WorldWideScience

Sample records for material anomaly detection

  1. Structural material anomaly detection system using water chemistry data

    International Nuclear Information System (INIS)

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

    1992-01-01

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

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

    International Nuclear Information System (INIS)

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

    1993-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Paolo Napoletano

    2018-01-01

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

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

    International Nuclear Information System (INIS)

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

    1998-01-01

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

  5. 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...

  6. Ferret Workflow Anomaly Detection System

    National Research Council Canada - National Science Library

    Smith, Timothy J; Bryant, Stephany

    2005-01-01

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

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

    Science.gov (United States)

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

    2017-10-01

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

  8. Road Anomalies Detection System Evaluation.

    Science.gov (United States)

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

    2018-06-21

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

  9. Signal anomaly detection and characterization

    International Nuclear Information System (INIS)

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

    1988-08-01

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

  10. Anomaly detection in diurnal data

    NARCIS (Netherlands)

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

    2014-01-01

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

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

    International Nuclear Information System (INIS)

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

    1994-09-01

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

  12. 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

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

    Directory of Open Access Journals (Sweden)

    J. G. Rejas

    2012-07-01

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

  14. 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.

  15. 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

  16. Algorithms for Anomaly Detection - Lecture 1

    CERN Multimedia

    CERN. Geneva

    2017-01-01

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

  17. Algorithms for Anomaly Detection - Lecture 2

    CERN Multimedia

    CERN. Geneva

    2017-01-01

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

  18. 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.

  19. 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....

  20. Quantum machine learning for quantum anomaly detection

    Science.gov (United States)

    Liu, Nana; Rebentrost, Patrick

    2018-04-01

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

  1. Detection of cardiovascular anomalies: Hybrid systems approach

    KAUST Repository

    Ledezma, Fernando; Laleg-Kirati, Taous-Meriem

    2012-01-01

    In this paper, we propose a hybrid interpretation of the cardiovascular system. Based on a model proposed by Simaan et al. (2009), we study the problem of detecting cardiovascular anomalies that can be caused by variations in some physiological

  2. Residual generator for cardiovascular anomalies detection

    KAUST Repository

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

    2014-01-01

    This paper discusses the possibility of using observer-based approaches for cardiovascular anomalies detection and isolation. We consider a lumped parameter model of the cardiovascular system that can be written in a form of nonlinear state

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

    International Nuclear Information System (INIS)

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

    1982-11-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  5. Reducing customer minutes lost by anomaly detection?

    NARCIS (Netherlands)

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

    2012-01-01

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

  6. 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.

  7. 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.

  8. 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.

  9. Fusion and normalization to enhance anomaly detection

    Science.gov (United States)

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

    2009-05-01

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

  10. A Semiparametric Model for Hyperspectral Anomaly Detection

    Science.gov (United States)

    2012-01-01

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

  11. Detection of cardiovascular anomalies: Hybrid systems approach

    KAUST Repository

    Ledezma, Fernando

    2012-06-06

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

  12. Anomaly Detection using the "Isolation Forest" algorithm

    CERN Multimedia

    CERN. Geneva

    2015-01-01

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

  13. System and method for anomaly detection

    Science.gov (United States)

    Scherrer, Chad

    2010-06-15

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

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

    KAUST Repository

    Harrou, Fouzi; Sun, Ying; Khadraoui, Sofiane

    2016-01-01

    Accurate and effective anomaly detection and diagnosis of modern industrial systems are crucial for ensuring reliability and safety and for maintaining desired product quality. Anomaly detection based on principal component analysis (PCA) has been

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

    OpenAIRE

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

    2014-01-01

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

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

    OpenAIRE

    Zhang, James; Vukotic, Ilija; Gardner, Robert

    2018-01-01

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

  17. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1989-06-01

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

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

    Science.gov (United States)

    McIntosh, Dawn

    2006-01-01

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

  20. 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...

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

    International Nuclear Information System (INIS)

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

    1988-01-01

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

  2. Anomaly detection through information sharing under different topologies

    NARCIS (Netherlands)

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

    2017-01-01

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

  3. 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.

  4. Generative adversarial networks for anomaly detection in images

    OpenAIRE

    Batiste Ros, Guillem

    2018-01-01

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

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

    Science.gov (United States)

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

    2015-03-01

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

  6. Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

    Directory of Open Access Journals (Sweden)

    Shaonian Huang

    2018-01-01

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

  7. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

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

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    Wenqiang Cui

    2017-11-01

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

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

    OpenAIRE

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

    2013-01-01

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

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

    OpenAIRE

    Pham, Thai; Lee, Steven

    2016-01-01

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

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

    NARCIS (Netherlands)

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

    2010-01-01

    The EUROCAT website www.eurocat-network.eu publishes prenatal detection rates for major congenital anomalies using data from European population-based congenital anomaly registers, covering 28% of the EU population as well as non-EU countries. Data are updated annually. This information can be

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

    DEFF Research Database (Denmark)

    Garne, Ester; Dolk, Helen; Loane, Maria

    2010-01-01

    The EUROCAT website www.eurocat-network.eu publishes prenatal detection rates for major congenital anomalies using data from European population-based congenital anomaly registers, covering 28% of the EU population as well as non-EU countries. Data are updated annually. This information can be us...

  14. Low Count Anomaly Detection at Large Standoff Distances

    Science.gov (United States)

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

    2010-02-01

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

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

    Data.gov (United States)

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

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

    Science.gov (United States)

    2013-04-01

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

  17. Anomaly Detection and Diagnosis Algorithms for Discrete Symbols

    Data.gov (United States)

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

  18. 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.

  19. 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...

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

    Data.gov (United States)

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

  1. ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM

    Directory of Open Access Journals (Sweden)

    D. Amutha Guka

    2012-01-01

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

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

    National Research Council Canada - National Science Library

    Smetek, Timothy E

    2007-01-01

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

  3. 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)

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

    Science.gov (United States)

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

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

  5. A Survey on Anomaly Based Host Intrusion Detection System

    Science.gov (United States)

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

    2018-04-01

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

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

    International Nuclear Information System (INIS)

    Ivanov, K.N.

    2005-01-01

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

  7. 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.

  8. An incremental anomaly detection model for virtual machines

    Science.gov (United States)

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

    2017-01-01

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

  9. An incremental anomaly detection model for virtual machines.

    Directory of Open Access Journals (Sweden)

    Hancui Zhang

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-09-29

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

  11. Approaches in anomaly-based network intrusion detection systems

    NARCIS (Netherlands)

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

    2008-01-01

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

  12. Approaches in Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, Sandro

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

  13. 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.

  14. Online Anomaly Energy Consumption Detection Using Lambda Architecture

    DEFF Research Database (Denmark)

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

    2016-01-01

    problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark...... of the lambda detection system....

  15. 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.

  16. Identifying Threats Using Graph-based Anomaly Detection

    Science.gov (United States)

    Eberle, William; Holder, Lawrence; Cook, Diane

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

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

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

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

  18. Anomalies

    International Nuclear Information System (INIS)

    Bardeen, W.A.

    1985-08-01

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

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

    Directory of Open Access Journals (Sweden)

    Luis Martí

    2015-01-01

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

  20. 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...

  1. Unsupervised topic discovery by anomaly detection

    OpenAIRE

    Cheng, Leon

    2013-01-01

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

  2. Detection of data taking anomalies for the ATLAS experiment

    CERN Document Server

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

    2015-01-01

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

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

    NARCIS (Netherlands)

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

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

  4. 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

  5. Searching for Complex Patterns Using Disjunctive Anomaly Detection

    OpenAIRE

    Sabhnani, Maheshkumar; Dubrawski, Artur; Schneider, Jeff

    2013-01-01

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

  6. Hyperspectral anomaly detection using Sony PlayStation 3

    Science.gov (United States)

    Rosario, Dalton; Romano, João; Sepulveda, Rene

    2009-05-01

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

  7. Anomaly detection in real-time gross payment data

    NARCIS (Netherlands)

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

    2017-01-01

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

  8. Anomaly detection in VoIP traffic with trends

    NARCIS (Netherlands)

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

    2012-01-01

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

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

    Science.gov (United States)

    Rustam, Z.; Talita, A. S.

    2017-07-01

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

  10. Evaluation of Anomaly Detection Techniques for SCADA Communication Resilience

    OpenAIRE

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

    2016-01-01

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

  11. Development of anomaly detection models for deep subsurface monitoring

    Science.gov (United States)

    Sun, A. Y.

    2017-12-01

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

  12. Boiling anomaly detection by various signal characterization methods

    International Nuclear Information System (INIS)

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

    1996-01-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

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

    CSIR Research Space (South Africa)

    Mkuzangwe, NNP

    2015-08-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-01-01

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

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

    International Nuclear Information System (INIS)

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

    1996-01-01

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

  17. 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

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  19. 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.

  20. Occurrence and Detectability of Thermal Anomalies on Europa

    Science.gov (United States)

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

    2017-10-01

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

  1. Anomaly-based intrusion detection for SCADA systems

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  2. 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.

  3. Detecting Illicit Nuclear Materials

    International Nuclear Information System (INIS)

    Kouzes, Richard T.

    2005-01-01

    The threat that weapons of mass destruction might enter the United States has led to a number of efforts for the detection and interdiction of nuclear, radiological, chemical, and biological weapons at our borders. There have been multiple deployments of instrumentation to detect radiation signatures to interdict radiological material, including weapons and weapons material worldwide

  4. Using Physical Models for Anomaly Detection in Control Systems

    Science.gov (United States)

    Svendsen, Nils; Wolthusen, Stephen

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

  5. Inflight and Preflight Detection of Pitot Tube Anomalies

    Science.gov (United States)

    Mitchell, Darrell W.

    2014-01-01

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

  6. 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

  7. Rule-based expert system for maritime anomaly detection

    Science.gov (United States)

    Roy, Jean

    2010-04-01

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

  8. 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.

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

    Science.gov (United States)

    Lauzon, N.; Lence, B. J.

    2002-12-01

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

  10. 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.

  11. 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.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Peter Christiansen

    2016-11-01

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

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

    Directory of Open Access Journals (Sweden)

    Chunyong Yin

    2017-01-01

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

  15. An anomaly detector applied to a materials control and accounting system

    International Nuclear Information System (INIS)

    Whiteson, R.; Kelso, F.; Baumgart, C.; Tunnell, T.W.

    1994-01-01

    Large amounts of safeguards data are automatically gathered and stored by monitoring instruments used in nuclear chemical processing plants, nuclear material storage facilities, and nuclear fuel fabrication facilities. An integrated safeguards approach requires the ability to identify anomalous activities or states in these data. Anomalies in the data could be indications of error, theft, or diversion of material. The large volume of the data makes analysis and evaluation by human experts very tedious, and the complex and diverse nature of the data makes these tasks difficult to automate. This paper describes the early work in the development of analysis tools to automate the anomaly detection process. Using data from accounting databases, the authors are modeling the normal behavior of processes. From these models they hope to be able to identify activities or data that deviate from that norm. Such tools would be used to reveal trends, identify errors, and recognize unusual data. Thus the expert's attention can be focused directly on significant phenomena

  16. 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

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

    Directory of Open Access Journals (Sweden)

    B. Ravi Kiran

    2018-02-01

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

  18. Fissile materials detection

    International Nuclear Information System (INIS)

    Dumesnil, P.

    1977-03-01

    Description is given of three types of apparatus intended for controlling fossile materials in view of avoiding their diversion or preventing said products to be mixed to less dangerous radioactive wastes. The gantry-type apparatus is intended for the detection of small masses of fissile materials moving through a crossing place; the neutron gantry consists of helium 3 detectors of the type 150NH100, located inside polyethylene blocks; as for the gamma gantry, it consists of two large plastic scintillators integrated to the vertical legs of said gantry. The second apparatus is a high-efficiency detector intended for controlling Pu inside waste casks. It can detect 10mg of Pu inside a 100 liters drum for one minute counting. The third apparatus intended for persons and things monitoring is still on study. Such as the gantries it is based on sampled measurement of the background noise [fr

  19. Early Detection and Identification of Anomalies in Chemical Regime

    International Nuclear Information System (INIS)

    Figedy, Stefan; Smiesko, Ivan

    2011-01-01

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

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

    Science.gov (United States)

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

    2015-08-01

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

  1. Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2017-04-01

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

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

    KAUST Repository

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

    2014-01-01

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

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    Yuejun Guo

    2017-06-01

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

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

    Science.gov (United States)

    Goldstein, Markus; Uchida, Seiichi

    2016-01-01

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

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

    KAUST Repository

    Ledezma, Fernando

    2015-07-01

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

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

    Directory of Open Access Journals (Sweden)

    Yang Dan

    2008-12-01

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

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

    International Nuclear Information System (INIS)

    Nagamatsu, Takashi; Gofuku, Akio

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Fei Li

    2017-05-01

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

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

    OpenAIRE

    Singh, Akash

    2017-01-01

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

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

    Science.gov (United States)

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

    2016-12-01

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

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

    Science.gov (United States)

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

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

  14. 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.

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

    Science.gov (United States)

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

    2014-03-01

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

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

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena; Gehrke, Oliver

    2016-01-01

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

  17. 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.

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

    Science.gov (United States)

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

    2018-05-11

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

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

    Directory of Open Access Journals (Sweden)

    Xinhua Nie

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

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

    Directory of Open Access Journals (Sweden)

    Jun Liu

    2016-01-01

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

  1. Multivariate diagnostics and anomaly detection for nuclear safeguards

    International Nuclear Information System (INIS)

    Burr, T.

    1994-01-01

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

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

    Data.gov (United States)

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

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

    KAUST Repository

    Harrou, Fouzi; Sun, Ying; Madakyaru, Muddu

    2016-01-01

    Accurate and effective anomaly detection and diagnosis of modern engineering systems by monitoring processes ensure reliability and safety of a product while maintaining desired quality. In this paper, an innovative method based on Kullback

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

    International Nuclear Information System (INIS)

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

    1993-02-01

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

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

    International Nuclear Information System (INIS)

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

    1995-01-01

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

  6. Bio-Inspired Distributed Decision Algorithms for Anomaly Detection

    Science.gov (United States)

    2017-03-01

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

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

    KAUST Repository

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

    2015-01-01

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

  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. Lunar magnetic anomalies detected by the Apollo substatellite magnetometers

    Science.gov (United States)

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

    1979-01-01

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

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

    Directory of Open Access Journals (Sweden)

    M. Flach

    2017-08-01

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

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

    Science.gov (United States)

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

    2001-01-01

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

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

    Science.gov (United States)

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

    2015-03-01

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

  14. 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.

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

    Directory of Open Access Journals (Sweden)

    Hongchao Song

    2017-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1995-11-01

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

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

    International Nuclear Information System (INIS)

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

    1995-01-01

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

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

    Science.gov (United States)

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

    2016-10-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

  20. 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.

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2011-06-01

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

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

    Science.gov (United States)

    Jang, J; Seo, J K

    2015-06-01

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

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

    NARCIS (Netherlands)

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

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

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

    NARCIS (Netherlands)

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

    2009-01-01

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

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

    NARCIS (Netherlands)

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

    2009-01-01

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

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

    NARCIS (Netherlands)

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

    2009-01-01

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

  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. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2015-01-01

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

  10. Materials science for nuclear detection

    OpenAIRE

    Peurrung, Anthony

    2008-01-01

    The increasing importance of nuclear detection technology has led to a variety of research efforts that seek to accelerate the discovery and development of useful new radiation detection materials. These efforts aim to improve our understanding of how these materials perform, develop formalized discovery tools, and enable rapid and effective performance characterization. We provide an overview of these efforts along with an introduction to the history, physics, and taxonomy of radiation detec...

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

    Directory of Open Access Journals (Sweden)

    Arup Ghosh

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  13. Revisiting Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.

    2009-01-01

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

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

    NARCIS (Netherlands)

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

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

  15. Early detection of materials degradation

    Science.gov (United States)

    Meyendorf, Norbert

    2017-02-01

    Lightweight components for transportation and aerospace applications are designed for an estimated lifecycle, taking expected mechanical and environmental loads into account. The main reason for catastrophic failure of components within the expected lifecycle are material inhomogeneities, like pores and inclusions as origin for fatigue cracks, that have not been detected by NDE. However, material degradation by designed or unexpected loading conditions or environmental impacts can accelerate the crack initiation or growth. Conventional NDE methods are usually able to detect cracks that are formed at the end of the degradation process, but methods for early detection of fatigue, creep, and corrosion are still a matter of research. For conventional materials ultrasonic, electromagnetic, or thermographic methods have been demonstrated as promising. Other approaches are focused to surface damage by using optical methods or characterization of the residual surface stresses that can significantly affect the creation of fatigue cracks. For conventional metallic materials, material models for nucleation and propagation of damage have been successfully applied for several years. Material microstructure/property relations are well established and the effect of loading conditions on the component life can be simulated. For advanced materials, for example carbon matrix composites or ceramic matrix composites, the processes of nucleation and propagation of damage is still not fully understood. For these materials NDE methods can not only be used for the periodic inspections, but can significantly contribute to the material scientific knowledge to understand and model the behavior of composite materials.

  16. 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...

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

    Science.gov (United States)

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

    1981-05-22

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-09-01

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

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

    Science.gov (United States)

    Sivaraks, Haemwaan

    2015-01-01

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

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

    KAUST Repository

    Harrou, Fouzi

    2016-09-09

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

  1. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2016-01-01

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

  2. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi

    2016-06-13

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

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

    Science.gov (United States)

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

    2011-07-01

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

  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. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Al Mouhamed, Mayez

    1977-09-15

    In a number of complex physical systems the accessible signals are often characterized by random fluctuations about a mean value. The fluctuations (signature) often transmit information about the state of the system that the mean value cannot predict. This study is undertaken to elaborate statistical methods of anomaly detection on the basis of signature analysis of the noise inherent in the process. The algorithm presented first learns the characteristics of normal operation of a complex process. Then it detects small deviations from the normal behavior. The algorithm can be implemented in a medium-sized computer for on line application. (author) [French] Dans de nombreux systemes physiques complexes les grandeurs accessibles a l'homme sont souvent caracterisees par des fluctuations aleatoires autour d'une valeur moyenne. Les fluctuations (signatures) transmettent souvent des informations sur l'etat du systeme que la valeur moyenne ne peut predire. Cette etude est entreprise pour elaborer des methodes statistiques de detection d'anomalies de fonctionnement sur la base de l'analyse des signatures contenues dans les signaux de bruit provenant du processus. L'algorithme presente est capable de: 1/ Apprendre les caracteristiques des operations normales dans un processus complexe. 2/ Detecter des petites deviations par rapport a la conduite normale du processus. L'algorithme peut etre implante sur un calculateur de taille moyenne pour les applications en ligne. (auteur)

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

    Energy Technology Data Exchange (ETDEWEB)

    Kemeny, L.G

    1998-12-31

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

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

    International Nuclear Information System (INIS)

    Kemeny, L.G.

    1998-01-01

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

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

    NARCIS (Netherlands)

    Drugan, M.M.

    2017-01-01

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

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

    NARCIS (Netherlands)

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

    2017-01-01

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

  11. Instrumentation for Detecting Hazardous Materials.

    Science.gov (United States)

    1980-06-01

    equipment a detector for monitoring radioactivity . A portable device for detecting the presence of hazardous mate- rials should also be included in the...Acrylonitrile 2 Natural Gas/LNG 2 211 ----- Material Name (Cont’d.) Number of Times Listed Radioactive Materials 2 Fertilizers 1 Cellulose Nitrate 1 Acrolein...Birnbaum, and Curtis Fincher, L "Fluorescence Determination of the Atmospheric Polutant NO2 in Impact of Lasers in Spectroscopy, Vol. 49 of Proceed

  12. 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.

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

    International Nuclear Information System (INIS)

    Zhang, Liangwei; Lin, Jing; Karim, Ramin

    2015-01-01

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

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

    Science.gov (United States)

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

    2017-09-01

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

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

    CERN Document Server

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

    2018-01-01

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

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

    Science.gov (United States)

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

    2017-07-01

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

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

    Science.gov (United States)

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

    2016-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Mikhail Emelianov

    2012-09-01

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

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

    KAUST Repository

    Madakyaru, Muddu

    2017-02-16

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

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

    KAUST Repository

    Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying

    2017-01-01

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

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

    OpenAIRE

    Laxhammar , Rikard; Falkman , Göran

    2012-01-01

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

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

    Science.gov (United States)

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

    2018-06-01

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

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

    Science.gov (United States)

    Trichias, Konstantinos; Pijpers, Richard; Meeuwissen, Erik

    2014-03-01

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

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

    Science.gov (United States)

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

    2016-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Hongtao Wang

    2017-03-01

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

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

    Science.gov (United States)

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

    2013-11-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

  9. 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

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

    Directory of Open Access Journals (Sweden)

    Zhu Fuying

    2011-05-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

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

    Science.gov (United States)

    2016-09-01

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

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

    OpenAIRE

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

    2018-01-01

    Home Automation and Smart Homes diffusion are providing an interesting opportunity to implement elderly monitoring. This is a new valid technological support to allow in-place aging of seniors by means of a detection system to notify potential anomalies. Monitoring has been implemented by means of Complex Event Processing on live streams of home automation data: this allows the analysis of the behavior of the house inhabitant through quantitative indicators. Different kinds of quantitative in...

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

    Directory of Open Access Journals (Sweden)

    Matti Mantere

    2013-09-01

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

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

    Directory of Open Access Journals (Sweden)

    Jingyue Pang

    2018-03-01

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

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

    Science.gov (United States)

    Simon, Donald L.; Rinehart, Aidan Walker

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Antonio Plaza

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Paz Abel

    2010-01-01

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

  19. Contraband and threat material detection

    International Nuclear Information System (INIS)

    Lowrey, J. D.; Dunn, W.L.

    2008-01-01

    Full text: A pressing threat in modern society is the effective use of improvised explosive devices or IED's. One of the commonly used techniques to detect explosives is radiography. A primary drawback of this method is that humans are required in order to examine the image of each target. This requires trained personnel, who are subject to fatigue if many targets are being examined in rapid succession. Other trace element techniques generally require collection of samples from or near the surface of suspect targets. The signature-based radiation scanning (SBRS) technology has been developed to counter this threat. This technology can result in automated systems, requiring minimal operator involvement, that can rapidly identify IEDs from standoff. Preliminary research indicates that explosive samples of 5-10 kg or greater hidden in various targets can be detected from standoffs of more than a meter, with high sensitivity and high specificity. Many common explosives have similar concentrations of hydrogen, carbon, nitrogen and oxygen (HCNO). As neutrons interact with HCNO materials, unique signatures are created based on the specific composition of the material. We collect signatures from the HCNO prompt and inelastically scattered gamma rays and from scattered neutrons. Two neutron detectors (one bare and one cadmium-covered) are used in order to provide some measure of the back-scattered neutron spectrum. A library of signature templates, based on signatures detected from known targets containing known explosives in various configurations, is created. Similar signatures can be collected for suspect targets. Then a template-matching technique is used to construct two figure-of-merit metrics. The values of these metrics can be used to differentiate between safe targets and IEDs. Laboratory tests have been conducted using a high purity Germanium (HPGe) detector and two europium-doped lithium-iodide neutron detectors (one bare and one covered with cadmium) are used to

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

    Directory of Open Access Journals (Sweden)

    Christian M. Mueller

    2010-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Gabriel Martos

    2018-01-01

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

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

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  3. 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.

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

    Directory of Open Access Journals (Sweden)

    Xiaoyu Zhang

    2017-01-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  7. 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.

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

    Science.gov (United States)

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

    2010-01-01

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

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

    KAUST Repository

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

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

    Science.gov (United States)

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

    2011-06-01

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

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

    Data.gov (United States)

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

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

    KAUST Repository

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

    2016-01-01

    Detecting anomalies is important for reliable operation of several engineering systems. Multivariate statistical monitoring charts are an efficient tool for checking the quality of a process by identifying abnormalities. Principal component analysis

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

    Science.gov (United States)

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

    2017-11-01

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

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

    Science.gov (United States)

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

    2014-03-01

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

  17. Freeman's transorbital lobotomy as an anomaly: A material culture examination of surgical instruments and operative spaces.

    Science.gov (United States)

    Collins, Brianne M; Stam, Henderikus J

    2015-05-01

    In 1946, Walter Freeman introduced the transorbital ice pick lobotomy. Touted as a procedure that could be learned and subsequently performed by psychiatrists outside of the operating room, the technique was quickly criticized by neurosurgeons. In this article, we take a material culture approach to consider 2 grounds upon which neurosurgeons based their objections-surgical instruments and operative spaces. On both counts, Freeman was in contravention of established normative neurosurgical practices and, ultimately, his technique was exposed as an anomaly by neurosurgeons. Despite its rejection, the transorbital lobotomy became entrenched in contemporary memory and remains the emblematic procedure of the psychosurgery era. (c) 2015 APA, all rights reserved).

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Tomasz Andrysiak

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-07-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Ilia Nouretdinov

    2017-05-01

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

  3. 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

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

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

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

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

    Directory of Open Access Journals (Sweden)

    Ling Zou

    2014-07-01

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

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

    International Nuclear Information System (INIS)

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

    1994-01-01

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

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

    Science.gov (United States)

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

    2016-09-01

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

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

    Science.gov (United States)

    Jakkula, V; Cook, D J

    2008-01-01

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

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

    Science.gov (United States)

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

    2013-12-01

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

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

    OpenAIRE

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

    2016-01-01

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

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

    International Nuclear Information System (INIS)

    VALENTE, J.; FISHBONE, L.

    2003-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Muhammad Hilmi Kamarudin

    2017-01-01

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

  13. Strain-Detecting Composite Materials

    Science.gov (United States)

    Wallace, Terryl A. (Inventor); Smith, Stephen W. (Inventor); Piascik, Robert S. (Inventor); Horne, Michael R. (Inventor); Messick, Peter L. (Inventor); Alexa, Joel A. (Inventor); Glaessgen, Edward H. (Inventor); Hailer, Benjamin T. (Inventor)

    2016-01-01

    A composite material includes a structural material and a shape-memory alloy embedded in the structural material. The shape-memory alloy changes crystallographic phase from austenite to martensite in response to a predefined critical macroscopic average strain of the composite material. In a second embodiment, the composite material includes a plurality of particles of a ferromagnetic shape-memory alloy embedded in the structural material. The ferromagnetic shape-memory alloy changes crystallographic phase from austenite to martensite and changes magnetic phase in response to the predefined critical macroscopic average strain of the composite material. A method of forming a composite material for sensing the predefined critical macroscopic average strain includes providing the shape-memory alloy having an austenite crystallographic phase, changing a size and shape of the shape-memory alloy to thereby form a plurality of particles, and combining the structural material and the particles at a temperature of from about 100-700.degree. C. to form the composite material.

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

    Science.gov (United States)

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

    2015-08-01

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

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

    International Nuclear Information System (INIS)

    Figedy, Stefan; Smiesko, Ivan

    2012-01-01

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

  16. 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

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

    Science.gov (United States)

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

    2015-02-01

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

  18. 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...

  19. 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.

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

    Science.gov (United States)

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

    2013-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Antonio Candelieri

    2017-03-01

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

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

    International Nuclear Information System (INIS)

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

    1992-01-01

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

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

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

    Science.gov (United States)

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

    2009-04-01

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

  5. 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.

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

    Science.gov (United States)

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

    2013-07-12

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

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

    Directory of Open Access Journals (Sweden)

    Daniela Gamba GARIB

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-04-01

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

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

    Science.gov (United States)

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

    2016-05-01

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

  11. 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.

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

    Science.gov (United States)

    Silva, Jorge; Willett, Rebecca

    2009-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Fabio Veronese

    2018-01-01

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

  14. Material detection method and device

    International Nuclear Information System (INIS)

    Shigenaka, Naoto; Fujimori, Haruo; Ono, Shigeki; Fuse, Motomasa; Uchida, Shunsuke.

    1994-01-01

    A specimen A sampled from an objective member for integrity evaluation, as well as a virgin specimen B having the same composition as the member are prepared. Ion injection, for example, is performed to the specimens A and B under the same condition to form deposits derived from ions, and the shape of the deposits of the specimens A and B are compared. The deposits formed on the crystal grain boundary has a convex shape, and a relative value for the energy of crystal grain boundary can be determined based on the aspect ratio. In addition, since the energy of the crystal grain boundary is in proportion to the grain boundary corrosion rate, the relative value for the grain boundary corrosion rate can be evaluated by measuring the shape of the deposits formed in the crystal grain boundary. If the grain boundary corrosion rate of the virgin specimen is previously measured, the change of the grain boundary corrosion rate can quantitatively be evaluated. A crack propagating rate of the reactor material upon evaluation of integrity, which has been difficult so far, can be determined, thereby enabling to forecast the remaining life time of the material at high accuracy. (N.H.)

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

    NARCIS (Netherlands)

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

    2017-01-01

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

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

    NARCIS (Netherlands)

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

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

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

    NARCIS (Netherlands)

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

    2017-01-01

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

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

    International Nuclear Information System (INIS)

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

    1980-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Anne-Hélène Rêve-Lamarche

    2017-12-01

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

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

    Science.gov (United States)

    2015-06-01

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

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

    Science.gov (United States)

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

    2018-02-22

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

  2. 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%.

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

    Directory of Open Access Journals (Sweden)

    Oscar Rojas

    2013-04-01

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

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

    Science.gov (United States)

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

    2018-05-16

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

  5. 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%.

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

    Science.gov (United States)

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

    2009-04-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Omar Chavez

    2016-05-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

    2016-08-01

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

  12. Technologies for detection of nuclear materials

    International Nuclear Information System (INIS)

    DeVolpi, A.

    1996-01-01

    Detection of smuggled nuclear materials at transit points requires monitoring unknown samples in large closed packages. This review contends that high-confidence nuclear-material detection requires induced fission as the primary mechanism, with passive radiation screening in a complementary role. With the right equipment, even small quantities of nuclear materials are detectable with a high probability at transit points. The equipment could also be linked synergistically with detectors of other contrabond. For screening postal mail and packages, passive monitors are probably more cost-effective. When a suspicious item is detected, a single active probe could then be used. Until active systems become mass produced, this two-stage screening/interrogation role for active/passive equipment is more economic for cargo at border crossings. For widespread monitoring of nuclear smuggling, it will probably be necessary to develop a system for simultaneously detecting most categories of contraband, including explosives and illicit drugs. With control of nuclear materials at known storage sites being the first line of defense, detection capabilities at international borders could establish a viable second line of defense against smuggling

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

    Directory of Open Access Journals (Sweden)

    Kamran Siddique

    2017-09-01

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

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

    Science.gov (United States)

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

    2009-12-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    Science.gov (United States)

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

    2012-12-01

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

  17. Wireless sensor for detecting explosive material

    Science.gov (United States)

    Lamberti, Vincent E; Howell, Jr., Layton N; Mee, David K; Sepaniak, Michael J

    2014-10-28

    Disclosed is a sensor for detecting explosive devices. The sensor includes a ferromagnetic metal and a molecular recognition reagent coupled to the ferromagnetic metal. The molecular recognition reagent is operable to expand upon absorption of vapor from an explosive material such that the molecular recognition reagent changes a tensile stress upon the ferromagnetic metal. The explosive device is detected based on changes in the magnetic switching characteristics of the ferromagnetic metal caused by the tensile stress.

  18. 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.

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

    Science.gov (United States)

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

    1990-01-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

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

    Directory of Open Access Journals (Sweden)

    L. Perrone

    2018-03-01

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

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

    Science.gov (United States)

    Antunes, Mário J; Correia, Manuel E

    2010-01-01

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

  3. 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

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

    Science.gov (United States)

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

    2016-01-01

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

  5. Metabonomics for detection of nuclear materials processing.

    Energy Technology Data Exchange (ETDEWEB)

    Alam, Todd Michael; Luxon, Bruce A. (University Texas Medical Branch); Neerathilingam, Muniasamy (University Texas Medical Branch); Ansari, S. (University Texas Medical Branch); Volk, David (University Texas Medical Branch); Sarkar, S. (University Texas Medical Branch); Alam, Mary Kathleen

    2010-08-01

    Tracking nuclear materials production and processing, particularly covert operations, is a key national security concern, given that nuclear materials processing can be a signature of nuclear weapons activities by US adversaries. Covert trafficking can also result in homeland security threats, most notably allowing terrorists to assemble devices such as dirty bombs. Existing methods depend on isotope analysis and do not necessarily detect chronic low-level exposure. In this project, indigenous organisms such as plants, small mammals, and bacteria are utilized as living sensors for the presence of chemicals used in nuclear materials processing. Such 'metabolic fingerprinting' (or 'metabonomics') employs nuclear magnetic resonance (NMR) spectroscopy to assess alterations in organismal metabolism provoked by the environmental presence of nuclear materials processing, for example the tributyl phosphate employed in the processing of spent reactor fuel rods to extract and purify uranium and plutonium for weaponization.

  6. Metabonomics for detection of nuclear materials processing

    International Nuclear Information System (INIS)

    Alam, Todd Michael; Luxon, Bruce A.; Neerathilingam, Muniasamy; Ansari, S.; Volk, David; Sarkar, S.; Alam, Mary Kathleen

    2010-01-01

    Tracking nuclear materials production and processing, particularly covert operations, is a key national security concern, given that nuclear materials processing can be a signature of nuclear weapons activities by US adversaries. Covert trafficking can also result in homeland security threats, most notably allowing terrorists to assemble devices such as dirty bombs. Existing methods depend on isotope analysis and do not necessarily detect chronic low-level exposure. In this project, indigenous organisms such as plants, small mammals, and bacteria are utilized as living sensors for the presence of chemicals used in nuclear materials processing. Such 'metabolic fingerprinting' (or 'metabonomics') employs nuclear magnetic resonance (NMR) spectroscopy to assess alterations in organismal metabolism provoked by the environmental presence of nuclear materials processing, for example the tributyl phosphate employed in the processing of spent reactor fuel rods to extract and purify uranium and plutonium for weaponization.

  7. New challenges in nuclear material detection

    International Nuclear Information System (INIS)

    Dunlop, W.; Sale, K.; Dougan, A.; Luke, J.; Suski, N.

    2002-01-01

    Full text: Even before the attacks of September 11, 2001 the International Safeguards community recognized the magnitude of the threat posed by illicit trafficking of nuclear materials and the need for enhanced physical protection. For the first time, separate sessions on illicit trafficking and physical protection of nuclear materials were included in the IAEA Safeguards Symposium. In the aftermath of September 11, it is clear that the magnitude of the problem and the urgency with which it must be addressed will be a significant driver for advanced nuclear materials detection technologies for years to come. Trafficking in nuclear material and other radioactive sources is a global concern. According to the IAEA Illicit Trafficking Database Program, there have been confirmed cases in more than 40 countries and the number of cases per year have nearly doubled since 1996. The challenge of combating nuclear terrorism also brings with it many opportunities for the development of new tools and new approaches. In addition to the traditional gamma-ray imaging, spectrometry and neutron interrogation, there is a need for smaller, smarter, more energy-efficient sensors and sensor systems for detecting and tracking threats. These systems go by many names - correlated sensor networks, wide-area tracking systems, sensor or network fabrics - but the concept behind them is the same. Take a number of wireless sensors and tie them together with a communications network, develop a scheme for fusing the data and make the system easy to deploy. This paper will present a brief survey of nuclear materials detection capability, and discuss some advances in research and development that are particularly suited for illicit trafficking, detection of shielded highly enriched uranium, and border security. (author)

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-06-15

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

  10. Detection of radioactive materials at borders

    International Nuclear Information System (INIS)

    2003-08-01

    of Understanding (MOU) (1998) to promote co-operation at the international level in order to improve the control of radioactive materials. At the time of the drafting of this report, a similar MOU between the IAEA and the International Criminal Police Organization (INTERPOL) is pending. There are a number of measures that must be undertaken by States to combat the illicit trafficking and inadvertent movements of radioactive materials. These measures are, generally, shared between the regulatory and law enforcement agencies as part of a State's national arrangements. One of these measures id the subject of this TECDOC, namely detection of radioactive materials at borders. While effective detection involves many components of regulatory and law enforcement strategies, the major focus of this publication is on radiation detection and in particular, the instrumentation necessary for such purposes. Its intent is to assist Member State organizations in effectively detecting radioactive materials crossing their borders, whether importations, exportations, or shipments in transit. The purpose of this publication is to provide guidance for Member States for use by customs, police or other law enforcement bodies on the radiation monitoring of vehicles, people and commodities at border crossing facilities as a countermeasure to illicit trafficking and also to find inadvertent movement of radioactive materials. Such monitoring may be one component of efforts towards finding radioactive materials that have been lost from control and which may enter a Member State

  11. Detection of radioactive materials at borders

    International Nuclear Information System (INIS)

    2002-09-01

    of Understanding (MOU) (1998) to promote co-operation at the international level in order to improve the control of radioactive materials. At the time of the drafting of this report, a similar MOU between the IAEA and the International Criminal Police Organization (INTERPOL) is pending. There are a number of measures that must be undertaken by States to combat the illicit trafficking and inadvertent movements of radioactive materials. These measures are, generally, shared between the regulatory and law enforcement agencies as part of a State's national arrangements. One of these measures id the subject of this TECDOC, namely detection of radioactive materials at borders. While effective detection involves many components of regulatory and law enforcement strategies, the major focus of this publication is on radiation detection and in particular, the instrumentation necessary for such purposes. Its intent is to assist Member State organizations in effectively detecting radioactive materials crossing their borders, whether importations, exportations, or shipments in transit. The purpose of this publication is to provide guidance for Member States for use by customs, police or other law enforcement bodies on the radiation monitoring of vehicles, people and commodities at border crossing facilities as a countermeasure to illicit trafficking and also to find inadvertent movement of radioactive materials. Such monitoring may be one component of efforts towards finding radioactive materials that have been lost from control and which may enter a Member State

  12. Detection of radioactive materials at borders

    International Nuclear Information System (INIS)

    2004-05-01

    of Understanding (MOU) (1998) to promote co-operation at the international level in order to improve the control of radioactive materials. At the time of the drafting of this report, a similar MOU between the IAEA and the International Criminal Police Organization (INTERPOL) is pending. There are a number of measures that must be undertaken by States to combat the illicit trafficking and inadvertent movements of radioactive materials. These measures are, generally, shared between the regulatory and law enforcement agencies as part of a State's national arrangements. One of these measures id the subject of this TECDOC, namely detection of radioactive materials at borders. While effective detection involves many components of regulatory and law enforcement strategies, the major focus of this publication is on radiation detection and in particular, the instrumentation necessary for such purposes. Its intent is to assist Member State organizations in effectively detecting radioactive materials crossing their borders, whether importations, exportations, or shipments in transit. The purpose of this publication is to provide guidance for Member States for use by customs, police or other law enforcement bodies on the radiation monitoring of vehicles, people and commodities at border crossing facilities as a countermeasure to illicit trafficking and also to find inadvertent movement of radioactive materials. Such monitoring may be one component of efforts towards finding radioactive materials that have been lost from control and which may enter a Member State

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

    Czech Academy of Sciences Publication Activity Database

    Šťástka, J.; Radová, Michaela

    2013-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2001-04-01

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

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

    Directory of Open Access Journals (Sweden)

    Hao Wu

    2017-12-01

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

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

    Science.gov (United States)

    Pursiainen, S.; Kaasalainen, M.

    2014-09-01

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

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

    Science.gov (United States)

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

    2012-04-01

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

  18. Detection of radioactive materials at Astrakhan

    International Nuclear Information System (INIS)

    Cantut, L.; Dougan, A.; Hemberger, P.; Kravenchenko, Gromov A..; Martin, D.; Pohl, B.; Richardson, J. H.; Williams, H.; York, R.; Zaitsev, E.

    1999-01-01

    Astrakhan is the major Russian port on the Caspian Sea. Consequently, it is the node for significant river traffic up the Volga, as well as shipments to and from other seaports on the Caspian Sea. The majority of this latter trade across the Caspian Sea is with Iran. The Second Line of Defense and RF SCC identified Astrakhan as one of the top priorities for upgrading with modern radiation detection equipment. The purpose of the cooperative effort between RF SCC and DOE at Astrakhan is to provide the capability through equipment and training to monitor and detect illegal shipments of nuclear materials through Astrakhan. The first facility was equipped with vehicle and rail portal monitoring systems. The second facility was equipped with pedestrian, vehicle and rail portal monitoring systems. A second phase of this project will complete the equipping of Astrakhan by providing additional rail and handheld systems, along with completion of video systems. Associated with both phases is the necessary equipment and procedural training to ensure successful operation of the equipment in order to detect and deter illegal trafficking in nuclear materials. The presentation will described this project and its overall relationship to the Second Line of Defense Program

  19. Intelligent Image Segment for Material Composition Detection

    Directory of Open Access Journals (Sweden)

    Liang Xiaodan

    2017-01-01

    Full Text Available In the process of material composition detection, the image analysis is an inevitable problem. Multilevel thresholding based OTSU method is one of the most popular image segmentation techniques. How, with the increase of the number of thresholds, the computing time increases exponentially. To overcome this problem, this paper proposed an artificial bee colony algorithm with a two-level topology. This improved artificial bee colony algorithm can quickly find out the suitable thresholds and nearly no trap into local optimal. The test results confirm it good performance.

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Science.gov (United States)

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

    2015-06-01

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

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

    International Nuclear Information System (INIS)

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

    1998-01-01

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

  3. Meta-material for nuclear particle detection

    Science.gov (United States)

    Merlo, V.; Salvato, M.; Lucci, M.; Ottaviani, I.; Cirillo, M.; Scherillo, A.; Schooneveld, E. M.; Vannozzi, A.; Celentano, G.; Pietropaolo, A.

    2017-02-01

    Superconducting strips coated with boron were engineered with a view to subnuclear particle detection. Combining the characteristics of boron as a generator of α-particles (as a consequence of neutron absorption) and the ability of superconducting strips to act as resistive switches, it is shown that fabricated Nb-boron and NbN-boron strips represent a promising basis for implementing neutron detection devices. In particular, the superconducting transition of boron-coated NbN strips generates voltage outputs of the order of a few volts thanks to the relatively higher normal state resitivity of NbN with respect to Nb. This result, combined with the relatively high transition temperature of NbN (of the order of 16 K for the bulk material), is an appealing prospect for future developments. The coated strips are meta-devices since their constituting material does not exist in nature and it is engineered to accomplish a specific task, i.e. generate an output voltage signal upon α-particle irradiation.

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

    Directory of Open Access Journals (Sweden)

    M. H. H. Khairi

    2018-04-01

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

  5. 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.

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

    Science.gov (United States)

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

    2010-04-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  8. Detection of electromagnetic radiation using nonlinear materials

    Science.gov (United States)

    Hwang, Harold Y.; Liu, Mengkun; Averitt, Richard D.; Nelson, Keith A.; Sternbach, Aaron; Fan, Kebin

    2016-06-14

    An apparatus for detecting electromagnetic radiation within a target frequency range is provided. The apparatus includes a substrate and one or more resonator structures disposed on the substrate. The substrate can be a dielectric or semiconductor material. Each of the one or more resonator structures has at least one dimension that is less than the wavelength of target electromagnetic radiation within the target frequency range, and each of the resonator structures includes at least two conductive structures separated by a spacing. Charge carriers are induced in the substrate near the spacing when the resonator structures are exposed to the target electromagnetic radiation. A measure of the change in conductivity of the substrate due to the induced charge carriers provides an indication of the presence of the target electromagnetic radiation.

  9. 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

  10. 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.

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

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2015-01-01

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

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

    Science.gov (United States)

    2014-03-27

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

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

    Science.gov (United States)

    2016-02-12

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

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

    International Nuclear Information System (INIS)

    Schoonewelle, H.

    1995-01-01

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

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

    CSIR Research Space (South Africa)

    Machaka, P

    2015-01-01

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

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

    CSIR Research Space (South Africa)

    Mgabile, T

    2012-10-01

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

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

    Indian Academy of Sciences (India)

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

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

    Science.gov (United States)

    2016-04-25

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

  20. Radioactive anomaly discrimination from spectral ratios

    Science.gov (United States)

    Maniscalco, James; Sjoden, Glenn; Chapman, Mac Clements

    2013-08-20

    A method for discriminating a radioactive anomaly from naturally occurring radioactive materials includes detecting a first number of gamma photons having energies in a first range of energy values within a predetermined period of time and detecting a second number of gamma photons having energies in a second range of energy values within the predetermined period of time. The method further includes determining, in a controller, a ratio of the first number of gamma photons having energies in the first range and the second number of gamma photons having energies in the second range, and determining that a radioactive anomaly is present when the ratio exceeds a threshold value.

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

    OpenAIRE

    Garofalo, Mauro

    2017-01-01

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

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

    OpenAIRE

    Kokkonen, Tero

    2016-01-01

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

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

    OpenAIRE

    Schindler, Timo

    2018-01-01

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

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

    Science.gov (United States)

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

    2010-04-01

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

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

    International Nuclear Information System (INIS)

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

    1990-01-01

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

  6. Detecting anomalous nuclear materials accounting transactions: Applying machine learning to plutonium processing facilities

    International Nuclear Information System (INIS)

    Vaccaro, H.S.

    1989-01-01

    Nuclear materials accountancy is the only safeguards measure that provides direct evidence of the status of nuclear materials. Of the six categories that gives rise to inventory differences, the technical capability is now in place to implement the technical innovations necessary to reduce the human error categories. There are really three main approaches to detecting anomalies in materials control and accountability (MC ampersand A) data: (1) Statistical: numeric methods such as the Page's Test, CUSUM, CUMUF, SITMUF, etc., can detect anomalies in metric (numeric) data. (2) Expert systems: Human expert's rules can be encoded into software systems such as ART, KEE, or Prolog. (3) Machine learning: Training data, such as historical MC ampersand A records, can be fed to a classifier program or neutral net or other machine learning algorithm. The Wisdom ampersand Sense (W ampersand S) software is a combination of approaches 2 and 3. The W ampersand S program includes full features for adding administrative rules and expert judgment rules to the rule base. if desired, the software can enforce consistency among all rules in the rule base

  7. Gravitational anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Leutwyler, H; Mallik, S

    1986-12-01

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

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

    Directory of Open Access Journals (Sweden)

    Ramakalavathi Marapareddy

    2017-10-01

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

  9. Corrosion Detection of Reinforcement of Building Materials with Piezoelectric Sensors

    Directory of Open Access Journals (Sweden)

    Jia Peng

    2017-06-01

    Full Text Available The extensive use of reinforced materials in the construction industry has raised increased concerns about their safety and durability, while corrosion detection of steel materials is becoming increasingly important. For the scientific management, timely repair and health monitoring of construction materials, as well as to ensure construction safety and prevent accidents, this paper investigates corrosion detection on construction materials based on piezoelectric sensors. At present, the commonly used corrosion detection methods include physical and electrochemical methods, but there are shortcomings such as large equipment area, low detection frequency, and complex operation. In this study an improved piezoelectric ultrasonic sensor was designed, which could not only detect the internal defects of buildings while not causing structural damage, but also realize continuous detection and enable qualitative and quantitative assessment. Corrosion detection of reinforced building materials with piezoelectric sensors is quick and accurate, which can find hidden dangers and provide a reliable basis for the safety of the buildings.

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

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Taghva

    2016-10-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-08-15

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

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

    Science.gov (United States)

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

    2016-08-18

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

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

    Science.gov (United States)

    Raeesi, M.

    2009-05-01

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

  16. METHODS FOR DETECTING BACTERIA USING POLYMER MATERIALS

    NARCIS (Netherlands)

    Van Grinsven Bart Robert, Nicolaas; Cleij, Thomas

    2017-01-01

    A method for characterizing bacteria includes passing a liquid containing an analyte comprising a first bacteria and a second bacteria over and in contact with a polymer material on a substrate. The polymer material is formulated to bind to the first bacteria, and the first bacteria binds to the

  17. Combining item and bulk material loss-detection uncertainties

    International Nuclear Information System (INIS)

    Eggers, R.F.

    1982-01-01

    Loss detection requirements, such as five formula kilograms with 99% probability of detection, which apply to the sum of losses from material in both item and bulk form, constitute a special problem for the nuclear material statistician. Requirements of this type are included in the Material Control and Accounting Reform Amendments described in the Advance Notice of Proposed Rule Making (Federal Register, 46(175):45144-46151). Attribute test sampling of items is the method used to detect gross defects in the inventory of items in a given control unit. Attribute sampling plans are designed to detect a loss of a specificed goal quantity of material with a given probability. In contrast to the methods and statistical models used for item loss detection, bulk material loss detection requires all the material entering and leaving a control unit to be measured and the calculation of a loss estimator that will be tested against an appropriate alarm threshold. The alarm threshold is determined from an estimate of the error inherent in the components of the loss estimator. In this paper a simple grahical method of evaluating the combined capabilities of bulk material loss detection methods and item attribute testing procedures will be described. Quantitative results will be given for several cases, indicating how a decrease in the precision of the item loss detection method tends to force an increase in the precision of the bulk loss detection procedure in order to meet the overall detection requirement. 4 figures

  18. Muon radiography technology for detecting high-Z materials

    International Nuclear Information System (INIS)

    Ma Lingling; Wang Wenxin; Zhou Jianrong; Sun Shaohua; Liu Zuoye; Li Lu; Du Hongchuan; Zhang Xiaodong; Hu Bitao

    2010-01-01

    This paper studies the possibility of using the scattering of cosmic muons to identify threatening high-Z materials. Various scenarios of threat material detection are simulated with the Geant4 toolkit. PoCA (Point of Closest Approach) algorithm reconstructing muon track gives 3D radiography images of the target material. Z-discrimination capability, effects of the placement of high-Z materials, shielding materials inside the cargo, and spatial resolution of position sensitive detector for muon radiography are carefully studied. Our results show that a detector position resolution of 50 μm is good enough for shielded materials detection. (authors)

  19. Holonomy anomalies

    International Nuclear Information System (INIS)

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

    1985-05-01

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

  20. Method for detecting radiation dose utilizing thermoluminescent material

    International Nuclear Information System (INIS)

    Miller, S.D.; McDonald, J.C.; Eichner, F.N.; Durham, J.S.

    1992-01-01

    The amount of ionizing radiation to which a thermoluminescent material has been exposed is determined by first cooling the thermoluminescent material and then optically stimulating the thermoluminescent material by exposure to light. Visible light emitted by the thermoluminescent material as it is allowed to warm up to room temperature is detected and counted. The thermoluminescent material may be annealed by exposure to ultraviolet light. 5 figs

  1. Subthreshold neutron interrogator for detection of radioactive materials

    Science.gov (United States)

    Evans, Michael L.; Menlove, Howard O.; Baker, Michael P.

    1980-01-01

    A device for detecting fissionable material such as uranium in low concentrations by interrogating with photoneutrons at energy levels below 500 keV, and typically about 26 keV. Induced fast neutrons having energies above 500 keV by the interrogated fissionable material are detected by a liquid scintillator or recoil proportional counter which is sensitive to the induced fast neutrons. Since the induced fast neutrons are proportional to the concentration of fissionable material, detection of induced fast neutrons indicate concentration of the fissionable material.

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

    Directory of Open Access Journals (Sweden)

    Panda L

    2018-06-01

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

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

    Science.gov (United States)

    Bronshtein, Moshe; Solt, Ido; Blumenfeld, Zeev

    2014-06-01

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

  4. Detecting Patterns of Anomalies

    Science.gov (United States)

    2009-03-01

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

  5. Covariance Spectroscopy for Fissile Material Detection

    International Nuclear Information System (INIS)

    Trainham, Rusty; Tinsley, Jim; Hurley, Paul; Keegan, Ray

    2009-01-01

    Nuclear fission produces multiple prompt neutrons and gammas at each fission event. The resulting daughter nuclei continue to emit delayed radiation as neutrons boil off, beta decay occurs, etc. All of the radiations are causally connected, and therefore correlated. The correlations are generally positive, but when different decay channels compete, so that some radiations tend to exclude others, negative correlations could also be observed. A similar problem of reduced complexity is that of cascades radiation, whereby a simple radioactive decay produces two or more correlated gamma rays at each decay. Covariance is the usual means for measuring correlation, and techniques of covariance mapping may be useful to produce distinct signatures of special nuclear materials (SNM). A covariance measurement can also be used to filter data streams because uncorrelated signals are largely rejected. The technique is generally more effective than a coincidence measurement. In this poster, we concentrate on cascades and the covariance filtering problem

  6. Detection method for nuclear reactor material

    International Nuclear Information System (INIS)

    Isobe, Yusuke; Hashimoto, Motoyuki.

    1995-01-01

    A fine state of a test piece taken out of a reactor core is analyzed upon periodical inspection, and a new test piece previously reproducing the state described above at the outside of the reactor is disposed to the reactor core upon completion of the periodical inspection. Further, a fine state of the material at a time preceding to the operation time at a certain periodical inspection is forecast, and a test piece reproducing the state at the outside of the reactor is disposed to the reactor core upon the completion of the periodical inspection. Since a test piece previously reproducing the change of the state up to a certain periodical inspection by a method other than irradiation of neutrons is newly disposed, radiation of the test piece is not extremely increased even after an extremely long period of summed up reactor operation time, to provide substantially constant radiation level on every test piece. (T.M.)

  7. Material Property Estimation for Direct Detection of DNAPL using Integrated Ground-Penetrating Radar Velocity, Imaging and Attribute Analysis

    Energy Technology Data Exchange (ETDEWEB)

    John H. Bradford; Stephen Holbrook; Scott B. Smithson

    2004-12-09

    The focus of this project is direct detection of DNAPL's specifically chlorinated solvents, via material property estimation from multi-fold surface ground-penetrating radar (GPR) data. We combine state-of-the-art GPR processing methodology with quantitative attribute analysis and material property estimation to determine the location and extent of residual and/or pooled DNAPL in both the vadose and saturated zones. An important byproduct of our research is state-of-the-art imaging which allows us to pinpoint attribute anomalies, characterize stratigraphy, identify fracture zones, and locate buried objects.

  8. A multisignal detection of hazardous materials for homeland security

    Directory of Open Access Journals (Sweden)

    Alamaniotis Miltiadis

    2009-01-01

    Full Text Available The detection of hazardous materials has been identified as one of the most urgent needs of homeland security, especially in scanning cargo containers at United States ports. To date, special nuclear materials have been detected using neutron or gamma interrogation, and recently the nuclear resonance fluorescence has been suggested. We show a new paradigm in detecting the materials of interest by a method that combines four signals (radiography/computer tomography, acoustic, muon scattering, and nuclear resonance fluorescence in cargos. The intelligent decision making software system is developed to support the following scenario: initially, radiography or the computer tomography scan is constructed to possibly mark the region(s of interest. The acoustic interrogation is utilized in synergy to obtain information regarding the ultrasonic velocity of the cargo interior. The superposition of the computer tomography and acoustic images narrows down the region(s of interest, and the intelligent system guides the detection to the next stage: no threat and finish, or proceed to the next interrogation. If the choice is the latter, knowing that high Z materials yield large scattering angle for muons, the muon scattering spectrum is used to detect the existence of such materials in the cargo. Additionally, the nuclear resonance fluorescence scan yields a spectrum that can be likened to the fingerprint of a material. The proposed algorithm is tested for detection of special nuclear materials in a comprehensive scenario.

  9. A multisignal detection of hazardous materials for homeland security

    International Nuclear Information System (INIS)

    Alamaniotis, M.; Terrill, S.; Perry, J.; Gao, R.; Tsoukalas, L.; Jevremovic, T.

    2009-01-01

    The detection of hazardous materials has been identified as one of the most urgent needs of homeland security, especially in scanning cargo containers at United States ports. To date, special nuclear materials have been detected using neutron or gamma interrogation, and recently the nuclear resonance fluorescence has been suggested. We show a new paradigm in detecting the materials of interest by a method that combines four signals (radiography/computer tomography, acoustic, muon scattering, and nuclear resonance fluorescence) in cargos. The intelligent decision making software system is developed to support the following scenario: initially, radiography or the computer tomography scan is constructed to possibly mark the region(s) of interest. The acoustic interrogation is utilized in synergy to obtain information regarding the ultrasonic velocity of the cargo interior. The superposition of the computer tomography and acoustic images narrows down the region(s) of interest, and the intelligent system guides the detection to the next stage: no threat and finish, or proceed to the next interrogation. If the choice is the latter, knowing that high Z materials yield large scattering angle for muons, the muon scattering spectrum is used to detect the existence of such materials in the cargo. Additionally, the nuclear resonance fluorescence scan yields a spectrum that can be likened to the fingerprint of a material. The proposed algorithm is tested for detection of special nuclear materials in a comprehensive scenario. (author)

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

    International Nuclear Information System (INIS)

    Komai, Masafumi

    2001-07-01

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

  11. Active neutron technique for detecting attempted special nuclear material diversion

    International Nuclear Information System (INIS)

    Smith, G.W.; Rice, L.G. III.

    1979-01-01

    The identification of special nuclear material (SNM) diversion is necessary if SNM inventory control is to be maintained at nuclear facilities. (Special nuclear materials are defined for this purpose as either 235 U of 239 Pu.) Direct SNM identification by the detection of natural decay or fission radiation is inadequate if the SNM is concealed by appropriate shielding. The active neutron interrogation technique described combines direct SNM identification by delayed fission neutron (DFN) detection with implied SNM detection by the identification of materials capable of shielding SNM from direct detection. This technique is being developed for application in an unattended material/equipment portal through which items such as electronic instruments, packages, tool boxes, etc., will pass. The volume of this portal will be 41-cm wide, 53-cm high and 76-cm deep. The objective of this technique is to identify an attempted diversion of at least 20 grams of SNM with a measurement time of 30 seconds

  12. Material limitations on the detection limit in refractometry

    DEFF Research Database (Denmark)

    Skafte-Pedersen, Peder; Nunes, Pedro; Xiao, Sanshui

    2009-01-01

    We discuss the detection limit for refractometric sensors relying on high-Q optical cavities and show that the ultimate classical detection limit is given by min {Δn} ≳ η with n + iη being the complex refractive index of the material under refractometric investigation. Taking finite Q factors and...

  13. 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.

  14. Organic materials and devices for detecting ionizing radiation

    Science.gov (United States)

    Doty, F Patrick [Livermore, CA; Chinn, Douglas A [Livermore, CA

    2007-03-06

    A .pi.-conjugated organic material for detecting ionizing radiation, and particularly for detecting low energy fission neutrons. The .pi.-conjugated materials comprise a class of organic materials whose members are intrinsic semiconducting materials. Included in this class are .pi.-conjugated polymers, polyaromatic hydrocarbon molecules, and quinolates. Because of their high resistivities (.gtoreq.10.sup.9 ohmcm), these .pi.-conjugated organic materials exhibit very low leakage currents. A device for detecting and measuring ionizing radiation can be made by applying an electric field to a layer of the .pi.-conjugated polymer material to measure electron/hole pair formation. A layer of the .pi.-conjugated polymer material can be made by conventional polymer fabrication methods and can be cast into sheets capable of covering large areas. These sheets of polymer radiation detector material can be deposited between flexible electrodes and rolled up to form a radiation detector occupying a small volume but having a large surface area. The semiconducting polymer material can be easily fabricated in layers about 10 .mu.m to 100 .mu.m thick. These thin polymer layers and their associated electrodes can be stacked to form unique multi-layer detector arrangements that occupy small volume.

  15. MR imaging features of the congenital uterine anomalies

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  16. System to detect nuclear materials by active neutron method

    International Nuclear Information System (INIS)

    Koroev, M.; Korolev, Yu.; Lopatin, Yu.; Filonov, V.

    1999-01-01

    The report presents the results of the development of the system to detect nuclear materials by active neutron method measuring delayed neutrons. As the neutron source the neutron generator was used. The neutron generator was controlled by the system. The detectors were developed on the base of the helium-3 counters. Each detector consist of 6 counters. Using a number of such detectors it is possible to verify materials stored in different geometry. There is an spectrometric scintillator detector in the system which gives an additional functional ability to the system. The system could be used to estimate the nuclear materials in waste, to detect the unauthorized transfer of the nuclear materials, to estimate the material in tubes [ru

  17. 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

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

    Directory of Open Access Journals (Sweden)

    M. A. Belenguer-Plomer

    2016-12-01

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

  19. DOWN'S ANOMALY.

    Science.gov (United States)

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

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

  20. Passive nuclear material detection in a personnel portal

    International Nuclear Information System (INIS)

    Fehlau, P.E.; Eaton, M.J.

    1979-01-01

    The concepts employed in the development of gamma-ray and neutron detection systems for a special nuclear materials booth portal monitor are described. The portal is designed for unattended use in detecting diversion by a technically sophisticated adversary and has possible application to International Atomic Energy Agency safeguards of a fast critical assembly facility. Preliminary evaluation results are given and plans for further parameter studies are noted

  1. Miscellaneous radioactive materials detected during uranium mill tailings surveys

    International Nuclear Information System (INIS)

    Wilson, M.J.

    1993-10-01

    The Department of Energy's (DOE) Office of Environmental Restoration and Waste Management directed the Oak Ridge National Laboratory Pollutant Assessments Group in the conduct of radiological surveys on properties in Monticello, Utah, associated with the Mendaciously millsite National Priority List site. During these surveys, various radioactive materials were detected that were unrelated to the Monticello millsite. The existence and descriptions of these materials were recorded in survey reports and are condensed in this report. The radioactive materials detected are either naturally occurring radioactive material, such as rock and mineral collections, uranium ore, and radioactive coal or manmade radioactive material consisting of tailings from other millsites, mining equipment, radium dials, mill building scraps, building materials, such as brick and cinderblock, and other miscellaneous sources. Awareness of the miscellaneous and naturally occurring material is essential to allow DOE to forecast the additional costs and schedule changes associated with remediation activities. Also, material that may pose a health hazard to the public should be revealed to other regulatory agencies for consideration

  2. Material Limitations on the Detection Limit in Refractometry

    OpenAIRE

    Skafte-Pedersen, Peder; Nunes, Pedro S.; Xiao, Sanshui; Mortensen, Niels Asger

    2009-01-01

    We discuss the detection limit for refractometric sensors relying on high-Q optical cavities and show that the ultimate classical detection limit is given by min {Δn} ≳ η with n + iη being the complex refractive index of the material under refractometric investigation. Taking finite Q factors and filling fractions into account, the detection limit declines. As an example we discuss the fundamental limits of silicon-based high-Q resonators, such as photonic crystal resonators, for sensing in a...

  3. Material limitations on the detection limit in refractometry.

    Science.gov (United States)

    Skafte-Pedersen, Peder; Nunes, Pedro S; Xiao, Sanshui; Mortensen, Niels Asger

    2009-01-01

    We discuss the detection limit for refractometric sensors relying on high-Q optical cavities and show that the ultimate classical detection limit is given by min {Δn} ≳ η, with n + iη being the complex refractive index of the material under refractometric investigation. Taking finite Q factors and filling fractions into account, the detection limit declines. As an example we discuss the fundamental limits of silicon-based high-Q resonators, such as photonic crystal resonators, for sensing in a bio-liquid environment, such as a water buffer. In the transparency window (λ ≳ 1100 nm) of silicon the detection limit becomes almost independent on the filling fraction, while in the visible, the detection limit depends strongly on the filling fraction because the silicon absorbs strongly.

  4. Material Limitations on the Detection Limit in Refractometry

    Directory of Open Access Journals (Sweden)

    Niels Asger Mortensen

    2009-10-01

    Full Text Available We discuss the detection limit for refractometric sensors relying on high-Q optical cavities and show that the ultimate classical detection limit is given by min {Δn} ≳ η with n + iη being the complex refractive index of the material under refractometric investigation. Taking finite Q factors and filling fractions into account, the detection limit declines. As an example we discuss the fundamental limits of silicon-based high-Q resonators, such as photonic crystal resonators, for sensing in a bio-liquid environment, such as a water buffer. In the transparency window (λ ≳ 1100 nm of silicon the detection limit becomes almost independent on the filling fraction, while in the visible, the detection limit depends strongly on the filling fraction because the silicon absorbs strongly.

  5. New Monitoring System to Detect a Radioactive Material in Motion

    International Nuclear Information System (INIS)

    Boudergui, Karim; Kondrasovs, Vladimir; Coulon, Romain; Corre, Gwenole; Normand, Stephane

    2013-06-01

    Illegal radioactive material transportation detection, by terrorist for example, is problematic in urban public transportation. Academics and industrials systems include Radiation Portal Monitor (RPM) to detect radioactive matters transported in vehicles or carried by pedestrians. However, today's RPMs are not able to efficiently detect a radioactive material in movement. Due to count statistic and gamma background, false alarms may be triggered or at the contrary a radioactive material not detected. The statistical false alarm rate has to be as low as possible in order to limit useless intervention especially in urban mass transportation. The real-time approach depicted in this paper consists in using a time correlated detection technique in association with a sensor network. It is based on several low-cost and large area plastic scintillators and a digital signal processing designed for signal reconstruction from the sensor network. The number of sensors used in the network can be adapted to fit with applications requirements or cost. The reconstructed signal is improved by comparing other approaches. This allows us to increase the device speed that has to be scanned while decreasing the risk of false alarm. In the framework of a project called SECUR-ED Secured Urban Transportation - European Demonstration, this prototype system will be used during an experiment in the Milan urban mass transportation. (authors)

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

    Science.gov (United States)

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

    2017-12-01

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

  7. 'Surveyor': An Underwater System for Threat Material Detection

    International Nuclear Information System (INIS)

    Valkovic, Vladivoj; Sudac, Davorin; Nad, Karlo; Obhodas, Jasmina; Matika, Dario; Kollar, Robert

    2010-01-01

    The bottoms of the coastal seas, and oceans as well, are contaminated by many man-made objects including a variety of ammunition. This contamination is world wide spread with some areas being highly polluted presenting a serious threat to local population and to visitors as well. All littoral nations are investing lots of effort into the remediation of their coastal areas. Once the presence of the anomaly on the bottom of the shallow coastal sea water is confirmed (by visual identification and by using one or several sensors, namely magnetometer, sonar and optical cameras) it is necessary to establish if it contains explosive/chemical warfare charge. In our work we propose this to be performed by using neutron sensor installed within an underwater vessel - 'Surveyor'. When positioned above the object, or to its side, the system inspects the object for the presence of the threat material by using alpha particle tagged neutrons from the sealed tube d+t neutron generator. (author)

  8. Mobile Techniques for Rapid Detection of Concealed Nuclear Material

    International Nuclear Information System (INIS)

    Rosenstock, W.; Koeble, T.; Risse, M.; Berky, W.

    2015-01-01

    To prevent the diversion of nuclear material as well as illicit production, transport and use of nuclear material we investigated in mobile techniques to detect and identify such material in the field as early as possible. For that purpose we use a highly sensitive gamma measurement system installed in a car. It consists of two large volume plastic scintillators, one on each side of the car, each scintillator with 12 l active volume, and two extreme sensitive high purity Germanium detectors with 57 cm 2 crystal diameter, cooled electrically. The measured data are processed immediately with integrated, appropriate analysis software for direct assessment including material identification and classification within seconds. The software for the plastic scintillators can differentiate between natural and artificial radioactivity, thus giving a clear hint for the existence of unexpected material. In addition, the system is equipped with highly sensitive neutron detectors. We have performed numerous measurements by passing different radioactive and nuclear sources in relatively large distances with this measurement car. Even shielded as well as masked material was detected and identified in most of the cases. We will report on the measurements performed in the field (on an exercise area) and in the lab and discuss the capabilities of the system, especially with respect to timeliness and identification. This system will improve the nuclear verification capabilities also. (author)

  9. Safeguards: Modelling of the Detection and Characterization of Nuclear Materials

    International Nuclear Information System (INIS)

    Enqvist, Andreas

    2010-01-01

    Nuclear safeguards is a collective term for the tools and methods needed to ensure nonproliferation and safety in connection to utilization of nuclear materials. It encompasses a variety of concepts from legislation to measurement equipment. The objective of this thesis is to present a number of research results related to nuclear materials control and accountability, especially the area of nondestructive assay. Physical aspects of nuclear materials are often the same as for materials encountered in everyday life. One special aspect though is that nuclear materials also emit radiation allowing them to be qualitatively and quantitatively measured without direct interaction with the material. For the successful assay of the material, the particle generation and detection needs to be well understood, and verified with measurements, simulations and models. Four topics of research are included in the thesis. First the generation and multiplication of neutrons and gamma rays in a fissile multiplying sample is treated. The formalism used enables investigation of the number of generated, absorbed and detected particles, offering understanding of the different processes involved. Secondly, the issue of relating the coincident detector signals, generated by both neutrons and gamma rays, to sample parameters is dealt with. Fission rate depends directly on the sample mass, while parameters such as neutron generation by alpha decay and neutron leakage multiplication are parameters that depend on the size, composition and geometry of the sample. Artificial neural networks are utilized to solve the inverse problem of finding sample characteristics from the measured rates of particle multiples. In the third part the interactions between neutrons and organic scintillation detectors are treated. The detector material consists of hydrogen and carbon, on which the neutrons scatter and transfer energy. The problem shares many characteristics with the area of neutron moderation found in

  10. 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

  11. Neutron detection using boron gallium nitride semiconductor material

    Directory of Open Access Journals (Sweden)

    Katsuhiro Atsumi

    2014-03-01

    Full Text Available In this study, we developed a new neutron-detection device using a boron gallium nitride (BGaN semiconductor in which the B atom acts as a neutron converter. BGaN and gallium nitride (GaN samples were grown by metal organic vapor phase epitaxy, and their radiation detection properties were evaluated. GaN exhibited good sensitivity to α-rays but poor sensitivity to γ-rays. Moreover, we confirmed that electrons were generated in the depletion layer under neutron irradiation. This resulted in a neutron-detection signal after α-rays were generated by the capture of neutrons by the B atoms. These results prove that BGaN is useful as a neutron-detecting semiconductor material.

  12. Nondestructive detection of surface flaws in materials by infrared thermography

    International Nuclear Information System (INIS)

    Ishii, Toshimitsu; Ooka, Norikazu; Eto, Motokuni; Hoshiya, Taiji; Okamoto, Yoshizo

    1999-01-01

    Infrared thermography is one of the useful remote sensing techniques applied to the nondestructive detection of surface flaws in materials. Radiation temperatures of the specimen surface and surrounding walls as well as the difference in them are crucial factors to detect surface flaws from thermal images, and it is essential that these factors be properly evaluated beforehand in order to detect the flaws by infrared thermography. In this study, the radiation temperature of nuclear graphite specimens heated uniformly was measured by infrared thermography to evaluate the radiation characteristics such as emissivity, radiosity coefficient and variation of radiation temperature. The influence of the temperature difference between the test specimen and its surroundings on the limit of detection of pinhole flaws was discussed on the basis of the thermal images of graphite specimen with surface flaws. It was found that the thermal image of a small flaw was clearly visible with increase in the temperature difference. (author)

  13. Cascaded image analysis for dynamic crack detection in material testing

    Science.gov (United States)

    Hampel, U.; Maas, H.-G.

    Concrete probes in civil engineering material testing often show fissures or hairline-cracks. These cracks develop dynamically. Starting at a width of a few microns, they usually cannot be detected visually or in an image of a camera imaging the whole probe. Conventional image analysis techniques will detect fissures only if they show a width in the order of one pixel. To be able to detect and measure fissures with a width of a fraction of a pixel at an early stage of their development, a cascaded image analysis approach has been developed, implemented and tested. The basic idea of the approach is to detect discontinuities in dense surface deformation vector fields. These deformation vector fields between consecutive stereo image pairs, which are generated by cross correlation or least squares matching, show a precision in the order of 1/50 pixel. Hairline-cracks can be detected and measured by applying edge detection techniques such as a Sobel operator to the results of the image matching process. Cracks will show up as linear discontinuities in the deformation vector field and can be vectorized by edge chaining. In practical tests of the method, cracks with a width of 1/20 pixel could be detected, and their width could be determined at a precision of 1/50 pixel.

  14. Fast Detection of Material Deformation through Structural Dissimilarity

    Energy Technology Data Exchange (ETDEWEB)

    Ushizima, Daniela; Perciano, Talita; Parkinson, Dilworth

    2015-10-29

    Designing materials that are resistant to extreme temperatures and brittleness relies on assessing structural dynamics of samples. Algorithms are critically important to characterize material deformation under stress conditions. Here, we report on our design of coarse-grain parallel algorithms for image quality assessment based on structural information and on crack detection of gigabyte-scale experimental datasets. We show how key steps can be decomposed into distinct processing flows, one based on structural similarity (SSIM) quality measure, and another on spectral content. These algorithms act upon image blocks that fit into memory, and can execute independently. We discuss the scientific relevance of the problem, key developments, and decomposition of complementary tasks into separate executions. We show how to apply SSIM to detect material degradation, and illustrate how this metric can be allied to spectral analysis for structure probing, while using tiled multi-resolution pyramids stored in HDF5 chunked multi-dimensional arrays. Results show that the proposed experimental data representation supports an average compression rate of 10X, and data compression scales linearly with the data size. We also illustrate how to correlate SSIM to crack formation, and how to use our numerical schemes to enable fast detection of deformation from 3D datasets evolving in time.

  15. Dyonic anomalies

    International Nuclear Information System (INIS)

    Henningson, Mans; Johansson, Erik P.G.

    2005-01-01

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

  16. Detection of special nuclear materials with the associate particle technique

    International Nuclear Information System (INIS)

    Carasco, Cédric; Deyglun, Clément; Pérot, Bertrand; Eléon, Cyrille; Normand, Stéphane; Sannié, Guillaume; Boudergui, Karim; Corre, Gwenolé; Konzdrasovs, Vladimir; Pras, Philippe

    2013-01-01

    In the frame of the French trans-governmental R and D program against chemical, biological, radiological, nuclear and explosives (CBRN-E) threats, CEA is studying the detection of Special Nuclear Materials (SNM) by neutron interrogation with fast neutrons produced by an associated particle sealed tube neutron generator. The deuterium-tritium fusion reaction produces an alpha particle and a 14 MeV neutron almost back to back, allowing tagging neutron emission both in time and direction with an alpha particle position-sensitive sensor embedded in the generator. Fission prompt neutrons and gamma rays induced by tagged neutrons which are tagged by an alpha particle are detected in coincidence with plastic scintillators. This paper presents numerical simulations performed with the MCNP-PoliMi Monte Carlo computer code and with post processing software developed with the ROOT data analysis package. False coincidences due to neutron and photon scattering between adjacent detectors (cross talk) are filtered out to increase the selectivity between nuclear and benign materials. Accidental coincidences, which are not correlated to an alpha particle, are also taken into account in the numerical model, as well as counting statistics, and the time-energy resolution of the data acquisition system. Such realistic calculations show that relevant quantities of SNM (few kg) can be distinguished from cargo and shielding materials in 10 min acquisitions. First laboratory tests of the system under development in CEA laboratories are also presented.

  17. Detecting nuclear materials smuggling: performance evaluation of container inspection policies.

    Science.gov (United States)

    Gaukler, Gary M; Li, Chenhua; Ding, Yu; Chirayath, Sunil S

    2012-03-01

    In recent years, the United States, along with many other countries, has significantly increased its detection and defense mechanisms against terrorist attacks. A potential attack with a nuclear weapon, using nuclear materials smuggled into the country, has been identified as a particularly grave threat. The system for detecting illicit nuclear materials that is currently in place at U.S. ports of entry relies heavily on passive radiation detectors and a risk-scoring approach using the automated targeting system (ATS). In this article we analyze this existing inspection system and demonstrate its performance for several smuggling scenarios. We provide evidence that the current inspection system is inherently incapable of reliably detecting sophisticated smuggling attempts that use small quantities of well-shielded nuclear material. To counter the weaknesses of the current ATS-based inspection system, we propose two new inspection systems: the hardness control system (HCS) and the hybrid inspection system (HYB). The HCS uses radiography information to classify incoming containers based on their cargo content into "hard" or "soft" containers, which then go through different inspection treatment. The HYB combines the radiography information with the intelligence information from the ATS. We compare and contrast the relative performance of these two new inspection systems with the existing ATS-based system. Our studies indicate that the HCS and HYB policies outperform the ATS-based policy for a wide range of realistic smuggling scenarios. We also examine the impact of changes in adversary behavior on the new inspection systems and find that they effectively preclude strategic gaming behavior of the adversary. © 2011 Society for Risk Analysis.

  18. Detection of nuclear material by photon activation inside cargo containers

    Science.gov (United States)

    Gmar, Mehdi; Berthoumieux, Eric; Boyer, Sébastien; Carrel, Frédérick; Doré, Diane; Giacri, Marie-Laure; Lainé, Frédéric; Poumarède, Bénédicte; Ridikas, Danas; Van Lauwe, Aymeric

    2006-05-01

    Photons with energies above 6 MeV can be used to detect small amounts of nuclear material inside large cargo containers. The method consists in using an intense beam of high-energy photons (bremsstrahlung radiation) in order to induce reactions of photofission on actinides. The measurement of delayed neutrons and delayed gammas emitted by fission products brings specific information on localization and quantification of the nuclear material. A simultaneous measurement of both of these delayed signals can overcome some important limitations due to matrix effects like heavy shielding and/or the presence of light elements as hydrogen. We have a long experience in the field of nuclear waste package characterization by photon interrogation and we have demonstrated that presently the detection limit can be less than one gram of actinide per ton of package. Recently we tried to extend our knowledge to assess the performance of this method for the detection of special nuclear materials in sea and air freights. This paper presents our first results based on experimental measurements carried out in the SAPHIR facility, which houses a linear electron accelerator with the energy range from 15 MeV to 30 MeV. Our experiments were also modeled using the full scale Monte Carlo techniques. In addition, and in a more general frame, due to the lack of consistent data on photonuclear reactions, we have been working on the development of a new photonuclear activation file (PAF), which includes cross sections for more than 600 isotopes including photofission fragment distributions and delayed neutron tables for actinides. Therefore, this work includes also some experimental results obtained at the ELSA electron accelerator, which is more adapted for precise basic nuclear data measurements.

  19. Accelerating fissile material detection with a neutron source

    Science.gov (United States)

    Rowland, Mark S.; Snyderman, Neal J.

    2018-01-30

    A neutron detector system for discriminating fissile material from non-fissile material wherein a digital data acquisition unit collects data at high rate, and in real-time processes large volumes of data directly to count neutrons from the unknown source and detecting excess grouped neutrons to identify fission in the unknown source. The system includes a Poisson neutron generator for in-beam interrogation of a possible fissile neutron source and a DC power supply that exhibits electrical ripple on the order of less than one part per million. Certain voltage multiplier circuits, such as Cockroft-Walton voltage multipliers, are used to enhance the effective of series resistor-inductor circuits components to reduce the ripple associated with traditional AC rectified, high voltage DC power supplies.

  20. Energy nonlinearity in radiation detection materials: Causes and consequences

    International Nuclear Information System (INIS)

    Jaffe, J.E.; Jordan, D.V.; Peurrung, A.J.

    2007-01-01

    The phenomenology and present theoretical understanding of energy nonlinearity (nonproportionality) in radiation detection materials is reviewed, with emphasis on gamma-ray spectroscopy. Scintillators display varying degrees and patterns of nonlinearity, while semiconductor detectors are extremely linear, and gas detectors show a characteristic form of nonproportionality associated with core levels. The relation between nonlinear response (to both primary particles and secondary electrons) and spectrometer resolution is also discussed. We review the qualitative ideas about the origin of nonlinearity in scintillators that have been proposed to date, with emphasis on transport and recombination of electronic excitations. Recent computational and experimental work on the basic physics of scintillators is leading towards a better understanding of energy nonlinearity and should result in new, more linear scintillator materials in the near future

  1. Fast neutron attenuation measurements for detection of illicit materials

    International Nuclear Information System (INIS)

    Lee, Hee Seock; Chung, Chin Wha; Guon, Ki Il; Lee, Bo Young; Ko, Seung Kook; Shin, Yong Mu

    2002-01-01

    Experiments were carried out to develop a novel method using neutron attenuation for the detection of illicit materials. By using pulsed fast neutrons generated from a Bi target bombarded with a 2 GeV electron beam, attenuation spectra of C, N, and O have been measured to study the feasibility of a practical application. The spectral dependence on the material thickness and the geometrical distribution as well as the ability to identify different elements in a layered environment have been studied. For the elements mentioned here, the total cross sections have been obtained from the measured attenuation spectra and compared with ENDF-VI, which showed good agreement. The study confirms that a conventional low energy electron linac can be put into a practical use, and some practical idea is presented

  2. Economic effects of detecting and confiscating illicitly trafficked radioactive materials

    International Nuclear Information System (INIS)

    Montmayeul, J.P.

    1998-01-01

    Development smuggling and confiscation of illicit radioactive materials demands taking into account its financial implications. The real beneficiaries of smuggling are often difficult to identify. Generally the violation cases are impossible to solve. Who would pay? How the costs of detection, analysis and decontamination could be covered. One of the primary solutions could be better charge of different parties (prisoners). It could as well be the responsibility of involved parties (transporters, enterprises, etc.). Another possibility would be to apply the principle of responsibility for payment by the party responsible for contamination

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

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

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

  4. Damage detection in composite materials using Lamb wave methods

    Science.gov (United States)

    Kessler, Seth S.; Spearing, S. Mark; Soutis, Constantinos

    2002-04-01

    Cost-effective and reliable damage detection is critical for the utilization of composite materials. This paper presents part of an experimental and analytical survey of candidate methods for in situ damage detection of composite materials. Experimental results are presented for the application of Lamb wave techniques to quasi-isotropic graphite/epoxy test specimens containing representative damage modes, including delamination, transverse ply cracks and through-holes. Linear wave scans were performed on narrow laminated specimens and sandwich beams with various cores by monitoring the transmitted waves with piezoceramic sensors. Optimal actuator and sensor configurations were devised through experimentation, and various types of driving signal were explored. These experiments provided a procedure capable of easily and accurately determining the time of flight of a Lamb wave pulse between an actuator and sensor. Lamb wave techniques provide more information about damage presence and severity than previously tested methods (frequency response techniques), and provide the possibility of determining damage location due to their local response nature. These methods may prove suitable for structural health monitoring applications since they travel long distances and can be applied with conformable piezoelectric actuators and sensors that require little power.

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

    Directory of Open Access Journals (Sweden)

    Ilir Tasha

    2014-01-01

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

  6. Superheated emulsions for the detection of special nuclear material

    International Nuclear Information System (INIS)

    D’Errico, Francesco; Di Fulvio, Angela

    2011-01-01

    A novel solution for the detection and smuggling interdiction of special nuclear materials is presented here consisting of large detector modules which contain superheated emulsions and which are readout with an optical approach. The detectors can be produced to be fully sensitive to prompt fission neutrons and totally insensitive to the interrogation beam, whether X-rays or neutrons below a chosen energy threshold. Therefore, the detectors are able to operate while the selected interrogation beam is on and they will only pick up the signal from fission neutrons. A position-sensitive readout mechanism is used in our design, relying on the scattering of light by neutron-induced bubbles. A beam of coherent light crosses the active area of the detector, and local variations in scattered light due to the presence of bubbles are detected in real time by arrays of silicon planar photodiodes affixed along the whole length of the detector. The system may offer a variety of advantages compared to current approaches, such as the possibility of simultaneous irradiation and detection, i.e. a 100% duty cycle, without requiring complex signal analysis, and high signal-to-noise ratio, minimizing costly nuisance alarms, thanks to its inherent insensitivity to photons.

  7. Detecting superlight dark matter with Fermi-degenerate materials

    Energy Technology Data Exchange (ETDEWEB)

    Hochberg, Yonit [Theory Group, Lawrence Berkeley National Laboratory,Berkeley, CA 94709 (United States); Berkeley Center for Theoretical Physics, University of California, Berkeley, CA 94709 (United States); Pyle, Matt [Physics Department, University of California,Berkeley, CA 94709 (United States); Zhao, Yue [Michigan Center for Theoretical Physics, University of Michigan,Ann Arbor, MI 48109 (United States); Zurek, Kathryn M. [Theory Group, Lawrence Berkeley National Laboratory,Berkeley, CA 94709 (United States); Berkeley Center for Theoretical Physics, University of California,Berkeley, CA 94709 (United States)

    2016-08-08

    We examine in greater detail the recent proposal of using superconductors for detecting dark matter as light as the warm dark matter limit of O(keV). Detection of such light dark matter is possible if the entire kinetic energy of the dark matter is extracted in the scattering, and if the experiment is sensitive to O(meV) energy depositions. This is the case for Fermi-degenerate materials in which the Fermi velocity exceeds the dark matter velocity dispersion in the Milky Way of ∼10{sup −3}. We focus on a concrete experimental proposal using a superconducting target with a transition edge sensor in order to detect the small energy deposits from the dark matter scatterings. Considering a wide variety of constraints, from dark matter self-interactions to the cosmic microwave background, we show that models consistent with cosmological/astrophysical and terrestrial constraints are observable with such detectors. A wider range of viable models with dark matter mass below an MeV is available if dark matter or mediator properties (such as couplings or masses) differ at BBN epoch or in stellar interiors from those in superconductors. We also show that metal targets pay a strong in-medium suppression for kinetically mixed mediators; this suppression is alleviated with insulating targets.

  8. Detection of materials of interest to nonproliferation: A novel approach

    International Nuclear Information System (INIS)

    Ze, Frederic; Tittmann, Bernhard R.; Lenahan, P.M.

    2002-01-01

    We propose the development of a novel detector that can locate and identify materials of interest to Nuclear Arms Non Proliferation. The device will combine nuclear acoustic resonance (NAR) with superconducting quantum interference device (SQUID) widely used in nuclear magnetic resonance (NMR), geophysics, nondestructive evaluations, and biomagnetism, to name only few. NAR works like NMR. Thus resonant absorption (of applied ultrasonic energy) by a nuclear spin system occurs when the ultrasonic frequency is equal to the appropriate frequency separations between the magnetic nuclear energy levels. Ultrasonic energy couples to the nuclear spin system via spin-phonon interaction. The resulting nuclear acoustic resonance can be detected via the changes in (a) ultrasonic attenuation, (b) ultrasonic velocity, (c) material magnetization, (d) or nuclear magnetic susceptibility, all of which carries 'intrinsic and unique signatures' of the material under investigation. The device's sensitivity and penetration depth (into metals) will be enhanced by incorporating SQUID technology into the design. We will present the details of interaction physics and outline a plan of action needed to successfully transform the concepts into a practical detector

  9. Coronary anomalies: what the radiologist should know*

    Science.gov (United States)

    Neves, Priscilla Ornellas; Andrade, Joalbo; Monção, Henry

    2015-01-01

    Coronary anomalies comprise a diverse group of malformations, some of them asymptomatic with a benign course, and the others related to symptoms as chest pain and sudden death. Such anomalies may be classified as follows: 1) anomalies of origination and course; 2) anomalies of intrinsic coronary arterial anatomy; 3) anomalies of coronary termination. The origin and the proximal course of anomalous coronary arteries are the main prognostic factors, and interarterial course or a coronary artery is considered to be malignant due its association with increased risk of sudden death. Coronary computed tomography angiography has become the reference method for such an assessment as it detects not only anomalies in origination of these arteries, but also its course in relation to other mediastinal structures, which plays a relevant role in the definition of the therapeutic management. Finally, it is essential for radiologists to recognize and characterize such anomalies. PMID:26379322

  10. Micro-crack detection in high-performance cementitious materials

    DEFF Research Database (Denmark)

    Lura, Pietro; Guang, Ye; Tanaka, Kyoji

    2005-01-01

    of high-performance cement pastes in silicone moulds that exert minimal external restraint. Cast-in steel rods with varying diameter internally restrain the autogenous shrinkage and lead to crack formation. Dimensions of the steel rods are chosen so that the size of this restraining inclusion resembles......-ray tomography, do not allow sufficient resolution of microcracks. A new technique presented in this paper allows detection of microcracks in cement paste while avoiding artefacts induced by unwanted restraint, drying or temperature variations. The technique consists in casting small circular cylindrical samples...... aggregate size. Gallium intrusion of the cracks and subsequent examination by electron probe micro analysis, EPMA, are used to identify the cracks. The gallium intrusion technique allows controllable impregnation of cracks in the cement paste. A distinct contrast between gallium and the surrounding material...

  11. A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Wen Chen

    2011-08-01

    Full Text Available A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance. First, the self data set is replaced by the self cluster centers to compare with the detector candidates in each cluster level. As the number of self clusters is much less than the self set size, the detector generation efficiency is improved. Second, during the detector generation process, the detector candidates are restricted to the lower coverage space to reduce detector redundancy. In the article, the problem that the distances between antigens coverage to a constant value in the high dimensional space is analyzed, accordingly the Principle Component Analysis (PCA method is used to reduce the data dimension, and the fractional distance function is employed to enhance the distinctiveness between the self and non-self antigens. The detector generation procedure is terminated when the expected non-self coverage is reached. The theory analysis and experimental results demonstrate that the detection rate of HC-RNSA is higher than that of the traditional negative selection algorithms while the false alarm rate and time cost are reduced.

  12. Detection of fissionable materials in cargoes using monochromatic photon radiography

    Science.gov (United States)

    Danagoulian, Areg; Lanza, Richard; O'Day, Buckley; LNSP Team

    2015-04-01

    The detection of Special Nuclear Materials (e.g. Pu and U) and nuclear devices in the commercial cargo traffic is one of the challenges posed by the threat of nuclear terrorism. Radiography and active interrogation of heavily loaded cargoes require ~ 1 - 10MeV photons for penetration. In a proof-of-concept system under development at MIT, the interrogating monochromatic photon beam is produced via a 11B(d , nγ) 12C reaction. To achieve this, a boron target is used along with the 3 MeV d+ RFQ accelerator at MIT-Bates. The reactions results in the emission of very narrow 4.4 MeV and 15.1 MeV gammas lines. The photons, after traversing the cargo, are detected by an array of NaI(Tl) detectors. A spectral analysis of the transmitted gammas allows to independently determine the areal density and the atomic number (Z) of the cargo. The proposed approach could revolutionize cargo inspection, which, in its current fielded form has to rely on simple but high dose bremsstrahlung sources. Use of monochromatic sources would significantly reduce the necessary dose and allow for better determination of the cargo's atomic number. The general methodology will be described and the preliminary results from the proof-of-concept system will be presented and discussed. Supported by NSF/DNDO Collaborative Research ARI-LA Award ECCS-1348328.

  13. Carbon nanotubes for gas detection: materials preparation and device assembly

    International Nuclear Information System (INIS)

    Terranova, M L; Lucci, M; Orlanducci, S; Tamburri, E; Sessa, V; Reale, A; Carlo, A Di

    2007-01-01

    An efficient sensing device for NH 3 and NO x detection has been realized using ordered arrays of single-walled C nanotubes deposited onto an interdigitated electrode platform operating at room temperature. The sensing material has been prepared using several chemical-physical techniques for purification and positioning of the nanotubes inside the electrode gaps. In particular, both DC and AC fields have been applied in order to move and to align the nanostructures by electrophoresis and dielectrophoresis processes. We investigated the effects of different voltages applied to a gate contact on the back side of the substrate on the performances of the device and found that for different gas species (NH 3 , NO x ) a constant gate bias increases the sensitivity for gas detection. Moreover, in this paper we demonstrate that a pulsed bias applied to the gate contact facilitates the gas interaction with the nanotubes, either reducing the absorption times or accelerating the desorption times, thus providing a fast acceleration and a dramatic improvement of the time dependent behaviour of the device

  14. Acoustic damage detection in laser-cut CFRP composite materials

    Science.gov (United States)

    Nishino, Michiteru; Harada, Yoshihisa; Suzuki, Takayuki; Niino, Hiroyuki

    2012-03-01

    Carbon fiber reinforced plastics (CFRP) composite material, which is expected to reduce the weight of automotive, airplane and etc., was cut by laser irradiation with a pulsed-CO2 laser (TRUMPF TFL5000; P=800W, 20kHz, τ=8μs, λ=10.6μm, V=1m/min) and single-mode fiber lasers (IPG YLR-300-SM; P=300W, λ=1.07μm, V=1m/min)(IPG YLR- 2000-SM; P=2kW, λ=1.07μm, V=7m/min). To detect thermal damage at the laser cutting of CFRP materials consisting of thermoset resin matrix and PAN or PITCH-based carbon fiber, the cut quality was observed by X-ray CT. The effect of laser cutting process on the mechanical strength for CFRP tested at the tensile test. Acoustic emission (AE) monitoring, high-speed camera and scanning electron microscopy were used for the failure process analysis. AE signals and fractographic features characteristic of each laser-cut CFRP were identified.

  15. Detection of smuggling of nuclear material covered by a legal transport of radioactive material

    International Nuclear Information System (INIS)

    Safar, J.; Zsigrai, J.; Tam, N.C.; Lakosi, L.

    2001-01-01

    Full text: One of the worst scenarios for detection of illicit trafficking of nuclear material is when a legal transport of radioactive material is used to cover the radiation of the smuggled uranium. Feasibility study was performed in the Institute of Isotopes and Surface Chemistry of the Chemical Research Centre of the Hungarian Academy of Sciences (hereinafter: Institute) in order to study the possible on site measurement techniques and approaches applicable in such cases. As the type A and type B packages always incorporate a feature such as a seal, in a realistic scenario the confiscated nuclear material is expected to be placed outside the package. The passive neutron emission of the uranium is negligible for a reasonable isotopic abundance therefore the feasibility study was concentrating on non-destructive, passive gamma- spectrometric methods. Possible application of Nal (diameter 40x40 mm 3 , large planar (15x15x3 mm 3 ) and a hemispheric CdZnTe (500 mm 3 , and high purity Germanium detectors was investigated. During the on site measurements portable electronics, mini multichannel analyzer, palmtop and/or notebook computer were used. The shielding material of the packages was lead or depleted uranium. The smuggled material was simulated by a package of reactor fuel pellets containing low enriched or natural uranium (materials confiscated in earlier cases) and standards containing low enriched uranium. During the supposed scenario the portal monitor provides an indication of an elevated level of the environmental radioactivity. Then the responsible (e.g. customs) officer investigate the vehicle by a hand-held survey meter in order to search for peaks in dose rates. If a peak was localized, which is different from the position of the legally transported package(s) the officer requests for the expertise of the designated institutes. The following model cases provided the basic conclusion: 1. The legal transport of the radioactive material was simulated by a

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

    Science.gov (United States)

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

    2014-10-01

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

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

    Science.gov (United States)

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

    2017-11-21

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

  18. 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.).

  19. Chiral anomalies and differential geometry

    International Nuclear Information System (INIS)

    Zumino, B.

    1983-10-01

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

  20. Graph anomalies in cyber communications

    Energy Technology Data Exchange (ETDEWEB)

    Vander Wiel, Scott A [Los Alamos National Laboratory; Storlie, Curtis B [Los Alamos National Laboratory; Sandine, Gary [Los Alamos National Laboratory; Hagberg, Aric A [Los Alamos National Laboratory; Fisk, Michael [Los Alamos National Laboratory

    2011-01-11

    Enterprises monitor cyber traffic for viruses, intruders and stolen information. Detection methods look for known signatures of malicious traffic or search for anomalies with respect to a nominal reference model. Traditional anomaly detection focuses on aggregate traffic at central nodes or on user-level monitoring. More recently, however, traffic is being viewed more holistically as a dynamic communication graph. Attention to the graph nature of the traffic has expanded the types of anomalies that are being sought. We give an overview of several cyber data streams collected at Los Alamos National Laboratory and discuss current work in modeling the graph dynamics of traffic over the network. We consider global properties and local properties within the communication graph. A method for monitoring relative entropy on multiple correlated properties is discussed in detail.

  1. Dual-energy X-ray radiography for automatic high-Z material detection

    International Nuclear Information System (INIS)

    Chen Gongyin; Bennett, Gordon; Perticone, David

    2007-01-01

    There is an urgent need for high-Z material detection in cargo. Materials with Z > 74 can indicate the presence of fissile materials or radiation shielding. Dual (high) energy X-ray material discrimination is based on the fact that different materials have different energy dependence in X-ray attenuation coefficients. This paper introduces the basic physics and analyzes the factors that affect dual-energy material discrimination performance. A detection algorithm is also discussed

  2. Situs anomalies on prenatal MRI

    International Nuclear Information System (INIS)

    Nemec, Stefan F.; Brugger, Peter C.; Nemec, Ursula; Bettelheim, Dieter; Kasprian, Gregor; Amann, Gabriele; Rimoin, David L.; Graham, John M.; Prayer, Daniela

    2012-01-01

    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.

  3. Coded aperture material motion detection system for the ACPR

    International Nuclear Information System (INIS)

    McArthur, D.A.; Kelly, J.G.

    1975-01-01

    Single LMFBR fuel pins are being irradiated in Sandia's Annular Core Pulsed Reactor (ACPR). In these experiments single fuel pins have been driven well into the melt and vaporization regions in transients with pulse widths of about 5 ms. The ACPR is being upgraded so that it can be used to irradiate bundles of seven LMFBR fuel pins. The coded aperture material motion detection system described is being developed for this upgraded ACPR, and has for its design goals 1 mm transverse resolution (i.e., in the axial and radial directions), depth resolution of a few cm, and time resolution of 0.1 ms. The target date for development of this system is fall 1977. The paper briefly reviews the properties of coded aperture imaging, describes one possible system for the ACPR upgrade, discusses experiments which have been performed to investigate the feasibility of such a system, and describes briefly the further work required to develop such a system. The type of coded aperture to be used has not yet been fixed, but a one-dimensional section of a Fresnel zone plate appears at this time to have significant advantages

  4. Portal monitoring for detecting fissile materials and chemical explosives

    International Nuclear Information System (INIS)

    Albright, D.

    1992-01-01

    The portal monitoring of pedestrians, packages, equipment, and vehicles entering or leaving areas of high physical security has been common for many years. Many nuclear facilities rely on portal monitoring to prevent the theft or diversion of plutonium and highly enriched uranium. At commercial airports, portals are used to prevent firearms and explosives from being smuggled onto airplanes. An August 1989 Federal Aviation Administration (FAA) regulation requires US airlines to screen luggage on international flights for chemical explosives. This paper reports that portal monitoring is now being introduced into arms-control agreements. Because some of the portal-monitoring equipment that would be useful in verifying arms-control agreements is already widely used as part of the physical security systems at nuclear facilities and commercial airports, the authors review these uses of portal monitoring, as well as its role in verifying the INF treaty. Then the authors survey the major types of portal-monitoring equipment that would be most useful in detecting nuclear warheads or fissile material

  5. Special Nuclear Material Detection with a Water Cherenkov based Detector

    International Nuclear Information System (INIS)

    Sweany, M.; Bernstein, A.; Bowden, N.; Dazeley, S.; Svoboda, R.

    2008-01-01

    Fission events from Special Nuclear Material (SNM), such as highly enriched uranium or plutonium, produce a number of neutrons and high energy gamma-rays. Assuming the neutron multiplicity is approximately Poissonian with an average of 2 to 3, the observation of time correlations between these particles from a cargo container would constitute a robust signature of the presence of SNM inside. However, in order to be sensitive to the multiplicity, one would require a high total efficiency. There are two approaches to maximize the total efficiency; maximizing the detector efficiency or maximizing the detector solid angle coverage. The advanced detector group at LLNL is investigating one way to maximize the detector size. We are designing and building a water Cerenkov based gamma and neutron detector for the purpose of developing an efficient and cost effective way to deploy a large solid angle car wash style detector. We report on our progress in constructing a larger detector and also present preliminary results from our prototype detector that indicates detection of neutrons

  6. 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...

  7. Anomaly detection using process mining

    NARCIS (Netherlands)

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

    2009-01-01

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

  8. Anomaly Detection for Complex Systems

    Data.gov (United States)

    National Aeronautics and Space Administration — In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about...

  9. Major congenital anomalies in a Danish region

    DEFF Research Database (Denmark)

    Garne, Ester; Hansen, Anne Vinkel; Birkelund, Anne Sofie

    2014-01-01

    INTRODUCTION: This study describes the prevalence of congenital anomalies and changes over time in birth outcome, mortality and chronic maternal diseases. MATERIAL AND METHODS: This study was based on population data from the EUROCAT registry covering the Funen County, Denmark, 1995...... mortality decreased significantly over time for cases with major congenital anomalies (p congenital anomaly cases, 8% had a registration of one of these chronic maternal diseases......: diabetes, epilepsy, mental disorder, thyroid disease, asthma, or inflammatory bowel disease. Medication for these conditions accounted for 46% of maternal drug use. CONCLUSION: Maternal morbidity and use of potentially teratogenic medication have increased among congenital anomaly cases. Foetal and infant...

  10. Renal anomalies in congenital heart disease

    International Nuclear Information System (INIS)

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

    1987-01-01

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

  11. A multi points ultrasonic detection method for material flow of belt conveyor

    Science.gov (United States)

    Zhang, Li; He, Rongjun

    2018-03-01

    For big detection error of single point ultrasonic ranging technology used in material flow detection of belt conveyor when coal distributes unevenly or is large, a material flow detection method of belt conveyor is designed based on multi points ultrasonic counter ranging technology. The method can calculate approximate sectional area of material by locating multi points on surfaces of material and belt, in order to get material flow according to running speed of belt conveyor. The test results show that the method has smaller detection error than single point ultrasonic ranging technology under the condition of big coal with uneven distribution.

  12. Systems and methods for neutron detection using scintillator nano-materials

    Science.gov (United States)

    Letant, Sonia Edith; Wang, Tzu-Fang

    2016-03-08

    In one embodiment, a neutron detector includes a three dimensional matrix, having nanocomposite materials and a substantially transparent film material for suspending the nanocomposite materials, a detector coupled to the three dimensional matrix adapted for detecting a change in the nanocomposite materials, and an analyzer coupled to the detector adapted for analyzing the change detected by the detector. In another embodiment, a method for detecting neutrons includes receiving radiation from a source, converting neutrons in the radiation into alpha particles using converter material, converting the alpha particles into photons using quantum dot emitters, detecting the photons, and analyzing the photons to determine neutrons in the radiation.

  13. Kohn anomalies in superconductors

    International Nuclear Information System (INIS)

    Flatte, M.E.

    1994-01-01

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

  14. Tracheobronchial Branching Anomalies

    International Nuclear Information System (INIS)

    Hong, Min Ji; Kim, Young Tong; Jou, Sung Shick; Park, A Young

    2010-01-01

    There are various congenital anomalies with respect to the number, length, diameter, and location of tracheobronchial branching patterns. The tracheobronchial anomalies are classified into two groups. The first one, anomalies of division, includes tracheal bronchus, cardiac bronchus, tracheal diverticulum, pulmonary isomerism, and minor variations. The second one, dysmorphic lung, includes lung agenesis-hypoplasia complex and lobar agenesis-aplasia complex

  15. 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

  16. Detecting strain in birefringent materials using spectral polarimetry

    Science.gov (United States)

    Garner, Harold R. (Inventor); Ragucci, Anthony J. (Inventor); Cisar, Alan J. (Inventor); Huebschman, Michael L. (Inventor)

    2010-01-01

    A method, computer program product and system for analyzing multispectral images from a plurality of regions of birefringent material, such as a polymer film, using polarized light and a corresponding polar analyzer to identify differential strain in the birefringent material. For example, the birefringement material may be low-density polyethylene (LDPE), high-density polyethylene (HDPE), polypropylene, polyethylene terephthalate (PET), polyvinyl chloride (PVC), polyvinylidene chloride, polyester, nylon, or cellophane film. Optionally, the method includes generating a real-time quantitative strain map.

  17. Revolution in Detection Affairs

    Energy Technology Data Exchange (ETDEWEB)

    Stern W.

    2013-11-02

    The detection of nuclear or radioactive materials for homeland or national security purposes is inherently difficult. This is one reason detection efforts must be seen as just one part of an overall nuclear defense strategy which includes, inter alia, material security, detection, interdiction, consequence management and recovery. Nevertheless, one could argue that there has been a revolution in detection affairs in the past several decades as the innovative application of new technology has changed the character and conduct of detection operations. This revolution will likely be most effectively reinforced in the coming decades with the networking of detectors and innovative application of anomaly detection algorithms.

  18. Dental anomalies of the deciduous dentition among Indian children: A survey from Jodhpur, Rajasthan, India

    Directory of Open Access Journals (Sweden)

    Shravani Govind Deolia

    2015-01-01

    Full Text Available Background: Anomalies and enamel hypoplasia of deciduous dentition are routinely encountered by dental professionals and early detection and careful management of such conditions facilitates may help in customary occlusal development. Objective: The aim of this study was to determine the prevalence of hypodontia, microdontia, double teeth, and hyperdontia of deciduous teeth among Indian children. Materials and Methods: The study group comprised 1,398 children (735 boys, 633 girls. The children were examined in department of Pedodontics and Preventive Dentistry in Jodhpur Dental College General Hospital, Jodhpur, Rajasthan, India. Clinical data were collected by single dentist according to Kreiborg criteria, which includes double teeth, hypodontia, microdontia, and supernumerary teeth. Statistical analysis of the data was performed using the descriptive analysis and chi-square test. Results: Dental anomalies were found in 4% of children. The distribution of dental anomalies were significantly more frequent (P = 0.001 in girls (5.8%, n = 38 than in boys (2.7%, n = 18. In relation to anomaly frequencies at different ages, significant difference was found between 2 and 3 years (P = 0.001. Conclusion: Double teeth were the most frequently (2.3% observed anomaly. The other anomalies followed as 0.3% supernumerary teeth, 0.6% microdontia, 0.6% hypodontia. Identification of dental anomalies at an early age is of great importance as it prevents malocclusions, functional and certain psychological problems.

  19. Branchial anomalies in children.

    Science.gov (United States)

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

    2011-08-01

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

  20. Exploiting Novel Radiation-Induced Electromagnetic Material Changes for Remote Detection and Monitoring: Final Progress Report

    Science.gov (United States)

    2016-04-01

    Exploiting Novel Radiation -Induced Electromagnetic Material Changes for Remote Detection and Monitoring: Final Progress Report Distribution...assess the effects of ionizing radiation on at least three classes of electromagnetic materials. The proposed approach for radiation detection was...that was desired to be monitored remotely. Microwave or low millimeter wave electromagnetic radiation would be used to interrogate the device

  1. Multi-colorimetric sensor array for detection of illegal materials

    DEFF Research Database (Denmark)

    Kostesha, Natalie; Boisen, Anja; Jakobsen, Mogens Havsteen

    2012-01-01

    The detection of low pressure illegal compounds is an important analytical problem which requires reliable, selective and sensitive detection methods which provide the highest level of confidence in the result. Therefore, to contribute in the successful development of the recognition technology...... and signal processing enhancements to sensing methods, recognition ability, data acquisition time and data processing algorithms are necessary. In this research we work towards the development of a rapid, easy in use, highly sensitive, specific (minimal false positives) sensor based on a colorimetric sensing...

  2. Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies

    Science.gov (United States)

    Chemura, Abel; Mutanga, Onisimo; Dube, Timothy

    2017-05-01

    The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α ≤ 0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy = 80.9% and 68.1% respectively) and for LSWI (Overall accuracy = 68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and

  3. TCT characterization of different semiconductor materials for particle detection

    International Nuclear Information System (INIS)

    Fink, J.; Lodomez, P.; Krueger, H.; Pernegger, H.; Weilhammer, P.; Wermes, N.

    2006-01-01

    The development of digital semiconductor based X-ray detectors necessitates a detailed understanding of the applied sensor material. Under this premise a broad-band transient current technique (TCT) setup has been developed and used to characterize different semiconductors. The measurements are based on the generation of electrical charges within the sensor material and the subsequent time-resolved analysis of the charge carrier movement. From the recorded current pulses the charge collection efficiency, the charge carrier mobility and the electric field profile have been extracted. The examined materials are silicon p in n diodes, ohmic and Schottky contacted CdTe detectors, CdZnTe (CZT) crystals with Schottky contacts as well as two single-crystal CVD-diamonds

  4. Detecting special nuclear material using muon-induced neutron emission

    Energy Technology Data Exchange (ETDEWEB)

    Guardincerri, Elena; Bacon, Jeffrey; Borozdin, Konstantin; Matthew Durham, J.; Fabritius II, Joseph [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Hecht, Adam [University of New Mexico, Albuquerque, NM 87131 (United States); Milner, Edward C. [Southern Methodist University, Dallas, TX 75205 (United States); Miyadera, Haruo; Morris, Christopher L. [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Perry, John [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); University of New Mexico, Albuquerque, NM 87131 (United States); Poulson, Daniel [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States)

    2015-07-21

    The penetrating ability of cosmic ray muons makes them an attractive probe for imaging dense materials. Here, we describe experimental results from a new technique that uses neutrons generated by cosmic-ray muons to identify the presence of special nuclear material (SNM). Neutrons emitted from SNM are used to tag muon-induced fission events in actinides and laminography is used to form images of the stopping material. This technique allows the imaging of SNM-bearing objects tagged using muon tracking detectors located above or to the side of the objects, and may have potential applications in warhead verification scenarios. During the experiment described here we did not attempt to distinguish the type or grade of the SNM.

  5. An anomaly analysis framework for database systems

    NARCIS (Netherlands)

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

    2015-01-01

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

  6. Materials Degradation and Detection (MD2): Deep Dive Final Report

    Energy Technology Data Exchange (ETDEWEB)

    McCloy, John S.; Montgomery, Robert O.; Ramuhalli, Pradeep; Meyer, Ryan M.; Hu, Shenyang Y.; Li, Yulan; Henager, Charles H.; Johnson, Bradley R.

    2013-02-01

    An effort is underway at Pacific Northwest National Laboratory (PNNL) to develop a fundamental and general framework to foster the science and technology needed to support real-time monitoring of early degradation in materials used in the production of nuclear power. The development of such a capability would represent a timely solution to the mounting issues operators face with materials degradation in nuclear power plants. The envisioned framework consists of three primary and interconnected “thrust” areas including 1) microstructural science, 2) behavior assessment, and 3) monitoring and predictive capabilities. A brief state-of-the-art assessment for each of these core technology areas is discussed in the paper.

  7. Congenital anomalies and normal skeletal variants

    International Nuclear Information System (INIS)

    Guebert, G.M.; Yochum, T.R.; Rowe, L.J.

    1987-01-01

    Congenital anomalies and normal skeletal variants are a common occurrence in clinical practice. In this chapter a large number of skeletal anomalies of the spine and pelvis are reviewed. Some of the more common skeletal anomalies of the extremities are also presented. The second section of this chapter deals with normal skeletal variants. Some of these variants may simulate certain disease processes. In some instances there are no clear-cut distinctions between skeletal variants and anomalies; therefore, there may be some overlap of material. The congenital anomalies are presented initially with accompanying text, photos, and references, beginning with the skull and proceeding caudally through the spine to then include the pelvis and extremities. The normal skeletal variants section is presented in an anatomical atlas format without text or references

  8. Trace detection of explosive materials in air cargo containers

    NARCIS (Netherlands)

    Jezierska, M.M.; Spreen, J.S.; Ruiter, J.C. de; Koomen, G.C.M.; Slegt, M.

    2011-01-01

    At the request of the National Coordinator for Counterterrorism in the Netherlands a research project called “Security through innovation - risk-oriented detection in a drive-through set-up” has been carried out by TNO Defense, Security and Safety (TNO) and by Dutch Customs. In 2009 and 2010, a

  9. Simulation of Neutron Backscattering applied to organic material detection

    International Nuclear Information System (INIS)

    Forero, N. C.; Cruz, A. H.; Cristancho, F.

    2007-01-01

    The Neutron Backscattering technique is tested when performing the task of localizing hydrogenated explosives hidden in soil. Detector system, landmine, soil and neutron source are simulated with Geant4 in order to obtain the number of neutrons detected when several parameters like mine composition, relative position mine-source and soil moisture are varied

  10. Applications of Kalman Filtering to nuclear material control. [Kalman filtering and linear smoothing for detecting nuclear material losses

    Energy Technology Data Exchange (ETDEWEB)

    Pike, D.H.; Morrison, G.W.; Westley, G.W.

    1977-10-01

    The feasibility of using modern state estimation techniques (specifically Kalman Filtering and Linear Smoothing) to detect losses of material from material balance areas is evaluated. It is shown that state estimation techniques are not only feasible but in most situations are superior to existing methods of analysis. The various techniques compared include Kalman Filtering, linear smoothing, standard control charts, and average cumulative summation (CUSUM) charts. Analysis results indicated that the standard control chart is the least effective method for detecting regularly occurring losses. An improvement in the detection capability over the standard control chart can be realized by use of the CUSUM chart. Even more sensitivity in the ability to detect losses can be realized by use of the Kalman Filter and the linear smoother. It was found that the error-covariance matrix can be used to establish limits of error for state estimates. It is shown that state estimation techniques represent a feasible and desirable method of theft detection. The technique is usually more sensitive than the CUSUM chart in detecting losses. One kind of loss which is difficult to detect using state estimation techniques is a single isolated loss. State estimation procedures are predicated on dynamic models and are well-suited for detecting losses which occur regularly over several accounting periods. A single isolated loss does not conform to this basic assumption and is more difficult to detect.

  11. Global gravitational anomalies

    International Nuclear Information System (INIS)

    Witten, E.

    1985-01-01

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

  12. Anomaly-free models for flavour anomalies

    Science.gov (United States)

    Ellis, John; Fairbairn, Malcolm; Tunney, Patrick

    2018-03-01

    We explore the constraints imposed by the cancellation of triangle anomalies on models in which the flavour anomalies reported by LHCb and other experiments are due to an extra U(1)^' gauge boson Z^' . We assume universal and rational U(1)^' charges for the first two generations of left-handed quarks and of right-handed up-type quarks but allow different charges for their third-generation counterparts. If the right-handed charges vanish, cancellation of the triangle anomalies requires all the quark U(1)^' charges to vanish, if there are either no exotic fermions or there is only one Standard Model singlet dark matter (DM) fermion. There are non-trivial anomaly-free models with more than one such `dark' fermion, or with a single DM fermion if right-handed up-type quarks have non-zero U(1)^' charges. In some of the latter models the U(1)^' couplings of the first- and second-generation quarks all vanish, weakening the LHC Z^' constraint, and in some other models the DM particle has purely axial couplings, weakening the direct DM scattering constraint. We also consider models in which anomalies are cancelled via extra vector-like leptons, showing how the prospective LHC Z^' constraint may be weakened because the Z^' → μ ^+ μ ^- branching ratio is suppressed relative to other decay modes.

  13. Radiation Detection System for Prevention of Illicit Trafficking of Nuclear and Radioactive Materials

    International Nuclear Information System (INIS)

    Kwak, Sung Woo; Chang, Sung Soon; Yoo, Ho Sik

    2010-01-01

    Fixed radiation portal monitors (RPMs) deployed at border, seaport, airport and key traffic checkpoints have played an important role in preventing the illicit trafficking and transport of nuclear and radioactive materials. However, the RPM is usually large and heavy and can't easily be moved to different locations. These reasons motivate us to develop a mobile radiation detection system. The objective of this paper is to report our experience on developing the mobile radiation detection system for search and detection of nuclear and radioactive materials during road transport. Field tests to characterize the developed detection system were performed at various speeds and distances between the radioactive isotope (RI) transporting car and the measurement car. Results of measurements and detection limits of our system are described in this paper. The mobile radiation detection system developed should contribute to defending public's health and safety and the environment against nuclear and radiological terrorism by detecting nuclear or radioactive material hidden illegally in a vehicle

  14. Anomaly-specified virtual dimensionality

    Science.gov (United States)

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

    2013-09-01

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

  15. First branchial groove anomaly.

    Science.gov (United States)

    Kumar, M; Hickey, S; Joseph, G

    2000-06-01

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

  16. A grey incidence algorithm to detect high-Z material using cosmic ray muons

    Science.gov (United States)

    He, W.; Xiao, S.; Shuai, M.; Chen, Y.; Lan, M.; Wei, M.; An, Q.; Lai, X.

    2017-10-01

    Muon scattering tomography (MST) is a method for using cosmic muons to scan cargo containers and vehicles for special nuclear materials. However, the flux of cosmic ray muons is low, in the real life application, the detection has to be done a short timescale with small numbers of muons. In this paper, we present a novel approach to detection of special nuclear material by using cosmic ray muons. We use the degree of grey incidence to distinguish typical waste fuel material, uranium, from low-Z material, medium-Z material and other high-Z materials of tungsten and lead. The result shows that using this algorithm, it is possible to detect high-Z materials with an acceptable timescale.

  17. Neutron interrogation system using high gamma ray signature to detect contraband special nuclear materials in cargo

    Science.gov (United States)

    Slaughter, Dennis R [Oakland, CA; Pohl, Bertram A [Berkeley, CA; Dougan, Arden D [San Ramon, CA; Bernstein, Adam [Palo Alto, CA; Prussin, Stanley G [Kensington, CA; Norman, Eric B [Oakland, CA

    2008-04-15

    A system for inspecting cargo for the presence of special nuclear material. The cargo is irradiated with neutrons. The neutrons produce fission products in the special nuclear material which generate gamma rays. The gamma rays are detecting indicating the presence of the special nuclear material.

  18. Gamma motes for detection of radioactive materials in shipping containers

    International Nuclear Information System (INIS)

    Harold McHugh; William Quam; Stephan Weeks; Brendan Sever

    2007-01-01

    Shipping containers can be effectively monitored for radiological materials using gamma (and neutron) motes in distributed mesh networks. The mote platform is ideal for collecting data for integration into operational management systems required for efficiently and transparently monitoring international trade. Significant reductions in size and power requirements have been achieved for room-temperature cadmium zinc telluride (CZT) gamma detectors. Miniaturization of radio modules and microcontroller units are paving the way for low-power, deeply-embedded, wireless sensor distributed mesh networks

  19. Detection of ultraviolet radiation using tissue equivalent radiochromic gel materials

    International Nuclear Information System (INIS)

    Bero, M A; Abukassem, I

    2009-01-01

    Ferrous Xylenol-orange Gelatin gel (FXG) is known to be sensitive to ionising radiation such as γ and X-rays. The effect of ionising radiation is to produce an increase in the absorption over a wide region of the visible spectrum, which is proportional to the absorbed dose. This study demonstrates that FXG gel is sensitive to ultraviolet radiation and therefore it could functions as UV detector. Short exposure to UV radiation produces linear increase in absorption measured at 550nm, however high doses of UV cause the ion indicator colour to fad away in a manner proportional to the incident UV energy. Light absorbance increase at the rate of 1.1% per minute of irradiation was monitored. The exposure level at which the detector has linear response is comparable to the natural summer UV radiation. Evaluating the UV ability to pass through tissue equivalent gel materials shows that most of the UV gets absorbed in the first 5mm of the gel materials, which demonstrate the damaging effects of this radiation type on human skin and eyes. It was concluded that FXG gel dosimeter has the potential to offer a simple, passive ultraviolet radiation detector with sensitivity suitable to measure and visualises the natural sunlight UV exposure directly by watching the materials colour changes.

  20. Detection of ultraviolet radiation using tissue equivalent radiochromic gel materials

    Science.gov (United States)

    Bero, M. A.; Abukassem, I.

    2009-05-01

    Ferrous Xylenol-orange Gelatin gel (FXG) is known to be sensitive to ionising radiation such as γ and X-rays. The effect of ionising radiation is to produce an increase in the absorption over a wide region of the visible spectrum, which is proportional to the absorbed dose. This study demonstrates that FXG gel is sensitive to ultraviolet radiation and therefore it could functions as UV detector. Short exposure to UV radiation produces linear increase in absorption measured at 550nm, however high doses of UV cause the ion indicator colour to fad away in a manner proportional to the incident UV energy. Light absorbance increase at the rate of 1.1% per minute of irradiation was monitored. The exposure level at which the detector has linear response is comparable to the natural summer UV radiation. Evaluating the UV ability to pass through tissue equivalent gel materials shows that most of the UV gets absorbed in the first 5mm of the gel materials, which demonstrate the damaging effects of this radiation type on human skin and eyes. It was concluded that FXG gel dosimeter has the potential to offer a simple, passive ultraviolet radiation detector with sensitivity suitable to measure and visualises the natural sunlight UV exposure directly by watching the materials colour changes.

  1. Detection of tiny amounts of fissile materials in large-sized containers with radioactive waste

    Science.gov (United States)

    Batyaev, V. F.; Skliarov, S. V.

    2018-01-01

    The paper is devoted to non-destructive control of tiny amounts of fissile materials in large-sized containers filled with radioactive waste (RAW). The aim of this work is to model an active neutron interrogation facility for detection of fissile ma-terials inside NZK type containers with RAW and determine the minimal detectable mass of U-235 as a function of various param-eters: matrix type, nonuniformity of container filling, neutron gen-erator parameters (flux, pulse frequency, pulse duration), meas-urement time. As a result the dependence of minimal detectable mass on fissile materials location inside container is shown. Nonu-niformity of the thermal neutron flux inside a container is the main reason of the space-heterogeneity of minimal detectable mass in-side a large-sized container. Our experiments with tiny amounts of uranium-235 (<1 g) confirm the detection of fissile materials in NZK containers by using active neutron interrogation technique.

  2. The IAEA concept of detection of diversion through nuclear material accountancy (2)

    International Nuclear Information System (INIS)

    Akiba, Mitsunori

    2005-01-01

    Diversion into D (falsification of accounting report) and diversion into MUF could be detected by the Inspectorate through nuclear material accountancy. The Inspectorate designs inspection activities to detect diversion into D in cost effective ways. As a result, detection of diversion into D is divided into two statistics, one is item difference statistics which could detect major defects and the other is material balance statistics which could detect remaining small defects. MUF statistics could detect Diversion into MUF. Item statistics has many useful characteristics from safeguards view points, so it is examined in details. Material balance statistics and MUF statistics stem from measurement error associated with equipment inevitably. The above-mentioned concept is called 'IAEA decision structure'. Hereafter, designing safeguards (inspection activities) approach will be based on the IAEA decision structure. (author)

  3. Neutrino scattering and the reactor antineutrino anomaly

    Science.gov (United States)

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

    2017-12-01

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

  4. Hybrid silica materials for detection of toxic species and clinical diagnosis

    OpenAIRE

    Pascual Vidal, Lluís

    2017-01-01

    The present PhD thesis entitled "Silica Hybrid Materials for detection of toxic species and clinical diagnosis" is focused on the design and synthesis of new hybrid materials, using different silica supports as inorganic scaffolds, with applications in recognition, sensing and diagnostic protocols. The first chapter of the PhD thesis is devoted to the definition and classification of hybrid materials, relying on concepts of Nanotechnology, Supramolecular and Materials Chemistry. State o...

  5. Radiation sensitive devices and systems for detection of radioactive materials and related methods

    Science.gov (United States)

    Kotter, Dale K

    2014-12-02

    Radiation sensitive devices include a substrate comprising a radiation sensitive material and a plurality of resonance elements coupled to the substrate. Each resonance element is configured to resonate responsive to non-ionizing incident radiation. Systems for detecting radiation from a special nuclear material include a radiation sensitive device and a sensor located remotely from the radiation sensitive device and configured to measure an output signal from the radiation sensitive device. In such systems, the radiation sensitive device includes a radiation sensitive material and a plurality of resonance elements positioned on the radiation sensitive material. Methods for detecting a presence of a special nuclear material include positioning a radiation sensitive device in a location where special nuclear materials are to be detected and remotely interrogating the radiation sensitive device with a sensor.

  6. Double-pulse standoff laser-induced breakdown spectroscopy for versatile hazardous materials detection

    Energy Technology Data Exchange (ETDEWEB)

    Gottfried, Jennifer L. [U.S. Army Research Laboratory, AMSRD-ARL-WM-BD, Aberdeen Proving Ground, MD, 21005-5069 (United States)], E-mail: jennifer.gottfried@arl.army.mil; De Lucia, Frank C.; Munson, Chase A.; Miziolek, Andrzej W. [U.S. Army Research Laboratory, AMSRD-ARL-WM-BD, Aberdeen Proving Ground, MD, 21005-5069 (United States)

    2007-12-15

    We have developed a double-pulse standoff laser-induced breakdown spectroscopy (ST-LIBS) system capable of detecting a variety of hazardous materials at tens of meters. The use of a double-pulse laser improves the sensitivity and selectivity of ST-LIBS, especially for the detection of energetic materials. In addition to various metallic and plastic materials, the system has been used to detect bulk explosives RDX and Composition-B, explosive residues, biological species such as the anthrax surrogate Bacillus subtilis, and chemical warfare simulants at 20 m. We have also demonstrated the discrimination of explosive residues from various interferents on an aluminum substrate.

  7. Dental Anomalies: An Update

    Directory of Open Access Journals (Sweden)

    Fatemeh Jahanimoghadam

    2016-01-01

    Full Text Available Dental anomalies are usual congenital malformation that can happen either as isolated findings or as a part of a syndrome. Developmental anomalies influencing the morphology exists in both deciduous and permanent dentition and shows different forms such as gemination, fusion, concrescence, dilaceration, dens evaginatus (DE, enamel pearls, taurodontism or peg-shaped laterals. All These anomalies have clinical significance concerning aesthetics, malocclusion and more necessary preparing of the development of dental decays and oral diseases. Through a search in PubMed, Google, Scopus and Medline, a total of eighty original research papers during 1928-2016 were found with the keywords such as dental anomaly, syndrome, tooth and hypodontia. One hundred review titles were identified, eighty reviews were retrieved that were finally included as being relevant and of sufficient quality. In this review, dental anomalies including gemination, fusion, concrescence, dilaceration, dens invaginatus, DE, taurodontism, enamel pearls, fluorosis, peg-shaped laterals, dentinal dysplasia, regional odontodysplasia and hypodontia are discussed. Diagnosing dental abnormality needs a thorough evaluation of the patient, involving a medical, dental, familial and clinical history. Clinical examination and radiographic evaluation and in some of the cases, specific laboratory tests are also needed. Developmental dental anomalies require careful examination and treatment planning. Where one anomaly is present, clinicians should suspect that other anomalies may also be present. Moreover, careful clinical and radiographical examination is required. Furthermore, more complex cases need multidisciplinary planning and treatment.

  8. National infrastructure for detecting, controlling and monitoring radioactive materials

    International Nuclear Information System (INIS)

    Othman, I.

    2001-01-01

    Full text: The Atomic Energy Commission of Syria (AECS) has the direct responsibility to assure proper safety for handling, accounting for and controlling of nuclear materials and radioactive sources which based on a solid regulatory infrastructure , its elements contains the following items: preventing, responding, training, exchanging of information. Based on the National Law for AECS's Establishment no. 12/1976, a Ministerial Decree for Radiation Safety no. 6514 dated 8.12.1997, issued by the Prime Minister. This Decree authorizes the Syrian Atomic Energy Commission to regulate all kinds of radiation sources. It fulfills the basic requirements of radiation protection and enforce the rules and regulations. The Radiation and Nuclear Regulatory Office (RNRO) is responsible for preparing all the draft regulations. In 1999 the General Regulations for Radiation Protection was issued by the Director General of the AECS, under Decision no. 112/99 dated 3.2.1999. It is based on an IAEA publication, Safety Series no. 115 (1996), and adopted to meet the national requirements. Syria has nine Boarding Centers seeking to prevent unauthorized movement of nuclear material and radioactive sources in and out side the country. They are related to the Atomic Energy Commission (AECS), and are located at the main entrances of the country. Each is provided with the practical tools and equipment in order to assist Radiation Protection Officers (RPO) in fulfilling their commitments, by promoting greater transparency in legal transfers of radioactive materials and devices. They apply complete procedures for the safe import, export and transit of radioactive sources. The RPOs provide authorizations by issuing an entry approval document, after making sure that each concerned shipments has an authorized license from the Syrian Regulatory Body (RNRO) before permitting shipments to leave, arrive or transit across their territory, enabling law enforcement to track the legal movement of

  9. Bias detection and certified reference materials for random measurands

    Science.gov (United States)

    Rukhin, Andrew L.

    2015-12-01

    A problem that frequently occurs in metrology is the bias checking of data obtained by a laboratory against the specified value and uncertainty estimate given in the certificate of analysis. The measurand—a property of a certified reference material (CRM)—is supposed to be random with a normal distribution whose parameters are given by the certificate specifications. The laboratory’s data from subsequent measurements of the CRM (a CRM experiment) are summarized by the sample mean value and its uncertainty which is commonly based on a repeatability standard deviation. New confidence intervals for the lab’s bias are derived. Although they may lack intuitive appeal, those obtained by using higher order asymptotic methods, compared and contrasted in this paper, are recommended.

  10. Orientation-dependent low field magnetic anomalies and room-temperature spintronic material – Mn doped ZnO films by aerosol spray pyrolysis

    CSIR Research Space (South Africa)

    Nkosi, SS

    2013-12-01

    Full Text Available of ferromagnetism, a relatively new phenomenon called “low-field microwave absorption” has been observed in ferromagnetic materials and other various materials such as high temperature superconductors, ferrites, manganites, doped silicate glasses and soft... absorption phenomenon has been observed in ferromagnetic materials and various other materials such as superconductors, ferrites, manganites, semiconductors, doped silicate glasses, in soft materials and recently in iron monosilicides films [41- 46...

  11. Additive Manufacturing Materials Study for Gaseous Radiation Detection

    Energy Technology Data Exchange (ETDEWEB)

    Steer, C.A.; Durose, A.; Boakes, J. [AWE Aldermaston, Reading, Berkshire, RG7 4PR (United Kingdom)

    2015-07-01

    Additive manufacturing (AM) techniques may lead to improvements in many areas of radiation detector construction; notably the rapid manufacturing time allows for a reduced time between prototype iterations. The additive nature of the technique results in a granular microstructure which may be permeable to ingress by atmospheric gases and make it unsuitable for gaseous radiation detector development. In this study we consider the application of AM to the construction of enclosures and frames for wire-based gaseous radiation tracking detectors. We have focussed on oxygen impurity ingress as a measure of the permeability of the enclosure, and the gas charging and discharging curves of several simplistic enclosure shapes are reported. A prototype wire-frame is also presented to examine structural strength and positional accuracy of an AM produced frame. We lastly discuss the implications of this study for AM based radiation detection technology as a diagnostic tool for incident response scenarios, such as the interrogation of a suspect radiation-emitting package. (authors)

  12. Additive Manufacturing Materials Study for Gaseous Radiation Detection

    International Nuclear Information System (INIS)

    Steer, C.A.; Durose, A.; Boakes, J.

    2015-01-01

    Additive manufacturing (AM) techniques may lead to improvements in many areas of radiation detector construction; notably the rapid manufacturing time allows for a reduced time between prototype iterations. The additive nature of the technique results in a granular microstructure which may be permeable to ingress by atmospheric gases and make it unsuitable for gaseous radiation detector development. In this study we consider the application of AM to the construction of enclosures and frames for wire-based gaseous radiation tracking detectors. We have focussed on oxygen impurity ingress as a measure of the permeability of the enclosure, and the gas charging and discharging curves of several simplistic enclosure shapes are reported. A prototype wire-frame is also presented to examine structural strength and positional accuracy of an AM produced frame. We lastly discuss the implications of this study for AM based radiation detection technology as a diagnostic tool for incident response scenarios, such as the interrogation of a suspect radiation-emitting package. (authors)

  13. Cross-validated detection of crack initiation in aerospace materials

    Science.gov (United States)

    Vanniamparambil, Prashanth A.; Cuadra, Jefferson; Guclu, Utku; Bartoli, Ivan; Kontsos, Antonios

    2014-03-01

    A cross-validated nondestructive evaluation approach was employed to in situ detect the onset of damage in an Aluminum alloy compact tension specimen. The approach consisted of the coordinated use primarily the acoustic emission, combined with the infrared thermography and digital image correlation methods. Both tensile loads were applied and the specimen was continuously monitored using the nondestructive approach. Crack initiation was witnessed visually and was confirmed by the characteristic load drop accompanying the ductile fracture process. The full field deformation map provided by the nondestructive approach validated the formation of a pronounced plasticity zone near the crack tip. At the time of crack initiation, a burst in the temperature field ahead of the crack tip as well as a sudden increase of the acoustic recordings were observed. Although such experiments have been attempted and reported before in the literature, the presented approach provides for the first time a cross-validated nondestructive dataset that can be used for quantitative analyses of the crack initiation information content. It further allows future development of automated procedures for real-time identification of damage precursors including the rarely explored crack incubation stage in fatigue conditions.

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

    Science.gov (United States)

    Gu, Yingxin; Wylie, B.K.

    2010-01-01

    This study identifies areas with ecosystem performance anomalies (EPA) within the Upper Colorado River Basin (UCRB) during 2005-2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions) at the 90% level of confidence. The EPA maps were validated using "percentage of bare soil" ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming) tended to have a higher (lower) percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005-2007) persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM) identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions. ?? 2010 by the authors.

  15. Prevalence of dental anomalies and enamel hypoplasia in primary dentition among preschool children of West Godavari District, Andhra Pradesh -A cross - sectional study

    Directory of Open Access Journals (Sweden)

    Suzan Sahana

    2013-01-01

    Full Text Available Background: It is axiomatic that Pediatric dental anomalies and enamel hypoplasia (E.H are routinely encountered in primary dentition and early detection and prudent management of the condition facilitates normal occlusal development. Objectives: To determine the prevalence of various dental anomalies and enamel hypoplasia in preschool children between two to six years of age. Materials & Method: A total of 1898 children, between two to six years were randomly selected and screened for dental anomalies and enamel hypoplasia The chi square test was used to analyze the data statistically. Results: The overall prevalence rate of dental anomalies and enamel hypoplasia in this study was 0.63% and 8.95% respectively. Double teeth were the most frequently reported dental anomaly while supernumerary teeth were least reported. None of them reported with hypodontia.

  16. Introduction to anomalies

    International Nuclear Information System (INIS)

    Alvarez-Gaume, L.

    1986-01-01

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

  17. MR imaging of paediatric uterovaginal anomalies

    International Nuclear Information System (INIS)

    Lang, I.M.; Babyn, P.; Oliver, G.D.

    1999-01-01

    Background. Transabdominal ultrasound (US) has not proved completely reliable in Muellerian duct anomalies. One study has shown it useful in obstructed uterovaginal anomalies. We are unaware of a study that has used endovaginal ultrasound in children to investigate uterovaginal anomalies. Magnetic resonance imaging (MRI) is now gaining wide acceptance in imaging congenital abnormalities of the genital tract. Objective. To identify the problems and potential pitfalls of using MRI to evaluate the female genital tract in paediatric patients. Materials and methods. A retrospective review of the MRI scans of 19 patients, aged 3 months to 19 years (mean 14 years), with uterovaginal anomalies. Results. The uterovaginal anomalies were categorised into three groups: (1) congenital absence of the Muellerian ducts, or the Mayer-Rokitansky-Kuster-Hauser syndrome (n = 7), (2) disorders of vertical fusion (n = 2) and (3) disorders of lateral fusion (n = 10). Conclusions. MRI is a reliable method for evaluating paediatric uterovaginal anomalies, but should be analysed in conjunction with other imaging modalities (US and genitography). Previous surgery makes interpretation more difficult and, if possible, MRI should be carried out prior to any surgery. An accurate MRI examination can be extremely helpful prior to surgery and it is important for the radiologist to have knowledge of how these complex anomalies are managed and what pitfalls to avoid. (orig.)

  18. Use of DSC and DMA Techniques to Help Investigate a Material Anomaly for PTFE Used in Processing a Piston Cup for the Urine Processor Assembly (UPA) on International Space Station (ISS)

    Science.gov (United States)

    Wingard, Doug

    2010-01-01

    Human urine and flush water are eventually converted into drinking water with the Urine Processor Assembly (UPA) aboard the International Space Station (ISS). This conversion is made possible through the Distillation Assembly (DA) of the UPA. One component of the DA is a molded circular piston cup made of virgin polytetrafluoroethylene (PTFE). The piston cup is assembled to a titanium component using eight fasteners and washers. Molded PTFE produced for spare piston cups in the first quarter of 2010 was different in appearance and texture, and softer than material molded for previous cups. For the suspect newer PTFE material, cup fasteners were tightened to only one-half the required torque value, yet the washers embedded almost halfway into the material. The molded PTFE used in the DA piston cup should be Type II, based on AMS 3667D and ASTM D4894 specifications. The properties of molded PTFE are considerably different between Type I and II materials. Engineers working with the DA thought that if Type I PTFE was molded by mistake instead of Type II material, that could have resulted in the anomalous material properties. Typically, the vendor molds flat sheet PTFE from the same material lot used to mold the piston cups, and tensile testing as part of quality control should verify that the PTFE is Type II material. However, for this discrepant lot of material, such tensile data was not available. Differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) were two of the testing techniques used at the NASA/Marshall Space Flight Center (MSFC) to investigate the anomaly for the PTFE material. Other techniques used on PTFE specimens were: Shore D hardness testing, tensile testing on dog bone specimens and a qualitative estimation of porosity by optical and scanning electron microscopy.

  19. Revolution in nuclear detection affairs

    International Nuclear Information System (INIS)

    Stern, Warren M.

    2014-01-01

    The detection of nuclear or radioactive materials for homeland or national security purposes is inherently difficult. This is one reason detection efforts must be seen as just one part of an overall nuclear defense strategy which includes, inter alia, material security, detection, interdiction, consequence management and recovery. Nevertheless, one could argue that there has been a revolution in detection affairs in the past several decades as the innovative application of new technology has changed the character and conduct of detection operations. This revolution will likely be most effectively reinforced in the coming decades with the networking of detectors and innovative application of anomaly detection algorithms

  20. 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.

  1. Examination of packaging materials in bakery products : a validated method for detection and quantification

    NARCIS (Netherlands)

    Raamsdonk, van L.W.D.; Pinckaers, V.G.Z.; Vliege, J.J.M.; Egmond, van H.J.

    2012-01-01

    Methods for the detection and quantification of packaging materials are necessary for the control of the prohibition of these materials according to Regulation (EC)767/2009. A method has been developed and validated at RIKILT for bakery products, including sweet bread and raisin bread. This choice

  2. Detecting the honeycomb sandwich composite material's moisture impregnating defects by using infrared thermography technique

    International Nuclear Information System (INIS)

    Kwon, Koo Ahn; Choi, Man Yong; Park, Jeong Hak; Choi, Won Jae; Park, Hee Sang

    2017-01-01

    Many composite materials are used in the aerospace industry because of their excellent mechanical properties. However, the nature of aviation exposes these materials to high temperature and high moisture conditions depending on climate, location, and altitude. Therefore, the molecular arrangement chemical properties, and mechanical properties of composite materials can be changed under these conditions. As a result, surface disruptions and cracks can be created. Consequently, moisture-impregnating defects can be induced due to the crack and delamination of composite materials as they are repeatedly exposed to moisture absorption moisture release, fatigue environment, temperature changes, and fluid pressure changes. This study evaluates the possibility of detecting the moisture-impregnating defects of CFRP and GFRP honeycomb structure sandwich composite materials, which are the composite materials in the aircraft structure, by using an active infrared thermography technology among non-destructive testing methods. In all experiments, it was possible to distinguish the area and a number of CFRP composite materials more clearly than those of GFRP composite material. The highest detection rate was observed in the heating duration of 50 mHz and the low detection rate was at the heating duration of over 500 mHz. The reflection method showed a higher detection rate than the transmission method

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

    Directory of Open Access Journals (Sweden)

    Adalet Elcin Yildiz

    2013-01-01

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

  4. Skyrmions and anomalies

    International Nuclear Information System (INIS)

    Rho, M.

    1987-02-01

    The author summarizes the works presented at the meeting on skyrmions and anomalies. He divides the principal issues of this workshop into five categories: QCD effective lagrangians, chiral bags and the Cheshire cat principle, strangeness problem, phenomenology, mathematical structure

  5. Kohn anomaly in graphene

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  6. Algebraic structure of chiral anomalies

    International Nuclear Information System (INIS)

    Stora, R.

    1985-09-01

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

  7. Anomalies and gravity

    International Nuclear Information System (INIS)

    Mielke, Eckehard W.

    2006-01-01

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

  8. Fivebrane gravitational anomalies

    International Nuclear Information System (INIS)

    Becker, Katrin; Becker, Melanie

    2000-01-01

    Freed, Harvey, Minasian and Moore (FHMM) have proposed a mechanism to cancel the gravitational anomaly of the M-theory fivebrane coming from diffeomorphisms acting on the normal bundle. This procedure is based on a modification of the conventional M-theory Chern-Simons term. We apply the FHMM mechanism in the ten-dimensional type IIA theory. We then analyze the relation to the anomaly cancellation mechanism for the type IIA fivebrane proposed by Witten

  9. Remote detection of radioactive material using high-power pulsed electromagnetic radiation.

    Science.gov (United States)

    Kim, Dongsung; Yu, Dongho; Sawant, Ashwini; Choe, Mun Seok; Lee, Ingeun; Kim, Sung Gug; Choi, EunMi

    2017-05-09

    Remote detection of radioactive materials is impossible when the measurement location is far from the radioactive source such that the leakage of high-energy photons or electrons from the source cannot be measured. Current technologies are less effective in this respect because they only allow the detection at distances to which the high-energy photons or electrons can reach the detector. Here we demonstrate an experimental method for remote detection of radioactive materials by inducing plasma breakdown with the high-power pulsed electromagnetic waves. Measurements of the plasma formation time and its dispersion lead to enhanced detection sensitivity compared to the theoretically predicted one based only on the plasma on and off phenomena. We show that lower power of the incident electromagnetic wave is sufficient for plasma breakdown in atmospheric-pressure air and the elimination of the statistical distribution is possible in the presence of radioactive material.

  10. Improved explosive collection and detection with rationally assembled surface sampling materials

    Energy Technology Data Exchange (ETDEWEB)

    Chouyyok, Wilaiwan; Bays, J. Timothy; Gerasimenko, Aleksandr A.; Cinson, Anthony D.; Ewing, Robert G.; Atkinson, David A.; Addleman, R. Shane

    2016-01-01

    Sampling and detection of trace explosives is a key analytical process in modern transportation safety. In this work we have explored some of the fundamental analytical processes for collection and detection of trace level explosive on surfaces with the most widely utilized system, thermal desorption IMS. The performance of the standard muslin swipe material was compared with chemically modified fiberglass cloth. The fiberglass surface was modified to include phenyl functional groups. When compared to standard muslin, the phenyl functionalized fiberglass sampling material showed better analyte release from the sampling material as well as improved response and repeatability from multiple uses of the same swipe. The improved sample release of the functionalized fiberglass swipes resulted in a significant increase in sensitivity. Various physical and chemical properties were systematically explored to determine optimal performance. The results herein have relevance to improving the detection of other explosive compounds and potentially to a wide range of other chemical sampling and field detection challenges.

  11. The Holographic Weyl anomaly

    CERN Document Server

    Henningson, M; Henningson, Mans; Skenderis, Kostas

    1998-01-01

    We calculate the Weyl anomaly for conformal field theories that can be described via the adS/CFT correspondence. This entails regularizing the gravitational part of the corresponding supergravity action in a manner consistent with general covariance. Up to a constant, the anomaly only depends on the dimension d of the manifold on which the conformal field theory is defined. We present concrete expressions for the anomaly in the physically relevant cases d = 2, 4 and 6. In d = 2 we find for the central charge c = 3 l/ 2 G_N in agreement with considerations based on the asymptotic symmetry algebra of adS_3. In d = 4 the anomaly agrees precisely with that of the corresponding N = 4 superconformal SU(N) gauge theory. The result in d = 6 provides new information for the (0, 2) theory, since its Weyl anomaly has not been computed previously. The anomaly in this case grows as N^3, where N is the number of coincident M5 branes, and it vanishes for a Ricci-flat background.

  12. Remote Detection and Location of Illegal Radioactive Materials Units in Uzbekistan

    International Nuclear Information System (INIS)

    Khaydarov, R. A.; Khaydarov, R. R.

    2007-01-01

    Uzbekistan is a checkpoint for transportation between Russia and some Asian countries, such as Iran, Pakistan, Afghanistan and Tajikistan that might be attractive destinations for those smuggling nuclear materials or weapons. Currently there are over 200 border crossing points. Most of them have equipped with monitors able to reliably detect nuclear materials. Uzbekistan also has substantial radioactive ore mining, and these monitors also allow the Customs Service to maintain safe conditions for their inspectors as well as for population of Uzbekistan and its neighbors. But it is very important to detect radioactive materials inland, their location and travel. This task cannot be solved by using stationary detectors which are used at border crossing points. New method, electronic scheme and software for remote detection, location and travel of radioactive sources were developed. The operation principle lies in detection of radiation by 6 detectors situated in a leaden cylindrical shield collimating gamma-radiation in 6 directions. Besides the detection system contains 6 amplifiers, 6 counters and JPS-system connected with computer. The detection system is transported by car. Field tests of the detection system have shown that the detection limit is 5. 106 Bq and 4.106 Bq for Co60 and Cs137 respectively when the radioactive sources distance is 400 m. (author)

  13. Material aging and degradation detection and remaining life assessment for plant life management

    International Nuclear Information System (INIS)

    Ramuhalli, P.; Henager, C.H. Jr.; Griffin, J.W.; Meyer, R.M.; Coble, J.B.; Pitman, S.G.; Bond, L.J.

    2012-01-01

    One of the major factors that may impact long-term operations is structural material degradation. Detecting materials degradation, estimating the remaining useful life (RUL) of the component, and determining approaches to mitigating the degradation are important from the perspective of long-term operations. In this study, multiple nondestructive measurement and monitoring methods were evaluated for their ability to assess the material degradation state. Metrics quantifying the level of damage from these measurements were defined and evaluated for their ability to provide estimates of remaining life of the component. An example of estimating the RUL from nondestructive measurements of material degradation condition is provided. (author)

  14. RARE BRANCHIAL ARCH ANOMALIES

    Directory of Open Access Journals (Sweden)

    Jayanta Kumar

    2016-03-01

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

  15. Development of international standards for instrumentation used for detection of illicit trafficking of radioactive material

    International Nuclear Information System (INIS)

    Voytchev, M.; Chiaro, P.; Radev, R.

    2006-01-01

    Subcommittee 45 B 'Radiation Protection Instrumentation' of the International Electrotechnical Commission (I.E.C.) is charged with the development of international standards for instrumentation used for monitoring of illicit trafficking of radioactive material through international boarders and territories, as well as inside countries. Currently three I.E.C. standards are in advanced stages of development. They are expected to be approved and published in 2006-2007. The international participation and the main characteristics of the following three standards are discussed and presented: I.E.C. 62327 'Hand-held Instruments for the Detection and Identification of Radionuclides and Additionally for the Indication of Ambient Dose Equivalent Rate from Photon Radiation', I.E.C. 62401 'Alarming Personal Radiation Devices for Detection of Illicit Trafficking of Radioactive Material' and I.E.C. 62244 'Installed Radiation Monitors for the Detection of Radioactive and Special Nuclear Materials at National Borders'

  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. Stationery equipment for detecting radioactive material on passing pedestrians developed and made in RFYC - VNIIEF

    International Nuclear Information System (INIS)

    Kapustin, D.S.

    2000-01-01

    At VNIIEF, radiation monitors have been designed that measure for transported or pedestrian carried radioactive material and allow a quick determination of an excess gamma and/or neutron background on natural or fixed levels. For this problem, neither the type of the material or its amount need to be determined, but other parameters are measured to detect highly sensitive or priority materials, defined by the threshold of detection, operational monitoring efficiency, simplicity of use and demonstrative imaging of results. All these parameters were considered at VNlIEF in the design of pedestrian radiation monitor KPRM-P1. Post KPRM-P1 is an automatic pedestrian radiation monitor, installed on communicating and KPP enterprises, and intended for checking for authorized or unauthorized radioactive material possessed by pedestrians crossing a controlled space. (authors)

  18. Equipment of high sensitivity to detect smuggled radioactive materials transported across the ''east-west'' border

    International Nuclear Information System (INIS)

    Antonovski, A.; Kagan, L.; Stavrov, A.

    1998-01-01

    An equipment specially developed for the customs radiation control is described. Its sensitivity is higher than requirements of western countries. The equipment ensures an alarm when a radioactive source (both shielded or not) is found in the controlled area, localizes and identifies the source detected, and provides the radiation protection of customs personnel. Most of devices have a non-volatile memory where the radiation situation history is stored and then transferred to PC. The equipment may be used by personnel of special services for secret detection of radioactive materials. Some Belarussian and Russian documents specifying measures to prevent an unauthorized transportation of radioactive materials are discussed. (author)

  19. Device for Detection of Explosives, Nuclear and Other Hazardous Materials in Luggage and Cargo Containers

    Science.gov (United States)

    Kuznetsov, Andrey; Evsenin, Alexey; Gorshkov, Igor; Osetrov, Oleg; Vakhtin, Dmitry

    2009-12-01

    Device for detection of explosives, radioactive and heavily shielded nuclear materials in luggage and cargo containers based on Nanosecond Neutron Analysis/Associated Particles Technique (NNA/APT) is under construction. Detection module consists of a small neutron generator with built-in position-sensitive detector of associated alpha-particles, and several scintillator-based gamma-ray detectors. Explosives and other hazardous chemicals are detected by analyzing secondary high-energy gamma-rays from reactions of fast neutrons with materials inside a container. The same gamma-ray detectors are used to detect unshielded radioactive and nuclear materials. An array of several neutron detectors is used to detect fast neutrons from induced fission of nuclear materials. Coincidence and timing analysis allows one to discriminate between fission neutrons and scattered probing neutrons. Mathematical modeling by MCNP5 and MCNP-PoliMi codes was used to estimate the sensitivity of the device and its optimal configuration. Comparison of the features of three gamma detector types—based on BGO, NaI and LaBr3 crystals is presented.

  20. Device for Detection of Explosives, Nuclear and Other Hazardous Materials in Luggage and Cargo Containers

    International Nuclear Information System (INIS)

    Kuznetsov, Andrey; Evsenin, Alexey; Osetrov, Oleg; Vakhtin, Dmitry; Gorshkov, Igor

    2009-01-01

    Device for detection of explosives, radioactive and heavily shielded nuclear materials in luggage and cargo containers based on Nanosecond Neutron Analysis/Associated Particles Technique (NNA/APT) is under construction. Detection module consists of a small neutron generator with built-in position-sensitive detector of associated alpha-particles, and several scintillator-based gamma-ray detectors. Explosives and other hazardous chemicals are detected by analyzing secondary high-energy gamma-rays from reactions of fast neutrons with materials inside a container. The same gamma-ray detectors are used to detect unshielded radioactive and nuclear materials. An array of several neutron detectors is used to detect fast neutrons from induced fission of nuclear materials. Coincidence and timing analysis allows one to discriminate between fission neutrons and scattered probing neutrons. Mathematical modeling by MCNP5 and MCNP-PoliMi codes was used to estimate the sensitivity of the device and its optimal configuration. Comparison of the features of three gamma detector types--based on BGO, NaI and LaBr 3 crystals is presented.