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. Theoretically Optimal Distributed Anomaly Detection

    Data.gov (United States)

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

  3. Seismic data fusion anomaly detection

    Science.gov (United States)

    Harrity, Kyle; Blasch, Erik; Alford, Mark; Ezekiel, Soundararajan; Ferris, David

    2014-06-01

    Detecting anomalies in non-stationary signals has valuable applications in many fields including medicine and meteorology. These include uses such as identifying possible heart conditions from an Electrocardiography (ECG) signals or predicting earthquakes via seismographic data. Over the many choices of anomaly detection algorithms, it is important to compare possible methods. In this paper, we examine and compare two approaches to anomaly detection and see how data fusion methods may improve performance. The first approach involves using an artificial neural network (ANN) to detect anomalies in a wavelet de-noised signal. The other method uses a perspective neural network (PNN) to analyze an arbitrary number of "perspectives" or transformations of the observed signal for anomalies. Possible perspectives may include wavelet de-noising, Fourier transform, peak-filtering, etc.. In order to evaluate these techniques via signal fusion metrics, we must apply signal preprocessing techniques such as de-noising methods to the original signal and then use a neural network to find anomalies in the generated signal. From this secondary result it is possible to use data fusion techniques that can be evaluated via existing data fusion metrics for single and multiple perspectives. The result will show which anomaly detection method, according to the metrics, is better suited overall for anomaly detection applications. The method used in this study could be applied to compare other signal processing algorithms.

  4. Hyperspectral Anomaly Detection in Urban Scenarios

    Science.gov (United States)

    Rejas Ayuga, J. G.; Martínez Marín, R.; Marchamalo Sacristán, M.; Bonatti, J.; Ojeda, J. C.

    2016-06-01

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

  5. Hyperspectral anomaly detection using enhanced global factors

    Science.gov (United States)

    Paciencia, Todd J.; Bauer, Kenneth W.

    2016-05-01

    Dimension reduction techniques have become one popular unsupervised approach used towards detecting anomalies in hyperspectral imagery. Although demonstrating promising results in the literature on specific images, these methods can become difficult to directly interpret and often require tuning of their parameters to achieve high performance on a specific set of images. This lack of generality is also compounded by the need to remove noise and atmospheric absorption spectral bands from the image prior to detection. Without a process for this band selection and to make the methods adaptable to different image compositions, performance becomes difficult to maintain across a wider variety of images. Here, we present a framework that uses factor analysis to provide a robust band selection and more meaningful dimension reduction with which to detect anomalies in the imagery. Measurable characteristics of the image are used to create an automated decision process that allows the algorithm to adjust to a particular image, while maintaining high detection performance. The framework and its algorithms are detailed, and results are shown for forest, desert, sea, rural, urban, anomaly-sparse, and anomaly-dense imagery types from different sensors. Additionally, the method is compared to current state-of-the-art methods and is shown to be computationally efficient.

  6. Survey of Anomaly Detection Methods

    Energy Technology Data Exchange (ETDEWEB)

    Ng, B

    2006-10-12

    This survey defines the problem of anomaly detection and provides an overview of existing methods. The methods are categorized into two general classes: generative and discriminative. A generative approach involves building a model that represents the joint distribution of the input features and the output labels of system behavior (e.g., normal or anomalous) then applies the model to formulate a decision rule for detecting anomalies. On the other hand, a discriminative approach aims directly to find the decision rule, with the smallest error rate, that distinguishes between normal and anomalous behavior. For each approach, we will give an overview of popular techniques and provide references to state-of-the-art applications.

  7. Kalman滤波在地震电离层TEC异常探测中的应用%APPLICATION OF KALMAN FILTERING IN DETECTING IONOSPHERIC TEC ANOMALY PRIOR TO EARTHQUAKE

    Institute of Scientific and Technical Information of China (English)

    聂兆生; 祝芙英; 付宁波

    2011-01-01

    It has been verified by plentiful existing observations that the ionospheric disturbance do appear prior to earthquake. In this paper,on the basis of the ionospheric TEC derived from the GPS observation data from the reference stations of Crustal Movement Observational Network of China, we processed and analyzed the ionospheric TEC data prior to the WENCHUAN Ms8.0 earthquake by Kalman filtering and then we compared the results with the previous conclusion, the results indicate that the establishment of the model of the Kalman filter is reasonable and reliable in the detection of the ionospheric TEC anomaly prior to earthquake effectively.%基于中国地壳运动观测网络GPS观测资料解算的电离层TEC数据,利用Kalman滤波方法对2008年5月12日汶川Ms8.0地震前的电离层TEC进行异常探测研究,并与以前的处理结果进行对比,对比结果表明:利用Kalman滤波方法能够有效地探测到震前的电离层TEC异常扰动.

  8. Anomaly detection in online social networks

    CERN Document Server

    Savage, David; Yu, Xinghuo; Chou, Pauline; Wang, Qingmai

    2016-01-01

    Anomalies in online social networks can signify irregular, and often illegal behaviour. Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being labelled or unlabelled, and survey methods for detecting these different types of anomalies. We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space. In addition, this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas of future research.

  9. Network anomaly detection a machine learning perspective

    CERN Document Server

    Bhattacharyya, Dhruba Kumar

    2013-01-01

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

  10. Anomaly Detection in Dynamic Networks

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-10-14

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

  11. 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.

  12. Efficient Accurate Context-Sensitive Anomaly Detection

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    For program behavior-based anomaly detection, the only way to ensure accurate monitoring is to construct an efficient and precise program behavior model. A new program behavior-based anomaly detection model,called combined pushdown automaton (CPDA) model was proposed, which is based on static binary executable analysis. The CPDA model incorporates the optimized call stack walk and code instrumentation technique to gain complete context information. Thereby the proposed method can detect more attacks, while retaining good performance.

  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. Comparison of Unsupervised Anomaly Detection Methods

    Data.gov (United States)

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

  15. Anomaly Detection in Power Quality at Data Centers

    Science.gov (United States)

    Grichine, Art; Solano, Wanda M.

    2015-01-01

    The goal during my internship at the National Center for Critical Information Processing and Storage (NCCIPS) is to implement an anomaly detection method through the StruxureWare SCADA Power Monitoring system. The benefit of the anomaly detection mechanism is to provide the capability to detect and anticipate equipment degradation by monitoring power quality prior to equipment failure. First, a study is conducted that examines the existing techniques of power quality management. Based on these findings, and the capabilities of the existing SCADA resources, recommendations are presented for implementing effective anomaly detection. Since voltage, current, and total harmonic distortion demonstrate Gaussian distributions, effective set-points are computed using this model, while maintaining a low false positive count.

  16. Anomaly detection for internet surveillance

    Science.gov (United States)

    Bouma, Henri; Raaijmakers, Stephan; Halma, Arvid; Wedemeijer, Harry

    2012-06-01

    Many threats in the real world can be related to activity of persons on the internet. Internet surveillance aims to predict and prevent attacks and to assist in finding suspects based on information from the web. However, the amount of data on the internet rapidly increases and it is time consuming to monitor many websites. In this paper, we present a novel method to automatically monitor trends and find anomalies on the internet. The system was tested on Twitter data. The results showed that it can successfully recognize abnormal changes in activity or emotion.

  17. Anomaly Detection Using Metaheuristic Firefly Harmonic Clustering

    OpenAIRE

    Mario H. A. C. Adaniya; Taufik Abr˜ao; Mario Lemes Proenc¸a Jr.

    2013-01-01

    The performance of communication networks can be affected by a number of factors including misconfiguration, equipments outages, attacks originated from legitimate behavior or not, software errors, among many other causes. These factors may cause an unexpected change in the traffic behavior and create what we call anomalies that may represent a loss of performance or breach of network security. Knowing the behavior pattern of the network is essential to detect and characterize an anomaly. The...

  18. Dendritic Cells for Anomaly Detection

    CERN Document Server

    Greensmith, Julie; Aickelin, Uwe

    2010-01-01

    Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human signals from the host tissue and correlate these signals with proteins know as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

  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. Detecting data anomalies methods in distributed systems

    Science.gov (United States)

    Mosiej, Lukasz

    2009-06-01

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

  1. 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. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

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

  3. Detection of cardiovascular anomalies: Hybrid systems approach

    KAUST Repository

    Diaz Ledezma, Fernando

    2012-06-06

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

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

    Directory of Open Access Journals (Sweden)

    Rimas Ciplinskas

    2016-06-01

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

  5. Anomaly Detection with Artificial Immune Network

    Institute of Scientific and Technical Information of China (English)

    PENG Lingxi; LI Tao; LIU Xiaojie; CHEN Yuefeng; LIU Caiming; LIU Sunjun

    2007-01-01

    Inspired by the immune network theory, an adaptive anomaly detection paradigm based on artificial immune network,referred as APAI, is proposed. The implementation of the paradigm includes: initially, the first is to create the initial antibody network; then, through the learning of each training antigen, the antibody network is evolved and updated by the optimal antibodies. Finally, anomaly detection process is accomplished by majority vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study, which is taken from the UCI machine learning database.The obtained detection accuracy of APAI was 97.7%, which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classification properties, APAI possesses biological immune networkproperties such as clonal selection, immune network, and immune memory, which can be applied to pattern recognition, classification, and etc.

  6. Surface latent heat flux anomalies prior to the Indonesia Mw9.0 earthquake of 2004

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The temporal and spatial variations of surface latent heat flux (SLHF) before and after the Mw9.0 earthquake that occurred on the west coast of Sumatra, Indonesia on 26 December 2004 are summarized. It is found that before the earthquake significant SLHF anomalies occurred at the epicentral area and its vicinity. The largest SLHF anomaly occurred on the subduction zone in the middle part of Burma micro-plate, where the middle part of the rupture zone is located and the aftershocks are concentrated. The developments of the anomaly involved growing of the anomaly from small to large and spreading of the anomaly from disordered to concentrated. The anomaly began to occur on the east extensional boundary of the Burma micro-plate and its adjacent oceanic basin, and then propagated to the west compressive boundary, where the subduction zone exists. Finally, the anomaly disappeared after the main shock. The seismic source is considered to be a dissipation system. The increase of stress prior to an earthquake may enhance the exchange of energy and material between the seismic source system and the outer system, resulting in the increase of the rate of energy exchange between sea surface and atmosphere, which is believed to be the main reason of the generation of SLHF anomaly.

  7. Anomaly Detection using the "Isolation Forest" algorithm

    CERN Document Server

    CERN. Geneva

    2015-01-01

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

  8. HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS

    OpenAIRE

    Rejas Ayuga, J. G.; Martínez Marín, R.; Marchamalo Sacristán, M.; Bonatti, J.; Ojeda, J. C.

    2016-01-01

    We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of t...

  9. Anomaly Detection Using Metaheuristic Firefly Harmonic Clustering

    Directory of Open Access Journals (Sweden)

    Mario H. A. C. Adaniya

    2013-01-01

    Full Text Available The performance of communication networks can be affected by a number of factors including misconfiguration, equipments outages, attacks originated from legitimate behavior or not, software errors, among many other causes. These factors may cause an unexpected change in the traffic behavior and create what we call anomalies that may represent a loss of performance or breach of network security. Knowing the behavior pattern of the network is essential to detect and characterize an anomaly. Therefore, this paper presents an algorithm based on the use of Digital Signature of Network Segment (DSNS, used to model the traffic behavior pattern. We propose a clustering algorithm, K-Harmonic means (KHM, combined with a new heuristic approach, named Firefly Algorithm (FA, for network volume anomaly detection. The KHM calculate the weighting function of each point to calculate new centroids and circumventing the initialization problem present in most center based clustering algorithm and exploits the search capability of FA from escaping local optima. Processing the DSNS data and real traffic data is possible to detect and classify intervals considered anomalous with a trade-off between the 80% true-positive rate and 20% false-positive rate.

  10. Trusted Anomaly Detection with Context Dependency

    Institute of Scientific and Technical Information of China (English)

    PENG Xin-guang; YAN Mei-feng

    2006-01-01

    Anomaly detection of privileged processes is one of the most important means to safeguard the host and system security. The key problem for improving detection performance is to identify local behavior of the short sequences in traces of system calls accurately. An alternative modeling method was proposed based on the typical pattern matching of short sequences, which builds upon the concepts of short sequences with context dependency and the specially designed aggregation algorithm. The experimental results indicate that the modeling method considering the context dependency improves clearly the sensitive decision threshold as compared with the previous modeling method.

  11. 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.

  12. Accumulating pyramid spatial-spectral collaborative coding divergence for hyperspectral anomaly detection

    Science.gov (United States)

    Sun, Hao; Zou, Huanxin; Zhou, Shilin

    2016-03-01

    Detection of anomalous targets of various sizes in hyperspectral data has received a lot of attention in reconnaissance and surveillance applications. Many anomaly detectors have been proposed in literature. However, current methods are susceptible to anomalies in the processing window range and often make critical assumptions about the distribution of the background data. Motivated by the fact that anomaly pixels are often distinctive from their local background, in this letter, we proposed a novel hyperspectral anomaly detection framework for real-time remote sensing applications. The proposed framework consists of four major components, sparse feature learning, pyramid grid window selection, joint spatial-spectral collaborative coding and multi-level divergence fusion. It exploits the collaborative representation difference in the feature space to locate potential anomalies and is totally unsupervised without any prior assumptions. Experimental results on airborne recorded hyperspectral data demonstrate that the proposed methods adaptive to anomalies in a large range of sizes and is well suited for parallel processing.

  13. Extracting Hidden Anomalies using Sketch and Non Gaussian Multiresolution Statistical Detection Procedures

    OpenAIRE

    Dewaele, Guillaume; Fukuda, Kensuke; Borgnat, Pierre; Abry, Patrice; Cho, Kenjiro

    2007-01-01

    International audience A new profile-based anomaly detection and characterization procedure is proposed. It aims at performing prompt and accurate detection of both short-lived and long-lasting low-intensity anomalies, without the recourse of any prior knowledge of the targetted traffic. Key features of the algorithm lie in the joint use of random projection techniques (sketches) and of a multiresolution non Gaussian marginal distribution modeling. The former enables both a reduction in th...

  14. Firewall policy anomaly detection and resolution

    Directory of Open Access Journals (Sweden)

    Ms. R.V.Darade

    2014-06-01

    Full Text Available Security of all private networks in businesses and institutions is achieved by firewall. Firewall provides protection by the quality of policy configured. Lack of Systematic analysis mechanism and Tools, Complex firewall configuration makes designing and managing firewall policies difficult. With help of segmentation rule, anomaly management framework is designed for accurate detection and effective resolution of anomalies. Using this technique, packets of network can be divided into set of disjoint packet space segments. Every segment is associated with unique set of firewall rules which specify an overlap relation among all firewall rules whic h could be conflicting or redundant. Flexible conflict resolution method is provided which has many resolution stra tegies for risk assessment of protected networks and its policy definition. Firewall logs are maintained by using association rule mining on these logs to find frequent logs, which in turned filtered to find malicious packets. Apriori algorithm is used to find frequent element from above logs. In each round, it computes the support for all candidate-item-sets. Candidate-item-sets with frequency above the minimum support parameter are selected at the end of each round; these frequent item-sets of round are used in the next round to construct candidate -item-sets. The algorithm halts when item-sets with desired frequency not found .

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

    OpenAIRE

    LE, Viet-Duc; Scholten, Hans; Havinga, Paul

    2013-01-01

    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 more sensors or retrieving information. However, most conventional anomaly detection methods are power hungry and computation consuming. This paper proposes a new online anomaly detection algorithm ...

  16. Method for detecting software anomalies based on recurrence plot analysis

    Directory of Open Access Journals (Sweden)

    Michał Mosdorf

    2012-03-01

    Full Text Available Presented paper evaluates method for detecting software anomalies based on recurrence plot analysis of trace log generated by software execution. Described method for detecting software anomalies is based on windowed recurrence quantification analysis for selected measures (e.g. Recurrence rate - RR or Determinism - DET. Initial results show that proposed method is useful in detecting silent software anomalies that do not result in typical crashes (e.g. exceptions.

  17. 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...

  18. DISTRIBUTED ANOMALY DETECTION USING SATELLITE DATA FROM MULTIPLE MODALITIES

    Data.gov (United States)

    National Aeronautics and Space Administration — DISTRIBUTED ANOMALY DETECTION USING SATELLITE DATA FROM MULTIPLE MODALITIES KANISHKA BHADURI*, KAMALIKA DAS, AND PETR VOTAVA* Abstract. There has been a tremendous...

  19. 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

  20. Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark

    OpenAIRE

    Lavin, Alexander; Ahmad, Subutai

    2015-01-01

    Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations; examples abound in domains such as finance, IT, security, medical, and energy. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, not batches, and learn while simultaneously making predictions. There are no benchmarks to adequately test and score the efficacy of real-time anomaly detectors. Here we propose t...

  1. Web Anomaly Misuse Intrusion Detection Framework for SQL Injection Detection

    Directory of Open Access Journals (Sweden)

    Shaimaa Ezzat Salama

    2012-03-01

    Full Text Available Databases at the background of e-commerce applications are vulnerable to SQL injection attack which is considered as one of the most dangerous web attacks. In this paper we propose a framework based on misuse and anomaly detection techniques to detect SQL injection attack. The main idea of this framework is to create a profile for legitimate database behavior extracted from applying association rules on XML file containing queries submitted from application to the database. As a second step in the detection process, the structure of the query under observation will be compared against the legitimate queries stored in the XML file thus minimizing false positive alarms

  2. 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)

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

    International Nuclear Information System (INIS)

    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)

  4. Anomaly detection enhanced classification in computer intrusion detection

    Energy Technology Data Exchange (ETDEWEB)

    Fugate, M. L. (Michael L.); Gattiker, J. R. (James R.)

    2002-01-01

    This report describes work with the goal of enhancing capabilities in computer intrusion detection. The work builds upon a study of classification performance, that compared various methods of classifying information derived from computer network packets into attack versus normal categories, based on a labeled training dataset. This previous work validates our classification methods, and clears the ground for studying whether and how anomaly detection can be used to enhance this performance, The DARPA project that initiated the dataset used here concluded that anomaly detection should be examined to boost the performance of machine learning in the computer intrusion detection task. This report investigates the data set for aspects that will be valuable for anomaly detection application, and supports these results with models constructed from the data. In this report, the term anomaly detection means learning a model from unlabeled data, and using this to make some inference about future data. Our data is a feature vector derived from network packets: an 'example' or 'sample'. On the other hand, classification means building a model from labeled data, and using that model to classify unlabeled (future) examples. There is some precedent in the literature for combining these methods. One approach is to stage the two techniques, using anomaly detection to segment data into two sets for classification. An interpretation of this is a method to combat nonstationarity in the data. In our previous work, we demonstrated that the data has substantial temporal nonstationarity. With classification methods that can be thought of as learning a decision surface between two statistical distributions, performance is expected to degrade significantly when classifying examples that are from regions not well represented in the training set. Anomaly detection can be seen as a problem of learning the density (landscape) or the support (boundary) of a statistical

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

    Directory of Open Access Journals (Sweden)

    Yoshiteru Ishida

    2009-11-01

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

  6. 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.

  7. Online Anomaly Energy Consumption Detection Using Lambda Architecture

    DEFF Research Database (Denmark)

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

    2016-01-01

    With the widely use of smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics p...

  8. Automated Network Anomaly Detection with Learning, Control and Mitigation

    Science.gov (United States)

    Ippoliti, Dennis

    2014-01-01

    Anomaly detection is a challenging problem that has been researched within a variety of application domains. In network intrusion detection, anomaly based techniques are particularly attractive because of their ability to identify previously unknown attacks without the need to be programmed with the specific signatures of every possible attack.…

  9. 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. PMID:26336154

  10. Generalization of GLRT-Based Magnetic Anomaly Detection

    OpenAIRE

    Pepe, Pascal; Zozor, Steeve; Rouve, Laure-Line; Coulomb, Jean-Louis; Servière, Christine; Muley, Jean

    2015-01-01

    International audience Magnetic anomaly detection (MAD) refers to a passive method used to reveal hidden magnetic masses and is most commonly based on a dipolar target model. This paper proposes a generalization of the MAD through a multipolar model that provides a more precise description of the anomaly and serves a twofold objective: to improve the detection performance , and to widen the variety of detectable targets. The dipole detection strategy – namely an orthonormal decomposition o...

  11. 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.

  12. Online Anomaly Energy Consumption Detection Using Lambda Architecture

    DEFF Research Database (Denmark)

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

    2016-01-01

    With the widely use of smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics...... problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark...... Streaming. The system supports not only iterative refreshing the detection models from scalable data sets, but also real-time anomaly detection on scalable live data streams. This paper empirically evaluates the system and the detection algorithm, and the results show the effectiveness and the scalability...

  13. Seismicity anomalies prior to 8 June 2008, Mw=6.4 earthquake in Western Greece

    Directory of Open Access Journals (Sweden)

    G. Chouliaras

    2009-03-01

    Full Text Available The epicentral area of the Mw=6.4, 8 June 2008 main shock in northwestern Peloponesus, Western Greece, had been forecasted as a candidate for the occurrence of a strong earthquake by independent scientific investigations. This study concerns the seismicity of a large area surrounding the epicenter of the main shock using the seismological data from the monthly bulletins of the Institute of Geodynamics of the National Observatory of Athens. This data set is the most detailed earthquake catalog available for anomalous seismicity pattern investigations in Greece. The results indicate a decrease in seismicity rate seven years prior to the 8 June main shock which constituted a two and a half year long seismic quiescence surrounding the epicentral area. This quiescence anomaly was succeeded by a period of acceleration in seismic activity for five years approximately, until the occurrence of the main shock.

  14. Fuzzy Based Anomaly Intrusion Detection System for Clustered WSN

    OpenAIRE

    Sumathy Murugan; Sundara Rajan, M.

    2015-01-01

    In Wireless Sensor Networks (WSN), the intrusion detection technique may result in increased computational cost, packet loss, performance degradation and so on. In order to overcome these issues, in this study, we propose a fuzzy based anomaly intrusion detection system for clustered WSN. Initially the cluster heads are selected based on the parameters such as link quality, residual energy and coverage. Then the anomaly intrusion is detected using fuzzy logic technique. This technique conside...

  15. 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...

  16. Online Anomaly Energy Consumption Detection Using Lambda Architecture

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  17. Network Traffic Anomalies Detection and Identification with Flow Monitoring

    CERN Document Server

    Nguyen, Huy; Kim, Dong Il; Choi, Deokjai

    2010-01-01

    Network management and security is currently one of the most vibrant research areas, among which, research on detecting and identifying anomalies has attracted a lot of interest. Researchers are still struggling to find an effective and lightweight method for anomaly detection purpose. In this paper, we propose a simple, robust method that detects network anomalous traffic data based on flow monitoring. Our method works based on monitoring the four predefined metrics that capture the flow statistics of the network. In order to prove the power of the new method, we did build an application that detects network anomalies using our method. And the result of the experiments proves that by using the four simple metrics from the flow data, we do not only effectively detect but can also identify the network traffic anomalies.

  18. Stillbirth Risk Among Fetuses With Ultrasound-Detected Isolated Congenital Anomalies

    Science.gov (United States)

    Frey, Heather A.; Odibo, Anthony O.; Dicke, Jeffrey M.; Shanks, Anthony L.; Macones, George A.; Cahill, Alison G.

    2014-01-01

    Objective To estimate the risk of stillbirth among pregnancies complicated by a major isolated congenital anomaly detected by antenatal ultrasound, and the influence of incidental growth restriction. Methods A retrospective cohort study of all consecutive singleton pregnancies undergoing routine anatomic survey between 1990 and 2009 was performed. Stillbirth rates among fetuses with an ultrasound-detected isolated major congenital anomaly were compared to fetuses without major anomalies. Stillbirth rates were calculated per 1,000 ongoing pregnancies. Exclusion criteria included delivery prior to 24 weeks of gestation, multiple fetal anomalies, minor anomalies and chromosomal abnormalities. Analyses were stratified by gestational age at delivery (prior to 32 weeks vs. 32 weeks of gestation or after) and birth weight less than the 10th percentile. We adjusted for confounders using logistic regression. Results Among 65,308 singleton pregnancies delivered at 24 weeks of gestation or after, 873 pregnancies with an isolated major congenital anomaly (1.3%) were identified. The overall stillbirth rate among fetuses with a major anomaly was 55/1,000 compared to 4/1,000 in nonanomalous fetuses (aOR 15.17, 95% CI 11.03–20.86). Stillbirth risk in anomalous fetuses was similar prior to 32 weeks of gestation (26/1,000) and 32 weeks of gestation or after (31/1,000). Among growth-restricted fetuses, the stillbirth rate increased among anomalous (127/1,000) and nonanomalous fetuses (18/1,000), and congenital anomalies remained associated with higher rates of stillbirth (aOR 8.20, 95% CI 5.27–12.74). Conclusion The stillbirth rate is increased in anomalous fetuses regardless of incidental growth restriction. These risks can assist practitioners designing care plans for anomalous fetuses who have elevated and competing risks of stillbirth and neonatal death. PMID:24901272

  19. An enhanced stream mining approach for network anomaly detection

    Science.gov (United States)

    Bellaachia, Abdelghani; Bhatt, Rajat

    2005-03-01

    Network anomaly detection is one of the hot topics in the market today. Currently, researchers are trying to find a way in which machines could automatically learn both normal and anomalous behavior and thus detect anomalies if and when they occur. Most important applications which could spring out of these systems is intrusion detection and spam mail detection. In this paper, the primary focus on the problem and solution of "real time" network intrusion detection although the underlying theory discussed may be used for other applications of anomaly detection (like spam detection or spy-ware detection) too. Since a machine needs a learning process on its own, data mining has been chosen as a preferred technique. The object of this paper is to present a real time clustering system; we call Enhanced Stream Mining (ESM) which could analyze packet information (headers, and data) to determine intrusions.

  20. 3D Scene Priors for Road Detection

    NARCIS (Netherlands)

    J.M. Alvarez; T. Gevers; A.M. Lopez

    2010-01-01

    Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homo

  1. On the Utility of Anonymized Flow Traces for Anomaly Detection

    CERN Document Server

    Burkhart, Martin; May, Martin

    2008-01-01

    The sharing of network traces is an important prerequisite for the development and evaluation of efficient anomaly detection mechanisms. Unfortunately, privacy concerns and data protection laws prevent network operators from sharing these data. Anonymization is a promising solution in this context; however, it is unclear if the sanitization of data preserves the traffic characteristics or introduces artifacts that may falsify traffic analysis results. In this paper, we examine the utility of anonymized flow traces for anomaly detection. We quantitatively evaluate the impact of IP address anonymization, namely variations of permutation and truncation, on the detectability of large-scale anomalies. Specifically, we analyze three weeks of un-sampled and non-anonymized network traces from a medium-sized backbone network. We find that all anonymization techniques, except prefix-preserving permutation, degrade the utility of data for anomaly detection. We show that the degree of degradation depends to a large exten...

  2. 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.

  3. 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...

  4. In-Flight Diagnosis and Anomaly Detection Project

    Data.gov (United States)

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

  5. 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...

  6. Comparative Analysis of Data-Driven Anomaly Detection Methods

    Data.gov (United States)

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

  7. Anomaly Detection from ASRS Databases of Textual Reports

    Data.gov (United States)

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

  8. Anomaly detection in GPS data based on visual analytics

    OpenAIRE

    Yu, Y.; Liao, Z; Chen, B

    2010-01-01

    Modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing high-level intelligence and domain-specific expertise. We combine the power of the two for anomaly detection in GPS data by integrating them through a visualization and human-computer interaction interface. In this paper we introduce GPSvas (GPS Visual Analytics System), a system that detects anomalies in GPS data using...

  9. 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)

  10. 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

  11. Lidar detection algorithm for time and range anomalies

    Science.gov (United States)

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

    2007-10-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Ivanov, K. N.

    2005-11-27

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

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

    International Nuclear Information System (INIS)

    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.

  14. 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.

  15. Unfolding the procedure of characterizing recorded ultra low frequency, kHZ and MHz electromagnetic anomalies prior to the L'Aquila earthquake as pre-seismic ones. Part II

    CERN Document Server

    Eftaxias, K; Contoyiannis, Y; Papadimitriou, C; Kalimeri, M; Kopanas, J; Antonopoulos, G; Nomicos, C

    2009-01-01

    Ultra low frequency-ULF (1 Hz or lower), kHz and MHz electromagnetic (EM) anomalies were recorded prior to the L'Aquila catastrophic earthquake (EQ) that occurred on April 6, 2009. The detected anomalies followed this temporal scheme. (i) The MHZ EM anomalies were detected on March 26, 2009 and April 2, 2009. The kHz EM anomalies were emerged on April, 4 2009. The ULF EM anomaly was continuously recorded from March 29, 2009 up to April 2, 2009. "Are EQs predictable?" is a question hotly debated in the science community. Its answer begs for another question: "Are there credible EQ precursors?". Despite fairly abundant circumstantial evidence pre-seismic EM signals have not been adequately accepted as real physical quantities. Therefore, the question effortlessly arises as to whether the observed anomalies before the L'Aquila EQ were seismogenic or not. The main goal of this work is to provide some insight into this issue.

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

    Science.gov (United States)

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

    2016-07-01

    This paper describes a computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has proven useful for medical decision support in previous works from our team. In the proposed framework, breasts are first partitioned adaptively into regions. Then, features derived from the detection of lesions (masses and microcalcifications) as well as textural features, are extracted from each region and combined in order to classify mammography examinations as "normal" or "abnormal". Whenever an abnormal examination record is detected, the regions that induced that automated diagnosis can be highlighted. Two strategies are evaluated to define this anomaly detector. In a first scenario, manual segmentations of lesions are used to train an SVM that assigns an anomaly index to each region; local anomaly indices are then combined into a global anomaly index. In a second scenario, the local and global anomaly detectors are trained simultaneously, without manual segmentations, using various MIL algorithms (DD, APR, mi-SVM, MI-SVM and MILBoost). Experiments on the DDSM dataset show that the second approach, which is only weakly-supervised, surprisingly outperforms the first approach, even though it is strongly-supervised. This suggests that anomaly detectors can be advantageously trained on large medical image archives, without the need for manual segmentation. PMID:26829783

  17. Discovering Emerging Topics in Social Streams via Link Anomaly Detection

    CERN Document Server

    Takahashi, Toshimitsu; Yamanishi, Kenji

    2011-01-01

    Detection of emerging topics are now receiving renewed interest motivated by the rapid growth of social networks. Conventional term-frequency-based approaches may not be appropriate in this context, because the information exchanged are not only texts but also images, URLs, and videos. We focus on the social aspects of theses networks. That is, the links between users that are generated dynamically intentionally or unintentionally through replies, mentions, and retweets. We propose a probability model of the mentioning behaviour of a social network user, and propose to detect the emergence of a new topic from the anomaly measured through the model. We combine the proposed mention anomaly score with a recently proposed change-point detection technique based on the Sequentially Discounting Normalized Maximum Likelihood (SDNML), or with Kleinberg's burst model. Aggregating anomaly scores from hundreds of users, we show that we can detect emerging topics only based on the reply/mention relationships in social net...

  18. Three-dimensional distribution of ionospheric anomalies prior to three large earthquakes in Chile

    Science.gov (United States)

    He, Liming; Heki, Kosuke

    2016-07-01

    Using regional Global Positioning System (GPS) networks, we studied three-dimensional spatial structure of ionospheric total electron content (TEC) anomalies preceding three recent large earthquakes in Chile, South America, i.e., the 2010 Maule (Mw 8.8), the 2014 Iquique (Mw 8.2), and the 2015 Illapel (Mw 8.3) earthquakes. Both positive and negative TEC anomalies, with areal extent dependent on the earthquake magnitudes, appeared simultaneously 20-40 min before the earthquakes. For the two midlatitude earthquakes (2010 Maule and 2015 Illapel), positive anomalies occurred to the north of the epicenters at altitudes 150-250 km. The negative anomalies occurred farther to the north at higher altitudes 200-500 km. This lets the epicenter, the positive and negative anomalies align parallel with the local geomagnetic field, which is a typical structure of ionospheric anomalies occurring in response to positive surface electric charges.

  19. SCADA Protocol Anomaly Detection Utilizing Compression (SPADUC) 2013

    Energy Technology Data Exchange (ETDEWEB)

    Gordon Rueff; Lyle Roybal; Denis Vollmer

    2013-01-01

    There is a significant need to protect the nation’s energy infrastructures from malicious actors using cyber methods. Supervisory, Control, and Data Acquisition (SCADA) systems may be vulnerable due to the insufficient security implemented during the design and deployment of these control systems. This is particularly true in older legacy SCADA systems that are still commonly in use. The purpose of INL’s research on the SCADA Protocol Anomaly Detection Utilizing Compression (SPADUC) project was to determine if and how data compression techniques could be used to identify and protect SCADA systems from cyber attacks. Initially, the concept was centered on how to train a compression algorithm to recognize normal control system traffic versus hostile network traffic. Because large portions of the TCP/IP message traffic (called packets) are repetitive, the concept of using compression techniques to differentiate “non-normal” traffic was proposed. In this manner, malicious SCADA traffic could be identified at the packet level prior to completing its payload. Previous research has shown that SCADA network traffic has traits desirable for compression analysis. This work investigated three different approaches to identify malicious SCADA network traffic using compression techniques. The preliminary analyses and results presented herein are clearly able to differentiate normal from malicious network traffic at the packet level at a very high confidence level for the conditions tested. Additionally, the master dictionary approach used in this research appears to initially provide a meaningful way to categorize and compare packets within a communication channel.

  20. Visual analytics of anomaly detection in large data streams

    Science.gov (United States)

    Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel A.; Sharma, Ratnesh K.; Mehta, Abhay

    2009-01-01

    Most data streams usually are multi-dimensional, high-speed, and contain massive volumes of continuous information. They are seen in daily applications, such as telephone calls, retail sales, data center performance, and oil production operations. Many analysts want insight into the behavior of this data. They want to catch the exceptions in flight to reveal the causes of the anomalies and to take immediate action. To guide the user in finding the anomalies in the large data stream quickly, we derive a new automated neighborhood threshold marking technique, called AnomalyMarker. This technique is built on cell-based data streams and user-defined thresholds. We extend the scope of the data points around the threshold to include the surrounding areas. The idea is to define a focus area (marked area) which enables users to (1) visually group the interesting data points related to the anomalies (i.e., problems that occur persistently or occasionally) for observing their behavior; (2) discover the factors related to the anomaly by visualizing the correlations between the problem attribute with the attributes of the nearby data items from the entire multi-dimensional data stream. Mining results are quickly presented in graphical representations (i.e., tooltip) for the user to zoom into the problem regions. Different algorithms are introduced which try to optimize the size and extent of the anomaly markers. We have successfully applied this technique to detect data stream anomalies in large real-world enterprise server performance and data center energy management.

  1. 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.

  2. Poseidon: a 2-tier anomaly-based intrusion detection system

    NARCIS (Netherlands)

    Bolzoni, Damiano; Zambon, Emmanuele; Etalle, Sandro; Hartel, Pieter

    2005-01-01

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

  3. 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.

  4. Anomalies.

    Science.gov (United States)

    Online-Offline, 1999

    1999-01-01

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

  5. 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

  6. 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.

  7. 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...

  8. 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.

  9. Novel anomaly detection approach for telecommunication network proactive performance monitoring

    Institute of Scientific and Technical Information of China (English)

    Yanhua YU; Jun WANG; Xiaosu ZHAN; Junde SONG

    2009-01-01

    The mode of telecommunication network management is changing from "network oriented" to "subscriber oriented". Aimed at enhancing subscribers'feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed taking advantage of time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1-α by constructing random variables satisfying t distribution. Experimental results verify the method's effectiveness.

  10. Anomaly detection using classified eigenblocks in GPR image

    Science.gov (United States)

    Kim, Min Ju; Kim, Seong Dae; Lee, Seung-eui

    2016-05-01

    Automatic landmine detection system using ground penetrating radar has been widely researched. For the automatic mine detection system, system speed is an important factor. Many techniques for mine detection have been developed based on statistical background. Among them, a detection technique employing the Principal Component Analysis(PCA) has been used for clutter reduction and anomaly detection. However, the PCA technique can retard the entire process, because of large basis dimension and a numerous number of inner product operations. In order to overcome this problem, we propose a fast anomaly detection system using 2D DCT and PCA. Our experiments use a set of data obtained from a test site where the anti-tank and anti- personnel mines are buried. We evaluate the proposed system in terms of the ROC curve. The result shows that the proposed system performs much better than the conventional PCA systems from the viewpoint of speed and false alarm rate.

  11. Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data

    OpenAIRE

    Hong, Chi-Yao; Caesar, Matthew; Duffield, Nick; Wang, Jia

    2012-01-01

    Operational network data, management data such as customer care call logs and equipment system logs, is a very important source of information for network operators to detect problems in their networks. Unfortunately, there is lack of efficient tools to automatically track and detect anomalous events on operational data, causing ISP operators to rely on manual inspection of this data. While anomaly detection has been widely studied in the context of network data, operational data presents sev...

  12. Anomaly detection for machine learning redshifts applied to SDSS galaxies

    CERN Document Server

    Hoyle, Ben; Paech, Kerstin; Bonnett, Christopher; Seitz, Stella; Weller, Jochen

    2015-01-01

    We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million 'clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 'anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed 'anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured stat...

  13. Simultaneous observation of VHF radio wave transmission anomaly propagated beyond line of site prior to earthquakes in multiple sites

    Science.gov (United States)

    Yamashita, H.; Mogi, T.; Moriya, T.; Takada, M.; Morisada, M.

    2010-12-01

    The VHF radio wave transmission anomalies propagated beyond line of site prior to earthquakes (M>4), (hereafter termed EQ-echo) have been observed more than 20 times from 2004 at the Erimo observatory (ERM) in Hokkaido, Northern Japan. A statistical relationship between magnitude of preceding earthquake and total duration time of the EQ-echo has been proposed (Moriya et al.2009). To confirm a region where the EQ-echo simultaneously observed for each earthquake, we installed another 3 observatory with approximately 5 km spacing in the surroundings of ERM. The EQ-echoes have been observed simultaneously at two observatories prior to four earthquakes since 2008. The initial time and duration of each EQ echo were same time in several cases but different at some minutes each other in other cases. The wave forms of the EQ-echoes were similar in both records. In the Fuyushima observatory (FYS, 10km away from ERM) , three-way antennas were installed at every 120 degree to detect an arrival direction of EQ-echoes. Simultaneous observations of EQ-echoes at ERM and FYS for the preceding EQ (M=4.7) that occurred in the Hidaka mountains revealed that this EQ-echo came from direction of the epicenter based on the FYS observation and this direction was consistent with that of EQ-echo observed simultaneously in ERM. Although some of simultaneous observed EQ-echoes were observed in same time completely at both observatories, but some of them were with time rag of duration of each EQ-echo between multiple observed sites. We discussed what these time rags mean by considering possibilities of moving of scattering objects, generation of a radio duct, and so on, as in response to this fact.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Abedin, Ahmad Firdaus Zainal; Ibrahim, Noorddin [Department of Defence Science, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, Kuala Lumpur 57000 (Malaysia); Zabidi, Noriza Ahmad; Demon, Siti Zulaikha Ngah [Centre for Foundation Studies, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, Kuala Lumpur 57000 (Malaysia)

    2016-01-22

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

  16. 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...

  17. 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 s

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

    NARCIS (Netherlands)

    Le, Viet-Duc; Scholten, Hans; Havinga, Paul

    2013-01-01

    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 m

  19. Extending TOPS: Knowledge Management System for Anomaly Detection and Analysis

    Science.gov (United States)

    Votava, P.; Nemani, R. R.; Michaelis, A.

    2009-12-01

    Terrestrial Observation and Prediction System (TOPS) is a flexible modeling software system that integrates ecosystem models with frequent satellite and surface weather observations to produce ecosystem nowcasts (assessments of current conditions) and forecasts useful in natural resources management, public health and disaster management. We have been extending the Terrestrial Observation and Prediction System (TOPS) to include capability for automated anomaly detection and analysis of both on-line (streaming) and off-line data. While there are large numbers of anomaly detection algorithms for multivariate datasets, we are extending this capability beyond the anomaly detection itself and towards an automated analysis that would discover the possible causes of the anomalies. There are often indirect connections between datasets that manifest themselves during occurrence of external events and rather than searching exhaustively throughout all the datasets, our goal is to capture this knowledge and provide it to the system during automated analysis. This results in more efficient processing. Since we don’t need to process all the datasets using the original anomaly detection algorithms, which is often compute intensive; we achieve data reduction as we don’t need to store all the datasets in order to search for possible connections but we can download selected data on-demand based on our analysis. For example, an anomaly observed in vegetation Net Primary Production (NPP) can relate to an anomaly in vegetation Leaf Area Index (LAI), which is a fairly direct connection, as LAI is one of the inputs for NPP, however the change in LAI could be caused by a fire event, which is not directly connected with NPP. Because we are able to capture this knowledge we can analyze fire datasets and if there is a match with the NPP anomaly, we can infer that a fire is a likely cause. The knowledge is captured using OWL ontology language, where connections are defined in a schema

  20. Method for Real-Time Model Based Structural Anomaly Detection

    Science.gov (United States)

    Smith, Timothy A. (Inventor); Urnes, James M., Sr. (Inventor); Reichenbach, Eric Y. (Inventor)

    2015-01-01

    A system and methods for real-time model based vehicle structural anomaly detection are disclosed. A real-time measurement corresponding to a location on a vehicle structure during an operation of the vehicle is received, and the real-time measurement is compared to expected operation data for the location to provide a modeling error signal. A statistical significance of the modeling error signal to provide an error significance is calculated, and a persistence of the error significance is determined. A structural anomaly is indicated, if the persistence exceeds a persistence threshold value.

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

    Directory of Open Access Journals (Sweden)

    Paula Zozzaro-Smith

    2014-07-01

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

  2. A new data normalization method for unsupervised anomaly intrusion detection

    Institute of Scientific and Technical Information of China (English)

    Long-zheng CAI; Jian CHEN; Yun KE; Tao CHEN; Zhi-gang LI

    2010-01-01

    Unsupervised anomaly detection can detect attacks without the need for clean or labeled training data.This paper studies the application of clustering to unsupervised anomaly detection(ACUAD).Data records are mapped to a feature space.Anomalies are detected by determining which points lie in the sparse regions of the feature space.A critical element for this method to be effective is the definition of the distance function between data records.We propose a unified normalization distance framework for records with numeric and nominal features mixed data.A heuristic method that computes the distance for nominal features is proposed,taking advantage of an important characteristic of nominal features-their probability distribution.Then,robust methods are proposed for mapping numeric features and computing their distance,these being able to tolerate the impact of the value difference in scale and diversification among features,and outliers introduced by intrusions.Empirical experiments with the KDD 1999 dataset showed that ACUAD can detect intrusions with relatively low false alarm rates compared with other approaches.

  3. ANOMALY DETECTION AND ATTRIBUTION USING AUTO FORECAST AND DIRECTED GRAPHS

    Directory of Open Access Journals (Sweden)

    Vivek Sankar

    2016-03-01

    Full Text Available In the business world, decision makers rely heavily on data to back their decisions. With the quantum of data increasing rapidly, traditional methods used to generate insights from reports and dashboards will soon become intractable. This creates a need for efficient systems which can substitute human intelligence and reduce time latency in decision making. This paper describes an approach to process time series data with multiple dimensions such as geographies, verticals, products, efficiently, and to detect anomalies in the data and further, to explain potential reasons for the occurrence of the anomalies. The algorithm implements auto selection of forecast models to make reliable forecasts and detect such anomalies. Depth First Search (DFS is applied to analyse each of these anomalies and find its root causes. The algorithm filters the redundant causes and reports the insights to the stakeholders. Apart from being a hair-trigger KPI tracking mechanism, this algorithm can also be customized for problems lke A/B testing, campaign tracking and product evaluations.

  4. 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

  5. Towards Periodicity Based Anomaly Detection in SCADA Networks

    OpenAIRE

    Barbosa, Rafael Ramos Regis; Sadre, Ramin; Pras, Aiko

    2012-01-01

    Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities. The polling mechanism used to retrieve data from field devices causes the data transmission to be highly periodic. In this paper, we propose an approach that exploits traffic periodicity to detect traffic anomalies, which represent potential intrusion attempts. We present a proof of concept to show the feasibility of our approach.

  6. Deep Structured Energy Based Models for Anomaly Detection

    OpenAIRE

    Zhai, Shuangfei; Cheng, Yu; Lu, Weining; Zhang, Zhongfei

    2016-01-01

    In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures. We propose deep structured energy based models (DSEBMs), where the energy function is the output of a deterministic deep neural network with structure. We develop novel model architectures to integrate EBMs with different types of data such as static data, sequential data, and spatial data, and apply appropriate model architectures to adapt to the data structure. Our trai...

  7. 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.

  8. 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.

  9. Anomaly Detection in Clutter using Spectrally Enhanced Ladar

    CERN Document Server

    Chhabra, Puneet S; Hopgood, James R

    2016-01-01

    Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a series of echoes that reflect from objects in a scene. These can be first, last or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure the intensity of light reflected from objects continuously over a period of time. In a camouflaged scenario, e.g., objects hidden behind dense foliage, a FW-Ladar penetrates such foliage and returns a sequence of echoes including buried faint echoes. The aim of this paper is to learn local-patterns of co-occurring echoes characterised by their measured spectra. A deviation from such patterns defines an abnormal event in a forest/tree depth profile. As far as the authors know, neither DR or FW-Ladar, along with several spectral measurements, has not been applied to anomaly detection. This work presents an algorithm that allows detection of spectral and temporal anomalies in FW-Multi Spectral Ladar (FW-MSL) data samples. An anomaly is defined as a full waveform temporal and spectral ...

  10. Beyond Trisomy 21: Additional Chromosomal Anomalies Detected through Routine Aneuploidy Screening

    Directory of Open Access Journals (Sweden)

    Amy Metcalfe

    2014-04-01

    Full Text Available Prenatal screening is often misconstrued by patients as screening for trisomy 21 alone; however, other chromosomal anomalies are often detected. This study aimed to systematically review the literature and use diagnostic meta-analysis to derive pooled detection and false positive rates for aneuploidies other than trisomy 21 with different prenatal screening tests. Non-invasive prenatal testing had the highest detection (DR and lowest false positive (FPR rates for trisomy 13 (DR: 90.3%; FPR: 0.2%, trisomy 18 (DR: 98.1%; FPR: 0.2%, and 45,X (DR: 92.2%; FPR: 0.1%; however, most estimates came from high-risk samples. The first trimester combined test also had high DRs for all conditions studied (trisomy 13 DR: 83.1%; FPR: 4.4%; trisomy 18 DR: 91.9%; FPR: 3.5%; 45,X DR: 70.1%; FPR: 5.4%; triploidy DR: 100%; FPR: 6.3%. Second trimester triple screening had the lowest DRs and highest FPRs for all conditions (trisomy 13 DR: 43.9%; FPR: 8.1%; trisomy 18 DR: 70.5%; FPR: 3.3%; 45,X DR: 77.2%; FPR: 9.3%. Prenatal screening tests differ in their ability to accurately detect chromosomal anomalies. Patients should be counseled about the ability of prenatal screening to detect anomalies other than trisomy 21 prior to undergoing screening.

  11. Detecting Humans in Dense Crowds Using Locally-Consistent Scale Prior and Global Occlusion Reasoning.

    Science.gov (United States)

    Idrees, Haroon; Soomro, Khurram; Shah, Mubarak

    2015-10-01

    Human detection in dense crowds is an important problem, as it is a prerequisite to many other visual tasks, such as tracking, counting, action recognition or anomaly detection in behaviors exhibited by individuals in a dense crowd. This problem is challenging due to the large number of individuals, small apparent size, severe occlusions and perspective distortion. However, crowded scenes also offer contextual constraints that can be used to tackle these challenges. In this paper, we explore context for human detection in dense crowds in the form of a locally-consistent scale prior which captures the similarity in scale in local neighborhoods and its smooth variation over the image. Using the scale and confidence of detections obtained from an underlying human detector, we infer scale and confidence priors using Markov Random Field. In an iterative mechanism, the confidences of detection hypotheses are modified to reflect consistency with the inferred priors, and the priors are updated based on the new detections. The final set of detections obtained are then reasoned for occlusion using Binary Integer Programming where overlaps and relations between parts of individuals are encoded as linear constraints. Both human detection and occlusion reasoning in proposed approach are solved with local neighbor-dependent constraints, thereby respecting the inter-dependence between individuals characteristic to dense crowd analysis. In addition, we propose a mechanism to detect different combinations of body parts without requiring annotations for individual combinations. We performed experiments on a new and extremely challenging dataset of dense crowd images showing marked improvement over the underlying human detector. PMID:26340254

  12. Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data

    CERN Document Server

    Hong, Chi-Yao; Duffield, Nick; Wang, Jia

    2012-01-01

    Operational network data, management data such as customer care call logs and equipment system logs, is a very important source of information for network operators to detect problems in their networks. Unfortunately, there is lack of efficient tools to automatically track and detect anomalous events on operational data, causing ISP operators to rely on manual inspection of this data. While anomaly detection has been widely studied in the context of network data, operational data presents several new challenges, including the volatility and sparseness of data, and the need to perform fast detection (complicating application of schemes that require offline processing or large/stable data sets to converge). To address these challenges, we propose Tiresias, an automated approach to locating anomalous events on hierarchical operational data. Tiresias leverages the hierarchical structure of operational data to identify high-impact aggregates (e.g., locations in the network, failure modes) likely to be associated w...

  13. 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.

  14. 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.

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

    International Nuclear Information System (INIS)

    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)

  16. 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.

  17. A Neural Network Approach for Misuse and Anomaly Intrusion Detection

    Institute of Scientific and Technical Information of China (English)

    YAO Yu; YU Ge; GAO Fu-xiang

    2005-01-01

    An MLP(Multi-Layer Perceptron)/Elman neural network is proposed in this paper, which realizes classification with memory of past events using the real-time classification of MLP and the memorial functionality of Elman. The system's sensitivity for the memory of past events can be easily reconfigured without retraining the whole network. This approach can be used for both misuse and anomaly detection system. The intrusion detection systems(IDSs) using the hybrid MLP/Elman neural network are evaluated by the intrusion detection evaluation data sponsored by U. S. Defense Advanced Research Projects Agency (DARPA). The results of experiment are presented in Receiver Operating Characteristic (ROC) curves. The capabilites of these IDSs to identify Deny of Service(DOS) and probing attacks are enhanced.

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

    Directory of Open Access Journals (Sweden)

    Tengfei Ma

    2012-01-01

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

  19. Log Summarization and Anomaly Detection for TroubleshootingDistributed Systems

    Energy Technology Data Exchange (ETDEWEB)

    Gunter, Dan; Tierney, Brian L.; Brown, Aaron; Swany, Martin; Bresnahan, John; Schopf, Jennifer M.

    2007-08-01

    Today's system monitoring tools are capable of detectingsystem failures such as host failures, OS errors, and network partitionsin near-real time. Unfortunately, the same cannot yet be said of theend-to-end distributed softwarestack. Any given action, for example,reliably transferring a directory of files, can involve a wide range ofcomplex and interrelated actions across multiple pieces of software:checking user certificates and permissions, getting details for allfiles, performing third-party transfers, understanding re-try policydecisions, etc. We present an infrastructure for troubleshooting complexmiddleware, a general purpose technique for configurable logsummarization, and an anomaly detection technique that works in near-realtime on running Grid middleware. We present results gathered using thisinfrastructure from instrumented Grid middleware and applications runningon the Emulab testbed. From these results, we analyze the effectivenessof several algorithms at accurately detecting a variety of performanceanomalies.

  20. System for Anomaly and Failure Detection (SAFD) system development

    Science.gov (United States)

    Oreilly, D.

    1992-07-01

    This task specified developing the hardware and software necessary to implement the System for Anomaly and Failure Detection (SAFD) algorithm, developed under Technology Test Bed (TTB) Task 21, on the TTB engine stand. This effort involved building two units; one unit to be installed in the Block II Space Shuttle Main Engine (SSME) Hardware Simulation Lab (HSL) at Marshall Space Flight Center (MSFC), and one unit to be installed at the TTB engine stand. Rocketdyne personnel from the HSL performed the task. The SAFD algorithm was developed as an improvement over the current redline system used in the Space Shuttle Main Engine Controller (SSMEC). Simulation tests and execution against previous hot fire tests demonstrated that the SAFD algorithm can detect engine failure as much as tens of seconds before the redline system recognized the failure. Although the current algorithm only operates during steady state conditions (engine not throttling), work is underway to expand the algorithm to work during transient condition.

  1. Anomaly Event Detection Method Based on Compressive Sensing and Iteration in Wireless Sensor Networks

    OpenAIRE

    Shihua Cao; Qihui Wang; Yaping Yuan; Junyang Yu

    2014-01-01

    Anomaly event detection is one of the research hotspots in wireless sensor networks. Aiming at the disadvantages of current detection solutions, a novel anomaly event detection algorithm based on compressed sensing and iteration is proposed. Firstly, a measured value can be sensed in each node, based on the compressed sensing. Then the problem of anomaly event detection is modeled as the minimization problem of weighted l1 norm, and OMP algorithm is adopted for solving the problem iteratively...

  2. DYNAMIC NETWORK ANOMALY INTRUSION DETECTION USING MODIFIED SOM

    Directory of Open Access Journals (Sweden)

    Aneetha.A.S

    2012-05-01

    Full Text Available Detection of unexpected and emerging new threats has become a necessity for secured internet communication with absolute data confidentiality, integrity and availability. Design and development of such a detection system shall not only be new, accurate and fast but also effective in a dynamic environment encompassing the surrounding network. In this paper, an algorithm is proposed for anomaly detection through modifying the Self – Organizing Map (SOM, by including new neighbourhood updating rules and learning rate dynamically in order to overcome the fixed architecture and random weight vector assignment. The algorithm initially starts with null network and grows with the original data space as initial weight vectors. New nodes are created using distance threshold parameter and their neighbourhood is identified using connection strength. Employing learning rule, the weight vector updation is carried out for neighbourhood nodes. Performance of the new algorithm is evaluated for using standard bench mark dataset. The result is compared with other neural network methods, shows 98% detection rate and 2% false alarm rate.

  3. A Dynamic Approach for Anomaly Detection in AODV

    Directory of Open Access Journals (Sweden)

    P.Vigneshwaran

    2011-02-01

    Full Text Available Mobile ad hoc networks (MANETs are relatively vuln erable to malicious network attacks, and therefore, security is a more significant issue than infrastru cture-based wire-less networks. In MANETs, it is di fficult to identify malicious hosts as the topology of the network dynamically changes. A malicious host can e asily interrupt a route for which it is one of the formin g nodes in the communication path. Since the topolo gy of a MANET dynamically changes, the mere use of a stat ic baseline profile is not efficient. We proposed a new anomaly-detection scheme based on a dynamic learnin g process that allows the training data to be updat ed at particular time intervals. Our dynamic learning process involves calculating the projection distanc es based on multidimensional statistics using weighted coefficients and a forgetting curve.

  4. Near-Real Time Anomaly Detection for Scientific Sensor Data

    Science.gov (United States)

    Gallegos, I.; Gates, A.; Tweedie, C. E.; goswami, S.; Jaimes, A.; Gamon, J. A.

    2011-12-01

    Verification (SDVe) prototype tool identified anomalies detected by the expert-specified data properties over the EC data. Scientists using DaProS and SDVe were able to detect environmental variability, instrument malfunctioning, and seasonal and diurnal variability in EC and hyperspectral datasets. The results of the experiment also yielded insights regarding the practices followed by scientists to specify data properties, and it exposed new data properties challenges and a potential method for capturing data quality confidence levels.

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

    International Nuclear Information System (INIS)

    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

  6. A Framework for an Adaptive Anomaly Detection System with Fuzzy Data Mining

    Institute of Scientific and Technical Information of China (English)

    GAO Xiang; WANG Min; ZHAO Rongchun

    2006-01-01

    In this paper, we present an adaptive anomaly detection framework that is applicable to network-based intrusion detection. Our framework employs fuzzy cluster algorithm to detect anomalies in an online, adaptive fashion without a priori knowledge of the underlying data. We evaluate our method by performing experiments over network records from the KDD CUP99 data set.

  7. Detection of motifs in anomalies from nuclear power plant data using data mining techniques

    International Nuclear Information System (INIS)

    Anomaly detection deals with the discovery of abnormal behaviour from the given data. In the recent times, there has been great research interest towards anomaly detection using data mining techniques. The reason being that in many real world applications, extraction of abnormalities is much more important than detection and analysis of normal behaviour. This is specifically significant in those applications wherein timely maintenance of anomalies is costly and very crucial to the application. In certain cases, it is also possible that there exist some pattern in the anomalies. In the present work, the focus is on detection of patterns in anomalies from Nuclear Power Plant (NPP) data. Further, an analysis has been done to identify the different types of patterns from the NPP data. These different types of patterns have been denoted as 'motifs' to signify the repetitive nature of various types of patterns in anomalies. Such analysis has been done for predictive maintenance in nuclear power plants. (author)

  8. Visibility Video Detection with Dark Channel Prior on Highway

    Directory of Open Access Journals (Sweden)

    Jiandong Zhao

    2016-01-01

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

  9. A Result Fusion based Distributed Anomaly Detection System for Android Smartphones

    Directory of Open Access Journals (Sweden)

    Zhizhong Wu

    2013-02-01

    Full Text Available In this paper we present an information fusion based distributed anomaly detection system for Android mobile phones. The proposed framework realizes a clientserver architecture, the client continuously extracts various features and transfers to the server, and the server’s major task is to detect anomaly using state-of-art detection algorithms implemented as anomaly detectors. Multiple distributed servers simultaneously analyzing the feature vector using different detectors and information fusion is used to fuse the results of detectors. We also propose a cycle-based statistical approach for smartphone anomaly detection as the smartphone users usual follow regular patterns due to their periodical patterns of lives. Empirical results suggest that the proposed framework and novel anomaly detection algorithm are effective in detecting malware on Android devices.

  10. Study of the anomalies in critical frequency of F2 layer prior to earthquakes in South American region

    Science.gov (United States)

    Ghosh, Soujan; Chakrabarti, Sandip Kumar; Sasmal, Sudipta; Midya, Subrata kumar

    2016-07-01

    Earthquake precursors study is very important. Earthquake preparation process starts almost 1-30 days before its occurrence. Several large earthquakes hit the west coast of South America after the year 2000. We consider five large earthquakes (mag > 6) and analyze temporal variation of Ionospheric parameters fifteen days before and fifteen days after main shocks. These parameters measured by the ground based station Jicamarca (lat 11.95 S, long 76.87 W) which lies at a distance within a radius of 1000km from the epicentre of these large earthquakes. In this paper, we define a parameter and named it as 'F-Parameter', containing critical frequency of F2 layer (f0F2) and virtual height of F layer (h'F) to examine the ionospheric variation during earthquake. The f0F2 values also revealed anomaly which has been considered as a supporting evidence for the observed correlation. We also check Dst, Kp, Ap index to confirm that these amomalies are due to seismic events only. The final results showed that the value of ionospheric parameter (F-Parameter) increased 12-3 days prior to earthquake. The increment is over +3σ from the normal variation of the parameter value.

  11. Real-time anomaly detection in full motion video

    Science.gov (United States)

    Konowicz, Glenn; Li, Jiang

    2012-06-01

    Improvement in sensor technology such as charge-coupled devices (CCD) as well as constant incremental improvements in storage space has enabled the recording and storage of video more prevalent and lower cost than ever before. However, the improvements in the ability to capture and store a wide array of video have required additional manpower to translate these raw data sources into useful information. We propose an algorithm for automatically detecting anomalous movement patterns within full motion video thus reducing the amount of human intervention required to make use of these new data sources. The proposed algorithm tracks all of the objects within a video sequence and attempts to cluster each object's trajectory into a database of existing trajectories. Objects are tracked by first differentiating them from a Gaussian background model and then tracked over subsequent frames based on a combination of size and color. Once an object is tracked over several frames, its trajectory is calculated and compared with other trajectories earlier in the video sequence. Anomalous trajectories are differentiated by their failure to cluster with other well-known movement patterns. Adding the proposed algorithm to an existing surveillance system could increase the likelihood of identifying an anomaly and allow for more efficient collection of intelligence data. Additionally, by operating in real-time, our algorithm allows for the reallocation of sensing equipment to those areas most likely to contain movement that is valuable for situational awareness.

  12. 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.

  13. Bayesian anomaly detection in heterogeneous media with applications to geophysical tomography

    Science.gov (United States)

    Simon, Martin

    2014-11-01

    In this paper, we consider the problem of detecting a parameterized anomaly in an isotropic, stationary and ergodic conductivity random field via electrical impedance tomography. A homogenization result for a stochastic forward problem built on the complete electrode model is derived, which serves as the basis for a two-stage numerical method in the framework of Bayesian inverse problems. The novelty of this method lies in the introduction of an enhanced error model accounting for the approximation errors that result from reducing the full forward model to a homogenized one. In the first stage, a MAP estimate for the reduced forward model equipped with the enhanced error model is computed. Then, in the second stage, a bootstrap prior based on the first stage results is defined and the resulting posterior distribution is sampled via Markov chain Monte Carlo. We provide the theoretical foundation of the proposed method, discuss different aspects of a numerical implementation and present numerical experiments to support our findings.

  14. 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.

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

    Directory of Open Access Journals (Sweden)

    Markus Goldstein

    Full Text Available 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.

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

    KAUST Repository

    Diaz Ledezma, F.

    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.

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

    Directory of Open Access Journals (Sweden)

    Yogendra Kumar JAIN

    2009-01-01

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

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

    OpenAIRE

    Haemwaan Sivaraks; Chotirat Ann Ratanamahatana

    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 fr...

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Yuchong Li

    2015-01-01

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

  1. Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

    Science.gov (United States)

    Zhou, Z.-G.; Tang, P.; Zhou, M.

    2016-06-01

    Anomaly regions in satellite images can reflect unexpected changes of land cover caused by flood, fire, landslide, etc. Detecting anomaly regions in satellite image time series is important for studying the dynamic processes of land cover changes as well as for disaster monitoring. Although several methods have been developed to detect land cover changes using satellite image time series, they are generally designed for detecting inter-annual or abrupt land cover changes, but are not focusing on detecting spatial-temporal changes in continuous images. In order to identify spatial-temporal dynamic processes of unexpected changes of land cover, this study proposes a method for detecting anomaly regions in each image of satellite image time series based on seasonal autocorrelation analysis. The method was validated with a case study to detect spatial-temporal processes of a severe flooding using Terra/MODIS image time series. Experiments demonstrated the advantages of the method that (1) it can effectively detect anomaly regions in each of satellite image time series, showing spatial-temporal varying process of anomaly regions, (2) it is flexible to meet some requirement (e.g., z-value or significance level) of detection accuracies with overall accuracy being up to 89% and precision above than 90%, and (3) it does not need time series smoothing and can detect anomaly regions in noisy satellite images with a high reliability.

  2. NADIR (Network Anomaly Detection and Intrusion Reporter): A prototype network intrusion detection system

    Energy Technology Data Exchange (ETDEWEB)

    Jackson, K.A.; DuBois, D.H.; Stallings, C.A.

    1990-01-01

    The Network Anomaly Detection and Intrusion Reporter (NADIR) is an expert system which is intended to provide real-time security auditing for intrusion and misuse detection at Los Alamos National Laboratory's Integrated Computing Network (ICN). It is based on three basic assumptions: that statistical analysis of computer system and user activities may be used to characterize normal system and user behavior, and that given the resulting statistical profiles, behavior which deviates beyond certain bounds can be detected, that expert system techniques can be applied to security auditing and intrusion detection, and that successful intrusion detection may take place while monitoring a limited set of network activities such as user authentication and access control, file movement and storage, and job scheduling. NADIR has been developed to employ these basic concepts while monitoring the audited activities of more than 8000 ICN users.

  3. Lunar magnetic anomalies detected by the Apollo subsatellite magnetometers

    Science.gov (United States)

    Hood, L. L.; Coleman, P. J., Jr.; 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 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. 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; individual anomaly sources have therefore not yet been identified. The mechanism of magnetization and the origin of the magnetizing field remain unresolved, but the uniformity with which the Reiner Gamma deposit is apparently magnetized, and the north-south depletion of magnetization intensity across a substantial portion of the far side, seem to require the existence of an ambient field, perhaps of global or larger extent.

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

    Data.gov (United States)

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

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

    Science.gov (United States)

    O’Hern, Corey S.; Shattuck, Mark D.; Ogle, Serenity; Forero, Adriana; Morrison, Juliet; Slayden, Richard; Katze, Michael G.

    2016-01-01

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

  6. Applying static code analysis to firewall policies for the purpose of anomaly detection

    OpenAIRE

    Zaliva, Vadim

    2011-01-01

    Treating modern firewall policy languages as imperative, special purpose programming languages, in this article we will try to apply static code analysis techniques for the purpose of anomaly detection. We will first abstract a policy in common firewall policy language into an intermediate language, and then we will try to apply anomaly detection algorithms to it. The contributions made by this work are: 1. An analysis of various control flow instructions in popular firewall policy languages ...

  7. Comparison of Ultrasound and MRI in Detecting Fetal Anomalies

    OpenAIRE

    R Abdi; H. Majidi

    2005-01-01

    Introduction & Background: Ultrasound (US) and MRI are considered complementary technologies, and MRI is utilized as an adjunct to US in the evaluation of fetal anomalies. Overall ultrasound remains the prime mo-dality for evaluating disorders of the fetus and pregnancy. Ultrasound continues to have several obvious advan-tages over MRI. It is safe and relatively inexpensive and is widely available It also allows for real-time imaging. However, US does have important limitations. First, it...

  8. 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.

  9. Software Tool Support to Specify and Verify Scientific Sensor Data Properties to Improve Anomaly Detection

    Science.gov (United States)

    Gallegos, I.; Gates, A. Q.; Tweedie, C.; Cybershare

    2010-12-01

    Advancements in scientific sensor data acquisition technologies, such as wireless sensor networks and robotic trams equipped with sensors, are increasing the amount of data being collected at field sites . This elevates the challenges of verifying the quality of streamed data and monitoring the correct operation of the instrumentation. Without the ability to evaluate the data collection process at near real-time, scientists can lose valuable time and data. In addition, scientists have to rely on their knowledge and experience in the field to evaluate data quality. Such knowledge is rarely shared or reused by other scientists mostly because of the lack of a well-defined methodology and tool support. Numerous scientific projects address anomaly detection, mostly as part of the verification system’s source code; however, anomaly detection properties, which often are embedded or hard-coded in the source code, are difficult to refine. In addition, a software developer is required to modify the source code every time a new anomaly detection property or a modification to an existing property is needed. This poster describes the tool support that has been developed, based on software engineering techniques, to address these challenges. The overall tool support allows scientists to specify and reuse anomaly detection properties generated using the specification tool and to use the specified properties to conduct automated anomaly detection at near-real time. The anomaly-detection mechanism is independent of the system used to collect the sensor data. With guidance provided by a classification and categorization of anomaly-detection properties, the user specifies properties on scientific sensor data. The properties, which can be associated with particular field sites or instrumentation, document knowledge about data anomalies that otherwise would have limited availability to the scientific community.

  10. Lunar magnetic anomalies detected by the Apollo substatellite magnetometers

    Science.gov (United States)

    Hood, L.L.; Coleman, P.J., Jr.; 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

  11. 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.

  12. A hyperspectral imagery anomaly detection algorithm based on local three-dimensional orthogonal subspace projection

    Science.gov (United States)

    Zhang, Xing; Wen, Gongjian

    2015-10-01

    Anomaly detection (AD) becomes increasingly important in hyperspectral imagery analysis with many practical applications. Local orthogonal subspace projection (LOSP) detector is a popular anomaly detector which exploits local endmembers/eigenvectors around the pixel under test (PUT) to construct background subspace. However, this subspace only takes advantage of the spectral information, but the spatial correlat ion of the background clutter is neglected, which leads to the anomaly detection result sensitive to the accuracy of the estimated subspace. In this paper, a local three dimensional orthogonal subspace projection (3D-LOSP) algorithm is proposed. Firstly, under the jointly use of both spectral and spatial information, three directional background subspaces are created along the image height direction, the image width direction and the spectral direction, respectively. Then, the three corresponding orthogonal subspaces are calculated. After that, each vector along three direction of the local cube is projected onto the corresponding orthogonal subspace. Finally, a composite score is given through the three direction operators. In 3D-LOSP, the anomalies are redefined as the target not only spectrally different to the background, but also spatially distinct. Thanks to the addition of the spatial information, the robustness of the anomaly detection result has been improved greatly by the proposed 3D-LOSP algorithm. It is noteworthy that the proposed algorithm is an expansion of LOSP and this ideology can inspire many other spectral-based anomaly detection methods. Experiments with real hyperspectral images have proved the stability of the detection result.

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

    NARCIS (Netherlands)

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

    2014-01-01

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

  14. The Anomaly Detection in SMTP Traffic Based on Leaky Integrate-and-Fire Model

    Institute of Scientific and Technical Information of China (English)

    LUO Hao; FANG Bin-xing; YUN Xiao-chun

    2006-01-01

    This paper investigated an effective and robust mechanism for detecting simple mail transfer protocol(SMTP) traffic anomaly. The detection method cumulates the deviation of current delivering status from history behavior based on a weighted sum method called the leaky integrate-and-fire model to detect anomaly. The simplicity of the detection method is that the method need not store history profile and low computation overhead, which makes the detection method itself immunes to attacks. The performance is investigated in terms of detection probability, the false alarm ratio, and the detection delay. The results show that leaky integrate-and-fire method is quite effective at detecting constant intensity attacks and increasing intensity attacks. Compared with the non-parametric cumulative sum method, the evaluation results show that the proposed detection method has shorter detection latency and higher detection probability.

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

    Science.gov (United States)

    Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

    2016-01-01

    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. PMID:27136561

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

    Science.gov (United States)

    Liu, Liansheng; Liu, Datong; Zhang, Yujie; Peng, Yu

    2016-04-29

    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.

  17. 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.

  18. Extending TOPS: A Prototype MODIS Anomaly Detection Architecture

    Science.gov (United States)

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

    2008-12-01

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

  19. 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.

  20. Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery

    OpenAIRE

    Ramakalavathi Marapareddy; James V. Aanstoos; Nicolas H. Younan

    2016-01-01

    Fully polarimetric Synthetic Aperture Radar (polSAR) data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR) to screen levees for anomalies. SAR technology, due to its high spatial resolution ...

  1. A Statistical Detection of an Anomaly from a Few Noisy Tomographic Projections

    Directory of Open Access Journals (Sweden)

    Fillatre Lionel

    2005-01-01

    Full Text Available The problem of detecting an anomaly/target from a very limited number of noisy tomographic projections is addressed from the statistical point of view. The imaged object is composed of an environment, considered as a nuisance parameter, with a possibly hidden anomaly/target. The GLR test is used to solve the problem. When the projection linearly depends on the nuisance parameters, the GLR test coincides with an optimal statistical invariant test.

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

    OpenAIRE

    HARPER, Lorie M.; Sutton, Amelia L. M.; LONGMAN, Ryan E.; Odibo, Anthony O.

    2014-01-01

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

  3. Unfolding the procedure of characterizing recorded ultra low frequency, kHZ and MHz electromagetic anomalies prior to the L'Aquila earthquake as pre-seismic ones – Part 1

    Directory of Open Access Journals (Sweden)

    K. Eftaxias

    2009-11-01

    Full Text Available Ultra low frequency, kHz and MHz electromagnetic (EM anomalies were recorded prior to the L'Aquila catastrophic earthquake that occurred on 6 April 2009. The main aims of this paper are threefold: (i suggest a procedure for the designation of detected EM anomalies as seismogenic ones. We do not expect to be able to provide a succinct and solid definition of a pre-seismic EM emission. Instead, we aim, through a multidisciplinary analysis, to provide the elements of a definition. (ii Link the detected MHz and kHz EM anomalies with equivalent last stages of the earthquake preparation process. (iii Put forward physically meaningful arguments for quantifying the time to global failure and the identification of distinguishing features beyond which the evolution towards global failure becomes irreversible. We emphasize that we try to specify not only whether a single EM anomaly is pre-seismic in itself, but also whether a combination of kHz, MHz, and ULF EM anomalies can be characterized as pre-seismic. The entire procedure unfolds in two consecutive parts. Here in Part 1 we focus on the detected kHz EM anomaly, which play a crucial role in our approach to these challenges. We try to discriminate clearly this anomaly from background noise. For this purpose, we analyze the data successively in terms of various concepts of entropy and information theory including, Shannon n-block entropy, conditional entropy, entropy of the source, Kolmogorov-Sinai entropy, T-entropy, approximate entropy, fractal spectral analysis, R/S analysis and detrended fluctuation analysis. We argue that this analysis reliably distinguishes the candidate kHz EM precursor from the noise: the launch of anomalies from the normal state is combined by a simultaneous appearance of a significantly higher level of organization, and persistency. This finding indicates that the process in which the anomalies are rooted is governed by a positive feedback mechanism. This

  4. Handling Web and Database Requests Using Fuzzy Rules for Anomaly Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Selvamani Kadirvelu

    2011-01-01

    Full Text Available Problem statement: It is necessary to propose suitable detection and prevention mechanisms to provide security for the information contents used by the web application. Many prevention mechanisms which are currently available are not able to classify anomalous, random and normal request. This leads to the problem of false positives which is classifying a normal request as anomalous and denying access to information. Approach: In this study, we propose an anomaly detection system which will act as a Web based anomaly detector called intelligent SQL Anomaly detector and it uses decision tree algorithm and a feedback mechanism for effective classification. Results: This newly proposed and implemented technique has higher probability for reducing false positives which are the drawbacks in the earlier systems. Hence, our system proves that it detects all anomalies and shows better results when compared with the existing system. Conclusion: A refreshing technique to improve the detection rate of web-based intrusion detection systems by serially framing a web request anomaly detector using fuzzy rules has been proposed and implemented and this system proves to be more efficient then the existing earlier system when compared with the obtained results.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Todd Vollmer; Milos Manic

    2012-08-01

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

  6. Anomaly Event Detection Method Based on Compressive Sensing and Iteration in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Shihua Cao

    2014-03-01

    Full Text Available Anomaly event detection is one of the research hotspots in wireless sensor networks. Aiming at the disadvantages of current detection solutions, a novel anomaly event detection algorithm based on compressed sensing and iteration is proposed. Firstly, a measured value can be sensed in each node, based on the compressed sensing. Then the problem of anomaly event detection is modeled as the minimization problem of weighted l1 norm, and OMP algorithm is adopted for solving the problem iteratively. And then the result of problem solving is judged according to detection functions. Finally, in the light of the judgment results, the weight value is updated for beginning a new round iteration. The loop won't stop until all the anomaly events are detected in wireless sensor networks. Simulation experimental results show the proposed algorithm has a better omission detection rate and false alarm rate in different noisy environments. In addition, the detection quality of this algorithm is higher than those of the traditional ones.

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

    NARCIS (Netherlands)

    Bolzoni, Damiano; Zambon, Emmanuele; Etalle, Sandro; Hartel, Pieter

    2006-01-01

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

  8. 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.

  9. Comparison of Ultrasound and MRI in Detecting Fetal Anomalies

    Directory of Open Access Journals (Sweden)

    R. Abdi

    2005-08-01

    Full Text Available Introduction & Background: Ultrasound (US and MRI are considered complementary technologies, and MRI is utilized as an adjunct to US in the evaluation of fetal anomalies. Overall ultrasound remains the prime mo-dality for evaluating disorders of the fetus and pregnancy. Ultrasound continues to have several obvious advan-tages over MRI. It is safe and relatively inexpensive and is widely available It also allows for real-time imaging. However, US does have important limitations. First, it is uniquely operator-and interpreter-dependent. In ad-dition, compared to MRI, US provides a smaller field-of-view, and the resolution of US images is restricted by penetration through soft tissues and bone. Thus, the sensitivity of US in evaluating the fetus is reduced in obese patients and in women whose pregnancies are complicated by low amniotic fluid volume. There is a growing body of literature on the use of MRI and has documented its usefulness in confirming or expanding upon US findings. On the contrary, MRI visualization of the fetus is not significantly limited by maternal obe-sity, fetal position, or oligohydramnios, and visualization of the brain is not restricted by the ossified skull. It provides superior soft-tissue contrast resolution and the ability to distinguish individual structures such as lung, liver, kidney, bowel, and gray and white matter. Patients & Methods: In this study, patients in the second and third trimesters of pregnancy were recruited on the basis of abnormal fetal US results within 2 days of MR imaging by another radiologist. Results: In some cases such as anencephaly which is associated with polyhydraminous or in multicystic dys-plastic kidney disease, MRI added no more information to ultrasonography; but in the following cases MRI had more data. In a fetus with bilateral hydronephrosis, MRI could differentiate PUV from UPJ stenosis by visualizing distention of the ureters. MRI allowed better depiction of complex anomalies

  10. Least Square Support Vector Machine for Detection of - Ionospheric Anomalies Associated with the Powerful Nepal Earthquake (Mw = 7.5) of 25 April 2015

    Science.gov (United States)

    Akhoondzadeh, M.

    2016-06-01

    Due to the irrepalable devastations of strong earthquakes, accurate anomaly detection in time series of different precursors for creating a trustworthy early warning system has brought new challenges. In this paper the predictability of Least Square Support Vector Machine (LSSVM) has been investigated by forecasting the GPS-TEC (Total Electron Content) variations around the time and location of Nepal earthquake. In 77 km NW of Kathmandu in Nepal (28.147° N, 84.708° E, depth = 15.0 km) a powerful earthquake of Mw = 7.8 took place at 06:11:26 UTC on April 25, 2015. For comparing purpose, other two methods including Median and ANN (Artificial Neural Network) have been implemented. All implemented algorithms indicate on striking TEC anomalies 2 days prior to the main shock. Results reveal that LSSVM method is promising for TEC sesimo-ionospheric anomalies detection.

  11. Revisiting anomaly-based network intrusion detection systems

    NARCIS (Netherlands)

    Bolzoni, Damiano

    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

  12. Anomaly Detection System Based on Principal Component Analysis and Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    LI Zhanchun; LI Zhitang; LIU Bin

    2006-01-01

    This article presents an anomaly detection system based on principal component analysis (PCA) and support vector machine (SVM). The system first creates a profile defining a normal behavior by frequency-based scheme, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is normal or anomaly. In order to avoid overfitting and reduce the computational burden, normal behavior principal features are extracted by the PCA method. SVM is used to distinguish normal or anomaly for user behavior after training procedure has been completed by learning. In the experiments for performance evaluation the system achieved a correct detection rate equal to 92.2% and a false detection rate equal to 2.8%.

  13. 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...

  14. A program to compute magnetic anomaly detection probabilities

    OpenAIRE

    Forrest, R. N.

    1988-01-01

    Approved for public release, distribution unlimited This report was prepared in conjunction with research conducted for the Chief of Naval Operations and funded by the Naval Postgraduate School Second Revision The report contains user instructions, a listing and documentation for a microcomputer BASIC program that can be used to compute an estimate of the probability that a magnetic anamoly detection (MAD) system such as the AN/ASQ-81 will detect a submarine during an encounter. (rh)

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

    Directory of Open Access Journals (Sweden)

    Haemwaan Sivaraks

    2015-01-01

    Full Text Available 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.

  16. Improvements in the method of radiation anomaly detection by spectral comparison ratios.

    Science.gov (United States)

    Pfund, D M; Anderson, K K; Detwiler, R S; Jarman, K D; McDonald, B S; Milbrath, B D; Myjak, M J; Paradis, N C; Robinson, S M; Woodring, M L

    2016-04-01

    We present a new procedure for configuring the Nuisance-rejection Spectral Comparison Ratio Anomaly Detection (N-SCRAD) method. The procedure minimizes detectable count rates of source spectra at a specified false positive rate using simulated annealing. We also present a new method for correcting the estimates of background variability used in N-SCRAD to current conditions of the total count rate. The correction lowers detection thresholds for a specified false positive rate, enabling greater sensitivity to targets. PMID:26807839

  17. An ECG T-wave Anomalies Detection Using a Lightweight Classification Model for Wireless Body Sensors

    OpenAIRE

    Hadjem, Medina; Naït-Abdesselam, Farid

    2015-01-01

    International audience Various wearable devices are foreseen to be the key components in the future for vital signs monitoring as they offer a non-invasive, remote and real-time medical monitoring means. Among those, Wireless Body Sensors (WBS) for cardiac monitoring are of prominent help to early detect cardioVascular Diseases (CVD) by analyzing 24/24 and 7/7 collected cardiac data. Today, most of these WBS systems for CVD detection, includeonly limited automatic anomalies detection, part...

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

    OpenAIRE

    LAHEEB MOHAMMAD IBRAHIM

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Gregório M.F. de Camargo

    2015-02-01

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

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

    Science.gov (United States)

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

    2015-02-01

    Indicine cattle have lower reproductive performance in comparison to taurine. A chromosomal anomaly characterized by the presence Y markers in females was reported and associated with infertility in cattle. The aim of this study was to investigate the occurrence of the anomaly in Brahman cows. Brahman cows (n = 929) were genotyped for a Y chromosome specific region using real time-PCR. Only six out of 929 cows had the anomaly (0.6%). The anomaly frequency was much lower in Brahman cows than in the crossbred population, in which it was first detected. It also seems that the anomaly doesn't affect pregnancy in the population. Due to the low frequency, association analyses couldn't be executed. Further, SNP signal of the pseudoautosomal boundary region of the Y chromosome was investigated using HD SNP chip. Pooled DNA of "non-pregnant" and "pregnant" cows were compared and no difference in SNP allele frequency was observed. Results suggest that the anomaly had a very low frequency in this Australian Brahman population and had no effect on reproduction. Further studies comparing pregnant cows and cows that failed to conceive should be executed after better assembly and annotation of the Y chromosome in cattle. PMID:25750859

  1. Quality Control of Temperature and Salinity from CTD based on Anomaly Detection

    CERN Document Server

    Castelão, Guilherme P

    2015-01-01

    The CTD is a set of sensors used by oceanographers to measure fundamental hydrographic properties of the oceans. It is characterized by a high precision product, only achieved if a quality control procedure identifies and removes the bad samples. Such procedure has been traditionally done by a sequence of independent tests that minimize false negatives. It is here proposed a novel approach to identify the bad samples as anomalies in respect to the typical behavior of good data. Several tests are combined into a single multidimensional evaluation to provide a more flexible classification criterion. The traditional approach is reproduced with an error of 0.04%, otherwise, the Anomaly Detection technique surpasses the reference if calibrated by visual inspection. CoTeDe is a Python package developed to apply the traditional and the Anomaly Detection quality control of temperature and salinity data from CTD, and can be extended to XBT, ARGO and other sensors.

  2. Critical features in electromagnetic anomalies detected prior to the L'Aquila earthquake

    CERN Document Server

    Contoyiannis, Y F; Kopanas, J; Antonopoulos, G; Contoyianni, L; Eftaxias, K

    2009-01-01

    Electromagnetic (EM) emissions in a wide frequency spectrum ranging from kHz to MHz are produced by opening cracks, which can be considered as the so-called precursors of general fracture. We emphasize that the MHz radiation appears earlier than the kHz in both laboratory and geophysical scale. An important challenge in this field of research is to distinguish characteristic epochs in the evolution of precursory EM activity and identify them with the equivalent last stages in the earthquake (EQ) preparation process. Recently, we proposed the following two epochs/stages model: (i) The second epoch, which includes the finally emerged strong impulsive kHz EM emission is due to the fracture of the high strength large asperities that are distributed along the activated fault sustaining the system. (ii) The first epoch, which includes the initially emerged MHz EM radiation is thought to be due to the fracture of a highly heterogeneous system that surrounds the family of asperities. A catastrophic EQ of magnitude Mw...

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

    Science.gov (United States)

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

    2015-10-01

    With the development of spectral imaging technology, hyperspectral anomaly detection is getting more and more widely used in remote sensing imagery processing. The traditional RX anomaly detection algorithm neglects spatial correlation of images. Besides, it does not validly reduce the data dimension, which costs too much processing time and shows low validity on hyperspectral data. The hyperspectral images follow Gauss-Markov Random Field (GMRF) in space and spectral dimensions. The inverse matrix of covariance matrix is able to be directly calculated by building the Gauss-Markov parameters, which avoids the huge calculation of hyperspectral data. This paper proposes an improved RX anomaly detection algorithm based on three-dimensional GMRF. The hyperspectral imagery data is simulated with GMRF model, and the GMRF parameters are estimated with the Approximated Maximum Likelihood method. The detection operator is constructed with GMRF estimation parameters. The detecting pixel is considered as the centre in a local optimization window, which calls GMRF detecting window. The abnormal degree is calculated with mean vector and covariance inverse matrix, and the mean vector and covariance inverse matrix are calculated within the window. The image is detected pixel by pixel with the moving of GMRF window. The traditional RX detection algorithm, the regional hypothesis detection algorithm based on GMRF and the algorithm proposed in this paper are simulated with AVIRIS hyperspectral data. Simulation results show that the proposed anomaly detection method is able to improve the detection efficiency and reduce false alarm rate. We get the operation time statistics of the three algorithms in the same computer environment. The results show that the proposed algorithm improves the operation time by 45.2%, which shows good computing efficiency. PMID:26904830

  4. 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.

  5. Adaptive Kalman filtering for anomaly detection in software appliances

    OpenAIRE

    Knorn, Florian; Leith, Douglas J.

    2008-01-01

    Availability and reliability are often important features of key software appliances such as firewalls, web servers, etc. In this paper we seek to go beyond the simple heartbeat monitoring that is widely used for failover control. We do this by integrating more fine grained measurements that are readily available on most platforms to detect possible faults or the onset of failures. In particular, we evaluate the use of adaptive Kalman Filtering for automated CPU usage prediction that...

  6. 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.

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

    International Nuclear Information System (INIS)

    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

  9. A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior

    NARCIS (Netherlands)

    Seraj, Fatjon; Zhang, Kui; Türkes, Okan; Meratnia, Nirvana; Havinga, Paul J.M.

    2015-01-01

    This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach

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

    Science.gov (United States)

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

    2004-01-01

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

  11. Anomaly Detection Using Power Signature of Consumer Electrical Devices

    Directory of Open Access Journals (Sweden)

    CERNAZANU-GLAVAN, C.

    2015-02-01

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

  12. Detection of elastic and electric conductivity anomalies in Potassium Sulphamate single crystal

    Energy Technology Data Exchange (ETDEWEB)

    Varughese, George, E-mail: gvushakoppara@yahoo.co.i [Department of Physics, Catholicate College, Pathanamthitta, Kerala 689645 (India); Santhosh Kumar, A. [SPAP, Mahatma Gandhi University, Kottayam, Kerala 686 560 (India); Louis, Godfrey [Department of Physics, Cochin University of Science and Technology, Cochin 22 (India)

    2010-04-01

    Elastic anomalies in Potassium Sulphamate, (KNH{sub 2}SO{sub 3}), above room temperature were detected from temperature variation of elastic constants measured by ultrasonic Pulse Echo Overlap technique. Potassium Sulphamate has been reported to be a ferroelectric and piezo electric material. The elastic constants C{sub 11}, C{sub 44}, C{sub 55} and C{sub 66} have exhibited weak anomalies around 350 K. The DC conductivity measurement along a, b, and c axes also supports this conclusion.

  13. Cluster analysis for anomaly detection in accounting data : an audit approach

    OpenAIRE

    Thiprungsri, Sutapat; Vasarhelyi, Miklos A.

    2011-01-01

    This study examines the application of cluster analysis in the accounting domain, particularly discrepancy detection in audit. Cluster analysis groups data so that points within a single group or cluster are similar to one another and distinct from points in other clusters. Clustering has been shown to be a good candidate for anomaly detection. The purpose of this study is to examine the use of clustering technology to automate fraud filtering during an audit. We use cluster analysis to help ...

  14. 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.

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

    Science.gov (United States)

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

    2005-01-01

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

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

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

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

  17. Genetic algorithm for TEC seismo-ionospheric anomalies detection around the time of the Solomon (Mw = 8.0) earthquake of 06 February 2013

    Science.gov (United States)

    Akhoondzadeh, M.

    2013-08-01

    On 6 February 2013, at 12:12:27 local time (01:12:27 UTC) a seismic event registering Mw 8.0 struck the Solomon Islands, located at the boundaries of the Australian and Pacific tectonic plates. Time series prediction is an important and widely interesting topic in the research of earthquake precursors. This paper describes a new computational intelligence approach to detect the unusual variations of the total electron content (TEC) seismo-ionospheric anomalies induced by the powerful Solomon earthquake using genetic algorithm (GA). The GA detected a considerable number of anomalous occurrences on earthquake day and also 7 and 8 days prior to the earthquake in a period of high geomagnetic activities. In this study, also the detected TEC anomalies using the proposed method are compared to the results dealing with the observed TEC anomalies by applying the mean, median, wavelet, Kalman filter, ARIMA, neural network and support vector machine methods. The accordance in the final results of all eight methods is a convincing indication for the efficiency of the GA method. It indicates that GA can be an appropriate non-parametric tool for anomaly detection in a non linear time series showing the seismo-ionospheric precursors variations.

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

    Science.gov (United States)

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

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

  19. Anomaly Detection in Gamma-Ray Vehicle Spectra with Principal Components Analysis and Mahalanobis Distances

    International Nuclear Information System (INIS)

    The goal of primary radiation monitoring in support of routine screening and emergency response is to detect characteristics in vehicle radiation signatures that indicate the presence of potential threats. Two conceptual approaches to analyzing gamma-ray spectra for threat detection are isotope identification and anomaly detection. While isotope identification is the time-honored method, an emerging technique is anomaly detection that uses benign vehicle gamma ray signatures to define an expectation of the radiation signature for vehicles that do not pose a threat. Newly acquired spectra are then compared to this expectation using statistical criteria that reflect acceptable false alarm rates and probabilities of detection. The gamma-ray spectra analyzed here were collected at a U.S. land Port of Entry (POE) using a NaI-based radiation portal monitor (RPM). The raw data were analyzed to develop a benign vehicle expectation by decimating the original pulse-height channels to 35 energy bins, extracting composite variables via principal components analysis (PCA), and estimating statistically weighted distances from the mean vehicle spectrum with the mahalanobis distance (MD) metric. This paper reviews the methods used to establish the anomaly identification criteria and presents a systematic analysis of the response of the combined PCA and MD algorithm to modeled mono-energetic gamma-ray sources

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

    Energy Technology Data Exchange (ETDEWEB)

    Whiteson, R.; Hench, K.; Yarbro, T. [Los Alamos National Lab., NM (United States); Baumgart, C. [Dept. of Energy, Albuquerque, NM (United States). Kansas City Plant

    1998-12-31

    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.

  1. Incorporating Prior Shape into Geometric Active Contours for Face Contour Detection

    Institute of Scientific and Technical Information of China (English)

    HUANGFuzhen; SUJianbo; XIYugeng

    2004-01-01

    In this paper a new method that incorporates prior shape information into geometric active contours for face contour detection is proposed. As in general a human face can be treated as an ellipse with a little shape variation, the prior face shape is represented as an elliptical curve. By combining the prior face shape with the powerful geometric active model proposed by Chan and Vese, the improved geometric active model can retain all the advantage of the Chan-Vese model and can detect face contours in images with complex backgrounds accurately even if the image is noisy. Moreover, by implementing the new model in a variational level set framework, automatic topological changes of the model can be achieved naturally and the transformation parameters that map the face boundary to the prior shape can be roughly estimated simultaneously. The experimental results show our procedure to be eiTicient.

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

    OpenAIRE

    Shyh-Chin Lan; Teng-To Yu; Cheinway Hwang; and Ricky Kao

    2011-01-01

    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 betwe...

  3. Boosting multi-features with prior knowledge for mini unmanned helicopter landmark detection

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Without sufficient real training data, the data driven classification algorithms based on boosting method cannot solely be utilized to applications such as the mini unmanned helicopter landmark image detection. In this paper, we propose an approach which uses a boosting algorithm with the prior knowledge for the mini unmanned helicopter landmark image detection. The stage forward stagewise additive model of boosting is analyzed, and the approach how to combine it with the prior knowledge model is presented. The approach is then applied to landmark image detection, where the multi-features are boosted to solve a series of problems, such as rotation, noises affected, etc. Results of real flight experiments demonstrate that for small training examples the boosted learning system using prior knowledge is dramatically better than the one driven by data only.

  4. A self-adaptive negative selection algorithm used for anomaly detection

    Institute of Scientific and Technical Information of China (English)

    Jinquan Zeng; Xiaojie Liu; Tao Li; Caiming Liu; Lingxi Peng; Feixian Sun

    2009-01-01

    A novel negative selection algorithm (NSA), which is referred to as ANSA, is presented. In many actual anomaly detection systems, the training data are just partially composed of the normal elements, and the seif/nonself space often varies over time. Therefore, anom-aly detection system has to build the profile of the system based on a part of self elements and adjust itself to adapt those variables. However, previous NSAs need a large number of self elements to build the profile of the system, and lack adaptability. In order to over-come these limitations, the proposed approach uses a novel technique to adjust the self radius and evolve the nonself-covering detectors to build an appropriate profile of the system. To determine the performance of the approach, the experiments with the well-known data-set were performed. Results exhibited that our proposed approach outperforms the previous techniques.

  5. GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

    Energy Technology Data Exchange (ETDEWEB)

    Harshaw, Chris R [ORNL; Bridges, Robert A [ORNL; Iannacone, Michael D [ORNL; Reed, Joel W [ORNL; Goodall, John R [ORNL

    2016-01-01

    This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \\textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\\% at the time-interval level, and 0.05\\% at the IP-level with 100\\% true positive rates at both.

  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. Shape anomaly detection under strong measurement noise: An analytical approach to adaptive thresholding

    Science.gov (United States)

    Krasichkov, Alexander S.; Grigoriev, Eugene B.; Bogachev, Mikhail I.; Nifontov, Eugene M.

    2015-10-01

    We suggest an analytical approach to the adaptive thresholding in a shape anomaly detection problem. We find an analytical expression for the distribution of the cosine similarity score between a reference shape and an observational shape hindered by strong measurement noise that depends solely on the noise level and is independent of the particular shape analyzed. The analytical treatment is also confirmed by computer simulations and shows nearly perfect agreement. Using this analytical solution, we suggest an improved shape anomaly detection approach based on adaptive thresholding. We validate the noise robustness of our approach using typical shapes of normal and pathological electrocardiogram cycles hindered by additive white noise. We show explicitly that under high noise levels our approach considerably outperforms the conventional tactic that does not take into account variations in the noise level.

  8. A new morphological anomaly detection algorithm for hyperspectral images and its GPU implementation

    Science.gov (United States)

    Paz, Abel; Plaza, Antonio

    2011-10-01

    Anomaly detection is considered a very important task for hyperspectral data exploitation. It is now routinely applied in many application domains, including defence and intelligence, public safety, precision agriculture, geology, or forestry. Many of these applications require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. However, with the recent explosion in the amount and dimensionality of hyperspectral imagery, this problem calls for the incorporation of parallel computing techniques. In the past, clusters of computers have offered an attractive solution for fast anomaly detection in hyperspectral data sets already transmitted to Earth. However, these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power integrated components are essential to reduce mission payload and obtain analysis results in (near) real-time, i.e., at the same time as the data is collected by the sensor. An exciting new development in the field of commodity computing is the emergence of commodity graphics processing units (GPUs), which can now bridge the gap towards on-board processing of remotely sensed hyperspectral data. In this paper, we develop a new morphological algorithm for anomaly detection in hyperspectral images along with an efficient GPU implementation of the algorithm. The algorithm is implemented on latest-generation GPU architectures, and evaluated with regards to other anomaly detection algorithms using hyperspectral data collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in the WTC complex. The proposed GPU implementation achieves real-time performance in the considered case study.

  9. Application of Distributed Optical Fiber Sensing Technology in the Anomaly Detection of Shaft Lining in Grouting

    OpenAIRE

    Chunde Piao; Jun Yuan; Bin Shi; Haijun Lu; Guangqing Wei; Chunsheng Gu

    2015-01-01

    The rupture of the shaft lining caused by grouting has seriously undermined the safety in coal mining. Based on BOTDR distributed optical fiber sensing technology, this paper studied the layout method of optical fiber sensors and the anomaly detection method of the deformation and obtained the evolution law of shaft deformation triggered by grouting. The research results showed that the bonding problem of optical fiber sensors in damp environment could be effectively solved, by applying the b...

  10. Fetal Central Nervous System Anomalies Detected by Magnetic Resonance Imaging: A Two-Year Experience

    Directory of Open Access Journals (Sweden)

    Sepideh Sefidbakht

    2016-06-01

    Full Text Available Background Magnetic resonance imaging (MRI is gradually becoming more common for thorough visualization of the fetus than ultrasound (US, especially for neurological anomalies, which are the most common indications for fetal MRI and are a matter of concern for both families and society. Objectives We investigated fetal MRIs carried out in our center for frequency of central nervous system anomalies. This is the first such report in southern Iran. Materials and Methods One hundred and seven (107 pregnant women with suspicious fetal anomalies in prenatal ultrasound entered a cross-sectional retrospective study from 2011 to 2013. A 1.5 T Siemens Avanto scanner was employed for sequences, including T2 HASTE and Trufisp images in axial, coronal, and sagittal planes to mother’s body, T2 HASTE and Trufisp relative to the specific fetal body part being evaluated, and T1 flash images in at least one plane based on clinical indication. We investigated any abnormality in the central nervous system and performed descriptive analysis to achieve index of frequency. Results Mean gestational age ± standard deviation (SD for fetuses was 25.54 ± 5.22 weeks, and mean maternal age ± SD was 28.38 ± 5.80 years Eighty out of 107 (74.7% patients who were referred with initial impression of borderline ventriculomegaly. A total of 18 out of 107 (16.82% patients were found to have fetuses with CNS anomalies and the remainder were neurologically normal. Detected anomalies were as follow: 3 (16.6% fetuses each had the Dandy-Walker variant and Arnold-Chiari II (with myelomeningocele. Complete agenesis of corpus callosum, partial agenesis of corpus callosum, and aqueductal stenosis were each seen in 2 (11.1% fetuses. Arnold-Chiari II without myelomeningocele, anterior spina bifida associated with neurenteric cyst, arachnoid cyst, lissencephaly, and isolated enlarged cisterna magna each presented in one (5.5% fetus. One fetus had concomitant schizencephaly and complete

  11. A Comparative Study of Unsupervised Anomaly Detection Techniques Using Honeypot Data

    Science.gov (United States)

    Song, Jungsuk; Takakura, Hiroki; Okabe, Yasuo; Inoue, Daisuke; Eto, Masashi; Nakao, Koji

    Intrusion Detection Systems (IDS) have been received considerable attention among the network security researchers as one of the most promising countermeasures to defend our crucial computer systems or networks against attackers on the Internet. Over the past few years, many machine learning techniques have been applied to IDSs so as to improve their performance and to construct them with low cost and effort. Especially, unsupervised anomaly detection techniques have a significant advantage in their capability to identify unforeseen attacks, i.e., 0-day attacks, and to build intrusion detection models without any labeled (i.e., pre-classified) training data in an automated manner. In this paper, we conduct a set of experiments to evaluate and analyze performance of the major unsupervised anomaly detection techniques using real traffic data which are obtained at our honeypots deployed inside and outside of the campus network of Kyoto University, and using various evaluation criteria, i.e., performance evaluation by similarity measurements and the size of training data, overall performance, detection ability for unknown attacks, and time complexity. Our experimental results give some practical and useful guidelines to IDS researchers and operators, so that they can acquire insight to apply these techniques to the area of intrusion detection, and devise more effective intrusion detection models.

  12. Accurate Anomaly Detection using Adaptive Monitoring and Fast Switching in SDN

    Directory of Open Access Journals (Sweden)

    Gagandeep Garg

    2015-10-01

    Full Text Available —Software defined networking (SDN is rapidly evolving technology which provides a suitable environment for easily applying efficient monitoring policies on the networks. SDN provides a centralized control of the whole network from which monitoring of network traffic and resources can be done with ease. SDN promises to drastically simplify network monitoring and management and also enable rapid innovation of networks through network programmability. SDN architecture separates the control of the network from the forwarding devices. With the higher innovation provided by the SDN, security threats at open interfaces of SDN also increases significantly as an attacker can target the single centralized point i.e. controller, to attack the network. Hence, efficient adaptive monitoring and measurement is required to detect and prevent malicious activities inside the network. Various such techniques have already been proposed by many researchers. This paper describes a work of applying efficient adaptive monitoring on the network while maintaining the performance of the network considering monitoring overhead over the controller. This work represents effective bandwidth utilization for calculation of threshold range while applying anomaly detection rules for monitoring of the network. Accurate detection of anomalies is implemented and also allows valid users and applications to transfer the data without any restrictions inside the network which otherwise were considered as anomalies in previous technique due to fluctuation of data and narrow threshold window. The concept of fast switching also used to improve the processing speed and performance of the networks.

  13. Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining.

    Science.gov (United States)

    Zhang, Dingwen; Han, Junwei; Han, Jungong; Shao, Ling

    2016-06-01

    As an interesting and emerging topic, cosaliency detection aims at simultaneously extracting common salient objects in multiple related images. It differs from the conventional saliency detection paradigm in which saliency detection for each image is determined one by one independently without taking advantage of the homogeneity in the data pool of multiple related images. In this paper, we propose a novel cosaliency detection approach using deep learning models. Two new concepts, called intrasaliency prior transfer and deep intersaliency mining, are introduced and explored in the proposed work. For the intrasaliency prior transfer, we build a stacked denoising autoencoder (SDAE) to learn the saliency prior knowledge from auxiliary annotated data sets and then transfer the learned knowledge to estimate the intrasaliency for each image in cosaliency data sets. For the deep intersaliency mining, we formulate it by using the deep reconstruction residual obtained in the highest hidden layer of a self-trained SDAE. The obtained deep intersaliency can extract more intrinsic and general hidden patterns to discover the homogeneity of cosalient objects in terms of some higher level concepts. Finally, the cosaliency maps are generated by weighted integration of the proposed intrasaliency prior, deep intersaliency, and traditional shallow intersaliency. Comprehensive experiments over diverse publicly available benchmark data sets demonstrate consistent performance gains of the proposed method over the state-of-the-art cosaliency detection methods. PMID:26571541

  14. Detection and Origin of Hydrocarbon Seepage Anomalies in the Barents Sea

    Science.gov (United States)

    Polteau, Stephane; Planke, Sverre; Stolze, Lina; Kjølhamar, Bent E.; Myklebust, Reidun

    2016-04-01

    We have collected more than 450 gravity cores in the Barents Sea to detect hydrocarbon seepage anomalies and for seismic-stratigraphic tie. The cores are from the Hoop Area (125 samples) and from the Barents Sea SE (293 samples). In addition, we have collected cores near seven exploration wells. The samples were analyzed using three different analytical methods; (1) the standard organic geochemical analyzes of Applied Petroleum Technologies (APT), (2) the Amplified Geochemical Imaging (AGI) method, and (3) the Microbial Prospecting for Oil and Gas (MPOG) method. These analytical approaches can detect trace amounts of thermogenic hydrocarbons in the sediment samples, and may provide additional information about the fluid phases and the depositional environment, maturation, and age of the source rocks. However, hydrocarbon anomalies in seabed sediments may also be related to shallow sources, such as biogenic gas or reworked source rocks in the sediments. To better understand the origin of the hydrocarbon anomalies in the Barents Sea we have studied 35 samples collected approximately 200 m away from seven exploration wells. The wells included three boreholes associated with oil discoveries, two with gas discoveries, one dry well with gas shows, and one dry well. In general, the results of this case study reveal that the oil wells have an oil signature, gas wells show a gas signature, and dry wells have a background signature. However, differences in results from the three methods may occur and have largely been explained in terms of analytical measurement ranges, method sensitivities, and bio-geochemical processes in the seabed sediments. The standard geochemical method applied by APT relies on measuring the abundance of compounds between C1 to C5 in the headspace gas and between C11 to C36 in the sediment extracts. The anomalies detected in the sediment samples from this study were in the C16 to C30 range. Since the organic matter yields were mostly very low, the

  15. Detection of airway anomalies in pediatric patients with cardiovascular anomalies with low dose prospective ECG-gated dual-source CT.

    Directory of Open Access Journals (Sweden)

    Hui Jiao

    Full Text Available 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 board and written informed consent was obtained from all patients' guardian. DSCT examinations were performed to detect cardiovascular abnormalities using weight-adjusted low-dose protocol. Two radiologists independently performed CT image analysis. The FTB reports were reviewed by an experienced pulmonologist. The sensitivity, specificity, positive predictive value (PPV, negative predictive value (NPV, and accuracy of DSCT in the detection of airway anomalies were assessed. The tracheobronchial stenoses revealed on FTB were graded. Effective radiation dose was calculated. RESULTS: Thirty cases were diagnosed with tracheobronchial narrowing and/or abnormality in 33 patients by FTB, while 3 patients had normal FTB findings. Twenty-eight cases were diagnosed with airway anomalies by CT, of which 27 were correct positive. 3 patients with normal findings at CT had findings of tracheobronchial narrowing due to tracheobronchomalacia at inspiration at FTB. Sensitivity and specificity of CT were 90.0% (95% CI: 72.3%, 97.4% and 66.7% (95% CI: 12.5 %, 98.2 %, respectively. PPV and NPV were 96.4% (95% CI: 79.8 %, 99.8% and 40.0% (95% CI: 7.3%, 83.0%, respectively. Overall accuracy of DSCT in detecting airway anomalies in pediatrics with cardiovascular anomalies was 87.9% (95% CI: 74.5%, 97.6%. In grading of tracheobronchial stenosis, images from CT correlated closely (r = 0.89 with those of FTB. Mean effective dose was 0.60 ± 0.20 mSv. CONCLUSION: In pediatric patients

  16. Small sample training and test selection method for optimized anomaly detection algorithms in hyperspectral imagery

    Science.gov (United States)

    Mindrup, Frank M.; Friend, Mark A.; Bauer, Kenneth W.

    2012-01-01

    There are numerous anomaly detection algorithms proposed for hyperspectral imagery. Robust parameter design (RPD) techniques provide an avenue to select robust settings capable of operating consistently across a large variety of image scenes. Many researchers in this area are faced with a paucity of data. Unfortunately, there are no data splitting methods for model validation of datasets with small sample sizes. Typically, training and test sets of hyperspectral images are chosen randomly. Previous research has developed a framework for optimizing anomaly detection in HSI by considering specific image characteristics as noise variables within the context of RPD; these characteristics include the Fisher's score, ratio of target pixels and number of clusters. We have developed method for selecting hyperspectral image training and test subsets that yields consistent RPD results based on these noise features. These subsets are not necessarily orthogonal, but still provide improvements over random training and test subset assignments by maximizing the volume and average distance between image noise characteristics. The small sample training and test selection method is contrasted with randomly selected training sets as well as training sets chosen from the CADEX and DUPLEX algorithms for the well known Reed-Xiaoli anomaly detector.

  17. 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.

  18. Anomaly Detection Rudiments for the Application of Hyperspectral Sensors in Aerospace Remote Sensing

    International Nuclear Information System (INIS)

    Hyperspectral imaging differs from conventional techniques by exploiting the spectral dimensionality of remote scenes. This additional information promotes discrimination of image elements, especially anomalies that are dissimilar with respect to global features. Algorithms for anomaly detection are designed to overcome the inherent difficulty of analysing hypercubes, which are the higher-dimensional analogues of conventional broadband images. Such algorithms are prolific in their variety and design, which could become an obstacle in choice or application for the neophyte researcher in this field. This paper seeks to consolidate this plethora of algorithms into succinct categories for clarity of rudimentary decision making. A duplicate of article 012048 Snapshot hyperspectral imaging and practical applications was originally published here, in error, as article 012051. The present article replaced the duplicate and was published on 18 August 2009.

  19. Behavior Based Anomaly Detection Technique to Mitigate the Routing Misbehavior in MANET

    Directory of Open Access Journals (Sweden)

    T.V.P.Sundararajan

    2009-05-01

    Full Text Available Mobile ad hoc network does not have traffic concentration points such as gateway or access points which perform behavior monitoring of individual nodes. Therefore, maintaining the network function for normal nodes when other nodes do not route and forward correctly is a big challenge. This paper, address the behavior based anomaly detection technique inspired by the biological immune system to enhance the performance of MANET to operate despite the presence of misbehaving nodes. Due to its reliance on overhearing, the existing watchdog technique may fail to detect misbehavior or raise false alarms in the presence of ambiguous collisions, receiver collisions, and limited transmission power. Our proposed scheme uses intelligent machine learning techniques that learns and detects each node by false alarm and negative selection approach. We consider DSR, AODV and DSDV [24],[25] as underlying routing protocol which are highly vulnerable to routing misbehavior. Analytical and simulation results are presented to evaluate the performance of the proposed scheme. Keywords: intrusion detection, anomaly detection, mobile ad hoc network, security.

  20. 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.

  1. Smartphone-Based Pedestrian’s Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Tsuyoshi Ishikawa

    2016-10-01

    Full Text Available Road anomalies, such as cracks, pits and puddles, have generally been identified by citizen reports made by e-mail or telephone; however, it is difficult for administrative entities to locate the anomaly for repair. An advanced smartphone-based solution that sends text and/or image reports with location information is not a long-lasting solution, because it depends on people’s active reporting. In this article, we show an opportunistic sensing-based system that uses a smartphone for road anomaly detection without any active user involvement. To detect road anomalies, we focus on pedestrians’ avoidance behaviors, which are characterized by changing azimuth patterns. Three typical avoidance behaviors are defined, and random forest is chosen as the classifier. Twenty-nine features are defined, in which features calculated by splitting a segment into the first half and the second half and considering the monotonicity of change were proven to be effective in recognition. Experiments were carried out under an ideal and controlled environment. Ten-fold cross-validation shows an average classification performance with an F-measure of 0.89 for six activities. The proposed recognition method was proven to be robust against the size of obstacles, and the dependency on the storing position of a smartphone can be handled by an appropriate classifier per storing position. Furthermore, an analysis implies that the classification of data from an “unknown” person can be improved by taking into account the compatibility of a classifier.

  2. 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...

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

    International Nuclear Information System (INIS)

    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)

  4. Automatic, Real-Time Algorithms for Anomaly Detection in High Resolution Satellite Imagery

    Science.gov (United States)

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

    2008-12-01

    Earth observing satellites are generating data at an unprecedented rate, surpassing almost all other data intensive applications. However, most of the data that arrives from the satellites is not analyzed directly. Rather, multiple scientific teams analyze only a small fraction of the total data available in the data stream. Although there are many reasons for this situation one paramount concern is developing algorithms and methods that can analyze the vast, high dimensional, streaming satellite images. This paper describes a new set of methods that are among the fastest available algorithms for real-time anomaly detection. These algorithms were built to maximize accuracy and speed for a variety of applications in fields outside of the earth sciences. However, our studies indicate that with appropriate modifications, these algorithms can be extremely valuable for identifying anomalies rapidly using only modest computational power. We review two algorithms which are used as benchmarks in the field: Orca, One-Class Support Vector Machines and discuss the anomalies that are discovered in MODIS data taken over the Central California region. We are especially interested in automatic identification of disturbances within the ecosystems (e,g, wildfires, droughts, floods, insect/pest damage, wind damage, logging). We show the scalability of the algorithms and demonstrate that with appropriately adapted technology, the dream of real-time analysis can be made a reality.

  5. Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery.

    Science.gov (United States)

    Marapareddy, Ramakalavathi; Aanstoos, James V; Younan, Nicolas H

    2016-01-01

    Fully polarimetric Synthetic Aperture Radar (polSAR) data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR) to screen levees for anomalies. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice for identifying problematic areas on earthen levees. Using the parameters entropy (H), anisotropy (A), alpha (α), and eigenvalues (λ, λ₁, λ₂, and λ₃), we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are H/α, H/A, A/α, Wishart H/α, Wishart H/A/α, and H/α/λ classification algorithms. 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. PMID:27322270

  6. Advanced Unsupervised Classification Methods to Detect Anomalies on Earthen Levees Using Polarimetric SAR Imagery

    Directory of Open Access Journals (Sweden)

    Ramakalavathi Marapareddy

    2016-06-01

    Full Text Available Fully polarimetric Synthetic Aperture Radar (polSAR data analysis has wide applications for terrain and ground cover classification. The dynamics of surface and subsurface water events can lead to slope instability resulting in slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We used L-band Synthetic Aperture Radar (SAR to screen levees for anomalies. SAR technology, due to its high spatial resolution and soil penetration capability, is a good choice for identifying problematic areas on earthen levees. Using the parameters entropy (H, anisotropy (A, alpha (α, and eigenvalues (λ, λ1, λ2, and λ3, we implemented several unsupervised classification algorithms for the identification of anomalies on the levee. The classification techniques applied are H/α, H/A, A/α, Wishart H/α, Wishart H/A/α, and H/α/λ classification algorithms. 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.

  7. Anomaly detection for network traffic flow%网络流量异常检测

    Institute of Scientific and Technical Information of China (English)

    单蓉胜; 李建华; 王明政

    2004-01-01

    提出了一种新颖的网络洪流攻击的异常检测机制.这种检测机制的无状态维护、低计算代价的特性保证了自身具有抗洪流攻击的能力.本文以检测SYN洪流攻击为实例详细阐述了异常检测机制.这个机制应用EWMA方法检测网络流的突变, 并运用对称性分析方法检测网络流的异常活动.测试结果表明本文所提出的检测机制具有很好的检测洪流攻击的准确度, 并具有低延时特性.%This paper presents a novel mechanism for detecting flooding-attacks. The simplicity of the mechanism lies in its statelessness and low computation overhead, which makes the detection mechanism itself immune to flooding-attacks. In this paper, SYN-flooding, as an instance of flooding-attack, is used to illustrate the anomaly detection mechanism. The mechanism applies an exponentially weighted moving average (EWMA) method to detect the abrupt net flow and applies a symmetry analysis method to detect the anomaly activity of the network flow. Experiment shows that the mechanism has high detection accuracy and low detection latency.

  8. GPU implementation of target and anomaly detection algorithms for remotely sensed hyperspectral image analysis

    Science.gov (United States)

    Paz, Abel; Plaza, Antonio

    2010-08-01

    Automatic target and anomaly detection are considered very important tasks for hyperspectral data exploitation. These techniques are now routinely applied in many application domains, including defence and intelligence, public safety, precision agriculture, geology, or forestry. Many of these applications require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. However, with the recent explosion in the amount and dimensionality of hyperspectral imagery, this problem calls for the incorporation of parallel computing techniques. In the past, clusters of computers have offered an attractive solution for fast anomaly and target detection in hyperspectral data sets already transmitted to Earth. However, these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power integrated components are essential to reduce mission payload and obtain analysis results in (near) real-time, i.e., at the same time as the data is collected by the sensor. An exciting new development in the field of commodity computing is the emergence of commodity graphics processing units (GPUs), which can now bridge the gap towards on-board processing of remotely sensed hyperspectral data. In this paper, we describe several new GPU-based implementations of target and anomaly detection algorithms for hyperspectral data exploitation. The parallel algorithms are implemented on latest-generation Tesla C1060 GPU architectures, and quantitatively evaluated using hyperspectral data collected by NASA's AVIRIS system over the World Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in the WTC complex.

  9. Anomalous Signals Prior to Wenchuan Earthquake Detected by Superconducting Gravimeter and Broadband Seismometers Records

    Institute of Scientific and Technical Information of China (English)

    Wenbin Shen; Dijin Wang; Cheinway Hwang

    2011-01-01

    Using 1 Hz sampling records at one superconducting gravimeter (SG) station and 11 broadband seismometer stations,we found anomalous signals prior to the 2008 Wenchuan(汶川)earthquake event.The tides are removed from the original SG records to obtain the gravity residuals.Applying the Hilbert-Huang transform (HHT) and the wavelet analysis to the SG gravity residuals leads to time-frequency spectra,which suggests that there is an anomalous signal series around 39 h prior to the event.The period and the magnitude of the anomalous signal series are about 8 s and 3×10-8 m/s2 (3 μGal),respectively.In another aspect,applying HHT analysis technique to 11 records at broadband seismometer stations shows that most of them contain anomalous signals prior to the Wenchuan event,and the marginal spectra of 8 inland stations show an apparent characteristic of double peaks in anomalous days compared to the only one peak of the marginal spectra in quiet days.Preliminary investigations suggest that the anomalous signals prior to the earthquake are closely related to the low-frequency earthquake (LFE).We concluded that the SG data as well as the broadband seismometers records might be significant information sources in detecting the anomalous signals prior to large earthquakes.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

    A problem of anomaly detection in homogenous populations consisting of linear stable systems is studied. The recently introduced sparse multiple kernel based regularization method is applied to solve the problem. A common problem with the existing regularization methods is that there lacks...... an efficient and systematic way to tune the involved regularization parameters. In contrast, the hyper-parameters (some of them can be interpreted as regularization parameters) involved in the proposed method are tuned in an automatic way, and in fact estimated by using the empirical Bayes method. What's more...

  11. 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

  12. 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.

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

    Indian Academy of Sciences (India)

    Saad Y Sait; Akshay Bhandari; Shreya Khare; Cyriac James; Hema A Murthy

    2015-09-01

    The Internet has become a vital source of information; internal and external attacks threaten the integrity of the LAN connected to the Internet. In this work, several techniques have been described for detection of such threats. We have focussed on anomaly-based intrusion detection in the campus environment at the network edge. A campus LAN consisting of more than 9000 users with a 90 Mbps internet access link is a large network. Therefore, efficient techniques are required to handle such big data and to model user behaviour. Proxy server logs of a campus LAN and edge router traces have been used for anomalies like abusive Internet access, systematic downloading (internal threats) and DDoS attacks (external threat); our techniques involve machine learning and time series analysis applied at different layers in TCP/IP stack. Accuracy of our techniques has been demonstrated through extensive experimentation on huge and varied datasets. All the techniques are applicable at the edge and can be integrated into a Network Intrusion Detection System.

  14. IMPROVEMENT OF ANOMALY DETECTION ALGORITHMS IN HYPERSPECTRAL IMAGES USING DISCRETE WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    Kamal Jamshidi

    2012-01-01

    Full Text Available Recently anomaly detection (AD has become an important application for target detection in hyperspectralremotely sensed images. In many applications, in addition to high accuracy of detection we need a fast andreliable algorithm as well. This paper presents a novel method to improve the performance of current ADalgorithms. The proposed method first calculates Discrete Wavelet Transform (DWT of every pixel vectorof image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of “Wavelet transform”matrix which are the approximation of main image. In this research some benchmark AD algorithmsincluding Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared ImagingSpectrometer (AVIRIS hyperspectral datasets. Experimental results demonstrate significant improvementof runtime in proposed method. In addition, this method improves the accuracy of AD algorithms becauseof DWT’s power in extracting approximation coefficients of signal, which contain the main behaviour ofsignal, and abandon the redundant information in hyperspectral image data.

  15. Detection of crustal deformation prior to the 2014 Mt. Ontake eruption by the stacking method

    Science.gov (United States)

    Miyaoka, Kazuki; Takagi, Akimichi

    2016-04-01

    The phreatic eruption of Mt. Ontake in central Japan occurred in September 27, 2014. No obvious crustal deformation was observed prior to the eruption, and the magnitudes of other precursor phenomena were very small. In this study, we used the stacking method to detect crustal deformation prior to the eruption. The stacking method is a technique to improve the signal-to-noise ratio by stacking multiple records of crustal deformation. We succeeded in detecting a slight crustal deformation caused by a volume change in the shallow region beneath the volcano's summit from 1 month prior to the eruption. We also detected a slight crustal deformation that may have been caused by a volume increase in the deep region from one and a half months before the eruption. The magnitude of the volume change in the shallow region did not differ significantly in the 2014 eruption compared to the volume change during the small Mt. Ontake eruption in 2007, and the volume change in the deep region was rather smaller in 2014 than in 2007.

  16. Temperature anomaly detection and estimation using microwave radiometry and anatomical information

    Science.gov (United States)

    Kelly, Patrick; Sobers, Tamara; St. Peter, Benjamin; Siqueira, Paul; Capraro, Geoffrey

    2011-03-01

    Many medically significant conditions (e.g., ischemia, carcinoma and inflammation) involve localized anomalies in physiological parameters such as the metabolic and blood perfusion rates. These in turn lead to deviations from normal tissue temperature patterns. Microwave radiometry is a passive system for sensing the radiation that objects emit naturally in the microwave frequency band. Since the emitted power depends on temperature, and since radiation at low microwave frequencies can propagate through several centimeters of tissue, microwave radiometry has the potential to provide valuable information about subcutaneous anomalies. The radiometric temperature measurement for a tissue region can be modeled as the inner product of the temperature pattern and a weighting function that depends on tissue properties and the radiometer's antenna. In the absence of knowledge of the weighting functions, it can be difficult to extract specific information about tissue temperature patterns (or the underlying physiological parameters) from the measurements. In this paper, we consider a scenario in which microwave radiometry works in conjunction with another imaging modality (e.g., 3D-CT or MRI) that provides detailed anatomical information. This information is used along with sensor properties in electromagnetic simulation software to generate weighting functions. It also is used in bio-heat equations to generate nominal tissue temperature patterns. We then develop a hypothesis testing framework that makes use of the weighting functions, nominal temperature patterns, and maximum likelihood estimates to detect anomalies. Simulation results are presented to illustrate the proposed detection procedures. The design and performance of an S-band (2-4 GHz) radiometer, and some of the challenges in using such a radiometer for temperature measurements deep in tissue, are also discussed.

  17. Feasibility of anomaly detection and characterization using trans-admittance mammography with 60 × 60 electrode array

    Science.gov (United States)

    Zhao, Mingkang; Wi, Hun; Lee, Eun Jung; Woo, Eung Je; In Oh, Tong

    2014-10-01

    Electrical impedance imaging has the potential to detect an early stage of breast cancer due to higher admittivity values compared with those of normal breast tissues. The tumor size and extent of axillary lymph node involvement are important parameters to evaluate the breast cancer survival rate. Additionally, the anomaly characterization is required to distinguish a malignant tumor from a benign tumor. In order to overcome the limitation of breast cancer detection using impedance measurement probes, we developed the high density trans-admittance mammography (TAM) system with 60 × 60 electrode array and produced trans-admittance maps obtained at several frequency pairs. We applied the anomaly detection algorithm to the high density TAM system for estimating the volume and position of breast tumor. We tested four different sizes of anomaly with three different conductivity contrasts at four different depths. From multifrequency trans-admittance maps, we can readily observe the transversal position and estimate its volume and depth. Specially, the depth estimated values were obtained accurately, which were independent to the size and conductivity contrast when applying the new formula using Laplacian of trans-admittance map. The volume estimation was dependent on the conductivity contrast between anomaly and background in the breast phantom. We characterized two testing anomalies using frequency difference trans-admittance data to eliminate the dependency of anomaly position and size. We confirmed the anomaly detection and characterization algorithm with the high density TAM system on bovine breast tissue. Both results showed the feasibility of detecting the size and position of anomaly and tissue characterization for screening the breast cancer.

  18. Accuracy of Ultrasound in Detection of Gross Prenatal Central Nervous System Anomalies after the Eighteenth Week of Gestation

    Directory of Open Access Journals (Sweden)

    M. Tahmasebi

    2007-10-01

    Full Text Available Background/Objective: Ultrasound (US detection of prenatal central nervous system (CNS anatomic anomalies is very important in making decision about therapeutic termination. In the present study, the accuracy of US in detection of gross prenatal CNS anatomic anomalies has been investigated."nPatients and Methods: 3012 pregnant women were scanned after 18 weeks of gestation by an expert operator in a referring center. All delivered fetuses were followed after birth through clinical examination and sonography."nResults: In this study, the accuracy of US in detection of gross CNS anatomic anomalies of fetuses after 18 weeks gestation was found to be 100%. The sensitivity, specificity, positive and negative predictive values of US were 100%. In sonographic examination of these 3012 pregnant women, 36 fetuses were detected with CNS anomalies, some of whom had more than one anomaly. Gross CNS anomalies observed included microcephaly, hydrocephaly, anencephaly, holoprosencephaly, ventriculomegaly, meningocele, encephalocele, lissencephaly, agenesis of corpus callosum, bilateral choroid plexus cysts and hypoplastic cerebellum."nConclusion: US is highly operator dependent and operator experience may be the most determinant affecting the results. Sonographic scanning after 18 weeks of gestation is associated with the best results.

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

    Energy Technology Data Exchange (ETDEWEB)

    Tamaoki, Tetsuo; Sonoda, Yukio; Sato, Masuo (Toshiba Corp., Kawasaki, Kanagawa (Japan)); Takahashi, Ryoichi

    1994-03-01

    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).

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

    Directory of Open Access Journals (Sweden)

    Zhang Lin

    2016-01-01

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

  1. Tracking Environmental Compliance and Remediation Trajectories Using Image-Based Anomaly Detection Methodologies

    Directory of Open Access Journals (Sweden)

    James K. Lein

    2011-11-01

    Full Text Available Recent interest in use of satellite remote sensing for environmental compliance and remediation assessment has been heightened by growing policy requirements and the need to provide more rapid and efficient monitoring and enforcement mechanisms. However, remote sensing solutions are attractive only to the extent that they can deliver environmentally relevant information in a meaningful and time-sensitive manner. Unfortunately, the extent to which satellite-based remote sensing satisfies the demands for compliance and remediation assessment under the conditions of an actual environmental accident or calamity has not been well documented. In this study a remote sensing solution to the problem of site remediation and environmental compliance assessment was introduced based on the use of the RDX anomaly detection algorithm and vegetation indices developed from the Tasseled Cap Transform. Results of this analysis illustrate how the use of standard vegetation transforms, integrated into an anomaly detection strategy, enable the time-sequenced tracking of site remediation progress. Based on these results credible evidence can be produced to support compliance evaluation and remediation assessment following major environmental disasters.

  2. Anomaly Detection Algorithm for Stay Cable Monitoring Data Based on Data Fusion

    Institute of Scientific and Technical Information of China (English)

    Xiaoling Liu,Qiao Huang∗; Yuan Ren

    2016-01-01

    In order to improve the accuracy and consistency of data in health monitoring system, an anomaly detection algorithm for stay cables based on data fusion is proposed. The monitoring data of Nanjing No. 3 Yangtze River Bridge is used as the basis of study. Firstly, an adaptive processing framework with feedback control is established based on the concept of data fusion. The data processing contains four steps: data specification, data cleaning, data conversion and data fusion. Data processing information offers feedback to the original data system, which further gives guidance for the sensor maintenance or replacement. Subsequently, the algorithm steps based on the continuous data distortion is investigated,which integrates the inspection data and the distribution test method. Finally, a group of cable force data is utilized as an example to verify the established framework and algorithm. Experimental results show that the proposed algorithm can achieve high detection accuracy, providing a valuable reference for other monitoring data processing.

  3. ADAPTIVE SUBSYSTEM FOR DETECTING AND PREVENTING ANOMALIES AS A PROTECTION MEANS AGAINST NETWORK ATTACKS

    Directory of Open Access Journals (Sweden)

    Simankov V. S.

    2015-06-01

    Full Text Available This article describes the results of networks anomalies detection system based on modular adaptive approach practical implementation. The list of specific modules used in the practical implementation of IPS, their architecture, algorithms, software, organizational and technical support determined at technical working design based on the results of the audit, evaluation and risk analysis. In the general list of modules (subsystems we may include: intrusion detection and prevention (IPS / IDS subsystems; monitoring, data collection, and event correlation, administration and management subsystem and others. We have demonstrated the specificity of formation requirements for the basic mechanisms of the subsystems in terms of development and implementation of specific architecture with some examples, plus practically implemented structure of system modules, as well as organizational and technical support system functioning

  4. Anomaly Detection Techniques with Real Test Data from a Spinning Turbine Engine-Like Rotor

    Science.gov (United States)

    Abdul-Aziz, Ali; Woike, Mark R.; Oza, Nikunj C.; Matthews, Bryan L.

    2012-01-01

    Online detection techniques to monitor the health of rotating engine components are becoming increasingly attractive to aircraft engine manufacturers in order to increase safety of operation and lower maintenance costs. Health monitoring remains a challenge to easily implement, especially in the presence of scattered loading conditions, crack size, component geometry, and materials properties. The current trend, however, is to utilize noninvasive types of health monitoring or nondestructive techniques to detect hidden flaws and mini-cracks before any catastrophic event occurs. These techniques go further to evaluate material discontinuities and other anomalies that have grown to the level of critical defects that can lead to failure. Generally, health monitoring is highly dependent on sensor systems capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system.

  5. Improved K-means Algorithm for Manufacturing Process Anomaly Detection and Recognition

    Institute of Scientific and Technical Information of China (English)

    ZHOU Xiaomin; PENG Wei; SHI Haibo

    2006-01-01

    Anomaly detection and recognition are of prime importance in process industries. Faults are usually rare, and, therefore, predicting them is difficult. In this paper, a new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Based on the clustering result of historical disqualification product data in manufacturing process which generated by the Improved-K-means algorithm, a prediction model which is used to detect and recognize the abnormal trend of the quality problems is constructed. This simple and robust alarm-system architecture for predicting incoming faults realizes the transition of quality problems from diagnosis afterward to prevention beforehand indeed. In the end, the alarm model was applied for prediction and avoidance of gear-wheel assembly faults at a gear-plant.

  6. System and method for the detection of anomalies in an image

    Science.gov (United States)

    Prasad, Lakshman; Swaminarayan, Sriram

    2013-09-03

    Preferred aspects of the present invention can include receiving a digital image at a processor; segmenting the digital image into a hierarchy of feature layers comprising one or more fine-scale features defining a foreground object embedded in one or more coarser-scale features defining a background to the one or more fine-scale features in the segmentation hierarchy; detecting a first fine-scale foreground feature as an anomaly with respect to a first background feature within which it is embedded; and constructing an anomalous feature layer by synthesizing spatially contiguous anomalous fine-scale features. Additional preferred aspects of the present invention can include detecting non-pervasive changes between sets of images in response at least in part to one or more difference images between the sets of images.

  7. Normalized edge detection, and the horizontal extent and depth of geophysical anomalies

    Institute of Scientific and Technical Information of China (English)

    Li Li-Li; Han Li-Guo; Huang Da-Nian

    2014-01-01

    Edge detection is an image processing technique for finding the boundaries of objects within images. It is typically used to interpret gravity and magnetic data, andfi nd the horizontal boundaries of geological bodies. Large deviations between model and true edges are common because of the interference of depth and errors in computing the derivatives; thus, edge detection methods cannot provide information about the depth of the source. To simultaneously obtain the horizontal extent and depth of geophysical anomalies, we use normalized edge detection filters, which normalize the edge detection function at different depths, and the maxima that correspond to the location of the source. The errors between model and actual edges are minimized as the depth of the source decreases and the normalized edge detection method recognizes the extent of the source based on the maxima, allowing for reliable model results. We demonstrate the applicability of the normalized edge detection fi lters in defi ning the horizontal extent and depth using synthetic and actual aeromagnetic data.

  8. Artificially Augmented Training for Anomaly-based Network Intrusion Detection Systems

    Directory of Open Access Journals (Sweden)

    Chockalingam Karuppanchetty

    2015-09-01

    Full Text Available Attacks on web servers are becoming increasingly prevalent; the resulting social and economic impact of successful attacks is also exacerbated by our dependency on web-based applications. There are many existing attack detection and prevention schemes, which must be carefully configured to ensure their efficacy. In this paper, we present a study challenges that arise in training network payload anomaly detection schemes that utilize collected network traffic for tuning and configuration. The advantage of anomaly-based intrusion detection is in its potential for detecting zero day attacks. These types of schemes, however, require extensive training to properly model the normal characteristics of the system being protected. Usually, training is done through the use of real data collected by monitoring the activity of the system. In practice, network operators or administrators may run into cases where they have limited availability of such data. This issue can arise due to the system being newly deployed (or heavily modified or due to the content or behavior that leads to normal characterization having been changed. We show that artificially generated packet payloads can be used to effectively augment the training and tuning. We evaluate the method using real network traffic collected at a server site; We illustrate the problem at first (use of highly variable and unsuitable training data resulting in high false positives of 3.6∼10%, then show improvements using the augmented training method (false positives as low as 0.2%. We also measure the impact on network performance, and present a lookup based optimization that can be used to improve latency and throughput.

  9. Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling.

    Science.gov (United States)

    Raghuram, Jayaram; Miller, David J; Kesidis, George

    2014-07-01

    We propose a method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), malware, and phishing. Our method is based on learning a (null hypothesis) probability model based on a large set of domain names that have been white listed by some reliable authority. Since these names are mostly assigned by humans, they are pronounceable, and tend to have a distribution of characters, words, word lengths, and number of words that are typical of some language (mostly English), and often consist of words drawn from a known lexicon. On the other hand, in the present day scenario, algorithmically generated domain names typically have distributions that are quite different from that of human-created domain names. We propose a fully generative model for the probability distribution of benign (white listed) domain names which can be used in an anomaly detection setting for identifying putative algorithmically generated domain names. Unlike other methods, our approach can make detections without considering any additional (latency producing) information sources, often used to detect fast flux activity. Experiments on a publicly available, large data set of domain names associated with fast flux service networks show encouraging results, relative to several baseline methods, with higher detection rates and low false positive rates.

  10. Detection of Seismic Anomalies Linked to Emanations of Hydrocarbons in the Cuban Northwest Coast

    Directory of Open Access Journals (Sweden)

    Guillermo Miró Pagés

    2014-11-01

    Full Text Available The exploration of hydrocarbons to international scale constitutes a very complex and expensive task. Traditionally in the coast areas like the ones in the present work, the location of the exploration wells has been based on derived structural and stratigraphic information of geophysical data, mainly seismic; however it is well-known that in several regions similar of the world, the detection of superficial seeps of hydrocarbons confirm the existence of oil systems, has contributed to achieve a bigger dependability of the carried out prospectings, what has great importance considering the millionaire character of the financial expenditures who demands. For that reason, the main objective was to try to identify seismic anomalies typically associate with existences of hydrocarbons in Cuban coastareas. The main conclusion of this article is that the identification of seismic anomalies similar to those observed in the course of the present work can constitute a valuable additional informative element for the prospecting of hydrocarbons in areas of the Cuban coast.

  11. 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

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

    Science.gov (United States)

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

    2016-04-01

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

  13. Scalable Algorithms for Unsupervised Classification and Anomaly Detection in Large Geospatiotemporal Data Sets

    Science.gov (United States)

    Mills, R. T.; Hoffman, F. M.; Kumar, J.

    2015-12-01

    The increasing availability of high-resolution geospatiotemporal datasets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery and mining of ecological data sets fused from disparate sources. Traditional algorithms and computing platforms are impractical for the analysis and synthesis of data sets of this size; however, new algorithmic approaches that can effectively utilize the complex memory hierarchies and the extremely high levels of available parallelism in state-of-the-art high-performance computing platforms can enable such analysis. We describe some unsupervised knowledge discovery and anomaly detection approaches based on highly scalable parallel algorithms for k-means clustering and singular value decomposition, consider a few practical applications thereof to the analysis of climatic and remotely-sensed vegetation phenology data sets, and speculate on some of the new applications that such scalable analysis methods may enable.

  14. 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.

  15. Realization and detection of Weyl semimetals and the chiral anomaly in cold atomic systems

    Science.gov (United States)

    He, Wen-Yu; Zhang, Shizhong; Law, K. T.

    2016-07-01

    In this work, we describe a method to realize a three-dimensional Weyl semimetal by coupling multilayers of a honeycomb optical lattice in the presence of a pair of Raman lasers. The Raman lasers render each isolated honeycomb layer a Chern insulator. With finite interlayer coupling, the bulk gap of the system closes at certain out-of-plane momenta due to Raman assisted tunneling and results in the Weyl semimetal phase. Using experimentally relevant parameters, we show that both one pair and two pairs of Weyl points can be realized by tuning the interlayer coupling strength. We suggest that Landau-Zener tunneling can be used to detect Weyl points and show that the transition probability increases dramatically when the Weyl point emerges. The realization of chiral anomaly by using a magnetic-field gradient is also discussed.

  16. Automated Anomaly Detection in Distribution Grids Using $\\mu$PMU Measurements

    CERN Document Server

    Jamei, Mahdi; Roberts, Ciaran; Stewart, Emma; Peisert, Sean; McParland, Chuck; McEachern, Alex

    2016-01-01

    The impact of Phasor Measurement Units (PMUs) for providing situational awareness to transmission system operators has been widely documented. Micro-PMUs ($\\mu$PMUs) are an emerging sensing technology that can provide similar benefits to Distribution System Operators (DSOs), enabling a level of visibility into the distribution grid that was previously unattainable. In order to support the deployment of these high resolution sensors, the automation of data analysis and prioritizing communication to the DSO becomes crucial. In this paper, we explore the use of $\\mu$PMUs to detect anomalies on the distribution grid. Our methodology is motivated by growing concern about failures and attacks to distribution automation equipment. The effectiveness of our approach is demonstrated through both real and simulated data.

  17. 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...

  18. Multiscale spatial density smoothing: an application to large-scale radiological survey and anomaly detection

    CERN Document Server

    Tansey, Wesley; Reinhart, Alex; Scott, James G

    2015-01-01

    We consider the problem of estimating a spatially varying density function, motivated by problems that arise in large-scale radiological survey and anomaly detection. In this context, the density functions to be estimated are the background gamma-ray energy spectra at sites spread across a large geographical area, such as nuclear production and waste-storage sites, military bases, medical facilities, university campuses, or the downtown of a city. Several challenges combine to make this a difficult problem. First, the spectral density at any given spatial location may have both smooth and non-smooth features. Second, the spatial correlation in these density functions is neither stationary nor locally isotropic. Third, the spatial correlation decays at different length scales at different locations in the support of the underlying density. Finally, at some spatial locations, there is very little data. We present a method called multiscale spatial density smoothing that successfully addresses these challenges. ...

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

    Energy Technology Data Exchange (ETDEWEB)

    Dumidu Wijayasekara; Ondrej Linda; Milos Manic; Craig Rieger

    2014-08-01

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

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

    International Nuclear Information System (INIS)

    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)

  1. Detection of an outburst one year prior to the explosion of SN 2011ht

    CERN Document Server

    Fraser, M; Kotak, R; Smartt, S J; Smith, K W; Polshaw, J; Drake, A J; Boles, T; Lee, C -H; Burgett, W S; Chambers, K C; Draper, P W; Flewelling, H; Hodapp, K W; Kaiser, N; Kudritzki, R -P; Magnier, E A; Price, P A; Tonry, J L; Wainscoat, R J; Waters, C

    2013-01-01

    Using imaging from the Pan-STARRS1 survey, we identify a precursor outburst at epochs 287 and 170 days prior to the reported explosion of the purported Type IIn supernova (SN) 2011ht. In the Pan-STARRS data, a source coincident with SN 2011ht is detected exclusively in the \\zps\\ and \\yps-bands. An absolute magnitude of M$_z\\simeq$-11.8 suggests that this was an outburst of the progenitor star. Unfiltered, archival Catalina Real Time Transient survey images also reveal a coincident source from at least 258 to 138 days before the main event. We suggest that the outburst is likely to be an intrinsically red eruption, although we cannot conclusively exclude a series of erratic outbursts which were observed only in the redder bands by chance. This is only the fourth detection of an outburst prior to a claimed SN, and lends credence to the possibility that many more interacting transients have pre-explosion outbursts, which have been missed by current surveys.

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

    International Nuclear Information System (INIS)

    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

  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. A Prior-based Transfer Learning Method for the Phishing Detection

    Directory of Open Access Journals (Sweden)

    Jianyi Zhang

    2012-08-01

    Full Text Available In this paper, we introduce a prior-based transfer  learning method for our statistical machine learning  classifier which based on the logistic regression to detect the  phishing sites that relies on our selected features of the  URLs. Because of the mismatched distributions of the  features in different phishing domains, we employ multiple  models for different regions. Since it is impossible for us to  collect enough data from a new region to rebuild the  detection model, we adjust the existing models by the  transfer learning algorithm to solve these problems. The  proposed algorithm was evaluated on a real-world task of  detecting the phishing websites. After a number of  experiments, our proposed transfer learning algorithm  achieves more than 97% accuracy. The result demonstrates  the use of this algorithm in the anti-phishing scenario is  feasible and ready for our large scale detection engine. 

  5. 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.

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

    International Nuclear Information System (INIS)

    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

  7. Plasmon mode as a detection of the chiral anomaly in Weyl semimetals

    OpenAIRE

    Zhou, Jianhui; Chang, Hao-Ran; Xiao, Di

    2014-01-01

    Weyl semimetals are one kind of three-dimensional gapless semimetal with nontrivial topology in the momentum space. The chiral anomaly in Weyl semimetals manifests as a charge imbalance between the Weyl nodes of opposite chiralities induced by parallel electric and magnetic fields. We investigate the chiral anomaly effect on the plasmon mode in both intrinsic and doped Weyl semimetals within the random phase approximation. We prove that the chiral anomaly gives rise to a different plasmon mod...

  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. Para-GMRF: parallel algorithm for anomaly detection of hyperspectral image

    Science.gov (United States)

    Dong, Chao; Zhao, Huijie; Li, Na; Wang, Wei

    2007-12-01

    The hyperspectral imager is capable of collecting hundreds of images corresponding to different wavelength channels for the observed area simultaneously, which make it possible to discriminate man-made objects from natural background. However, the price paid for the wealthy information is the enormous amounts of data, usually hundreds of Gigabytes per day. Turning the huge volume data into useful information and knowledge in real time is critical for geoscientists. In this paper, the proposed parallel Gaussian-Markov random field (Para-GMRF) anomaly detection algorithm is an attempt of applying parallel computing technology to solve the problem. Based on the locality of GMRF algorithm, we partition the 3-D hyperspectral image cube in spatial domain and distribute data blocks to multiple computers for concurrent detection. Meanwhile, to achieve load balance, a work pool scheduler is designed for task assignment. The Para-GMRF algorithm is organized in master-slave architecture, coded in C programming language using message passing interface (MPI) library and tested on a Beowulf cluster. Experimental results show that Para-GMRF algorithm successfully conquers the challenge and can be used in time sensitive areas, such as environmental monitoring and battlefield reconnaissance.

  10. Application of Distributed Optical Fiber Sensing Technology in the Anomaly Detection of Shaft Lining in Grouting

    Directory of Open Access Journals (Sweden)

    Chunde Piao

    2015-01-01

    Full Text Available The rupture of the shaft lining caused by grouting has seriously undermined the safety in coal mining. Based on BOTDR distributed optical fiber sensing technology, this paper studied the layout method of optical fiber sensors and the anomaly detection method of the deformation and obtained the evolution law of shaft deformation triggered by grouting. The research results showed that the bonding problem of optical fiber sensors in damp environment could be effectively solved, by applying the binder consisting of sodium silicate and cement. Through BOTDR-based deformation detection, the real-time deformation of the shaft lining caused by grouting was immediately spotted. By comparing the respective strain of shaft lining deformation and concrete deformation, the risk range of shaft lining grouting was identified. With the additional strain increment of the shaft lining triggered by each process of grouting, the saturated condition of grouting volume in strata was analyzed, providing an important technical insight into the field construction and the safety of the shaft lining.

  11. A Novel Network Traffic Anomaly Detection Model Based on Superstatistics Theory

    Directory of Open Access Journals (Sweden)

    Yue Yang

    2011-02-01

    Full Text Available With the development of network technology and growing enlargement of network size, the network structure is becoming more and more complicated. Mutual interactions of different network equipment, topology configurations, transmission protocols and cooperation and competition among the network users inevitably cause the network traffic flow which is controlled by several driving factors to appear non-stationary and complicated behavior. Because of its non-stationary property it can not easily use traditional way to analyze the complicated network traffic. A new detection method of non-stationary network traffic based on superstatistics theory is discussed in the paper. According to the superstatistics theory, the complex dynamic system may have a large fluctuation of intensive quantities on large time scales which cause the system to behave as non-stationary which is also the characteristic of network traffic. This new idea provides us a novel method to partition the non-stationary traffic time series into small stationary segments which can be modeled by discrete Generalized Pareto(GP distribution. Different segments follow GP distribution with different distribution parameters which are named slow parameters. We use this slow parameters of the segments as a key determinant factor of the system to describe the network characteristic and analyze the slow parameters with time series theory to detect network anomaly. The result of experiments indicates that this method can be effective.

  12. 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.

  13. A data driven approach for detection and isolation of anomalies in a group of UAVs

    Directory of Open Access Journals (Sweden)

    Wang Yin

    2015-02-01

    Full Text Available The use of groups of unmanned aerial vehicles (UAVs has greatly expanded UAV’s capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar (dynamic model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters. Simulation results have demonstrated the efficiency and flexibility of the proposed approach.

  14. A data driven approach for detection and isolation of anomalies in a group of UAVs

    Institute of Scientific and Technical Information of China (English)

    Wang Yin; Wang Daobo; Wang Jianhong

    2015-01-01

    The use of groups of unmanned aerial vehicles (UAVs) has greatly expanded UAV’s capa-bilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar (dynamic) model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters. Simulation results have demonstrated the efficiency and flexibility of the proposed approach.

  15. 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%.

  16. Sparse source travel-time tomography of a laboratory target: accuracy and robustness of anomaly detection

    CERN Document Server

    Pursiainen, Sampsa

    2014-01-01

    This study concerned conebeam travel-time tomography. The focus was on a sparse distribution of signal sources that can be necessary in a challenging in situ environment such as in asteroid tomography. The goal was to approximate the minimum number of source positions needed for robust detection of refractive anomalies, e.g., voids within an asteroid or a casting defects in concrete. Experimental ultrasonic data were recorded utilizing as a target a 150 mm plastic cast cube containing three stones with diameter between 22 and 41 mm. A signal frequency of 55 kHz (35 mm wavelength) was used. Source counts from one to six were tested for different placements. Based on our statistical inversion approach and analysis of the results, three or four sources were found to lead to reliable inversion. The source configurations investigated were also ranked according to their performance. Our results can be used, for example, in the planning of planetary missions as well as in material testing.

  17. 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

    2016-01-01

    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%. PMID:27304957

  18. Anomaly Identification from Super-Low Frequency Electromagnetic Data for the Coalbed Methane Detection

    Science.gov (United States)

    Zhao, S. S.; Wang, N.; Hui, J.; Ye, X.; Qin, Q.

    2016-06-01

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

  19. Sparse source travel-time tomography of a laboratory target: accuracy and robustness of anomaly detection

    International Nuclear Information System (INIS)

    This study concerned conebeam travel-time tomography. The focus was on a sparse distribution of signal sources that can be necessary in a challenging in situ environment such as in asteroid tomography. The goal was to approximate the minimum number of source positions needed for robust detection of refractive anomalies, e.g., voids within an asteroid or a casting defects in concrete. Experimental ultrasonic data were recorded utilizing as a target a 150 mm plastic cast cube containing three stones with diameter between 22 and 41 mm. A signal frequency of 55 kHz (35 mm wavelength) was used. Source counts from one to six were tested for different placements. Based on our statistical inversion approach and analysis of the results, three or four sources were found to lead to reliable inversion. The source configurations investigated were also ranked according to their performance. Our results can be used, for example, in the planning of planetary missions as well as in material testing. (paper)

  20. 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%.

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

    Science.gov (United States)

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

    2014-12-01

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

  2. 利用 IGS 数据分析伊朗、巴基斯坦两次强震前的 TEC 异常%Analysis of the TEC Anomaly prior to Two Earthquakes in Pakistan and Iran by Adopting Statistics of IGS

    Institute of Scientific and Technical Information of China (English)

    齐曙光; 王曦; 郭广猛

    2014-01-01

    利用 IGS 发布的总电子含量(TEC)数据,采用从点到面的滑动标准差法,分析了伊朗和巴基斯坦两次强震前的 TEC 变化,发现两次强震前都出现 TEC 异常,表现为:(1)4月16日震前14日和16日出现 TEC 减少异常;(2)9月24日震前的19日、20日和21日出现 TEC 增大异常;(3)从平面分析上看两次地震前都出现了一定区域的 TEC 异常,异常程度从异常中心到外逐渐减弱,但异常中心不一定在震中位置。%Exploring and detecting the portents of earthquakes are common puzzles for experts and scholars at home and abroad.While studying earthquake in Alaska,Leonard and Barnes deter-mined that the ionosphere changes prior to earthquakes,and they first recognized the causal nexus between them.In recent years,the Global Positioning System (GPS)has expanded rapidly to offer a new and more effective method for observing Turkish earthquake code (Total Electron Content (TEC))values and detecting portents of earthquakes.In 1998,the International GPS Service(IGS) began to release global ionosphere service products.Due to the broader covering range of IGS,its statistics have been adopted by an increasing number of scholars to investigate TEC values and their anomalies,aiming at detecting portents of earthquakes.On April 16 and September 24,2013, two earthquakes,both at a scale of 7.7,struck Iran and Pakistan.At such a high scale,the two quakes showed the traits of narrow space,time occurrence,and high casualties and losses;there-fore,we used them as study targets.Existing research proves that when the sun cycles and geo-magnetic activity show a relatively equal period,the TEC does not exhibit strong fluctuation un-less there are other strong disturbance sources.Accordingly,in seismically active zones,excessive TEC fluctuation is much more likely to indicate impending earthquakes.We chose the core of the two earthquakes as our study targets,and we developed two wave

  3. Detection of a thin sheet magnetic anomaly by squid-gradiometer systems: possibility of hydrofracture azimuth determination

    Energy Technology Data Exchange (ETDEWEB)

    Overton, W.C. Jr.

    1978-12-01

    A study of the signal physics of magnetic anomaly detection was carried out by superconducting gradiometer and magnetometer loop systems with SQUID sensors for possible application to the LASL geothermal energy program. In particular, the crack produced by hydrofracture of a deep HDR geothermal borehole would be filled with a magnetic material such as ferrofluid. When polarized by the earth's field, this material would produce a localized crack magnetic anomaly which is characteristic of the azimuth of the vertical crack with respect to magnetic north. Signatures of the anomaly would be determined by taking rotation data before and after filling the crack with magnetic material. A mathematical description was found for these signatures. To test the theory and the feasibility of the idea, the deep borehole vertical cracks were simulated by using panels to define sheets 1.5 mm thick, 1.2 m wide, and 2.5 m high. When filled with ferrofluid of suitable magnetic permeability, the local anomaly develops. Signatures were measured with a horizontal axial gradiometer rotated about a vertical axis. Good agreement was found between theory and experiment for aximuths in the east and west quadrants but only fair agreement in the north and south quadrants.

  4. 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.

  5. Research on Anomaly Detection Method in Android Application%Android应用异常检测方法研究

    Institute of Scientific and Technical Information of China (English)

    刘晓明

    2015-01-01

    目前面向Android系统的攻击越来越多,因此,分析与检测Android恶意应用已经成为了一个非常重要的研究课题.本文主要从恶意应用类型,国内外主流检测技术等方面分析了Android恶意应用的检测方法研究现状,并基于当前的检测技术,提出仅将良性样本作为训练集来实现对未知Android应用进行异常检测的方法,取得了良好的实验结果.最后,本文分析了Android应用异常检测方法的发展趋势及未来主要研究方向.%Attacks targeting on Android system have become more and more frequently. Analyzing and detecting Android malicious applications thus has become an important issue. In this work, we analyze the research status of Android malicious application detection methods based on different types of malware and domestic and international mainstream detection technology. Based on the current detection technology, we propose an anomaly detection approach for malapps based on benign Android apps only and achieved good results. Finally, this paper analyzes the development trend of Android application anomaly detection methods and future research direction.

  6. 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

  7. An earthquake from space: detection of precursory magnetic anomalies from Swarm satellites before the 2015 M8 Nepal Earthquake

    Science.gov (United States)

    De Santis, A.; Balasis, G.; Pavón-Carrasco, F. J.; Cianchini, G.; Mandea, M.

    2015-12-01

    A large earthquake of around 8 magnitude occurred on 25 April 2015, 06:26 UTC, with epicenter in Nepal, causing more than 9000 fatalities and devastating destruction. The contemporary orbiting in the topside ionosphere of the three Swarm satellites by ESA makes it possible to look for possible pre-earthquake magnetic anomalous signals, likely due to some lithosphere-atmosphere-ionosphere (LAI) coupling. First, a wavelet analysis has been performed during the same day of the earthquake (from the external magnetic point of view, an exceptionally quiet day) with the result that a ULF anomalous and persisting signal (from around 3 to 6 UTC), is clearly detected before the earthquake. After this single-spot analysis, we performed a more extensive analysis for two months around the earthquake occurrence, to confirm or refute the cause-effect relationship. From the series of the detected magnetic anomalies (during night and magnetically quiet times) from Swarm satellites, we show that the cumulative numbers of anomalies follows the same typical power-law behavior of a critical system approaching its critical time, in our case, the large seismic event of 25 April, 2015, and then it recovers as the typical recovery phase after a large earthquake. The impressive similarity of this behavior with the analogous of seismic data analysis, provides strong support to the lithospheric origin of the satellite magnetic anomalies, as due to the LAI coupling during the preparation phase of the Nepal earthquake.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-03-15

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

  9. Detection of Characteristic Precipitation Anomaly Patterns of El Nino / La Nina in Time- variable Gravity Fields by GRACE

    Science.gov (United States)

    Heki, K.; Morishita, Y.

    2007-12-01

    GRACE (Gravity Recovery and Climate Experiment) satellites, launched in March 2002, have been mapping monthly gravity fields of the Earth, allowing us to infer changes in surface mass, e.g. water and ice. Past findings include the ice mass loss in southern Greenland (Luthcke et al., 2006) and its acceleration in 2004 (Velicogna and Wahr, 2006), crustal dilatation by the 2004 Sumatra Earthquake (Han et al., 2006) and the postseismic movement of water in mantle (Ogawa and Heki, 2007). ENSO (El Nino and Southern Oscillation) brings about global climate impacts, together with its opposite phenomenon, La Nina. Ropelewski and Halpert (1987) showed typical precipitation patterns in ENSO years; characteristic regional-scale precipitation anomalies occur in India, tropical and southern Africa and South America. Nearly opposite precipitation anomalies are shown to occur in La Nina years (Ropelewski and Halpert, 1988). Here we report the detection of such precipitation anomaly patterns in the GRACE monthly gravity data 2002 - 2007, which includes both La Nina (2005 fall - 2006 spring) and El Nino (2006 fall - 2007 spring) periods. We modeled the worldwide gravity time series with constant trends and seasonal changes, and extracted deviations of gravity values at two time epochs, i.e. February 2006 and 2007, and converted them into the changes in equivalent surface water mass. East Africa showed negative gravity deviation (-20.5 cm in water) in 2006 February (La Nina), which reversed to positive (18.7 cm) in 2007 February (El Nino). Northern and southern parts of South America also showed similar see-saw patterns. Such patterns closely resemble to those found meteorologically (Ropelewski and Halpert, 1987; 1988), suggesting the potential of GRACE as a sensor of inter-annual precipitation anomalies through changes in continental water storage. We performed numerical simulations of soil moisture changes at grid points in land area incorporating the CMAP precipitation data, NCEP

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

    International Nuclear Information System (INIS)

    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.

  11. Interpretation of Magnetic Anomalies in Salihli (Turkey) Geothermal Area Using 3-D Inversion and Edge Detection Techniques

    Science.gov (United States)

    Timur, Emre

    2016-04-01

    There are numerous geophysical methods used to investigate geothermal areas. The major purpose of this magnetic survey is to locate the boudaries of active hydrothermal system in the South of Gediz Graben in Salihli (Manisa/Turkey). The presence of the hydrothermal system had already been inferred from surface evidence of hydrothermal activity and drillings. Firstly, 3-D prismatic models were theoretically investigated and edge detection methods were utilized with an iterative inversion method to define the boundaries and the parameters of the structure. In the first step of the application, it was necessary to convert the total field anomaly into a pseudo-gravity anomaly map. Then the geometric boudaries of the structures were determined by applying a MATLAB based software with 3 different edge detection algorithms. The exact location of the structures were obtained by using these boundary coordinates as initial geometric parameters in the inversion process. In addition to these methods, reduction to pole and horizontal gradient methods were applied to the data to achieve more information about the location and shape of the possible reservoir. As a result, the edge detection methods were found to be successful, both in the field and as theoretical data sets for delineating the boundaries of the possible geothermal reservoir structure. The depth of the geothermal reservoir was determined as 2,4 km from 3-D inversion and 2,1 km from power spectrum methods.

  12. OGLE-2008-BLG-510: first automated real-time detection of a weak microlensing anomaly - brown dwarf or stellar binary?

    CERN Document Server

    Bozza, V; Rattenbury, N J; Joergensen, U G; Tsapras, Y; Bramich, D M; Udalski, A; Bond, I A; Liebig, C; Cassan, A; Fouque, P; Fukui, A; Hundertmark, M; Shin, I -G; Lee, S H; Choi, J -Y; Park, S -Y; Gould, A; Allan, A; Mao, S; Wyrzykowski, L; Street, R A; Buckley, D; Nagayama, T; Mathiasen, M; Hinse, T C; Novati, S Calchi; Harpsoee, K; Mancini, L; Scarpetta, G; Anguita, T; Burgdorf, M J; Horne, K; Hornstrup, A; Kains, N; Kerins, E; Kjaergaard, P; Masi, G; Rahvar, S; Ricci, D; Snodgrass, C; Southworth, J; Steele, I A; Surdej, J; Thoene, C C; Wambsganss, J; Zub, M; Albrow, M D; Batista, V; Beaulieu, J -P; Bennett, D P; Caldwell, J A R; Cole, A; Cook, K H; Coutures, C; Dieters, S; Prester, D Dominis; Donatowicz, J; Greenhill, J; Kane, S R; Kubas, D; Marquette, J -B; Martin, R; Menzies, J; Pollard, K R; Sahu, K C; Williams, A; Szymanski, M K; Kubiak, M; Pietrzynski, G; Soszynski, I; Poleski, R; Ulaczyk, K; DePoy, D L; Dong, S; Han, C; Janczak, J; Lee, C -U; Pogge, R W; Abe, F; Furusawa, K; Hearnshaw, J B; Itow, Y; Kilmartin, P M; Korpela, A V; Lin, W; Ling, C H; Masuda, K; Matsubara, Y; Miyake, N; Muraki, Y; Ohnishi, K; Perrott, Y C; Saito, To; Skuljan, L; Sullivan, D J; Sumi, T; Suzuki, D; Sweatman, W L; Tristram, P J; Wada, K; Yock, P C M; Gulbis, A; Hashimoto, Y; Kniazev, A; Vaisanen, P

    2012-01-01

    The microlensing event OGLE-2008-BLG-510 is characterised by an evident asymmetric shape of the peak, promptly detected by the ARTEMiS system in real time. The skewness of the light curve appears to be compatible both with binary-lens and binary-source models, including the possibility that the lens system consists of an M dwarf orbited by a brown dwarf. The detection of this microlensing anomaly and our analysis demonstrates that: 1) automated real-time detection of weak microlensing anomalies with immediate feedback is feasible, 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 crucial, where systematics can be confused with real features, in particular small higher-order effects such as orbital motion signatures. It moreover becomes apparent that events wit...

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

    International Nuclear Information System (INIS)

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

  14. Detection of anticipatory postural adjustments prior to gait initiation using inertial wearable sensors

    Directory of Open Access Journals (Sweden)

    Sekine Masaki

    2011-04-01

    Full Text Available Abstract Background The present study was performed to evaluate and characterize the potential of accelerometers and angular velocity sensors to detect and assess anticipatory postural adjustments (APAs generated by the first step at the beginning of the gait. This paper proposes an algorithm to automatically detect certain parameters of APAs using only inertial sensors. Methods Ten young healthy subjects participated in this study. The subjects wore an inertial unit containing a triaxial accelerometer and a triaxial angular velocity sensor attached to the lower back and one footswitch on the dominant leg to detect the beginning of the step. The subjects were standing upright on a stabilometer to detect the center of pressure displacement (CoP generated by the anticipatory adjustments. The subjects were asked to take a step forward at their own speed and stride length. The duration and amplitude of the APAs detected by the accelerometer and angular velocity sensors were measured and compared with the results obtained from the stabilometer. The different phases of gait initiation were identified and compared using inertial sensors. Results The APAs were detected by all of the sensors. Angular velocity sensors proved to be adequate to detect the beginning of the step in a manner similar to the footswitch by using a simple algorithm, which is easy to implement in low computational power devices. The amplitude and duration of APAs detected using only inertial sensors were similar to those detected by the stabilometer. An automatic algorithm to detect APA duration using triaxial inertial sensors was proposed. Conclusions These results suggest that the feasibility of accelerometers is improved through the use of angular velocity sensors, which can be used to automatically detect and evaluate APAs. The results presented can be used to develop portable sensors that may potentially be useful for monitoring patients in the home environment, thus

  15. Anomaly detection driven active learning for identifying suspicious tracks and events in WAMI video

    Science.gov (United States)

    Miller, David J.; Natraj, Aditya; Hockenbury, Ryler; Dunn, Katherine; Sheffler, Michael; Sullivan, Kevin

    2012-06-01

    We describe a comprehensive system for learning to identify suspicious vehicle tracks from wide-area motion (WAMI) video. First, since the road network for the scene of interest is assumed unknown, agglomerative hierarchical clustering is applied to all spatial vehicle measurements, resulting in spatial cells that largely capture individual road segments. Next, for each track, both at the cell (speed, acceleration, azimuth) and track (range, total distance, duration) levels, extreme value feature statistics are both computed and aggregated, to form summary (p-value based) anomaly statistics for each track. Here, to fairly evaluate tracks that travel across different numbers of spatial cells, for each cell-level feature type, a single (most extreme) statistic is chosen, over all cells traveled. Finally, a novel active learning paradigm, applied to a (logistic regression) track classifier, is invoked to learn to distinguish suspicious from merely anomalous tracks, starting from anomaly-ranked track prioritization, with ground-truth labeling by a human operator. This system has been applied to WAMI video data (ARGUS), with the tracks automatically extracted by a system developed in-house at Toyon Research Corporation. Our system gives promising preliminary results in highly ranking as suspicious aerial vehicles, dismounts, and traffic violators, and in learning which features are most indicative of suspicious tracks.

  16. A Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a prior surface reflectance database

    Science.gov (United States)

    Sun, Lin; Wei, Jing; Wang, Jian; Mi, Xueting; Guo, Yamin; Lv, Yang; Yang, Yikun; Gan, Ping; Zhou, Xueying; Jia, Chen; Tian, Xinpeng

    2016-06-01

    Conventional cloud detection methods are easily affected by mixed pixels, complex surface structures, and atmospheric factors, resulting in poor cloud detection results. To minimize these problems, a new Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a priori surface reflectance database is proposed in this paper. A monthly surface reflectance database is constructed using long-time-sequenced MODerate resolution Imaging Spectroradiometer surface reflectance product (MOD09A1) to provide the surface reflectance of the underlying surfaces. The relationships between the apparent reflectance changes and the surface reflectance are simulated under different observation and atmospheric conditions with the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) model, and the dynamic threshold cloud detection models are developed. Two typical remote sensing data with important application significance and different sensor parameters, MODIS and Landsat 8, are selected for cloud detection experiments. The results were validated against the visual interpretation of clouds and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud measurements. The results showed that the UDTCDA can obtain a high precision in cloud detection, correctly identifying cloudy pixels and clear-sky pixels at rates greater than 80% with error rate and missing rate of less than 20%. The UDTCDA cloud product overall shows less estimation uncertainty than the current MODIS cloud mask products. Moreover, the UDTCDA can effectively reduce the effects of atmospheric factors and mixed pixels and can be applied to different satellite sensors to realize long-term, large-scale cloud detection operations.

  17. Selecting training and test images for optimized anomaly detection algorithms in hyperspectral imagery through robust parameter design

    Science.gov (United States)

    Mindrup, Frank M.; Friend, Mark A.; Bauer, Kenneth W.

    2011-06-01

    There are numerous anomaly detection algorithms proposed for hyperspectral imagery. Robust parameter design (RPD) techniques have been applied to some of these algorithms in an attempt to choose robust settings capable of operating consistently across a large variety of image scenes. Typically, training and test sets of hyperspectral images are chosen randomly. Previous research developed a frameworkfor optimizing anomaly detection in HSI by considering specific image characteristics as noise variables within the context of RPD; these characteristics include the Fisher's score, ratio of target pixels and number of clusters. This paper describes a method for selecting hyperspectral image training and test subsets yielding consistent RPD results based on these noise features. These subsets are not necessarily orthogonal, but still provide improvements over random training and test subset assignments by maximizing the volume and average distance between image noise characteristics. Several different mathematical models representing the value of a training and test set based on such measures as the D-optimal score and various distance norms are tested in a simulation experiment.

  18. Detection of anomalies in NLO sulphamic acid single crystals by ultrasonic and thermal studies

    Indian Academy of Sciences (India)

    GEORGE VARUGHESE

    2016-09-01

    The ultrasonic pulse echo overlap technique (PEO) has been used to measure the velocities of 10 MHz acoustic waves in sulphamic acid single crystals in the range of 300–400 K. This study evaluated all the elastic stiffnessconstants, compliance constants and Poisson’s ratios of the crystal. The temperature variations of the elastic constants have been determined. The phase transition studies above room temperature were investigated using ultrasonic PEO technique. This study has suggested new weak elastic anomalies for the crystal around 330 K. The transverse elastic constants C44 and C66 have shown clear thermal hysteresis of 2 K. The present differential scanningcalorimetric (DSC) studies carried out at a slow heating rate have also suggested weak phase transition around 331 K. The present elastic and thermal studies have been substantiated by already reported DC electrical conductivitystudies around 330 K.

  19. 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.

  20. A MACHINE LEARNING APPROACH TO ANOMALY-BASED DETECTION ON ANDROID PLATFORMS

    Directory of Open Access Journals (Sweden)

    Joshua Abah

    2015-11-01

    Full Text Available The emergence of mobile platforms with increased storage and computing capabilities and the pervasive use of these platforms for sensitive applications such as online banking, e-commerce and the storage of sensitive information on these mobile devices have led to increasing danger associated with malware targeted at these devices. Detecting such malware presents inimitable challenges as signature-based detection techniques available today are becoming inefficient in detecting new and unknown malware. In this research, a machine learning approach for the detection of malware on Android platforms is presented. The detection system monitors and extracts features from the applications while in execution and uses them to perform in-device detection using a trained K-Nearest Neighbour classifier. Results shows high performance in the detection rate of the classifier with accuracy of 93.75%, low error rate of 6.25% and low false positive rate with ability of detecting real Android malware.

  1. 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.

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

    OpenAIRE

    Firoozeh Ahmadi; Hadieh Haghighi

    2011-01-01

    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-...

  3. Thermal and TEC anomalies detection using an intelligent hybrid system around the time of the Saravan, Iran, (Mw = 7.7) earthquake of 16 April 2013

    Science.gov (United States)

    Akhoondzadeh, M.

    2014-02-01

    A powerful earthquake of Mw = 7.7 struck the Saravan region (28.107° N, 62.053° E) in Iran on 16 April 2013. Up to now nomination of an automated anomaly detection method in a non linear time series of earthquake precursor has been an attractive and challenging task. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) have revealed strong potentials in accurate time series prediction. This paper presents the first study of an integration of ANN and PSO method in the research of earthquake precursors to detect the unusual variations of the thermal and total electron content (TEC) seismo-ionospheric anomalies induced by the strong earthquake of Saravan. In this study, to overcome the stagnation in local minimum during the ANN training, PSO as an optimization method is used instead of traditional algorithms for training the ANN method. The proposed hybrid method detected a considerable number of anomalies 4 and 8 days preceding the earthquake. Since, in this case study, ionospheric TEC anomalies induced by seismic activity is confused with background fluctuations due to solar activity, a multi-resolution time series processing technique based on wavelet transform has been applied on TEC signal variations. In view of the fact that the accordance in the final results deduced from some robust methods is a convincing indication for the efficiency of the method, therefore the detected thermal and TEC anomalies using the ANN + PSO method were compared to the results with regard to the observed anomalies by implementing the mean, median, Wavelet, Kalman filter, Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM) and Genetic Algorithm (GA) methods. The results indicate that the ANN + PSO method is quite promising and deserves serious attention as a new tool for thermal and TEC seismo anomalies detection.

  4. COLLABORATIVE ANOMALY-BASED INTRUSION DETECTION IN MOBILE AD HOC NETWORKS

    Directory of Open Access Journals (Sweden)

    SUNIL K. PARYANI,

    2011-05-01

    Full Text Available Intrusion Prevention is first line of defense against attacks in MANET. Intrusion Detection and response presents a second line of defense. New vulnerabilities will continue to invent new attack methods so new technology such as MANET, we focus on developing effective detection approaches In this paper, we present an intrusion detection system for detection of malicious node in mobile ad hoc network. The technique is designed for detection of malicious nodes in a neighborhood in which each pair of nodes are within radio range of each other. Such a neighborhood of nodes is known as a clique. [1] This technique is aimed to reduce the computation and communication costs to select a monitor node and reduces the message passing between the nodes to detect a malicious node from the cluster hence there very less traffic and less chances of a collision.

  5. The Cyborg Astrobiologist: Matching of Prior Textures by Image Compression for Geological Mapping and Novelty Detection

    CERN Document Server

    McGuire, P C; Bruner, K R; Gross, C; Ormö, J; Smosna, R A; Walter, S; Wendt, L

    2013-01-01

    (abridged) We describe an image-comparison technique of Heidemann and Ritter that uses image compression, and is capable of: (i) detecting novel textures in a series of images, as well as of: (ii) alerting the user to the similarity of a new image to a previously-observed texture. This image-comparison technique has been implemented and tested using our Astrobiology Phone-cam system, which employs Bluetooth communication to send images to a local laptop server in the field for the image-compression analysis. We tested the system in a field site displaying a heterogeneous suite of sandstones, limestones, mudstones and coalbeds. Some of the rocks are partly covered with lichen. The image-matching procedure of this system performed very well with data obtained through our field test, grouping all images of yellow lichens together and grouping all images of a coal bed together, and giving a 91% accuracy for similarity detection. Such similarity detection could be employed to make maps of different geological unit...

  6. 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

  7. 64. The prevalence of coronary artery anomalies in Qassim province detected by cardiac computed tomography angiography

    Directory of Open Access Journals (Sweden)

    O. smettei

    2016-07-01

    Full Text Available Coronary artery anomalies (CAAs affect about 1% of the general population based on invasive coronary angiography (ICA data, computed tomography angiography (CTA enables better visualization of the origin, course, relation to the adjacent structures, and termination of CAAs compared to ICA. The aim of our work is to estimate the frequency of CAAs in Qassim province among patients underwent cardiac CTA at PSCCQ. Retrospective analysis of the CTA data of 2235 patients between 2009 and 2015. The prevalence of CAAs in our study was 1.029%. Among the 2235 patients, 241 (10.78% had CAAs or coronary variants, 198 (8.85% had myocardial bridging, 34 (1.52% had a variable location of the Coronary Ostia, Twenty two (0.98% had a separate origin of left anterior descending (LAD and left circumflex coronary (LCX arteries, ten (0.447% had a separate origin of the RCA and the Conus artery. Seventeen (0.76% had an anomalous origin of the coronaries. Six (0.268% had a coronary artery fistula, which is connected mainly to the right heart chambers, one of these fistulas was complicated by acute myocardial infarction. The incidence of CAAs in our patient population was similar to the former studies, CTA is an excellent tool for diagnosis and guiding the management of the CAAs.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  9. Subsurface faults detection based on magnetic anomalies investigation: A field example at Taba protectorate, South Sinai

    Science.gov (United States)

    Khalil, Mohamed H.

    2016-08-01

    Quantitative interpretation of the magnetic data particularly in a complex dissected structure necessitates using of filtering techniques. In Taba protectorate, Sinai synthesis of different filtering algorithms was carried out to distinct and verifies the subsurface structure and estimates the depth of the causative magnetic sources. In order to separate the shallow-seated structure, filters of the vertical derivatives (VDR), Butterworth high-pass (BWHP), analytic signal (AS) amplitude, and total horizontal derivative of the tilt derivative (TDR_THDR) were conducted. While, filters of the apparent susceptibility and Butterworth low-pass (BWLP) were conducted to identify the deep-seated structure. The depths of the geological contacts and faults were calculated by the 3D Euler deconvolution. Noteworthy, TDR_THDR was independent of geomagnetic inclination, significantly less susceptible to noise, and more sensitive to the details of the shallow superimposed structures. Whereas, the BWLP proved high resolution capabilities in attenuating the shorter wavelength of the near surface anomalies and emphasizing the longer wavelength derived from deeper causative structure. 3D Euler deconvolution (SI = 0) was quite amenable to estimate the depths of superimposed subsurface structure. The pattern, location, and trend of the deduced shallow and deep faults were conformed remarkably to the addressed fault system.

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

    Directory of Open Access Journals (Sweden)

    Borja Rodríguez-Cuenca

    2015-09-01

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

  11. 四川汶川8.0级地震地下流体异常分析%Analysis of Underground Fluid Anomalies Prior to the Wenchuan MS8.0 Earthquake

    Institute of Scientific and Technical Information of China (English)

    王小娟; 李旭升; 牛延平; 田野

    2014-01-01

    .Second,because the fracture surface continued for several hundred kilometers through the town,the earthquake wreaked havoc on the buildings.Third,the earth-quake occurred in the mountains.Fourth,secondary effects aggravated the disaster.Field investi-gation and the precursory data research by previous scholars revealed that the Wenchuan earth-quake included macro-precursor abnormalities and seismic effects.This earthquake had the largest magnitude of those occurring in north-south seismic belt in recent years.Therefore,it is necessa-ry to summarize the Wenchuan earthquake precursor anomaly. In recent years,China’s seismic monitoring network has become denser,and the observation scale has become greater.Data have been accumulated for moderate earthquakes.In addition, many achievements have been in fundamental theories,earthquake prediction methods,and the precursory mechanism.In the history of more than 40 years of earthquake monitoring and fore-casting,20 destructive events have been predicted with varying degrees of success.Despite some progress in the method of forecasting,it is quite difficult to predict earthquakes.Therefore, earthquake prediction is still in the primary stage.Although the Wenchuan earthquake was not predicted,some abnormal fluid phenomena appeared before the earthquake.By summarizing and analyzing data after the earthquake,some scholars detected 28 credible underground fluid anoma-lies within 1000 km in addition to 1 1 suspected abnormalities and 1 94 coseismic abnormalities.A month after the Wenchuan earthquake,the China Earthquake Administration subsurface fluid disciplinary technical coordination group found 39 suspected underground fluid abnormalities within 1,000 km from the epicenter.According to the Sichuan Seismological Bureau of Statistics, nine underground fluid anomalies may be related to the Wenchuan earthquake.Despite an excep-tion,it is certain that subsurface fluid exists before the earthquake anomalies.The following ex-ception is divided into

  12. On the possibility of detecting large-scale crustal remanent magnetization with Magsat vector magnetic anomaly data

    Science.gov (United States)

    Galliher, S. C.; Mayhew, M. A.

    1982-01-01

    Magnetic anomaly component data measured by Magsat is compared with synthetic anomaly component fields arising from an equivalent source dipole array at the earth's surface generated from total field anomaly data alone. It is found that the synthetic components fit the component data regardless of the dipole orientation assigned to the equivalent sources and of the dipole spacing. Tentative conclusions are: (1) over the U.S., vector anomaly fields can be determined to the accuracy of the measurements from the total field anomaly data alone; and (2) the equivalent source technique is not useful for determining the direction of large-scale crustal magnetization.

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

    Energy Technology Data Exchange (ETDEWEB)

    VALENTE,J.FISHBONE,L.ET AL.

    2003-07-13

    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

  14. 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.

  15. ADRISYA: A Flow Based Anomaly Detection System for Slow and Fast Scan

    Directory of Open Access Journals (Sweden)

    Muraleedharan N

    2010-10-01

    Full Text Available Attackers perform port scan to find reachability, liveness and running services in a system or network.Current day scanning tools provide different scanning options and capable of evading various securitytools like firewall, IDS and IPS. So in order to detect and prevent attacks in the early stages, an accuratedetection of scanning activity in real time is very much essential. In this paper we present a flow basedprotocol behaviour analysis system to detect TCP based slow and fast scan. This system providesscalable, accurate and generic solution to TCP based scanning by means of automatic behaviour analysisof the network traffic. Detection capability of proposed system is compared with SNORT and resultproves the high detection rate of the system over SNORT.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-07-15

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

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

    International Nuclear Information System (INIS)

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

  18. Exocrine-to-endocrine differentiation is detectable only prior to birth in the uninjured mouse pancreas

    Directory of Open Access Journals (Sweden)

    Murtaugh L Charles

    2010-04-01

    Full Text Available Abstract Background Histological evidence suggests that insulin-producing beta (β-cells arise in utero from duct-like structures of the fetal exocrine pancreas, and genetic lineage tracing studies indicate that they are maintained in the adult by self-renewal. These studies have not addressed the origin of the new β-cells that arise in large numbers shortly after birth, and contradictory lineage tracing results have been published regarding the differentiation potential of duct cells in this period. We established an independent approach to address this question directly. Results We generated mice in which duct and acinar cells, comprising the exocrine pancreas, can be genetically marked by virtue of their expressing the mucin gene Muc1. Using these mice, we performed time-specific lineage tracing to determine if these cells undergo endocrine transdifferentiation in vivo. We find that Muc1+ cells do give rise to β-cells and other islet cells in utero, providing formal proof that mature islets arise from embryonic duct structures. From birth onwards, Muc1 lineage-labeled cells are confined to the exocrine compartment, with no detectable contribution to islet cells. Conclusions These results argue against a significant contribution by exocrine transdifferentiation to the normal postnatal expansion and maintenance of β-cell mass. Exocrine transdifferentiation has been proposed to occur during injury and regeneration, and our experimental model is suited to test this hypothesis in vivo.

  19. Hardening and termination of long-duration gamma rays detected prior to lightning

    CERN Document Server

    Tsuchiya, H; Iwata, K; Yamada, S; Yuasa, T; Kitaguchi, T; Kawaharada, M; Nakazawa, K; Kokubun, M; Kato, H; Okano, M; Tamagawa, T; Makishima, K

    2013-01-01

    We report the first observation of 3$-$30 MeV prolonged gamma-ray emission that was abruptly terminated by lightning. The gamma-ray detection was made during winter thunderstorms on December 30, 2010 by the Gamma-Ray Observation of Winter THunderclouds (GROWTH) experiment carried out in a coastal area along the Sea of Japan. The gamma-ray flux lasted for less than 3 min, continuously hardening closer to the lightning occurrence. The hardening at energies of 3$-$10 MeV energies was most prominent. The gamma-ray flux abruptly ceased less than 800 ms before the lightning flash that occurred over 5 km away from the experimental site. In addition, we observed a clear difference in the duration of the 3$-$10 MeV gamma rays and those $>$10 MeV, suggesting that the area of $>$10 MeV gamma-ray emission is considerably smaller than that of the lower-energy gamma rays. This work may give a manifestation that a local region emitting prolonged gamma rays connects with a distant region to initiate lightning.

  20. Using a nano-flare probe to detect RNA in live donor cells prior to somatic cell nuclear transfer.

    Science.gov (United States)

    Fu, Bo; Ren, Liang; Liu, Di; Ma, Jian-Zhang; An, Tie-Zhu; Yang, Xiu-Qin; Ma, Hong; Guo, Zhen-Hua; Zhu, Meng; Bai, Jing

    2016-01-01

    Many transgenes are silenced in mammalian cells (donor cells used for somatic cell nuclear transfer [SCNT]). Silencing correlated with a repressed chromatin structure or suppressed promoter, and it impeded the production of transgenic animals. Gene transcription studies in live cells are challenging because of the drawbacks of reverse-transcription polymerase chain reaction and fluorescence in situ hybridization. Nano-flare probes provide an effective approach to detect RNA in living cells. We used 18S RNA, a housekeeping gene, as a reference gene. This study aimed to establish a platform to detect RNA in single living donor cells using a Nano-flare probe prior to SCNT and to verify the safety and validity of the Nano-flare probe in order to provide a technical foundation for rescuing silenced transgenes in transgenic cloned embryos. We investigated cytotoxic effect of the 18S RNA-Nano-flare probe on porcine fetal fibroblasts, characterized the distribution of the 18S RNA-Nano-flare probe in living cells and investigated the effect of the 18S RNA-Nano-flare probe on the development of cloned embryos after SCNT. The cytotoxic effect of the 18S RNA-Nano-flare probe on porcine fetal fibroblasts was dose-dependent, and 18S RNA was detected using the 18S RNA-Nano-flare probe. In addition, treating donor cells with 500 pM 18S RNA-Nano-flare probe did not have adverse effects on the development of SCNT embryos at the pre-implantation stage. In conclusion, we established a preliminary platform to detect RNA in live donor cells using a Nano-flare probe prior to SCNT.

  1. A prior-knowledge-based threshold segmentation method of forward-looking sonar images for underwater linear object detection

    Science.gov (United States)

    Liu, Lixin; Bian, Hongyu; Yagi, Shin-ichi; Yang, Xiaodong

    2016-07-01

    Raw sonar images may not be used for underwater detection or recognition directly because disturbances such as the grating-lobe and multi-path disturbance affect the gray-level distribution of sonar images and cause phantom echoes. To search for a more robust segmentation method with a reasonable computational cost, a prior-knowledge-based threshold segmentation method of underwater linear object detection is discussed. The possibility of guiding the segmentation threshold evolution of forward-looking sonar images using prior knowledge is verified by experiment. During the threshold evolution, the collinear relation of two lines that correspond to double peaks in the voting space of the edged image is used as the criterion of termination. The interaction is reflected in the sense that the Hough transform contributes to the basis of the collinear relation of lines, while the binary image generated from the current threshold provides the resource of the Hough transform. The experimental results show that the proposed method could maintain a good tradeoff between the segmentation quality and the computational time in comparison with conventional segmentation methods. The proposed method redounds to a further process for unsupervised underwater visual understanding.

  2. Detection of CD2 expression in chicken hematogenic embryo yolk sac lymphoid cells prior to thymus genesis

    Institute of Scientific and Technical Information of China (English)

    Dongyu Zhou; Jigui Wang; Weiquan Liu; Rongxiu Liu; Yuehu Pei

    2008-01-01

    Lymphoid mononuclear cells from chick embryos at stage 16 were collected prior to fetal liver and thymus genesis to study the differentiation and function of the hematogenic yolk sac and to detect whether CD2 occurs on the surface of lymphoid mononuclear cells.The phenotype and functional activity of the cell surface protein E receptor and the ultrastructure of embryonic E+ cells were compared with those of mature T cells.Our results indicate 99.36% homology between the E receptors of embryonic lymphocytes and mature T cells.Other similarities,including molecular distribution,motivation,the ability to form an erythrocyte rosette,the structure of the receptor-ligand complex,and the conformation of the signal channel,were detected between embryonic lymphocytes and mature CD2-expressing T cells.These results indicate that CD2 is already expressed prior to fetal fiver and thymus genesis and that its expression is not dependent on the thymic microenvironment.

  3. 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.

  4. Information-theoretic analysis of x-ray scatter and phase architectures for anomaly detection

    Science.gov (United States)

    Coccarelli, David; Gong, Qian; Stoian, Razvan-Ionut; Greenberg, Joel A.; Gehm, Michael E.; Lin, Yuzhang; Huang, Liang-Chih; Ashok, Amit

    2016-05-01

    Conventional performance analysis of detection systems confounds the effects of the system architecture (sources, detectors, system geometry, etc.) with the effects of the detection algorithm. Previously, we introduced an information-theoretic approach to this problem by formulating a performance metric, based on Cauchy-Schwarz mutual information, that is analogous to the channel capacity concept from communications engineering. In this work, we discuss the application of this metric to study novel screening systems based on x-ray scatter or phase. Our results show how effective use of this metric can impact design decisions for x-ray scatter and phase systems.

  5. ANOMALY INTRUSION DETECTION DESIGN USING HYBRID OF UNSUPERVISED AND SUPERVISED NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    M. Bahrololum

    2009-07-01

    Full Text Available This paper proposed a new approach to design the system using a hybrid of misuse and anomalydetection for training of normal and attack packets respectively. The utilized method for attack training isthe combination of unsupervised and supervised Neural Network (NN for Intrusion Detection System. Bythe unsupervised NN based on Self Organizing Map (SOM, attacks will be classified into smallercategories considering their similar features, and then unsupervised NN based on Backpropagation willbe used for clustering. By misuse approach known packets would be identified fast and unknown attackswill be able to detect by this method.

  6. A survey on anomaly and signature based intrusion detection system (IDS

    Directory of Open Access Journals (Sweden)

    Mrs.Anshu Gangwar

    2014-04-01

    Full Text Available Security is considered as one of the most critical parameter for the acceptance of any networking technology. Information in transit must be protected from unauthorized release and modification, and the connection itself must be established and maintained securely malicious users have taken advantage of this to achieve financial gain or accomplish some corporate or personal agenda. Denial of Service (DoS and distributed DoS (DDoS attacks are evolving continuously. These attacks make network resources unavailable for legitimate users which results in massive loss of data, resources and money. Combination of Intrusion detection System and Firewall is used by Business Organizations to detect and p revent Organizations‟ network from these attacks. Signatures to detect them are not available. This paper presents a light-Weight mechanism to detect novel DoS/DDoS (Resource Consumption attacks and automatic signature generation process to represent them in real time. Experimental results are provided to support the proposed mechanism.

  7. Automated detection and analysis of volcanic thermal anomalies through the combined use of SEVIRI and MODIS

    OpenAIRE

    Ganci, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Del Negro, C.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Vicari, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; Fortuna, L.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia

    2010-01-01

    Multispectral infrared observations carried out by the spacecrafts have shown that spaceborne remote sensing of high-temperature volcanic features is feasible and robust enough to turn into volcano monitoring. Especially meteorological satellites have proven a powerful instrument to detect and monitor dynamic phenomena, such as volcanic processes, allowing very high temporal resolution despite of their low spatial resolution. An automated system that uses both EOS-MODIS and ...

  8. Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform

    OpenAIRE

    Zhou, Bowen; Shariat, Shahriar

    2016-01-01

    Online media offers opportunities to marketers to deliver brand messages to a large audience. Advertising technology platforms enables the advertisers to find the proper group of audiences and deliver ad impressions to them in real time. The recent growth of the real time bidding has posed a significant challenge on monitoring such a complicated system. With so many components we need a reliable system that detects the possible changes in the system and alerts the engineering team. In this pa...

  9. 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)

    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

  10. Détection d'anomalies bathymétriques à partir de profils altimétriques = Detection of bathymetric anomalies from altimetric profiles

    OpenAIRE

    Le Quentrec, M. F.

    1992-01-01

    De nombreux travaux ont montré l'intérêt des données des satellites altimétriques pour la détection des anomalies bathymétriques océaniques. La bonne corrélation entre les altitudes du géoïde déduites des mesures altimétriques et les structures bathymétriques de courtes longueurs d'onde (de 35 à 245 km pour le satellite SEASAT) a permis, soit de découvrir de nouveaux reliefs sous-marins (plus d'une centaine de monts sous-marins ont été détectés dans le Pacifique Sud par cette méthode) soit de...

  11. Anomaly Detection in Electroencephalogram Signals Using Unconstrained Minimum Average Correlation Energy Filter

    Directory of Open Access Journals (Sweden)

    Aini Hussain

    2009-01-01

    Full Text Available Problem statement: Electroencepharogram (EEG is an extremely complex signal with very low signal to noise ratio and these attributed to difficulty in analyzing the signal. Hence for detecting abnormal segment, a distinctive method is required to train the technologist to distinguish the anomalous in EEG data. The objective of this study was to create a framework to analyze EEG signals recorded from epileptic patients by evaluating the potential of UMACE filter to detect changes in single-channel EEG data during routine epilepsy monitoring. Approach: Normally, the peak to side lobe ratio (PSR of a UMACE filter was employed as an indicator if a test data is similar to an authentic class or vice versa, however in this study, the consistent changes of the correlation output known as Region Of Interest (ROI was plotted and monitored. Based on this approach, a novel method to analyze and distinguish variances in scalp EEG as well as comparing both normal and abnormal regions of the patient’s EEG was assessed. The performance of the novelty detection was examined based on the onset and end time of each seizure in the ROI plot. Results: Results showed that using ROI plot of variances one can distinguish irregularities in the EEG data. The advantage of the proposed technique was that it did not require large amount of data for training. Conclusion: As such, it was feasible to perform seizure analysis as well as localizing seizure onsets. In short, the technique can be used as a guideline for faster diagnosis in a lengthy EEG recording.

  12. Detection of Entamoeba histolytica DNA in the saliva of amoebic liver abscess patients who received prior treatment with metronidazole.

    Science.gov (United States)

    Khairnar, Krishna; Parija, Subhash Chandra

    2008-12-01

    Saliva is an easily-accessible and a non-invasive clinical specimen alternate to blood and liver pus. An attempt was made to detect Entamoeba histolytica DNA released in the saliva of amoebic liver abscess (ALA) patients by applying 16S-like rRNA gene-based nested multiplex polymerase chain reaction (NM-PCR). The NM-PCR detected E. histolytica DNA in the saliva of eight (28.6%) of 28 ALA patients. The NM-PCR result was negative for E. histolytica DNA in the saliva of all the eight ALA patients who were tested prior to treatment with metronidazole but was positive in the saliva of eight (40%) of 20 ALA patient who were tested after therapy with metronidazole. The NM-PCR detected E. histolytica DNA in liver abscess pus of all 28 (100%) patients with ALA. The TechLab E. histolytica II enzyme-linked immunosorbent assay was positive for E. histolytica Gal/GalNAc lectin antigen in the liver abscess pus of 13 (46.4%) of the 28 ALA patients. The indirect haemagglutination (IHA) test was positive for anti-amoebic antibodies in the serum of 22 (78.6%) of the 28 ALA patients and 2 (5.7%) of 35 healthy controls. The present study, for the first time, demonstrates the release of E. histolytica DNA in the saliva of ALA patients by applying NM-PCR. PMID:19069620

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

    Science.gov (United States)

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

    2016-08-01

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

  14. Structural Anomalies Detected in Ceramic Matrix Composites Using Combined Nondestructive Evaluation and Finite Element Analysis (NDE and FEA)

    Science.gov (United States)

    Abdul-Aziz, Ali; Baaklini, George Y.; Bhatt, Ramakrishna T.

    2003-01-01

    and the experimental data. Furthermore, modeling of the voids collected via NDE offered an analytical advantage that resulted in more accurate assessments of the material s structural strength. The top figure shows a CT scan image of the specimen test section illustrating various hidden structural entities in the material and an optical image of the test specimen considered in this study. The bottom figure represents the stress response predicted from the finite element analyses (ref .3 ) for a selected CT slice where it clearly illustrates the correspondence of the high stress risers due to voids in the material with those predicted by the NDE. This study is continuing, and efforts are concentrated on improving the modeling capabilities to imitate the structural anomalies as detected.

  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

    screening policies in 18 countries and 1.13 million births in 12 countries in 2002-04. METHODS: (i) Questionnaire on national screening policies and termination of pregnancy for fetal anomaly (TOPFA) laws in 2004. (ii) Analysis of data on prenatal detection and termination for Down's syndrome and neural...... 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...... associated with wide country variation in prenatal detection rates for Down's syndrome and NTD....

  16. Urinary System anomalies at birth

    Directory of Open Access Journals (Sweden)

    Sharada B. Menasinkai

    2015-06-01

    Full Text Available Background: Congenital anomalies of urinary system are common and are found in 3-4% of population, and lethal urinary anomalies account for 10% of termination of pregnancy. Methods: A study was done to know the incidence of congenital anomalies at birth for the period of 4 months from May 99 - Sept 99 at Cheluvamba hospital attached to Mysore medical college. Congenital anomalies in the still births, live births and aborted fetuses >20 weeks were studied along with the case history and ultrasound reports. Aborted fetuses and still born babies were collected for autopsy after the consent of parents. These babies were fixed in 10% formalin and autopsy was done after fixing, and anomalies were noted. Results: Total births during study period were 3000. There were 61 babies with congenital anomalies and 6 babies had anomalies of urinary system. Among the urinary system anomalies 1 baby had bilateral renal agenesis, 1 baby had unilateral renal agenesis with anophthalmia (Fraser syndrome, 2 babies had Multicystic dysplastic kidney disease (MCDK and 1 live baby had hydronephrosis due to obstruction at pelvi ureteric junction, and 1 live female baby had polycystic kidneys. Conclusion: Incidence of urinary system anomalies in the present study was 2 per 1000 births. U/S detection of urinary anomalies varies with period of gestation, amniotic fluid volume and visualisation of urinary bladder. Autopsy helps to detect renal agenesis. [Int J Res Med Sci 2015; 3(3.000: 743-748

  17. SADM potentiometer anomaly investigations

    Science.gov (United States)

    Wood, Brian; Mussett, David; Cattaldo, Olivier; Rohr, Thomas

    2005-07-01

    During the last 3 years Contraves Space have been developing a Low Power (1-2kW) Solar Array Drive Mechanism (SADM) aimed at small series production. The mechanism was subjected to two test programmes in order to qualify the SADM to acceptable levels. During the two test programmes, anomalies were experienced with the Potentiometers provided by Eurofarad SA and joint investigations were undertaken to resolve why these anomalies had occurred. This paper deals with the lessons learnt from the failure investigation on the two Eurofarad (rotary) Potentiometer anomaly. The Rotary Potentiometers that were used were fully redundant; using two back to back mounted "plastic tracks". It is a pancake configuration mounted directly to the shaft of the Slip Ring Assembly at the extreme in-board end of the SADM. It has no internal bearings. The anomaly initially manifested itself as a loss of performance in terms of linearity, which was first detected during Thermal Vacuum testing. A subsequent anomaly manifested itself by the complete failure of the redundant potentiometer again during thermal vacuum testing. This paper will follow and detail the chain of events following this anomaly and identifies corrective measures to be applied to the potentiometer design and assembly process.

  18. 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.

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

    Directory of Open Access Journals (Sweden)

    M. Akhoondzadeh

    2013-04-01

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

  20. 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.

  1. 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

  2. Inverter Anomaly Detection Algorithm Research and Simulation%变频器异常检测方法研究与仿真

    Institute of Scientific and Technical Information of China (English)

    莫桂江

    2012-01-01

    Put forward a kind of abnormal parameter mapping estimate of the frequency converter dynamic parameters abnormal detection algorithm is proposed. Extraction frequency converter dynamic anomalies parameters, establishes the dynamic parameter and converter the mapping relationship between the parts of frequency converter dynamic parameters for nonlinear transform, the calculation of frequency converter dynamic parameters abnormal interval remove interference. Experiments show that the detection means to be able lo improve the frequency converter anomaly detection accuracy, can accurate detection fault components.%提出了一种异常参数映射估计的变频器异常动态参数检测算法.提取变频器异常动态参数,建立动态参数与变频器部件之间的映射关系,对变频器动态参数进行非线性变换,计算变频器动态参数异常区间排除干扰.实验证明,这种检测方式能够提高变频器异常检测的准确率,能够准确检测故障部件.

  3. Detection of Fluoroquinolone-Resistant Organisms from Rectal Swabs by Use of Selective Media Prior to a Transrectal Prostate Biopsy▿

    OpenAIRE

    Liss, Michael A; Peeples, Amy N.; Peterson, Ellena M.

    2011-01-01

    Sepsis caused by fluoroquinolone-resistant Escherichia coli is a risk for patients undergoing an ultrasound-guided, transrectal prostate biopsy. A method incorporating selective broth and media was evaluated using rectal swabs obtained from 136 patients prior to a biopsy procedure. Fluoroquinolone-resistant organisms were isolated from 22% of the patients included in this study.

  4. 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 of

  5. 基于数字属性和符号属性混合数据的网络异常入侵检测方法%Network-based anomaly intrusion detection with numeric-and-nominal mixed data

    Institute of Scientific and Technical Information of China (English)

    蔡龙征; 余胜生; 王晓峰; 周敬利

    2006-01-01

    Anomaly detection is a key element of intrusion detection systems and a necessary complement of widely used misuse intrusion detection systems. Data sources used by network intrusion detection, like network packets or connections, often contain both numeric and nominal features. Both of these features contain important information for intrusion detection. These two features, on the other hand, have different characteristics. This paper presents a new network based anomaly intrusion detection approach that works well by building profiles for numeric and nominal features in different ways. During training, for each numeric feature, a normal profile is build through statistical distribution inference and parameter estimation, while for each nominal feature, a normal profile is setup through statistical method. These profiles are used as detection models during testing to judge whether a data being tested is benign or malicious. Experiments with the data set of 1999 DARPA (defense advanced research project agency) intrusion detection evaluation show that this approach can detect attacks effectively.

  6. Quantitative identification of mutant alleles derived from lung cancer in plasma cell-free DNA via anomaly detection using deep sequencing data.

    Directory of Open Access Journals (Sweden)

    Yoji Kukita

    Full Text Available The detection of rare mutants using next generation sequencing has considerable potential for diagnostic applications. Detecting circulating tumor DNA is the foremost application of this approach. The major obstacle to its use is the high read error rate of next-generation sequencers. Rather than increasing the accuracy of final sequences, we detected rare mutations using a semiconductor sequencer and a set of anomaly detection criteria based on a statistical model of the read error rate at each error position. Statistical models were deduced from sequence data from normal samples. We detected epidermal growth factor receptor (EGFR mutations in the plasma DNA of lung cancer patients. Single-pass deep sequencing (>100,000 reads was able to detect one activating mutant allele in 10,000 normal alleles. We confirmed the method using 22 prospective and 155 retrospective samples, mostly consisting of DNA purified from plasma. A temporal analysis suggested potential applications for disease management and for therapeutic decision making to select epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI.

  7. Using memory for prior aircraft events to detect conflicts under conditions of proactive air traffic control and with concurrent task requirements.

    Science.gov (United States)

    Bowden, Vanessa K; Loft, Shayne

    2016-06-01

    In 2 experiments we examined the impact of memory for prior events on conflict detection in simulated air traffic control under conditions where individuals proactively controlled aircraft and completed concurrent tasks. Individuals were faster to detect conflicts that had repeatedly been presented during training (positive transfer). Bayesian statistics indicated strong evidence for the null hypothesis that conflict detection was not impaired for events that resembled an aircraft pair that had repeatedly come close to conflicting during training. This is likely because aircraft altitude (the feature manipulated between training and test) was attended to by participants when proactively controlling aircraft. In contrast, a minor change to the relative position of a repeated nonconflicting aircraft pair moderately impaired conflict detection (negative transfer). There was strong evidence for the null hypothesis that positive transfer was not impacted by dividing participant attention, which suggests that part of the information retrieved regarding prior aircraft events was perceptual (the new aircraft pair "looked" like a conflict based on familiarity). These findings extend the effects previously reported by Loft, Humphreys, and Neal (2004), answering the recent strong and unanimous calls across the psychological science discipline to formally establish the robustness and generality of previously published effects. (PsycINFO Database Record PMID:27295467

  8. Detection of Entamoeba histolytica DNA in the Saliva of Amoebic Liver Abscess Patients Who Received Prior Treatment with Metronidazole

    OpenAIRE

    Khairnar, Krishna; Parija, Subhash Chandra

    2008-01-01

    Saliva is an easily-accessible and a non-invasive clinical specimen alternate to blood and liver pus. An attempt was made to detect Entamoeba histolytica DNA released in the saliva of amoebic liver abscess (ALA) patients by applying 16S-like rRNA gene-based nested multiplex polymerase chain reaction (NM-PCR). The NM-PCR detected E. histolytica DNA in the saliva of eight (28.6%) of 28 ALA patients. The NM-PCR result was negative for E. histolytica DNA in the saliva of all the eight ALA patient...

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

    International Nuclear Information System (INIS)

    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/m3. 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

  10. Vortex-assisted liquid-liquid microextraction of bisphenol S prior to its determination by HPLC with UV detection

    International Nuclear Information System (INIS)

    Vortex-assisted liquid-liquid microextraction (VALLME) for the rapid extraction of trace bisphenol S (BPS) in environmental water is presented. In order to simplify the procedure, an in-house fabricated glass dropper with different internal diameters of the two ends is exploited. The solidification-melt step was cut in VALLME by means of the in-house fabricated glass dropper. After extraction with 2-ethylhexanol, BPS was detected by high performance liquid chromatography (HPLC) with ultraviolet (UV) detection. Factors such as type and volume of extraction solvent, extraction time, sample pH and ionic strength were evaluated. Under optimized conditions, the linearity range varied from 0.10 to 50 μg L−1 with a squared regression coefficient r2 of 0.9995. The relative standard deviation (RSD) is 2.3 % (n = 7). The limit of detection (LOD) and limit of quantification (LOQ) are 0.02 and 0.06 μg L−1, respectively. The presented method was employed for the determination of BPS in real water samples. The relative recoveries are 81.8–87.3 % for the two real water samples. The method is shown to be economical, fast and can be routinely performed. (author)

  11. The Pioneer Anomaly

    Directory of Open Access Journals (Sweden)

    Viktor T. Toth

    2010-09-01

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

  12. Anomaly Structure of Supergravity and Anomaly Cancellation

    CERN Document Server

    Butter, Daniel

    2009-01-01

    We display the full anomaly structure of supergravity, including new D-term contributions to the conformal anomaly. This expression has the super-Weyl and chiral U(1)_K transformation properties that are required for implementation of the Green-Schwarz mechanism for anomaly cancellation. We outline the procedure for full anomaly cancellation. Our results have implications for effective supergravity theories from the weakly coupled heterotic string theory.

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

    OpenAIRE

    Freeborn, Patrick H.; Wooster, Martin J.; Gareth Roberts; Weidong Xu

    2014-01-01

    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 e...

  14. 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

  15. The Pioneer Anomaly

    CERN Document Server

    Turyshev, Slava G

    2010-01-01

    Radio-metric Doppler tracking data received from the Pioneer 10 and 11 spacecraft from heliocentric distances of 20-70 AU has consistently indicated the presence of a small, anomalous, blue-shifted frequency drift uniformly changing with a rate of ~6 x 10^{-9} Hz/s. Ultimately, the drift was interpreted as a constant sunward deceleration of each particular spacecraft at the level of a_P = (8.74 +/- 1.33) x 10^{-10} m/s^2. This apparent violation of the Newton's gravitational inverse-square law has become known as the Pioneer anomaly; the nature of this anomaly remains unexplained. In this review, we summarize the current knowledge of the physical properties of the discovered effect and the conditions that led to its detection and characterization. We review various mechanisms proposed to explain the anomaly and discuss the current state of efforts to determine its nature. A comprehensive new investigation of the anomalous behavior of the two Pioneers has begun recently. The new efforts rely on the much-extend...

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

    OpenAIRE

    Borja Rodríguez-Cuenca; Silverio García-Cortés; Celestino Ordóñez; Maria C. Alonso

    2015-01-01

    Detecting and modeling urban furniture are of particular interest for urban management and the development of autonomous driving systems. This paper presents a novel method for detecting and classifying vertical urban objects and trees from unstructured three-dimensional mobile laser scanner (MLS) or terrestrial laser scanner (TLS) point cloud data. The method includes an automatic initial segmentation to remove the parts of the original cloud that are not of interest for detecting vertical o...

  17. Network traffic anomaly detection based on relative entropy%基于相对熵的网络流量异常检测方法

    Institute of Scientific and Technical Information of China (English)

    张登银; 廖建飞

    2012-01-01

    The anomaly detection of network traffic, which aims at detecting abrupt attacks timely and accurately, is important in the field of network security. Existing detection methods, such as the methods based on data mining and wavelet analysis, fail to meet the application requirements of online traffic detection either due to the high complexity of algorithm or the poor detection effect. By introducing the concept of information entropy and calculating relative entropy of the network traffic on the vision of the traffic S dimensions and hierarchies in real-time, this paper proposes a relative entropy based detection method with the time complexity of algorithm at O(N ×log2N ×D) . Experiment analysis shows that the false a-larm rate can be controlled only in 0. 03 ~0. 05 when the detection rate reaches 0. 8 ~0. 85 , which meets the requirements of real-time and accuracy simultaneously.%网络流量的异常检测是网络安全领域一个重要分支,目标是及时准确地检测网络中发生的突发攻击事件.现有流量异常检测方法如数据挖掘、小波分析等方法或因检测效果较差,或因算法复杂,难以满足实时在线流量检测的应用需求.文中引入信息熵概念,通过对网络流量进行分维和分层实时计算网络流量相对熵,提出了一种基于相对熵的流量异常检测方法,算法时间复杂度为O(N×log2N× D).实验分析表明,当检测率达到0.80 ~0.85时,误报率控制在0.03 ~O.05,可同时满足系统实时性和准确性要求.

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

    International Nuclear Information System (INIS)

    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 226Ra-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

  19. An Anomaly Detection Scheme Based on DBI-PD Clustering Algorithm%一种基于DBI-PD聚类算法的异常检测机制

    Institute of Scientific and Technical Information of China (English)

    丁姝郁

    2015-01-01

    分析了网络数据维数和检测准确度之间的关系,介绍了常用于入侵检测的聚类分析方法及其优缺点。在此基础上,提出一种以戴维森堡丁指数(DBI)为聚类准则、基于划分和密度方法的聚类算法(DBI-PD)。该方法通过信息增益率(IGR)提取网络数据中对检测攻击最有用的“特征”,并以DBI准则确定最优聚类个数、划分和密度两种聚类分析方法结合使用用于异常检测。提出的基于DBI-PD的异常检测机制能有效避免聚类分析在入侵检测中的“维数灾难”问题、避免无用数据特征干扰,还能改善聚类质量,从而提高检测准确度。%In this paper, the relationship between the dimensions of network data and the detection accuracy is analyzed. In addition, this paper introduces clustering analysis methods which are often used in intrusion detection and compare their advantages and disadvantages. On the basis of that, this paper proposes a partition and density-based clustering algorithm used Davies-Bouldin Index (DBI-PD). DBI-PD method firstly selects the most related features for detection in network data using information gain ratio (IGR), then determines the optimal number of clusters based on DBI, and finally combines the partition and density clustering methods to detect. The DBI-PD based anomaly detection scheme proposed in this paper can effectively avoid the "dimension disaster" problem in clustering analysis, as well as avoid the interferences because of the useless data features. Furthermore, this scheme can improve the clustering quality, so as to improve the accuracy of detection.

  20. 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.).

  1. Chiral anomalies and differential geometry

    Energy Technology Data Exchange (ETDEWEB)

    Zumino, B.

    1983-10-01

    Some properties of chiral anomalies are described from a geometric point of view. Topics include chiral anomalies and differential forms, transformation properties of the anomalies, identification and use of the anomalies, and normalization of the anomalies. 22 references. (WHK)

  2. 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.

  3. A HOST ANOMALY DETECTION METHOD BASED ON LDA MODEL%基于LDA模型的主机异常检测方法

    Institute of Scientific and Technical Information of China (English)

    贺喜; 蒋建春; 丁丽萍; 王永吉; 廖晓峰

    2012-01-01

    基于系统调用序列的入侵检测是分析主机系统调用数据进而发现入侵的一种安全检测技术,其关键技术是如何能够更准确地抽取系统调用序列的特征,并进行分类.为此,引进LDA( Latent Dirichlet Allocation )文本挖掘模型构建新的入侵检测分类算法.该方法将系统调用短序列视为word,利用LDA模型提取进程系统调用序列的主题特征,并结合系统调用频率特征,运用kNN(k-Nearest Neighbor)分类算法进行异常检测.针对DAPRA数据集的实验结果表明,该方法提高了入侵检测的准确度,降低了误报率.%The technique of intrusion detection based on sequence of host system call is a security detection technique mainly focusing on analysing the data set of host system call and further finding the intrusion. Its key technology relies on how to extract the characteristics of system call sequence more accurately and then followed by classification. In this paper, aiming at this, LDA (Latent Dirichlet Allocation) text mining model is introduced to build a new intrusion detection classification algorithm. In this method, topic characteristics of system call sequence are extracted using LDA model which the short sequence of system call is regarded by the method as word. Combined with the frequency characteristics of system calls, kNN (k-Nearest Neighbor) classification algorithm is used for anomaly detection. Experiment is evaluated on 1998 DAPRA data set, the result shows that the method improves the accuracy of intrusion detection, and reduces the false alarm rate.

  4. Anomaly Detection in Stock Marketplace Based on Market Microstructure%市场微结构的股市交易异常行为检测

    Institute of Scientific and Technical Information of China (English)

    林杨

    2013-01-01

    It is well known that many defects exist in current stock market, such as information abuse and price manipulation. Anomaly detection is helpful to enhance the integrity, fairness and transparence of stock market so it becomes a key link in financial regulatory system. Unfortunately , existing approaches were low performing as they rarely focused on analyzing the intraday information and mining potential trading behaviors. It proposed a method, which based on market mi-crostructure, to detect abnormal trading behaviors. An experiment was presented demonstrating the feasibility and effectiveness of this approach.%股票市场存在诸多弊端,如滥用客户信息,价格操纵等.股市监控是金融监管体系中不可缺少的一环,它对市场交易的诚信、公平和公开透明起到重要作用.现有检测交易异常行为的诸多方法中,很少分析股市即日数据并挖掘潜在的交易行为来检测异常.股市是一个复杂的非线性系统,一套可行高效的异常行为检测方法是股市异常行为监控的重要课题.提出一种基于市场微结构的异常交易行为检测方法,该方法能较有效地检测出股市存在的异常交易行为.最后,通过实例说明该方法的可行性和有效性.

  5. Generation of volatile copper species after in situ ionic liquid formation dispersive liquid-liquid microextraction prior to atomic absorption spectrometric detection.

    Science.gov (United States)

    Stanisz, Ewa; Zgoła-Grześkowiak, Agnieszka; Matusiewicz, Henryk

    2014-11-01

    The new procedure using in situ synthesis of ionic liquid extractant for dispersive liquid-liquid microextraction (in situ IL DLLME) combined with generation of volatile species prior to electrothermal atomic absorption spectrometry (ET AAS) for the determination of copper in soil samples was developed. Analytical signals were obtained without the back-extraction of copper from the IL phase prior to its determination. Under optimal conditions, the extraction in 10 mL of sample solution employing 8 μL of 1-hexyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide (HmimNTf2) (as the extraction solvent) was conducted. The ionic liquid served as two-task reagent: the efficient extractant and enhancement substance for generation step. The chemical generation of volatile species was performed by reduction of acidified copper solution (HCl 0.8 mol L(-1)) with NaBH4 (1.5%). Some essential parameters of the chemical generation such as NaBH4 and HCl concentrations, the kind and concentration of ionic liquid, carrier gas (Ar) flow rate, reaction and trapping time as well as pyrolysis and atomization temperatures were studied. For photogeneration the effect of the parameters such as the kind and concentration of low molecular weight organic acids and ionic liquid, carrier gas (Ar) flow rate, UV irradiation and ultrasonication time on the analytical signals were studied. The detection limit was found as 1.8 ng mL(-1) and the relative standard deviation (RSD) for seven replicate measurements of 100 µg mL(-1) in sample solution was 7%. The accuracy of the proposed method was evaluated by analysis of the certified reference materials. The measured copper contents in the reference materials were in satisfactory agreement with the certified values. The method was successfully applied to analysis of the soil and sediment samples. PMID:25127592

  6. Considerations in the Interpretation of Cosmological Anomalies

    CERN Document Server

    Peiris, Hiranya V

    2014-01-01

    Anomalies drive scientific discovery -- they are associated with the cutting edge of the research frontier, and thus typically exploit data in the low signal-to-noise regime. In astronomy, the prevalence of systematics --- both "known unknowns" and "unknown unknowns" --- combined with increasingly large datasets, the widespread use of ad hoc estimators for anomaly detection, and the "look-elsewhere" effect, can lead to spurious false detections. In this informal note, I argue that anomaly detection leading to discoveries of new physics requires a combination of physical understanding, careful experimental design to avoid confirmation bias, and self-consistent statistical methods. These points are illustrated with several concrete examples from cosmology.

  7. Anomaly detection for internet surveillance

    NARCIS (Netherlands)

    Bouma, H.; Raaijmakers, S.A.; Halma, A.H.R.; Wedemeijer, H.

    2012-01-01

    Many threats in the real world can be related to activity of persons on the internet. Internet surveillance aims to predict and prevent attacks and to assist in finding suspects based on information from the web. However, the amount of data on the internet rapidly increases and it is time consuming

  8. 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...

  9. 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...

  10. 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)

    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

  11. Network Traffic Anomalies Identification Based on Classification Methods

    Directory of Open Access Journals (Sweden)

    Donatas Račys

    2015-07-01

    Full Text Available A problem of network traffic anomalies detection in the computer networks is analyzed. Overview of anomalies detection methods is given then advantages and disadvantages of the different methods are analyzed. Model for the traffic anomalies detection was developed based on IBM SPSS Modeler and is used to analyze SNMP data of the router. Investigation of the traffic anomalies was done using three classification methods and different sets of the learning data. Based on the results of investigation it was determined that C5.1 decision tree method has the largest accuracy and performance and can be successfully used for identification of the network traffic anomalies.

  12. 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

  13. Dynamic and real-time network anomaly detection model inspired by immune%基于免疫的网络动态实时异常检测模型

    Institute of Scientific and Technical Information of China (English)

    彭凌西; 曾金全

    2012-01-01

    网络异常检测已成为入侵检测系统发展的重要方向.现有异常检测模型对检测模式描述为一种静态方式,缺乏良好的自适应性和协同性,检测率低,难以满足高速网络环境下实时检测的需求.针对此,借鉴人体免疫系统优异的自学习自适应机制,提出了一种新的基于免疫的网络动态实时异常检测模型NAIM.该模型通过对检测模式进行动态描述,结合抗体细胞动态克隆原理,探讨种痘及疫苗分发机制,实现检测模式随真实网络环境同步演化,从而提高网络异常检测的准确性和及时性.%The network anomaly detection has become the promising direction of intrusion detection system. The existing anomaly detection models depict the detection pattern with a static way, which lack good adaptability and interoperability with low detection rate, so it is difficult to implement the real-time detection under the high- speed network environment. Our research uses the excellent mechanism of Self-learning and adaptability of the human immune system, and a novel real-time immune-based anomaly detection model(NAIM) is proposed. The model dynamically depicts detection model, combining the antibody's clone theory and disscussing the vaccina- tion and bacterin distribution mechanism, which achieves the detection mode's synchronous evolvement with the real network enviroment, thus improves the network anomaly detection's veracity and timeliness.

  14. Dual left anterior descending artery with anomalous origin of long LAD from pulmonary artery - rare coronary anomaly detected on computed tomography coronary angiography

    Science.gov (United States)

    Vohra, Aditi; Narula, Harneet

    2016-01-01

    Dual left anterior descending artery is a rare coronary artery anomaly showing two left anterior descending arteries. Short anterior descending artery usually arises from the left coronary artery, while long anterior descending artery has anomalous origin and course. Dual left anterior descending artery with origin of long anterior descending artery from the pulmonary artery (ALCAPA) is a very rare coronary artery anomaly which has not been reported previously in the literature. We present the computed tomography coronary angiographic findings of this rare case in a young female patient who presented with atypical chest pain.

  15. Fighting Prior Review.

    Science.gov (United States)

    Bowen, John

    1990-01-01

    Reviews arguments for and against prior administrative review and censorship of student expression. Suggests that prior review strips any pretense of democracy from many American educational institutions. Argues that prior review is journalistically inappropriate, educationally unsound, and practically illogical. (KEH)

  16. Morning glory disc anomaly with Chiari type I malformation.

    Science.gov (United States)

    Arlow, Tim; Arepalli, Sruthi; Flanders, Adam E; Shields, Carol L

    2014-04-30

    Morning glory disc anomaly is a rare optic nerve dysplasia associated with various neovascular abnormalities. Due to these associations, children with morning glory disc anomaly have brain imaging and angiography to detect other congenital defects. The authors report the case of an infant with morning glory disc anomaly and coexisting Chiari type I malformation.

  17. 基于磁异常检测的潜艇探测探头类型分析%Analysis of different types of magnetic probes for submarine detection based on magnetic anomaly

    Institute of Scientific and Technical Information of China (English)

    陈宇沁; 周宏威; 袁建生

    2015-01-01

    The development of submarine noise reduction technology has led to the difficulty for traditional sonar e -quipments to detect a submarine , so there is more demand of non-acoustics antisubmarine detecting technology , and magnetic anomaly detection method is one of better future .Magnetic anomaly detection needs high-precision magnetic probes, so this paper analyzes the requirement of the magnetic probes based on the principle of magnetic anomaly de -tection.With the analysis of the high-precision fluxgate probe , the probe is found to have the characteristic of large er-rors in the total field measurement , and its non-availability is discussed .At last , the selection of magnetic measure-ment probes for submarine detection is analyzed .%潜艇降噪技术的发展导致传统声纳探潜手段的探测效果下降,需要寻求非声探潜新方法,而磁异常探潜方法是其中前景较好的一种。实现磁异常探潜,需要高精度的磁场测量探头。文章从磁异常探潜原理出发,分析了其对磁场测量探头特性的要求。具体分析了高精度磁通门探头,发现三分量组合磁通门探头测量总场值的误差过大,论述了其对磁异常探潜的不可用性。最后对潜艇探测中磁场测量探头的选型进行了分析。

  18. 基于改进符号化度量方法的机场噪声异常检测%An Anomaly Detection Method of Airport-noise Time Series Based on Improved SAX Measurement

    Institute of Scientific and Technical Information of China (English)

    王伟; 王建东; 张霞

    2014-01-01

    With the expansion of airport transportation scale , the airport noise issue is becoming one of the obstacles for the sustain-able development of the aviation industry .Anomalies in the airport noise are of great significance for the timely improvement of the equipments of aircraft and airports .In this paper , according to the characteristics of airport noise , a time series anomaly detection method for single monitoring point is proposed , which is based on the improved symbolic aggregate approximation similarity measure-ment .This method calculates the measure by applying the improved similarity measure , and finally finds anomalies using the k-nearest neighbor anomaly detection method.The proposed method is applied in practice after the theoretical verification of its feasibility .%机场噪声中的异常情况拥有很大价值,利用它能够及时完善飞机和机场的设备。结合机场噪声数据的特点,对上述问题进行研究并提出一种基于改进的符号化聚集近似( Symbolic Aggregate Approximation ,SAX)相似性度量的单监测点的时间序列异常检测方法。其运用相似性度量方法计算出度量结果,再运用k近邻异常检测方法进行异常发现,最后发现异常时间段。该方法在理论验证可行性之后在某机场的实测数据中进行应用,取得了良好的效果。

  19. Diagnosing Traffic Anomalies Using a Two-Phase Model

    Institute of Scientific and Technical Information of China (English)

    Bin Zhang; Jia-Hai Yang; Jian-Ping Wu; Ying-Wu Zhu

    2012-01-01

    Network traffic anomalies are unusual changes in a network,so diagnosing anomalies is important for network management.Feature-based anomaly detection models (ab)normal network traffic behavior by analyzing packet header features. PCA-subspace method (Principal Component Analysis) has been verified as an efficient feature-based way in network-wide anomaly detection.Despite the powerful ability of PCA-subspace method for network-wide traffic detection,it cannot be effectively used for detection on a single link.In this paper,different from most works focusing on detection on flow-level traffic,based on observations of six traffc features for packet-level traffic,we propose a new approach B6SVM to detect anomalies for packet-level traffic on a single link.The basic idea of B6-SVM is to diagnose anomalies in a multi-dimensional view of traffic features using Support Vector Machine (SVM).Through two-phase classification,B6-SVM can detect anomalies with high detection rate and low false alarm rate.The test results demonstrate the effectiveness and potential of our technique in diagnosing anomalies.Further,compared to previous feature-based anomaly detection approaches,B6-SVM provides a framework to automatically identify possible anomalous types.The framework of B6-SVM is generic and therefore,we expect the derived insights will be helpful for similar future research efforts.

  20. Ionic liquid-based zinc oxide nanofluid for vortex assisted liquid liquid microextraction of inorganic mercury in environmental waters prior to cold vapor atomic fluorescence spectroscopic detection.

    Science.gov (United States)

    Amde, Meseret; Liu, Jing-Fu; Tan, Zhi-Qiang; Bekana, Deribachew

    2016-03-01

    Zinc oxide nanofluid (ZnO-NF) based vortex assisted liquid liquid microextraction (ZnO-NF VA-LLME) was developed and employed in extraction of inorganic mercury (Hg(2+)) in environmental water samples, followed by cold vapor atomic fluorescence spectrometry (CV-AFS). Unlike other dispersive liquid liquid microextraction techniques, ZnO-NF VA-LLME is free of volatile organic solvents and dispersive solvent consumption. Analytical signals were obtained without back-extraction from the ZnO-NF phase prior to CV-AFS determination. Some essential parameters of the ZnO-NF VA-LLME and cold vapor generation such as composition and volume of the nanofluid, vortexing time, pH of the sample solution, amount of the chelating agent, ionic strength and matrix interferences have been studied. Under optimal conditions, efficient extraction of 1ng/mL of Hg(2+) in 10mL of sample solution was achieved using 50μL of ZnO-NF. The enrichment factor before dilution, detection limits and limits of quantification of the method were about 190, 0.019 and 0.064ng/mL, respectively. The intra and inter days relative standard deviations (n=8) were found to be 4.6% and 7.8%, respectively, at 1ng/mL spiking level. The accuracy of the current method was also evaluated by the analysis of certified reference materials, and the measured Hg(2+) concentration of GBW08603 (9.6ng/mL) and GBW(E)080392 (8.9ng/mL) agreed well with their certified value (10ng/mL). The method was applied to the analysis of Hg(2+) in effluent, influent, lake and river water samples, with recoveries in the range of 79.8-92.8% and 83.6-106.1% at 1ng/mL and 5ng/mL spiking levels, respectively. Overall, ZnO-NF VA-LLME is fast, simple, cost-effective and environmentally friendly and it can be employed for efficient enrichment of the analyte from various water samples. PMID:26717850

  1. Familial Poland anomaly.

    OpenAIRE

    David, T J

    1982-01-01

    The Poland anomaly is usually a non-genetic malformation syndrome. This paper reports two second cousins who both had a typical left sided Poland anomaly, and this constitutes the first recorded case of this condition affecting more than one member of a family. Despite this, for the purposes of genetic counselling, the Poland anomaly can be regarded as a sporadic condition with an extremely low recurrence risk.

  2. 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. PMID:26877180

  3. 混合PLS特征提取和CVM的异常入侵检测研究%Research on anomaly intrusion detection by hybrid Partial Least Square feature extrac-tion and Core Vector Machine

    Institute of Scientific and Technical Information of China (English)

    余文利; 方建文

    2014-01-01

    为提高异常入侵检测的效率,提出一种混合偏最小二乘特征提取和核心向量机算法的入侵检测模型。模型使用偏最小二乘算法在入侵数据集上进行主成分提取,在此基础上构建特征集,引入适用于解决大规模样本训练问题的核心向量机算法,在特征集上建立入侵检测模型,使用该模型对异常入侵行为进行检测和判断。通过基于KDD99数据集上的入侵检测实验,验证了混合模型的可行性和有效性。%To improve the efficiency of anomaly detecting intrusions, a hybrid model is proposed based on Partial Least Square(PLS)feature extraction and Core Vector Machine(CVM)algorithms. Principal elements are extracted from the intrusion data set by the feature extraction of PLS algorithm to establish the feature set, and then the anomaly intrusion detec-tion model for the feature set is constructed by virtue of speediness superiority of CVM algorithm in processing large-scale sample data. Anomaly intrusion actions are checked and judged using this model. Intrusion detection experiments based on KDD99 data set verify the feasibility and validity of the hybrid model.

  4. Competing Orders and Anomalies.

    Science.gov (United States)

    Moon, Eun-Gook

    2016-01-01

    A conservation law is one of the most fundamental properties in nature, but a certain class of conservation "laws" could be spoiled by intrinsic quantum mechanical effects, so-called quantum anomalies. Profound properties of the anomalies have deepened our understanding in quantum many body systems. Here, we investigate quantum anomaly effects in quantum phase transitions between competing orders and striking consequences of their presence. We explicitly calculate topological nature of anomalies of non-linear sigma models (NLSMs) with the Wess-Zumino-Witten (WZW) terms. The non-perturbative nature is directly related with the 't Hooft anomaly matching condition: anomalies are conserved in renormalization group flow. By applying the matching condition, we show massless excitations are enforced by the anomalies in a whole phase diagram in sharp contrast to the case of the Landau-Ginzburg-Wilson theory which only has massive excitations in symmetric phases. Furthermore, we find non-perturbative criteria to characterize quantum phase transitions between competing orders. For example, in 4D, we show the two competing order parameter theories, CP(1) and the NLSM with WZW, describe different universality class. Physical realizations and experimental implication of the anomalies are also discussed. PMID:27499184

  5. Anomalies and topology

    International Nuclear Information System (INIS)

    The lectures given cover the topological effects in gauge field theories, fermionic chiral anomalies, and some relationships between the two. Gauge field theories in three and four space-time dimensions are considered. Topological terms as external U(1) functional gauge potential connections in field space are discussed. Both the structure and physical impact of anomalies are described. 17 refs

  6. Competing Orders and Anomalies

    Science.gov (United States)

    Moon, Eun-Gook

    2016-08-01

    A conservation law is one of the most fundamental properties in nature, but a certain class of conservation “laws” could be spoiled by intrinsic quantum mechanical effects, so-called quantum anomalies. Profound properties of the anomalies have deepened our understanding in quantum many body systems. Here, we investigate quantum anomaly effects in quantum phase transitions between competing orders and striking consequences of their presence. We explicitly calculate topological nature of anomalies of non-linear sigma models (NLSMs) with the Wess-Zumino-Witten (WZW) terms. The non-perturbative nature is directly related with the ’t Hooft anomaly matching condition: anomalies are conserved in renormalization group flow. By applying the matching condition, we show massless excitations are enforced by the anomalies in a whole phase diagram in sharp contrast to the case of the Landau-Ginzburg-Wilson theory which only has massive excitations in symmetric phases. Furthermore, we find non-perturbative criteria to characterize quantum phase transitions between competing orders. For example, in 4D, we show the two competing order parameter theories, CP(1) and the NLSM with WZW, describe different universality class. Physical realizations and experimental implication of the anomalies are also discussed.

  7. The resolution of a magnetic anomaly map expected from GRM data

    Science.gov (United States)

    Strangway, D. W.; Arkani-Hamed, J.; Teskey, D. J.; Hood, P. J.

    1985-01-01

    Data from the MAGSAT mission were used to derive a global scalar magnetic anomaly map at an average altitude of about 400 km. It was possible to work with 2 data sets corresponding to dawn and dusk. The anomalies which were repeatable at dawn and at dusk was identified and the error limits of these anomalies were estimated. The repeatable anomalies were downward continued to about 10 km altitude. The anomalies over Canada were correlated quantitatively with bandpass filtered magnetic anomalies derived from aeromagnetic surveys. The close correlation indicates that the repeatable anomalies detected from orbit are due to geological causes. This correlation supports the geological significance of the global anomaly map.

  8. Overcoming priors anxiety

    OpenAIRE

    D'Agostini, G

    1999-01-01

    The choice of priors may become an insoluble problem if priors and Bayes' rule are not seen and accepted in the framework of subjectivism. Therefore, the meaning and the role of subjectivity in science is considered and defended from the pragmatic point of view of an ``experienced scientist''. The case for the use of subjective priors is then supported and some recommendations for routine and frontier measurement applications are given. The issue of reference priors is also considered from th...

  9. Anomaly Event Detection Scheme Based on Compressive Sensing in Wireless Sensor Network%WSN中基于压缩感知的异常事件检测方案

    Institute of Scientific and Technical Information of China (English)

    姜参; 马荣娟

    2014-01-01

    异常事件检测问题是无线传感器网络中的研究热点之一。为提高检测效率,提出一种基于压缩感知的异常事件检测方案。通过压缩采样得到各个节点感知数据的测量值,将异常事件检测问题建模为带权的 l1范数最小化问题,采用正交匹配追踪算法进行迭代求解,根据检测函数对求解结果进行判断,并依据判断结果更新权值,开始下一轮迭代,直到检测出无线传感器网络中存在的所有异常事件。仿真实验结果表明,该方案的漏检率和误警率较低,与 CCM和 GEP-ADS方案相比,分别能节省约4.1%和5.8%的能耗。%The anomaly event detection problem in Wireless Sensor Network(WSN) is currently a hot topic. In order to improve the detection efficiency, this paper proposes an anomaly event detection scheme based on compressive sensing. The measurements of the sensed data are obtained based on the compressive sampling, and the anomaly event detection problem is modeled as the reweighted l1 minimization problem, which is iteratively solved by the Orthogonal Matching Pursuit(OMP) algorithm. Furthermore, the solution is judged by the detection function. The weight is refreshed in the next iteration according to the judgments, until all abnormal events are detected in Wireless Sensor Network(WSN). Experimental results show that the proposed scheme can obtain the lower probability of missed detection and false alarm in different noise environments. Compared with the CCM and GEP-ADS scheme, the energy consumption of this scheme id saved by approximately 4.1%and 5.8%.

  10. 调试中基于文法编码的日志异常检测算法%A Log Anomaly Detection Algorithm for Debugging Based on Grammar-Based Codes

    Institute of Scientific and Technical Information of China (English)

    王楠; 韩冀中; 方金云

    2013-01-01

    调试软件中的非确定错误对软件开发有重要意义.近年来,随着云计算系统的快速发展和对录制重放调试方法研究的深入,使用异常检测方法从大量文本日志或控制流日志等数据中找出异常的信息对调试愈发重要.传统的异常检测算法大多是为检测和防范攻击而设计的,它们很多基于马尔可夫假设,对事件流上的剧烈变化很敏感.但是新的问题要求异常检测能够检出语义级别的异常行为.实验表明现有的基于马尔可夫假设的异常检测算法在这方面表现不佳.提出了一种新的基于文法编码的异常检测算法.该算法不依赖于统计模型、概率模型、机器学习及马尔可夫假设,设计和实现都极为简单.实验表明在检测高层次的语义异常方面,该算法比传统方法有优势.%Debugging non-deterministic bugs has long been an important research area in software development. In recent years, with the rapid emerging of large cloud computing systems and the development of record replay debugging, the key of such debugging problem becomes mining anomaly information from text console logs and/or execution flow logs. Anomaly detection algorithms can therefore be used in this area. However, although many approaches have been proposed, traditional anomaly detection algorithms are designed for detecting network attacking and not suitable for the new problems. One important reason is the Markov assumption on which many traditional anomaly detection methods are based. Markov-based methods are sensitive to harshly trashing in event transitions. In contrast, the new problems in system diagnosing require the abilities of detecting semantic misbehaviors. Experiment results show the powerless of Markov-based methods on those problems. This paper presents a novel anomaly detection algorithm which is based on grammar-based codes. Different from previous approaches, our algorithm is a non-Markov approach. It doesn

  11. 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, Bruce 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.

  12. 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.

  13. Imaging of facial anomalies.

    Science.gov (United States)

    Castillo, M; Mukherji, S K

    1995-01-01

    Anomalies of the face may occur in its lower or middle segments. Anomalies of the lower face generally involve the derivatives of the branchial apparatus and therefore manifest as defects in the mandible, pinnae, external auditory canals, and portions of the middle ears. These anomalies are occasionally isolated, but most of them occur in combination with systemic syndromes. These anomalies generally do not occur with respiratory compromise. Anomalies of the midface may extend from the upper lip to the forehead, reflecting the complex embryology of this region. Most of these deformities are isolated, but some patients with facial clefts, notably the midline cleft syndrome and holoprosencephaly, have anomalies in other sites. This is important because these patients will require detailed imaging of the face and brain. Anomalies of the midface tend to involve the nose and its air-conducting passages. We prefer to divide these anomalies into those with and without respiratory obstruction. The most common anomalies that result in airway compromise include posterior choanal stenoses and atresias, bilateral cysts (mucoceles) of the distal lacrimal ducts, and stenosis of the pyriform (anterior) nasal aperture. These may be optimally evaluated with computed tomography (CT) and generally require immediate treatment to ensure adequate ventilation. Rare nasal anomalies that also result in airway obstruction are agenesis of the pharynx, agenesis of the nose, and hypoplasia of the nasal alae. Agenesis of the nasopharynx and nose are complex anomalies that require both CT and magnetic resonance imaging (MRI). The diagnosis of hypoplasia of the nasal alae is a clinical one; these anomalies do not require imaging studies. Besides facial clefts, anomalies of the nose without respiratory obstruction tend to be centered around the nasofrontal region. This is the site of the most common sincipital encephaloceles. Patients with frontonasal and nasoethmoidal encephaloceles require both

  14. 基于用户行为周期的移动设备异常检测方法%User BehaviorCycle-Based Statistical Approach for Anomaly Detecting on Mobile Devices

    Institute of Scientific and Technical Information of China (English)

    吴志忠; 周学海

    2015-01-01

    In this paper, we present a distributed anomaly detection system for mobile devices. The proposed framework realizes a client-server architecture, the client continuously extracts various features of mobile device and transfers to the server, and the server’s major task is to detect anomaly using state-of-art detection algorithms. According to the regularity of human daily activity and the periodic of using mobile device, we also propose a novel user behavior cycle based statistical approach, in which the abnormal is determined by the distance from the undetermined feature vector to the similar time segments’ vectors of previous cycles. We use the Mahalanobis distance as distance metric since it is rarely affected by the correlate and value range of features. Evaluation results demonstrated that the proposed framework and novel anomaly detection algorithm could effectively improve the detection rate of malwares on mobile devices.%本文提出了一种分布式的移动设备异常检测系统,该系统采用客户端-服务器架构,客户端程序在移动设备上持续提取特征并传送给服务器,服务器使用异常检测算法分析特征。根据人类日常活动的规律性以及用户使用移动设备的周期性,我们还提出了一种基于用户行为周期的异常检测方法,通过比较待检测特征向量和以往周期相近时间段的特征向量集的距离即可判定该特征向量是否异常,向量比较时采用不受特征间关联以及特征取值范围影响的马氏距离作为距离衡量的标准。实验证明我们采用的移动设备异常检测系统框架和检测方法能够有效提高对移动设备恶意程序的检测率。

  15. Design and Implementation of an Anomaly Detector

    Energy Technology Data Exchange (ETDEWEB)

    Bagherjeiran, A; Cantu-Paz, E; Kamath, C

    2005-07-11

    This paper describes the design and implementation of a general-purpose anomaly detector for streaming data. Based on a survey of similar work from the literature, a basic anomaly detector builds a model on normal data, compares this model to incoming data, and uses a threshold to determine when the incoming data represent an anomaly. Models compactly represent the data but still allow for effective comparison. Comparison methods determine the distance between two models of data or the distance between a model and a point. Threshold selection is a largely neglected problem in the literature, but the current implementation includes two methods to estimate thresholds from normal data. With these components, a user can construct a variety of anomaly detection schemes. The implementation contains several methods from the literature. Three separate experiments tested the performance of the components on two well-known and one completely artificial dataset. The results indicate that the implementation works and can reproduce results from previous experiments.

  16. Learning about Poland Anomaly

    Science.gov (United States)

    ... performed too early, while the individual is growing, asymmetry can result or be made greater than before. ... Anomaly About.com- Poland Syndrome [rarediseases.about.com] Information about Poland syndrome produced by Mary Kugler, M.S. ...

  17. Scattering anomaly in optics

    CERN Document Server

    Silveirinha, Mario G

    2016-01-01

    In time-reversal invariant electronic systems the scattering matrix is anti-symmetric. This property enables an effect, designated here as "scattering anomaly", such that the electron transport does not suffer from back reflections, independent of the specific geometry of the propagation path or the presence of time-reversal invariant defects. In contrast, for a generic time-reversal invariant photonic system the scattering matrix is symmetric and there is no similar anomaly. Here, it is theoretically proven that despite these fundamental differences there is a wide class of photonic platforms - in some cases formed only by time-reversal invariant media - in which the scattering anomaly can occur. It is shown that an optical system invariant under the action of the composition of the time-reversal, parity and duality operators is characterized by an anti-symmetric scattering matrix. Specific examples of photonic platforms wherein the scattering anomaly occurs are given, and it is demonstrated with full wave n...

  18. Neutrino anomalies without oscillations

    Indian Academy of Sciences (India)

    Sandip Pakvasa

    2000-01-01

    I review explanations for the three neutrino anomalies (solar, atmospheric and LSND) which go beyond the `conventional' neutrino oscillations induced by mass-mixing. Several of these require non-zero neutrino masses as well.

  19. Skyrmions and anomalies

    International Nuclear Information System (INIS)

    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

  20. Congenital laryngeal anomalies,

    OpenAIRE

    Rutter, Michael J.

    2014-01-01

    Introduction: It is essential for clinicians to understand issues relevant to the airway management of infants and to be cognizant of the fact that infants with congenital laryngeal anomalies are at particular risk for an unstable airway. Objectives: To familiarize clinicians with issues relevant to the airway management of infants and to present a succinct description of the diagnosis and management of an array of congenital laryngeal anomalies. Methods: Revision article, in which the ma...

  1. The Pioneer Anomaly

    CERN Document Server

    de Diego, Jose A

    2008-01-01

    Analysis of the radio-metric data from Pioneer 10 and 11 spacecrafts has indicated the presence of an unmodeled acceleration starting at 20 AU, which has become known as the Pioneer anomaly. The nature of this acceleration is uncertain. In this paper we give a description of the effect and review some relevant mechanisms proposed to explain the observed anomaly. We also discuss on some future projects to investigate this phenomenon.

  2. Volume anomaly in ferrimagnetism

    OpenAIRE

    Pascard, H.; Globus, A.

    1981-01-01

    The volume anomaly ΔV/V due to the magnetic energy corresponding to the exchange interactions is experimentally determined for YIG. The experimental values (from 77 K to Tc) agree with the values deduced from the theoretical expression based on the Néel's theories of volume anomaly and of ferrimagnetism. These results are compared with those obtained by other authors on ferromagnetic and antiferromagnetic materials with localized magnetic moments : a reduced curve is obtained.

  3. Anomalies and Entanglement Entropy

    CERN Document Server

    Nishioka, Tatsuma

    2015-01-01

    We initiate a systematic study of entanglement and Renyi entropies in the presence of gauge and gravitational anomalies in even-dimensional quantum field theories. We argue that the mixed and gravitational anomalies are sensitive to boosts and obtain a closed form expression for their behavior under such transformations. Explicit constructions exhibiting the dependence of entanglement entropy on boosts is provided for theories on spacetimes with non-trivial magnetic fluxes and (or) non-vanishing Pontryagin classes.

  4. Dual diaphragmatic anomalies.

    Science.gov (United States)

    Padmanabhan, Arjun; Thomas, Abin Varghese

    2016-01-01

    Although diaphragmatic anomalies such as an eventration and hiatus hernia are commonly encountered in incidental chest X-ray imaging, the presence of concomitant multiple anomalies is extremely rare. This is all the more true in adults. Herein, we present the case of a 75-year-old female, while undergoing a routine chest X-ray imaging, was found to have eventration of right hemidiaphragm along with a hiatus hernia as well. PMID:27625457

  5. Dual diaphragmatic anomalies

    Directory of Open Access Journals (Sweden)

    Arjun Padmanabhan

    2016-01-01

    Full Text Available Although diaphragmatic anomalies such as an eventration and hiatus hernia are commonly encountered in incidental chest X-ray imaging, the presence of concomitant multiple anomalies is extremely rare. This is all the more true in adults. Herein, we present the case of a 75-year-old female, while undergoing a routine chest X-ray imaging, was found to have eventration of right hemidiaphragm along with a hiatus hernia as well.

  6. The Holographic Supercurrent Anomaly

    CERN Document Server

    Chaichian, Masud

    2004-01-01

    The \\gamma-trace anomaly of supersymmetry current in a supersymmetric gauge theory shares a superconformal anomaly multiplet with the chiral R-symmetry anomaly and the Weyl anomaly, and its holographic reproduction is a valuable test to the AdS/CFT correspondence conjecture. We investigate how the \\gamma-trace anomaly of the supersymmetry current of {\\cal N}=1 four-dimensional supersymmetric gauge theory in an {\\cal N}=1 conformal supergravity background can be extracted out from the ${\\cal N}=2$ gauged supergravity in five dimensions. It is shown that the reproduction of this super-Weyl anomaly originates from the following two facts: First the {\\cal N}=2 bulk supersymmetry transformation converts into {\\cal N}=1 superconformal transformation on the boundary, which consists of {\\cal N}=1 supersymmetry transformation and special conformal supersymmetry (or super-Weyl) transformation; second the supersymmetry variation of the bulk action of five-dimensional gauged supergravity is a total derivative. The non-co...

  7. Quick Anomaly Detection by the Newcomb--Benford Law, with Applications to Electoral Processes Data from the USA, Puerto Rico and Venezuela

    CERN Document Server

    Pericchi, Luis; 10.1214/09-STS296

    2012-01-01

    A simple and quick general test to screen for numerical anomalies is presented. It can be applied, for example, to electoral processes, both electronic and manual. It uses vote counts in officially published voting units, which are typically widely available and institutionally backed. The test examines the frequencies of digits on voting counts and rests on the First (NBL1) and Second Digit Newcomb--Benford Law (NBL2), and in a novel generalization of the law under restrictions of the maximum number of voters per unit (RNBL2). We apply the test to the 2004 USA presidential elections, the Puerto Rico (1996, 2000 and 2004) governor elections, the 2004 Venezuelan presidential recall referendum (RRP) and the previous 2000 Venezuelan Presidential election. The NBL2 is compellingly rejected only in the Venezuelan referendum and only for electronic voting units. Our original suggestion on the RRP (Pericchi and Torres, 2004) was criticized by The Carter Center report (2005). Acknowledging this, Mebane (2006) and The...

  8. Bayesian priors for transiting planets

    CERN Document Server

    Kipping, David M

    2016-01-01

    As astronomers push towards discovering ever-smaller transiting planets, it is increasingly common to deal with low signal-to-noise ratio (SNR) events, where the choice of priors plays an influential role in Bayesian inference. In the analysis of exoplanet data, the selection of priors is often treated as a nuisance, with observers typically defaulting to uninformative distributions. Such treatments miss a key strength of the Bayesian framework, especially in the low SNR regime, where even weak a priori information is valuable. When estimating the parameters of a low-SNR transit, two key pieces of information are known: (i) the planet has the correct geometric alignment to transit and (ii) the transit event exhibits sufficient signal-to-noise to have been detected. These represent two forms of observational bias. Accordingly, when fitting transits, the model parameter priors should not follow the intrinsic distributions of said terms, but rather those of both the intrinsic distributions and the observational ...

  9. Overcoming priors anxiety

    CERN Document Server

    D'Agostini, Giulio

    1999-01-01

    The choice of priors may become an insoluble problem if priors and Bayes' rule are not seen and accepted in the framework of subjectivism. Therefore, the meaning and the role of subjectivity in science is considered and defended from the pragmatic point of view of an ``experienced scientist''. The case for the use of subjective priors is then supported and some recommendations for routine and frontier measurement applications are given. The issue of reference priors is also considered from the practical point of view and in the general context of ``Bayesian dogmatism''.

  10. Detection of dwarf gourami iridovirus (Infectious spleen and kidney necrosis virus) in populations of ornamental fish prior to and after importation into Australia, with the first evidence of infection in domestically farmed Platy (Xiphophorus maculatus).

    Science.gov (United States)

    Rimmer, Anneke E; Becker, Joy A; Tweedie, Alison; Lintermans, Mark; Landos, Matthew; Stephens, Fran; Whittington, Richard J

    2015-11-01

    The movement of ornamental fish through international trade is a major factor for the transboundary spread of pathogens. In Australia, ornamental fish which may carry dwarf gourami iridovirus (DGIV), a strain of Infectious spleen and kidney necrosis virus (ISKNV), have been identified as a biosecurity risk despite relatively stringent import quarantine measures being applied. In order to gain knowledge of the potential for DGIV to enter Australia, imported ornamental fish were sampled prior to entering quarantine, during quarantine, and post quarantine from wholesalers and aquatic retail outlets in Australia. Samples were tested by quantitative polymerase chain reaction (qPCR) for the presence of megalocytivirus. Farmed and wild ornamental fish were also tested. Megalocytivirus was detected in ten of fourteen species or varieties of ornamental fish. Out of the 2086 imported gourami tested prior to entering quarantine, megalocytivirus was detected in 18.7% of fish and out of the 51 moribund/dead ornamental fish tested during the quarantine period, 68.6% were positive for megalocytivirus. Of fish from Australian wholesalers and aquatic retail outlets 14.5% and 21.9%, respectively, were positive. Out of 365 farmed ornamental fish, ISKNV-like megalocytivirus was detected in 1.1%; these were Platy (Xiphophorus maculatus). Megalocytivirus was not detected in free-living breeding populations of Blue gourami (Trichopodus trichopterus) caught in Queensland. This study showed that imported ornamental fish are vectors for DGIV and it was used to support an import risk analysis completed by the Australian Department of Agriculture. Subsequently, the national biosecurity policy was revised and from 1 March 2016, a health certification is required for susceptible families of fish to be free of this virus prior to importation. PMID:26452601

  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. Congenital Anomalies in Infant with Congenital Hypothyroidism

    Directory of Open Access Journals (Sweden)

    Zahra Razavi

    2012-09-01

    Full Text Available bjective: Congenital hypothyroidism is characterized by inadequate thyroid hormone production in newborn infants. Many infants with CH have co-occurring congenital malformations. This is an investigation on the frequency and types of congenital anomalies in infants with congenital hypothyroidism born from May 2006-2010 in Hamadan, west province of Iran.Methods: The Iranian neonatal screening program for congenital hypothyroidism was initiated in May 2005. This prospective descriptive study was conducted in infants diagnosed with congenital hypothyroidism being followed up in Pediatric Endocrinology Clinicof Besat Hospital, a tertiary care centre in Hamadan. Cases included all infants with congenital hypothyroidism diagnosed through newborn screening program or detected clinically. Anomalies were identified by clinical examination, echocardiography, and X-ray of the hip during the infant’s first year of life.Results: A total of 150 infants with biochemically confirmed primary congenital hypothyroidism (72 females and 78 males were recruited during the period between May 2006-2010. Overall, 30 (20% infants had associated congenital anomalies. The most common type of anomaly was Down syndrome. Seven infants (3.1% had congenital cardiac anomalies such as: ASD (n=3, VSD (n=2, PS (n =1, PDA (n=1. Three children (2.6% had developmental displasia of the hip (n=3.Conclusion: The overall frequency of Down syndrome, cardiac malformation and other birth defect was high in infants with CH. This reinforces the need to examine all infants with congenital hypothyroidism for the presence of associated congenital anomalies.

  13. Ventriculomegaly with non-CNS anomalies

    International Nuclear Information System (INIS)

    We correlated fetal magnetic resonance (MR) imaging findings with postnatal clinical findings to assess ventriculomegaly with non-CNS anomalies. From 2002 to 2010, 52 fetuses underwent a MRI for evaluation of ventriculomegaly after ultrasonography (US). Ten of the 52 demonstrated anomalies outside the central nervous system (CNS), including trisomy 8, trisomy 18, X-linked hydrocephalus, CHARGE/Potter sequences, VATER association, oral-facial-digital syndrome, esophageal atresia type C, or external auditory canal stenosis. Examinations were performed between 24 and 35 weeks' gestation. MR imaging was performed in a 1.5-tesla unit using a phased-array coil without preparation. Fetal MR imaging showed abnormalities of the kidney, bladder, duodenum, and thumbs but did not permit diagnosis of esophageal atresia type C or craniofacial, anorectal, or skeletal anomalies. Cardiac anomaly was most frequent, but fetal MR imaging did not allow final diagnosis of congenital heart disease. On both US and MR imaging, esophageal atresia type C and anorectal anomaly were undetected; normal rectal signal in a case of anorectal anomaly without urorectal fistula did not lead to suspicion of anomaly. Observation of adducted thumbs on MR imaging is an important sign of X-linked hydrocephalus. The slice area used in this study did not cover polydactyly, which accompanies oral-facial-digital syndrome. US and MR imaging are complementary imaging methods used to evaluate ventriculomegaly. Fetal MR imaging should cover the kidney, bladder, and fingers. Further work is needed to determine the anomalies that can be clearly detected by fetal MR imaging. (author)

  14. Accurate Detection Of Left Atrial Thrombus Prior To Atrial Fibrillation Ablation In Patients With Therapeutic Anticoagulation: Does Transesophageal Echocardiography Beat Conventional Wisdom?

    Directory of Open Access Journals (Sweden)

    Dhanunjaya Lakkireddy MD, FACC

    2009-02-01

    Full Text Available Atrial fibrillation (AF significantly increases the risk of left atrial (LA thrombus and systemic thromboembolism.1-4 Screening transesophageal echo (TEE to rule out left atrial thrombus has become standard of care over the years.5 Conventional thinking of therapeutic anticoagulation for 4-6 weeks prior to cardioversion may not reduce the risk of left atrial thrombus completely. Left atrial thrombi can be seen on 2-9% of screening TEEs in AF patients with various levels of anticoagulation.5 Radiofrequency ablation of atria with pulmonary vein isolation (PVI with or without various additional ablative techniques has evolved into very important strategy in the treatment of patients with AF.6-10 Even though the relative risk of systemic thromboembolism after non TEE guided cardioversion after 3 weeks of anticoagulation remains lower (approximately 0.8% despite 7% prevalence of LA thrombi, the same may not be applicable to invasive treatment modalities like AF ablation.6,11-13 The presence of LA thrombi may increase the risk of clot dislodgment and subsequent thromboembolism with catheter manipulation during AF ablation and is considered to be an absolute contraindication.

  15. 3D electrical resistivity inversion using prior spatial shape constraints

    Institute of Scientific and Technical Information of China (English)

    Li Shu-Cai; Nie Li-Chao; Liu Bin; Song Jie; Liu Zheng-Yu; Su Mao-Xin; Xu Lei

    2013-01-01

    To minimize the number of solutions in 3D resistivity inversion, an inherent problem in inversion, the amount of data considered have to be large and prior constraints need to be applied. Geological and geophysical data regarding the extent of a geological anomaly are important prior information. We propose the use of shape constraints in 3D electrical resistivity inversion. Three weighted orthogonal vectors (a normal and two tangent vectors) were used to control the resistivity differences at the boundaries of the anomaly. The spatial shape of the anomaly and the constraints on the boundaries of the anomaly are thus established. We incorporated the spatial shape constraints in the objective function of the 3D resistivity inversion and constructed the 3D resistivity inversion equation with spatial shape constraints. Subsequently, we used numerical modeling based on prior spatial shape data to constrain the direction vectors and weights of the 3D resistivity inversion. We established a reasonable range between the direction vectors and weights, and verified the feasibility and effectiveness of using spatial shape prior constraints in reducing excessive structures and the number of solutions. We applied the prior spatially shape-constrained inversion method to locate the aquifer at the Guangzhou subway. The spatial shape constraints were taken from ground penetrating radar data. The inversion results for the location and shape of the aquifer agree well with drilling data, and the number of inversion solutions is significantly reduced.

  16. FOETAL ULTRASOUND - NEUROECTODERMAL ANOMALIES IN RURAL PREGNANT WOMEN

    Directory of Open Access Journals (Sweden)

    Mala Venkata

    2016-06-01

    Full Text Available BACKGROUND A prospective clinical study to know the various types of congenital Neuroectodermal Anomalies on obstetric Ultrasound, in rural pregnant women. To reduce the maternal morbidity and mortality by early detection of these Congenital Neuroectodermal Anomalies. To calculate the incidence and prevalence of different types of Congenital Neuroectodermal Anomalies, in these rural pregnant women. To assist the obstetrician in taking decisions regarding the termination or continuation of the pregnancy in relation to the type of malformation and its prognosis. METHODS A prospective clinical study of Congenital Neuroectodermal Anomalies in 22,000 rural pregnant women coming to the Santhiram Medical College, Radiology Department for a routine obstetric scan. 44 cases of neuroectodermal anomalies were detected out of the 22000 cases, within an incidence of 2 per 1000 cases. Approximately 1 in every 500 cases showed an anomaly. RESULTS The most common lesions detected were hydrocephalus, and spina bifida followed by anencephaly. Association of these lesions with consanguinity, previous history of similar anomaly and intake of iron and folic acid tablets was noted. CONCLUSION Ultrasound is an excellent modality for the diagnosis and characterisation of the neuroectodermal anomalies. Its multiplanar imaging property along with real time image visualisation make it an excellent tool for the diagnosis and characterisation of these anomalies

  17. 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)

    Decaro, Nicola; Mari, Viviana; Lucente, Maria Stella; Sciarretta, Rossana; Elia, Gabriella; Ridpath, Julia F; Buonavoglia, Canio

    2013-12-27

    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 nested PCR protocol for rapid characterization of bovine pestiviruses. Phylogeny conducted with full-length pestivirus genomes and three informative genomic sequences, placed the strain detected in the samples, Italy-129/07, into the Hobi-like virus branch. Italy-129/07, similar to other Hobi-like viruses isolated in Italy, was more closely related to viruses of South American origin, than Hobi-like viruses of Southeast Asian origin. This suggests a possible introduction of this emerging group of pestiviruses into Italy as a consequence of using contaminated biological products such as fetal bovine serum originating in South America. This report of a Hobi-like virus associated with respiratory disease along with the full-genomic characterization of the virus detected provides new data that contributes to the body of knowledge regarding the epidemiology, pathobiology and genetic diversity of this emerging group of pestiviruses. Importantly, it dates the circulation of Hobi-like viruses in Italy to 2007, at least three years before previous reports.

  18. Anomalies without Massless Particles

    CERN Document Server

    Gurlanik, Z

    1994-01-01

    Baryon and lepton number in the standard model are violated by anomalies, even though the fermions are massive. This problem is studied in the context of a two dimensional model. In a uniform background field, fermion production arise from non-adiabatic behavior that compensates for the absence of massless modes. On the other hand, for localized instanton-like configurations, there is an adiabatic limit. In this case, the anomaly is produced by bound states which travel across the mass gap. The sphaleron corresponds to a bound state at the halfway point.

  19. 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.

  20. La detection des cyanobacteries en milieu lacustre par l'etude des anomalies des spectres de reflectance de l'eau

    Science.gov (United States)

    Constantin, Gabriel

    Proliferation of cyanobacteria is a growing problem in lacustrine environment that results in rapid degradation of water quality. Moreover, certain cyanobacteria species produce harmful toxins. Phycocyanin (PC) is a photosynthetic pigment typical of cyanobacteria and affects the water color: it is therefore possible to study them using remote sensing. At least three algorithms to estimate PC concentration ([PC]) have been published, but their relative errors are important, especially for lower concentration. In this study, we are presenting the results of a new algorithm that uses the second order variability (anomalies) of water's reflectance spectrum to estimate [PC]. This method has never been used in lacustrine environment. The dataset used to develop and validate the algorithm was obtained between 2001 and 2005 in 57 different lakes and reservoirs of the Netherlands and Spain. The performance of the second order algorithm is equivalent or better than the three previously published algorithms. For the subset were [PC] > 32 mg m-3, the contribution of the second order term (R2=0.68 and RMSE=0.25) seems to improve considerably the first order algorithm (R2=0.50 and RMSE=0.35). The accuracy of the second order algorithm for [PC] > 32 mg m-3 is superior to the one calculated for the whole dataset (R2=0.69 and RMSE=0.44). The algorithm can also be adapted to the. bands of satellite sensor MERIS for the study of cyanobacteria. The application of this algorithm to a MERIS image acquired the 29 August 2010 taken over the Missisquoi Bay (Quebec, Canada) demonstrates the potential of this new algorithm for a future cyanobacteria' monitoring system. Note that all the statistical results presented above are for the logarithm of [PC] and the units of the RMSE are log(mg/m 3).

  1. 无线自组织网络中多层综合的节点行为异常检测方法%Multi-layer Integrated Anomaly Detection of Mobile Nodes Behaviors in Mobile Ad Hoc Networks

    Institute of Scientific and Technical Information of China (English)

    王涛; 余顺争

    2009-01-01

    Mobile Ad hoc Networks are very vulnerable to malicious attacks due to the nature of mobile computing envi-ronment such as wireless communication channels, limited power and bandwidth, dynamically changing and distributed network topology,etc.The general existing Intrusion Detection Systems (IDS) have provided little evidence that they are applicable to a broader range threats.Based on the generalized and cooperative intrusion detection architecture pro-posed as the foundation for all intrusion detection, we presented an anomaly detection mechanism to discriminate the il-legitimate network behaviors of mobile nodes.By collecting the observation sequences of multiple protocol layers, Hid-den semi-Markov Model (HSMM) was explored to describe the network behaviors of legitimate nodes and to implement the anomaly detection for various malicious attacks.We conducted extensive experiments using the na-2 simulation envi-ronment to evaluate and validate our research.%Ad hoe网络由于采用无线信道、有限的电源和带宽、分布式控制等,会比有线网络更易受到入侵攻击.通常的入侵检测技术具有检测能力单一、缺乏对抗新入侵方式的能力等缺陷.在分布式入侵检测系统(IDS)的基础上,提出一种针对移动节点网络行为的异常检测机制.基于多层综合的观测值序列,采用隐半马尔可夫模型(HSMM)建立描述网络中合法节点正常行为的检测模型,继而对网络中的正常与异常行为进行判断与识别.实验表明,此方法能针对现有多种入侵方式进行有效的检测.

  2. Anomaly Busters II

    International Nuclear Information System (INIS)

    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

  3. 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...

  4. 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...

  5. North Atlantic Temperature Anomaly

    OpenAIRE

    Vukcevic, M.A.

    2009-01-01

    The author postulates the existence of a high correlation between North Atlantic Temperature Anomaly and the variations of magnetic field over the Hudson Bay region. Post-glacial uplift and convection in the underlying mantle uplift (as reflected in changes of the area's magnetic intensity) are making significant contribution to the Atlantic basin climate change.

  6. Anomalies and elliptic operators

    International Nuclear Information System (INIS)

    The coefficients of asymptotic expansion Spexp(-tA) at t→0 are calculated for the quantum field theory operators. It is shown how to apply these results to the calculations of axial and conformal anomalies, the charge renormalization in gauge theory and effective action in twodimensional electrodynamics

  7. 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 BACKGROUND: 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. METHODS AND FINDINGS: 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. CONCLUSIONS: 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

  8. MR imaging of fetal cerebral anomalies

    International Nuclear Information System (INIS)

    Background. Prenatal diagnosis of fetal brain anomalies relies mainly upon ultrasonography. However, even in the most experienced hands, the technique has limitations for some difficult diagnoses. MRI is an excellent imaging modality for the paediatric and adult brain. Objective. To assess the value of prenatal MRI when a cerebral anomaly was detected by US and where the prognosis depended on the identification of other anomalies undetectable by US, or where fetuses were at risk for a CNS lesion even when the US was normal. Materials and methods. Four hundred prenatal MRI examinations were performed since 1988, and confirmed by postnatal follow-up or pathological examination. Two-thirds of the examinations were performed after 25 weeks of gestation, one-third between 21 and 26 weeks. Fetal immobilisation was obtained by maternal premedication with flunitrazepam, administered orally 1 h before the examination. The examinations were performed on 1.5 T scanners using one or two surface coils. Results. Prenatal MRI allowed the diagnosis of serious unsuspected lesions such as neuronal migration disorders, ischaemic and haemorrhagic lesions and the abnormalities observed in tuberous sclerosis. It helped to characterise ventricular dilatation and anomalies of the corpus callosum and of the posterior fossa. Conclusions. MRI is a valuable complementary tool when prenatal US is incomplete, doubtful or limited. Prenatal MRI is particularly useful for the detection of ischaemic and haemorrhagic lesions, neuronal migration disorders and tuberous sclerosis lesions. Detection of these associated anomalies worsens the fetal prognosis, has medico-legal implications and modifies obstetric management. Normal prenatal MRI does not exclude an anomaly. (orig.)

  9. MR imaging of fetal cerebral anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Sonigo, P.C.; Carteret, M.; Brunelle, F.O. [Department of Radiology, Hopital Des Enfants Malades, Paris (France); Rypens, F.F. [Department of Radiology, Hopital Des Enfants Malades, Paris (France)]|[Department of Radiology, Hopital Erasme, Brussels (Belgium); Delezoide, A.L. [Department of Pathology, Hopital Des Enfants Malades, Paris (France)

    1998-04-01

    Background. Prenatal diagnosis of fetal brain anomalies relies mainly upon ultrasonography. However, even in the most experienced hands, the technique has limitations for some difficult diagnoses. MRI is an excellent imaging modality for the paediatric and adult brain. Objective. To assess the value of prenatal MRI when a cerebral anomaly was detected by US and where the prognosis depended on the identification of other anomalies undetectable by US, or where fetuses were at risk for a CNS lesion even when the US was normal. Materials and methods. Four hundred prenatal MRI examinations were performed since 1988, and confirmed by postnatal follow-up or pathological examination. Two-thirds of the examinations were performed after 25 weeks of gestation, one-third between 21 and 26 weeks. Fetal immobilisation was obtained by maternal premedication with flunitrazepam, administered orally 1 h before the examination. The examinations were performed on 1.5 T scanners using one or two surface coils. Results. Prenatal MRI allowed the diagnosis of serious unsuspected lesions such as neuronal migration disorders, ischaemic and haemorrhagic lesions and the abnormalities observed in tuberous sclerosis. It helped to characterise ventricular dilatation and anomalies of the corpus callosum and of the posterior fossa. Conclusions. MRI is a valuable complementary tool when prenatal US is incomplete, doubtful or limited. Prenatal MRI is particularly useful for the detection of ischaemic and haemorrhagic lesions, neuronal migration disorders and tuberous sclerosis lesions. Detection of these associated anomalies worsens the fetal prognosis, has medico-legal implications and modifies obstetric management. Normal prenatal MRI does not exclude an anomaly. (orig.) With 19 figs., 33 refs.

  10. Astrometric solar system anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Nieto, Michael Martin [Los Alamos National Laboratory; Anderson, John D [PROPULSION LABORATORY

    2009-01-01

    There are at least four unexplained anomalies connected with astrometric data. perhaps the most disturbing is the fact that when a spacecraft on a flyby trajectory approaches the Earth within 2000 km or less, it often experiences a change in total orbital energy per unit mass. next, a secular change in the astronomical unit AU is definitely a concern. It is increasing by about 15 cm yr{sup -1}. The other two anomalies are perhaps less disturbing because of known sources of nongravitational acceleration. The first is an apparent slowing of the two Pioneer spacecraft as they exit the solar system in opposite directions. Some astronomers and physicists are convinced this effect is of concern, but many others are convinced it is produced by a nearly identical thermal emission from both spacecraft, in a direction away from the Sun, thereby producing acceleration toward the Sun. The fourth anomaly is a measured increase in the eccentricity of the Moon's orbit. Here again, an increase is expected from tidal friction in both the Earth and Moon. However, there is a reported unexplained increase that is significant at the three-sigma level. It is produent to suspect that all four anomalies have mundane explanations, or that one or more anomalies are a result of systematic error. Yet they might eventually be explained by new physics. For example, a slightly modified theory of gravitation is not ruled out, perhaps analogous to Einstein's 1916 explanation for the excess precession of Mercury's perihelion.

  11. 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...

  12. Expanded spectrum of limb anomalies in the VATER association

    Energy Technology Data Exchange (ETDEWEB)

    Fernbach, S.K.; Glass, R.B.J.

    1988-04-01

    The radiographs of 230 children who had undergone neonatal surgery for imperforate anus and/or esophageal atresia/tracheoesophageal fistula were reviewed. Of the 31 children with limb anomalies thus detected, the 24 who had no radiologic or laboratory evidence of chromosomal abnormality form the basis of this report. In 16 children the limb anomalies fell within the commonly described spectrum of the VATER association. In the other 8 children and in 3 of the children with typical VATER limb anomalies additional anomalies were encountered: Sprengel deformity (2), hypoplasia of the humerus (3), radioulnar synostosis (1), midline anomalies of the hand (1), absence of the pubis, femur, tibia, and fibula and two rays of the foot (1), and other foot deformities. Subtle anomalies of the hand were common and included: clinodactyly, syndactyly, shortening of the middle phalanx of the fifth digit, and rotary malposition of the digits.

  13. 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

    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...

  14. Research and Implementation on Network Trafifc Anomaly Detection without Guidance Learning with Spark%Spark框架下基于无指导学习环境的网络流量异常检测研究与实现

    Institute of Scientific and Technical Information of China (English)

    吴晓平; 周舟; 李洪成

    2016-01-01

    In view of the massive data intrusion detection, this paper designs and implements a network trafifc anomaly detection system based on Spark framework. Data preprocessing use Python and Python data, an upgraded version of the IPython implementation. Anomaly detection usesK-means predict and classify flow records represent the type of attack. In order to avoid time overhead uses traditional distributed computing framework, this paper designs and implements an anomalyK-means detection method under the framework of Spark. The method storages temporary data into memory rather than the hard drive, and improve the computational efifciency. In order to solve the problem ofK value select dififcult, through the Spark iterative calculation and comparison of the different K-means value of theK algorithm in the cluster center to all points in the cluster average value of all points, to achieve the best selection ofK value. Finally, the performance and function of the system are tested. The test result shows that the system achieves the predetermined design requirements, and has high computational efifciency and detection accuracy.%针对海量数据进行入侵检测的困难性问题,文章设计并实现了一套基于Spark框架的网络流量无指导学习异常检测系统。数据的预处理采用Python和Python的数据升级版IPython实现,异常检测采用无指导学习环境下的快速聚类方法K-means预测以及划分流量方法,记录所代表的攻击类型。为了避免MapReduce等传统分布式计算框架频繁的硬盘读写带来的巨大时间开销,文章设计实现了Spark框架下的K-means异常检测方法,通过将每轮迭代产生的临时数据存入内存而非硬盘中,有效提高了K-means聚类检测算法的计算效率。此外,为解决K-means算法中K值选取难的问题,通过Spark迭代计算与比较不同K值下的K-means算法中各聚类中心到所属簇中所有点距离的平均值,实现最佳K值

  15. Homogeneous Liquid-Liquid Microextraction for Determination of Organophosphorus Pesticides in Environmental Water Samples Prior to Gas Chromatography-Flame Photometric Detection.

    Science.gov (United States)

    Berijani, Sana; Sadigh, Mirhanif; Pournamdari, Elham

    2016-07-01

    In this study, homogeneous liquid-liquid microextraction (HLLME) was developed for preconcentration and extraction of 15 organophosphorus pesticides (OPPs) from water samples coupling with gas chromatography followed by a flame photometric detector (HLLME-GC-FPD). In this method, OPPs were extracted by the homogeneous phase in a ternary solvent system (water/acetic acid/chloroform). The homogeneous solution was excluded by the addition of sodium hydroxide as a phase separator reagent and a cloudy solution was formed. After centrifugation (3 min at 5,000 rpm), the fine particles of extraction solvent (chloroform) were sedimented at the bottom of the conical test tube (10.0 ± 0.5 µL). Furthermore, 0.5 µL of the sedimented phase was injected into the GC for separation and determination of OPPs. Optimal results were obtained under the following conditions: volume of the extracting solvent (chloroform), 53 µL; volume of the consolute solvent (acetic acid), 0.76 mL and concentration of sodium hydroxide, 40% (w/v). Under the optimum conditions, the enrichment factors of (260-665), the extraction percent of 75.8-104%, the dynamic linear range of 0.03-300 µg L(-1) and the limits of detection of 0.004-0.03 µg L(-1) were obtained for the OPPs. This method was successfully applied for the extraction and determination of the OPPs in environmental water samples.

  16. Homogeneous Liquid-Liquid Microextraction for Determination of Organophosphorus Pesticides in Environmental Water Samples Prior to Gas Chromatography-Flame Photometric Detection.

    Science.gov (United States)

    Berijani, Sana; Sadigh, Mirhanif; Pournamdari, Elham

    2016-07-01

    In this study, homogeneous liquid-liquid microextraction (HLLME) was developed for preconcentration and extraction of 15 organophosphorus pesticides (OPPs) from water samples coupling with gas chromatography followed by a flame photometric detector (HLLME-GC-FPD). In this method, OPPs were extracted by the homogeneous phase in a ternary solvent system (water/acetic acid/chloroform). The homogeneous solution was excluded by the addition of sodium hydroxide as a phase separator reagent and a cloudy solution was formed. After centrifugation (3 min at 5,000 rpm), the fine particles of extraction solvent (chloroform) were sedimented at the bottom of the conical test tube (10.0 ± 0.5 µL). Furthermore, 0.5 µL of the sedimented phase was injected into the GC for separation and determination of OPPs. Optimal results were obtained under the following conditions: volume of the extracting solvent (chloroform), 53 µL; volume of the consolute solvent (acetic acid), 0.76 mL and concentration of sodium hydroxide, 40% (w/v). Under the optimum conditions, the enrichment factors of (260-665), the extraction percent of 75.8-104%, the dynamic linear range of 0.03-300 µg L(-1) and the limits of detection of 0.004-0.03 µg L(-1) were obtained for the OPPs. This method was successfully applied for the extraction and determination of the OPPs in environmental water samples. PMID:26944949

  17. Fetal Kidney Anomalies: Next Generation Sequencing

    DEFF Research Database (Denmark)

    Rasmussen, Maria; Sunde, Lone; Nielsen, Marlene Louise;

    with prenatally detected kidney anomalies in order to uncover genetic explanations and assess recurrence risk. Also, we aim to study the relation between genetic findings and post mortem kidney histology. Methods The study comprises fetuses diagnosed prenatally with bilateral kidney anomalies that have undergone...... in the nephronophthisis associated gene, TMEM67 and six fetuses had mutations in kidney developmental genes. For these fetuses kidney histology is presented. Conclusion and Perspectives In eight (14%) fetuses we identified a likely genetic cause of the kidney anomalies. Ten fetuses from eight families, in which......Aim and Introduction Identification of abnormal kidneys in the fetus may lead to termination of the pregnancy and raises questions about the underlying cause and recurrence risk in future pregnancies. In this study, we investigate the effectiveness of targeted next generation sequencing in fetuses...

  18. 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.

  19. Pictorial review of MRI/CT Scan in congenital temporal bone anomalies, in patients for cochlear implant

    International Nuclear Information System (INIS)

    High-resolution CT scan (HRCT) and MRI are routinely performed prior to cochlear implant surgery. These modalities help assess the status of the inner ear structures. A few patients have significant anomalies, which need to be assessed and understood in detail. We present a pictorial essay of these anomalies and described our HRCT and MRI techniques in patients being imaged prior to surgery

  20. Arthur Prior and 'Now'

    DEFF Research Database (Denmark)

    Blackburn, Patrick Rowan; Jørgensen, Klaus Frovin

    2015-01-01

    ’s search led him through the work of Castaneda, 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...

  1. Anomalies, Branes, and Currents

    OpenAIRE

    Cheung, Yeuk-Kwan E.; Yin, Zheng

    1997-01-01

    When a D-brane wraps around a cycle of a curved manifold, the twisting of its normal bundle can induce chiral asymmetry in its worldvolume theory. We obtain the general form of the resulting anomalies for D-branes and their intersections. They are not cancelled among themselves, and the standard inflow mechanism does not apply at first sight because of their apparent lack of factorizability and the apparent vanishing of the corresponding inflow. We show however after taking into consideration...

  2. A Framework for Security Components Anomalies Severity Evaluation and Classification

    OpenAIRE

    Kamel Karoui; Fakher Ben Ftima; Henda Ben Ghezala

    2013-01-01

    Security components such as firewalls, IDS and IPS, are the most widely adopted security devices fornetwork protection.These components are often implemented with several errors (or anomalies) that aresometimes critical. To ensure the security of their networks, administrators should detect these anomaliesand correct them. Before correcting the detected anomalies, the administrator should evaluate and classifythese latter to determine the best strategy to correct them. In this work, we propos...

  3. [Fetal ocular anomalies: the advantages of prenatal magnetic resonance imaging].

    Science.gov (United States)

    Brémond-Gignac, D; Copin, H; Elmaleh, M; Milazzo, S

    2010-05-01

    Congenital ocular malformations are uncommon and require prenatal diagnosis. Severe anomalies are more often detected by trained teams and minor anomalies are more difficult to identify and must be systematically sought, particularly when multiple malformations or a family and maternal history is known. The prenatal diagnosis-imaging tool most commonly used is ultrasound but it can be completed by magnetic resonance imaging (MRI), which contributes crucial information. Fetal dysmorphism can occur in various types of dysfunction and prenatal diagnosis must recognize fetal ocular anomalies. After systematic morphologic ultrasound imaging, different abnormalities detected by MRI are studied. Classical parameters such as binocular and interorbital measurements are used to detect hypotelorism and hypertelorism. Prenatal ocular anomalies such as cataract microphthalmia, anophthalmia, and coloboma have been described. Fetal MRI added to prenatal sonography is essential in detecting cerebral and general anomalies and can give more information on the size and morphology of the eyeball. Fetal abnormality detection includes a detailed family and maternal history, an amniotic fluid sample for karyotype, and other analyses for a better understanding of the images. Each pregnancy must be discussed with all specialists for genetic counseling. With severe malformations, termination of pregnancy is proposed because of risk of blindness and associated cerebral or systemic anomalies. Early prenatal diagnosis of ocular malformations can also detect associated abnormalities, taking congenital cataracts that need surgical treatment into account as early as possible. Finally, various associated syndromes need a pediatric check-up that could lead to emergency treatment.

  4. Combined application of alpha-track and fission-track techniques for detection of plutonium particles in environmental samples prior to isotopic measurement using thermo-ionization mass spectrometry.

    Science.gov (United States)

    Lee, Chi-Gyu; Suzuki, Daisuke; Esaka, Fumitaka; Magara, Masaaki; Kimura, Takaumi

    2011-07-15

    The fission track technique is a sensitive detection method for particles which contain radio-nuclides like (235)U or (239)Pu. However, when the sample is a mixture of plutonium and uranium, discrimination between uranium particles and plutonium particles is difficult using this technique. In this study, we developed a method for detecting plutonium particles in a sample mixture of plutonium and uranium particles using alpha track and fission track techniques. The specific radioactivity (Bq/g) for alpha decay of plutonium is several orders of magnitude higher than that of uranium, indicating that the formation of the alpha track due to alpha decay of uranium can be disregarded under suitable conditions. While alpha tracks in addition to fission tracks were detected in a plutonium particle, only fission tracks were detected in a uranium particle, thereby making the alpha tracks an indicator for detecting particles containing plutonium. In addition, it was confirmed that there is a linear relationship between the numbers of alpha tracks produced by plutonium particles made of plutonium certified standard material and the ion intensities of the various plutonium isotopes measured by thermo-ionization mass spectrometry. Using this correlation, the accuracy in isotope ratios, signal intensity and measurement errors is presumable from the number of alpha tracks prior to the isotope ratio measurements by thermal ionization mass spectrometry. It is expected that this method will become an effective tool for plutonium particle analysis. The particles used in this study had sizes between 0.3 and 2.0 μm.

  5. An anomaly of positron parameter appearing in the study of an electron-brush-plated material -- Is it possible to use positron to detect molecular hydrogen content?

    International Nuclear Information System (INIS)

    On account of the ease with which positron annihilation experiments may be made and the well-known manner in which positrons behave much in the way that hydrogen protons do, the positron technique is thus commonly used to study hydrogen-charging and hydrogen-absorbed metals and alloys. Indeed, the positron technique has been an indispensable and powerful tool to investigate the influence of hydrogen on microstructures and properties of materials, thus a lot of the related papers have been published. However, papers regarding the use of positrons to detect molecular hydrogen content are few in the literature. This is because hydrogen-charging of metallic materials is a quite complicated process. Generally speaking, when hydrogen-charging, the content of hydrogen increases but occasionally it becomes lower instead of higher with increasing charging time. In addition, positrons, after entering a material, are affected not only by the old defects and the newly generated defects, but by hydrogen proton screening as well. Therefore, the existence of certain relationships between the measured positron parameter and hydrogen content in the material has not been found. Among the curves of positron parameter as a function of charging time which appeared in publications, the one which has a wavelike characteristic was frequently seen. In this letter, the author found that the measured Doppler lineshape parameter happens to be roughly directly proportional to the molecular hydrogen content involved in a particular material

  6. An anomaly of positron parameter appearing in the study of an electron-brush-plated material -- Is it possible to use positron to detect molecular hydrogen content

    Energy Technology Data Exchange (ETDEWEB)

    Wang Jingcheng; Qin Yunjie (Shanghai Iron and Steel Research Inst. (China). Applied Physics Dept.); Zhang Guilin; Zheng Wanhui (Shanghai Nuclear Research Inst. (China))

    1994-11-01

    On account of the ease with which positron annihilation experiments may be made and the well-known manner in which positrons behave much in the way that hydrogen protons do, the positron technique is thus commonly used to study hydrogen-charging and hydrogen-absorbed metals and alloys. Indeed, the positron technique has been an indispensable and powerful tool to investigate the influence of hydrogen on microstructures and properties of materials, thus a lot of the related papers have been published. However, papers regarding the use of positrons to detect molecular hydrogen content are few in the literature. This is because hydrogen-charging of metallic materials is a quite complicated process. Generally speaking, when hydrogen-charging, the content of hydrogen increases but occasionally it becomes lower instead of higher with increasing charging time. In addition, positrons, after entering a material, are affected not only by the old defects and the newly generated defects, but by hydrogen proton screening as well. Therefore, the existence of certain relationships between the measured positron parameter and hydrogen content in the material has not been found. Among the curves of positron parameter as a function of charging time which appeared in publications, the one which has a wavelike characteristic was frequently seen. In this letter, the author found that the measured Doppler lineshape parameter happens to be roughly directly proportional to the molecular hydrogen content involved in a particular material.

  7. Minor physical anomalies in Tourette syndrome

    Directory of Open Access Journals (Sweden)

    Györgyi Csábi

    2008-09-01

    Full Text Available Background and Objectives: The prevalence of minor physical anomalies (prenatal errors of morphogenesis was evaluated in patients with Tourette syndrome to get indirect data on the possible role of aberrant neurodevelopment in the aetiology of Tourette syndrome. No published study is known on the minor physical anomaly prevalence in this recently intensively investigated disorder, and connecting to current opinions on a possible role of aberrant neurodevelopment in Tourette syndrome it seems important to introduce trait marker research focusing on brain maldevelopment. Methods: A scale developed by Méhes1,2 was used to detect the presence or absence of 57 minor physical anomalies in 24 patients with Tourette syndrome and in 24 matched controls 21 boys and 3 girls were evaluated, the age of onset of illness among the Tourette patients was between the age of 5 and 13. Results: The mean value of all minor physical anomalies was significantly higher among the group of patients compared with controls. (Mann - Whitney U - value: 49, 50, -Z = - 4,92, p = 0,001 In the case of 7 minor physical anomalies we could demonstrate statistically significant differences between the Tourette and the control sample. In the case of 4 minor malformations (supernumary nipples, prominent forehead, tongue with smooth and rough spots, double posterior hair whorl and of 3 phenogenetic variants (antimongoloid slant, inner epicanthic folds, high arched palate a significantly higher frequency was observed compared with control individuals. However after Bonferroni correction for the Fisher’s Exact test, only double posterior hair whorl and high arched palate showed a significantly higher frequency compared to control children (p = 0.001. Conclusions: The overrepresentation of minor physical anomalies in Tourette syndrome can strongly support the view that this disorder is related to pathological factors operating early in development.

  8. A study of associated congenital anomalies with biliary atresia

    Directory of Open Access Journals (Sweden)

    Lucky Gupta

    2016-01-01

    Full Text Available Background/Purpose: This study aims to analyze the incidence and type of various associated anomalies among infants with extrahepatic biliary atresia (EHBA, compare their frequency with those quoted in the existing literature and assess their role in the overall management. Materials and Methods: A retrospective study was performed on 137 infants who underwent the Kasai procedure for EHBA during the past 12 years. The medical records were reviewed for the incidence and type of associated anomalies in addition to the details of the management of the EHBA. Results: Of the137 infants, 40 (29.2% were diagnosed as having 58 anomalies. The majority of patients had presented in the 3 rd month of life; mean age was 81 ± 33 days (range = 20-150 days. There were 32 males and 8 females; boys with EHBA had a higher incidence of associated anomalies. Of these 40 patients, 22 (37.9% had vascular anomalies, 13 patients (22.4% had hernias (umbilical-10, inguinal-3, 7 patients (12.1% had intestinal malrotation, 4 patients (6.8% had choledochal cyst, 1 patient (1.7% had Meckel′s diverticulum, 3 patients (5% had undergone prior treatment for jejunoileal atresias (jejunal-2, ileal-1, 2 patients (3.4% had undergone prior treatment for esophageal atresia and tracheoesophageal fistula, 2 patients (3.4% had spleniculi, and 2 patients (3.4% were diagnosed as having situs inversus. Conclusions: The most common associated anomalies in our study were related to the vascular variation at the porta hepatis and the digestive system. The existence of anomalies in distantly developing anatomic regions in patients with EHBA supports the possibility of a "generalized" insult during embryogenesis rather than a "localized" defect. In addition, male infants were observed to have significantly more associated anomalies as compared with the female infants in contrast to earlier reports.

  9. Weather Satellite Thermal IR Responses Prior to Earthquakes

    Science.gov (United States)

    OConnor, Daniel P.

    2005-01-01

    A number of observers claim to have seen thermal anomalies prior to earthquakes, but subsequent analysis by others has failed to produce similar findings. What exactly are these anomalies? Might they be useful for earthquake prediction? It is the purpose of this study to determine if thermal anomalies can be found in association with known earthquakes by systematically co-registering weather satellite images at the sub-pixel level and then determining if statistically significant responses occurred prior to the earthquake event. A new set of automatic co-registration procedures was developed for this task to accommodate all properties particular to weather satellite observations taken at night, and it relies on the general condition that the ground cools after sunset. Using these procedures, we can produce a set of temperature-sensitive satellite images for each of five selected earthquakes (Algeria 2003; Bhuj, India 2001; Izmit, Turkey 2001; Kunlun Shan, Tibet 2001; Turkmenistan 2000) and thus more effectively investigate heating trends close to the epicenters a few hours prior to the earthquake events. This study will lay tracks for further work in earthquake prediction and provoke the question of the exact nature of the thermal anomalies.

  10. A Framework for Security Components Anomalies Severity Evaluation and Classification

    Directory of Open Access Journals (Sweden)

    Kamel Karoui

    2013-07-01

    Full Text Available Security components such as firewalls, IDS and IPS, are the most widely adopted security devices fornetwork protection.These components are often implemented with several errors (or anomalies that aresometimes critical. To ensure the security of their networks, administrators should detect these anomaliesand correct them. Before correcting the detected anomalies, the administrator should evaluate and classifythese latter to determine the best strategy to correct them. In this work, we propose a framework to assessand classify the detected anomalies using a three evaluation criteria: a quantitative evaluation, a semanticevaluation and multi-anomalies evaluation. The proposed process, convenient in an audit process, will bedetailed by a case study to demonstrate its usefulness

  11. Anomaly indicators for time-reversal symmetric topological orders

    CERN Document Server

    Wang, Chenjie

    2016-01-01

    Some time-reversal symmetric topological orders are anomalous in that they cannot be realized in strictly two-dimensions without breaking time reversal symmetry; instead, they can only be realized on the surface of certain three-dimensional systems. We propose two quantities, which we call {\\it anomaly indicators}, that can detect if a time-reversal symmetric topological order is anomalous in this sense. Both anomaly indicators are expressed in terms of the quantum dimensions, topological spins, and time-reversal properties of the anyons in the given topological order. The first indicator, $\\eta_2$, applies to bosonic systems while the second indicator, $\\eta_f$, applies to fermionic systems in the DIII class. We conjecture that $\\eta_2$, together with a previously known indicator $\\eta_1$, can detect the two known $\\mathbb Z_2$ anomalies in the bosonic case, while $\\eta_f$ can detect the $\\mathbb Z_{16}$ anomaly in the fermionic case.

  12. Anomaly Traceback using Software Defined Networking

    OpenAIRE

    François, Jérôme; Festor, Olivier

    2014-01-01

    While the threats in Internet are still increasing and evolving (like intra multi-tenant data center attacks), protection and detection mechanisms are not fully accurate. Therefore, forensics is vital for recovering from an attack but also to identify the responsible entities. Therefore, this paper focuses on tracing back to the sources of an anomaly in the network. In this paper, we propose a method leveraging the Software Defined Networking (SDN) paradigm to passively identify switches comp...

  13. Observational manifestations of anomaly inflow

    OpenAIRE

    Boyarsky, Alexey; Ruchayskiy, Oleg; Shaposhnikov, Mikhail

    2005-01-01

    In theories with chiral couplings, one of the important consistency requirements is that of the cancellation of a gauge anomaly. In particular, this is one of the conditions imposed on the hypercharges in the standard model. However, anomaly cancellation condition of the standard model looks unnatural from the perspective of a theory with extra dimensions. Indeed, if our world were embedded into an odd-dimensional space, then the full theory would be automatically anomaly-free. In this paper ...

  14. 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.

  15. MR imaging features of the congenital uterine anomalies

    International Nuclear Information System (INIS)

    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

  16. Turtle carapace anomalies: the roles of genetic diversity and environment.

    Directory of Open Access Journals (Sweden)

    Guillermo Velo-Antón

    Full Text Available BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSIONS/SIGNIFICANCE: 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.

  17. Prevalence of Associated Anomalies in Cleft Lip and/or Palate Patients

    Directory of Open Access Journals (Sweden)

    Shahin Abdollahi Fakhim

    2016-03-01

    Full Text Available Introduction: Orofacial clefts are among the most common congenital anomalies. Patients presenting with orofacial clefts often require surgery or other complex procedures. A cleft lip or palate can be a single anomaly or a part of multiple congenital anomalies. The reported prevalence of cleft disease and associated anomalies varies widely across the literature, and is dependent on the diagnostic procedure used. In this study we determined the prevalence of associated anomalies in patients with a cleft lip and/or palate, with a specific focus on cardiac anomalies. Materials and Methods: In this cross-sectional study, 526 patients with a cleft lip and /or palate admitted to the children’s referral hospital between 2006 and 2011 were evaluated. All associated anomalies were detected and recorded. Patient information collected included age, gender, type and side of cleft, craniofacial anomalies and presence of other anomalies, including cardiac anomalies. Data were analyzed using SPSS version 16.   Results: Of the 526 patients enrolled in the study, 58% (305 were male and 42% (221 were female. In total, 75% of patients (396 were aged between 4 and 8 years and 25% (130 were aged less than 4 years. The most common cleft type in our study was bilateral cleft palate. The most commonly associated anomaly among cleft patients, in 12% of cleft patients, was a cardiac anomaly. The most common cardiac anomaly was atrial septal defect (ASD.   Conclusion:  The prevalence of associated anomalies among orofacial cleft patients is high. The most common associated anomaly is cardiac anomaly, with ASD being the most common cardiac anomaly. There are no significant relationships between type of cleft and associated cardiac anomalies.

  18. Anomaly poles as common signatures of chiral and conformal anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Armillis, Roberta, E-mail: roberta.armillis@le.infn.i [Dipartimento di Fisica, Universita del Salento and INFN Sezione di Lecce, Via Arnesano, 73100 Lecce (Italy); Coriano, Claudio, E-mail: claudio.coriano@le.infn.i [Dipartimento di Fisica, Universita del Salento and INFN Sezione di Lecce, Via Arnesano, 73100 Lecce (Italy); Department of Physics, University of Crete, Heraklion, Crete (Greece); Delle Rose, Luigi, E-mail: luigi.dellerose@le.infn.i [Dipartimento di Fisica, Universita del Salento and INFN Sezione di Lecce, Via Arnesano, 73100 Lecce (Italy)

    2009-12-07

    One feature of the chiral anomaly, analyzed in a perturbative framework, is the appearance of massless poles which account for it. They are identified by a spectral analysis of the anomaly graph and are usually interpreted as being of an infrared origin. Recent investigations show that their presence is not just confined in the infrared, but that they appear in the effective action under the most general kinematical conditions, even if they decouple in the infrared. Further studies reveal that they are responsible for the non-unitary behaviour of these theories in the ultraviolet (UV) region. We extend this analysis to the case of the conformal anomaly, showing that the effective action describing the interaction of gauge fields with gravity is characterized by anomaly poles that give the entire anomaly and are decoupled in the infrared (IR), in complete analogy with the chiral case. This complements a related analysis by Giannotti and Mottola on the trace anomaly in gravity, in which an anomaly pole has been identified in the corresponding correlator using dispersion theory in the IR. Our extension is based on an exact computation of the off-shell correlation function involving an energy-momentum tensor and two vector currents (the gauge-gauge-graviton vertex) which is responsible for the appearance of the anomaly.

  19. Rare Upper Airway Anomalies.

    Science.gov (United States)

    Windsor, Alanna; Clemmens, Clarice; Jacobs, Ian N

    2016-01-01

    A broad spectrum of congenital upper airway anomalies can occur as a result of errors during embryologic development. In this review, we will describe the clinical presentation, diagnosis, and management strategies for a few select, rare congenital malformations of this system. The diagnostic tools used in workup of these disorders range from prenatal tests to radiological imaging, swallowing evaluations, indirect or direct laryngoscopy, and rigid bronchoscopy. While these congenital defects can occur in isolation, they are often associated with disorders of other organ systems or may present as part of a syndrome. Therefore workup and treatment planning for patients with these disorders often involves a team of multiple specialists, including paediatricians, otolaryngologists, pulmonologists, speech pathologists, gastroenterologists, and geneticists. PMID:26277452

  20. Advanced Ground Systems Maintenance Anomaly Detection Project

    Data.gov (United States)

    National Aeronautics and Space Administration — This project will develop the capability to identify anomalous conditions (indications to potential impending system failure) in ground system operations before...

  1. To detect anomalies in diaphragm walls

    NARCIS (Netherlands)

    Spruit, R.

    2015-01-01

    Diaphragm walls are potentially ideal retaining walls for deep excavations in densely built-up areas, as they cause no vibrations during their construction and provide structural elements with high strength and stiffness. In the recent past, however, several projects using diaphragm walls as soil an

  2. 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...

  3. 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...

  4. Anomaly Detection and Degradation Prediction of MOSFET

    OpenAIRE

    Li-Feng Wu; Yong Guan; Xiao-Juan Li; Jie Ma

    2015-01-01

    The MOSFET is an important power electronic transistor widely used in electrical systems. Its reliability has an effect on the performance of systems. In this paper, the failure models and mechanisms of MOSFETs are briefly analyzed. The on-resistance Ron is the key failure precursor parameter representing the degree of degradation. Based on the experimental data, a nonlinear dual-exponential degradation model for MOSFETs is obtained. Then, we present an approach for MOSFET degradation state p...

  5. 云环境下 SDN 的流量异常检测性能分析%Performance Analysis of Traffic Anomaly Detection in Cloud-based Software-defined Network

    Institute of Scientific and Technical Information of China (English)

    马超; 程力; 孔玲玲

    2015-01-01

    The increasing complexity of hybrid cloud networks becomes a bottleneck of cloud computing.As a potential solution, SDN has gained great attentions from both industry and academia, especially in the network security domain.Research on utili-zing SDN in network attack detection is still in its inception phase.Specifically, it has not been evaluated whether SDN can effi-ciently detect internal network attacks in a cloud environment.In this research we implement both SDN and traditional network in-frastructures based on OpenStack platform.We simulate both flood and port-scan attacks and utilize two types of traffic anomaly detection algorithms.Experiment results indicate that the SDN method shows better performance in memory usage without degrad-ing its accuracy, while it also suffers performance bottleneck when directly deployed into SDN controllers.%随着复杂的混合云网络逐渐成为云计算发展的瓶颈,软件定义网络( SDN)技术近年来成为学术界和工业界关注的热点。在网络安全领域,对于应用SDN来解决网络攻击的研究尚处于起步阶段,SDN是否能够高效检测来自内部的网络攻击尚无定论。针对该问题,在分析SDN技术框架的基础上,设计基于OpenStack的云环境实验方案。在传统云环境网络和SDN环境下同时测试2种流量异常检测算法,模拟Flood攻击和端口扫描攻击,分析SDN在检测攻击时的精确度和资源使用率。结果表明,在云环境下利用SDN检测内部威胁时比传统网络环境占用更少的物理内存而不影响精确度,但直接在SDN控制器上部署安全应用的方式也存在性能瓶颈。

  6. Trace Anomaly in Geometric Discretization

    OpenAIRE

    Czech, Bartlomiej

    2007-01-01

    I develop the simplest geometric-discretized analogue of two dimensional scalar field theory, which qualitatively reproduces the trace anomaly of the continuous theory. The discrete analogue provides an interpretation of the trace anomaly in terms of a non-trivial transformation of electric-magnetic duality-invariant modes of resistor networks that accommodate both electric and magnetic charge currents.

  7. Algebraic study of chiral anomalies

    Indian Academy of Sciences (India)

    Juan Mañes; Raymond Stora; Bruno Zumino

    2012-06-01

    The algebraic structure of chiral anomalies is made globally valid on non-trivial bundles by the introduction of a fixed background connection. Some of the techniques used in the study of the anomaly are improved or generalized, including a systematic way of generating towers of ‘descent equations’.

  8. Anomaly mediation deformed by axion

    International Nuclear Information System (INIS)

    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

  9. Anomaly Poles as Common Signatures of Chiral and Conformal Anomalies

    CERN Document Server

    Armillis, Roberta; Rose, Luigi Delle

    2009-01-01

    One feature of the chiral anomaly, analyzed in a perturbative framework, is the appearance of massless poles which account for it. They are identified by a spectral analysis of the anomaly graph and are usually interpreted as being of an infrared origin. Recent investigations shown that their presence is not just confined in the infrared, but that they appear in the effective action under the most general kinematical conditions, even if they decouple in the infrared. Further studies reveal that they are responsible for the non-unitary behaviour of these theories in the ultraviolet (UV) region. We extend this analysis to the case of the conformal anomaly, showing that the effective action describing the interaction of gauge fields with gravity is characterized by anomaly poles that give the entire anomaly and are decoupled in the infrared (IR), in complete analogy with the chiral case. This complements a related analysis by Giannotti and Mottola on the trace anomaly in gravity, in which an anomaly pole has bee...

  10. Fetal central nervous system anomalies: fast MRI vs ultrasonography

    International Nuclear Information System (INIS)

    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)

  11. Methods and Systems for Characterization of an Anomaly Using Infrared Flash Thermography

    Science.gov (United States)

    Koshti, Ajay M. (Inventor)

    2013-01-01

    A method for characterizing an anomaly in a material comprises (a) extracting contrast data; (b) measuring a contrast evolution; (c) filtering the contrast evolution; (d) measuring a peak amplitude of the contrast evolution; (d) determining a diameter and a depth of the anomaly, and (e) repeating the step of determining the diameter and the depth of the anomaly until a change in the estimate of the depth is less than a set value. The step of determining the diameter and the depth of the anomaly comprises estimating the depth using a diameter constant C.sub.D equal to one for the first iteration of determining the diameter and the depth; estimating the diameter; and comparing the estimate of the depth of the anomaly after each iteration of estimating to the prior estimate of the depth to calculate the change in the estimate of the depth of the anomaly.

  12. Satellite Infrared Radiation Measurements Prior to the Major Earthquakes

    Science.gov (United States)

    Ouzounov, Dimitar; Pulintes, S.; Bryant, N.; Taylor, Patrick; Freund, F.

    2005-01-01

    This work describes our search for a relationship between tectonic stresses and increases in mid-infrared (IR) flux as part of a possible ensemble of electromagnetic (EM) phenomena that may be related to earthquake activity. We present and &scuss observed variations in thermal transients and radiation fields prior to the earthquakes of Jan 22, 2003 Colima (M6.7) Mexico, Sept. 28 .2004 near Parkfield (M6.0) in California and Northern Sumatra (M8.5) Dec. 26,2004. Previous analysis of earthquake events has indicated the presence of an IR anomaly, where temperatures increased or did not return to its usual nighttime value. Our procedures analyze nighttime satellite data that records the general condtion of the ground after sunset. We have found from the MODIS instrument data that five days before the Colima earthquake the IR land surface nighttime temperature rose up to +4 degrees C in a 100 km radius around the epicenter. The IR transient field recorded by MODIS in the vicinity of Parkfield, also with a cloud free environment, was around +1 degree C and is significantly smaller than the IR anomaly around the Colima epicenter. Ground surface temperatures near the Parkfield epicenter four days prior to the earthquake show steady increase. However, on the night preceding the quake, a significant drop in relative humidity was indicated, process similar to those register prior to the Colima event. Recent analyses of continuous ongoing long- wavelength Earth radiation (OLR) indicate significant and anomalous variability prior to some earthquakes. The cause of these anomalies is not well understood but could be the result of a triggering by an interaction between the lithosphere-hydrosphere and atmospheric related to changes in the near surface electrical field and/or gas composition prior to the earthquake. The OLR anomaly usually covers large areas surrounding the main epicenter. We have found strong anomalies signal (two sigma) along the epicentral area signals on Dec 21

  13. Binning of satellite magnetic anomalies

    Science.gov (United States)

    Goyal, H. K.; Vonfrese, R. R. B.; Hinze, W. J.

    1985-01-01

    Crustal magnetic anomaly signals over satellite orbits were simulated to investigate numerical averaging as an anomaly estimator. Averaging as an anomaly estimator involves significant problems concerning spatial and amplitude smoothing of the satellite magnetic observations. The results of simulations suggest that the error of numerical averaging constitutes a small and relatively minor component of the total error-budget of higher orbital anomaly estimates, whereas for lower orbital estimates numerical averaging error increases substantially. As an alternative to numerical averaging, least-squares collocation was investigated and observed to produce substantially more accurate anomaly estimates, particularly as the orbital elevation of prediction was decreased towards the crustal sources. In contrast to averaging, collocation is a significantly more resource-intensive procedure to apply because of the practical, but surmountable problems related to establishing and inverting the covariance matrix for accurate anomaly prediction. However, collocation may be much more effectively used to exploit the anomaly details contained in the lower orbital satellite magnetic data for geologic analysis.

  14. SYSTEMS OF REMOVING NETWORK ANOMALIES AND METHODS OF CREATION THEIR ARCHITECTURE

    Directory of Open Access Journals (Sweden)

    Kucher V. A.

    2015-06-01

    Full Text Available Different stages of designing architecture of detection systems and opposition to network anomalies are analyzed in this article. It is pointed that common classification can be to determine state of network: “normal”, “critical”, “faulted”. Bases for building architecture of detection and removing anomalies are offered

  15. 基于Shell命令和DTMC模型的用户行为异常检测新方法%Novel Method for Anomaly Detection of User Behavior Based on Shell Commands and DTMC Models

    Institute of Scientific and Technical Information of China (English)

    肖喜; 翟起滨; 田新广; 陈小娟

    2011-01-01

    提出一种新的基于离散时间Markov链模型的用户行为异常检测方法,主要用于以shell命令为审计数据的入侵检测系统.该方法在训练阶段充分考虑了用户行为复杂多变的特点和审计数据的短时相关性,将shell命令序列作为基本数据处理单元,依据其出现频率利用阶梯式的数据归并方法来确定Markov链的状态,同现有方法相比提高了用户行为轮廓描述的准确性和对用户行为变化的适应性,并且大幅度减少了状态个数,节约了存储成本.在检测阶段,针对检测实时性和准确度需求,通过计算状态序列的出现概率分析用户行为异常程度,并提供了基于固定窗长度和可变窗长度的两种均值滤噪处理及行为判决方案.实验表明,该方法具有很高的检测性能,其可操作性也优于同类方法.%This paper presented a novel method for anomaly detection of user behavior based on the discrete-time Mar kov chain model;which is applicable to intrusion detection systems using shell commands as audit data. In the training period;the uncertainty of the user's behavior and the relevance of the operation of shell commands in short time were fully considered. This method takes the sequences of shell commands as the basic processing units. It merges the se quences into sets in terms of their ordered frequencies and then constructs states of the Markov chain on the merged re sults. Therefore this method increases the accuracy of describing the normal behavior profile and the adaptability to the variations of the user's behavior and sharply reduces the number of states and the required storage space. In the detec tion stage;considering the real-time performance and the accuracy requirement of the detection system; it analyzes the a nomaly degree of the user's behavior by computing the occurrence probabilities of the state sequences;and then pro vides two schemes;based on the probability stream filtered with single

  16. ALP hints from cooling anomalies

    CERN Document Server

    Giannotti, Maurizio

    2015-01-01

    We review the current status of the anomalies in stellar cooling and argue that, among the new physics candidates, an axion-like particle would represent the best option to account for the hinted additional cooling.

  17. Notes on Anomaly Induced Transport

    CERN Document Server

    Landsteiner, Karl

    2016-01-01

    Chiral anomalies give rise to dissipationless transport phenomena such as the chiral magnetic and vortical effects. In these notes I review the theory from a quantum field theoretic, hydrodynamic and holographic perspective. A physical interpretation of the otherwise somewhat obscure concepts of consistent and covariant anomalies will be given. Vanishing of the CME in strict equilibrium will be connected to the boundary conditions in momentum space imposed by the regularization. The role of the gravitational anomaly will be explained. That it contributes to transport in an unexpectedly low order in the derivative expansion can be easiest understood via holography. Anomalous transport is supposed to play also a key role in understanding the electronics of advanced materials, the Dirac- and Weyl (semi)metals. Anomaly related phenomena such as negative magnetoresistivity, anomalous Hall effect, thermal anomalous Hall effect and Fermi arcs can be understood via anomalous transport. Finally I briefly review a holo...

  18. On renormalization of axial anomaly

    International Nuclear Information System (INIS)

    It is shown that multiplicative renormalization of the axial singlet current results in renormalization of the axial anomaly in all orders of perturbation theory. It is a necessary condition for the Adler - Bardeen theorem being valid. 10 refs.; 2 figs

  19. Role of Sonography and MRI in Fetal CNS Anomaly

    Directory of Open Access Journals (Sweden)

    Jalal Jalalshokouhi

    2010-05-01

    Full Text Available Current ultrasound equipment allows the antenatal identification of many central nervous system anomalies from early gestation. Diagnostic accuracy, however, remains heavily dependent upon the expertise of the sonologist. Fetal ultrasound is effective in identifying CNS anomalies. Magnetic resonance imaging may play a major role in the evaluation of cases with suboptimal ultrasound visualization, or when specific anomalies are suspected, such as intracranial haemorrhage or migrational disorders."nThis study was performed in two centers, of which anomaly sonography scan was carried out in Nasle Omid clinic by high end ultrasound machines (Aloka a10-version 2009 and Medison Accuvix-XQ and the fetal MRI was performed in Jaam e Jam Imaging center."nAnomaly ultrasound scan and detailed CNS scan was done by checking the size and shape of the skull, symmetry of the CNS, cerebellum, cisterna magna, CSP, lateral ventricles and thalami by 2-6 MHZ abdominal convex transducer and in some cases, high resolution transvaginal sonography was performed for better images."nCases were referred for fetal CNS MRI when ultra-sound was not conclusive for CNS anomaly or better evaluation of the background anomaly."nIn this study, in more than 20 cases we could confirm sonography is the major diagnostic tool for CNS anomalies, if performed by an experienced sonologist and proper equipment. "nMRI has a very important role in confirming ultra-sound findings or detecting CNS anomalies when sonography is not conclusive, if MRI is accomplished based on a proper protocol and read by an experienced radiologist.

  20. Gravitational anomaly and transport phenomena

    OpenAIRE

    Landsteiner, Karl; Megías Fernández, Eugenio; Pena-Benítez, Francisco

    2011-01-01

    Quantum anomalies give rise to new transport phenomena. In particular, a magnetic field can induce an anomalous current via the chiral magnetic effect and a vortex in the relativistic fluid can also induce a current via the chiral vortical effect. The related transport coefficients can be calculated via Kubo formulas. We evaluate the Kubo formula for the anomalous vortical conductivity at weak coupling and show that it receives contributions proportional to the gravitational anomaly coefficie...

  1. Satellite elevation magnetic anomaly maps

    Science.gov (United States)

    Braile, L. W.; Hinze, W. J. (Principal Investigator)

    1982-01-01

    The problem of inverting 2 deg average MAGSAT scalar anomalies for the region 80 W, 60 E longitude and 40 S, 70 N latitude was attempted on the LARS computer; however, the effort was aborted due to insufficient allocation of CPU-time. This problem is currently being resubmitted and should be implemented shortly for quantitative comparison with free-air gravity anomaly, geothermal, and tectonic data.

  2. Boundary Anomalies and Correlation Functions

    OpenAIRE

    Huang, Kuo-Wei(C.N. Yang Institute for Theoretical Physics, Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, 11794, U.S.A.)

    2016-01-01

    It was shown recently that boundary terms of conformal anomalies recover the universal contribution to the entanglement entropy and also play an important role in the boundary monotonicity theorem of odd-dimensional quantum field theories. Motivated by these results, we investigate relationships between boundary anomalies and the stress tensor correlation functions in conformal field theories. In particular, we focus on how the conformal Ward identity and the renormalization group equation ar...

  3. Situs anomalies on prenatal MRI

    International Nuclear Information System (INIS)

    Objective: Situs anomalies refer to an abnormal organ arrangement, which may be associated with severe errors of development. Due regard being given to prenatal magnetic resonance imaging (MRI) as an adjunct to ultrasonography (US), this study sought to demonstrate the in utero visualization of situs anomalies on MRI, compared to US. Materials and methods: This retrospective study included 12 fetuses with situs anomalies depicted on fetal MRI using prenatal US as a comparison modality. With an MRI standard protocol, the whole fetus was assessed for anomalies, with regard to the position and morphology of the following structures: heart; venous drainage and aorta; stomach and intestines; liver and gallbladder; and the presence and number of spleens. Results: Situs inversus totalis was found in 3/12 fetuses; situs inversus with levocardia in 1/12 fetuses; situs inversus abdominis in 2/12 fetuses; situs ambiguous with polysplenia in 3/12 fetuses, and with asplenia in 2/12 fetuses; and isolated dextrocardia in 1/12 fetuses. Congenital heart defects (CHDs), vascular anomalies, and intestinal malrotations were the most frequent associated malformations. In 5/12 cases, the US and MRI diagnoses were concordant. Compared to US, in 7/12 cases, additional MRI findings specified the situs anomaly, but CHDs were only partially visualized in six cases. Conclusions: Our initial MRI results demonstrate the visualization of situs anomalies and associated malformations in utero, which may provide important information for perinatal management. Using a standard protocol, MRI may identify additional findings, compared to US, which confirm and specify the situs anomaly, but, with limited MRI visualization of fetal CHDs.

  4. The accrual anomaly: Evidence from Borsa Istanbul

    Directory of Open Access Journals (Sweden)

    Nasif Ozkan

    2015-06-01

    Full Text Available In this study, we seek to answer whether stock prices fully reflect information in accruals and cash flows about future earnings. Following prior research, we perform Mishkin test and hedge portfolio analysis. The results based on full sample do not indicate mispricing in the components of earnings on Borsa Istanbul. When we exclude loss firms from the full sample, mispricing of total accruals and its components, and thus the presence of accrual anomaly on Borsa Istanbul, is revealed. Using trading strategy based on total accruals of profit firms, investors may generate abnormal returns of 18.58%. These results may suggest that Borsa Istanbul is not efficient in semi-strong form.

  5. Space weather and space anomalies

    Directory of Open Access Journals (Sweden)

    L. I. Dorman

    2005-11-01

    Full Text Available A large database of anomalies, registered by 220 satellites in different orbits over the period 1971-1994 has been compiled. For the first time, data from 49 Russian Kosmos satellites have been included in a statistical analysis. The database also contains a large set of daily and hourly space weather parameters. A series of statistical analyses made it possible to quantify, for different satellite orbits, space weather conditions on the days characterized by anomaly occurrences. In particular, very intense fluxes (>1000 pfu at energy >10 MeV of solar protons are linked to anomalies registered by satellites in high-altitude (>15000 km, near-polar (inclination >55° orbits typical for navigation satellites, such as those used in the GPS network, NAVSTAR, etc. (the rate of anomalies increases by a factor ~20, and to a much smaller extent to anomalies in geostationary orbits, (they increase by a factor ~4. Direct and indirect connections between anomaly occurrence and geomagnetic perturbations are also discussed.

  6. Anomalies of abdominal organs in polysplenia syndrome: Multidetector computed tomography findings

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sung Won; Lee, Yong Seok; Jung, Jin Hee [Dept. of Radiology, Dongguk University Ilsan Hospital, Dongguk University School of Medicine, Goyang (Korea, Republic of)

    2016-02-15

    Polysplenia syndrome is a rare situs ambiguous anomaly associated with multiple spleens and anomalies of abdominal organs. Because most of the minor anomalies do not cause clinical symptoms, polysplenia syndrome is detected incidentally in the adults. Anomalies of abdominal organs may include multiple spleens of variable size or right-sided spleen, large midline or left-sided liver, midline gallbladder, biliary tract anomalies, short pancreas, right-sided stomach, intestinal malrotation, inferior vena cava interruption with azygos or hemiazygos continuation, and a preduodenal portal vein. As the multidetector computed tomography is increasingly used, situs anomalies will likely to be found with greater frequency in the adults. Therefore, radiologists should become familiar with these rare and peculiar anomalies of abdominal organs in polysplenia syndrome.

  7. MODIS/AQUA MYD14A1 Thermal Anomalies & Fire Daily L3 Global 1km

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  8. MODIS/TERRA MOD14A1 Thermal Anomalies & Fire Daily L3 Global 1km

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  9. MODIS/AQUA MYD14 Thermal Anomalies & Fire 5-Min L2 Swath 1km

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  10. MODIS/TERRA MOD14 Thermal Anomalies & Fire 5-Min L2 Swath 1km

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  11. Euro-African MAGSAT anomaly-tectonic observations

    Science.gov (United States)

    Hinze, W. J.; Olivier, R.; Vonfrese, R. R. B.

    1985-01-01

    Preliminary satellite (MAGSAT) scalar magnetic anomaly data are compiled and differentially reduced to radial polarization by equivalent point source inversion for comparison with tectonic data of Africa, Europe and adjacent marine areas. A number of associations are evident to constrain analyses of the tectonic features and history of the region. The Precambrian shields of Africa and Europe exhibit varied magnetic signatures. All shields are not magnetic highs and, in fact, the Baltic shield is a marked minimum. The reduced-to-the-pole magnetic map shows a marked tendency for northeasterly striking anomalies in the eastern Atlantic and adjacent Africa, which is coincident to the track of several hot spots for the past 100 million years. However, there is little consistency in the sign of the magnetic anomalies and the track of the hot spots. Comparison of the radially polarized anomalies of Africa and Europe with other reduced-to-the-pole magnetic satellite anomaly maps of the Western Hemisphere support the reconstruction of the continents prior to the origin of the present-day Atlantic Ocean in the Mesozoic Era.

  12. Imaging evaluation of fetal vascular anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Calvo-Garcia, Maria A.; Kline-Fath, Beth M.; Koch, Bernadette L.; Laor, Tal [MLC 5031 Cincinnati Children' s Hospital Medical Center, Department of Radiology, Cincinnati, OH (United States); Adams, Denise M. [Cincinnati Children' s Hospital Medical Center, Department of Pediatrics and Hemangioma and Vascular Malformation Center, Cincinnati, OH (United States); Gupta, Anita [Cincinnati Children' s Hospital Medical Center, Department of Pathology, Cincinnati, OH (United States); Lim, Foong-Yen [Cincinnati Children' s Hospital Medical Center, Pediatric Surgery and Fetal Center of Cincinnati, Cincinnati, OH (United States)

    2015-08-15

    Vascular anomalies can be detected in utero and should be considered in the setting of solid, mixed or cystic lesions in the fetus. Evaluation of the gray-scale and color Doppler US and MRI characteristics can guide diagnosis. We present a case-based pictorial essay to illustrate the prenatal imaging characteristics in 11 pregnancies with vascular malformations (5 lymphatic malformations, 2 Klippel-Trenaunay syndrome, 1 venous-lymphatic malformation, 1 Parkes-Weber syndrome) and vascular tumors (1 congenital hemangioma, 1 kaposiform hemangioendothelioma). Concordance between prenatal and postnatal diagnoses is analyzed, with further discussion regarding potential pitfalls in identification. (orig.)

  13. Automatic Construction of Anomaly Detectors from Graphical Models

    Energy Technology Data Exchange (ETDEWEB)

    Ferragut, Erik M [ORNL; Darmon, David M [ORNL; Shue, Craig A [ORNL; Kelley, Stephen [ORNL

    2011-01-01

    Detection of rare or previously unseen attacks in cyber security presents a central challenge: how does one search for a sufficiently wide variety of types of anomalies and yet allow the process to scale to increasingly complex data? In particular, creating each anomaly detector manually and training each one separately presents untenable strains on both human and computer resources. In this paper we propose a systematic method for constructing a potentially very large number of complementary anomaly detectors from a single probabilistic model of the data. Only one model needs to be trained, but numerous detectors can then be implemented. This approach promises to scale better than manual methods to the complex heterogeneity of real-life data. As an example, we develop a Latent Dirichlet Allocation probability model of TCP connections entering Oak Ridge National Laboratory. We show that several detectors can be automatically constructed from the model and will provide anomaly detection at flow, sub-flow, and host (both server and client) levels. This demonstrates how the fundamental connection between anomaly detection and probabilistic modeling can be exploited to develop more robust operational solutions.

  14. Prenatal sonographic diagnosis of focal musculoskeletal anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Ryu, Jung Kyu; Cho, Jeong Yeon; Lee, Young Ho; Kim, Ei Jeong; Chun, Yi Kyeong [Samsung Cheil Hospital, Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

    2002-09-15

    Focal musculoskeletal anomalies are various and may be an isolated finding or may be found in conjunction with numerous associations, including genetic syndromes, Karyotype abnormals, central nervous system anomalies and other general musculoskeletal disorders. Early prenatal diagnosis of these focal musculoskeletal anomalies nor only affects prenatal care and postnatal outcome but also helps in approaching other numerous associated anomalies.

  15. Expanding the spectrum of human ganglionic eminence region anomalies on fetal magnetic resonance imaging

    Energy Technology Data Exchange (ETDEWEB)

    Righini, Andrea; Parazzini, Cecilia; Izzo, Giana [Children' s Hospital ' ' V. Buzzi' ' , Department of Radiology and Neuroradiology, Milan (Italy); Cesaretti, Claudia [Children' s Hospital ' ' V. Buzzi' ' , Department of Radiology and Neuroradiology, Milan (Italy); Ospedale Maggiore Policlinico, Medical Genetics Unit, Fondazione I.R.C.C.S. Ca' Granda, Milan (Italy); Conte, Giorgio [Children' s Hospital ' ' V. Buzzi' ' , Department of Radiology and Neuroradiology, Milan (Italy); University of Milan, Department of Health Sciences, Milan (Italy); Frassoni, Carolina; Inverardi, Francesca [Fondazione I.R.C.C.S. Istituto Neurologico ' ' C. Besta' ' , Clinical Epileptology and Experimental Neurophysiology Unit, Milan (Italy); Bulfamante, Gaetano; Avagliano, Laura [San Paolo Hospital, Division of Human Pathology, Milan (Italy); Rustico, Mariangela [Children' s Hospital ' ' V. Buzzi' ' , Department of Obstetrics and Gynaecology, Prenatal Diagnosis, Milan (Italy)

    2016-03-15

    Ganglionic eminence (GE) is a transient fetal brain structure that harvests a significant amount of precursors of cortical GABA-ergic interneurons. Prenatal magnetic resonance (MR) imaging features of GE anomalies (i.e., cavitations) have already been reported associated with severe micro-lissencephaly. The purpose of this report was to illustrate the MR imaging features of GE anomalies in conditions other than severe micro-lissencephalies. Among all the fetuses submitted to prenatal MR imaging at our center from 2005 to 2014, we collected eight cases with GE anomalies and only limited associated brain anomalies. The median gestational age at the time of MR imaging was 21 weeks ranging from 19 to 29 weeks. Two senior pediatric neuroradiologists categorized the anomalies of the GE region in two groups: group one showing cavitation in the GE region and group two showing enlarged GE region. For each fetal case, associated cranial anomalies were also reported. Five out of the eight cases were included in group one and three in group two. Besides the GE region abnormality, all eight cases had additional intracranial anomalies, such as mild partial callosal agenesis, vermian hypoplasia and rotation, cerebellar hypoplasia, ventriculomegaly, enlarged subarachnoid spaces, molar tooth malformation. Ultrasound generally detected most of the associated intracranial anomalies, prompting the MR investigation; on the contrary in none of the cases, GE anomalies had been detected by ultrasound. Our observation expands the spectrum of human GE anomalies, demonstrating that these may take place also without associated severe micro-lissencephalies. (orig.)

  16. Cluster Based Cost Efficient Intrusion Detection System For Manet

    OpenAIRE

    Kumarasamy, Saravanan; B, Hemalatha; P, Hashini

    2013-01-01

    Mobile ad-hoc networks are temporary wireless networks. Network resources are abnormally consumed by intruders. Anomaly and signature based techniques are used for intrusion detection. Classification techniques are used in anomaly based techniques. Intrusion detection techniques are used for the network attack detection process. Two types of intrusion detection systems are available. They are anomaly detection and signature based detection model. The anomaly detection model uses the historica...

  17. Menarche: Prior Knowledge and Experience.

    Science.gov (United States)

    Skandhan, K. P.; And Others

    1988-01-01

    Recorded menstruation information among 305 young women in India, assessing the differences between those who did and did not have knowledge of menstruation prior to menarche. Those with prior knowledge considered menarche to be a normal physiological function and had a higher rate of regularity, lower rate of dysmenorrhea, and earlier onset of…

  18. A pulmonary sequestered segment with an aberrant pulmonary arterial supply: A case of unique anomaly

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Min Chul; Woo, Jeong Joo; An, Jin Kyung; Jung, Yoon Young; Choi, Yun Sun [Dept. of Radiology, Eulji Hospital, Eulji University, Seoul (Korea, Republic of)

    2016-04-15

    We presented a rare case of a 64-year-old man with a combined anomaly of the bronchus and pulmonary artery that was detected incidentally. Computed tomography showed a hyperlucent, aerated sequestered segment of the right lower lung with an independent ectopic bronchus, which had no connection to the other airway. The affected segment was supplied by its own aberrant pulmonary artery branch from the right pulmonary trunk. This anomaly cannot be classified with any of the previously reported anomalies.

  19. Ant colony optimization-based firewall anomaly mitigation engine.

    Science.gov (United States)

    Penmatsa, Ravi Kiran Varma; Vatsavayi, Valli Kumari; Samayamantula, Srinivas Kumar

    2016-01-01

    A firewall is the most essential component of network perimeter security. Due to human error and the involvement of multiple administrators in configuring firewall rules, there exist common anomalies in firewall rulesets such as Shadowing, Generalization, Correlation, and Redundancy. There is a need for research on efficient ways of resolving such anomalies. The challenge is also to see that the reordered or resolved ruleset conforms to the organization's framed security policy. This study proposes an ant colony optimization (ACO)-based anomaly resolution and reordering of firewall rules called ACO-based firewall anomaly mitigation engine. Modified strategies are also introduced to automatically detect these anomalies and to minimize manual intervention of the administrator. Furthermore, an adaptive reordering strategy is proposed to aid faster reordering when a new rule is appended. The proposed approach was tested with different firewall policy sets. The results were found to be promising in terms of the number of conflicts resolved, with minimal availability loss and marginal security risk. This work demonstrated the application of a metaheuristic search technique, ACO, in improving the performance of a packet-filter firewall with respect to mitigating anomalies in the rules, and at the same time demonstrated conformance to the security policy. PMID:27441151

  20. GEOMAGNETIC ANOMALY FIELD VECTOR OFF WESTERN AUSTRALIA

    OpenAIRE

    ノギ, ヨシフミ; エグチ, ヨシアキ; セアマ, ノブカズ; イセザキ, ノブヒロ; Yoshifumi, NOGI; Yoshiaki, EGUCHI; Nobukazu, SEAMA; Nobuhiro, ISEZAKI

    1993-01-01

    Vector data of the geomagnetic anomaly field were obtained during the 32nd Japanese Antarctic Research Expedition (JARE-32) off Western Australia. The strikes of the magnetic boundaries at their position were derived from vector data of the geomagnetic anomaly field. These strikes were interpreted as the directions of magnetic anomaly lineations originated either by seafloor spreading (seafloor spreading anomaly) or by morphological structures (structural magnetic anomaly). Some strikes of st...