coupling model like a precursor signal of a strong earthquake, especially when the appearance of other different precursors (thermal anomalies and/or gas fluxes) could be detected. In this work, a six-month long series of ionospheric maps produced from GNSS data collected by a network of 49 GPS permanent stations distributed within an area around the city of L'Aquila (Abruzzi, Italy), where an earthquake (M = 6.3) occurred on April 6, 2009

  18. Using new edges for anomaly detection in computer networks

    Science.gov (United States)

    Neil, Joshua Charles

    2015-05-19

    Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

  19. Structural material anomaly detection system using water chemistry data, (7)

    International Nuclear Information System (INIS)

    Nagase, Makoto; Uchida, Shunsuke; Asakura, Yamato; Ohsumi, Katsumi.

    1993-01-01

    A method to detect small changes in water quality and diagnose their causes by analyzing on-line conductivity and pH data was proposed. Laboratory tests showed that effective noise reduction of measured on-line data could be got by using median filter to detect small changes of conductivity ; a relative change of 0.001 μS/cm was distinguishable. By simulating the changes of pH and conductivity in the reactor water against a small concentration change of sodium ion or sulfate ion in the feedwater, it was found that an adequate elapsed time for the diagnosis was 4 h from the start of the concentration change. A conductivity difference of 0.001 μS/cm in the reactor water made it theoretically possible to distinguish between a sodium ion concentration change of 4.6 ppt and a sulfate ion concentration change of 9.6 ppt in the feedwater. (author)

  20. Multivariate diagnostics and anomaly detection for nuclear safeguards

    International Nuclear Information System (INIS)

    Burr, T.

    1994-01-01

    For process control and other reasons, new and future nuclear reprocessing plants are expected to be increasingly more automated than older plants. As a consequence of this automation, the quantity of data potentially available for safeguards may be much greater in future reprocessing plants than in current plants. The authors first review recent literature that applies multivariate Shewhart and multivariate cumulative sum (Cusum) tests to detect anomalous data. These tests are used to evaluate residuals obtained from a simulated three-tank problem in which five variables (volume, density, and concentrations of uranium, plutonium, and nitric acid) in each tank are modeled and measured. They then present results from several simulations involving transfers between the tanks and between the tanks and the environment. Residuals from a no-fault problem in which the measurements and model predictions are both correct are used to develop Cusum test parameters which are then used to test for faults for several simulated anomalous situations, such as an unknown leak or diversion of material from one of the tanks. The leak can be detected by comparing measurements, which estimate the true state of the tank system, with the model predictions, which estimate the state of the tank system as it ''should'' be. The no-fault simulation compares false alarm behavior for the various tests, whereas the anomalous problems allow one to compare the power of the various tests to detect faults under possible diversion scenarios. For comparison with the multivariate tests, univariate tests are also applied to the residuals

  1. 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.

  2. Statistical methods for anomaly detection in the complex process; Methodes statistiques de detection d'anomalies de fonctionnement dans les processus complexes

    Energy Technology Data Exchange (ETDEWEB)

    Al Mouhamed, Mayez

    1977-09-15

    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) [French] Dans de nombreux systemes physiques complexes les grandeurs accessibles a l'homme sont souvent caracterisees par des fluctuations aleatoires autour d'une valeur moyenne. Les fluctuations (signatures) transmettent souvent des informations sur l'etat du systeme que la valeur moyenne ne peut predire. Cette etude est entreprise pour elaborer des methodes statistiques de detection d'anomalies de fonctionnement sur la base de l'analyse des signatures contenues dans les signaux de bruit provenant du processus. L'algorithme presente est capable de: 1/ Apprendre les caracteristiques des operations normales dans un processus complexe. 2/ Detecter des petites deviations par rapport a la conduite normale du processus. L'algorithme peut etre implante sur un calculateur de taille moyenne pour les applications en ligne. (auteur)

  3. 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

  4. 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

  5. A Bayesian model for anomaly detection in SQL databases for security systems

    NARCIS (Netherlands)

    Drugan, M.M.

    2017-01-01

    We focus on automatic anomaly detection in SQL databases for security systems. Many logs of database systems, here the Townhall database, contain detailed information about users, like the SQL queries and the response of the database. A database is a list of log instances, where each log instance is

  6. A white-box anomaly-based framework for database leakage detection

    NARCIS (Netherlands)

    Costante, E.; den Hartog, J.; Petkovic, M.; Etalle, S.; Pechenizkiy, M.

    2017-01-01

    Data leakage is at the heart most of the privacy breaches worldwide. In this paper we present a white-box approach to detect potential data leakage by spotting anomalies in database transactions. We refer to our solution as white-box because it builds self explanatory profiles that are easy to

  7. 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.

  8. Spectral analysis of geological materials in the Central Volcanic Range of Costa Rica and its relationship to the remote detection of anomalies

    OpenAIRE

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

    2014-01-01

    The aim of this work is the comparative study of methods for calculating spectral anomalies from imaging spectrometry in several test areas of the Central Volcanic Range (CVR) of Costa Rica. In the detection of anomalous responses it is assumed no prior knowledge of the targets, so that the pixels are automatically separated according to their spectral information significantly differentiated with respect to a background to be estimated, either globally for the full scene, either locally by i...

  9. An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection

    International Nuclear Information System (INIS)

    Zhang, Liangwei; Lin, Jing; Karim, Ramin

    2015-01-01

    The accuracy of traditional anomaly detection techniques implemented on full-dimensional spaces degrades significantly as dimensionality increases, thereby hampering many real-world applications. This work proposes an approach to selecting meaningful feature subspace and conducting anomaly detection in the corresponding subspace projection. The aim is to maintain the detection accuracy in high-dimensional circumstances. The suggested approach assesses the angle between all pairs of two lines for one specific anomaly candidate: the first line is connected by the relevant data point and the center of its adjacent points; the other line is one of the axis-parallel lines. Those dimensions which have a relatively small angle with the first line are then chosen to constitute the axis-parallel subspace for the candidate. Next, a normalized Mahalanobis distance is introduced to measure the local outlier-ness of an object in the subspace projection. To comprehensively compare the proposed algorithm with several existing anomaly detection techniques, we constructed artificial datasets with various high-dimensional settings and found the algorithm displayed superior accuracy. A further experiment on an industrial dataset demonstrated the applicability of the proposed algorithm in fault detection tasks and highlighted another of its merits, namely, to provide preliminary interpretation of abnormality through feature ordering in relevant subspaces. - Highlights: • An anomaly detection approach for high-dimensional reliability data is proposed. • The approach selects relevant subspaces by assessing vectorial angles. • The novel ABSAD approach displays superior accuracy over other alternatives. • Numerical illustration approves its efficacy in fault detection applications

  10. 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.

  11. 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.

  12. 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...

  13. Detecting anomalies in crowded scenes via locality-constrained affine subspace coding

    Science.gov (United States)

    Fan, Yaxiang; Wen, Gongjian; Qiu, Shaohua; Li, Deren

    2017-07-01

    Video anomaly event detection is the process of finding an abnormal event deviation compared with the majority of normal or usual events. The main challenges are the high structure redundancy and the dynamic changes in the scenes that are in surveillance videos. To address these problems, we present a framework for anomaly detection and localization in videos that is based on locality-constrained affine subspace coding (LASC) and a model updating procedure. In our algorithm, LASC attempts to reconstruct the test sample by its top-k nearest subspaces, which are obtained by segmenting the normal samples space using a clustering method. A sample with a large reconstruction cost is detected as abnormal by setting a threshold. To adapt to the scene changes over time, a model updating strategy is proposed. We experiment on two public datasets: the UCSD dataset and the Avenue dataset. The results demonstrate that our method achieves competitive performance at a 700 fps on a single desktop PC.

  14. A scalable architecture for online anomaly detection of WLCG batch jobs

    Science.gov (United States)

    Kuehn, E.; Fischer, M.; Giffels, M.; Jung, C.; Petzold, A.

    2016-10-01

    For data centres it is increasingly important to monitor the network usage, and learn from network usage patterns. Especially configuration issues or misbehaving batch jobs preventing a smooth operation need to be detected as early as possible. At the GridKa data and computing centre we therefore operate a tool BPNetMon for monitoring traffic data and characteristics of WLCG batch jobs and pilots locally on different worker nodes. On the one hand local information itself are not sufficient to detect anomalies for several reasons, e.g. the underlying job distribution on a single worker node might change or there might be a local misconfiguration. On the other hand a centralised anomaly detection approach does not scale regarding network communication as well as computational costs. We therefore propose a scalable architecture based on concepts of a super-peer network.

  15. 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.

  16. 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.

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

    KAUST Repository

    Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying

    2017-01-01

    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.

  18. Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor

    OpenAIRE

    Laxhammar , Rikard; Falkman , Göran

    2012-01-01

    Part 4: First Conformal Prediction and Its Applications Workshop (COPA 2012); International audience; Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases. Second, they are insensitive to loca...

  19. Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

    Science.gov (United States)

    Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

    2018-06-01

    The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

  20. 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.

  1. OceanXtremes: Scalable Anomaly Detection in Oceanographic Time-Series

    Science.gov (United States)

    Wilson, B. D.; Armstrong, E. M.; Chin, T. M.; Gill, K. M.; Greguska, F. R., III; Huang, T.; Jacob, J. C.; Quach, N.

    2016-12-01

    The oceanographic community must meet the challenge to rapidly identify features and anomalies in complex and voluminous observations to further science and improve decision support. Given this data-intensive reality, we are developing an anomaly detection system, called OceanXtremes, powered by an intelligent, elastic Cloud-based analytic service backend that enables execution of domain-specific, multi-scale anomaly and feature detection algorithms across the entire archive of 15 to 30-year ocean science datasets.Our parallel analytics engine is extending the NEXUS system and exploits multiple open-source technologies: Apache Cassandra as a distributed spatial "tile" cache, Apache Spark for in-memory parallel computation, and Apache Solr for spatial search and storing pre-computed tile statistics and other metadata. OceanXtremes provides these key capabilities: Parallel generation (Spark on a compute cluster) of 15 to 30-year Ocean Climatologies (e.g. sea surface temperature or SST) in hours or overnight, using simple pixel averages or customizable Gaussian-weighted "smoothing" over latitude, longitude, and time; Parallel pre-computation, tiling, and caching of anomaly fields (daily variables minus a chosen climatology) with pre-computed tile statistics; Parallel detection (over the time-series of tiles) of anomalies or phenomena by regional area-averages exceeding a specified threshold (e.g. high SST in El Nino or SST "blob" regions), or more complex, custom data mining algorithms; Shared discovery and exploration of ocean phenomena and anomalies (facet search using Solr), along with unexpected correlations between key measured variables; Scalable execution for all capabilities on a hybrid Cloud, using our on-premise OpenStack Cloud cluster or at Amazon. The key idea is that the parallel data-mining operations will be run "near" the ocean data archives (a local "network" hop) so that we can efficiently access the thousands of files making up a three decade time

  2. 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

  3. 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.

  4. 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.

  5. A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes

    Science.gov (United States)

    Sato, Daisuke; Hanaoka, Shouhei; Nomura, Yukihiro; Takenaga, Tomomi; Miki, Soichiro; Yoshikawa, Takeharu; Hayashi, Naoto; Abe, Osamu

    2018-02-01

    Purpose: The target disorders of emergency head CT are wide-ranging. Therefore, people working in an emergency department desire a computer-aided detection system for general disorders. In this study, we proposed an unsupervised anomaly detection method in emergency head CT using an autoencoder and evaluated the anomaly detection performance of our method in emergency head CT. Methods: We used a 3D convolutional autoencoder (3D-CAE), which contains 11 layers in the convolution block and 6 layers in the deconvolution block. In the training phase, we trained the 3D-CAE using 10,000 3D patches extracted from 50 normal cases. In the test phase, we calculated abnormalities of each voxel in 38 emergency head CT volumes (22 abnormal cases and 16 normal cases) for evaluation and evaluated the likelihood of lesion existence. Results: Our method achieved a sensitivity of 68% and a specificity of 88%, with an area under the curve of the receiver operating characteristic curve of 0.87. It shows that this method has a moderate accuracy to distinguish normal CT cases to abnormal ones. Conclusion: Our method has potentialities for anomaly detection in emergency head CT.

  6. 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.

  7. Electrical resistivity anomaly observed in and around the epicentral area prior to the T'angshan Earthquake of 1976

    Energy Technology Data Exchange (ETDEWEB)

    Chao, Y L; Ch' ien, F Y

    1978-07-01

    Before the T'angshan earthquake, magnitude 7.8 on 28 July 1976, 9 of the 14 resistivity stations located within the region of Peking, Tientsin, and T'angshan recorded gradual decreases in apparent electrical resistivity for a period of 2 to 3 years. The anomalous region as deduced from the records of these nine stations has a semimajor axis of about 150 km surrounding the epicenter, and yet two of the stations located very close to the epicenter recorded rapidly decreasing apparent resistivity anomaly during a period of more than two months immediately before the earthquake. From results of some field measurements in situ of the apparent resistivity-decrease due to compression of rock or soil layers near the earth's surface, the linear compressional strain of shallow layers in and around the epicentral area was estimated to be about 3 x 10/sup -5/. Compressional strain of much the same order of magnitude was observed by base line survey before the T'angshan earthquake.

  8. 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.

  9. 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.

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

    Science.gov (United States)

    2016-09-01

    tool has been developed for many platforms: Android , iOS, and Windows. The Windows version has been developed as a web server that allows the...Microsoft Windows. 15. SUBJECT TERMS Applied Anomaly Detection Tool, AADT, Windows, server, web service, installation 16. SECURITY CLASSIFICATION OF: 17...instructional information about identifying them as groups and individually. The software has been developed for several different platforms: Android

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

    OpenAIRE

    Fabio Veronese; Andrea Masciadri; Sara Comai; Matteo Matteucci; Fabio Salice

    2018-01-01

    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 in...

  12. Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network

    Directory of Open Access Journals (Sweden)

    Matti Mantere

    2013-09-01

    Full Text Available The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.

  13. 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.

  14. 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.

  15. 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.

  16. Improving Anomaly Detection for Text-Based Protocols by Exploiting Message Structures

    Directory of Open Access Journals (Sweden)

    Christian M. Mueller

    2010-12-01

    Full Text Available Service platforms using text-based protocols need to be protected against attacks. Machine-learning algorithms with pattern matching can be used to detect even previously unknown attacks. In this paper, we present an extension to known Support Vector Machine (SVM based anomaly detection algorithms for the Session Initiation Protocol (SIP. Our contribution is to extend the amount of different features used for classification (feature space by exploiting the structure of SIP messages, which reduces the false positive rate. Additionally, we show how combining our approach with attribute reduction significantly improves throughput.

  17. Entropy Measures for Stochastic Processes with Applications in Functional Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Gabriel Martos

    2018-01-01

    Full Text Available We propose a definition of entropy for stochastic processes. We provide a reproducing kernel Hilbert space model to estimate entropy from a random sample of realizations of a stochastic process, namely functional data, and introduce two approaches to estimate minimum entropy sets. These sets are relevant to detect anomalous or outlier functional data. A numerical experiment illustrates the performance of the proposed method; in addition, we conduct an analysis of mortality rate curves as an interesting application in a real-data context to explore functional anomaly detection.

  18. 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)

  19. On-road anomaly detection by multimodal sensor analysis and multimedia processing

    Science.gov (United States)

    Orhan, Fatih; Eren, P. E.

    2014-03-01

    The use of smartphones in Intelligent Transportation Systems is gaining popularity, yet many challenges exist in developing functional applications. Due to the dynamic nature of transportation, vehicular social applications face complexities such as developing robust sensor management, performing signal and image processing tasks, and sharing information among users. This study utilizes a multimodal sensor analysis framework which enables the analysis of sensors in multimodal aspect. It also provides plugin-based analyzing interfaces to develop sensor and image processing based applications, and connects its users via a centralized application as well as to social networks to facilitate communication and socialization. With the usage of this framework, an on-road anomaly detector is being developed and tested. The detector utilizes the sensors of a mobile device and is able to identify anomalies such as hard brake, pothole crossing, and speed bump crossing. Upon such detection, the video portion containing the anomaly is automatically extracted in order to enable further image processing analysis. The detection results are shared on a central portal application for online traffic condition monitoring.

  20. Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm

    Directory of Open Access Journals (Sweden)

    Xiaoyu Zhang

    2017-01-01

    Full Text Available Thousands of flights datasets should be analyzed per day for a moderate sized fleet; therefore, flight datasets are very large. In this paper, an improved kernel principal component analysis (KPCA method is proposed to search for signatures of anomalies in flight datasets through the squared prediction error statistics, in which the number of principal components and the confidence for the confidence limit are automatically determined by OpenMP-based K-fold cross-validation algorithm and the parameter in the radial basis function (RBF is optimized by GPU-based kernel learning method. Performed on Nvidia GeForce GTX 660, the computation of the proposed GPU-based RBF parameter is 112.9 times (average 82.6 times faster than that of sequential CPU task execution. The OpenMP-based K-fold cross-validation process for training KPCA anomaly detection model becomes 2.4 times (average 1.5 times faster than that of sequential CPU task execution. Experiments show that the proposed approach can effectively detect the anomalies with the accuracy of 93.57% and false positive alarm rate of 1.11%.

  1. 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)

  2. Radiation anomaly detection algorithms for field-acquired gamma energy spectra

    Science.gov (United States)

    Mukhopadhyay, Sanjoy; Maurer, Richard; Wolff, Ron; Guss, Paul; Mitchell, Stephen

    2015-08-01

    The Remote Sensing Laboratory (RSL) is developing a tactical, networked radiation detection system that will be agile, reconfigurable, and capable of rapid threat assessment with high degree of fidelity and certainty. Our design is driven by the needs of users such as law enforcement personnel who must make decisions by evaluating threat signatures in urban settings. The most efficient tool available to identify the nature of the threat object is real-time gamma spectroscopic analysis, as it is fast and has a very low probability of producing false positive alarm conditions. Urban radiological searches are inherently challenged by the rapid and large spatial variation of background gamma radiation, the presence of benign radioactive materials in terms of the normally occurring radioactive materials (NORM), and shielded and/or masked threat sources. Multiple spectral anomaly detection algorithms have been developed by national laboratories and commercial vendors. For example, the Gamma Detector Response and Analysis Software (GADRAS) a one-dimensional deterministic radiation transport software capable of calculating gamma ray spectra using physics-based detector response functions was developed at Sandia National Laboratories. The nuisance-rejection spectral comparison ratio anomaly detection algorithm (or NSCRAD), developed at Pacific Northwest National Laboratory, uses spectral comparison ratios to detect deviation from benign medical and NORM radiation source and can work in spite of strong presence of NORM and or medical sources. RSL has developed its own wavelet-based gamma energy spectral anomaly detection algorithm called WAVRAD. Test results and relative merits of these different algorithms will be discussed and demonstrated.

  3. 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.

  4. 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.

  5. 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.

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

    KAUST Repository

    Kadri, Farid; Harrou, Fouzi; Chaabane, Sondè s; Sun, Ying; Tahon, Christian

    2015-01-01

    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.

  7. 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

  8. Caldera unrest detected with seawater temperature anomalies at Deception Island, Antarctic Peninsula

    Science.gov (United States)

    Berrocoso, M.; Prates, G.; Fernández-Ros, A.; Peci, L. M.; de Gil, A.; Rosado, B.; Páez, R.; Jigena, B.

    2018-04-01

    Increased thermal activity was detected to coincide with the onset of volcano inflation in the seawater-filled caldera at Deception Island. This thermal activity was manifested in pulses of high water temperature that coincided with ocean tide cycles. The seawater temperature anomalies were detected by a thermometric sensor attached to the tide gauge (bottom pressure sensor). This was installed where the seawater circulation and the locations of known thermal anomalies, fumaroles and thermal springs, together favor the detection of water warmed within the caldera. Detection of the increased thermal activity was also possible because sea ice, which covers the entire caldera during the austral winter months, insulates the water and thus reduces temperature exchange between seawater and atmosphere. In these conditions, the water temperature data has been shown to provide significant information about Deception volcano activity. The detected seawater temperature increase, also observed in soil temperature readings, suggests rapid and near-simultaneous increase in geothermal activity with onset of caldera inflation and an increased number of seismic events observed in the following austral summer.

  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. 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,...

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

    KAUST Repository

    Harrou, Fouzi; Madakyaru, Muddu; Sun, Ying; Khadraoui, Sofiane

    2016-01-01

    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

  12. Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores.

    Science.gov (United States)

    Pham, Thuy T; Moore, Steven T; Lewis, Simon John Geoffrey; Nguyen, Diep N; Dutkiewicz, Eryk; Fuglevand, Andrew J; McEwan, Alistair L; Leong, Philip H W

    2017-11-01

    Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From

  13. 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.

  14. 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)

  15. Anomaly Detection in Smart Metering Infrastructure with the Use of Time Series Analysis

    Directory of Open Access Journals (Sweden)

    Tomasz Andrysiak

    2017-01-01

    Full Text Available The article presents solutions to anomaly detection in network traffic for critical smart metering infrastructure, realized with the use of radio sensory network. The structure of the examined smart meter network and the key security aspects which have influence on the correct performance of an advanced metering infrastructure (possibility of passive and active cyberattacks are described. An effective and quick anomaly detection method is proposed. At its initial stage, Cook’s distance was used for detection and elimination of outlier observations. So prepared data was used to estimate standard statistical models based on exponential smoothing, that is, Brown’s, Holt’s, and Winters’ models. To estimate possible fluctuations in forecasts of the implemented models, properly parameterized Bollinger Bands was used. Next, statistical relations between the estimated traffic model and its real variability were examined to detect abnormal behavior, which could indicate a cyberattack attempt. An update procedure of standard models in case there were significant real network traffic fluctuations was also proposed. The choice of optimal parameter values of statistical models was realized as forecast error minimization. The results confirmed efficiency of the presented method and accuracy of choice of the proper statistical model for the analyzed time series.

  16. Anomaly Detection for Beam Loss Maps in the Large Hadron Collider

    Science.gov (United States)

    Valentino, Gianluca; Bruce, Roderik; Redaelli, Stefano; Rossi, Roberto; Theodoropoulos, Panagiotis; Jaster-Merz, Sonja

    2017-07-01

    In the LHC, beam loss maps are used to validate collimator settings for cleaning and machine protection. This is done by monitoring the loss distribution in the ring during infrequent controlled loss map campaigns, as well as in standard operation. Due to the complexity of the system, consisting of more than 50 collimators per beam, it is difficult to identify small changes in the collimation hierarchy, which may be due to setting errors or beam orbit drifts with such methods. A technique based on Principal Component Analysis and Local Outlier Factor is presented to detect anomalies in the loss maps and therefore provide an automatic check of the collimation hierarchy.

  17. Anomaly Detection for Beam Loss Maps in the Large Hadron Collider

    International Nuclear Information System (INIS)

    Valentino, Gianluca; Bruce, Roderik; Redaelli, Stefano; Rossi, Roberto; Theodoropoulos, Panagiotis; Jaster-Merz, Sonja

    2017-01-01

    In the LHC, beam loss maps are used to validate collimator settings for cleaning and machine protection. This is done by monitoring the loss distribution in the ring during infrequent controlled loss map campaigns, as well as in standard operation. Due to the complexity of the system, consisting of more than 50 collimators per beam, it is difficult to identify small changes in the collimation hierarchy, which may be due to setting errors or beam orbit drifts with such methods. A technique based on Principal Component Analysis and Local Outlier Factor is presented to detect anomalies in the loss maps and therefore provide an automatic check of the collimation hierarchy. (paper)

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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)

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Anomaly detection in heterogeneous media via saliency analysis of propagating wavefields

    Science.gov (United States)

    Druce, Jeffrey M.; Haupt, Jarvis D.; Gonella, Stefano

    2014-03-01

    This work investigates the problem of anomaly detection by means of an agnostic inference strategy based on the concepts of spatial saliency and data sparsity. Specifically, it addresses the implementation and experimental validation aspects of a salient feature extraction methodology that was recently proposed for laser-based diagnostics and leverages the wavefield spatial reconstruction capability offered by scanning laser vibrometers. The methodology consists of two steps. The first is a spatiotemporal windowing strategy designed to partition the structural domain in small sub-domains and replicate impinging wave conditions at each location. The second is the construction of a low-rank-plus-outlier model of the regional data set using principal component analysis. Regions are labeled salient when their behavior does not belong to a common low-dimensional subspace that successfully describes the typical behavior of the anomaly-free portion of the surrounding medium. The most at­ tractive feature of this method is that it requires virtually no knowledge of the structural and material properties of the medium. This property makes it a powerful diagnostic tool for the inspection of media with pronounced heterogeneity or with unknown or unreliable material property distributions, e.g., as a result of severe material degradation over large portions of their domain.

  10. Detection of Anomalies in Citrus Leaves Using Laser-Induced Breakdown Spectroscopy (LIBS).

    Science.gov (United States)

    Sankaran, Sindhuja; Ehsani, Reza; Morgan, Kelly T

    2015-08-01

    Nutrient assessment and management are important to maintain productivity in citrus orchards. In this study, laser-induced breakdown spectroscopy (LIBS) was applied for rapid and real-time detection of citrus anomalies. Laser-induced breakdown spectroscopy spectra were collected from citrus leaves with anomalies such as diseases (Huanglongbing, citrus canker) and nutrient deficiencies (iron, manganese, magnesium, zinc), and compared with those of healthy leaves. Baseline correction, wavelet multivariate denoising, and normalization techniques were applied to the LIBS spectra before analysis. After spectral pre-processing, features were extracted using principal component analysis and classified using two models, quadratic discriminant analysis and support vector machine (SVM). The SVM resulted in a high average classification accuracy of 97.5%, with high average canker classification accuracy (96.5%). LIBS peak analysis indicated that high intensities at 229.7, 247.9, 280.3, 393.5, 397.0, and 769.8 nm were observed of 11 peaks found in all the samples. Future studies using controlled experiments with variable nutrient applications are required for quantification of foliar nutrients by using LIBS-based sensing.

  11. Early detection and identification of anomalies in chemical regime based on computational intelligence techniques

    International Nuclear Information System (INIS)

    Figedy, Stefan; Smiesko, Ivan

    2012-01-01

    This article provides brief information about the fundamental features of a newly-developed diagnostic system for early detection and identification of anomalies being generated in water chemistry regime of the primary and secondary circuit of the VVER-440 reactor. This system, which is called SACHER (System of Analysis of CHEmical Regime), was installed within the major modernization project at the NPP-V2 Bohunice in the Slovak Republic. The SACHER system has been fully developed on MATLAB environment. It is based on computational intelligence techniques and inserts various elements of intelligent data processing modules for clustering, diagnosing, future prediction, signal validation, etc, into the overall chemical information system. The application of SACHER would essentially assist chemists to identify the current situation regarding anomalies being generated in the primary and secondary circuit water chemistry. This system is to be used for diagnostics and data handling, however it is not intended to fully replace the presence of experienced chemists to decide upon corrective actions. (author)

  12. 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

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

    Science.gov (United States)

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

    2015-02-01

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

  14. 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...

  15. 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.

  16. Stochastic anomaly detection in eye-tracking data for quantification of motor symptoms in Parkinson's disease

    Science.gov (United States)

    Jansson, Daniel; Medvedev, Alexander; Axelson, Hans; Nyholm, Dag

    2013-10-01

    Two methods for distinguishing between healthy controls and patients diagnosed with Parkinson's disease by means of recorded smooth pursuit eye movements are presented and evaluated. Both methods are based on the principles of stochastic anomaly detection and make use of orthogonal series approximation for probability distribution estimation. The first method relies on the identification of a Wiener-type model of the smooth pursuit system and attempts to find statistically significant differences between the estimated parameters in healthy controls and patientts with Parkinson's disease. The second method applies the same statistical method to distinguish between the gaze trajectories of healthy and Parkinson subjects attempting to track visual stimuli. Both methods show promising results, where healthy controls and patients with Parkinson's disease are effectively separated in terms of the considered metric. The results are preliminary because of the small number of participating test subjects, but they are indicative of the potential of the presented methods as diagnosing or staging tools for Parkinson's disease.

  17. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Jiamin; Hoffman, Joanne; Zhao, Jocelyn; Yao, Jianhua; Lu, Le; Kim, Lauren; Turkbey, Evrim B.; Summers, Ronald M., E-mail: rms@nih.gov [Imaging Biomarkers and Computer-aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center Building, 10 Room 1C224 MSC 1182, Bethesda, Maryland 20892-1182 (United States)

    2016-07-15

    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.

  19. 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.

  20. 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

  1. Reliable detection of fluence anomalies in EPID-based IMRT pretreatment quality assurance using pixel intensity deviations

    International Nuclear Information System (INIS)

    Gordon, J. J.; Gardner, J. K.; Wang, S.; Siebers, J. V.

    2012-01-01

    Purpose: This work uses repeat images of intensity modulated radiation therapy (IMRT) fields to quantify fluence anomalies (i.e., delivery errors) that can be reliably detected in electronic portal images used for IMRT pretreatment quality assurance. Methods: Repeat images of 11 clinical IMRT fields are acquired on a Varian Trilogy linear accelerator at energies of 6 MV and 18 MV. Acquired images are corrected for output variations and registered to minimize the impact of linear accelerator and electronic portal imaging device (EPID) positioning deviations. Detection studies are performed in which rectangular anomalies of various sizes are inserted into the images. The performance of detection strategies based on pixel intensity deviations (PIDs) and gamma indices is evaluated using receiver operating characteristic analysis. Results: Residual differences between registered images are due to interfraction positional deviations of jaws and multileaf collimator leaves, plus imager noise. Positional deviations produce large intensity differences that degrade anomaly detection. Gradient effects are suppressed in PIDs using gradient scaling. Background noise is suppressed using median filtering. In the majority of images, PID-based detection strategies can reliably detect fluence anomalies of ≥5% in ∼1 mm 2 areas and ≥2% in ∼20 mm 2 areas. Conclusions: The ability to detect small dose differences (≤2%) depends strongly on the level of background noise. This in turn depends on the accuracy of image registration, the quality of the reference image, and field properties. The longer term aim of this work is to develop accurate and reliable methods of detecting IMRT delivery errors and variations. The ability to resolve small anomalies will allow the accuracy of advanced treatment techniques, such as image guided, adaptive, and arc therapies, to be quantified.

  2. 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.

  3. 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.

  4. Paternal psychological response after ultrasonographic detection of structural fetal anomalies with a comparison to maternal response: a cohort study.

    Science.gov (United States)

    Kaasen, Anne; Helbig, Anne; Malt, Ulrik Fredrik; Naes, Tormod; Skari, Hans; Haugen, Guttorm Nils

    2013-07-12

    In Norway almost all pregnant women attend one routine ultrasound examination. Detection of fetal structural anomalies triggers psychological stress responses in the women affected. Despite the frequent use of ultrasound examination in pregnancy, little attention has been devoted to the psychological response of the expectant father following the detection of fetal anomalies. This is important for later fatherhood and the psychological interaction within the couple. We aimed to describe paternal psychological responses shortly after detection of structural fetal anomalies by ultrasonography, and to compare paternal and maternal responses within the same couple. A prospective observational study was performed at a tertiary referral centre for fetal medicine. Pregnant women with a structural fetal anomaly detected by ultrasound and their partners (study group,n=155) and 100 with normal ultrasound findings (comparison group) were included shortly after sonographic examination (inclusion period: May 2006-February 2009). Gestational age was >12 weeks. We used psychometric questionnaires to assess self-reported social dysfunction, health perception, and psychological distress (intrusion, avoidance, arousal, anxiety, and depression): Impact of Event Scale. General Health Questionnaire and Edinburgh Postnatal Depression Scale. Fetal anomalies were classified according to severity and diagnostic or prognostic ambiguity at the time of assessment. Median (range) gestational age at inclusion in the study and comparison group was 19 (12-38) and 19 (13-22) weeks, respectively. Men and women in the study group had significantly higher levels of psychological distress than men and women in the comparison group on all psychometric endpoints. The lowest level of distress in the study group was associated with the least severe anomalies with no diagnostic or prognostic ambiguity (p < 0.033). Men had lower scores than women on all psychometric outcome variables. The correlation in

  5. GNSS reflectometry aboard the International Space Station: phase-altimetry simulation to detect ocean topography anomalies

    Science.gov (United States)

    Semmling, Maximilian; Leister, Vera; Saynisch, Jan; Zus, Florian; Wickert, Jens

    2016-04-01

    An ocean altimetry experiment using Earth reflected GNSS signals has been proposed to the European Space Agency (ESA). It is part of the GNSS Reflectometry Radio Occultation Scatterometry (GEROS) mission that is planned aboard the International Space Station (ISS). Altimetric simulations are presented that examine the detection of ocean topography anomalies assuming GNSS phase delay observations. Such delay measurements are well established for positioning and are possible due to a sufficient synchronization of GNSS receiver and transmitter. For altimetric purpose delays of Earth reflected GNSS signals can be observed similar to radar altimeter signals. The advantage of GNSS is the synchronized separation of transmitter and receiver that allow a significantly increased number of observation per receiver due to more than 70 GNSS transmitters currently in orbit. The altimetric concept has already been applied successfully to flight data recorded over the Mediterranean Sea. The presented altimetric simulation considers anomalies in the Agulhas current region which are obtained from the Region Ocean Model System (ROMS). Suitable reflection events in an elevation range between 3° and 30° last about 10min with ground track's length >3000km. Typical along-track footprints (1s signal integration time) have a length of about 5km. The reflection's Fresnel zone limits the footprint of coherent observations to a major axis extention between 1 to 6km dependent on the elevation. The altimetric performance depends on the signal-to-noise ratio (SNR) of the reflection. Simulation results show that precision is better than 10cm for SNR of 30dB. Whereas, it is worse than 0.5m if SNR goes down to 10dB. Precision, in general, improves towards higher elevation angles. Critical biases are introduced by atmospheric and ionospheric refraction. Corresponding correction strategies are still under investigation.

  6. 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.

  7. Airborne detection of magnetic anomalies associated with soils on the Oak Ridge Reservation, Tennessee

    International Nuclear Information System (INIS)

    Doll, W.E.; Beard, L.P.; Helm, J.M.

    1995-01-01

    Reconnaissance airborne geophysical data acquired over the 35,000-acre Oak Ridge Reservation (ORR), TN, show several magnetic anomalies over undisturbed areas mapped as Copper Ridge Dolomite (CRD). The anomalies of interest are most apparent in magnetic gradient maps where they exceed 0.06 nT/m and in some cases exceed 0.5 nT/m. Anomalies as large as 25nT are seen on maps. Some of the anomalies correlate with known or suspected karst, or with apparent conductivity anomalies calculated from electromagnetic data acquired contemporaneously with the magnetic data. Some of the anomalies have a strong correlation with topographic lows or closed depressions. Surface magnetic data have been acquired over some of these sites and have confirmed the existence of the anomalies. Ground inspections in the vicinity of several of the anomalies has not led to any discoveries of manmade surface materials of sufficient size to generate the observed anomalies. One would expect an anomaly of approximately 1 nT for a pickup truck from 200 ft altitude. Typical residual magnetic anomalies have magnitudes of 5--10 nT, and some are as large as 25nT. The absence of roads or other indications of culture (past or present) near the anomalies and the modeling of anomalies in data acquired with surface instruments indicate that man-made metallic objects are unlikely to be responsible for the anomaly. The authors show that observed anomalies in the CRD can reasonably be associated with thickening of the soil layer. The occurrence of the anomalies in areas where evidences of karstification are seen would follow because sediment deposition would occur in topographic lows. Linear groups of anomalies on the maps may be associated with fracture zones which were eroded more than adjacent rocks and were subsequently covered with a thicker blanket of sediment. This study indicates that airborne magnetic data may be of use in other sites where fracture zones or buried collapse structures are of interest

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

    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

  13. 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

  14. Possibility of detecting triple gluon coupling and Adler-Bell-Jackiw anomaly in polarized deep inelastic scattering

    International Nuclear Information System (INIS)

    Lam, C.S.; Li, B.A.

    1980-05-01

    A way to detect experimentally the existence of triple gluon coupling and the Adler-Bell-Jackiw anomaly is to measure the Q 2 -dependence of polarized deep inelastic scattering. These effects lead to a ln ln Q 2 term which we calculate by introducing a new gluon operator in the Wilson expansion

  15. 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.

  16. Confabulation Based Real-time Anomaly Detection for Wide-area Surveillance Using Heterogeneous High Performance Computing Architecture

    Science.gov (United States)

    2015-06-01

    CONFABULATION BASED REAL-TIME ANOMALY DETECTION FOR WIDE-AREA SURVEILLANCE USING HETEROGENEOUS HIGH PERFORMANCE COMPUTING ARCHITECTURE SYRACUSE...DETECTION FOR WIDE-AREA SURVEILLANCE USING HETEROGENEOUS HIGH PERFORMANCE COMPUTING ARCHITECTURE 5a. CONTRACT NUMBER FA8750-12-1-0251 5b. GRANT...processors including graphic processor units (GPUs) and Intel Xeon Phi processors. Experimental results showed significant speedups, which can enable

  17. Detection of Congenital Mullerian Anomalies Using Real-Time 3D Sonography

    Directory of Open Access Journals (Sweden)

    Firoozeh Ahmadi

    2011-01-01

    Full Text Available A 35 year-old woman referred to Royan Institute (Reproductive Biomedicine Research Center for infertilitytreatment. She had an eleven-year history of primary infertility with a normal abdominal ultrasound.Hysterosalpingography (HSG was obtained one month prior to referral in another center (Fig A.The HSG finding of an apparent unicorn uterus followed by a normal vaginal ultrasound led us toperform a three-dimensional vaginal ultrasound before resorting to hysteroscopy. Results of thethree-dimensional vaginal ultrasound revealed a normal uterus (Fig B, C.Accurate characterization of congenital Mullerian anomalies (MDAs such as an arcuate, unicornuate,didelphys, bicornuate or septate uterus is challenging. While HSG has been the standard test in the diagnosisof MDAs, some limitations may favor the use of three-dimensional ultrasound. The most difficult partof HSG is interpreting the two-dimensional radiographic image into a complex, three-dimensional livingorgan (1. A variety of technical problems may occur while performing HSG. In this case, only an obliqueview could lead to a correct interpretation. It is advisable for the interpreter to perform the procedure ratherthan to inspect only the finished radiographic images (2.One of the most useful scan planes obtained on three-dimensional ultrasound is the coronal view ofthe uterus. This view is known to be a valuable problem-solving tool that assists in differentiatingbetween various types of MDAs due to the high level of agreement between three-dimensionalultrasound and HSG (3, 4.Recently, three-dimensional ultrasound has become the sole mandatory step in the initial investigationof MDAs due to its superiority to other techniques that have been used for the same purpose (5.

  18. Application of Ground-Penetrating Radar for Detecting Internal Anomalies in Tree Trunks with Irregular Contours.

    Science.gov (United States)

    Li, Weilin; Wen, Jian; Xiao, Zhongliang; Xu, Shengxia

    2018-02-22

    To assess the health conditions of tree trunks, it is necessary to estimate the layers and anomalies of their internal structure. The main objective of this paper is to investigate the internal part of tree trunks considering their irregular contour. In this respect, we used ground penetrating radar (GPR) for non-invasive detection of defects and deteriorations in living trees trunks. The Hilbert transform algorithm and the reflection amplitudes were used to estimate the relative dielectric constant. The point cloud data technique was applied as well to extract the irregular contours of trunks. The feasibility and accuracy of the methods were examined through numerical simulations, laboratory and field measurements. The results demonstrated that the applied methodology allowed for accurate characterizations of the internal inhomogeneity. Furthermore, the point cloud technique resolved the trunk well by providing high-precision coordinate information. This study also demonstrated that cross-section tomography provided images with high resolution and accuracy. These integrated techniques thus proved to be promising for observing tree trunks and other cylindrical objects. The applied approaches offer a great promise for future 3D reconstruction of tomographic images with radar wave.

  19. 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%.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Victor Garcia-Font

    2016-06-01

    Full Text Available 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%.

  2. Edge detection of magnetic anomalies using analytic signal of tilt angle (ASTA)

    Science.gov (United States)

    Alamdar, K.; Ansari, A. H.; Ghorbani, A.

    2009-04-01

    Magnetic is a commonly used geophysical technique to identify and image potential subsurface targets. Interpretation of magnetic anomalies is a complex process due to the superposition of multiple magnetic sources, presence of geologic and cultural noise and acquisition and positioning error. Both the vertical and horizontal derivatives of potential field data are useful; horizontal derivative, enhance edges whereas vertical derivative narrow the width of anomaly and so locate source bodies more accurately. We can combine vertical and horizontal derivative of magnetic field to achieve analytic signal which is independent to body magnetization direction and maximum value of this lies over edges of body directly. Tilt angle filter is phased-base filter and is defined as angle between vertical derivative and total horizontal derivative. Tilt angle value differ from +90 degree to -90 degree and its zero value lies over body edge. One of disadvantage of this filter is when encountering with deep sources the detected edge is blurred. For overcome this problem many authors introduced new filters such as total horizontal derivative of tilt angle or vertical derivative of tilt angle which Because of using high-order derivative in these filters results may be too noisy. If we combine analytic signal and tilt angle, a new filter termed (ASTA) is produced which its maximum value lies directly over body edge and is easer than tilt angle to delineate body edge and no complicity of tilt angle. In this work new filter has been demonstrated on magnetic data from an area in Sar- Cheshme region in Iran. This area is located in 55 degree longitude and 32 degree latitude and is a copper potential region. The main formation in this area is Andesith and Trachyandezite. Magnetic surveying was employed to separate the boundaries of Andezite and Trachyandezite from adjacent area. In this regard a variety of filters such as analytic signal, tilt angle and ASTA filter have been applied which

  3. Anomaly detection for medical images based on a one-class classification

    Science.gov (United States)

    Wei, Qi; Ren, Yinhao; Hou, Rui; Shi, Bibo; Lo, Joseph Y.; Carin, Lawrence

    2018-02-01

    Detecting an anomaly such as a malignant tumor or a nodule from medical images including mammogram, CT or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis. A conventional way to address this is to learn a discriminative model using training datasets of negative and positive samples. The learned model can be used to classify a testing sample into a positive or negative class. However, in medical applications, the high unbalance between negative and positive samples poses a difficulty for learning algorithms, as they will be biased towards the majority group, i.e., the negative one. To address this imbalanced data issue as well as leverage the huge amount of negative samples, i.e., normal medical images, we propose to learn an unsupervised model to characterize the negative class. To make the learned model more flexible and extendable for medical images of different scales, we have designed an autoencoder based on a deep neural network to characterize the negative patches decomposed from large medical images. A testing image is decomposed into patches and then fed into the learned autoencoder to reconstruct these patches themselves. The reconstruction error of one patch is used to classify this patch into a binary class, i.e., a positive or a negative one, leading to a one-class classifier. The positive patches highlight the suspicious areas containing anomalies in a large medical image. The proposed method has been tested on InBreast dataset and achieves an AUC of 0.84. The main contribution of our work can be summarized as follows. 1) The proposed one-class learning requires only data from one class, i.e., the negative data; 2) The patch-based learning makes the proposed method scalable to images of different sizes and helps avoid the large scale problem for medical images; 3) The training of the proposed deep convolutional neural network (DCNN) based auto-encoder is fast and stable.

  4. Reliability of the exercise ECG in detecting silent ischemia in patients with prior myocardial infarction

    International Nuclear Information System (INIS)

    Yamagishi, Takashi; Matsuda, Yasuo; Satoh, Akira

    1991-01-01

    To assess the reliability of the exercise ECG in detecting silent ischemia, ECG results were compared with those of stress-redistribution thallium-201 single-photon emission computed tomography (SPECT) in 116 patients with prior myocardial infarction and in 20 normal subjects used as a control. The left ventricle (LV) was divided into 20 segmental images, which were scored blindly on a 5-point scale. The redistribution score was defined as thallium defect score of exercise subtracted by that of redistribution image and was used as a measure of amount of ischemic but viable myocardium. The upper limit of normal redistribution score (=4.32) was defined as mean+2 standard deviations derived from 20 normal subjects. Of 116 patients, 47 had the redistribution score above the normal range. Twenty-five (53%) of the 47 patients showed positive ECG response. Fourteen (20%) of the 69 patients, who had the normal redistribution score, showed positive ECG response. Thus, the ECG response had a sensitivity of 53% and a specificity of 80% in detecting transient ischemia. Furthermore, the 116 patients were subdivided into 4 groups according to the presence or absence of chest pain and ECG change during exercise. Fourteen patients showed both chest pain and ECG change and all these patients had the redistribution score above the normal range. Twenty-five patients showed ECG change without chest pain and 11 (44%) of the 25 patients had the abnormal redistribution. Three (43%) of 7 patients who showed chest pain without ECG change had the abnormal redistribution score. Of 70 patients who had neither chest pain nor ECG change, 19 (27%) had the redistribution score above the normal range. Thus, limitations exist in detecting silent ischemia by ECG in patients with a prior myocardial infarction, because the ECG response to the exercise test may have a low degree of sensitivity and a high degree of false positive and false negative results in detecting silent ischemia. (author)

  5. 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.

  6. 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.

  7. Detection of sinkholes or anomalies using full seismic wave fields : phase II.

    Science.gov (United States)

    2016-08-01

    A new 2-D Full Waveform Inversion (FWI) software code was developed to characterize layering and anomalies beneath the ground surface using seismic testing. The software is capable of assessing the shear and compression wave velocities (Vs and Vp) fo...

  8. 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.

  9. MODVOLC2: A Hybrid Time Series Analysis for Detecting Thermal Anomalies Applied to Thermal Infrared Satellite Data

    Science.gov (United States)

    Koeppen, W. C.; Wright, R.; Pilger, E.

    2009-12-01

    We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found

  10. 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

  11. Detection of geothermal anomalies in Tengchong, Yunnan Province, China from MODIS multi-temporal night LST imagery

    Science.gov (United States)

    Li, H.; Kusky, T. M.; Peng, S.; Zhu, M.

    2012-12-01

    Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using multi-temporal MODIS LST (Land Surface Temperature). The monthly night MODIS LST data from Mar. 2000 to Mar. 2011 of the study area were collected and analyzed. The 132 month average LST map was derived and three geothermal anomalies were identified. The findings of this study agree well with the results from relative geothermal gradient measurements. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect geothermal anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.

  12. 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.

  13. Can Sepsis Be Detected in the Nursing Home Prior to the Need for Hospital Transfer?

    Science.gov (United States)

    Sloane, Philip D; Ward, Kimberly; Weber, David J; Kistler, Christine E; Brown, Benjamin; Davis, Katherine; Zimmerman, Sheryl

    2018-06-01

    To determine whether and to what extent simple screening tools might identify nursing home (NH) residents who are at high risk of becoming septic. Retrospective chart audit of all residents who had been hospitalized and returned to participating NHs during the study period. A total of 236 NH residents, 59 of whom returned from hospitals with a diagnosis of sepsis and 177 who had nonsepsis discharge diagnoses, from 31 community NHs that are typical of US nursing homes overall. NH documentation of vital signs, mental status change, and medical provider visits 0-12 and 13-72 hours prior to the hospitalization. The specificity and sensitivity of 5 screening tools were evaluated for their ability to detect residents with incipient sepsis during 0-12 and 13-72 hours prior to hospitalization: The Systemic Inflammatory Response Syndrome criteria, the quick Sequential Organ Failure Assessment (SOFA), the 100-100-100 Early Detection Tool, and temperature thresholds of 99.0°F and 100.2°F. In addition, to validate the hospital diagnosis of sepsis, hospital discharge records in the NHs were audited to calculate SOFA scores. Documentation of 1 or more vital signs was absent in 26%-34% of cases. Among persons with complete vital sign documentation, during the 12 hours prior to hospitalization, the most sensitive screening tools were the 100-100-100 Criteria (79%) and an oral temperature >99.0°F (51%); and the most specific tools being a temperature >100.2°F (93%), the quick SOFA (88%), the Systemic Inflammatory Response Syndrome criteria (86%), and a temperature >99.0°F (85%). Many SOFA data points were missing from the record; in spite of this, 65% of cases met criteria for sepsis. NHs need better systems to monitor NH residents whose status is changing, and to present that information to medical providers in real time, either through rapid medical response programs or telemetry. Copyright © 2018 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. Using Generalized Entropies and OC-SVM with Mahalanobis Kernel for Detection and Classification of Anomalies in Network Traffic

    Directory of Open Access Journals (Sweden)

    Jayro Santiago-Paz

    2015-09-01

    Full Text Available Network anomaly detection and classification is an important open issue in network security. Several approaches and systems based on different mathematical tools have been studied and developed, among them, the Anomaly-Network Intrusion Detection System (A-NIDS, which monitors network traffic and compares it against an established baseline of a “normal” traffic profile. Then, it is necessary to characterize the “normal” Internet traffic. This paper presents an approach for anomaly detection and classification based on Shannon, Rényi and Tsallis entropies of selected features, and the construction of regions from entropy data employing the Mahalanobis distance (MD, and One Class Support Vector Machine (OC-SVM with different kernels (Radial Basis Function (RBF and Mahalanobis Kernel (MK for “normal” and abnormal traffic. Regular and non-regular regions built from “normal” traffic profiles allow anomaly detection, while the classification is performed under the assumption that regions corresponding to the attack classes have been previously characterized. Although this approach allows the use of as many features as required, only four well-known significant features were selected in our case. In order to evaluate our approach, two different data sets were used: one set of real traffic obtained from an Academic Local Area Network (LAN, and the other a subset of the 1998 MIT-DARPA set. For these data sets, a True positive rate up to 99.35%, a True negative rate up to 99.83% and a False negative rate at about 0.16% were yielded. Experimental results show that certain q-values of the generalized entropies and the use of OC-SVM with RBF kernel improve the detection rate in the detection stage, while the novel inclusion of MK kernel in OC-SVM and k-temporal nearest neighbors improve accuracy in classification. In addition, the results show that using the Box-Cox transformation, the Mahalanobis distance yielded high detection rates with

  19. Anomalies in the detection of change: When changes in sample size are mistaken for changes in proportions.

    Science.gov (United States)

    Fiedler, Klaus; Kareev, Yaakov; Avrahami, Judith; Beier, Susanne; Kutzner, Florian; Hütter, Mandy

    2016-01-01

    Detecting changes, in performance, sales, markets, risks, social relations, or public opinions, constitutes an important adaptive function. In a sequential paradigm devised to investigate detection of change, every trial provides a sample of binary outcomes (e.g., correct vs. incorrect student responses). Participants have to decide whether the proportion of a focal feature (e.g., correct responses) in the population from which the sample is drawn has decreased, remained constant, or increased. Strong and persistent anomalies in change detection arise when changes in proportional quantities vary orthogonally to changes in absolute sample size. Proportional increases are readily detected and nonchanges are erroneously perceived as increases when absolute sample size increases. Conversely, decreasing sample size facilitates the correct detection of proportional decreases and the erroneous perception of nonchanges as decreases. These anomalies are however confined to experienced samples of elementary raw events from which proportions have to be inferred inductively. They disappear when sample proportions are described as percentages in a normalized probability format. To explain these challenging findings, it is essential to understand the inductive-learning constraints imposed on decisions from experience.

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

    International Nuclear Information System (INIS)

    Williams, Jason M.; Arlinghaus, Lori R.; Rani, Sudheer D.; Shone, Martha D.; Abramson, Vandana G.; Pendyala, Praveen; Chakravarthy, A.B.; Gorge, William J.; Knowland, Joshua G.; Lattanze, Ronald K.; Perrin, Steven R.; Scarantino, Charles W.; Townsend, David W.; Abramson, Richard G.; Yankeelov, Thomas E.

    2016-01-01

    To dynamically detect and characterize "1"8F-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_p_e_a_k and SUV_m_a_x 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 may also

  1. 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

  2. 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

  3. Properties of the geoelectric structure that promote the detection of electrotelluric anomalies. The case of Ioannina, Greece

    Energy Technology Data Exchange (ETDEWEB)

    Makris, J. P. [Technological Educational Institute of Crete, Dept. of Electronics (Branch of Chania), Chalepa, Chania, Crete (Greece)

    2001-04-01

    The reliable detection and identification of electrotelluric anomalies that could be considered as precursory phenomena of earthquakes become fundamental aspects of earthquake prediction research. Special arrangements, in local and/or regional scale, of the geoelectric structure beneath the measuring point, may act as natural realtime filters on the ULF electrotelluric data improving considerably the signal to magnetotelluric-noise ratio of anomalies originated by probably non-magnetotelluric sources. Linear polarization, i.e. local channelling of the electric field on the surface is expected in cases where 3D-local inhomogeneities, producing strong shear distortion, are present in the vicinity of the monitoring site and/or when a 2D-regional geoelectrical setting exhibits high anisotropy. By assuming different generation mechanisms and modes of propagation for the electrotelluric anomalies that could be considered earthquake precursory phenomena, a rotationally originated residual electrotelluric field results, eliminating background magnetotelluric-noise and revealing hidden transient variations that could be associated to earthquakes. The suggested method is applicable in real-time data collection, thus simplifies and accelerates the tedious task of identification of suspicious signals. As an indicative example, the case of Ioannina (located in Northwestern Greece) is presented. The local polarization of the electrotelluric field varies dramatically even at neighboring points although the regional geoelectric strike direction does not change.

  4. 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.

  5. Detection of anomalies in radio tomography of asteroids: Source count and forward errors

    Science.gov (United States)

    Pursiainen, S.; Kaasalainen, M.

    2014-09-01

    The purpose of this study was to advance numerical methods for radio tomography in which asteroid's internal electric permittivity distribution is to be recovered from radio frequency data gathered by an orbiter. The focus was on signal generation via multiple sources (transponders) providing one potential, or even essential, scenario to be implemented in a challenging in situ measurement environment and within tight payload limits. As a novel feature, the effects of forward errors including noise and a priori uncertainty of the forward (data) simulation were examined through a combination of the iterative alternating sequential (IAS) inverse algorithm and finite-difference time-domain (FDTD) simulation of time evolution data. Single and multiple source scenarios were compared in two-dimensional localization of permittivity anomalies. Three different anomaly strengths and four levels of total noise were tested. Results suggest, among other things, that multiple sources can be necessary to obtain appropriate results, for example, to distinguish three separate anomalies with permittivity less or equal than half of the background value, relevant in recovery of internal cavities.

  6. Prostate specific antigen velocity does not aid prostate cancer detection in men with prior negative biopsy.

    Science.gov (United States)

    Vickers, Andrew J; Wolters, Tineke; Savage, Caroline J; Cronin, Angel M; O'Brien, M Frank; Roobol, Monique J; Aus, Gunnar; Scardino, Peter T; Hugosson, Jonas; Schröder, Fritz H; Lilja, Hans

    2010-09-01

    Prostate specific antigen velocity has been proposed as a marker to aid in prostate cancer detection. We determined whether prostate specific antigen velocity could predict repeat biopsy results in men with persistently increased prostate specific antigen after initial negative biopsy. We identified 1,837 men who participated in the Göteborg or Rotterdam section of the European Randomized Screening study of Prostate Cancer and who underwent 1 or more subsequent prostate biopsies after an initial negative finding. We evaluated whether prostate specific antigen velocity improved predictive accuracy beyond that of prostate specific antigen alone. Of the 2,579 repeat biopsies 363 (14%) were positive for prostate cancer, of which 44 (1.7%) were high grade (Gleason score 7 or greater). Prostate specific antigen velocity was statistically associated with cancer risk but had low predictive accuracy (AUC 0.55, p <0.001). There was some evidence that prostate specific antigen velocity improved AUC compared to prostate specific antigen for high grade cancer. However, the small increase in risk associated with high prostate specific antigen velocity (from 1.7% to 2.8% as velocity increased from 0 to 1 ng/ml per year) had questionable clinical relevance. Men with prior negative biopsy are at lower risk for prostate cancer at subsequent biopsies with high grade disease particularly rare. We found little evidence to support prostate specific antigen velocity to aid in decisions about repeat biopsy for prostate cancer. 2010 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  7. 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.

  8. 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.)

  9. 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.

  10. A new segmentation strategy for processing magnetic anomaly detection data of shallow depth ferromagnetic pipeline

    Science.gov (United States)

    Feng, Shuo; Liu, Dejun; Cheng, Xing; Fang, Huafeng; Li, Caifang

    2017-04-01

    Magnetic anomalies produced by underground ferromagnetic pipelines because of the polarization of earth's magnetic field are used to obtain the information on the location, buried depth and other parameters of pipelines. In order to achieve a fast inversion and interpretation of measured data, it is necessary to develop a fast and stable forward method. Magnetic dipole reconstruction (MDR), as a kind of integration numerical method, is well suited for simulating a thin pipeline anomaly. In MDR the pipeline model must be cut into small magnetic dipoles through different segmentation methods. The segmentation method has an impact on the stability and speed of forward calculation. Rapid and accurate simulation of deep-buried pipelines has been achieved by exciting segmentation method. However, in practical measurement, the depth of underground pipe is uncertain. When it comes to the shallow-buried pipeline, the present segmentation may generate significant errors. This paper aims at solving this problem in three stages. First, the cause of inaccuracy is analyzed by simulation experiment. Secondly, new variable interval section segmentation is proposed based on the existing segmentation. It can help MDR method to obtain simulation results in a fast way under the premise of ensuring the accuracy of different depth models. Finally, the measured data is inversed based on new segmentation method. The result proves that the inversion based on the new segmentation can achieve fast and accurate inversion of depth parameters of underground pipes without being limited by pipeline depth.

  11. Prenatal detection of structural cardiac defects and presence of associated anomalies: a retrospective observational study of 1262 fetal echocardiograms.

    Science.gov (United States)

    Mone, Fionnuala; Walsh, Colin; Mulcahy, Cecelia; McMahon, Colin J; Farrell, Sinead; MacTiernan, Aoife; Segurado, Ricardo; Mahony, Rhona; Higgins, Shane; Carroll, Stephen; McParland, Peter; McAuliffe, Fionnuala M

    2015-06-01

    The aim of this study is to document the detection of fetal congenital heart defect (CHD) in relation to the following: (1) indication for referral, (2) chromosomal and (3) extracardiac abnormalities. All fetal echocardiograms performed in our institution from 2007 to 2011 were reviewed retrospectively. Indication for referral, cardiac diagnosis based on the World Health Organization International Classification of Diseases tenth revision criteria and the presence of chromosomal and extracardiac defects were recorded. Of 1262 echocardiograms, 287 (22.7%) had CHD. Abnormal anatomy scan in pregnancies originally considered to be at low risk of CHD was the best indicator for detecting CHD (91.2% of positive cardiac diagnoses), compared with other indications of family history (5.6%) or maternal medical disorder (3.1%). Congenital anomalies of the cardiac septa comprised the largest category (n = 89), within which atrioventricular septal defects were the most common anomaly (n = 36). Invasive prenatal testing was performed for 126 of 287 cases, of which 44% (n = 55) had a chromosomal abnormality. Of 232 fetuses without chromosomal abnormalities, 31% had an extracardiac defect (n = 76). Most CHDs occur in pregnancies regarded to be at low risk, highlighting the importance of a routine midtrimester fetal anatomy scan. Frequent association of fetal CHD and chromosomal and extracardiac pathology emphasises the importance of thorough evaluation of any fetus with CHD. © 2015 John Wiley & Sons, Ltd.

  12. A Review of Anomaly Detection Techniques and Distributed Denial of Service (DDoS on Software Defined Network (SDN

    Directory of Open Access Journals (Sweden)

    M. H. H. Khairi

    2018-04-01

    Full Text Available Software defined network (SDN is a network architecture in which the network traffic may be operated and managed dynamically according to user requirements and demands. Issue of security is one of the big challenges of SDN because different attacks may affect performance and these attacks can be classified into different types. One of the famous attacks is distributed denial of service (DDoS. SDN is a new networking approach that is introduced with the goal to simplify the network management by separating the data and control planes. However, the separation leads to the emergence of new types of distributed denial-of-service (DDOS attacks on SDN networks. The centralized role of the controller in SDN makes it a perfect target for the attackers. Such attacks can easily bring down the entire network by bringing down the controller. This research explains DDoS attacks and the anomaly detection as one of the famous detection techniques for intelligent networks.

  13. 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.

  14. 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.

  15. 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 (pdental 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.

  16. 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.

  17. 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.

  18. Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

    Directory of Open Access Journals (Sweden)

    Patrick H. Freeborn

    2014-02-01

    Full Text Available Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI Fire Thermal Anomaly (FTA detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS across the Central African Republic (CAR. Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI’s omission rate can be attributed to a coupling between SEVIRI’s low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI’s commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i future improvements to the SEVIRI FTA detection algorithm; (ii the assimilation of the SEVIRI and MODIS active fire products; and (iii the potential inclusion of SEVIRI into a network of geostationary

  19. Satellite detection of carbon monoxide emission prior to the Gujarat earthquake of 26 January 2001

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Ramesh P., E-mail: rsingh@chapman.edu [Department of Physics, Computational Science and Engineering, Schmid College of Science, Chapman University, One University Drive, Orange CA 92866 (United States); Senthil Kumar, J.; Zlotnicki, Jacques [Observatoire de Physique du Globe de Clermont-Ferrand, OPGC-CNRS, Campus des Cuzeaux, 24, av. des Landais, 63177 Aubiere Cedex (France); Kafatos, Menas [Department of Physics, Computational Science and Engineering, Schmid College of Science, Chapman University, One University Drive, Orange CA 92866 (United States)

    2010-04-15

    NOAA AVHRR images have clearly shown anomalous changes in land surface temperature associated with earthquakes in the past two decades. Soon after the Gujarat earthquake of January 26, 2001, an anomalous increase in land surface temperature was inferred from MODIS satellite data a few days prior to the main earthquake event. The cause of such an anomalous change in surface temperature prior to the earthquake is attributed to many probable phenomena, but no definite cause has been identified. In the present study, changes of a complementary nature were found of land surface temperature associated with the emission of CO from the epicentral region. The observed changes on land and atmosphere associated with the Gujarat earthquake of 26 January, 2001, show the existence of strong coupling between land, atmosphere and ionosphere.

  20. 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

  1. 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.

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

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2015-01-01

    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.

  3. Analysis of a SCADA System Anomaly Detection Model Based on Information Entropy

    Science.gov (United States)

    2014-03-27

    Gerard, 2005:3) The NTSB report lists alarm management as one of the top five areas for improvement in pipeline SCADA systems (Gerard, 2005:1...Zhang, Qin, Wang, and Liang for leak detection in a SCADA -run pipeline system. A concept derived from information theory improved leak detection...System for West Products Pipeline . Journal of Loss Prevention in the Process Industries, 22(6), 981-989. Zhu, B., & Sastry, S. (2010). SCADA

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

    Science.gov (United States)

    2016-02-12

    characteristic and precision recall curves. New 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12. DISTRIBUTION...detection on data in the compressed domain. Detection performance was analyzed using receiver operating characteristic and precision recall curves. New...following categories: (b) Papers published in non-peer-reviewed journals (N/A for none) (c) Presentations Received Paper TOTAL : Received Paper TOTAL

  5. 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.)

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

    Science.gov (United States)

    2009-06-01

    des ign a c lassifier f or t he differentiation of the abnormal from the normal structures . In this project, we have also investigated the...were trained: one with the current m ammograms and the other with the prior mammograms. A feed- forward backpropagation artificial neural network...Sahin er, B., Helvie, M. A., Petri ck, N., Roubidou x, M. A., Wi lson, T. E., Adler, D. D., Pa ramagul, C., Ne wman, J . S . a nd G opal , S. S

  7. 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.

  8. 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...

  9. 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...

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

    Indian Academy of Sciences (India)

    The Internet has become a vital source of information; internal and exter- .... (iii) DDos detection: Distributed Denial of Service (DDoS) is a common malicious ...... Guirguis M, Bestavros A, Matta I and Zhang Y 2005a Reduction of quality (roq) ...

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

    Science.gov (United States)

    2016-04-25

    2012. [5] Phil Muncaster. Indian navy computers stormed by malware-ridden USBs. 2012. [6] Ponemon. 2011 Second Annual Cost of Cyber Crime Study...Zhang, and Shanshan Sun. “A mixed unsu- pervised clustering-based intrusion detection model”. In: Genetic and Evolutionary Computing, 2009. WGEC’09

  12. Big Data Analytics for Flow-based Anomaly Detection in High-Speed Networks

    OpenAIRE

    Garofalo, Mauro

    2017-01-01

    The Cisco VNI Complete Forecast Highlights clearly states that the Internet traffic is growing in three different directions, Volume, Velocity, and Variety, bringing computer network into the big data era. At the same time, sophisticated network attacks are growing exponentially. Such growth making the existing signature-based security tools, like firewall and traditional intrusion detection systems, ineffective against new kind of attacks or variations of known attacks. In this dissertati...

  13. Anomaly-based online intrusion detection system as a sensor for cyber security situational awareness system

    OpenAIRE

    Kokkonen, Tero

    2016-01-01

    Almost all the organisations and even individuals rely on complex structures of data networks and networked computer systems. That complex data ensemble, the cyber domain, provides great opportunities, but at the same time it offers many possible attack vectors that can be abused for cyber vandalism, cyber crime, cyber espionage or cyber terrorism. Those threats produce requirements for cyber security situational awareness and intrusion detection capability. This dissertation conc...

  14. Anomaly Detection in Log Data using Graph Databases and Machine Learning to Defend Advanced Persistent Threats

    OpenAIRE

    Schindler, Timo

    2018-01-01

    Advanced Persistent Threats (APTs) are a main impendence in cyber security of computer networks. In 2015, a successful breach remains undetected 146 days on average, reported by [Fi16].With our work we demonstrate a feasible and fast way to analyse real world log data to detect breaches or breach attempts. By adapting well-known kill chain mechanisms and a combine of a time series database and an abstracted graph approach, it is possible to create flexible attack profiles. Using this approach...

  15. Anomaly detection in forward looking infrared imaging using one-class classifiers

    Science.gov (United States)

    Popescu, Mihail; Stone, Kevin; Havens, Timothy; Ho, Dominic; Keller, James

    2010-04-01

    In this paper we describe a method for generating cues of possible abnormal objects present in the field of view of an infrared (IR) camera installed on a moving vehicle. The proposed method has two steps. In the first step, for each frame, we generate a set of possible points of interest using a corner detection algorithm. In the second step, the points related to the background are discarded from the point set using an one class classifier (OCC) trained on features extracted from a local neighborhood of each point. The advantage of using an OCC is that we do not need examples from the "abnormal object" class to train the classifier. Instead, OCC is trained using corner points from images known to be abnormal object free, i.e., that contain only background scenes. To further reduce the number of false alarms we use a temporal fusion procedure: a region has to be detected as "interesting" in m out of n, mGM). The comparison is performed using a set of about 900 background point neighborhoods for training and 400 for testing. The best performing OCC is then used to detect abnormal objects in a set of IR video sequences obtained on a 1 mile long country road.

  16. Early detection and diagnosis of plant anomalies using parallel simulation and knowledge engineering techniques

    International Nuclear Information System (INIS)

    Ness, E.; Berg, Oe.; Soerenssen, A.

    1990-01-01

    A conventional alarm system in a nuclear power plant surveys pressures, temperatures and similar physical quantities and triggers if they get too high or too low. To avoid false alarms for a dynamic process, the alarm limits should be rather wide. This means that a disturbance may develop quite a bit before it is detected. In order to get warnings sufficiently early to avoid taking drastic countermeasures in restoring normal plant conditions, the alarm limits should be put close to the desired operating points. As a extension of several years' activity on alarm reduction methods, the OECD Halden Reactor Project started in 1985 to develop a fault detection system based on the application of reference models for process sections. The system looks at groups of variables rather than single variables. In this way each variable within the group may have a legal value, but the group as a whole may indicate that something is wrong. Therefore faults are detected earlier than by conventional alarm systems, even in dynamic situations. Reference models for the feedwater system of a PWR nuclear power plant have been successfully evaluated with real process data. A test installation is now running on the Loviisa NPP, Finland

  17. 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.

  18. Insufficient evidence for the use of a physical examination to detect maltreatment in children without prior suspicion: a systematic review.

    Science.gov (United States)

    Hoytema van Konijnenburg, Eva Mm; Teeuw, Arianne H; Sieswerda-Hoogendoorn, Tessa; Leenders, Arnold G E; van der Lee, Johanna H

    2013-12-06

    Although it is often performed in clinical practice, the diagnostic value of a screening physical examination to detect maltreatment in children without prior suspicion has not been reviewed. This article aims to evaluate the diagnostic value of a complete physical examination as a screening instrument to detect maltreatment in children without prior suspicion. We systematically searched the databases of MEDLINE, EMBASE, PsychINFO, CINAHL, and ERIC, using a sensitive search strategy. Studies that i) presented medical findings of a complete physical examination for screening purposes in children 0-18 years, ii) specifically recorded the presence or absence of signs of child maltreatment, and iii) recorded child maltreatment confirmed by a reference standard, were included. Two reviewers independently performed study selection, data extraction, and quality appraisal using the QUADAS-2 tool. The search yielded 4,499 titles, of which three studies met the eligibility criteria. The prevalence of confirmed signs of maltreatment during screening physical examination varied between 0.8% and 13.5%. The designs of the studies were inadequate to assess the diagnostic accuracy of a screening physical examination for child maltreatment. Because of the lack of informative studies, we could not draw conclusions about the diagnostic value of a screening physical examination in children without prior suspicion of child maltreatment.

  19. Integrating prior knowledge in multiple testing under dependence with applications to detecting differential DNA methylation.

    Science.gov (United States)

    Kuan, Pei Fen; Chiang, Derek Y

    2012-09-01

    DNA methylation has emerged as an important hallmark of epigenetics. Numerous platforms including tiling arrays and next generation sequencing, and experimental protocols are available for profiling DNA methylation. Similar to other tiling array data, DNA methylation data shares the characteristics of inherent correlation structure among nearby probes. However, unlike gene expression or protein DNA binding data, the varying CpG density which gives rise to CpG island, shore and shelf definition provides exogenous information in detecting differential methylation. This article aims to introduce a robust testing and probe ranking procedure based on a nonhomogeneous hidden Markov model that incorporates the above-mentioned features for detecting differential methylation. We revisit the seminal work of Sun and Cai (2009, Journal of the Royal Statistical Society: Series B (Statistical Methodology)71, 393-424) and propose modeling the nonnull using a nonparametric symmetric distribution in two-sided hypothesis testing. We show that this model improves probe ranking and is robust to model misspecification based on extensive simulation studies. We further illustrate that our proposed framework achieves good operating characteristics as compared to commonly used methods in real DNA methylation data that aims to detect differential methylation sites. © 2012, The International Biometric Society.

  20. 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

  1. Gravitational anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Leutwyler, H; Mallik, S

    1986-12-01

    The effective action for fermions moving in external gravitational and gauge fields is analyzed in terms of the corresponding external field propagator. The central object in our approach is the covariant energy-momentum tensor which is extracted from the regular part of the propagator at short distances. It is shown that the Lorentz anomaly, the conformal anomaly and the gauge anomaly can be expressed in terms of the local polynomials which determine the singular part of the propagator. (There are no coordinate anomalies). Except for the conformal anomaly, for which we give explicit representations only in dless than or equal to4, we consider an arbitrary number of dimensions.

  2. Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery

    Directory of Open Access Journals (Sweden)

    Ramakalavathi Marapareddy

    2017-10-01

    Full Text Available This paper analyzes the use of a synthetic aperture radar (SAR imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides. If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD using Euclidean and Mahalanobis distance, support vector machine (SVM, and maximum likelihood (ML, using SAR technology to detect slough slides on earthen levees. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s uninhabited aerial vehicle synthetic aperture radar (UAVSAR. The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers.

  3. Fraud Detection in Credit Card Transactions; Using Parallel Processing of Anomalies in Big Data

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Taghva

    2016-10-01

    Full Text Available In parallel to the increasing use of electronic cards, especially in the banking industry, the volume of transactions using these cards has grown rapidly. Moreover, the financial nature of these cards has led to the desirability of fraud in this area. The present study with Map Reduce approach and parallel processing, applied the Kohonen neural network model to detect abnormalities in bank card transactions. For this purpose, firstly it was proposed to classify all transactions into the fraudulent and legal which showed better performance compared with other methods. In the next step, we transformed the Kohonen model into the form of parallel task which demonstrated appropriate performance in terms of time; as expected to be well implemented in transactions with Big Data assumptions.

  4. ORP and pH measurements to detect redox and acid-base anomalies from hydrothermal activity

    Science.gov (United States)

    Santana-Casiano, J. M.; González-Dávila, M.; Fraile-Nuez, E.

    2017-12-01

    The Tagoro submarine volcano is located 1.8 km south of the Island of El Hierro at 350 m depth and rises up to 88 m below sea level. It was erupting melting material for five months, from October 2011 to March 2012, changing drastically the physical-chemical properties of the water column in the area. After this eruption, the system evolved to a hydrothermal system. The character of both reduced and acid of the hydrothermal emissions in the Tagoro submarine volcano allowed us to detect anomalies related with changes in the chemical potential and the proton concentration using ORP and pH sensors, respectively. Tow-yos using a CTD-rosette with these two sensors provided the locations of the emissions plotting δ(ORP)/δt and ΔpH versus the latitude or longitude. The ORP sensor responds very quickly to the presence of reduced chemicals in the water column. Changes in potential are proportional to the amount of reduced chemical species present in the water. The magnitude of these changes are examined by the time derivative of ORP, δ(ORP)/δt. To detect changes in the pH, the mean pH for each depth at a reference station in an area not affected by the vent emission is subtracted from each point measured near the volcanic edifice, defining in this way ΔpH. Detailed surveys of the volcanic edifice were carried out between 2014 and 2016 using several CTD-pH-ORP tow-yo studies, localizing the ORP and pH changes, which were used to obtain surface maps of anomalies. Moreover, meridional tow-yos were used to calculate the amount of volcanic CO2 added to the water column. The inputs of CO2 along multiple sections combined with measurements of oceanic currents produced an estimated volcanic CO2 flux = 6.0 105 ± 1.1 105 kg d-1 which increases the acidity above the volcano by 20%. Sites like the Tagoro submarine volcano, in its degasification stage, provide an excellent opportunity to study the carbonate system in a high CO2 world, the volcanic contribution to the global

  5. 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

  6. 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.)

  7. 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.)

  8. Detection of prior irradiation in dried fruits by electron spin resonance (ESR)

    International Nuclear Information System (INIS)

    Esteves, M.P.; Andrade, M.E.; Empis, J.

    1998-01-01

    Complete text of publication follows. Dried almonds, raisins, dates and pistachio were irradiated using gamma radiation and electron beam with an average absorbed dose of 5 kGy. The radiation treatment was detected by ESR spectroscopy. Different parts of the dried fruits were analysed as follows: almonds: skin; raisins: dried pulp (at 30 deg C for one week); dates: dried pulp (at 30 deg C for one week) and stone; pistachio: nutshell. A Bruker ECS 106 EPR Spectrometer, at a microwave power of 0.4 mW, at room temperature was used. The analysis were carried out 2-3 months and 6 months after irradiation. A series of signals, tentatively described as 'cellulose-like', 'sugar-like' and 'complex' were observed, and some slight differences between spectra recorded from samples irradiated with gamma and electrons were evident

  9. Detection of prior irradiation in dried fruits by electron spin resonance (ESR)

    Energy Technology Data Exchange (ETDEWEB)

    Esteves, M P [Instituto Nacional de Investigacao Agraria (EAN), Oeiras (Portugal); Andrade, M E [Instituto Tecnologico e Nuclear (ITN), Sacavem (Portugal); Empis, J [Instituto Superior Tecnico (IST), Lisboa (Portugal)

    1999-12-31

    Complete text of publication follows. Dried almonds, raisins, dates and pistachio were irradiated using gamma radiation and electron beam with an average absorbed dose of 5 kGy. The radiation treatment was detected by ESR spectroscopy. Different parts of the dried fruits were analysed as follows: almonds: skin; raisins: dried pulp (at 30 deg C for one week); dates: dried pulp (at 30 deg C for one week) and stone; pistachio: nutshell. A Bruker ECS 106 EPR Spectrometer, at a microwave power of 0.4 mW, at room temperature was used. The analysis were carried out 2-3 months and 6 months after irradiation. A series of signals, tentatively described as `cellulose-like`, `sugar-like` and `complex` were observed, and some slight differences between spectra recorded from samples irradiated with gamma and electrons were evident.

  10. Anomaly detection using simulated MTI data cubes derived from HYDICE data

    International Nuclear Information System (INIS)

    Moya, M.M.; Taylor, J.G.; Stallard, B.R.; Motomatsu, S.E.

    1998-01-01

    The US Department of Energy is funding the development of the Multi-spectral Thermal Imager (MTI), a satellite-based multi-spectral (MS) thermal imaging sensor scheduled for launch in October 1999. MTI is a research and development (R and D) platform to test the applicability of multispectral and thermal imaging technology for detecting and monitoring signs of proliferation of weapons of mass destruction. During its three-year mission, MTI will periodically record images of participating government, industrial and natural sites in fifteen visible and infrared spectral bands to provide a variety of image data associated with weapons production activities. The MTI satellite will have spatial resolution in the visible bands that is five times better than LANDSAT TM in each dimension and will have five thermal bands. In this work, the authors quantify the separability between specific materials and the natural background by applying Receiver Operating Curve (ROC) analysis to the residual errors from a linear unmixing. The authors apply the ROC analysis to quantify performance of the MTI. They describe the MTI imager and simulate its data by filtering HYDICE hyperspectral imagery both spatially and spectrally and by introducing atmospheric effects corresponding to the MTI satellite altitude. They compare and contrast the individual effects on performance of spectral resolution, spatial resolution, atmospheric corrections, and varying atmospheric conditions

  11. 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

    2016-08-18

    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.

  12. Trench Parallel Bouguer Anomaly (TPBA): A robust measure for statically detecting asperities along the forearc of subduction zones

    Science.gov (United States)

    Raeesi, M.

    2009-05-01

    During 1970s some researchers noticed that large earthquakes occur repeatedly at the same locations. These observations led to the asperity hypothesis. At the same times some researchers noticed that there was a relationship between the location of great interplate earthquakes and the submarine structures, basins in particular, over the rupture area in the forearc regions. Despite these observations there was no comprehensive and reliable hypothesis explaining the relationship. There were numerous cons and pros to the various hypotheses given in this regard. In their pioneering study, Song and Simons (2003) approached the problem using gravity data. This was a turning point in seismology. Although their approach was correct, appropriate gravity anomaly had to be used in order to reveal the location and extent of the asperities. Following the method of Song and Simons (2003) but using the Bouguer gravity anomaly that we called "Trench Parallel Bouguer Anomaly", TPBA, we found strong, logical, and convincing relation between the TPBA-derived asperities and the slip distribution as well as earthquake distribution, foreshocks and aftershocks in particular. Various parameters with different levels of importance are known that affect the contact between the subducting and the overriding plates, We found that the TPBA can show which are the important factors. Because the TPBA-derived asperities are based on static physical properties (gravity and elevation), they do not suffer from instabilities due to the trade-offs, as it happens for asperities derived in dynamic studies such as waveform inversion. Comparison of the TPBA-derived asperities with rupture processes of the well-studied great earthquakes, reveals the high level of accuracy of the TPBA. This new measure opens a forensic viewpoint on the rupture process along the subduction zones. The TPBA reveals the reason behind 9+ earthquakes and it explains where and why they occur. The TPBA reveals the areas that can

  13. Muscle MRS detects elevated PDE/ATP ratios prior to fatty infiltration in Becker muscular dystrophy.

    Science.gov (United States)

    Wokke, B H; Hooijmans, M T; van den Bergen, J C; Webb, A G; Verschuuren, J J; Kan, H E

    2014-11-01

    Becker muscular dystrophy (BMD) is characterized by progressive muscle weakness. Muscles show structural changes (fatty infiltration, fibrosis) and metabolic changes, both of which can be assessed using MRI and MRS. It is unknown at what stage of the disease process metabolic changes arise and how this might vary for different metabolites. In this study we assessed metabolic changes in skeletal muscles of Becker patients, both with and without fatty infiltration, quantified via Dixon MRI and (31) P MRS. MRI and (31) P MRS scans were obtained from 25 Becker patients and 14 healthy controls using a 7 T MR scanner. Five lower-leg muscles were individually assessed for fat and muscle metabolite levels. In the peroneus, soleus and anterior tibialis muscles with non-increased fat levels, PDE/ATP ratios were higher (P < 0.02) compared with controls, whereas in all muscles with increased fat levels PDE/ATP ratios were higher compared with healthy controls (P ≤ 0.05). The Pi /ATP ratio in the peroneus muscles was higher in muscles with increased fat fractions (P = 0.005), and the PCr/ATP ratio was lower in the anterior tibialis muscles with increased fat fractions (P = 0.005). There were no other significant changes in metabolites, but an increase in tissue pH was found in all muscles of the total group of BMD patients in comparison with healthy controls (P < 0.05). These findings suggest that (31) P MRS can be used to detect early changes in individual muscles of BMD patients, which are present before the onset of fatty infiltration. Copyright © 2014 John Wiley & Sons, Ltd.

  14. Holonomy anomalies

    International Nuclear Information System (INIS)

    Bagger, J.; Nemeschansky, D.; Yankielowicz, S.

    1985-05-01

    A new type of anomaly is discussed that afflicts certain non-linear sigma models with fermions. This anomaly is similar to the ordinary gauge and gravitational anomalies since it reflects a topological obstruction to the reparametrization invariance of the quantum effective action. Nonlinear sigma models are constructed based on homogeneous spaces G/H. Anomalies arising when the fermions are chiral are shown to be cancelled sometimes by Chern-Simons terms. Nonlinear sigma models are considered based on general Riemannian manifolds. 9 refs

  15. 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.

  16. 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...

  17. 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

  18. Tribal Odisha Eye Disease Study (TOES # 2 Rayagada school screening program: efficacy of multistage screening of school teachers in detection of impaired vision and other ocular anomalies

    Directory of Open Access Journals (Sweden)

    Panda L

    2018-06-01

    Full Text Available Lapam Panda,1 Taraprasad Das,1 Suryasmita Nayak,1 Umasankar Barik,2 Bikash C Mohanta,1 Jachin Williams,3 Vivekanand Warkad,4 Guha Poonam Tapas Kumar,5 Rohit C Khanna3 1Indian Oil Center for Rural Eye Health, GPR ICARE, L V Prasad Eye Institute, MTC Campus, Bhubaneswar, India; 2Naraindas Morbai Budhrani Eye Centre, L V Prasad Eye Institute, Rayagada, India; 3Gullapalli Pratibha Rao International Center for Advancement of Rural Eye Care, L V Prasad Eye Institute, KAR Campus, Hyderabad, India; 4Miriam Hyman Children Eye Care Center, L V Prasad Eye Institute, MTC Campus, Bhubaneswar, India; 5District Administration, Government of Odisha, Rayagada, India Purpose: To describe program planning and effectiveness of multistage school eye screening and assess accuracy of teachers in vision screening and detection of other ocular anomalies in Rayagada District School Sight Program, Odisha, India.Methods: This multistage screening of students included as follows: stage I: screening for vision and other ocular anomalies by school teachers in the school; stage II: photorefraction, subjective correction and other ocular anomaly confirmation by optometrists in the school; stage III: comprehensive ophthalmologist examination in secondary eye center; and stage IV: pediatric ophthalmologist examination in tertiary eye center. Sensitivity, specificity, positive predictive value (PPV and negative predictive value (NPV of teachers for vision screening and other ocular anomaly detection were calculated vis-à-vis optometrist (gold standard.Results: In the study, 216 teachers examined 153,107 (95.7% of enrolled students aged 5–15 years. Teachers referred 8,363 (5.4% of examined students and 5,990 (71.6% of referred were examined in stage II. After prescribing spectacles to 443, optometrists referred 883 students to stage III. The sensitivity (80.51% and PPV (93.05% of teachers for vision screening were high, but specificity (53.29% and NPV (26.02% were low. The

  19. [The advantages of early midtrimester targeted fetal systematic organ screening for the detection of fetal anomalies--will a global change start in Israel?].

    Science.gov (United States)

    Bronshtein, Moshe; Solt, Ido; Blumenfeld, Zeev

    2014-06-01

    Despite more than three decades of universal popularity of fetal sonography as an integral part of pregnancy evaluation, there is still no unequivocal agreement regarding the optimal dating of fetal sonographic screening and the type of ultrasound (transvaginal vs abdominal). TransvaginaL systematic sonography at 14-17 weeks for fetal organ screening. The evaluation of over 72.000 early (14-17 weeks) and late (18-24 weeks) fetal ultrasonographic systematic organ screenings revealed that 96% of the malformations are detectable in the early screening with an incidence of 1:50 gestations. Only 4% of the fetal anomalies are diagnosed later in pregnancy. Over 99% of the fetal cardiac anomalies are detectable in the early screening and most of them appear in low risk gestations. Therefore, we suggest a new platform of fetal sonographic evaluation and follow-up: The extensive systematic fetal organ screening should be performed by an expert sonographer who has been trained in the detection of fetal malformations, at 14-17 weeks gestation. This examination should also include fetal cardiac echography Three additional ultrasound examinations are suggested during pregnancy: the first, performed by the patient's obstetrician at 6-7 weeks for the exclusion of ectopic pregnancy, confirmation of fetal viability, dating, assessment of chorionicity in multiple gestations, and visualization of maternal adnexae. The other two, at 22-26 and 32-34 weeks, require less training and should be performed by an obstetrician who has been qualified in the sonographic detection of fetal anomalies. The advantages of early midtrimester targeted fetal systematic organ screening for the detection of fetal anomalies may dictate a global change.

  20. Detecting Patterns of Anomalies

    Science.gov (United States)

    2009-03-01

    notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does...Report (SAR) 18. NUMBER OF PAGES 174 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified...Tillett and I. L. Spencer. Influenza surveillance in england and wales using routine statistics. Journal of Hygine , 88:83–94, 1982. Shobha Venkataraman

  1. 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

  2. Development of early core anomaly detection system by using in-sodium microphone in JOYO. Fundamental characteristics test of in-sodium microphone in water and examination of improvement of detection accuracy

    International Nuclear Information System (INIS)

    Komai, Masafumi

    2001-07-01

    Fast reactor core anomalies can be detected in near real-time with acoustic sensors. An acoustic detection system senses an in-core anomaly immediately from the fast acoustic signals that propagate through the sodium coolant. One example of a detectable anomaly is sodium boiling due to local blockage in a sub-assembly; the slight change in background acoustic signals can be detected. A key advantage of the acoustic detector is that it can be located outside the core. The location of the anomaly in the core can be determined by correlating multiple acoustic signals. This report describes the testing and fundamental characteristics of a microphone suitable for use in the sodium coolant and examines methods to improve the system's S/N ratio. Testing in water confirmed that the in-sodium microphone has good impulse and wide band frequency responses. These tests used impulse and white noise signals that imitate acoustic signals from boiling sodium. Correlation processing of multiple microphone signals to improve S/N ratio is also described. (author)

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

    Science.gov (United States)

    Akhoondzadeh, M.

    2013-04-01

    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.

  4. First and second trimester screening for fetal structural anomalies.

    Science.gov (United States)

    Edwards, Lindsay; Hui, Lisa

    2018-04-01

    Fetal structural anomalies are found in up to 3% of all pregnancies and ultrasound-based screening has been an integral part of routine prenatal care for decades. The prenatal detection of fetal anomalies allows for optimal perinatal management, providing expectant parents with opportunities for additional imaging, genetic testing, and the provision of information regarding prognosis and management options. Approximately one-half of all major structural anomalies can now be detected in the first trimester, including acrania/anencephaly, abdominal wall defects, holoprosencephaly and cystic hygromata. Due to the ongoing development of some organ systems however, some anomalies will not be evident until later in the pregnancy. To this extent, the second trimester anatomy is recommended by professional societies as the standard investigation for the detection of fetal structural anomalies. The reported detection rates of structural anomalies vary according to the organ system being examined, and are also dependent upon factors such as the equipment settings and sonographer experience. Technological advances over the past two decades continue to support the role of ultrasound as the primary imaging modality in pregnancy, and the safety of ultrasound for the developing fetus is well established. With increasing capabilities and experience, detailed examination of the central nervous system and cardiovascular system is possible, with dedicated examinations such as the fetal neurosonogram and the fetal echocardiogram now widely performed in tertiary centers. Magnetic resonance imaging (MRI) is well recognized for its role in the assessment of fetal brain anomalies; other potential indications for fetal MRI include lung volume measurement (in cases of congenital diaphragmatic hernia), and pre-surgical planning prior to fetal spina bifida repair. When a major structural abnormality is detected prenatally, genetic testing with chromosomal microarray is recommended over

  5. The Persistence of Experience: Prior Attentional and Emotional State Affects Network Functioning in a Target Detection Task.

    Science.gov (United States)

    Stern, Emily R; Muratore, Alexandra F; Taylor, Stephan F; Abelson, James L; Hof, Patrick R; Goodman, Wayne K

    2015-09-01

    Efficient, adaptive behavior relies on the ability to flexibly move between internally focused (IF) and externally focused (EF) attentional states. Despite evidence that IF cognitive processes such as event imagination comprise a significant amount of awake cognition, the consequences of internal absorption on the subsequent recruitment of brain networks during EF tasks are unknown. The present functional magnetic resonance imaging (fMRI) study employed a novel attentional state switching task. Subjects imagined positive and negative events (IF task) or performed a working memory task (EF task) before switching to a target detection (TD) task also requiring attention to external information, allowing for the investigation of neural functioning during external attention based on prior attentional state. There was a robust increase of activity in frontal, parietal, and temporal regions during TD when subjects were previously performing the EF compared with IF task, an effect that was most pronounced following negative IF. Additionally, dorsolateral prefrontal cortex was less negatively coupled with ventromedial prefrontal and posterior cingulate cortices during TD following IF compared with EF. These findings reveal the striking consequences for brain activity following immersion in an IF attentional state, which have strong implications for psychiatric disorders characterized by excessive internal focus. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  6. Focal skin defect, limb anomalies and microphthalmia.

    NARCIS (Netherlands)

    Jackson, K.E.; Andersson, H.C.

    2004-01-01

    We describe two unrelated female patients with congenital single focal skin defects, unilateral microphthalmia and limb anomalies. Growth and psychomotor development were normal and no brain malformation was detected. Although eye and limb anomalies are commonly associated, clinical anophthalmia and

  7. 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

  8. 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

  9. 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.

  10. 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)…

  11. 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.

  12. 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.

  13. 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

  14. Comparison of single-dose and double-dose thallium-201 myocardial perfusion scintigraphy for the detection of coronary artery disease and prior myocardial infarction

    International Nuclear Information System (INIS)

    Blood, D.K.; McCarthy, D.M.; Sciacca, R.R.; Cannon, P.J.

    1978-01-01

    Thallium-201 myocardial perfusion scintigraphy was performed after exercise, 4 hours after exercise (redistribution) and after a separate rest injection in 87 patients undergoing coronary arteriography. Significant coronary lesions were present in 62 of the patients. Interpretation of the rest and redistribution scintiscans was the same in 69 patients, 45 of whom had coronary artery disease (CAD). In 16 of the 17 patients with CAD and differing interpretations, defects were present on redistribution scintiscans but not on rest scintiscans; 11 of these patients had evidence of prior transmural myocardial infarction and the other five had an occluded coronary artery supplying the region of the defect. Redistribution scintiscans were more sensitive than rest scintiscans for the detection of prior myocardial infarction (93% vs 54%; P < 0.01). The increased sensitivity was confined to the detection of prior inferior myocardial infarctions. In 36 of 38 patients with persistent perfusion defects on 4-hour redistribution scintiscans, either a prior infarction or an occluded coronary vessel was present. These data demonstrate that redistribution thallium-201 scintiscans may be substituted for conventional rest scintiscans, resulting in reduced cost and radiation exposure to the patients

  15. Application of process monitoring to anomaly detection in nuclear material processing systems via system-centric event interpretation of data from multiple sensors of varying reliability

    International Nuclear Information System (INIS)

    Garcia, Humberto E.; Simpson, Michael F.; Lin, Wen-Chiao; Carlson, Reed B.; Yoo, Tae-Sic

    2017-01-01

    Highlights: • Process monitoring can strengthen nuclear safeguards and material accountancy. • Assessment is conducted at a system-centric level to improve safeguards effectiveness. • Anomaly detection is improved by integrating process and operation relationships. • Decision making is benefited from using sensor and event sequence information. • Formal framework enables optimization of sensor and data processing resources. - Abstract: In this paper, we apply an advanced safeguards approach and associated methods for process monitoring to a hypothetical nuclear material processing system. The assessment regarding the state of the processing facility is conducted at a system-centric level formulated in a hybrid framework. This utilizes architecture for integrating both time- and event-driven data and analysis for decision making. While the time-driven layers of the proposed architecture encompass more traditional process monitoring methods based on time series data and analysis, the event-driven layers encompass operation monitoring methods based on discrete event data and analysis. By integrating process- and operation-related information and methodologies within a unified framework, the task of anomaly detection is greatly improved. This is because decision-making can benefit from not only known time-series relationships among measured signals but also from known event sequence relationships among generated events. This available knowledge at both time series and discrete event layers can then be effectively used to synthesize observation solutions that optimally balance sensor and data processing requirements. The application of the proposed approach is then implemented on an illustrative monitored system based on pyroprocessing and results are discussed.

  16. 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

  17. 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

    control. An intrusion detection system observes distributed energy resource’s behaviour, control actions and the power system impact, and is tested together with an ongoing voltage control attack in a co-simulation set-up. The simulation results obtained with a real photovoltaic rooftop power plant data...

  18. The Association of H1N1 Pandemic Influenza with Congenital Anomaly Prevalence in Europe

    DEFF Research Database (Denmark)

    Luteijn, Johannes Michiel; Addor, Marie-Claude; Arriola, Larraitz

    2015-01-01

    BACKGROUND: In the context of the European Surveillance of Congenital Anomalies (EUROCAT) surveillance response to the 2009 influenza pandemic, we sought to establish whether there was a detectable increase of congenital anomaly prevalence among pregnancies exposed to influenza seasons in general......, and whether any increase was greater during the 2009 pandemic than during other seasons. METHODS: We performed an ecologic time series analysis based on 26,967 pregnancies with nonchromosomal congenital anomaly conceived from January 2007 to March 2011, reported by 15 EUROCAT registries. Analysis...... and tricuspid atresia and stenosis during pandemic influenza season 2009, but not during 2007-2011 influenza seasons. For congenital anomalies, where there was no prior hypothesis, the prevalence of tetralogy of Fallot was strongly reduced during influenza seasons. CONCLUSIONS: Our data do not suggest...

  19. Prenatal Detection of Cardiac Anomalies in Fetuses with Single Umbilical Artery: Diagnostic Accuracy Comparison of Maternal-Fetal-Medicine and Pediatric Cardiologist

    Directory of Open Access Journals (Sweden)

    Ilir Tasha

    2014-01-01

    Full Text Available Aim. To determine agreement of cardiac anomalies between maternal fetal medicine (MFM physicians and pediatric cardiologists (PC in fetuses with single umbilical artery (SUA. Methods. A retrospective review of all fetuses with SUA between 1999 and 2008. Subjects were studied by MFM and PC, delivered at our institution, and had confirmation of SUA and cardiac anomaly by antenatal and neonatal PC follow-up. Subjects were divided into four groups: isolated SUA, SUA and isolated cardiac anomaly, SUA and multiple anomalies without heart anomalies, and SUA and multiple malformations including cardiac anomaly. Results. 39,942 cases were studied between 1999 and 2008. In 376 of 39,942 cases (0.94%, SUA was diagnosed. Only 182 (48.4% met inclusion criteria. Cardiac anomalies were found in 21% (38/182. Agreement between MFM physicians and PC in all groups combined was 94% (171/182 (95% CI [89.2, 96.8]. MFM physicians overdiagnosed cardiac anomalies in 4.4% (8/182. MFM physicians and PC failed to antenatally diagnose cardiac anomaly in the same two cases. Conclusions. Good agreement was noted between MFM physicians and PC in our institution. Studies performed antenatally by MFM physicians and PC are less likely to uncover the entire spectrum of cardiac abnormalities and thus neonatal follow-up is suggested.

  20. Effect of prior dust collection on detection, counting efficiency, and energy resolution for alpha continuous air monitors

    International Nuclear Information System (INIS)

    Newton, G.J.; Hoover, M.D.

    1994-01-01

    For the past several years, we have supported the DOE Waste Isolation Pilot Plant (WIPP) project by evaluating the capabilities and performance of the Eberline Alpha 6 continuous air monitor (CAM). This evalution has focused on the ability of the CAM to correctly report plutonium in the presence of salt dust. Tests involving the simultaneous collection of plutonium and salt have shown that burial by salt can degrade the detection of plutonium, but that this interference is negligible when salt concentrations are below about 0.2 mg/m 3 . Throughout the evalution, it has been assumed that salt burial is a concern for slow, chronic release of plutonium, but that any acute release of plutonium would be collected on the top surface of the filter or salt and would be unattenuated. The spectral quality of alpha radiation detection on membrane filters is observed to improve with filter loading. This is attributed to the probability that accumulations of dust tend to fill in surface irregularities of the collection filter at a a faster rate than they create additional surface irregularities. The validity of these assumptions about the improved detection of plutonium on salt-layer surfaces has recently been questioned. Based on electron micrographic examination of salt-laden filters, it has been speculated that collection of salt dust on a membrane filter results in formation of pores, fissures, and dendritic shapes of salt on the filter surface. If plutonium were collected, particles could penetrate into the pores and fissures, resulting in a degraded or lost signal from the plutonium. Because no experimental evidence existed to answer the concern, the purpose of the current study was to quantify any differences between detection of plutonium on clean or salt-laden filters

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. Magnetic sulfur-doped porous carbon for preconcentration of trace mercury in environmental water prior to ICP-MS detection.

    Science.gov (United States)

    Peng, Chuyu; He, Man; Chen, Beibei; Huang, Lijin; Hu, Bin

    2017-11-20

    A novel magnetic sulfur-doped porous carbon (MSPC) was fabricated via a simple one-step carbonization of a mixture of sucrose, basic magnesium sulfate whiskers and Fe 3 O 4 @SiO 2 nanoparticles. Due to the high S content, the prepared MSPC possessed high adsorption capacity for Hg 2+ (343 mg g -1 ) with good selectivity. Based on this, a method coupling magnetic solid phase extraction (MSPE) with inductively coupled plasma mass spectrometry (ICP-MS) was developed for the determination of trace Hg 2+ in environmental water samples. Various parameters such as pH, desorption solvent and its concentration, desorption volume and time, sample volume, and adsorption time that affect the determination have been optimized. Under the optimal conditions, a high enrichment factor of 100-fold was obtained, the limit of detection (LOD) was found to be 0.52 pg mL -1 with a relative standard deviation (c = 10 pg mL -1 , n = 7) of 7.1%, and a good linearity was obtained within the concentration range of 2-5000 pg mL -1 for Hg 2+ . Besides, the proposed method has very fast adsorption/desorption kinetics, target Hg 2+ could be rapidly adsorbed on the prepared MSPC in 2 min and desorbed from the MSPC in 2 min with the assistance of a permanent magnet. Therefore, the proposed method of MSPE-ICP-MS exhibits good application potential in the determination of trace Hg 2+ in environmental water samples.

  6. Using exceedance probabilities to detect anomalies in routinely recorded animal health data, with particular reference to foot-and-mouth disease in Viet Nam.

    Science.gov (United States)

    Richards, K K; Hazelton, M L; Stevenson, M A; Lockhart, C Y; Pinto, J; Nguyen, L

    2014-10-01

    The widespread availability of computer hardware and software for recording and storing disease event information means that, in theory, we have the necessary information to carry out detailed analyses of factors influencing the spatial distribution of disease in animal populations. However, the reliability of such analyses depends on data quality, with anomalous records having the potential to introduce significant bias and lead to inappropriate decision making. In this paper we promote the use of exceedance probabilities as a tool for detecting anomalies when applying hierarchical spatio-temporal models to animal health data. We illustrate this methodology through a case study data on outbreaks of foot-and-mouth disease (FMD) in Viet Nam for the period 2006-2008. A flexible binomial logistic regression was employed to model the number of FMD infected communes within each province of the country. Standard analyses of the residuals from this model failed to identify problems, but exceedance probabilities identified provinces in which the number of reported FMD outbreaks was unexpectedly low. This finding is interesting given that these provinces are on major cattle movement pathways through Viet Nam. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Path scanning for the detection of anomalous subgraphs and use of DNS requests and host agents for anomaly/change detection and network situational awareness

    Science.gov (United States)

    Neil, Joshua Charles; Fisk, Michael Edward; Brugh, Alexander William; Hash, Curtis Lee; Storlie, Curtis Byron; Uphoff, Benjamin; Kent, Alexander

    2017-11-21

    A system, apparatus, computer-readable medium, and computer-implemented method are provided for detecting anomalous behavior in a network. Historical parameters of the network are determined in order to determine normal activity levels. A plurality of paths in the network are enumerated as part of a graph representing the network, where each computing system in the network may be a node in the graph and the sequence of connections between two computing systems may be a directed edge in the graph. A statistical model is applied to the plurality of paths in the graph on a sliding window basis to detect anomalous behavior. Data collected by a Unified Host Collection Agent ("UHCA") may also be used to detect anomalous behavior.

  8. Aeromagnetic anomalies over faulted strata

    Science.gov (United States)

    Grauch, V.J.S.; Hudson, Mark R.

    2011-01-01

    High-resolution aeromagnetic surveys are now an industry standard and they commonly detect anomalies that are attributed to faults within sedimentary basins. However, detailed studies identifying geologic sources of magnetic anomalies in sedimentary environments are rare in the literature. Opportunities to study these sources have come from well-exposed sedimentary basins of the Rio Grande rift in New Mexico and Colorado. High-resolution aeromagnetic data from these areas reveal numerous, curvilinear, low-amplitude (2–15 nT at 100-m terrain clearance) anomalies that consistently correspond to intrasedimentary normal faults (Figure 1). Detailed geophysical and rock-property studies provide evidence for the magnetic sources at several exposures of these faults in the central Rio Grande rift (summarized in Grauch and Hudson, 2007, and Hudson et al., 2008). A key result is that the aeromagnetic anomalies arise from the juxtaposition of magnetically differing strata at the faults as opposed to chemical processes acting at the fault zone. The studies also provide (1) guidelines for understanding and estimating the geophysical parameters controlling aeromagnetic anomalies at faulted strata (Grauch and Hudson), and (2) observations on key geologic factors that are favorable for developing similar sedimentary sources of aeromagnetic anomalies elsewhere (Hudson et al.).

  9. Chiral anomalies and differential geometry

    International Nuclear Information System (INIS)

    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

  10. 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.

  11. Seismic Monitoring Prior to and During DFDP-2 Drilling, Alpine Fault, New Zealand: Matched-Filter Detection Testing and the Real-Time Monitoring System

    Science.gov (United States)

    Boese, C. M.; Chamberlain, C. J.; Townend, J.

    2015-12-01

    In preparation for the second stage of the Deep Fault Drilling Project (DFDP) and as part of related research projects, borehole and surface seismic stations were installed near the intended DFDP-2 drill-site in the Whataroa Valley from late 2008. The final four borehole stations were installed within 1.2 km of the drill-site in early 2013 to provide near-field observations of any seismicity that occurred during drilling and thus provide input into operational decision-making processes if required. The basis for making operational decisions in response to any detected seismicity had been established as part of a safety review conducted in early 2014 and was implemented using a "traffic light" system, a communications plan, and other operational documents. Continuous real-time earthquake monitoring took place throughout the drilling period, between September and late December 2014, and involved a team of up to 15 seismologists working in shifts near the drill-site and overseas. Prior to drilling, records from 55 local earthquakes and 14 quarry blasts were used as master templates in a matched-filter detection algorithm to test the capabilities of the seismic network for detecting seismicity near the drill site. The newly detected microseismicity was clustered near the DFDP-1 drill site at Gaunt Creek, 7.4 km southwest of DFDP-2. Relocations of these detected events provide more information about the fault geometry in this area. Although no detectable seismicity occurred within 5 km of the drill site during the drilling period, the region is capable of generating earthquakes that would have required an operational response had they occurred while drilling was underway (including a M2.9 event northwest of Gaunt Creek on 15 August 2014). The largest event to occur while drilling was underway was of M4.5 and occurred approximately 40 km east of the DFDP-2 drill site. In this presentation, we summarize the setup and operations of the seismic network and discuss key

  12. 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...

  13. Anomaly detection using process mining

    NARCIS (Netherlands)

    Bezerra, F.; Wainer, J.; Aalst, van der W.M.P.; Halpin, T.; Krogstie, J.; Nurcan, S.; Proper, E.; Schmidt, R.; Soffer, P.; Ukor, R.

    2009-01-01

    Recently, several large companies have been involved in financial scandals related to mismanagement, resulting in financial damages for their stockholders. In response, certifications and manuals for best practices of governance were developed, and in some cases, tougher federal laws were

  14. 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...

  15. Discrimination between preseismic electromagnetic anomalies and solar activity effects

    Science.gov (United States)

    Koulouras, Gr; Balasis, G.; Kontakos, K.; Ruzhin, Y.; Avgoustis, G.; Kavouras, D.; Nomicos, C.

    2009-04-01

    Laboratory studies suggest that electromagnetic emissions in a wide frequency spectrum ranging from kHz to very high MHz frequencies are produced by the opening of microcracks, with the MHz radiation appearing earlier than the kHz radiation. Earthquakes are large-scale fracture phenomena in the Earth's heterogeneous crust. Thus, the radiated kHz-MHz electromagnetic emissions are detectable not only at laboratory but also at geological scale. Clear MHz-to-kHz electromagnetic anomalies have been systematically detected over periods ranging from a few days to a few hours prior to recent destructive earthquakes in Greece. We bear in mind that whether electromagnetic precursors to earthquakes exist is an important question not only for earthquake prediction but mainly for understanding the physical processes of earthquake generation. An open question in this field of research is the classification of a detected electromagnetic anomaly as a pre-seismic signal associated to earthquake occurrence. Indeed, electromagnetic fluctuations in the frequency range of MHz are known to related to a few sources, i.e., they might be atmospheric noise (due to lightning), man-made composite noise, solar-terrestrial noise (resulting from the Sun-solar wind-magnetosphere-ionosphere-Earth's surface chain) or cosmic noise, and finally, lithospheric effect, namely pre-seismic activity. We focus on this point. We suggest that if a combination of detected kHz and MHz electromagnetic anomalies satisfies the herein presented set of criteria these anomalies could be considered as candidate precursory phenomena of an impending earthquake.

  16. Renal anomalies in congenital heart disease

    International Nuclear Information System (INIS)

    Lee, Byung Hee; Kim, In One; Yeon, Kyung Mo; Yoon, Yong Soo

    1987-01-01

    In general, the incidence of urinary tract anomalies in congenital heart disease is higher than that in general population. So authors performed abdominal cineradiography in 1045 infants and children undergoing cineangiographic examinations for congenital heart disease, as a screening method for the detection, the incidence, and the nature of associated urinary tract anomalies. The results were as follows: 1. The incidence of urinary tract anomaly associated with congenital heart disease was 4.1% (<2% in general population). 2. Incidence of urinary tract anomalies was 4.62% in 671 acyanotic heart diseases, 3.20% in 374 cyanotic heart diseases. 3. There was no constant relationship between the type of cardiac anomaly and the type of urinary tract anomaly

  17. 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.)

  18. 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.

  19. Kohn anomalies in superconductors

    International Nuclear Information System (INIS)

    Flatte, M.E.

    1994-01-01

    The detailed behavior of phonon dispersion curves near momenta which span the electronic Fermi sea in a superconductor is presented. An anomaly, similar to the metallic Kohn anomaly, exists in a superconductor's dispersion curves when the frequency of the photon spanning the Fermi sea exceeds twice the superconducting energy gap. This anomaly occurs at approximately the same momentum but is stronger than the normal-state Kohn anomaly. It also survives at finite temperature, unlike the metallic anomaly. Determination of Fermi-surface diameters from the location of these anomalies, therefore, may be more successful in the superconducting phase than in the normal state. However, the superconductor's anomaly fades rapidly with increased phonon frequency and becomes unobservable when the phonon frequency greatly exceeds the gap. This constraint makes these anomalies useful only in high-temperature superconductors such as La 1.85 Sr 0.15 CuO 4

  20. 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

  1. 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

  2. CSAX: Characterizing Systematic Anomalies in eXpression Data

    Science.gov (United States)

    Noto, Keith; Majidi, Saeed; Edlow, Andrea G.; Wick, Heather C.; Bianchi, Diana W.

    2015-01-01

    Abstract Methods for translating gene expression signatures into clinically relevant information have typically relied upon having many samples from patients with similar molecular phenotypes. Here, we address the question of what can be done when it is relatively easy to obtain healthy patient samples, but when abnormalities corresponding to disease states may be rare and one-of-a-kind. The associated computational challenge, anomaly detection, is a well-studied machine-learning problem. However, due to the dimensionality and variability of expression data, existing methods based on feature space analysis or individual anomalously expressed genes are insufficient. We present a novel approach, CSAX, that identifies pathways in an individual sample in which the normal expression relationships are disrupted. To evaluate our approach, we have compiled and released a compendium of public expression data sets, reformulated to create a test bed for anomaly detection. We demonstrate the accuracy of CSAX on the data sets in our compendium, compare it to other leading methods, and show that CSAX aids in both identifying anomalies and explaining their underlying biology. We describe an approach to characterizing the difficulty of specific expression anomaly detection tasks. We then illustrate CSAX's value in two developmental case studies. Confirming prior hypotheses, CSAX highlights disruption of platelet activation pathways in a neonate with retinopathy of prematurity and identifies, for the first time, dysregulated oxidative stress response in second trimester amniotic fluid of fetuses with obese mothers. Our approach provides an important step toward identification of individual disease patterns in the era of precision medicine. PMID:25651392

  3. Barley husk carbon as the fiber coating for the solid-phase microextraction of twelve pesticides in vegetables prior to gas chromatography-mass spectrometric detection.

    Science.gov (United States)

    Liang, Weiqian; Wang, Juntao; Zang, Xiaohuan; Dong, Wenhuan; Wang, Chun; Wang, Zhi

    2017-03-31

    In this work, a barley husk biomaterial was successfully carbonized by hydrothermal method. The carbon had a high specific surface area and good stability. It was coated onto a stainless steel wire through sol-gel technique to prepare a solid-phase microextraction fiber for the extraction of trace levels of twelve pesticides (tsumacide, fenobucarb, indoxacarb, diethofencarb, thimet, terbufos, malathion, thiamethoxam, imidacloprid, buprofezin, acetamiprid, thiamethoxam) from vegetable samples prior to gas chromatography-mass spectrometric (GC-MS) detection. The main experimental parameters that could influence the extraction efficiency such as extraction time, extraction temperature, sample pH, sample salinity, stirring rate, desorption temperature and desorption time, were investigated. Under the optimized conditions, the linearity was observed in the range of 0.2-75.0μgkg -1 for tomato samples, and 0.3-60.0μgkg -1 for cucumber samples, with the correlation coefficients (r) ranging from 0.9959 to 0.9983. The limits of detection of the method were 0.01-0.05μgkg -1 for tomato samples, and 0.03-0.10μgkg -1 for cucumber samples. The recoveries of the analytes for the method from spiked samples were in the range of 76%-104%, and the precision, expressed as the relative standard deviations, was less than 12%. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. 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.

  5. Branchial anomalies in children.

    Science.gov (United States)

    Bajaj, Y; Ifeacho, S; Tweedie, D; Jephson, C G; Albert, D M; Cochrane, L A; Wyatt, M E; Jonas, N; Hartley, B E J

    2011-08-01

    Branchial cleft anomalies are the second most common head and neck congenital lesions seen in children. Amongst the branchial cleft malformations, second cleft lesions account for 95% of the branchial anomalies. This article analyzes all the cases of branchial cleft anomalies operated on at Great Ormond Street Hospital over the past 10 years. All children who underwent surgery for branchial cleft sinus or fistula from January 2000 to December 2010 were included in this study. In this series, we had 80 patients (38 female and 42 male). The age at the time of operation varied from 1 year to 14 years. Amongst this group, 15 patients had first branchial cleft anomaly, 62 had second branchial cleft anomaly and 3 had fourth branchial pouch anomaly. All the first cleft cases were operated on by a superficial parotidectomy approach with facial nerve identification. Complete excision was achieved in all these first cleft cases. In this series of first cleft anomalies, we had one complication (temporary marginal mandibular nerve weakness. In the 62 children with second branchial cleft anomalies, 50 were unilateral and 12 were bilateral. In the vast majority, the tract extended through the carotid bifurcation and extended up to pharyngeal constrictor muscles. Majority of these cases were operated on through an elliptical incision around the external opening. Complete excision was achieved in all second cleft cases except one who required a repeat excision. In this subgroup, we had two complications one patient developed a seroma and one had incomplete excision. The three patients with fourth pouch anomaly were treated with endoscopic assisted monopolar diathermy to the sinus opening with good outcome. Branchial anomalies are relatively common in children. There are three distinct types, first cleft, second cleft and fourth pouch anomaly. Correct diagnosis is essential to avoid inadequate surgery and multiple procedures. The surgical approach needs to be tailored to the type

  6. Patent hepatic falciform artery detected after Tc-99m-macroaggregated albumin injection on SPECT/CT prior to Yttrium-90 microsphere radioembolization: a case report

    International Nuclear Information System (INIS)

    Karaman, B.; Aslan, A.; Hamcan, S.; Ugurel, M.

    2012-01-01

    Full text: Introduction: Yttrium-90 (Y-90) microsphere radioembolization is increasingly used for the treatment of unresectable hepatocellular carcinoma and liver metastasis. Objectives and tasks: We aim to present the upper abdominal wall skin involvement detected during routine pre-therapy Technetium-99m-macroaggregated albumin (Tc-99m-MAA) on SPECT/CT due to patent hepatic falciform artery and the precautions to avoid this potential complication. Material and methods: 38-year-old male with colon cancer and multiple liver metastasis was evaluated prior to radioembolization and Tc-99 MAA was slowly hand injected at the bifurcation of the proper hepatic artery. Then, the SPECT/CT scan was performed in order to investigate the systemic shunt or gastric involvement. Results: On SPECT/CT scan, involvement of the upper abdominal wall through falciform ligament was seen. Re-evaluation of the hepatic angiogram identified a patent hepatic falciform artery arising from the left hepatic artery. Y-90 microspheres were slowly hand injected to the left hepatic artery superselectively and no extra-hepatic activity was seen on SPECT/CT scan. Conclusion: Upper abdominal pain and dermatitis are uncommon findings after radioembolization and may occur due to inadvertent delivery of Y-90 microspheres into patent hepatic falciform artery. To prevent these complications, either patent hepatic falciform artery must be embolized by coil or Y-90 injection must be performed superselectively

  7. 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.

  8. 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.

  9. 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

  10. An anomaly analysis framework for database systems

    NARCIS (Netherlands)

    Vavilis, S.; Egner, A.I.; Petkovic, M.; Zannone, N.

    2015-01-01

    Anomaly detection systems are usually employed to monitor database activities in order to detect security incidents. These systems raise an alert when anomalous activities are detected. The raised alerts have to be analyzed to timely respond to the security incidents. Their analysis, however, is

  11. Global gravitational anomalies

    International Nuclear Information System (INIS)

    Witten, E.

    1985-01-01

    A general formula for global gauge and gravitational anomalies is derived. It is used to show that the anomaly free supergravity and superstring theories in ten dimensions are all free of global anomalies that might have ruined their consistency. However, it is shown that global anomalies lead to some restrictions on allowed compactifications of these theories. For example, in the case of O(32) superstring theory, it is shown that a global anomaly related to π 7 (O(32)) leads to a Dirac-like quantization condition for the field strength of the antisymmetric tensor field. Related to global anomalies is the question of the number of fermion zero modes in an instanton field. It is argued that the relevant gravitational instantons are exotic spheres. It is shown that the number of fermion zero modes in an instanton field is always even in ten dimensional supergravity. (orig.)

  12. 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.

  13. Anomaly-specified virtual dimensionality

    Science.gov (United States)

    Chen, Shih-Yu; Paylor, Drew; Chang, Chein-I.

    2013-09-01

    Virtual dimensionality (VD) has received considerable interest where VD is used to estimate the number of spectral distinct signatures, denoted by p. Unfortunately, no specific definition is provided by VD for what a spectrally distinct signature is. As a result, various types of spectral distinct signatures determine different values of VD. There is no one value-fit-all for VD. In order to address this issue this paper presents a new concept, referred to as anomaly-specified VD (AS-VD) which determines the number of anomalies of interest present in the data. Specifically, two types of anomaly detection algorithms are of particular interest, sample covariance matrix K-based anomaly detector developed by Reed and Yu, referred to as K-RXD and sample correlation matrix R-based RXD, referred to as R-RXD. Since K-RXD is only determined by 2nd order statistics compared to R-RXD which is specified by statistics of the first two orders including sample mean as the first order statistics, the values determined by K-RXD and R-RXD will be different. Experiments are conducted in comparison with widely used eigen-based approaches.

  14. First branchial groove anomaly.

    Science.gov (United States)

    Kumar, M; Hickey, S; Joseph, G

    2000-06-01

    First branchial groove anomalies are very rare. We report a case of a first branchial groove anomaly presented as an infected cyst in an 11-month-old child. Management of such lesions is complicated because of their close association with the facial nerve. Surgical management must include identification and protection of the facial nerve. Embryology and facial nerve disposition in relation to the anomaly are reviewed.

  15. Neutrino scattering and the reactor antineutrino anomaly

    Science.gov (United States)

    Garcés, Estela; Cañas, Blanca; Miranda, Omar; Parada, Alexander

    2017-12-01

    Low energy threshold reactor experiments have the potential to give insight into the light sterile neutrino signal provided by the reactor antineutrino anomaly and the gallium anomaly. In this work we analyze short baseline reactor experiments that detect by elastic neutrino electron scattering in the context of a light sterile neutrino signal. We also analyze the sensitivity of experimental proposals of coherent elastic neutrino nucleus scattering (CENNS) detectors in order to exclude or confirm the sterile neutrino signal with reactor antineutrinos.

  16. 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.

  17. Detecting ecosystem performance anomalies for land management in the upper colorado river basin using satellite observations, climate data, and ecosystem models

    Science.gov (United States)

    Gu, Yingxin; Wylie, B.K.

    2010-01-01

    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. ?? 2010 by the authors.

  18. Introduction to anomalies

    International Nuclear Information System (INIS)

    Alvarez-Gaume, L.

    1986-01-01

    These lectures are dedicated to the study of the recent progress and implications of anomalies in quantum field theory. In this introduction the author recapitulates some of the highlights in the history of the subject. The outline of these lectures is as follows: Section II contains a quick review of spinors in Euclidean and Minkowski space, some other group theory results relevant for the computation of anomalies in various dimensions, and an exposition of the index theorem. Section III starts the analysis of fermion determinants and chiral effective actions by deriving the non-Abelian anomaly from index theory. Using the results of Section II, the anomaly cancellation recently discovered by Green and Schwarz will be presented in Section IV as well as the connection of these results of Section III with the descent equations and the Wess-Zumino-Witten Lagrangians. Section V contains the generalization of anomalies to σ-models and some of its application in string theory. Section VI will deal with the anomalies from the Hamiltonian point of view. An exact formula for the imaginary part of the effective action for chiral fermions in the presence of arbitrary external gauge and gravitational fields will be derived in Section VII, and used in Section VIII for the study of global anomalies. 85 references

  19. 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.

  20. Application of ASTER SWIR bands in mapping anomaly pixels for Antarctic geological mapping

    International Nuclear Information System (INIS)

    Beiranvand Pour, Amin; Hashim, Mazlan; Park, Yongcheol

    2017-01-01

    Independent component analysis (ICA) was applied to shortwave infrared (SWIR) bands of ASTER satellite data for detailed mapping of alteration mineral zones in the context of polar environments, where little prior information is available. The Oscar II coast area north-eastern Graham Land, Antarctic Peninsula (AP) was selected to conduct a remote sensing satellite-based mapping approach to detect alteration mineral assemblages. Anomaly pixels in the ICA image maps related to spectral features of Al-O-H, Fe, Mg-O-H and CO3 groups were detected using SWIR datasets of ASTER. ICA method provided image maps of alteration mineral assemblages and discriminate lithological units with little available geological data for poorly mapped regions and/or without prior geological information for unmapped regions in northern and southern sectors of Oscar II coast area, Graham Land. The results of this investigation demonstrated the applicability of ASTER spectral data for lithological and alteration mineral mapping in poorly exposed lithologies and inaccessible regions, particularly using the image processing algorithm that are capable to detect anomaly pixels targets in the remotely sensed images, where no prior information is available. (paper)

  1. Splenic Anomalies of Shape, Size, and Location: Pictorial Essay

    Directory of Open Access Journals (Sweden)

    Adalet Elcin Yildiz

    2013-01-01

    Full Text Available Spleen can have a wide range of anomalies including its shape, location, number, and size. Although most of these anomalies are congenital, there are also acquired types. Congenital anomalies affecting the shape of spleen are lobulations, notches, and clefts; the fusion and location anomalies of spleen are accessory spleen, splenopancreatic fusion, and wandering spleen; polysplenia can be associated with a syndrome. Splenosis and small spleen are acquired anomalies which are caused by trauma and sickle cell disease, respectively. These anomalies can be detected easily by using different imaging modalities including ultrasonography, computed tomography, magnetic resonance imaging, and also Tc-99m scintigraphy. In this pictorial essay, we review the imaging findings of these anomalies which can cause diagnostic pitfalls and be interpreted as pathologic processes.

  2. 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

  3. Kohn anomaly in graphene

    International Nuclear Information System (INIS)

    Milosevic, I.; Kepcija, N.; Dobardzic, E.; Damnjanovic, M.; Mohr, M.; Maultzsch, J.; Thomsen, C.

    2011-01-01

    Symmetry based analysis of the Kohn anomaly is performed. Kohn phonon frequencies and displacements are calculated by force constant method. It is shown that Kohn phonon vibrations cause electronic band gap opening.

  4. Algebraic structure of chiral anomalies

    International Nuclear Information System (INIS)

    Stora, R.

    1985-09-01

    I will describe first the algebraic aspects of chiral anomalies, exercising however due care about the topological delicacies. I will illustrate the structure and methods in the context of gauge anomalies and will eventually make contact with results obtained from index theory. I will go into two sorts of generalizations: on the one hand, generalizing the algebraic set up yields e.g. gravitational and mixed gauge anomalies, supersymmetric gauge anomalies, anomalies in supergravity theories; on the other hand most constructions applied to the cohomologies which characterize anomalies easily extend to higher cohomologies. Section II is devoted to a description of the general set up as it applies to gauge anomalies. Section III deals with a number of algebraic set ups which characterize more general types of anomalies: gravitational and mixed gauge anomalies, supersymmetric gauge anomalies, anomalies in supergravity theories. It also includes brief remarks on σ models and a reminder on the full BRST algebra of quantized gauge theories

  5. Anomalies and gravity

    International Nuclear Information System (INIS)

    Mielke, Eckehard W.

    2006-01-01

    Anomalies in Yang-Mills type gauge theories of gravity are reviewed. Particular attention is paid to the relation between the Dirac spin, the axial current j5 and the non-covariant gauge spin C. Using diagrammatic techniques, we show that only generalizations of the U(1)- Pontrjagin four-form F and F = dC arise in the chiral anomaly, even when coupled to gravity. Implications for Ashtekar's canonical approach to quantum gravity are discussed

  6. 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

  7. 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.

  8. 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.

  9. Fourth branchial complex anomalies: a case series.

    Science.gov (United States)

    Shrime, Mark; Kacker, Ashutosh; Bent, John; Ward, Robert F

    2003-11-01

    Anomalies of the fourth branchial arch complex are exceedingly rare, with approximately forty cases reported in the literature since 1972. The authors report experience with six fourth arch anomalies. Retrospective chart review of six consecutive patients presenting to the pediatric otolaryngology service at a tertiary care center with anomalies referable to the fourth branchial arch. All six patients presented within the first or second decade of life. All six had left-sided disease. Four patients presented with recurrent neck infection, one with asymptomatic cervical masses, and one with a neck mass and respiratory compromise. One patient had prior surgery presented with a recurrence. Diagnosis of fourth arch anomalies was suggested or confirmed by computed tomography and flexible laryngoscopy. Treatment was surgical in five patients; one patient is awaiting surgery. Surgical procedures included resection of the mass and endoscopic cauterization of the inner opening of the cyst. The presentation of a cervical mass, especially with recurrent infections and especially on the left side, in a child in the first or second decade of life heightens suspicion for an anomaly of the fourth branchial arch. Diagnosis can be difficult, but is aided by the use of flexible laryngoscopy, Computed tomography (CT) scanning and ultrasonography. Surgical resection of the cyst and cauterization of its pyriform sinus opening should be undertaken to minimize recurrence.

  10. 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.

  11. RARE BRANCHIAL ARCH ANOMALIES

    Directory of Open Access Journals (Sweden)

    Jayanta Kumar

    2016-03-01

    Full Text Available AIM Amongst the branchial arch anomalies third arch anomaly occurs rarely and more so the fourth arch anomalies. We present our experience with cases of rare branchial arch anomalies. PATIENTS AND METHODS From June 2006 to January 2016, cases having their external opening in the lower third of sternocleidomastoid muscle with the tract going through thyroid gland and directing to pyriform sinus (PFS or cysts with internal opening in the PFS were studied. RESULTS No fourth arch anomaly was encountered. One cyst with internal opening which later on formed a fistula, three fistulae from beginning and two sinuses were encountered. The main stay of diagnosis was the fistula in the PFS and the tract lying posterior to the internal carotid artery. Simple excision technique with a small incision around the external opening was done. There was no recurrence. CONCLUSION Third arch fistula is not very rare as it was thought. Internal fistula is found in most of the cases. Though radiological investigations are helpful, fistulae can be diagnosed clinically and during operation. Extensive operation of the neck, mediastinum and pharynx is not required.

  12. 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

  13. Low Risk Anomalies?

    DEFF Research Database (Denmark)

    Schneider, Paul; Wagner, Christian; Zechner, Josef

    . Empirically, we find that option-implied ex-ante skewness is strongly related to ex-post residual coskewness and alphas. Beta- and volatility-based low risk anomalies are largely driven by a single principal component, which is in turn largely explained by skewness. Controlling for skewness renders the alphas......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...... of betting-against-beta and -volatility insignificant....

  14. Positively deflected anomaly mediation

    International Nuclear Information System (INIS)

    Okada, Nobuchika

    2002-01-01

    We generalize the so-called 'deflected anomaly mediation' scenario to the case where threshold corrections of heavy messengers to the sparticle squared masses are positive. A concrete model realizing this scenario is also presented. The tachyonic slepton problem can be fixed with only a pair of messengers. The resultant sparticle mass spectrum is quite different from that in the conventional deflected anomaly mediation scenario, but is similar to the one in the gauge mediation scenario. The lightest sparticle is mostly B-ino

  15. Congenital laryngeal anomalies,

    Directory of Open Access Journals (Sweden)

    Michael J. Rutter

    2014-12-01

    Full Text Available 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 main aspects concerning airway management of infants will be analyzed. Conclusions: It is critical for clinicians to understand issues relevant to the airway management of infants.

  16. Prevalence of dental developmental anomalies: a radiographic study.

    Science.gov (United States)

    Ezoddini, Ardakani F; Sheikhha, M H; Ahmadi, H

    2007-09-01

    To determine the prevalence of developmental dental anomalies in patients attending the Dental Faculty of Medical University of Yazd, Iran and the gender differences of these anomalies. A retrospective study based on the panoramic radiographs of 480 patients. Patients referred for panoramic radiographs were clinically examined, a detailed family history of any dental anomalies in their first and second degree relatives was obtained and finally their radiographs were studied in detail for the presence of dental anomalies. 40.8% of the patients had dental anomalies. The more common anomalies were dilaceration (15%), impacted teeth (8.3%) and taurodontism (7.5%) and supernumerary teeth (3.5%). Macrodontia and fusion were detected in a few radiographs (0.2%). 49.1% of male patients had dental anomalies compared to 33.8% of females. Dilaceration, taurodontism and supernumerary teeth were found to be more prevalent in men than women, whereas impacted teeth, microdontia and gemination were more frequent in women. Family history of dental anomalies was positive in 34% of the cases.. Taurodontism, gemination, dens in dente and talon cusp were specifically limited to the patients under 20 year's old, while the prevalence of other anomalies was almost the same in all groups. Dilaceration, impaction and taurodontism were relatively common in the studied populaton. A family history of dental anomalies was positive in a third of cases.

  17. Prevalence of dental anomalies in Saudi orthodontic patients.

    Science.gov (United States)

    Al-Jabaa, Aljazi H; Aldrees, Abdullah M

    2013-07-01

    This study aimed to investigate the prevalence of dental anomalies and study the association of these anomalies with different types of malocclusion in a random sample of Saudi orthodontic patients. Six hundred and two randomly selected pretreatment records including orthopantomographs (OPG), and study models were evaluated. The molar relationship was determined using pretreatment study models, and OPG were examined to investigate the prevalence of dental anomalies among the sample. The most common types of the investigated anomalies were: impaction followed by hypodontia, microdontia, macrodontia, ectopic eruption and supernumerary. No statistical significant correlations were observed between sex and dental anomalies. Dental anomalies were more commonly found in class I followed by asymmetric molar relation, then class II and finally class III molar relation. No malocclusion group had a statistically significant relation with any individual dental anomaly. The prevalence of dental anomalies among Saudi orthodontic patients was higher than the general population. Although, orthodontic patients have been reported to have high rates of dental anomalies, orthodontists often fail to consider this. If not detected, dental anomalies can complicate dental and orthodontic treatment; therefore, their presence should be carefully investigated during orthodontic diagnosis and considered during treatment planning.

  18. Discrimination between pre-seismic electromagnetic anomalies and solar activity effects

    Science.gov (United States)

    Koulouras, G.; Balasis, G.; Kiourktsidis, I.; Nannos, E.; Kontakos, K.; Stonham, J.; Ruzhin, Y.; Eftaxias, K.; Cavouras, D.; Nomicos, C.

    2009-04-01

    Laboratory studies suggest that electromagnetic emissions in a wide frequency spectrum ranging from kilohertz (kHz) to very high megahertz (MHz) frequencies are produced by the opening of microcracks, with the MHz radiation appearing earlier than the kHz radiation. Earthquakes are large-scale fracture phenomena in the Earth's heterogeneous crust. Thus, the radiated kHz-MHz electromagnetic emissions are detectable not only in the laboratory but also at a geological scale. Clear MHz-to-kHz electromagnetic anomalies have been systematically detected over periods ranging from a few days to a few hours prior to recent destructive earthquakes in Greece. We should bear in mind that whether electromagnetic precursors to earthquakes exist is an important question not only for earthquake prediction but mainly for understanding the physical processes of earthquake generation. An open question in this field of research is the classification of a detected electromagnetic anomaly as a pre-seismic signal associated with earthquake occurrence. Indeed, electromagnetic fluctuations in the frequency range of MHz are known to be related to a few sources, including atmospheric noise (due to lightning), man-made composite noise, solar-terrestrial noise (resulting from the Sun-solar wind-magnetosphere-ionosphere-Earth's surface chain) or cosmic noise, and finally, the lithospheric effect, namely pre-seismic activity. We focus on this point in this paper. We suggest that if a combination of detected kHz and MHz electromagnetic anomalies satisfies the set of criteria presented herein, these anomalies could be considered as candidate precursory phenomena of an impending earthquake.

  19. Discrimination between pre-seismic electromagnetic anomalies and solar activity effects

    International Nuclear Information System (INIS)

    Koulouras, G; Kiourktsidis, I; Stonham, J; Balasis, G; Nannos, E; Kontakos, K; Nomicos, C; Ruzhin, Y; Eftaxias, K; Cavouras, D

    2009-01-01

    Laboratory studies suggest that electromagnetic emissions in a wide frequency spectrum ranging from kilohertz (kHz) to very high megahertz (MHz) frequencies are produced by the opening of microcracks, with the MHz radiation appearing earlier than the kHz radiation. Earthquakes are large-scale fracture phenomena in the Earth's heterogeneous crust. Thus, the radiated kHz-MHz electromagnetic emissions are detectable not only in the laboratory but also at a geological scale. Clear MHz-to-kHz electromagnetic anomalies have been systematically detected over periods ranging from a few days to a few hours prior to recent destructive earthquakes in Greece. We should bear in mind that whether electromagnetic precursors to earthquakes exist is an important question not only for earthquake prediction but mainly for understanding the physical processes of earthquake generation. An open question in this field of research is the classification of a detected electromagnetic anomaly as a pre-seismic signal associated with earthquake occurrence. Indeed, electromagnetic fluctuations in the frequency range of MHz are known to be related to a few sources, including atmospheric noise (due to lightning), man-made composite noise, solar-terrestrial noise (resulting from the Sun-solar wind-magnetosphere-ionosphere-Earth's surface chain) or cosmic noise, and finally, the lithospheric effect, namely pre-seismic activity. We focus on this point in this paper. We suggest that if a combination of detected kHz and MHz electromagnetic anomalies satisfies the set of criteria presented herein, these anomalies could be considered as candidate precursory phenomena of an impending earthquake.

  20. 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...

  1. Prevalence and distribution of dental anomalies in orthodontic patients.

    Science.gov (United States)

    Montasser, Mona A; Taha, Mahasen

    2012-01-01

    To study the prevalence and distribution of dental anomalies in a sample of orthodontic patients. The dental casts, intraoral photographs, and lateral panoramic and cephalometric radiographs of 509 Egyptian orthodontic patients were studied. Patients were examined for dental anomalies in number, size, shape, position, and structure. The prevalence of each dental anomaly was calculated and compared between sexes. Of the total study sample, 32.6% of the patients had at least one dental anomaly other than agenesis of third molars; 32.1% of females and 33.5% of males had at least one dental anomaly other than agenesis of third molars. The most commonly detected dental anomalies were impaction (12.8%) and ectopic eruption (10.8%). The total prevalence of hypodontia (excluding third molars) and hyperdontia was 2.4% and 2.8%, respectively, with similiar distributions in females and males. Gemination and accessory roots were reported in this study; each of these anomalies was detected in 0.2% of patients. In addition to genetic and racial factors, environmental factors could have more important influence on the prevalence of dental anomalies in every population. Impaction, ectopic eruption, hyperdontia, hypodontia, and microdontia were the most common dental anomalies, while fusion and dentinogenesis imperfecta were absent.

  2. 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

  3. 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

  4. Anomaly Busters II

    Energy Technology Data Exchange (ETDEWEB)

    Anon.

    1985-11-15

    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. 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.

  8. 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...

  9. Generalized zero point anomaly

    International Nuclear Information System (INIS)

    Nogueira, Jose Alexandre; Maia Junior, Adolfo

    1994-01-01

    It is defined Zero point Anomaly (ZPA) as the difference between the Effective Potential (EP) and the Zero point Energy (ZPE). It is shown, for a massive and interacting scalar field that, in very general conditions, the renormalized ZPA vanishes and then the renormalized EP and ZPE coincide. (author). 3 refs

  10. Minnesota Bouguer Anomaly Grid

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — A 1.5 kilometer Bouguer anomaly grid for the state of Minnesota. Number of columns is 404 and number of rows is 463. The order of the data is from the lower left to...

  11. 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...

  12. Routine screening for fetal anomalies: expectations.

    Science.gov (United States)

    Goldberg, James D

    2004-03-01

    Ultrasound has become a routine part of prenatal care. Despite this, the sensitivity and specificity of the procedure is unclear to many patients and healthcare providers. In a small study from Canada, 54.9% of women reported that they had received no information about ultrasound before their examination. In addition, 37.2% of women indicated that they were unaware of any fetal problems that ultrasound could not detect. Most centers that perform ultrasound do not have their own statistics regarding sensitivity and specificity; it is necessary to rely on large collaborative studies. Unfortunately, wide variations exist in these studies with detection rates for fetal anomalies between 13.3% and 82.4%. The Eurofetus study is the largest prospective study performed to date and because of the time and expense involved in this type of study, a similar study is not likely to be repeated. The overall fetal detection rate for anomalous fetuses was 64.1%. It is important to note that in this study, ultrasounds were performed in tertiary centers with significant experience in detecting fetal malformations. The RADIUS study also demonstrated a significantly improved detection rate of anomalies before 24 weeks in tertiary versus community centers (35% versus 13%). Two concepts seem to emerge from reviewing these data. First, patients must be made aware of the limitations of ultrasound in detecting fetal anomalies. This information is critical to allow them to make informed decisions whether to undergo ultrasound examination and to prepare them for potential outcomes.Second, to achieve the detection rates reported in the Eurofetus study, ultrasound examination must be performed in centers that have extensive experience in the detection of fetal anomalies.

  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. Algebraic study of chiral anomalies

    Indian Academy of Sciences (India)

    Chiral anomalies; gauge theories; bundles; connections; quantum field ... The algebraic structure of chiral anomalies is made globally valid on non-trivial bundles by the introduction of a fixed background connection. ... Current Issue : Vol.

  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. 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

    , efficient and sensitive, (2) rather common weak features intrinsically come with ambiguities that are not easily resolved from photometric light curves, (3) a modelling approach that finds all features of parameter space rather than just the ‘favourite model’ is required and (4) the data quality is most......The microlensing event OGLE‐2008‐BLG‐510 is characterized by an evident asymmetric shape of the peak, promptly detected by the Automated Robotic Terrestrial Exoplanet Microlensing Search (ARTEMiS) system in real time. The skewness of the light curve appears to be compatible both with binary...

  17. 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

  18. Branchial Cleft Anomalies

    Science.gov (United States)

    McPhail, Neil; Mustard, Robert A.

    1966-01-01

    The embryology, anatomy and pathology of branchial cleft anomalies are discussed and 87 cases reviewed. The most frequent anomaly was branchial cleft cyst, of which there were 77 cases. Treatment in all cases consisted of complete excision. There were five cases of external branchial sinus and five cases of complete branchial fistula. Sinograms were helpful in demonstrating these lesions. Excision presented little difficulty. No proved case of branchiogenic carcinoma has been found in the Toronto General Hospital. Five cases are described in which the original diagnosis was branchiogenic carcinoma—in four of these a primary tumour has already been found. The authors believe that the diagnosis of branchiogenic carcinoma should never be accepted until repeated examinations over a period of at least five years have failed to reveal a primary tumour. ImagesFig. 1Fig. 2Fig. 3Fig. 4Fig. 5 PMID:5901161

  19. 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.

  20. Constrained noninformative priors

    International Nuclear Information System (INIS)

    Atwood, C.L.

    1994-10-01

    The Jeffreys noninformative prior distribution for a single unknown parameter is the distribution corresponding to a uniform distribution in the transformed model where the unknown parameter is approximately a location parameter. To obtain a prior distribution with a specified mean but with diffusion reflecting great uncertainty, a natural generalization of the noninformative prior is the distribution corresponding to the constrained maximum entropy distribution in the transformed model. Examples are given

  1. 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.

  2. Penile anomalies in adolescence.

    Science.gov (United States)

    Wood, Dan; Woodhouse, Christopher

    2011-03-07

    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.

  3. 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.

  4. Fialuridine induces acute liver failure in chimeric TK-NOG mice: a model for detecting hepatic drug toxicity prior to human testing.

    Directory of Open Access Journals (Sweden)

    Dan Xu

    2014-04-01

    Full Text Available Seven of 15 clinical trial participants treated with a nucleoside analogue (fialuridine [FIAU] developed acute liver failure. Five treated participants died, and two required a liver transplant. Preclinical toxicology studies in mice, rats, dogs, and primates did not provide any indication that FIAU would be hepatotoxic in humans. Therefore, we investigated whether FIAU-induced liver toxicity could be detected in chimeric TK-NOG mice with humanized livers.Control and chimeric TK-NOG mice with humanized livers were treated orally with FIAU 400, 100, 25, or 2.5 mg/kg/d. The response to drug treatment was evaluated by measuring plasma lactate and liver enzymes, by assessing liver histology, and by electron microscopy. After treatment with FIAU 400 mg/kg/d for 4 d, chimeric mice developed clinical and serologic evidence of liver failure and lactic acidosis. Analysis of liver tissue revealed steatosis in regions with human, but not mouse, hepatocytes. Electron micrographs revealed lipid and mitochondrial abnormalities in the human hepatocytes in FIAU-treated chimeric mice. Dose-dependent liver toxicity was detected in chimeric mice treated with FIAU 100, 25, or 2.5 mg/kg/d for 14 d. Liver toxicity did not develop in control mice that were treated with the same FIAU doses for 14 d. In contrast, treatment with another nucleotide analogue (sofosbuvir 440 or 44 mg/kg/d po for 14 d, which did not cause liver toxicity in human trial participants, did not cause liver toxicity in mice with humanized livers.FIAU-induced liver toxicity could be readily detected using chimeric TK-NOG mice with humanized livers, even when the mice were treated with a FIAU dose that was only 10-fold above the dose used in human participants. The clinical features, laboratory abnormalities, liver histology, and ultra-structural changes observed in FIAU-treated chimeric mice mirrored those of FIAU-treated human participants. The use of chimeric mice in preclinical toxicology

  5. Fialuridine induces acute liver failure in chimeric TK-NOG mice: a model for detecting hepatic drug toxicity prior to human testing.

    Science.gov (United States)

    Xu, Dan; Nishimura, Toshi; Nishimura, Sachiko; Zhang, Haili; Zheng, Ming; Guo, Ying-Ying; Masek, Marylin; Michie, Sara A; Glenn, Jeffrey; Peltz, Gary

    2014-04-01

    Seven of 15 clinical trial participants treated with a nucleoside analogue (fialuridine [FIAU]) developed acute liver failure. Five treated participants died, and two required a liver transplant. Preclinical toxicology studies in mice, rats, dogs, and primates did not provide any indication that FIAU would be hepatotoxic in humans. Therefore, we investigated whether FIAU-induced liver toxicity could be detected in chimeric TK-NOG mice with humanized livers. Control and chimeric TK-NOG mice with humanized livers were treated orally with FIAU 400, 100, 25, or 2.5 mg/kg/d. The response to drug treatment was evaluated by measuring plasma lactate and liver enzymes, by assessing liver histology, and by electron microscopy. After treatment with FIAU 400 mg/kg/d for 4 d, chimeric mice developed clinical and serologic evidence of liver failure and lactic acidosis. Analysis of liver tissue revealed steatosis in regions with human, but not mouse, hepatocytes. Electron micrographs revealed lipid and mitochondrial abnormalities in the human hepatocytes in FIAU-treated chimeric mice. Dose-dependent liver toxicity was detected in chimeric mice treated with FIAU 100, 25, or 2.5 mg/kg/d for 14 d. Liver toxicity did not develop in control mice that were treated with the same FIAU doses for 14 d. In contrast, treatment with another nucleotide analogue (sofosbuvir 440 or 44 mg/kg/d po) for 14 d, which did not cause liver toxicity in human trial participants, did not cause liver toxicity in mice with humanized livers. FIAU-induced liver toxicity could be readily detected using chimeric TK-NOG mice with humanized livers, even when the mice were treated with a FIAU dose that was only 10-fold above the dose used in human participants. The clinical features, laboratory abnormalities, liver histology, and ultra-structural changes observed in FIAU-treated chimeric mice mirrored those of FIAU-treated human participants. The use of chimeric mice in preclinical toxicology studies could improve

  6. Anomaly General Circulation Models.

    Science.gov (United States)

    Navarra, Antonio

    The feasibility of the anomaly model is assessed using barotropic and baroclinic models. In the barotropic case, both a stationary and a time-dependent model has been formulated and constructed, whereas only the stationary, linear case is considered in the baroclinic case. Results from the barotropic model indicate that a relation between the stationary solution and the time-averaged non-linear solution exists. The stationary linear baroclinic solution can therefore be considered with some confidence. The linear baroclinic anomaly model poses a formidable mathematical problem because it is necessary to solve a gigantic linear system to obtain the solution. A new method to find solution of large linear system, based on a projection on the Krylov subspace is shown to be successful when applied to the linearized baroclinic anomaly model. The scheme consists of projecting the original linear system on the Krylov subspace, thereby reducing the dimensionality of the matrix to be inverted to obtain the solution. With an appropriate setting of the damping parameters, the iterative Krylov method reaches a solution even using a Krylov subspace ten times smaller than the original space of the problem. This generality allows the treatment of the important problem of linear waves in the atmosphere. A larger class (nonzonally symmetric) of basic states can now be treated for the baroclinic primitive equations. These problem leads to large unsymmetrical linear systems of order 10000 and more which can now be successfully tackled by the Krylov method. The (R7) linear anomaly model is used to investigate extensively the linear response to equatorial and mid-latitude prescribed heating. The results indicate that the solution is deeply affected by the presence of the stationary waves in the basic state. The instability of the asymmetric flows, first pointed out by Simmons et al. (1983), is active also in the baroclinic case. However, the presence of baroclinic processes modifies the

  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

    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.

  9. 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.

  10. Radon anomalies precursory to the 2003 Mw = 6.8 Chengkung and 2006 Mw = 6.1 Taitung earthquakes in Taiwan

    International Nuclear Information System (INIS)

    Kuo, T.; Lin, C.; Fan, K.; Chang, G.; Lewis, C.; Han, Y.; Wu, Y.; Chen, W.; Tsai, C.

    2009-01-01

    Contrary to the normally observed increase in groundwater radon that occurs prior to earthquakes, we have measured anomalous decreases in radon concentration prior to the 2003 M W = 6.8 Chengkung and 2006 M W = 6.1 Taitung earthquakes that occurred within a 55 km radius from the Antung D1 monitoring well in eastern Taiwan. The v-shaped pattern of radon anomalies recognized at Antung is valuable for detecting the aseismic strain precursory to potentially disastrous earthquakes in a fractured aquifer surrounded by ductile aquitard in seismotectonic environments in this area.

  11. SU-E-T-261: Development of An Automated System to Detect Patient Identification and Positioning Errors Prior to Radiotherapy Treatment

    Energy Technology Data Exchange (ETDEWEB)

    Jani, S; Low, D; Lamb, J [UCLA, Los Angeles, CA (United States)

    2015-06-15

    Purpose: To develop a system that can automatically detect patient identification and positioning errors using 3D computed tomography (CT) setup images and kilovoltage CT (kVCT) planning images. Methods: Planning kVCT images were collected for head-and-neck (H&N), pelvis, and spine treatments with corresponding 3D cone-beam CT (CBCT) and megavoltage CT (MVCT) setup images from TrueBeam and TomoTherapy units, respectively. Patient identification errors were simulated by registering setup and planning images from different patients. Positioning errors were simulated by misaligning the setup image by 1cm to 5cm in the six anatomical directions for H&N and pelvis patients. Misalignments for spine treatments were simulated by registering the setup image to adjacent vertebral bodies on the planning kVCT. A body contour of the setup image was used as an initial mask for image comparison. Images were pre-processed by image filtering and air voxel thresholding, and image pairs were assessed using commonly-used image similarity metrics as well as custom -designed metrics. A linear discriminant analysis classifier was trained and tested on the datasets, and misclassification error (MCE), sensitivity, and specificity estimates were generated using 10-fold cross validation. Results: Our workflow produced MCE estimates of 0.7%, 1.7%, and 0% for H&N, pelvis, and spine TomoTherapy images, respectively. Sensitivities and specificities ranged from 98.0% to 100%. MCEs of 3.5%, 2.3%, and 2.1% were obtained for TrueBeam images of the above sites, respectively, with sensitivity and specificity estimates between 96.2% and 98.4%. MCEs for 1cm H&N/pelvis misalignments were 1.3/5.1% and 9.1/8.6% for TomoTherapy and TrueBeam images, respectively. 2cm MCE estimates were 0.4%/1.6% and 3.1/3.2%, respectively. Vertebral misalignment MCEs were 4.8% and 4.9% for TomoTherapy and TrueBeam images, respectively. Conclusion: Patient identification and gross misalignment errors can be robustly and

  12. SU-E-T-261: Development of An Automated System to Detect Patient Identification and Positioning Errors Prior to Radiotherapy Treatment

    International Nuclear Information System (INIS)

    Jani, S; Low, D; Lamb, J

    2015-01-01

    Purpose: To develop a system that can automatically detect patient identification and positioning errors using 3D computed tomography (CT) setup images and kilovoltage CT (kVCT) planning images. Methods: Planning kVCT images were collected for head-and-neck (H&N), pelvis, and spine treatments with corresponding 3D cone-beam CT (CBCT) and megavoltage CT (MVCT) setup images from TrueBeam and TomoTherapy units, respectively. Patient identification errors were simulated by registering setup and planning images from different patients. Positioning errors were simulated by misaligning the setup image by 1cm to 5cm in the six anatomical directions for H&N and pelvis patients. Misalignments for spine treatments were simulated by registering the setup image to adjacent vertebral bodies on the planning kVCT. A body contour of the setup image was used as an initial mask for image comparison. Images were pre-processed by image filtering and air voxel thresholding, and image pairs were assessed using commonly-used image similarity metrics as well as custom -designed metrics. A linear discriminant analysis classifier was trained and tested on the datasets, and misclassification error (MCE), sensitivity, and specificity estimates were generated using 10-fold cross validation. Results: Our workflow produced MCE estimates of 0.7%, 1.7%, and 0% for H&N, pelvis, and spine TomoTherapy images, respectively. Sensitivities and specificities ranged from 98.0% to 100%. MCEs of 3.5%, 2.3%, and 2.1% were obtained for TrueBeam images of the above sites, respectively, with sensitivity and specificity estimates between 96.2% and 98.4%. MCEs for 1cm H&N/pelvis misalignments were 1.3/5.1% and 9.1/8.6% for TomoTherapy and TrueBeam images, respectively. 2cm MCE estimates were 0.4%/1.6% and 3.1/3.2%, respectively. Vertebral misalignment MCEs were 4.8% and 4.9% for TomoTherapy and TrueBeam images, respectively. Conclusion: Patient identification and gross misalignment errors can be robustly and

  13. Polymer-coated magnetic nanospheres for preconcentration of organochlorine and pyrethroid pesticides prior to their determination by gas chromatography with electron capture detection

    International Nuclear Information System (INIS)

    Yang, Genggeng; He, Zeying; Liu, Xueke; Liu, Chang; Zhan, Jing; Liu, Donghui; Wang, Peng; Zhou, Zhiqiang

    2016-01-01

    Magnetic polymer nanospheres were prepared and used as adsorbents for the extraction of organochlorine and pyrethroid pesticides from water samples. The adsorbents were synthesized by mini emulsion polymerization of N-vinylimidazole and divinylbenzene and simultaneous encapsulation of oleic acid-coated Fe 3 O 4 nanoparticles. Following desorption with ethyl acetate, the target analytes β-hexachlorocyclohexane, δ-hexachlorocyclohexane, p,p’-DDE, heptachlor, trans-chlordan, cis-chlordan, bifenthrin, β-cypermethrin, δ-methrin, λ-cyhalothrin and esfenvalerate were determined by gas chromatography with electron capture detection. Desorption conditions, extraction times and sample volume were screened by Plackett-Burman design and optimized by Box-Behnken design. Under the optimum conditions, the organochlorines can be quantified in the 20 to 400 ng L −1 concentration range, and the pyrethroids in the 400 to 4000 ng L −1 concentration range. The recoveries of organochlorines and pyrethroids from spiked real water samples are between 77.6 and 97.3 %, with relative standard deviations between 0.9 and 10.0 %. The method for magnetic solid phase extraction described here is fast, simple and friendly to the environment. (author)

  14. First branchial cleft anomaly.

    Science.gov (United States)

    Al-Fallouji, M. A.; Butler, M. F.

    1983-01-01

    A 15-year-old girl presented with a cystic swelling since birth behind the ramus of the right mandible and diagnosed clinically as a dermoid cyst. Surgical exploration, however, showed that it was closely related to the external auditory canal, with an extension running medially behind the parotid gland and ending in the bony middle ear. The facial nerve was closely related to the deep part of the cyst. Such an anatomical position indicates that this was a first branchial cleft anomaly. Surgical excision of the cyst was performed. PMID:6622327

  15. Quick and simple sample treatment for multiresidue analysis of bisphenols, bisphenol diglycidyl ethers and their derivatives in canned food prior to liquid chromatography and fluorescence detection.

    Science.gov (United States)

    Alabi, A; Caballero-Casero, N; Rubio, S

    2014-04-04

    We report herein a multiresidue method for canned food determination of 12 bisphenols [bisphenol A (BPA), bisphenol B (BPB), bisphenol F (BPF), bisphenol E (BPE)], bisphenol diglycidyl ethers [bisphenol F diglycidyl ether (BFDGE), bisphenol A diglycidyl ether (BADGE)] and their derivatives [BADGE·2H2O, BADGE·H2O, BADGE·HCl·H2O, BADGE·HCl, BADGE·2HCl and BFDGE·2HCl]. The method was based on the microextraction of the target contaminants in 200mg food sample with 600 μL of a supramolecular solvent made up of inverse aggregates of tetradecanol, followed by analysis of the extract by liquid chromatography/fluorescence detection using external calibration. Chromatographic separation of all target compounds, including the ortho-ortho, ortho-para and para-para isomers of BFDGE and BFDGE·2HCl, was achieved with baseline separation (Resolution ≥ 1.52). No concentration of the extracts was required, the microextraction took about 30 min and several samples could be simultaneous treated. Method validation was carried out according to the recommendations of the European Commission Decision 2002/657/EC. Quantitation limits for the different analytes ranged between 0.9 and 3.5 μg kg(-1). Repeatability and reproducibility, expressed as relative standard deviation, were in the ranges 1.8-6.8% and 4.4-8.1%. The method was applied to the analysis of the target compounds in different food categories including vegetables, legumes, fruits, fish and seafood, meat product and grain. Recoveries in samples were within the range 80-110%. Only BPF and BPE were undetected in the canned food analyzed. The concentration found for the rest of bisphenols, diglycidyl ethers and derivatives was in the range 7.1-959 μg kg(-1). The study of the isomeric distribution of BFDGE and BFDGE·2HCl in food showed that they are preferentially present as one of the isomeric forms, that highlighting for further studies. The analytical and operational characteristics of this multiresidue method make

  16. Expanding the clinical spectrum of ocular anomalies in Noonan syndrome: Axenfeld-anomaly in a child with PTPN11 mutation.

    Science.gov (United States)

    Guerin, Andrea; So, Joyce; Mireskandari, Kamiar; Jougeh-Doust, Soghra; Chisholm, Caitlin; Klatt, Regan; Richer, Julie

    2015-02-01

    Ocular anomalies have been frequently reported in Noonan syndrome. Anterior segment anomalies have been described in 57% of PTPN11 positive patients, with the most common findings being corneal changes and in particular, prominent corneal nerves and cataracts. We report on a neonate with a confirmed PTPN11 mutation and ocular findings consistent with Axenfeld anomaly. The patient initially presented with non-immune hydrops and subsequently developed hypertrophic cardiomyopathy and dysmorphic features typical of Noonan syndrome. While a pathogenic mutation in PTPN11 was confirmed, prior testing for the two common genes associated with Axenfeld-Rieger syndrome, PITX2, and FOXC1 was negative. This finding expands the spectrum of anterior chamber anomalies seen in Noonan syndrome and perhaps suggests a common neural crest related mechanism that plays a critical role in the development of the eye and other organs. © 2014 Wiley Periodicals, Inc.

  17. Einstein, Entropy and Anomalies

    Science.gov (United States)

    Sirtes, Daniel; Oberheim, Eric

    2006-11-01

    This paper strengthens and defends the pluralistic implications of Einstein's successful, quantitative predictions of Brownian motion for a philosophical dispute about the nature of scientific advance that began between two prominent philosophers of science in the second half of the twentieth century (Thomas Kuhn and Paul Feyerabend). Kuhn promoted a monistic phase-model of scientific advance, according to which a paradigm driven `normal science' gives rise to its own anomalies, which then lead to a crisis and eventually a scientific revolution. Feyerabend stressed the importance of pluralism for scientific progress. He rejected Kuhn's model arguing that it fails to recognize the role that alternative theories can play in identifying exactly which phenomena are anomalous in the first place. On Feyerabend's account, Einstein's predictions allow for a crucial experiment between two incommensurable theories, and are an example of an anomaly that could refute the reigning paradigm only after the development of a competitor. Using Kuhn's specification of a disciplinary matrix to illustrate the incommensurability between the two paradigms, we examine the different research strategies available in this peculiar case. On the basis of our reconstruction, we conclude by rebutting some critics of Feyerabend's argument.

  18. 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.

  19. MR imaging features of the congenital uterine anomalies

    International Nuclear Information System (INIS)

    Hamcan, S.; Akgun, V.; Battal, B.; Kocaoglu, M.

    2012-01-01

    Full text: Introduction: Congenital uterine anomalies are common and usually asymptomatic. The agenesis, malfusion or deficient resorption of the Mullerian canals during embryogenesis may lead to these anomalies. Although ultrasonography (US) is the first step imaging technique in assessment of the uterine pathologies, it can be insufficient in differentiation of them. Magnetic resonance (MR) imaging is an adequate imaging technique in depicting pelvic anatomy and different types of uterine anomalies. Objectives and tasks: In this article, we aimed to present imaging features of the uterine anomalies. Material and methods: Pelvic MR scans of the cases who were referred to our radiology department for suspicious uterine anomaly were evaluated retrospectively. Results: We determined uniconuate uterus (type II), uterus didelphys (type III), bicornuate uterus (type IV), uterine septum (type V) and arcuate uterus (type VI) anomalies according to ASRM (American Society of Reproductive Medicine) classification. Conclusion: In cases with such pathologies leading to obstruction, dysmenorrhea or palpable pelvic mass in the puberty are the main clinical presentations. In cases without obstruction, infertility or multiple abortions can be encountered in reproductive ages. The identification of the subtype of the uterine anomalies is important for the preoperative planning of the management. MR that has multiplanar imaging capability and high soft tissue resolution is a non-invasive and the most important imaging modality for the detection and classification of the uterine anomalies

  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. 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

  2. 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...

  3. 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

  4. 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

  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. 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

  7. 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...

  8. 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.

  9. Low Risk Anomalies?

    DEFF Research Database (Denmark)

    Schneider, Paul; Wagner, Christian; Zechner, Josef

    This paper shows theoretically and empirically that beta- and volatility-based low risk anomalies are driven by return skewness. The empirical patterns concisely match the predictions of our model that endogenizes the role of skewness for stock returns through default risk. With increasing downside...... risk, the standard capital asset pricing model (CAPM) increasingly overestimates expected equity returns relative to firms' true (skew-adjusted) market risk. Empirically, the profitability of betting against beta/volatility increases with firms' downside risk, and the risk-adjusted return differential...... of betting against beta/volatility among low skew firms compared to high skew firms is economically large. Our results suggest that the returns to betting against beta or volatility do not necessarily pose asset pricing puzzles but rather that such strategies collect premia that compensate for skew risk...

  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. Nolen-Schiffer anomaly

    International Nuclear Information System (INIS)

    Pieper, S.C.; Wiringa, R.B.

    1995-01-01

    The Argonne v 18 potential contains a detailed treatment of the pp, pn and nn electromagnetic potential, including Coulomb, vacuum polarization, Darwin Foldy and magnetic moment terms, all with suitable form factors and was fit to pp and pn data using the appropriate nuclear masses. In addition, it contains a nuclear charge-symmetry breaking (CSB) term adjusted to reproduce the difference in the experimental pp and nn scattering lengths. We have used these potential terms to compute differences in the binding energies of mirror isospin-1/2 nuclei (Nolen-Schiffer [NS] anomaly). Variational Monte Carlo calculations for the 3 He- 3 H system and cluster variational Monte Carlo for the 15 O- 15 N and 17 F- 17 O systems were made. In the first case, the best variational wave function for the A = 3 nuclei was used. However, because our 16 O wave function does not reproduce accurately the 16 O rms radius, to which the NS anomaly is very sensitive, we adjusted the A = 15 and A = 17 wave functions to reproduce the experimental density profiles. Our computed energy differences for these three systems are 0.757 ± .001, 3.544 ± .018 and 3.458 ± .040 MeV respectively, which are to be compared with the experimental differences of 0.764, 3.537, and 3.544 MeV. Most of the theoretical uncertainties are due to uncertainties in the experimental rms radii. The nuclear CSB potential contributes 0.066, 0.188, and 0.090 MeV to these totals. We also attempted calculations for A = 39 and A = 41. However, in these cases, the experimental uncertainties in the rms radius make it impossible to extract useful information about the contribution of the nuclear CSB potential

  12. The Prior-project

    DEFF Research Database (Denmark)

    Engerer, Volkmar Paul; Roued-Cunliffe, Henriette; Albretsen, Jørgen

    digitisation of Arthur Prior’s Nachlass kept in the Bodleian Library, Oxford. The DH infrastructure in question is the Prior Virtual Lab (PVL). PVL was established in 2011 in order to provide researchers in the field of temporal logic easy access to the papers of Arthur Norman Prior (1914-1969), and officially......In this paper, we present a DH research infrastructure which relies heavily on a combination of domain knowledge with information technology. The general goal is to develop tools to aid scholars in their interpretations and understanding of temporal logic. This in turn is based on an extensive...

  13. Prevalence and distribution of selected dental anomalies among saudi children in Abha, Saudi Arabia.

    Science.gov (United States)

    Yassin, Syed M

    2016-12-01

    Dental anomalies are not an unusual finding in routine dental examination. The effect of dental anomalies can lead to functional, esthetic and occlusal problems. The Purpose of the study was to determine the prevalence and distribution of selected developmental dental anomalies in Saudi children. The study was based on clinical examination and Panoramic radiographs of children who visited the Pediatric dentistry clinics at King Khalid University College of Dentistry, Saudi Arabia. These patients were examined for dental anomalies in size, shape, number, structure and position. Data collected were entered and analyzed using statistical package for social sciences version. Of the 1252 children (638 Boys, 614 girls) examined, 318 subjects (25.39%) presented with selected dental anomalies. The distribution by gender was 175 boys (27.42%) and 143 girls (23.28%). On intergroup comparison, number anomalies was the most common anomaly with Hypodontia (9.7%) being the most common anomaly in Saudi children, followed by hyperdontia (3.5%). The Prevalence of size anomalies were Microdontia (2.6%) and Macrodontia (1.8%). The prevalence of Shape anomalies were Talon cusp (1.4%), Taurodontism (1.4%), Fusion (0.8%).The prevalence of Positional anomalies were Ectopic eruption (2.3%) and Rotation (0.4%). The prevalence of structural anomalies were Amelogenesis imperfecta (0.3%) Dentinogenesis imperfecta (0.1%). A significant number of children had dental anomaly with Hypodontia being the most common anomaly and Dentinogenesis imperfecta being the rare anomaly in the study. Early detection and management of these anomalies can avoid potential orthodontic and esthetic problems in a child. Key words: Dental anomalies, children, Saudi Arabia.

  14. 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)

  15. Anomaly mediation deformed by axion

    Energy Technology Data Exchange (ETDEWEB)

    Nakayama, Kazunori, E-mail: kazunori@hep-th.phys.s.u-tokyo.ac.jp [Department of Physics, University of Tokyo, Bunkyo-ku, Tokyo 113-0033 (Japan); Kavli Institute for the Physics and Mathematics of the Universe, University of Tokyo, Kashiwa 277-8583 (Japan); Yanagida, Tsutomu T. [Kavli Institute for the Physics and Mathematics of the Universe, University of Tokyo, Kashiwa 277-8583 (Japan)

    2013-05-13

    We show that in supersymmetric axion models the axion supermultiplet obtains a sizable F-term due to a non-supersymmetric dynamics and it generally gives the gaugino masses comparable to the anomaly mediation contribution. Thus the gaugino mass relation predicted by the anomaly mediation effect can be significantly modified in the presence of axion to solve the strong CP problem.

  16. Novel topological invariants and anomalies

    International Nuclear Information System (INIS)

    Hirayama, M.; Sugimasa, N.

    1987-01-01

    It is shown that novel topological invariants are associated with a class of Dirac operators. Trace formulas which are similar to but different from Callias's formula are derived. Implications of these topological invariants to anomalies in quantum field theory are discussed. A new class of anomalies are calculated for two models: one is two dimensional and the other four dimensional

  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. Arthur Prior and 'Now'

    DEFF Research Database (Denmark)

    Blackburn, Patrick Rowan; Jørgensen, Klaus Frovin

    2016-01-01

    ’s search led him through the work of Castañeda, and back to his own work on hybrid logic: the first made temporal reference philosophically respectable, the second made it technically feasible in a modal framework. With the aid of hybrid logic, Prior built a bridge from a two-dimensional UT calculus...

  19. Prior Knowledge Assessment Guide

    Science.gov (United States)

    2014-12-01

    assessment in a reasonable amount of time. Hands-on assessments can be extremely diverse in makeup and administration depending on the subject matter...DEVELOPING AND USING PRIOR KNOWLEDGE ASSESSMENTS TO TAILOR TRAINING D-3 ___ Brush and scrub ___ Orchards ___ Rice

  20. 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.