WorldWideScience

Sample records for based anomaly detection

  1. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

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

  2. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

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

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

    Directory of Open Access Journals (Sweden)

    Yoshiteru Ishida

    2009-11-01

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

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

    Science.gov (United States)

    Okamoto, Takeshi; Ishida, Yoshiteru

    2009-01-01

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

  5. 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.

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

    Science.gov (United States)

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

    2016-06-01

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

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

    NARCIS (Netherlands)

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

    2005-01-01

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

  8. 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.

  9. Anomaly detection based on zero appearances in subspaces

    OpenAIRE

    Pang, Guansong

    2017-01-01

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

  10. 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

  11. 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

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

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena; Gehrke, Oliver

    2016-01-01

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

  13. Hyperspectral anomaly detection based on stacked denoising autoencoders

    Science.gov (United States)

    Zhao, Chunhui; Li, Xueyuan; Zhu, Haifeng

    2017-10-01

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

  14. 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.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-01-27

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

  17. 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.

  18. Anomaly based Intrusion Detection using Modified Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    B.S. Harish

    2017-12-01

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

  19. 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)

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

    KAUST Repository

    Ledezma, Fernando

    2012-07-01

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

  1. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi

    2015-12-07

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

  2. 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

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

    Science.gov (United States)

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

    2016-12-01

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

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

    Science.gov (United States)

    Chen, Xiao-Yun; Zhan, Yan-Yan

    2008-04-01

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

  5. 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.

  6. 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...

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

    NARCIS (Netherlands)

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

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

  8. 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

  9. 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

  10. 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

  11. Theoretically Optimal Distributed Anomaly Detection

    Data.gov (United States)

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

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

    Directory of Open Access Journals (Sweden)

    Yubin Niu

    2016-03-01

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

  13. 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.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2015-10-01

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

  17. 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...

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

    Science.gov (United States)

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

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

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

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

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

  1. 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

  2. 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.

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

    Science.gov (United States)

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

    2015-01-01

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

  4. 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

  5. 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.

  6. 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)

  7. 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.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Yuchong Li

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Xing Hu

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Zhang Lin

    2016-01-01

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

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

    Data.gov (United States)

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

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

    International Nuclear Information System (INIS)

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

    1994-01-01

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

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

    Science.gov (United States)

    Solano, Wanda; Banerjee, Bikramjit; Kraemer, Landon

    2012-01-01

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

  15. Anomaly Detection from Hyperspectral Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Qiandong Guo

    2016-12-01

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

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS

    Directory of Open Access Journals (Sweden)

    J. G. Rejas Ayuga

    2016-06-01

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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  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. 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.

  11. 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.

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

    OpenAIRE

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

    2016-01-01

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

  13. 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...

  14. 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...

  15. 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.

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

    Science.gov (United States)

    2014-04-01

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

  17. Statistical Anomaly Detection for Monitoring of Human Dynamics

    Science.gov (United States)

    Kamiya, K.; Fuse, T.

    2015-05-01

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

  18. A model for anomaly classification in intrusion detection systems

    Science.gov (United States)

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

    2015-09-01

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

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

    Science.gov (United States)

    2009-03-01

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

  20. 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.

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  2. 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....

  3. 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.

  4. 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.

  5. Comparison of Unsupervised Anomaly Detection Methods

    Data.gov (United States)

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

  6. 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

  7. 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...

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

    OpenAIRE

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

    2017-01-01

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

  9. Online Anomaly Energy Consumption Detection Using Lambda Architecture

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  10. 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...

  11. 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.

  12. 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.

  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. Amalgamation of Anomaly-Detection Indices for Enhanced Process Monitoring

    KAUST Repository

    Harrou, Fouzi

    2016-01-29

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

  15. Detecting data anomalies methods in distributed systems

    Science.gov (United States)

    Mosiej, Lukasz

    2009-06-01

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

  16. 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...

  17. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ivanov, K. N.

    2005-11-27

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

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

    DEFF Research Database (Denmark)

    Garne, Ester; Dolk, Helen; Loane, Maria

    2010-01-01

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

  20. 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...

  1. Lidar detection algorithm for time and range anomalies

    Science.gov (United States)

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

    2007-10-01

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

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

    International Nuclear Information System (INIS)

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

    1989-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Rimas Ciplinskas

    2016-06-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2015-07-01

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

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

    Directory of Open Access Journals (Sweden)

    Tamer F. Ghanem

    2015-07-01

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

  7. Anomaly Detection using the "Isolation Forest" algorithm

    CERN Document Server

    CERN. Geneva

    2015-01-01

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

  8. An incremental anomaly detection model for virtual machines

    Science.gov (United States)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2011-12-01

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

  10. Adaptive Anomaly Detection using Isolation Forest

    Science.gov (United States)

    2009-12-20

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

  11. Applying rule-base anomalies to KADS inference structures

    NARCIS (Netherlands)

    Van Harmelen, Frank

    1997-01-01

    The literature on validation and verification of knowledge-based systems contains a catalogue of anomalies for knowledge-based systems, such as redundant, contradictory or deficient knowledge. Detecting such anomalies is a method for verifying knowledge-based systems. Unfortunately, the traditional

  12. Anomaly Detection and Visualization of School Electricity Consumption Data

    OpenAIRE

    Cui, Wenqiang; Wang, Hao

    2017-01-01

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

  13. 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

  14. 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.

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

    Science.gov (United States)

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

    2011-02-01

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

  16. Operating experiences with an on-line, computer based nuclear plant surveillance and anomaly detection system based on pattern recognition and artificial intelligence

    International Nuclear Information System (INIS)

    Kemeny, L.G.

    1988-01-01

    The control room of a nuclear power plant can represent a hostile work environment for all but a highly trained operating team. Whilst disciplined training and long professional experience will guarantee assurance of safety and reliability in nuclear plant operation, any surveillance system which has the ability to minimise human error and provide additional safeguards is a desirable asset. This paper proposes a scheme whereby some key parameters of a nuclear power plant, precisely known through detailed calculation and accurate measurement are stored as a data base in an on-line computer. Through the systematic statistical analysis of key stochastic variates a comparison is made by the on-line system with the data base at regular intervals. These time intervals may be as short as seconds during periods of reactor transients such as at start-up or shut down. Alternatively, during steady state operation, the parameters are calculated and displayed at intervals of an hour or greater. An anomaly, or an indication of unusual operational behaviour is indicated both numerically and graphically by the computer if it detects a variance greater than a few percent from the mean value of the reference data base. (author)

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

    Directory of Open Access Journals (Sweden)

    Paula Zozzaro-Smith

    2014-07-01

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

  18. 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.

  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. 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.

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

    Science.gov (United States)

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

    2016-11-11

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

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

    OpenAIRE

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

    2014-01-01

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

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

    OpenAIRE

    Cui, Wenqiang; Wang, Hao

    2017-01-01

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

  5. 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.

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

    Science.gov (United States)

    Noto, Keith; Brodley, Carla; Slonim, Donna

    2012-01-01

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

  7. 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

  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 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.

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

    NARCIS (Netherlands)

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

    2005-01-01

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

  11. Anomaly Detection from ASRS Databases of Textual Reports

    Data.gov (United States)

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

  12. 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...

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

    Data.gov (United States)

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

  14. Comparative Analysis of Data-Driven Anomaly Detection Methods

    Data.gov (United States)

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

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

    Science.gov (United States)

    2013-04-01

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

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

    Data.gov (United States)

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

  17. In-Flight Diagnosis and Anomaly Detection Project

    Data.gov (United States)

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

  18. Density Estimation and Anomaly Detection in Large Social Networks

    Science.gov (United States)

    2014-07-15

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

  19. 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.

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

    Science.gov (United States)

    Shen, Y; Cooper, G F

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Yogendra Kumar JAIN

    2009-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  3. 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.

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

    Science.gov (United States)

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

    2016-07-01

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

  5. 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.

  6. 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.

  7. Anomalies.

    Science.gov (United States)

    Online-Offline, 1999

    1999-01-01

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

  8. 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...

  9. 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.

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

    Science.gov (United States)

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

    2012-06-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-01-01

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

  12. A 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.

  13. Rate based failure detection

    Science.gov (United States)

    Johnson, Brett Emery Trabun; Gamage, Thoshitha Thanushka; Bakken, David Edward

    2018-01-02

    This disclosure describes, in part, a system management component and failure detection component for use in a power grid data network to identify anomalies within the network and systematically adjust the quality of service of data published by publishers and subscribed to by subscribers within the network. In one implementation, subscribers may identify a desired data rate, a minimum acceptable data rate, desired latency, minimum acceptable latency and a priority for each subscription. The failure detection component may identify an anomaly within the network and a source of the anomaly. Based on the identified anomaly, data rates and or data paths may be adjusted in real-time to ensure that the power grid data network does not become overloaded and/or fail.

  14. 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...

  15. 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.

  16. 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

  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. Advanced Ground Systems Maintenance Anomaly Detection

    Data.gov (United States)

    National Aeronautics and Space Administration — The Inductive Monitoring System (IMS) software utilizes techniques from the fields of model-based reasoning, machine learning, and data mining to build system...

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

    Directory of Open Access Journals (Sweden)

    Belacel Madani

    2012-06-01

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

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

    Indian Academy of Sciences (India)

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

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

    Science.gov (United States)

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

    2015-05-01

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

  2. MODEL-BASED VALIDATION AND VERIFICATION OF ANOMALIES IN LEGISLATION

    Directory of Open Access Journals (Sweden)

    Vjeran Strahonja

    2006-12-01

    Full Text Available An anomaly in legislation is absence of completeness, consistency and other desirable properties, caused by different semantic, syntactic or pragmatic reasons. In general, the detection of anomalies in legislation comprises validation and verification. The basic idea of research, as presented in this paper, is modelling legislation by capturing domain knowledge of legislation and specifying it in a generic way by using commonly agreed and understandable modelling concepts of the Unified Modelling Language (UML. Models of legislation enable to understand the system better, support the detection of anomalies and help to improve the quality of legislation by validation and verification. By implementing model-based approach, the object of validation and verification moves from legislation to its model. The business domain of legislation has two distinct aspects: a structural or static aspect (functionality, business data etc., and a behavioural or dynamic part (states, transitions, activities, sequences etc.. Because anomalism can occur on two different levels, on the level of a model, or on the level of legislation itself, a framework for validation and verification of legal regulation and its model is discussed. The presented framework includes some significant types of semantic and syntactic anomalies. Some ideas for assessment of pragmatic anomalies of models were found in the field of software quality metrics. Thus pragmatic features and attributes can be determined that could be relevant for evaluation purposes of models. Based on analogue standards for the evaluation of software, a qualitative and quantitative scale can be applied to determine the value of some feature for a specific model.

  3. 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.

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Todd Vollmer; Milos Manic

    2012-08-01

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

  6. Bio-Inspired Distributed Decision Algorithms for Anomaly Detection

    Science.gov (United States)

    2017-03-01

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

  7. 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

  8. Detecting Anomaly in Traffic Flow from Road Similarity Analysis

    KAUST Repository

    Liu, Xinran

    2016-06-02

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

  9. 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.

  10. Statistical methods for anomaly detection in the complex process

    International Nuclear Information System (INIS)

    Al Mouhamed, Mayez

    1977-09-01

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

  11. 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

  12. 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

  13. Steganography anomaly detection using simple one-class classification

    Science.gov (United States)

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

    2007-04-01

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

  14. 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.

  15. 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.

  16. 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.

  17. 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

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

    Science.gov (United States)

    Akhoondzadeh, M.

    2013-02-01

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

  19. 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.

  20. 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...

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

    Directory of Open Access Journals (Sweden)

    Gang Li

    2016-09-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

  3. 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

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

    Science.gov (United States)

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

    2013-11-01

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

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

    Science.gov (United States)

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

    2014-05-01

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

  6. 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

  7. Autonomic parameter tuning of anomaly-based IDSs: an SSH case study

    NARCIS (Netherlands)

    Sperotto, A.; Mandjes, M.; Sadre, R.; de Boer, P.-T.; Pras, A.

    2012-01-01

    Anomaly-based intrusion detection systems classify network traffic instances by comparing them with a model of the normal network behavior. To be effective, such systems are expected to precisely detect intrusions (high true positive rate) while limiting the number of false alarms (low false

  8. Autonomic Parameter Tuning of Anomaly-Based IDSs: an SSH Case Study

    NARCIS (Netherlands)

    Sperotto, Anna; Mandjes, M.R.H.; Sadre, R.; de Boer, Pieter-Tjerk; Pras, Aiko

    Anomaly-based intrusion detection systems classify network traffic instances by comparing them with a model of the normal network behavior. To be effective, such systems are expected to precisely detect intrusions (high true positive rate) while limiting the number of false alarms (low false

  9. 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.

  10. Learning patterns of human activity for anomaly detection

    Science.gov (United States)

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

    2007-04-01

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

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

    KAUST Repository

    Harrou, Fouzi

    2016-08-11

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

  12. 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

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

    Science.gov (United States)

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

    2008-12-01

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

  14. 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)

  15. 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.

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

    Science.gov (United States)

    Mehmood, Asif; Nasrabadi, Nasser M

    2011-06-10

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

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

    Science.gov (United States)

    Mehmood, Asif; Nasrabadi, Nasser M

    2010-08-20

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

  18. 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.

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

    NARCIS (Netherlands)

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

    2001-01-01

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

  20. 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.

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

    NARCIS (Netherlands)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2010-01-01

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

  3. An Anomaly Detector Based on Multi-aperture Mapping for Hyperspectral Data

    Directory of Open Access Journals (Sweden)

    LI Min

    2016-10-01

    Full Text Available Considering the correlationship of spectral content between anomaly and clutter background, inaccurate selection of background pixels induced estimation error of background model. In order to solve the above problems, a multi-aperture mapping based anomaly detector was proposed in this paper. Firstly, differing from background model which focused on feature extraction of background, multi-aperture mapping of hyperspectral data characterized the feature of whole hyperspectral data. According to constructed basis set of multi-aperture mapping, anomaly salience index of every test pixel was proposed to measure the relative statistic difference. Secondly, in order to analysis the moderate salience anomaly precisely, membership value was constructed to identify anomaly salience of test pixels continuously based on fuzzy logical theory. At same time, weighted iterative estimation of multi-aperture mapping was expected to converge adaptively with membership value as weight. Thirdly, classical defuzzification was proposed to fuse different detection results. Hyperspectral data was used in the experiments, and the robustness and sensitivity to anomaly with lower silence of proposed detector were tested.

  4. 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...

  5. 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

  6. A Gradient Analysis-Based Study of Aeromagnetic Anomalies of ...

    African Journals Online (AJOL)

    An aeromagnetic intensity contour map of a part of Nupe Basin of Nigeria was acquired, digitized and analysed. This work was carried out for a better understanding of the study area using the Gradient analysis-based technique to calculate depth to basement and to interpret the aeromagnetic anomaly map of the area.

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

    Science.gov (United States)

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

    2008-08-01

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

  8. Anomaly Detection Using Power Signature of Consumer Electrical Devices

    Directory of Open Access Journals (Sweden)

    CERNAZANU-GLAVAN, C.

    2015-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Karna Bryan

    2013-06-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

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

    Science.gov (United States)

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

    2018-02-28

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

  12. 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.

  13. 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.

  14. 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)

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  16. Extending TOPS: A Prototype MODIS Anomaly Detection Architecture

    Science.gov (United States)

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

    2008-12-01

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

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

    OpenAIRE

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

    2014-01-01

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

  18. 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.

  19. Enhanced detection sensitivity of prostate-specific antigen via PSA-conjugated gold nanoparticles based on localized surface plasmon resonance: GNP-coated anti-PSA/LSPR as a novel approach for the identification of prostate anomalies.

    Science.gov (United States)

    Jazayeri, M H; Amani, H; Pourfatollah, A A; Avan, A; Ferns, G A; Pazoki-Toroudi, H

    2016-10-01

    Prostate-specific antigen (PSA) is used to screen for prostate disease, although it has several limitations in its application as an organ-specific or cancer-specific marker. Furthermore, a highly specific/sensitive and/or label-free identification of PSA still remains a challenge in the diagnosis of prostate anomalies. We aimed to develop a gold nanoparticle (GNP)-conjugated anti-PSA antibody-based localized surface plasmon resonance (LSPR) as a novel approach to detect prostatic disease. A total of 25 nm colloidal gold particles were prepared followed by conjugation with anti-PSA pAb (GNPs-PSA pAb). LSPR was used to monitor the absorption changes of the aggregation of the particles. The size, shape and stability of the GNP-anti-PSA were evaluated by dynamic light scattering transmission electron microscopy (TEM) and zetasizer. The GNPs-conjugated PSA-pAb was successfully synthesized and subsequently characterized using ultraviolet absorption spectroscopy and TEM to determine the size distribution, crystallinity and stability of the particles (for example, stability of GNP: 443 mV). To increase the stability of the particles, we pegylated GNPs using an N-(3-dimethylaminopropyl)-N*-ethylcarbodiimide hydrochloride (EDC)/N-hydroxylsuccinimide (NHS) linker (for example, stability of GNP after pegylation: 272 mV). We found a significant increase in the absorbance and intensity of the particles with extinction peak at 545/2 nm, which was shifted by ~1 nm after conjugation. To illustrate the potential of the GNPs-PSA pAb to bind specifically to PSA, LSPR was used. We found that the extinction peak shifted 3 nm for a solution of 100 nM unlabeled antigen. In summary, we have established a novel approach for improving the efficacy/sensitivity of PSA in the assessment of prostate disease, supporting further investigation on the diagnostic value of GNP-conjugated anti-PSA/LSPR for the detection of prostate cancer.

  20. Using Machine Learning for Advanced Anomaly Detection and Classification

    Science.gov (United States)

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

    2016-09-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Dumidu Wijayasekara; Ondrej Linda; Milos Manic; Craig Rieger

    2014-08-01

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

  2. 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...

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

    Directory of Open Access Journals (Sweden)

    Antonio Candelieri

    2017-03-01

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

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

    Science.gov (United States)

    Kumar, Sricharan; Srivistava, Ashok N.

    2012-01-01

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

  5. 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

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

    Science.gov (United States)

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

    2015-02-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2004-01-01

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

  9. Application of Inductive Monitoring System to Plug Load Anomaly Detection

    Data.gov (United States)

    National Aeronautics and Space Administration — NASA Ames Research Center’s Sustainability Base is a new 50,000 sq. ft. LEED Platinum office building. Plug loads are expected to account for a significant portion...

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

    Science.gov (United States)

    2016-04-25

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

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

    Science.gov (United States)

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

    2005-01-01

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

  12. Electrical discharge in gases: a technique for detecting metal anomalies

    International Nuclear Information System (INIS)

    Lord, D.E.

    1979-01-01

    Optical ionization effects in gases appear to be very sensitive indicators of nonuniformities caused by contamination, deformation, and other factors affecting a metal surface. These optical effects are influenced by surface electron emission, which is influenced in turn by the chemical, metallurgical, and mechanical condition of the metal surface. Based on these effects, a general technique for inspection of critical parts that is fast, inexpensive, nondestructive, and not limited by size or geometry is presented. Ionization effects that reveal nonuniformities and were recorded with standard photographic equipment are shown

  13. Robust tracking and anomaly detection in video surveillance sequences

    Science.gov (United States)

    Rueda, Hoover F.; Polania, Luisa F.; Barner, Kenneth E.

    2012-06-01

    In this paper, the authors examine the problem of tracking people in both bright and dark video sequences. In particular, this problem is treated as a background/foreground decomposition problem, where the static part corresponds to the background, and moving objects to the foreground. Having this into account, the problem is formulated as a rank minimization problem of the form X = L + S + E, where X is the captured scene, L is the low-rank part (background), S is the sparse part (foreground) and E is the corrupting uniform noise introduced in the capture process. Actually, low-rank and sparse structures are widely studied and some areas such as Robust Principal Component Analysis (RPCA) and Matrix Completion (MC) have emerged to solve this kind of problems. Here we compare the performance of three different methods in solving the RPCA optimization problem for background separation: augmented lagrange multiplier method, Bayesian markov dependency method, and bilateral random projections method. Furthermore, a preprocessing light normalization stage and a mathematical morphology based post-processing stage are proposed to obtain better results.

  14. Improved Anomaly Detection using Integrated Supervised and Unsupervised Processing

    Science.gov (United States)

    Hunt, B.; Sheppard, D. G.; Wetterer, C. J.

    There are two broad technologies of signal processing applicable to space object feature identification using nonresolved imagery: supervised processing analyzes a large set of data for common characteristics that can be then used to identify, transform, and extract information from new data taken of the same given class (e.g. support vector machine); unsupervised processing utilizes detailed physics-based models that generate comparison data that can then be used to estimate parameters presumed to be governed by the same models (e.g. estimation filters). Both processes have been used in non-resolved space object identification and yield similar results yet arrived at using vastly different processes. The goal of integrating the results of the two is to seek to achieve an even greater performance by building on the process diversity. Specifically, both supervised processing and unsupervised processing will jointly operate on the analysis of brightness (radiometric flux intensity) measurements reflected by space objects and observed by a ground station to determine whether a particular day conforms to a nominal operating mode (as determined from a training set) or exhibits anomalous behavior where a particular parameter (e.g. attitude, solar panel articulation angle) has changed in some way. It is demonstrated in a variety of different scenarios that the integrated process achieves a greater performance than each of the separate processes alone.

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

    Directory of Open Access Journals (Sweden)

    Mikhail Emelianov

    2012-09-01

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

  16. 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.

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

    Science.gov (United States)

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

    2017-01-01

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

  18. 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.

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

    Science.gov (United States)

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

    2017-03-09

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

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

    Science.gov (United States)

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

    2013-01-01

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

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    Mikel Iturbe

    2017-01-01

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

  8. 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%.

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

    Directory of Open Access Journals (Sweden)

    S. S. Zhao

    2016-06-01

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

  10. Long-term observations of theWeddell Sea Anomaly based on the Swarm, CHAMP and DEMETER missions

    Science.gov (United States)

    Slominska, E.

    2016-12-01

    Normalized density difference index (INDD) was introduced for the purpose of detection of such phenomena as the Weddell Sea Anomaly (WSA). With this basic approach, we are capable of identifying spatial and temporal occurrence of anomalies exhibiting reversed diurnal cycle, characterized by greater ionospheric plasma densities observed in the post-sunset hours, when compared to day-time ones. Development of the WSA together with similar phenomenon observed in the Northern Hemisphere, named as the Mid-latitude Summer Nighttime Anomaly is documented with three satellite missions Swarm, DEMETER, and CHAMP. Since the generation of discussed anomalies is still an open issue, multi-mission and multi-instrumental observations at various altitudes should improve our understanding of the phenomena, and verify the role of several potential mechanisms used for explanation. Among mentioned mechanisms, combined result of thermospheric wind, solar photo-ionization, and the local magnetic field configuration is taken as a most comprehensive explanation. Analysis based on long-term trends of observations from three missions and six satellites are aimed at the proper parametrization of the phenomenon. Using spatial gradients in the magnetic field components derived from Swarm A/B/C magnetometers, we discuss longitudinal distributions and variations of anomalies. The study quantifies hemispheric differences between two anomalies, as well as temporal trends concerning the solar cycle.

  11. 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)

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

    Science.gov (United States)

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

    2014-02-01

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

  15. 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)

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

    Science.gov (United States)

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

    2017-12-01

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

  17. 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.

  18. 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.

  19. 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

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

    Science.gov (United States)

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

    2014-12-01

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

  1. 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.

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

    Science.gov (United States)

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

    2005-01-01

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

  3. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-06-15

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

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

    Directory of Open Access Journals (Sweden)

    Borja Rodríguez-Cuenca

    2015-09-01

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

  6. Precursor Analysis for Flight- and Ground-Based Anomaly Risk Significance Determination

    Science.gov (United States)

    Groen, Frank

    2010-01-01

    This slide presentation reviews the precursor analysis for flight and ground based anomaly risk significance. It includes information on accident precursor analysis, real models vs. models, and probabilistic analysis.

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

    Directory of Open Access Journals (Sweden)

    Kun Wang

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

  8. Deviant early pregnancy maternal triglyceride levels and increased risk of congenital anomalies: a prospective community-based cohort study

    NARCIS (Netherlands)

    Nederlof, M.; de Walle, H.E.K.; van Poppel, M.N.M.; Vrijkotte, T.G.M.; Gademan, M.G.J.

    2015-01-01

    Objective The maternal lipid profile could be of importance in congenital anomaly development. This study therefore investigates whether the maternal lipid profile during early pregnancy is associated with major nonsyndromic congenital anomalies (MNCA). Design Prospective community-based cohort

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Science.gov (United States)

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

    2016-04-01

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

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

    Science.gov (United States)

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

    2016-08-10

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-03-06

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

  13. 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...

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

    Directory of Open Access Journals (Sweden)

    Zhao Ying

    2013-01-01

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

  15. 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

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

    Science.gov (United States)

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

    2004-12-01

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

  17. 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.

  18. 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.

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Bin Liu

    2014-01-01

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

  1. Coronary artery anomalies. Diagnosis and classification based on cardiac CT and MRI (CMR) - from ALCAPA to anomalies of termination; Koronararterienanomalien. Diagnostik und Klassifikation auf Basis der CT und MRT des Herzens - von ALCAPA bis Terminationsanomalie

    Energy Technology Data Exchange (ETDEWEB)

    Heermann, Philipp; Heindel, Walter; Schuelke, Christoph [University Hospital Muenster (UKM) (Germany). Dept. of Clinical Radiology

    2017-01-15

    Coronary artery anomalies encompass a clinically and anatomically variable spectrum including physiological variants and pathophysiologically relevant anomalies. The majority of the variants has no hemodynamic relevance and is often detected accidentally. The recognition of the rare and relevant anomalies that cause either relevant shunt volumes leading to myocardial ischemia or ventricular tachyarrhythmias with the risk of sudden cardiac death is of major importance. This review is based on a literature search in PubMed conducted using the key words ''coronary artery'' and/or ''anomaly'' and/or ''anomalous origin'' and/or ''myocardial bridging'' and/or ''coronary artery fistula'' and/or ''Bland-White-Garland'' and/or ''ALCAPA''. Coronary artery anomalies can be anatomically subdivided into anomalies of origin, course and termination. The method of choice for anatomical imaging is ECG-triggered or gated multislice CT (MSCT) that provides high spatial resolution and the capability of multiplanar reconstructions. It facilitates the delineation of the precise course of all three coronary arteries and thus allows for correct classification in the anatomical classification system of coronary artery anomalies. The strengths of cardiac magnetic resonance imaging (CMR) are the evaluation of cardiac morphology, myocardial tissue properties and myocardial function. Basic methods are the analysis of myocardial contraction and perfusion with and without pharmacologic stress. Furthermore, potential shunt volumes could be quantified by phase contrast imaging or volumetry.

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

    Science.gov (United States)

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

    2013-04-01

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

  3. 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

  4. Global Assessment of Groundwater Sustainability Based On Storage Anomalies

    Science.gov (United States)

    Thomas, Brian F.; Caineta, Júlio; Nanteza, Jamiat

    2017-11-01

    The world's largest aquifers are a fundamental source of freshwater used for agricultural irrigation and to meet human water needs. Therefore, their stored volume of groundwater is linked with water security, which becomes more relevant during periods of drought. This work focuses on understanding large-scale groundwater changes, where we introduce an approach to evaluate groundwater sustainability at a global scale. We employ a groundwater drought index to assess performance metrics (reliability, resilience, vulnerability, and a combined sustainability index) for the largest and most productive global aquifers. Spatiotemporal changes in total water storage are derived from remote sensing observations of gravity anomalies, from which the groundwater drought index is inferred. The results reveal a complex relationship between the indicators, while considering monthly variability in groundwater storage. Combining the drought and sustainability indexes, as presented in this work, constitutes a measure for quantifying groundwater sustainability. This framework integrates changes in groundwater resources due to human influences and climate changes, thus opening a path to assess progress toward sustainable use and water security.

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

    Science.gov (United States)

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

    2006-12-01

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

  6. Pattern-based approach to fetal congenital cardiovascular anomalies using the transverse aortic arch view on prenatal cardiac MRI

    Energy Technology Data Exchange (ETDEWEB)

    Dong, Su-Zhen; Zhu, Ming [Shanghai Jiaotong University School of Medicine, Department of Radiology, Shanghai Children' s Medical Center, Shanghai (China)

    2015-05-01

    Fetal echocardiography is the imaging modality of choice for prenatal diagnosis of congenital cardiovascular anomalies. However, echocardiography has limitations. Fetal cardiac magnetic resonance imaging (MRI) has the potential to complement US in detecting congenital cardiovascular anomalies. This article draws on our experience; it describes the transverse aortic arch view on fetal cardiac MRI and important clues on an abnormal transverse view at the level of the aortic arch to the diagnosis of fetal congenital cardiovascular anomalies. (orig.)

  7. 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.

  8. Overheating Anomalies during Flight Test Due to the Base Bleeding

    Science.gov (United States)

    Luchinsky, Dmitry; Hafiychuck, Halyna; Osipov, Slava; Ponizhovskaya, Ekaterina; Smelyanskiy, Vadim; Dagostino, Mark; Canabal, Francisco; Mobley, Brandon L.

    2012-01-01

    In this paper we present the results of the analytical and numerical studies of the plume interaction with the base flow in the presence of base out-gassing. The physics-based analysis and CFD modeling of the base heating for single solid rocket motor performed in this research addressed the following questions: what are the key factors making base flow so different from that in the Shuttle [1]; why CFD analysis of this problem reveals small plume recirculation; what major factors influence base temperature; and why overheating was initiated at a given time in the flight. To answer these questions topological analysis of the base flow was performed and Korst theory was used to estimate relative contributions of radiation, plume recirculation, and chemically reactive out-gassing to the base heating. It was shown that base bleeding and small base volume are the key factors contributing to the overheating, while plume recirculation is effectively suppressed by asymmetric configuration of the flow formed earlier in the flight. These findings are further verified using CFD simulations that include multi-species gas environment both in the plume and in the base. Solid particles in the exhaust plume (Al2O3) and char particles in the base bleeding were also included into the simulations and their relative contributions into the base temperature rise were estimated. The results of simulations are in good agreement with the temperature and pressure in the base measured during the test.

  9. 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.

  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. Skeleton-Based Abnormal Gait Detection

    Directory of Open Access Journals (Sweden)

    Trong-Nguyen Nguyen

    2016-10-01

    Full Text Available Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%.

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

    Directory of Open Access Journals (Sweden)

    R. M. Parinussa

    2011-10-01

    Full Text Available For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS and future Soil Moisture Active and Passive (SMAP satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres. A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC approach and the Rvalue data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of

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

    National Research Council Canada - National Science Library

    Skormin, Victor A

    2007-01-01

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

  14. Paper 3: EUROCAT data quality indicators for population-based registries of congenital anomalies.

    Science.gov (United States)

    Loane, Maria; Dolk, Helen; Garne, Ester; Greenlees, Ruth

    2011-03-01

    The European Surveillance of Congenital Anomalies (EUROCAT) network of population-based congenital anomaly registries is an important source of epidemiologic information on congenital anomalies in Europe covering live births, fetal deaths from 20 weeks gestation, and terminations of pregnancy for fetal anomaly. EUROCAT's policy is to strive for high-quality data, while ensuring consistency and transparency across all member registries. A set of 30 data quality indicators (DQIs) was developed to assess five key elements of data quality: completeness of case ascertainment, accuracy of diagnosis, completeness of information on EUROCAT variables, timeliness of data transmission, and availability of population denominator information. This article describes each of the individual DQIs and presents the output for each registry as well as the EUROCAT (unweighted) average, for 29 full member registries for 2004-2008. This information is also available on the EUROCAT website for previous years. The EUROCAT DQIs allow registries to evaluate their performance in relation to other registries and allows appropriate interpretations to be made of the data collected. The DQIs provide direction for improving data collection and ascertainment, and they allow annual assessment for monitoring continuous improvement. The DQI are constantly reviewed and refined to best document registry procedures and processes regarding data collection, to ensure appropriateness of DQI, and to ensure transparency so that the data collected can make a substantial and useful contribution to epidemiologic research on congenital anomalies. Copyright © 2011 Wiley-Liss, Inc.

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

    Science.gov (United States)

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

    2017-05-01

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

  16. Congenital anomalies in children with cerebral palsy: a population-based record linkage study

    DEFF Research Database (Denmark)

    Rankin, Judith; Cans, Christine; Garne, Ester

    2010-01-01

    Our aim was to determine the proportion of children with cerebral palsy (CP) who have a congenital anomaly (CA) in three regions (Isère Region, French Alps; Funen County, Denmark; Northern Region, England) where population-based CP and CA registries exist, and to classify the children according t...

  17. Is Host-Based Anomaly Detection + Temporal Correlation = Worm Causality

    National Research Council Canada - National Science Library

    Sekar, Vyas; Xie, Yinglian; Reiter, Michael K; Zhang, Hui

    2007-01-01

    Epidemic-spreading attacks (e.g., worm and botnet propagation) have a natural notion of attack causality - a single network flow causes a victim host to get infected and subsequently spread the attack...

  18. Anomaly Detection in SCADA Systems - A Network Based Approach

    NARCIS (Netherlands)

    Barbosa, R.R.R.

    2014-01-01

    Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities, such as water treatment facilities. Historically, these networks were composed by special-purpose embedded devices communicating through proprietary protocols.

  19. Anomaly-Based Intrusion Detection Systems Utilizing System Call Data

    Science.gov (United States)

    2012-03-01

    2.1.1 Viruses The first use of the term “computer virus ” is attributed to Fred Cohen in 1983. Fred Cohen originally defined a computer virus as a...agents. Once compromised, these systems become part of what is known as a “zombie” network. 2.1.3 Trojans A Trojan horse is malware pretending to...be benign or useful software. When activated, Trojans perform unauthorized actions such as collecting, modifying, and forging data. Unlike viruses

  20. Anomaly detection in SCADA systems: a network based approach

    NARCIS (Netherlands)

    Barbosa, R.R.R.

    2014-01-01

    Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities, such as water treatment facilities. Historically, these networks were composed by special-purpose embedded devices communicating through proprietary protocols.

  1. Towards Periodicity Based Anomaly Detection in SCADA Networks

    NARCIS (Netherlands)

    Barbosa, R.R.R.; Sadre, R.; Pras, Aiko

    Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities. The polling mechanism used to retrieve data from field devices causes the data transmission to be highly periodic. In this paper, we propose an approach that exploits

  2. Radio Frequency Based Programmable Logic Controller Anomaly Detection

    Science.gov (United States)

    2013-09-01

    Heydt-Benjamin, and S. Capkun. “Physical-layer Identification of RFID Devices.” 18th Conf on USENIX Security Symposium. SSYM’09. 199–214. 2009. 17...MacKenzie, H. Shamoon Malware and SCADA Security What are the Im- pacts? . Technical Report, Tofino Security, Sep 2012. 61. Mateti,P. Hacking Techniques

  3. 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

  4. A case of generalized lymphatic anomaly causing skull-base leakage and bacterial meningitis.

    Science.gov (United States)

    Suga, Kenichi; Goji, Aya; Inoue, Miki; Kawahito, Masami; Taki, Masako; Mori, Kazuhiro

    2017-05-01

    Generalized lymphatic anomaly is a multifocal lymphatic malformation that affects the skin, thoracic viscera, and bones. A 3year-old Japanese boy presented with right facial palsy due to cystic tumors in the ipsilateral petrous bone. Pericardial effusion had been found incidentally and generalized lymphatic anomaly had been diagnosed by pericardial biopsy. Petrous bone tumor had been followed up without surgery. At the age of seven he presented with fever and disturbance of consciousness, and bacterial meningitis due to Streptococcus pneumoniae was diagnosed. Computed tomography and magnetic resonance imaging revealed middle skull-base leakage due to lymphatic malformation. He achieved complete recovery under intensive care with antibiotics and mechanical ventilation. One year later, he presented with multiple cystic formations in bilateral femora. At the 3-year follow-up, the patient was healthy with no recurrence of meningitis and osteolytic lesions in the femora were non-progressive. Computed tomography and magnetic resonance imaging are useful for demonstration of skull-base leakage by generalized lymphatic anomaly. We should consider generalized lymphatic anomaly among the differential diagnoses for skull-base leakage. Copyright © 2017 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

  5. ISHM Anomaly Lexicon for Rocket Test

    Science.gov (United States)

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

    2007-01-01

    byproducts of the anomaly lexicon compilation effort. For example, (1) Allows determination of the frequency distribution of anomalies to help identify those with the potential for high return on investment if included in automated detection as part of an ISHM system, (2) Availability of a regular lexicon could provide the base anomaly name choices to help maintain consistency in the DR collection process, and (3) Although developed for the rocket engine test environment, most of the anomalies are not specific to rocket testing, and thus can be reused in other applications.

  6. 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.

  7. 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.

  8. 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

  9. Anomica: Fast Support Vector Based Novelty Detection

    Data.gov (United States)

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

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

    NARCIS (Netherlands)

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

    2002-01-01

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

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

    Indian Academy of Sciences (India)

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

  12. Deviant early pregnancy maternal triglyceride levels and increased risk of congenital anomalies : a prospective community-based cohort study

    NARCIS (Netherlands)

    Nederlof, M.; de Walle, H. E. K.; van Poppel, M. N. M.; Vrijkotte, T. G. M.; Gademan, M. G. J.

    ObjectiveThe maternal lipid profile could be of importance in congenital anomaly development. This study therefore investigates whether the maternal lipid profile during early pregnancy is associated with major nonsyndromic congenital anomalies (MNCA). DesignProspective community-based cohort study.

  13. 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

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

    International Nuclear Information System (INIS)

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

    2000-01-01

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

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

    DEFF Research Database (Denmark)

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

    2012-01-01

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

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

    Science.gov (United States)

    2015-09-17

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

  17. 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

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

    Science.gov (United States)

    2016-09-01

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

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

    Science.gov (United States)

    Sherwin, Jason; Sajda, Paul

    2013-11-01

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

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

    Science.gov (United States)

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

    2005-01-01

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

  1. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2002-07-01

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

  3. 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.

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

    Science.gov (United States)

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

    2013-05-21

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

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

    Science.gov (United States)

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

    2016-08-01

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

  6. 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. Epidemiology of Congenital Anomalies in a Population-based Birth Registry in Taiwan, 2002

    Directory of Open Access Journals (Sweden)

    Bing-Yu Chen

    2009-06-01

    Conclusion: The occurrence rates for individual congenital anomalies in Taiwan were reported. Older maternal age was a risk factor for the occurrence of chromosomal and orofacial anomalies. More active prenatal screening and further investigation of causal factors of congenital anomalies are of major importance.

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

    Science.gov (United States)

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

    2017-10-01

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

  9. Integrated GRASS GIS based techniques to identify thermal anomalies on water surface. Taranto case study.

    Science.gov (United States)

    Massarelli, Carmine; Matarrese, Raffaella; Felice Uricchio, Vito

    2014-05-01

    In the last years, thermal images collected by airborne systems have made the detection of thermal anomalies possible. These images are an important tool to monitor natural inflows and legal or illegal dumping in coastal waters. By the way, the potential of these kinds of data is not well exploited by the Authorities who supervises the territory. The main reason is the processing of remote sensing data that requires very specialized operators and softwares which are usually expensive and complex. In this study, we adopt a simple methodology that uses GRASS, a free open-source GIS software, which has allowed us to map surface water thermal anomalies and, consequently, to identify and locate coastal inflows, as well as manmade or natural watershed drains or submarine springs (in italian citri) in the Taranto Sea (South of Italy). Taranto sea represents a coastal marine ecosystem that has been gradually modified by mankind. One of its inlet, the Mar Piccolo, is a part of the National Priority List site identified by the National Program of Environmental Remediation and Restoration because of the size and high presence of industrial activities, past and present, that have had and continue to seriously compromise the health status of the population and the environment. In order to detect thermal anomalies, two flights have been performed respectively on March 3rd and on April 7th, 2013. A total of 13 TABI images have been acquired to map the whole Mar Piccolo with 1m of spatial resolution. TABI-320 is an airborne thermal camera by ITRES, with a continuous spectral range between 8 and 12 microns. On July 15th, 2013, an in-situ survey was carried out along the banks to retrieve clear visible points of natural or artificial inflows, detecting up to 72 of discharges. GRASS GIS (Geographic Resources Analysis Support System), is a free and open source Geographic Information System (GIS) software suite used for geospatial data management and analysis, image processing

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

    Science.gov (United States)

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

    2010-11-23

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

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  12. SALVAGE D2.2 Description of the developed algorithms for intrusion detection in smart grid components

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena; Korman, Matus; Heussen, Kai

    2016-01-01

    This report presents developed model-based anomaly detection techniques used for intrusion detection in smart grid.......This report presents developed model-based anomaly detection techniques used for intrusion detection in smart grid....

  13. High risk for major nonlimb anomalies associated with lower-limb deficiency: a population-based study.

    Science.gov (United States)

    Syvänen, Johanna; Nietosvaara, Yrjänä; Ritvanen, Annukka; Koskimies, Eeva; Kauko, Tommi; Helenius, Ilkka

    2014-11-19

    The aims of this study were to determine the prevalence of congenital lower-limb reduction defects and associated mortality, to evaluate lower-limb deficiencies by type of reduction, and to identify patterns of associated anomalies. We conducted a population-based study with use of data from the Finnish Register of Congenital Malformations and Care Register for Health Care. All cases of lower-limb deficiency among live births, stillbirths, spontaneous abortions, and terminations of pregnancy due to fetal anomalies from 1993 to 2008 were included. We analyzed medical records and classified lower-limb reduction defects. Associated major anomalies were recorded, and perinatal mortality and infant mortality were calculated. Two hundred and sixty-six cases with lower-limb deficiency were identified, with a total prevalence of 2.8 per 10,000 births, a birth prevalence of 2.2 per 10,000 births, and a live-birth prevalence of 2.1 per 10,000 live births. Terminal transverse limb reductions accounted for 44.7% of the cases; longitudinal reductions, 22.9%; intercalary reductions, 7.9%; multiple reductions, 8.3%; and split-foot malformations, 4.5%. In addition to lower-limb deficiency, 47.7% of the cases had other major anomalies; anomalies of internal organs were noted in 26.3% of the cases, anomalies of the axial skeleton in 13.5% of cases, and central nervous system anomalies in 12.8%. Upper-limb reductions were observed in 32.0% of the cases. The relative risk (RR) for associated major anomalies was 12.54 (95% confidence interval [CI], 11.06 to 14.23) compared with the general figures for major congenital anomalies in Finland. The RR for associated anomalies was higher (1.75; 95% CI, 1.20 to 2.53) for longitudinal preaxial lower-limb deficiencies than for the other types of lower-limb reductions. Perinatal mortality was seventy-eight per 1000 births. All infant deaths were associated with chromosomal abnormalities, other known syndromes, or additional congenital

  14. 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

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

    Indian Academy of Sciences (India)

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

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

    Science.gov (United States)

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

    2016-01-01

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

  17. 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.)

  18. Spherical cap harmonic analysis of regional magnetic anomalies based on CHAMP satellite data

    Science.gov (United States)

    Feng, Yan; Jiang, Yong; Jiang, Yi; Liu, Bao-Jia; Jiang, Jin; Liu, Zhong-Wei; Ye, Mei-Chen; Wang, Hong-Shen; Li, Xiu-Ming

    2016-09-01

    We used CHAMP satellite vector data and the latest IGRF12 model to investigate the regional magnetic anomalies over mainland China. We assumed satellite points on the same surface (307.69 km) and constructed a spherical cap harmonic model of the satellite magnetic anomalies for elements X, Y, Z, and F over Chinese mainland for 2010.0 (SCH2010) based on selected 498 points. We removed the external field by using the CM4 model. The pole of the spherical cap is 36N° and 104°E, and its half-angle is 30°. After checking and comparing the root mean square (RMS) error of Δ X, Δ Y, and Δ Z and X, Y, and Z, we established the truncation level at K max = 9. The results suggest that the created China Geomagnetic Referenced Field at the satellite level (CGRF2010) is consistent with the CM4 model. We compared the SCH2010 with other models and found that the intensities and distributions are consistent. In view of the variation of F at different altitudes, the SCH2010 model results obey the basics of the geomagnetic field. Moreover, the change rate of X, Y, and Z for SCH2010 and CM4 are consistent. The proposed model can successfully reproduce the geomagnetic data, as other data-fitting models, but the inherent sources of error have to be considered as well.

  19. Evidence of Urban Precipitation Anomalies from Satellite and Ground-Based Measurements

    Science.gov (United States)

    Shepherd, J. Marshall; Manyin, M.; Negri, Andrew

    2004-01-01

    Urbanization is one of the extreme cases of land use change. Most of world's population has moved to urban areas. Although currently only 1.2% of the land is considered urban, the spatial coverage and density of cities are expected to rapidly increase in the near future. It is estimated that by the year 2025, 60% of the world's population will live in cities. Human activity in urban environments also alters weather and climate processes. However, our understanding of urbanization on the total Earth-weather-climate system is incomplete. Recent literature continues to provide evidence that anomalies in precipitation exist over and downwind of major cities. Current and future research efforts are actively seeking to verify these literature findings and understand potential cause-effect relationships. The novelty of this study is that it utilizes rainfall data from multiple satellite data sources (e.g. TRMM precipitation radar, TRMM-geosynchronous-rain gauge merged product, and SSM/I) and ground-based measurements to identify spatial anomalies and temporal trends in precipitation for cities around the world. Early results will be presented and placed within the context of weather prediction, climate assessment, and societal applications.

  20. Antenatal diagnosis, prevalence and outcome of major congenital anomalies in Saudi Arabia: A hospital based study

    International Nuclear Information System (INIS)

    Sallout, Bahauddin I.; Al-Hoshan, Manal S.; Attyyaa, Rehman A.; Al-Suleimat, Abdelmane A.

    2008-01-01

    The exact antenatal prevalence of congenital anomalies in Saudi society is unknown. Early antenatal diagnosis of congenital anomalies is crucial for early counseling, intervention and possible fetal therapy. The objective of this study was to evaluate the antenatal frequency of major congenital anomalies and malformations patterns in our hospital population and to evaluate the outcome and perinatal mortality rates for major congenital anomalies. This was a prospective study of the antenatal diagnosis of major fetal congenital anomalies conducted in the ultrasound Department of the Women's Specialized Hospital at King Fahd Medical City from for 7762 patients and 5379 babies delivered in our institution. We diagnosed 217 cases of fetal anomalies. The antenatal prevalence of congenital anomalies was 27.96 per 1000. The median maternal age at diagnosis was 27.5 years. Te median gestational age at diagnosis was 31 weeks. Genitourinary and cranial anomalies were the commonest; for 186 patients delivered in our institution, the birth prevalence was 34.57 per 1000 births. The median gestational age at delivery was 38 weeks. The perinatal mortality arte was 34.9% (65/186), including all cases of intrauterine fetal and neonatal deaths. The prevalence of major congenital anomalies in our population appears to be similar to international figures. Major congenital anomalies are a major cause of perinatal mortality. (author)

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

    Science.gov (United States)

    Ho, Shuyuan Mary

    2009-01-01

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

  2. 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...

  3. Craniofacial anomalies associated with hypospadias. Description of a hospital based population in South America

    Directory of Open Access Journals (Sweden)

    Nicolas Fernandez

    Full Text Available ABSTRACT Introduction: Hypospadias is a congenital abnormality of the penis, in which there is incomplete development of the distal urethra. There are numerous reports showing an increase of prevalence of hypospadias. Association of craniofacial malformations in patients diagnosed with hypospadias is rare. The aim of this study is to describe the association between hypospadias and craniofacial congenital anomalies. Materials and Methods: A retrospective review of the Latin-American collaborative study of congenital malformations (ECLAMC data was performed between January 1982 and December 2011. We included children diagnosed with associated hypospadias and among them we selected those that were associated with any craniofacial congenital anomaly. Results: Global prevalence was 11.3 per 10.000 newborns. In this population a total of 809 patients with 1117 associated anomalies were identified. On average there were 1.7 anomalies per patient. Facial anomalies were present in 13.2%. The most commonly major facial anomaly associated to hypospadias was cleft lip/palate with 52 cases. We identified that 18% have an association with other anomalies, and found an association between craniofacial anomalies and hypospadias in 0.59 cases/10.000 newborns. Discussion: Hypospadias is the most common congenital anomaly affecting the genitals. Its association with other anomalies is rare. It has been reported that other malformations occur in 29.3% of the cases with hypospadias. The more proximal the meatus, the higher the risk for having another associated anomaly. Conclusion: Associated hypospadias are rare, and it is important to identify the concurrent occurrence of craniofacial anomalies to better treat patients that might need a multidisciplinary approach.

  4. Craniofacial anomalies associated with hypospadias. Description of a hospital based population in South America.

    Science.gov (United States)

    Fernandez, Nicolas; Escobar, Rebeca; Zarante, Ignacio

    2016-01-01

    Hypospadias is a congenital abnormality of the penis, in which there is incomplete development of the distal urethra. There are numerous reports showing na increase of prevalence of hypospadias. Association of craniofacial malformations in patients diagnosed with hypospadias is rare. The aim of this study is to describe the association between hypospadias and craniofacial congenital anomalies. A retrospective review of the Latin-American collaborative study of congenital malformations (ECLAMC) data was performed between January 1982 and December 2011. We included children diagnosed with associated hypospadias and among them we selected those that were associated with any craniofacial congenital anomaly. Global prevalence was 11.3 per 10.000 newborns. In this population a total of 809 patients with 1117 associated anomalies were identified. On average there were 1.7 anomalies per patient. Facial anomalies were present in 13.2%. The most commonly major facial anomaly associated to hypospadias was cleft lip/palate with 52 cases. We identified that 18% have an association with other anomalies, and found an association between craniofacial anomalies and hypospadias in 0.59 cases/10.000 newborns. Hypospadias is the most common congenital anomaly affecting the genitals. Its association with other anomalies is rare. It has been reported that other malformations occur in 29.3% of the cases with hypospadias. The more proximal the meatus, the higher the risk for having another associated anomaly. Associated hypospadias are rare, and it is important to identify the concurrent occurrence of craniofacial anomalies to better treat patients that might need a multidisciplinary approach. Copyright© by the International Brazilian Journal of Urology.

  5. Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rupinder Singh

    2017-01-01

    Full Text Available In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack. For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

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

    Science.gov (United States)

    2016-02-12

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

  7. 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.

  8. 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.

  9. Congenital anomalies in children with cerebral palsy: a population-based record linkage study

    DEFF Research Database (Denmark)

    Rankin, Judith; Cans, Christine; Garne, Ester

    2009-01-01

    with ataxic CP (41.7%) and lowest in those with dyskinetic CP (2.1%). Cerebral anomalies were found in 8.4% and 7% of children with bilateral and unilateral spastic CP respectively. The most frequent cerebral anomalies were primary microcephaly (26.5%) and congenital hydrocephalus (17.3%). The most common non...

  10. Congenital basis of posterior fossa anomalies

    Science.gov (United States)

    Cotes, Claudia; Bonfante, Eliana; Lazor, Jillian; Jadhav, Siddharth; Caldas, Maria; Swischuk, Leonard

    2015-01-01

    The classification of posterior fossa congenital anomalies has been a controversial topic. Advances in genetics and imaging have allowed a better understanding of the embryologic development of these abnormalities. A new classification schema correlates the embryologic, morphologic, and genetic bases of these anomalies in order to better distinguish and describe them. Although they provide a better understanding of the clinical aspects and genetics of these disorders, it is crucial for the radiologist to be able to diagnose the congenital posterior fossa anomalies based on their morphology, since neuroimaging is usually the initial step when these disorders are suspected. We divide the most common posterior fossa congenital anomalies into two groups: 1) hindbrain malformations, including diseases with cerebellar or vermian agenesis, aplasia or hypoplasia and cystic posterior fossa anomalies; and 2) cranial vault malformations. In addition, we will review the embryologic development of the posterior fossa and, from the perspective of embryonic development, will describe the imaging appearance of congenital posterior fossa anomalies. Knowledge of the developmental bases of these malformations facilitates detection of the morphological changes identified on imaging, allowing accurate differentiation and diagnosis of congenital posterior fossa anomalies. PMID:26246090

  11. 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.)

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

    Science.gov (United States)

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

    2017-08-01

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

  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. Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

    Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

  15. Real-Time Monitoring and Analysis of Zebrafish Electrocardiogram with Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Michael Lenning

    2017-12-01

    Full Text Available Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac diseases are lacking, largely due to an incomplete understanding of their genetic basis and underlying molecular mechanisms. Zebrafish (Danio rerio are an excellent model system for studying heart disease as they enable a forward genetic approach to tackle this unmet medical need. In recent years, our team has been employing electrocardiogram (ECG as an efficient tool to study the zebrafish heart along with conventional approaches, such as immunohistochemistry, DNA and protein analyses. We have overcome various challenges in the small size and aquatic environment of zebrafish in order to obtain ECG signals with favorable signal-to-noise ratio (SNR, and high spatial and temporal resolution. In this paper, we highlight our recent efforts in zebrafish ECG acquisition with a cost-effective simplified microelectrode array (MEA membrane providing multi-channel recording, a novel multi-chamber apparatus for simultaneous screening, and a LabVIEW program to facilitate recording and processing. We also demonstrate the use of machine learning-based programs to recognize specific ECG patterns, yielding promising results with our current limited amount of zebrafish data. Our solutions hold promise to carry out numerous studies of heart diseases, drug screening, stem cell-based therapy validation, and regenerative medicine.

  16. Original Research. Correlation Between Cranial Base Morphology And Various Types Of Skeletal Anomalies

    Directory of Open Access Journals (Sweden)

    Panainte Irinel

    2017-03-01

    Full Text Available Background: Previous studies regarding various types of malocclusions have found correlations between the angle of the base of the skull and prognathism. Aim of the study: This cephalometric study sought to investigate the function of the cranium base angle in different types of malocclusion on a group of Romanian subjects. Materials and methods: Forty-four cephalometric radiographs were selected from patients referred to orthodontic treatment. The cephalometric records were digitized, and with the CorelDRAW Graphics Suite X5 software 22 landmarks have been marked on each radiograph. A number of linear and angular variables were calculated. Results: The angle of the base of the skull was found to be higher in Class II Division 1 subjects compared to the Class I group. The cranial base lengths, N-S and S-Ba, were significantly larger in both categories of Class II malocclusion than in Class I patients, but measurements were comparable in Class I and Class III. The SNA angle showed no considerable variation between Class I subjects and the other groups. SNA-SNP was significantly increased above Class I values in Class II Division1 and Class II Division 2 groups. No significant dissimilarities were observed for these lengths between Class I and Class III patients. Conclusions: The angle of the cranium base (S-N-Ba, S-N-Ar does not have a major role in the progression of malocclusion. In Angle Class II malocclusion the SNA angle is increased, and SNB is increased in malocclusion Class III. The anterior skull base length is increased in Class II anomalies. The length of the maxillary bone base is increased in Class II malocclusions type; in Class III type of malocclusion the length of the mandible bone is increased.

  17. Cellular telephone-based radiation detection instrument

    Energy Technology Data Exchange (ETDEWEB)

    Craig, William W [Pittsburg, CA; Labov, Simon E [Berkeley, CA

    2011-06-14

    A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

  18. A novel interacting multiple model based network intrusion detection scheme

    Science.gov (United States)

    Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

    2006-04-01

    In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

  19. A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

    Science.gov (United States)

    Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

    2017-12-01

    The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.

  20. Epidemiological Features of Congenital Anomalies in Tabriz District, a Population Based Study

    Directory of Open Access Journals (Sweden)

    Majid Karamooz

    2015-08-01

    Full Text Available Background and objectives : Congenital anomalies are responsible for a remarkable proportion of mortalities and morbidities of the newborn population. The aim of this study was to investigate the epidemiological features of birth defects in rural areas of Tabriz, northwest of Iran. Material and Methods : The study population comprised live births under 8 years old in Tabriz district. All health records of the children under 8 years old were evaluated. Results : Out of 22500 live births, we documented 254 cases with congenital anomalies. The prevalence rate of birth defects was 113 per 10000 births (95% CI: 99 to 126. Anomalies of the nervous system were the most common defects, accounting for 24% of birth defects. It was followed by the heart diseases anomalies and the eye/ear anomalies. The highest prevalence rate for birth defects was observed in the north-eastern region with 386 per 10000 live births (95% CI: 215 to 556 and the lowest prevalence rate was observed in the north-western region with 15 per 10000 live births     (95% CI: -14 to 45. Conclusion : The remarkable geographic disparities in the prevalence of birth defects in the region may indicate for a new investigation for the etiology of congenital anomalies.

  1. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2011-01-01

    This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal...

  2. 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

  3. 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.

  4. 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...

  5. Major congenital anomalies in babies born with Down syndrome

    DEFF Research Database (Denmark)

    Morris, Joan K; Garne, Ester; Wellesley, Diana

    2014-01-01

    Previous studies have shown that over 40% of babies with Down syndrome have a major cardiac anomaly and are more likely to have other major congenital anomalies. Since 2000, many countries in Europe have introduced national antenatal screening programs for Down syndrome. This study aimed...... to determine if the introduction of these screening programs and the subsequent termination of prenatally detected pregnancies were associated with any decline in the prevalence of additional anomalies in babies born with Down syndrome. The study sample consisted of 7,044 live births and fetal deaths with Down...... syndrome registered in 28 European population-based congenital anomaly registries covering seven million births during 2000-2010. Overall, 43.6% (95% CI: 42.4-44.7%) of births with Down syndrome had a cardiac anomaly and 15.0% (14.2-15.8%) had a non-cardiac anomaly. Female babies with Down syndrome were...

  6. Current practice of distraction osteogenesis for craniofacial anomalies in Europe: a web based survey.

    Science.gov (United States)

    Nada, Rania M; Sugar, Adrian W; Wijdeveld, Maarten G M M; Borstlap, Wilfred A; Clauser, Luigi; Hoffmeister, Bodo; Kuijpers-Jagtman, Anne Marie

    2010-03-01

    Aim of the study was to get more insight into the opinion of European surgeons and orthodontists on the use of distraction osteogenesis (DO) for patients with different diagnoses and treatment protocols. A web based survey was set up, showing records of four patients with different conditions: hemifacial microsomia (case 1), bilateral mandibular deficiency (case 2), cleft lip and palate (case 3) and Crouzon syndrome (case 4). Respondents from 181 Eurocleft centres were asked to fill out a questionnaire for each patient. Most of the respondents considered case 1 (80%), case 3 (81%) and case 4 (86%) suitable for DO, while only 31% were considering case 2 for DO. There was lack of consensus among the respondents about many aspects of DO. Out of six different treatment parameters, an acceptable degree of agreement was only seen in two: a latency period of 3-7 days and a distraction rate of 1mm per day. Furthermore, there was noticeable disagreement on the ideal age for treatment, surgical technique, distraction device, and retention period. Our results showed that there is a wide variety in treatment approaches for craniofacial anomalies in Europe. There is disagreement on essential steps in the distraction procedures.

  7. A community-based survey of visible congenital anomalies in rural Tamil Nadu

    Directory of Open Access Journals (Sweden)

    Sridhar K

    2009-10-01

    Full Text Available An extensive community-based survey of visible congenital defects covering 12.8 million children in rural Tamil Nadu state was conducted during the years 2004-05. A door-to-door survey was done utilizing the existing health care delivery system. More than 10,000 village health nurses were involved to collect the data. All children between the ages of 0 and 15 years were seen. The children with defects were seen by a medical officer and diagnosis was made as per chart. A total of 1.30% of children were born with some visible anomalies. The male:female ratio was 1.3:1. There was a family history in 9% and consanguinity in 32%. More than 5% mothers had taken some medication in the first trimester of pregnancy out of which anti-convulsants were 3.4%. Facial clefts showed a lower incidence of 1 in 1976 live births with peak incidence between March and June. Cleft palate alone showed a higher percentage (30% than other studies.

  8. Trends in the prevalences of congenital anomalies and age at motherhood in a southern European region: a population-based study

    Science.gov (United States)

    Cambra, K; Ibañez, B; Urzelai, D; Portillo, I; Montoya, I; Esnaola, S; Cirarda, F B

    2014-01-01

    Objectives To estimate the prevalences of the main groups of congenital anomalies and to assess their trend over time. Design Population-based study of prevalences. Setting The Basque Country, Spain. Participants All births and all congenital anomalies diagnosed prenatally, at birth or during the first year of age, in all hospitals of the country, from 1999 to 2008. Main outcomes measures Total diagnosed prevalences and prevalences at birth of all chromosomal and non-chromosomal anomalies, Down's syndrome, anomalies of the nervous system, urinary, limbs, digestive system and congenital heart defects. Results Mean age (SD) of women at childbirth and the proportion of them over 35 years of age shifted from 32.1 (4.5) years, with 18.3% in 1999–2001, to 32.3 (4.7) years, with 23.9% in 2006–2008. Between 1999 and 2008, 991 cases of chromosomal anomalies and 3090 of non-chromosomal anomalies were diagnosed, which yields, respectively, total prevalences of 5.2‰ and of 16.2‰. Among chromosomal anomalies, Down's syndrome is the most frequent (2.9‰). With marginal statistical significance, the results point at an increasing trend in total diagnosed chromosomal anomalies, but a decreasing one in prevalences at birth. Among non-chromosomal congenital anomalies, congenital heart defects are the most frequent (5.2‰) one. Rates of all non-chromosomal, urinary and limb anomalies grew during the study period, whereas those of congenital heart defects and anomalies of the digestive system did not change significantly. Conclusions In the Basque Country, rates of chromosomal anomalies are higher than the overall estimated prevalence in European countries, and continue to increase slightly, which may be related to the rise in maternal age. Rates of non-chromosomal anomalies are within the European frequent range of values, and the increases observed need to be checked in the following years. PMID:24589823

  9. A triangular climate-based decision model to forecast crop anomalies in Kenya

    Science.gov (United States)

    Guimarães Nobre, G.; Davenport, F.; Veldkamp, T.; Jongman, B.; Funk, C. C.; Husak, G. J.; Ward, P.; Aerts, J.

    2017-12-01

    By the end of 2017, the world is expected to experience unprecedented demands for food assistance where, across 45 countries, some 81 million people will face a food security crisis. Prolonged droughts in Eastern Africa are playing a major role in these crises. To mitigate famine risk and save lives, government bodies and international donor organisations are increasingly building up efforts to resolve conflicts and secure humanitarian relief. Disaster-relief and financing organizations traditionally focus on emergency response, providing aid after an extreme drought event, instead of taking actions in advance based on early warning. One of the reasons for this approach is that the seasonal risk information provided by early warning systems is often considered highly uncertain. Overcoming the reluctance to act based on early warnings greatly relies on understanding the risk of acting in vain, and assessing the cost-effectiveness of early actions. This research develops a triangular climate-based decision model for multiple seasonal time-scales to forecast strong anomalies in crop yield shortages in Kenya using Casual Discovery Algorithms and Fast and Frugal Decision Trees. This Triangular decision model (1) estimates the causality and strength of the relationship between crop yields and hydro climatological predictors (extracted from the Famine Early Warning Systems Network's data archive) during the crop growing season; (2) provides probabilistic forecasts of crop yield shortages in multiple time scales before the harvesting season; and (3) evaluates the cost-effectiveness of different financial mechanisms to respond to early warning indicators of crop yield shortages obtained from the model. Furthermore, we reflect on how such a model complements and advances the current state-of-art FEWS Net system, and examine its potential application to improve the management of agricultural risks in Kenya.

  10. Radon anomalies in ground water before earthquakes in Tokyo

    International Nuclear Information System (INIS)

    Saito, Masaaki

    1992-01-01

    Radon contents in ground waters in Tokyo have been measured since 1976. The correlation between earthquake and radon anomaly will be evaluated easily, when both earthquakes and radon anomalies are a few. In addition, the high reliability of the correlation will be obtained, if an earthquake and an anomaly occur at almost same time. The six earthquakes occurred in 1976∼1990 were chosen based on the magnitude (≥6.0) and the epicentral distance (<100 km). Radon anomalies shortly before the six earthquakes were investigated at the stations where few anomalies have been detected. Anomalies which can be considered to relate with earthquakes appeared near around the dates of the Ibaraki-Chiba (1985) and the Yamanashi-Kanagawa (1983) earthquakes. The anomalies appeared in 6 d before ∼4 d after the earthquakes, and no other anomalies had appeared in over 600 d before the earthquakes. Then it is presumed that these anomalies would be earthquake precursors. The anomalies were found at the stations distributed in 50∼70 km epicentral distances and on the compress quadrants of the earthquake mechanism. (author)

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

    African Journals Online (AJOL)

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

  12. A mechanism for Indian Summer Monsoon Intraseasonal Variability based on PV anomalies in the Somali Jet

    Science.gov (United States)

    Rai, Praveen; Joshi, Manoj; Dimri, Ashok; Turner, Andrew

    2017-04-01

    Intraseasonal variability during the Indian summer monsoon is characterized by periods of rainfall interspersed by dry periods, which are known as active and break events respectively. Understanding and predicting such events is important for predicting societally important changes such as water resources. The Somali Jet, lying over the Arabian Sea, is known to be a key regional feature of this circulation. In the present study, we analyse the spatial structure of Somali Jet potential vorticity (PV) anomalies and show that they vary considerably during active and break periods. Analysis of these PV anomalies suggests a mechanism joining sea surface temperatures (SST) anomalies, convection, modification of PV by diabatic heating and mixing in the atmospheric boundary layer, wind stress curl, and ocean upwelling processes. The feedback mechanism is consistent with observed variability in the coupled ocean-atmosphere system on timescales of approximately 20 days.

  13. MODIS/Aqua Thermal Anomalies/Fire 5-Min L2 Swath 1km V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  14. MODIS/Terra Thermal Anomalies/Fire 5-Min L2 Swath 1km V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  15. Epidemiology of multiple congenital anomalies in Europe: A EUROCAT population-based registry study

    DEFF Research Database (Denmark)

    Calzolari, Elisa; Barisic, Ingeborg; Loane, Maria

    2014-01-01

    for classification of congenital anomaly cases followed by manual review of potential MCA cases by geneticists. MCA cases are defined as cases with two or more major anomalies of different organ systems, excluding sequences, chromosomal and monogenic syndromes. RESULTS: The combination of an epidemiological...... and clinical approach for classification of cases has improved the quality and accuracy of the MCA data. Total prevalence of MCA cases was 15.8 per 10,000 births. Fetal deaths and termination of pregnancy were significantly more frequent in MCA cases compared with isolated cases (p ... more frequently prenatally diagnosed (p Live born infants with MCA were more often born preterm (p 

  16. Congenital anomalies associated with trisomy 18 or trisomy 13: A registry-based study in 16 European countries, 2000-2011.

    Science.gov (United States)

    Springett, Anna; Wellesley, Diana; Greenlees, Ruth; Loane, Maria; Addor, Marie-Claude; Arriola, Larraitz; Bergman, Jorieke; Cavero-Carbonell, Clara; Csaky-Szunyogh, Melinda; Draper, Elizabeth S; Garne, Ester; Gatt, Miriam; Haeusler, Martin; Khoshnood, Babak; Klungsoyr, Kari; Lynch, Catherine; Dias, Carlos Matias; McDonnell, Robert; Nelen, Vera; O'Mahony, Mary; Pierini, Anna; Queisser-Luft, Annette; Rankin, Judith; Rissmann, Anke; Rounding, Catherine; Stoianova, Sylvia; Tuckerz, David; Zymak-Zakutnia, Natalya; Morris, Joan K

    2015-12-01

    The aim of this study was to examine the prevalence of trisomies 18 and 13 in Europe and the prevalence of associated anomalies. Twenty-five population-based registries in 16 European countries provided data from 2000-2011. Cases included live births, fetal deaths (20+ weeks' gestation), and terminations of pregnancy for fetal anomaly (TOPFAs). The prevalence of associated anomalies was reported in live births. The prevalence of trisomy 18 and trisomy 13 were 4.8 (95%CI: 4.7-5.0) and 1.9 (95%CI: 1.8-2.0) per 10,000 total births. Seventy three percent of cases with trisomy 18 or trisomy 13 resulted in a TOPFA. Amongst 468 live born babies with trisomy 18, 80% (76-83%) had a cardiac anomaly, 21% (17-25%) had a nervous system anomaly, 8% (6-11%) had esophageal atresia and 10% (8-13%) had an orofacial cleft. Amongst 240 Live born babies with trisomy 13, 57% (51-64%) had a cardiac anomaly, 39% (33-46%) had a nervous system anomaly, 30% (24-36%) had an eye anomaly, 44% (37-50%) had polydactyly and 45% (39-52%) had an orofacial cleft. For babies with trisomy 18 boys were less likely to have a cardiac anomaly compared with girls (OR = 0.48 (0.30-0.77) and with trisomy 13 were less likely to have a nervous system anomaly [OR = 0.46 (0.27-0.77)]. Babies with trisomy 18 or trisomy 13 do have a high proportion of associated anomalies with the distribution of anomalies being different in boys and girls. © 2015 Wiley Periodicals, Inc.

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

    Directory of Open Access Journals (Sweden)

    M. Akhoondzadeh

    2013-04-01

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

  18. 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

  19. Deceiving entropy-based DoS detection

    Science.gov (United States)

    Özçelik, Ä.°lker; Brooks, Richard R.

    2014-06-01

    Denial of Service (DoS) attacks disable network services for legitimate users. A McAfee report shows that eight out of ten Critical Infrastructure Providers (CIPs) surveyed had a significant Distributed DoS (DDoS) attack in 2010.1 Researchers proposed many approaches for detecting these attacks in the past decade. Anomaly based DoS detection is the most common. In this approach, the detector uses statistical features; such as the entropy of incoming packet header fields like source IP addresses or protocol type. It calculates the observed statistical feature and triggers an alarm if an extreme deviation occurs. However, intrusion detection systems (IDS) using entropy based detection can be fooled by spoofing. An attacker can sniff the network to collect header field data of network packets coming from distributed nodes on the Internet and fuses them to calculate the entropy of normal background traffic. Then s/he can spoof attack packets to keep the entropy value in the expected range during the attack. In this study, we present a proof of concept entropy spoofing attack that deceives entropy based detection approaches. Our preliminary results show that spoofing attacks cause significant detection performance degradation.

  20. Presence of vascular anomalies with congenital hemihypertrophy and Wilms tumor: an evidence-based evaluation.

    Science.gov (United States)

    Kundu, Roopal V; Frieden, Ilona J

    2003-01-01

    Congenital hemihypertrophy is an uncommon condition of unknown etiology characterized by unilateral overgrowth of part or all of one side of the body. Hemihypertrophy is known to be associated with certain childhood tumors, most notably Wilms tumor (or nephroblastoma), and for this reason infants with hemihypertrophy are often followed with serial abdominal ultrasounds. Klippel-Trénaunay syndrome (KTS) is the triad of port-wine stain, venous varicosities, and soft tissue and/or bony hypertrophy. Children with KTS typically have localized rather than generalized hemihypertrophy, but occasionally the hypertrophy is more extensive than the vascular anomaly itself. Information is lacking about whether hemihypertrophy in this setting can also be a risk factor for Wilms tumor. We systematically reviewed the medical literature to determine whether well-documented cases of Wilms tumor in the setting of both hemihypertrophy and vascular anomalies have been described, and if found, whether the association was sufficiently frequent that routine screening for Wilms tumor in this setting should be recommended. A review of case reports and case series in the pediatric population was undertaken using specific inclusion and exclusion criteria. We found 4 of 58 subjects with hemihypertrophy and Wilms tumor had a reported vascular anomaly, but in only one case was a clear-cut diagnosis of KTS confirmed. The relationship of the other three vascular anomalies reported was of uncertain significance. In conclusion, our review suggests that the risk of Wilms tumor in the setting of localized soft-tissue hypertrophy in conjunction with a vascular malformation is quite low. More extensive hemihypertrophy extending to body sites remote from the vascular malformation itself could have a higher risk of Wilms tumor, although the magnitude of this risk is uncertain. Our findings suggest that routine serial abdominal ultrasounds in patients with vascular malformations in association with

  1. 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

  2. A Cross-Layer, Anomaly-Based IDS for WSN and MANET.

    Science.gov (United States)

    Amouri, Amar; Morgera, Salvatore D; Bencherif, Mohamed A; Manthena, Raju

    2018-02-22

    Intrusion detection system (IDS) design for mobile adhoc networks (MANET) is a crucial component for maintaining the integrity of the network. The need for rapid deployment of IDS capability with minimal data availability for training and testing is an important requirement of such systems, especially for MANETs deployed in highly dynamic scenarios, such as battlefields. This work proposes a two-level detection scheme for detecting malicious nodes in MANETs. The first level deploys dedicated sniffers working in promiscuous mode. Each sniffer utilizes a decision-tree-based classifier that generates quantities which we refer to as correctly classified instances (CCIs) every reporting time. In the second level, the CCIs are sent to an algorithmically run supernode that calculates quantities, which we refer to as the accumulated measure of fluctuation (AMoF) of the received CCIs for each node under test (NUT). A key concept that is used in this work is that the variability of the smaller size population which represents the number of malicious nodes in the network is greater than the variance of the larger size population which represents the number of normal nodes in the network. A linear regression process is then performed in parallel with the calculation of the AMoF for fitting purposes and to set a proper threshold based on the slope of the fitted lines. As a result, the malicious nodes are efficiently and effectively separated from the normal nodes. The proposed scheme is tested for various node velocities and power levels and shows promising detection performance even at low-power levels. The results presented also apply to wireless sensor networks (WSN) and represent a novel IDS scheme for such networks.

  3. Risk of congenital anomalies around a municipal solid waste incinerator: a GIS-based case-control study

    Directory of Open Access Journals (Sweden)

    Garavelli Livia

    2009-02-01

    Full Text Available Abstract Background Waste incineration releases into the environment toxic substances having a teratogenic potential, but little epidemiologic evidence is available on this topic. We aimed at examining the relation between exposure to the emissions from a municipal solid waste incinerator and risk of birth defects in a northern Italy community, using Geographical Information System (GIS data to estimate exposure and a population-based case-control study design. By modelling the incinerator emissions, we defined in the GIS three areas of increasing exposure according to predicted dioxins concentrations. We mapped the 228 births and induced abortions with diagnosis of congenital anomalies observed during the 1998–2006 period, together with a corresponding series of control births matched for year and hospital of birth/abortion as well as maternal age, using maternal address in the first three months of pregnancy to geocode cases and controls. Results Among women residing in the areas with medium and high exposure, prevalence of anomalies in the offspring was substantially comparable to that observed in the control population, nor dose-response relations for any of the major categories of birth defects emerged. Furthermore, odds ratio for congenital anomalies did not decrease during a prolonged shut-down period of the plant. Conclusion Overall, these findings do not lend support to the hypothesis that the environmental contamination occurring around an incineration plant such as that examined in this study may induce major teratogenic effects.

  4. Deviant early pregnancy maternal triglyceride levels and increased risk of congenital anomalies: a prospective community-based cohort study.

    Science.gov (United States)

    Nederlof, M; de Walle, H E K; van Poppel, M N M; Vrijkotte, T G M; Gademan, M G J

    2015-08-01

    The maternal lipid profile could be of importance in congenital anomaly development. This study therefore investigates whether the maternal lipid profile during early pregnancy is associated with major nonsyndromic congenital anomalies (MNCA). Prospective community-based cohort study. Amsterdam Born Children and their Development (ABCD) study. A cohort of 3074 pregnant women recruited in 2003-2004 and their offspring. Non-fasting blood samples from pregnant women participating in the ABCD-study (median 12.9 weeks of gestation) were analysed for triglycerides (TG), cholesterol (TC), free fatty acids (FFA), apolipoprotein B (ApoB), and apolipoprotein A1 (ApoA) (n = 3074). The perinatal outcome (MNCA) was obtained from the Youth Health Care Registration and two questionnaires. Adjustment was made for ethnicity. MNCA prevalence. The prevalence of MNCA was 2.2% (n = 68: 20 cardiovascular, 25 bone and muscle, and 23 other single anomalies). A nonlinear association was found between maternal TG levels and MNCA prevalence. With a lower or higher level of maternal TG, the estimated probability increased: a TG level of 0.73 mmol/l (5th percentile), of 1.28 mmol/l (50th percentile), and of 2.35 mmol/l (95th percentile) corresponded with an estimated probability of 3.6, 2.1, and 2.9%, respectively. Unadjusted subgroup analyses showed that the U-shaped association was most prominent for cardiovascular congenital anomalies. Other lipids were not associated with MNCA. Both low and high maternal TG levels during early pregnancy were associated with an increased risk of MNCA in offspring. This suggests that an attempt should be made to normalise TG levels before or during early pregnancy; however, replication of our results is necessary before clinical practice recommendations can be made. © 2015 Royal College of Obstetricians and Gynaecologists.

  5. 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.

  6. 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)…

  7. Paper 3: EUROCAT data quality indicators for population-based registries of congenital anomalies

    DEFF Research Database (Denmark)

    Loane, Maria; Dolk, Helen; Garne, Ester

    2011-01-01

    for fetal anomaly. EUROCAT's policy is to strive for high-quality data, while ensuring consistency and transparency across all member registries. A set of 30 data quality indicators (DQIs) was developed to assess five key elements of data quality: completeness of case ascertainment, accuracy of diagnosis...... for 2004-2008. This information is also available on the EUROCAT website for previous years. The EUROCAT DQIs allow registries to evaluate their performance in relation to other registries and allows appropriate interpretations to be made of the data collected. The DQIs provide direction for improving data...

  8. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-07-15

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

  10. DOM Based XSS Detecting Method Based on Phantomjs

    Science.gov (United States)

    Dong, Ri-Zhan; Ling, Jie; Liu, Yi

    Because malicious code does not appear in html source code, DOM based XSS cannot be detected by traditional methods. By analyzing the causes of DOM based XSS, this paper proposes a detection method of DOM based XSS based on phantomjs. This paper uses function hijacking to detect dangerous operation and achieves a prototype system. Comparing with existing tools shows that the system improves the detection rate and the method is effective to detect DOM based XSS.

  11. Multi-lane detection based on multiple vanishing points detection

    Science.gov (United States)

    Li, Chuanxiang; Nie, Yiming; Dai, Bin; Wu, Tao

    2015-03-01

    Lane detection plays a significant role in Advanced Driver Assistance Systems (ADAS) for intelligent vehicles. In this paper we present a multi-lane detection method based on multiple vanishing points detection. A new multi-lane model assumes that a single lane, which has two approximately parallel boundaries, may not parallel to others on road plane. Non-parallel lanes associate with different vanishing points. A biological plausibility model is used to detect multiple vanishing points and fit lane model. Experimental results show that the proposed method can detect both parallel lanes and non-parallel lanes.

  12. Maternal Overweight and Obesity and Genital Anomalies in Male Offspring: A Population-Based Swedish Cohort Study.

    Science.gov (United States)

    Arendt, Linn Håkonsen; Ramlau-Hansen, Cecilia Høst; Lindhard, Morten Søndergaard; Henriksen, Tine Brink; Olsen, Jørn; Yu, Yongfu; Cnattingius, Sven

    2017-07-01

    Overweight and obese pregnant women face higher risk of several critical birth outcomes, including an overall increased risk of congenital abnormalities. Only few studies have focused on associations between maternal overweight and the genital anomalies in boys, cryptorchidism and hypospadias, and results are inconclusive. We performed a population-based cohort study and assessed the associations between maternal body mass index (BMI) in early pregnancy and occurrence of cryptorchidism and hypospadias. All live-born singleton boys born in Sweden from 1992 to 2012 were included. From the Swedish Patient Register, information on cryptorchidism and hypospadias was available. Data were analysed using Cox proportional hazards regression adjusted for potential confounders. Mediation analyses were performed to estimate how much of the association between BMI and genital anomalies were mediated through obesity-related diseases. Of the 1 055 705 live-born singleton boys born from 1992 to 2012, 6807 (6.4 per 1000) were diagnosed with hypospadias and 16 469 (15.6 per 1000) were diagnosed with cryptorchidism, of which 9768 (9.3 per 1000) underwent corrective surgery for cryptorchidism. We observed dose-response associations between maternal BMI and hypospadias and cryptorchidism. Boys of mothers with BMI ≥40.0 kg/m 2 had the highest adjusted hazard ratios for hypospadias (HR 1.35, 95% confidence interval [CI] 1.04, 1.76) and cryptorchidism (HR 1.25, 95% CI 1.00, 1.58). A substantial proportion of the associations between BMI and the genital anomalies were mediated through preeclampsia. This large register-based study adds to the current literature and indicates that the occurrence of hypospadias and cryptorchidism increase with maternal overweight and obesity severity. © 2017 John Wiley & Sons Ltd.

  13. MODIS/Aqua Thermal Anomalies/Fire 8-Day L3 Global 1km SIN Grid V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  14. MODIS/Aqua Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  15. MODIS/Terra Thermal Anomalies/Fire 8-Day L3 Global 1km SIN Grid V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  16. MODIS/Aqua Coarse Thermal Anomalies/Fire 5-Min L2 Swath 5km V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  17. MODIS/Terra Coarse Thermal Anomalies/Fire 5-Min L2 Swath 5km V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  18. MODIS/Terra Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid V005

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of...

  19. 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

  20. An Unsupervised Deep Hyperspectral Anomaly Detector.

    Science.gov (United States)

    Ma, Ning; Peng, Yu; Wang, Shaojun; Leong, Philip H W

    2018-02-26

    Hyperspectral image (HSI) based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN) based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption. To reduce contamination by local anomalies, adaptive weights are constructed from reconstruction errors and statistical information. By using the code image which is generated during the inference of DBN and modified by adaptively updated weights, a local Euclidean distance between under test pixels and their neighboring pixels is used to determine the anomaly targets. Experimental results on synthetic and recorded HSI datasets show the performance of proposed method outperforms the classic global Reed-Xiaoli detector (RXD), local RX detector (LRXD) and the-state-of-the-art Collaborative Representation detector (CRD).

  1. An Unsupervised Deep Hyperspectral Anomaly Detector

    Directory of Open Access Journals (Sweden)

    Ning Ma

    2018-02-01

    Full Text Available Hyperspectral image (HSI based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN based anomaly detector is proposed. The high-level features and reconstruction errors are learned through the network in a manner which is not affected by previous background distribution assumption. To reduce contamination by local anomalies, adaptive weights are constructed from reconstruction errors and statistical information. By using the code image which is generated during the inference of DBN and modified by adaptively updated weights, a local Euclidean distance between under test pixels and their neighboring pixels is used to determine the anomaly targets. Experimental results on synthetic and recorded HSI datasets show the performance of proposed method outperforms the classic global Reed-Xiaoli detector (RXD, local RX detector (LRXD and the-state-of-the-art Collaborative Representation detector (CRD.

  2. Identifying frauds and anomalies in Medicare-B dataset.

    Science.gov (United States)

    Jiwon Seo; Mendelevitch, Ofer

    2017-07-01

    Healthcare industry is growing at a rapid rate to reach a market value of $7 trillion dollars world wide. At the same time, fraud in healthcare is becoming a serious problem, amounting to 5% of the total healthcare spending, or $100 billion dollars each year in US. Manually detecting healthcare fraud requires much effort. Recently, machine learning and data mining techniques are applied to automatically detect healthcare frauds. This paper proposes a novel PageRank-based algorithm to detect healthcare frauds and anomalies. We apply the algorithm to Medicare-B dataset, a real-life data with 10 million healthcare insurance claims. The algorithm successfully identifies tens of previously unreported anomalies.

  3. Potential fire detection based on Kalman-driven change detection

    CSIR Research Space (South Africa)

    Van Den Bergh, F

    2009-07-01

    Full Text Available A new active fire event detection algorithm for data collected with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, based on the extended Kalman filter, is introduced. Instead of using the observed temperatures of the spatial...

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

    Science.gov (United States)

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

    2018-03-01

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

  5. Population-based screening versus case detection.

    Directory of Open Access Journals (Sweden)

    Thomas Ravi

    2002-01-01

    Full Text Available India has a large burden of blindness and population-based screening is a strategy commonly employed to detect disease and prevent morbidity. However, not all diseases are amenable to screening. This communication examines the issue of "population-based screening" versus "case detection" in the Indian scenario. Using the example of glaucoma, it demonstrates that given the poor infrastructure, for a "rare" disease, case detection is more effective than population-based screening.

  6. Subsurface structure identification of active fault based on magnetic anomaly data (Case study: Toru fault in Sumatera fault system)

    Science.gov (United States)

    Simanjuntak, Andrean V. H.; Husni, Muhammad; Syirojudin, Muhammad

    2017-07-01

    Toru segment, which is one of the active faults and located in the North of Sumatra, broke in 1984 ago on Pahae Jahe's earthquake with a magnitude 6.4 at the northern part of the fault which has a length of 23 km, and also broke again at the same place in 2008. The event of recurrence is very fast, which only 25 years old have repeatedly returned. However, in the elastic rebound theory, it probably happen with a fracture 50 cm and an average of the shear velocity 20 mm/year. The average focus of the earthquake sourced at a depth of 10 km and 23 km along its fracture zones, which can generate enough shaking 7 MMI and could breaking down buildings and create landslides on the cliff. Due to its seismic activity, this study was made to identify the effectiveness of this fault with geophysical methods. Geophysical methods such as gravity, geomagnetic and seismology are powerful tools for detecting subsurface structures of local, regional as well as of global scales. This study used to geophysical methods to discuss about total intensity of the geomagnetic anomaly data, resulted in the distribution of susceptibility values corresponding to the fault movement. The geomagnetic anomalies data was obtained from Geomag, such as total intensity measured by satellite. Data acquisition have been corrected for diurnal variations and reduced by IGRF. The study of earthquake records can be used for differentiating the active and non active fault elements. Modeling has been done using several methods, such as pseudo-gravity, reduce to pole, and upward or downward continuation, which is used to filter the geomagnetic anomaly data because the data has not fully representative of the fault structure. The results indicate that rock layers of 0 - 100 km depth encountered the process of intrusion and are dominated by sedimentary rocks that are paramagnetic, and that the ones of 100 - 150 km depth experienced the activity of subducting slab consisting of basalt and granite which are

  7. Online Monitoring of Water-Quality Anomaly in Water Distribution Systems Based on Probabilistic Principal Component Analysis by UV-Vis Absorption Spectroscopy

    Directory of Open Access Journals (Sweden)

    Dibo Hou

    2014-01-01

    Full Text Available This study proposes a probabilistic principal component analysis- (PPCA- based method for online monitoring of water-quality contaminant events by UV-Vis (ultraviolet-visible spectroscopy. The purpose of this method is to achieve fast and sound protection against accidental and intentional contaminate injection into the water distribution system. The method is achieved first by properly imposing a sliding window onto simultaneously updated online monitoring data collected by the automated spectrometer. The PPCA algorithm is then executed to simplify the large amount of spectrum data while maintaining the necessary spectral information to the largest extent. Finally, a monitoring chart extensively employed in fault diagnosis field methods is used here to search for potential anomaly events and to determine whether the current water-quality is normal or abnormal. A small-scale water-pipe distribution network is tested to detect water contamination events. The tests demonstrate that the PPCA-based online monitoring model can achieve satisfactory results under the ROC curve, which denotes a low false alarm rate and high probability of detecting water contamination events.

  8. Radiologic analysis of congenital limb anomalies

    International Nuclear Information System (INIS)

    Chung, Hong Jun; Kim, Ok Hwa; Shinn, Kyung Sub; Kim, Nam Ae

    1994-01-01

    Congenital limb anomalies are manifested in various degree of severity and complexity bearing conclusion for description and nomenclature of each anomaly. We retrospectively analyzed the roentgenograms of congenital limb anomalies for the purpose of further understanding of the radiologic manifestations based on the embryonal defect and also to find the incidence of each anomaly. Total number of the patients was 89 with 137 anomalies. Recently the uniform system of classification for congenital anomalies of the upper limb was adopted by International Federation of Societies for Surgery of the Hand (IFSSH), which were categorized as 7 classifications. We used the IFSSH classification with some modification as 5 classifications; failure of formation of parts, failure of differentiation of parts, duplications, overgrowth and undergrowth. The patients with upper limb anomalies were 65 out of 89(73%), lower limb were 21(24%), and both upper and lower limb anomalies were 3(4%). Failure of formation was seen in 18%, failure of differentiation 39%, duplications 39%, overgrowth 8%, and undergrowth in 12%. Thirty-five patients had more than one anomaly, and 14 patients had intergroup anomalies. The upper limb anomalies were more common than lower limb. Among the anomalies, failure of differentiation and duplications were the most common types of congenital limb anomalies. Patients with failure of formation, failure of differentiation, and undergrowth had intergroup association of anomalies, but duplication and overgrowth tended to be isolated anomalies

  9. A Population-Based Case-Control Study of Drinking-Water Nitrate and Congenital Anomalies Using Geographic Information Systems (GIS) to Develop Individual-Level Exposure Estimates

    Science.gov (United States)

    Holtby, Caitlin E.; Guernsey, Judith R.; Allen, Alexander C.; VanLeeuwen, John A.; Allen, Victoria M.; Gordon, Robert J.

    2014-01-01

    Animal studies and epidemiological evidence suggest an association between prenatal exposure to drinking water with elevated nitrate (NO3-N) concentrations and incidence of congenital anomalies. This study used Geographic Information Systems (GIS) to derive individual-level prenatal drinking-water nitrate exposure estimates from measured nitrate concentrations from 140 temporally monitored private wells and 6 municipal water supplies. Cases of major congenital anomalies in Kings County, Nova Scotia, Canada, between 1988 and 2006 were selected from province-wide population-based perinatal surveillance databases and matched to controls from the same databases. Unconditional multivariable logistic regression was performed to test for an association between drinking-water nitrate exposure and congenital anomalies after adjusting for clinically relevant risk factors. Employing all nitrate data there was a trend toward increased risk of congenital anomalies for increased nitrate exposure levels though this was not statistically significant. After stratification of the data by conception before or after folic acid supplementation, an increased risk of congenital anomalies for nitrate exposure of 1.5–5.56 mg/L (2.44; 1.05–5.66) and a trend toward increased risk for >5.56 mg/L (2.25; 0.92–5.52) was found. Though the study is likely underpowered, these results suggest that drinking-water nitrate exposure may contribute to increased risk of congenital anomalies at levels below the current Canadian maximum allowable concentration. PMID:24503976

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

    Science.gov (United States)

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

    2004-01-01

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

  11. Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

    Science.gov (United States)

    Sharma, Manali; Das, Kamalika; Bilgic, Mustafa; Matthews, Bryan; Nielsen, David Lynn; Oza, Nikunj C.

    2016-01-01

    A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

  12. Radar-based hail detection

    Czech Academy of Sciences Publication Activity Database

    Skripniková, Kateřina; Řezáčová, Daniela

    2014-01-01

    Roč. 144, č. 1 (2014), s. 175-185 ISSN 0169-8095 R&D Projects: GA ČR(CZ) GAP209/11/2045; GA MŠk LD11044 Institutional support: RVO:68378289 Keywords : hail detection * weather radar * hail damage risk Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.844, year: 2014 http://www.sciencedirect.com/science/article/pii/S0169809513001804

  13. Data reduction and tying in regional gravity surveys—results from a new gravity base station network and the Bouguer gravity anomaly map for northeastern Mexico

    Science.gov (United States)

    Hurtado-Cardador, Manuel; Urrutia-Fucugauchi, Jaime

    2006-12-01

    Since 1947 Petroleos Mexicanos (Pemex) has conducted oil exploration projects using potential field methods. Geophysical exploration companies under contracts with Pemex carried out gravity anomaly surveys that were referred to different floating data. Each survey comprises observations of gravity stations along highways, roads and trails at intervals of about 500 m. At present, 265 separate gravimeter surveys that cover 60% of the Mexican territory (mainly in the oil producing regions of Mexico) are available. This gravity database represents the largest, highest spatial resolution information, and consequently has been used in the geophysical data compilations for the Mexico and North America gravity anomaly maps. Regional integration of gravimeter surveys generates gradients and spurious anomalies in the Bouguer anomaly maps at the boundaries of the connected surveys due to the different gravity base stations utilized. The main objective of this study is to refer all gravimeter surveys from Pemex to a single new first-order gravity base station network, in order to eliminate problems of gradients and spurious anomalies. A second objective is to establish a network of permanent gravity base stations (BGP), referred to a single base from the World Gravity System. Four regional loops of BGP covering eight States of Mexico were established to support the tie of local gravity base stations from each of the gravimeter surveys located in the vicinity of these loops. The third objective is to add the gravity constants, measured and calculated, for each of the 265 gravimeter surveys to their corresponding files in the Pemex and Instituto Mexicano del Petroleo database. The gravity base used as the common datum is the station SILAG 9135-49 (Latin American System of Gravity) located in the National Observatory of Tacubaya in Mexico City. We present the results of the installation of a new gravity base network in northeastern Mexico, reference of the 43 gravimeter surveys

  14. RIDES: Robust Intrusion Detection System for IP-Based Ubiquitous Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sungwon Lee

    2009-05-01

    Full Text Available TheIP-based Ubiquitous Sensor Network (IP-USN is an effort to build the “Internet of things”. By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System called RIDES (Robust Intrusion DEtection System for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components.

  15. RIDES: Robust Intrusion Detection System for IP-Based Ubiquitous Sensor Networks.

    Science.gov (United States)

    Amin, Syed Obaid; Siddiqui, Muhammad Shoaib; Hong, Choong Seon; Lee, Sungwon

    2009-01-01

    The IP-based Ubiquitous Sensor Network (IP-USN) is an effort to build the "Internet of things". By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System) called RIDES (Robust Intrusion DEtection System) for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control) technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components.

  16. Detecting Ecosystem Performance Anomalies for Land Management in the Upper Colorado River Basin Using Satellite Observations, Climate Data, and Ecosystem Models

    Directory of Open Access Journals (Sweden)

    Bruce K. Wylie

    2010-07-01

    Full Text Available This study identifies areas with ecosystem performance anomalies (EPA within the Upper Colorado River Basin (UCRB during 2005–2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions at the 90% level of confidence. The EPA maps were validated using “percentage of bare soil” ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming tended to have a higher (lower percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005–2007 persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions.

  17. 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

  18. Audiovisual laughter detection based on temporal features

    NARCIS (Netherlands)

    Petridis, Stavros; Nijholt, Antinus; Nijholt, A.; Pantic, M.; Pantic, Maja; Poel, Mannes; Poel, M.; Hondorp, G.H.W.

    2008-01-01

    Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved

  19. The prevalence of congenital anomalies in Europe

    DEFF Research Database (Denmark)

    Dolk, Helen; Loane, Maria; Garne, Ester

    2010-01-01

    EUROCAT (European Surveillance of Congenital Anomalies) is the network of population-based registers of congenital anomaly in Europe, with a common protocol and data quality review, covering 1.5 million annual births in 22 countries. EUROCAT recorded a total prevalence of major congenital anomalies...... anomalies overwhelmingly concern children surviving the early neonatal period, who have important medical, social or educational needs. The prevalence of chromosomal anomalies was 3.6 per 1,000 births, contributing 28% of stillbirths/fetal deaths from 20 weeks gestation with congenital anomaly, and 48...

  20. The Pioneer Anomaly

    Directory of Open Access Journals (Sweden)

    Viktor T. Toth

    2010-09-01

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

  1. Rare chromosome abnormalities, prevalence and prenatal diagnosis rates from population-based congenital anomaly registers in Europe.

    Science.gov (United States)

    Wellesley, Diana; Dolk, Helen; Boyd, Patricia A; Greenlees, Ruth; Haeusler, Martin; Nelen, Vera; Garne, Ester; Khoshnood, Babak; Doray, Berenice; Rissmann, Anke; Mullaney, Carmel; Calzolari, Elisa; Bakker, Marian; Salvador, Joaquin; Addor, Marie-Claude; Draper, Elizabeth; Rankin, Judith; Tucker, David

    2012-05-01

    The aim of this study is to quantify the prevalence and types of rare chromosome abnormalities (RCAs) in Europe for 2000-2006 inclusive, and to describe prenatal diagnosis rates and pregnancy outcome. Data held by the European Surveillance of Congenital Anomalies database were analysed on all the cases from 16 population-based registries in 11 European countries diagnosed prenatally or before 1 year of age, and delivered between 2000 and 2006. Cases were all unbalanced chromosome abnormalities and included live births, fetal deaths from 20 weeks gestation and terminations of pregnancy for fetal anomaly. There were 10,323 cases with a chromosome abnormality, giving a total birth prevalence rate of 43.8/10,000 births. Of these, 7335 cases had trisomy 21,18 or 13, giving individual prevalence rates of 23.0, 5.9 and 2.3/10,000 births, respectively (53, 13 and 5% of all reported chromosome errors, respectively). In all, 473 cases (5%) had a sex chromosome trisomy, and 778 (8%) had 45,X, giving prevalence rates of 2.0 and 3.3/10,000 births, respectively. There were 1,737 RCA cases (17%), giving a prevalence of 7.4/10,000 births. These included triploidy, other trisomies, marker chromosomes, unbalanced translocations, deletions and duplications. There was a wide variation between the registers in both the overall prenatal diagnosis rate of RCA, an average of 65% (range 5-92%) and the prevalence of RCA (range 2.4-12.9/10,000 births). In all, 49% were liveborn. The data provide the prevalence of families currently requiring specialised genetic counselling services in the perinatal period for these conditions and, for some, long-term care.

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

    NARCIS (Netherlands)

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

    1998-01-01

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

  3. Community-Based Intrusion Detection

    OpenAIRE

    Weigert, Stefan

    2017-01-01

    Today, virtually every company world-wide is connected to the Internet. This wide-spread connectivity has given rise to sophisticated, targeted, Internet-based attacks. For example, between 2012 and 2013 security researchers counted an average of about 74 targeted attacks per day. These attacks are motivated by economical, financial, or political interests and commonly referred to as “Advanced Persistent Threat (APT)” attacks. Unfortunately, many of these attacks are successful and the advers...

  4. Daytime Water Detection Based on Sky Reflections

    Science.gov (United States)

    Rankin, Arturo; Matthies, Larry; Bellutta, Paolo

    2011-01-01

    A water body s surface can be modeled as a horizontal mirror. Water detection based on sky reflections and color variation are complementary. A reflection coefficient model suggests sky reflections dominate the color of water at ranges > 12 meters. Water detection based on sky reflections: (1) geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground (2) predicts if the ground pixel is water based on color similarity and local terrain features. Water detection has been integrated on XUVs.

  5. Analyzing Spatiotemporal Anomalies through Interactive Visualization

    Directory of Open Access Journals (Sweden)

    Tao Zhang

    2014-06-01

    Full Text Available As we move into the big data era, data grows not just in size, but also in complexity, containing a rich set of attributes, including location and time information, such as data from mobile devices (e.g., smart phones, natural disasters (e.g., earthquake and hurricane, epidemic spread, etc. We are motivated by the rising challenge and build a visualization tool for exploring generic spatiotemporal data, i.e., records containing time location information and numeric attribute values. Since the values often evolve over time and across geographic regions, we are particularly interested in detecting and analyzing the anomalous changes over time/space. Our analytic tool is based on geographic information system and is combined with spatiotemporal data mining algorithms, as well as various data visualization techniques, such as anomaly grids and anomaly bars superimposed on the map. We study how effective the tool may guide users to find potential anomalies through demonstrating and evaluating over publicly available spatiotemporal datasets. The tool for spatiotemporal anomaly analysis and visualization is useful in many domains, such as security investigation and monitoring, situation awareness, etc.

  6. About the nature of regional thermal anomaly in the Semipalatinsk Test Site region revealed basing on remote space sensing data

    International Nuclear Information System (INIS)

    Melent'ev, M.I.; Velikanov, A.E.

    2003-01-01

    A thermal anomaly, (more than 20,000 sq. km) discovered in the Semipalatinsk Test Site region in the pictures from space, is observed every year on certain days mainly in winter-spring season. Appearance of the thermal anomaly often coincides with days of intensive fall of atmospheric precipitation and possible thawing of snow cover together with decreasing of ozone concentration in atmosphere. The explanation of thermal anomaly in the Semipalatinsk Test Site region due to nuclear reaction caused by the energy of radionuclide radioactive decay deposited in a soil layer after ground and air nuclear explosions and radiolysis processes in soil solutions is given in this article. (author)

  7. Congenital Anomalies Associated with Trisomy 18 or Trisomy 13 : A Registry-Based Study in 16 European Countries, 2000-2011

    NARCIS (Netherlands)

    Springett, Anna; Wellesley, Diana; Greenlees, Ruth; Loane, Maria; Addor, Marie-Claude; Arriola, Larraitz; Bergman, Jorieke; Cavero-Carbonell, Clara; Csaky-Szunyogh, Melinda; Draper, Elizabeth S.; Garne, Ester; Gatt, Miriam; Haeusler, Martin; Khoshnood, Babak; Klungsoyr, Kari; Lynch, Catherine; Dias, Carlos Matias; McDonnell, Robert; Nelen, Vera; O'Mahony, Mary; Pierini, Anna; Queisser-Luft, Annette; Rankin, Judith; Rissmann, Anke; Rounding, Catherine; Stoianova, Sylvia; Tuckerz, David; Zymak-Zakutnia, Natalya; Morris, Joan K.

    2015-01-01

    The aim of this study was to examine the prevalence of trisomies 18 and 13 in Europe and the prevalence of associated anomalies. Twenty-five population-based registries in 16 European countries provided data from 2000-2011. Cases included live births, fetal deaths (20+ weeks' gestation), and

  8. Spike detection II: automatic, perception-based detection and clustering.

    Science.gov (United States)

    Wilson, S B; Turner, C A; Emerson, R G; Scheuer, M L

    1999-03-01

    We developed perception-based spike detection and clustering algorithms. The detection algorithm employs a novel, multiple monotonic neural network (MMNN). It is tested on two short-duration EEG databases containing 2400 spikes from 50 epilepsy patients and 10 control subjects. Previous studies are compared for database difficulty and reliability and algorithm accuracy. Automatic grouping of spikes via hierarchical clustering (using topology and morphology) is visually compared with hand marked grouping on a single record. The MMNN algorithm is found to operate close to the ability of a human expert while alleviating problems related to overtraining. The hierarchical and hand marked spike groupings are found to be strikingly similar. An automatic detection algorithm need not be as accurate as a human expert to be clinically useful. A user interface that allows the neurologist to quickly delete artifacts and determine whether there are multiple spike generators is sufficient.

  9. Power Consumption Based Android Malware Detection

    Directory of Open Access Journals (Sweden)

    Hongyu Yang

    2016-01-01

    Full Text Available In order to solve the problem that Android platform’s sand-box mechanism prevents security protection software from accessing effective information to detect malware, this paper proposes a malicious software detection method based on power consumption. Firstly, the mobile battery consumption status information was obtained, and the Gaussian mixture model (GMM was built by using Mel frequency cepstral coefficients (MFCC. Then, the GMM was used to analyze power consumption; malicious software can be classified and detected through classification processing. Experiment results demonstrate that the function of an application and its power consumption have a close relationship, and our method can detect some typical malicious application software accurately.

  10. Apriori-based network intrusion detection system

    International Nuclear Information System (INIS)

    Wang Wenjin; Liu Junrong; Liu Baoxu

    2012-01-01

    With the development of network communication technology, more and more social activities run by Internet. In the meantime, the network information security is getting increasingly serious. Intrusion Detection System (IDS) has greatly improved the general security level of whole network. But there are still many problem exists in current IDS, e.g. high leak rate detection/false alarm rates and feature library need frequently upgrade. This paper presents an association-rule based IDS. This system can detect unknown attack by generate rules from training data. Experiment in last chapter proved the system has great accuracy on unknown attack detection. (authors)

  11. Anomaly Monitoring Method for Key Components of Satellite

    Directory of Open Access Journals (Sweden)

    Jian Peng

    2014-01-01

    Full Text Available This paper presented a fault diagnosis method for key components of satellite, called Anomaly Monitoring Method (AMM, which is made up of state estimation based on Multivariate State Estimation Techniques (MSET and anomaly detection based on Sequential Probability Ratio Test (SPRT. On the basis of analysis failure of lithium-ion batteries (LIBs, we divided the failure of LIBs into internal failure, external failure, and thermal runaway and selected electrolyte resistance (Re and the charge transfer resistance (Rct as the key parameters of state estimation. Then, through the actual in-orbit telemetry data of the key parameters of LIBs, we obtained the actual residual value (RX and healthy residual value (RL of LIBs based on the state estimation of MSET, and then, through the residual values (RX and RL of LIBs, we detected the anomaly states based on the anomaly detection of SPRT. Lastly, we conducted an example of AMM for LIBs, and, according to the results of AMM, we validated the feasibility and effectiveness of AMM by comparing it with the results of threshold detective method (TDM.

  12. Current practice of distraction osteogenesis for craniofacial anomalies in Europe: a web based survey.

    NARCIS (Netherlands)

    Nada, R.M.; Sugar, A.W.; Wijdeveld, M.G.M.M.; Borstlap, W.A.; Clauser, L.; Hoffmeister, B.; Kuijpers-Jagtman, A.M.

    2010-01-01

    Aim of the study was to get more insight into the opinion of European surgeons and orthodontists on the use of distraction osteogenesis (DO) for patients with different diagnoses and treatment protocols. A web based survey was set up, showing records of four patients with different conditions:

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

    International Nuclear Information System (INIS)

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

    1982-11-01

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

  14. Coronary anomalies: what the radiologist should know

    Directory of Open Access Journals (Sweden)

    Priscilla Ornellas Neves

    2015-08-01

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

  15. Coronary anomalies: what the radiologist should know*

    Science.gov (United States)

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

    2015-01-01

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

  16. A machine learning based methodology for anomaly detection in dam behaviour

    OpenAIRE

    Salazar González, Fernando

    2017-01-01

    Dam behaviour is difficult to predict with high accuracy. Numerical models for structural calculation solve the equations of continuum mechanics, but are subject to considerable uncertainty as to the characterisation of materials, especially with regard to the foundation. As a result, these models are often incapable to calculate dam behaviour with sufficient precision. Thus, it is difficult to determine whether a given deviation between model results and monitoring data represent a relevant ...

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

    Science.gov (United States)

    2014-03-27

    National Transportation Safety Board (NTSB) accident report, a ruptured pipe in Marshall, Michigan led to the release of “843,444 gallons of crude oil...3). This is true for some attempted applications of information theory such as psychology. R. Duncan Luce authored an article in the journal...applied” ( Luce , 2003:183). For a time following Shannon’s paper, psychologists attempted to apply information theory in their experiments with little

  18. Countering Botnets: Anomaly-Based Detection, Comprehensive Analysis, and Efficient Mitigation

    Science.gov (United States)

    2011-05-01

    Mariposa Working Group (MWG), which performed attribution and traceback on the so-called Mariposa botnet. In the end, several groups were arrested in...August 5, 2010 International Conference on Cyber Security: Law enforcement agencies alone cannot defeat our cyber adversaries. In the Mariposa case...our private sector partners also provided valuable help. The Mariposa Working Group, an informal band of security researchers and volunteers, gave

  19. Temporal Methods to Detect Content-Based Anomalies in Social Media

    Energy Technology Data Exchange (ETDEWEB)

    Skryzalin, Jacek [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Field, Jr., Richard [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Fisher, Andrew N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bauer, Travis L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    Here, we develop a method for time-dependent topic tracking and meme trending in social media. Our objective is to identify time periods whose content differs signifcantly from normal, and we utilize two techniques to do so. The first is an information-theoretic analysis of the distributions of terms emitted during different periods of time. In the second, we cluster documents from each time period and analyze the tightness of each clustering. We also discuss a method of combining the scores created by each technique, and we provide ample empirical analysis of our methodology on various Twitter datasets.

  20. Neural network based tomographic approach to detect earthquake-related ionospheric anomalies

    Directory of Open Access Journals (Sweden)

    S. Hirooka

    2011-08-01

    Full Text Available A tomographic approach is used to investigate the fine structure of electron density in the ionosphere. In the present paper, the Residual Minimization Training Neural Network (RMTNN method is selected as the ionospheric tomography with which to investigate the detailed structure that may be associated with earthquakes. The 2007 Southern Sumatra earthquake (M = 8.5 was selected because significant decreases in the Total Electron Content (TEC have been confirmed by GPS and global ionosphere map (GIM analyses. The results of the RMTNN approach are consistent with those of TEC approaches. With respect to the analyzed earthquake, we observed significant decreases at heights of 250–400 km, especially at 330 km. However, the height that yields the maximum electron density does not change. In the obtained structures, the regions of decrease are located on the southwest and southeast sides of the Integrated Electron Content (IEC (altitudes in the range of 400–550 km and on the southern side of the IEC (altitudes in the range of 250–400 km. The global tendency is that the decreased region expands to the east with increasing altitude and concentrates in the Southern hemisphere over the epicenter. These results indicate that the RMTNN method is applicable to the estimation of ionospheric electron density.

  1. Reconstruction Error and Principal Component Based Anomaly Detection in Hyperspectral Imagery

    Science.gov (United States)

    2014-03-27

    NIR Spectroscopy with Applications in Food and Beverage Analysis. Essex: Longman Scientific & Technical. Peres-Neto, P. R., Jackson, D. A...Presented to the Faculty Department of Aeronautics and Astronautics Graduate School of Engineering and Management Air Force Institute of Technology...2003), and (Jackson D. A., 1993). In 1933, Hotelling ( Hotelling , 1933), who coined the term ‘principal components,’ surmised that there was a

  2. Creating Robust Relation Extract and Anomaly Detect via Probabilistic Logic-Based Reasoning and Learning

    Science.gov (United States)

    2017-11-01

    reasoning, reasoning and learning, machine learning, relationship extraction, implicit information understanding, Natural language understanding, NLU...transferring across seemingly unrelated domains (eg: Sports and Finance , NELL ontologies). We refer to our ICDM paper [19] for further details and...variants, and phenotypes from the literature, and statistically infer their relationships , presently being applied to clinical genetic diagnostics

  3. Neonatal outcomes in fetuses with cardiac anomalies and the impact of delivery route.

    Science.gov (United States)

    Parikh, Laura I; Grantz, Katherine L; Iqbal, Sara N; Huang, Chun-Chih; Landy, Helain J; Fries, Melissa H; Reddy, Uma M

    2017-10-01

    Congenital fetal cardiac anomalies compromise the most common group of fetal structural anomalies. Several previous reports analyzed all types of fetal cardiac anomalies together without individualized neonatal morbidity outcomes based on cardiac defect. Mode of delivery in cases of fetal cardiac anomalies varies greatly as optimal mode of delivery in these complex cases is unknown. We sought to determine rates of neonatal outcomes for fetal cardiac anomalies and examine the role of attempted route of delivery on neonatal morbidity. Gravidas with fetal cardiac anomalies and delivery >34 weeks, excluding stillbirths and aneuploidies (n = 2166 neonates, n = 2701 cardiac anomalies), were analyzed from the Consortium on Safe Labor, a retrospective cohort study of electronic medical records. Cardiac anomalies were determined using International Classification of Diseases, Ninth Revision codes and organized based on morphology. Neonates were assigned to each cardiac anomaly classification based on the most severe cardiac defect present. Neonatal outcomes were determined for each fetal cardiac anomaly. Composite neonatal morbidity (serious respiratory morbidity, sepsis, birth trauma, hypoxic ischemic encephalopathy, and neonatal death) was compared between attempted vaginal delivery and planned cesarean delivery for prenatal and postnatal diagnosis. We used multivariate logistic regression to calculate adjusted odds ratio for composite neonatal morbidity controlling for race, parity, body mass index, insurance, gestational age, maternal disease, single or multiple anomalies, and maternal drug use. Most cardiac anomalies were diagnosed postnatally except hypoplastic left heart syndrome, which had a higher prenatal than postnatal detection rate. Neonatal death occurred in 8.4% of 107 neonates with conotruncal defects. Serious respiratory morbidity occurred in 54.2% of 83 neonates with left ventricular outflow tract defects. Overall, 76.3% of pregnancies with fetal

  4. Vision Based Obstacle Detection in Uav Imaging

    Science.gov (United States)

    Badrloo, S.; Varshosaz, M.

    2017-08-01

    Detecting and preventing incidence with obstacles is crucial in UAV navigation and control. Most of the common obstacle detection techniques are currently sensor-based. Small UAVs are not able to carry obstacle detection sensors such as radar; therefore, vision-based methods are considered, which can be divided into stereo-based and mono-based techniques. Mono-based methods are classified into two groups: Foreground-background separation, and brain-inspired methods. Brain-inspired methods are highly efficient in obstacle detection; hence, this research aims to detect obstacles using brain-inspired techniques, which try to enlarge the obstacle by approaching it. A recent research in this field, has concentrated on matching the SIFT points along with, SIFT size-ratio factor and area-ratio of convex hulls in two consecutive frames to detect obstacles. This method is not able to distinguish between near and far obstacles or the obstacles in complex environment, and is sensitive to wrong matched points. In order to solve the above mentioned problems, this research calculates the dist-ratio of matched points. Then, each and every point is investigated for Distinguishing between far and close obstacles. The results demonstrated the high efficiency of the proposed method in complex environments.

  5. VEGFA polymorphisms and cardiovascular anomalies in 22q11 microdeletion syndrome: a case-control and family-based study

    Directory of Open Access Journals (Sweden)

    JUAN FRANCISCO CALDERÓN

    2009-01-01

    Full Text Available Microdeletion 22q11 in humans causes velocardiofacial and DiGeorge syndromes. Most patients share a common 3Mb deletion, but the clinical manifestations are very heterogeneous. Congenital heart disease is present in 50-80% of patients and is a significant cause of morbidity and mortality. The phenotypic variability suggests the presence of modifiers. Polymorphisms in the VEGFA gene, coding for the vascular endothelial growth factor A, have been associated with non-syndromic congenital heart disease, as well as with the presence of cardiovascular anomalies in patients with microdeletion 22q11. We evaluated the association of VEGFA polymorphisms c.-2578C>A (rs699947, c.-1154G>A (rs1570360 and c.-634C>G (rs2010963 with congenital heart disease in Chilean patients with microdeletion 22q11. The study was performed using case-control and family-based association designs. We evaluated 122 patients with microdeletion 22q11 and known anatomy of the heart and great vessels, and their parents. Half the patients had congenital heart disease. We obtained no evidence of association by either method of analysis. Our results provide further evidence of the incomplete penetrance of the cardiovascular phenotype of microdeletion 22ql 1, but do not support association between VEGFA promoter polymorphisms and the presence of congenital heart disease in Chilean patients with this syndrome.

  6. MODIS/Aqua Near Real Time (NRT) Coarse Thermal Anomalies/Fire 5-Min L2 Swath 5km

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Near Real Time (NRT) Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on...

  7. MODIS/Aqua Near Real Time (NRT) Thermal Anomalies/Fire 5-Min L2 Swath 1km

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Near Real Time (NRT) Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on...

  8. MODIS/Terra Near Real Time (NRT) Coarse Thermal Anomalies/Fire 5-Min L2 Swath 5km

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Near Real Time (NRT) Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on...

  9. MODIS/Terra Near Real Time (NRT) Thermal Anomalies/Fire 5-Min L2 Swath 1km

    Data.gov (United States)

    National Aeronautics and Space Administration — MODIS Near Real Time (NRT) Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on...

  10. Animal anomalies and earthquake. [Earthquake forecasts based on the abnormal behavior of animals

    Energy Technology Data Exchange (ETDEWEB)

    Lan, C.

    1976-11-01

    Although earthquakes cannot yet be controlled, a great deal of evidence indicates that earthquakes will someday be mastered by men. In China, the earthquakes of Hai-ch'eng of South Liaoning on 2 February 1975, Lung-ling to Lu-hsi of Yunnan, and Sung-pan to P'ing-wu of Szechwan this year were successfully forecasted. The technique of forecasting earthquakes remains in need of being further perfected, however. This paper describes the abnormal reactions of over 80 species of animals just before an earthquake. Of these, the more accurate sources for forecasting include over 20 species of dogs, chickens, rodents, fish, birds, cats, and pigs. Through accumulation of feelings and heredity, the sense organs and nervous system possess a special ability to sense minute changes in the environment. The abnormal behaviors of the animals are perhaps reactions to certain physical or chemical changes, including changes of the electromagnetic field. Earthquake forecasting should be based upon comprehensive analyses of data including the abnormal behavior of animals observed by the masses.

  11. Recent advances in biosensor based endotoxin detection.

    Science.gov (United States)

    Das, A P; Kumar, P S; Swain, S

    2014-01-15

    Endotoxins also referred to as pyrogens are chemically lipopolysaccharides habitually found in food, environment and clinical products of bacterial origin and are unavoidable ubiquitous microbiological contaminants. Pernicious issues of its contamination result in high mortality and severe morbidities. Standard traditional techniques are slow and cumbersome, highlighting the pressing need for evoking agile endotoxin detection system. The early and prompt detection of endotoxin assumes prime importance in health care, pharmacological and biomedical sectors. The unparalleled recognition abilities of LAL biosensors perched with remarkable sensitivity, high stability and reproducibility have bestowed it with persistent reliability and their possible fabrication for commercial applicability. This review paper entails an overview of various trends in current techniques available and other possible alternatives in biosensor based endotoxin detection together with its classification, epidemiological aspects, thrust areas demanding endotoxin control, commercially available detection sensors and a revolutionary unprecedented approach narrating the influence of omics for endotoxin detection. Copyright © 2013 Elsevier B.V. All rights reserved.

  12. An SNMP based failure detection service

    OpenAIRE

    Wiesmann, Matthias; Urban, Peter; Defago, Xavier

    2006-01-01

    In this paper, we present the SNMP-FD system. This system is a novel failure detection service entirely based on the SNMP standard. The advantage of this approach is better interoperability, and the possibility to rely on different sources of information for failure detection, including network equipment. This, in turn, gives us more precise failure information. This paper presents the architecture of the SNMP-FD system and discusses its advantages, both from the system engineering and intero...

  13. Chiral anomalies and differential geometry

    Energy Technology Data Exchange (ETDEWEB)

    Zumino, B.

    1983-10-01

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

  14. Plagiarism Detection Based on SCAM Algorithm

    DEFF Research Database (Denmark)

    Anzelmi, Daniele; Carlone, Domenico; Rizzello, Fabio

    2011-01-01

    Plagiarism is a complex problem and considered one of the biggest in publishing of scientific, engineering and other types of documents. Plagiarism has also increased with the widespread use of the Internet as large amount of digital data is available. Plagiarism is not just direct copy but also...... paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. Our proposed detection process is based on natural language...... document. Our plagiarism detection system, like many Information Retrieval systems, is evaluated with metrics of precision and recall....

  15. Quantum Endpoint Detection Based on QRDA

    Science.gov (United States)

    Wang, Jian; Wang, Han; Song, Yan

    2017-10-01

    Speech recognition technology is widely used in many applications for man - machine interaction. To face more and more speech data, the computation of speech processing needs new approaches. The quantum computation is one of emerging computation technology and has been seen as useful computation model. So we focus on the basic operation of speech recognition processing, the voice activity detection, to present quantum endpoint detection algorithm. In order to achieve this algorithm, the n-bits quantum comparator circuit is given firstly. Then based on QRDA(Quantum Representation of Digital Audio), a quantum endpoint detection algorithm is presented. These quantum circuits could efficient process the audio data in quantum computer.

  16. 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.

  17. Cellular telephone-based wide-area radiation detection network

    Energy Technology Data Exchange (ETDEWEB)

    Craig, William W [Pittsburg, CA; Labov, Simon E [Berkeley, CA

    2009-06-09

    A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

  18. Multiprobe in-situ measurement of magnetic field in a minefield via a distributed network of miniaturized low-power integrated sensor systems for detection of magnetic field anomalies

    Science.gov (United States)

    Javadi, Hamid H. S.; Bendrihem, David; Blaes, B.; Boykins, Kobe; Cardone, John; Cruzan, C.; Gibbs, J.; Goodman, W.; Lieneweg, U.; Michalik, H.; Narvaez, P.; Perrone, D.; Rademacher, Joel D.; Snare, R.; Spencer, Howard; Sue, Miles; Weese, J.

    1998-09-01

    Based on technologies developed for the Jet Propulsion Laboratory (JPL) Free-Flying-Magnetometer (FFM) concept, we propose to modify the present design of FFMs for detection of mines and arsenals with large magnetic signature. The result will be an integrated miniature sensor system capable of identifying local magnetic field anomaly caused by a magnetic dipole moment. Proposed integrated sensor system is in line with the JPL technology road-map for development of autonomous, intelligent, networked, integrated systems with a broad range of applications. In addition, advanced sensitive magnetic sensors (e.g., silicon micromachined magnetometer, laser pumped helium magnetometer) are being developed for future NASA space plasma probes. It is envisioned that a fleet of these Integrated Sensor Systems (ISS) units will be dispersed on a mine-field via an aerial vehicle (a low-flying airplane or helicopter). The number of such sensor systems in each fleet and the corresponding in-situ probe-grid cell size is based on the strength of magnetic anomaly of the target and ISS measurement resolution of magnetic field vector. After a specified time, ISS units will transmit the measured magnetic field and attitude data to an air-borne platform for further data processing. The cycle of data acquisition and transmission will be continued until batteries run out. Data analysis will allow a local deformation of the Earth's magnetic field vector by a magnetic dipole moment to be detected. Each ISS unit consists of miniaturized sensitive 3- axis magnetometer, high resolution analog-to-digital converter (ADC), Field Programmable Gate Array (FPGA)-based data subsystem, Li-batteries and power regulation circuitry, memory, S-band transmitter, single-patch antenna, and a sun angle sensor. ISS unit is packaged with non-magnetic components and the electronic design implements low-magnetic signature circuits. Care is undertaken to guarantee no corruption of magnetometer sensitivity as a result

  19. On event-based optical flow detection

    Directory of Open Access Journals (Sweden)

    Tobias eBrosch

    2015-04-01

    Full Text Available Event-based sensing, i.e. the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER. Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency. A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations.

  20. Improved biosensor-based detection system

    DEFF Research Database (Denmark)

    2015-01-01

    Described is a new biosensor-based detection system for effector compounds, useful for in vivo applications in e.g. screening and selecting of cells which produce a small molecule effector compound or which take up a small molecule effector compound from its environment. The detection system...... comprises a protein or RNA-based biosensor for the effector compound which indirectly regulates the expression of a reporter gene via two hybrid proteins, providing for fewer false signals or less 'noise', tuning of sensitivity or other advantages over conventional systems where the biosensor directly...

  1. Image denoising based on noise detection

    Science.gov (United States)

    Jiang, Yuanxiang; Yuan, Rui; Sun, Yuqiu; Tian, Jinwen

    2018-03-01

    Because of the noise points in the images, any operation of denoising would change the original information of non-noise pixel. A noise detection algorithm based on fractional calculus was proposed to denoise in this paper. Convolution of the image was made to gain direction gradient masks firstly. Then, the mean gray was calculated to obtain the gradient detection maps. Logical product was made to acquire noise position image next. Comparisons in the visual effect and evaluation parameters after processing, the results of experiment showed that the denoising algorithms based on noise were better than that of traditional methods in both subjective and objective aspects.

  2. Neutron-based techniques for detection of explosives and drugs

    CERN Document Server

    Kiraly, B; Csikai, J

    2001-01-01

    Systematic measurements were carried out on the possible use of elastically backscattered Pu-Be neutrons combined with the thermal neutron reflection method for the identification of land mines and illicit drugs via he detection of H, C, N, and O elements as their major constituents. While ur present results show that these methods are capable of indicating the anomalies in bulky materials and observation of the major elements, e termination of the exact atom fractions needs further investigation.

  3. Major congenital anomalies in a Danish region

    DEFF Research Database (Denmark)

    Garne, Ester; Hansen, Anne Vinkel; Birkelund, Anne Sofie

    2014-01-01

    congenital anomaly, 13.9% had a chromosomal anomaly and 7.7% were multiple congenital anomalies. The combined foetal and infant mortality in the study area was 11.6 per 1,000 births. 19% (2.2 per 1,000) of these deaths were foetuses and infants with major congenital anomalies. Combined foetal and infant......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......-2008. The registry covers live births, foetal deaths with a gestational age (GA) of 20 weeks or more, and terminations of pregnancy due to congenital anomalies (TOPFA). RESULTS: The overall prevalence of congenital anomalies was 2.70% (95% confidence interval: 2.58-2.80). The majority of cases had an isolated...

  4. Research on Abnormal Detection Based on Improved Combination of K - means and SVDD

    Science.gov (United States)

    Hao, Xiaohong; Zhang, Xiaofeng

    2018-01-01

    In order to improve the efficiency of network intrusion detection and reduce the false alarm rate, this paper proposes an anomaly detection algorithm based on improved K-means and SVDD. The algorithm first uses the improved K-means algorithm to cluster the training samples of each class, so that each class is independent and compact in class; Then, according to the training samples, the SVDD algorithm is used to construct the minimum superspheres. The subordinate relationship of the samples is determined by calculating the distance of the minimum superspheres constructed by SVDD. If the test sample is less than the center of the hypersphere, the test sample belongs to this class, otherwise it does not belong to this class, after several comparisons, the final test of the effective detection of the test sample.In this paper, we use KDD CUP99 data set to simulate the proposed anomaly detection algorithm. The results show that the algorithm has high detection rate and low false alarm rate, which is an effective network security protection method.

  5. 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...

  6. Seizure detection algorithms based on EMG signals

    DEFF Research Database (Denmark)

    Conradsen, Isa

    Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective......: to show whether medical signal processing of EMG data is feasible for detection of epileptic seizures. Methods: EMG signals during generalised seizures were recorded from 3 patients (with 20 seizures in total). Two possible medical signal processing algorithms were tested. The first algorithm was based...... the frequency-based algorithm was efficient for detecting the seizures in the third patient. Conclusion: Our results suggest that EMG signals could be used to develop an automatic seizuredetection system. However, different patients might require different types of algorithms /approaches....

  7. Tornado Detection Based on Seismic Signal.

    Science.gov (United States)

    Tatom, Frank B.; Knupp, Kevin R.; Vitton, Stanley J.

    1995-02-01

    At the present time the only generally accepted method for detecting when a tornado is on the ground is human observation. Based on theoretical considerations combined with eyewitness testimony, there is strong reason to believe that a tornado in contact with the ground transfers a significant amount of energy into the ground. The amount of energy transferred depends upon the intensity of the tornado and the characteristics of the surface. Some portion of this energy takes the form of seismic waves, both body and surface waves. Surface waves (Rayleigh and possibly Love) represent the most likely type of seismic signal to be detected. Based on the existence of such a signal, a seismic tornado detector appears conceptually possible. The major concerns for designing such a detector are range of detection and discrimination between the tornadic signal and other types of surface waves generated by ground transportation equipment, high winds, or other nontornadic sources.

  8. Accelerometer and Camera-Based Strategy for Improved Human Fall Detection

    KAUST Repository

    Zerrouki, Nabil

    2016-10-29

    In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow’s. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.

  9. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Directory of Open Access Journals (Sweden)

    S. Ganapathy

    2012-01-01

    Full Text Available Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

  10. Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

    Science.gov (United States)

    Ganapathy, S; Yogesh, P; Kannan, A

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

  11. Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

    Science.gov (United States)

    Ganapathy, S.; Yogesh, P.; Kannan, A.

    2012-01-01

    Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

  12. Using scan statistics for congenital anomalies surveillance: the EUROCAT methodology.

    Science.gov (United States)

    Teljeur, Conor; Kelly, Alan; Loane, Maria; Densem, James; Dolk, Helen

    2015-11-01

    Scan statistics have been used extensively to identify temporal clusters of health events. We describe the temporal cluster detection methodology adopted by the EUROCAT (European Surveillance of Congenital Anomalies) monitoring system. Since 2001, EUROCAT has implemented variable window width scan statistic for detecting unusual temporal aggregations of congenital anomaly cases. The scan windows are based on numbers of cases rather than being defined by time. The methodology is imbedded in the EUROCAT Central Database for annual application to centrally held registry data. The methodology was incrementally adapted to improve the utility and to address statistical issues. Simulation exercises were used to determine the power of the methodology to identify periods of raised risk (of 1-18 months). In order to operationalize the scan methodology, a number of adaptations were needed, including: estimating date of conception as unit of time; deciding the maximum length (in time) and recency of clusters of interest; reporting of multiple and overlapping significant clusters; replacing the Monte Carlo simulation with a lookup table to reduce computation time; and placing a threshold on underlying population change and estimating the false positive rate by simulation. Exploration of power found that raised risk periods lasting 1 month are unlikely to be detected except when the relative risk and case counts are high. The variable window width scan statistic is a useful tool for the surveillance of congenital anomalies. Numerous adaptations have improved the utility of the original methodology in the context of temporal cluster detection in congenital anomalies.

  13. Rare chromosome abnormalities, prevalence and prenatal diagnosis rates from population-based congenital anomaly registers in Europe

    NARCIS (Netherlands)

    Wellesley, Diana; Dolk, Helen; Boyd, Patricia A.; Greenlees, Ruth; Haeusler, Martin; Nelen, Vera; Garne, Ester; Khoshnood, Babak; Doray, Berenice; Rissmann, Anke; Mullaney, Carmel; Calzolari, Elisa; Bakker, Marian; Salvador, Joaquin; Addor, Marie-Claude; Draper, Elizabeth; Rankin, Judith; Tucker, David

    The aim of this study is to quantify the prevalence and types of rare chromosome abnormalities (RCAs) in Europe for 2000-2006 inclusive, and to describe prenatal diagnosis rates and pregnancy outcome. Data held by the European Surveillance of Congenital Anomalies database were analysed on all the

  14. Water Detection Based on Object Reflections

    Science.gov (United States)

    Rankin, Arturo L.; Matthies, Larry H.

    2012-01-01

    Water bodies are challenging terrain hazards for terrestrial unmanned ground vehicles (UGVs) for several reasons. Traversing through deep water bodies could cause costly damage to the electronics of UGVs. Additionally, a UGV that is either broken down due to water damage or becomes stuck in a water body during an autonomous operation will require rescue, potentially drawing critical resources away from the primary operation and increasing the operation cost. Thus, robust water detection is a critical perception requirement for UGV autonomous navigation. One of the properties useful for detecting still water bodies is that their surface acts as a horizontal mirror at high incidence angles. Still water bodies in wide-open areas can be detected by geometrically locating the exact pixels in the sky that are reflecting on candidate water pixels on the ground, predicting if ground pixels are water based on color similarity to the sky and local terrain features. But in cluttered areas where reflections of objects in the background dominate the appearance of the surface of still water bodies, detection based on sky reflections is of marginal value. Specifically, this software attempts to solve the problem of detecting still water bodies on cross-country terrain in cluttered areas at low cost.

  15. Community detection based on network communicability

    Science.gov (United States)

    Estrada, Ernesto

    2011-03-01

    We propose a new method for detecting communities based on the concept of communicability between nodes in a complex network. This method, designated as N-ComBa K-means, uses a normalized version of the adjacency matrix to build the communicability matrix and then applies K-means clustering to find the communities in a graph. We analyze how this method performs for some pathological cases found in the analysis of the detection limit of communities and propose some possible solutions on the basis of the analysis of the ratio of local to global densities in graphs. We use four different quality criteria for detecting the best clustering and compare the new approach with the Girvan-Newman algorithm for the analysis of two "classical" networks: karate club and bottlenose dolphins. Finally, we analyze the more challenging case of homogeneous networks with community structure, for which the Girvan-Newman completely fails in detecting any clustering. The N-ComBa K-means approach performs very well in these situations and we applied it to detect the community structure in an international trade network of miscellaneous manufactures of metal having these characteristics. Some final remarks about the general philosophy of community detection are also discussed.

  16. Wavelet-frame-based microcalcification detection

    Science.gov (United States)

    Chang, Charles C.; Wu, Hsien-Hsun S.; Liu, Jyh-Charn S.; Chui, Charles K.

    1997-10-01

    As the leading cause of death for adult women under 54 years of age in the United States, breast cancer accounts for 29% of all cancers in women. Early diagnosis of breast cancer is the most effective approach to reduce death rate. The rapid climbing of the health care cost further reiterates the importance of cost-effective, accurate screening tools for breast cancer. This paper proposes a wavelet frame based computer algorithm for screening of microcalcifications on digitized mammographical imagery. Despite its simplicity, the discrete wavelet transform (DWT) of compactly supported wavelets has been effectively used for detection of various types of signals. However, the shifting variant property of DWT makes it very unstable for detection of minute microcalcifications. Although increasing the sampling rate will improve the detection probability, this approach will drastically increase the size of mammographical images. The wavelet frame transform can be easily derived from the DWT algorithm by eliminating its down sampling step. The subtle difference between DWT and WF in down sampling is shown to be critical to the accuracy of microcalcifications detection. Without any down sampling, local image information at different scales is preserved. By joint thresholding of wavelet coefficients at different scales, one can accurately pin point suspected microcalcifications. A simple partitioning technique enables the detection algorithm to process image blocks independently. Four different partitioning techniques have been compared, and the method of repeating the end value on each partition boundary has the least significant impact on the detection accuracy.

  17. DIFFERENTIAL SEARCH ALGORITHM BASED EDGE DETECTION

    Directory of Open Access Journals (Sweden)

    M. A. Gunen

    2016-06-01

    Full Text Available In this paper, a new method has been presented for the extraction of edge information by using Differential Search Optimization Algorithm. The proposed method is based on using a new heuristic image thresholding method for edge detection. The success of the proposed method has been examined on fusion of two remote sensed images. The applicability of the proposed method on edge detection and image fusion problems have been analysed in detail and the empirical results exposed that the proposed method is useful for solving the mentioned problems.

  18. Microcomputer-based video motion detection system

    International Nuclear Information System (INIS)

    Howington, L.C.

    1979-01-01

    This system was developed to enhance the volumetric intrusion detection capability of the Oak Ridge Y-12 Plant's security program. Not only does the system exhibit an extended range of detection over present infrared, microwave, and ultrasonic devices, it also provides an instantaneous assessment capability by providing the operator with a closed-circuit television (CCTV) image of the alarm scene as soon as motion is detected. The system consists of a custom-built, microcomputer-based, video processor which analyzes the signals received from a network of video cameras. The operator can view the camera images as they are displayed on a CCTV monitor while alarm scenes are displayed on a second monitor. Motion is detected by digitizing and comparing successive video frames and making an alarm decision based on the degree of mismatch. The software-based nature of the microcomputer lends a great deal of flexibility and adaptability in making the alarm decision. Alarm decision variables which are easily adjusted through software are the percent change in gray level required to label a pixel (picture element) as suspect, the number of suspect pixels required to generate an alarm, the pixel pattern to be sampled from the image, and the rate at which a new reference frame is taken. The system is currently being evaluated in a warehouse for potential application in several areas of the Plant. This paper discusses the hardware and software design of the system as well as problems encountered in its implementation and results obtained

  19. Imaging evaluation of fetal vascular anomalies

    International Nuclear Information System (INIS)

    Calvo-Garcia, Maria A.; Kline-Fath, Beth M.; Koch, Bernadette L.; Laor, Tal; Adams, Denise M.; Gupta, Anita; Lim, Foong-Yen

    2015-01-01

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

  20. Imaging evaluation of fetal vascular anomalies

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-08-15

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

  1. Learning about Poland Anomaly

    Science.gov (United States)

    ... these symptoms occur on one side of the body (unilateral). Also, it is important to note that Poland anomaly does not typically affect intelligence. Top of page What causes Poland anomaly? The ...

  2. Vascular Anomalies in Pediatrics.

    Science.gov (United States)

    Foley, Lisa S; Kulungowski, Ann M

    2015-08-01

    A standardized classification system allows improvements in diagnostic accuracy. Multidisciplinary vascular anomaly centers combine medical, surgical, radiologic, and pathologic expertise. This collaborative approach tailors treatment and management of vascular anomalies for affected individuals.

  3. TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

    DEFF Research Database (Denmark)

    Christiansen, Peter

    ) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset...... the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time...... algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is - compared to a state-of-the-art object detector Faster R-CNN - for an agricultural use-case able to detect humans better and at longer ranges (45-90m...

  4. Congenital vascular anomalies: current perspectives on diagnosis, classification, and management

    Directory of Open Access Journals (Sweden)

    Blei F

    2016-07-01

    Full Text Available Francine Blei,1 Mark E Bittman2 1Vascular Anomalies Program, Lenox Hill Hospital, Northwell Health, 2Department of Radiology, New York University Langone Medical Center, New York, NY, USA Abstract: The term "congenital vascular anomalies" encompasses those vascular lesions present at birth. Many of these lesions may be detected in utero. This review serves to apprise the readership of newly identified diagnoses and updated classification schemes. Attention is focused on clinical features, patterns of presentation, clinical manifestations and behavior, diagnostic tools, and treatment modalities. It is an invigorating period for this field, with a surge in vascular anomalies-related basic and clinical research, genetics, pharmacology, clinical trials, and patient advocacy. A large number of professional conferences now include vascular anomalies in the agenda, and trainees in multiple specialties are gaining expertise in this discipline. We begin with a summary of classification schemes and introduce the updated classification adopted by the International Society for the Study of Vascular Anomalies. Disease entities are described, with liberal use of photographs, as many diagnoses can be established based on a thorough history and visual appearance and it is thus essential to develop a familiarity with diagnosis-specific physical features. Peripheral (non-central nervous system vascular anomalies are the focus of this review. We focus on those entities in which diagnostic radiology is routinely used and accentuate when histologic confirmation is essential. We also underscore some differences in approach to the pediatric vs adolescent or adult patient. A list of Internet-based resources is included, with hyperlinks to informative sites. References are limited to seminal discoveries and review articles. We hope that our enthusiasm in writing this review will be shared by those who read this review. Keywords: vascular anomalies, hemangiomas, vascular

  5. Frequency Based Fault Detection in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    In order to obtain lower cost of energy for wind turbines fault detection and accommodation is important. Expensive condition monitoring systems are often used to monitor the condition of rotating and vibrating system parts. One example is the gearbox in a wind turbine. This system is operated...... in parallel to the control system, using different computers and additional often expensive sensors. In this paper a simple filter based algorithm is proposed to detect changes in a resonance frequency in a system, exemplified with faults resulting in changes in the resonance frequency in the wind turbine...... turbine fault detection and fault tolerant control benchmark model, in which one of the included faults results in a change in the gear box resonance frequency. This evaluation shows the potential of the proposed scheme to monitor the condition of wind turbine gear boxes in the existing control system....

  6. Magnetic hyperfine anomalies

    International Nuclear Information System (INIS)

    Buettgenbach, S.

    1984-01-01

    This study is concerned with the measurement and interpretation of magnetic hyperfine anomalies in electronic and muonic atoms, i.e. effects of the distribution of nuclear magnetization on the magnetic dipole hyperfine interaction. After a summary of the relevant theory and a review of experimental techniques, hyperfine anomaly results are discussed in terms of various nuclear models. The use of the anomaly for yielding information about the origin of magnetic hyperfine interactions is outlined. Experimental and theoretical hyperfine anomalies are tabulated. (Auth.)

  7. Water Detection Based on Color Variation

    Science.gov (United States)

    Rankin, Arturo L.

    2012-01-01

    This software has been designed to detect water bodies that are out in the open on cross-country terrain at close range (out to 30 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). This detector exploits the fact that the color variation across water bodies is generally larger and more uniform than that of other naturally occurring types of terrain, such as soil and vegetation. Non-traversable water bodies, such as large puddles, ponds, and lakes, are detected based on color variation, image intensity variance, image intensity gradient, size, and shape. At ranges beyond 20 meters, water bodies out in the open can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. But at closer range, the color coming out of a water body dominates sky reflections, and the water cue from sky reflections is of marginal use. Since there may be times during UGV autonomous navigation when a water body does not come into a perception system s field of view until it is at close range, the ability to detect water bodies at close range is critical. Factors that influence the perceived color of a water body at close range are the amount and type of sediment in the water, the water s depth, and the angle of incidence to the water body. Developing a single model of the mixture ratio of light reflected off the water surface (to the camera) to light coming out of the water body (to the camera) for all water bodies would be fairly difficult. Instead, this software detects close water bodies based on local terrain features and the natural, uniform change in color that occurs across the surface from the leading edge to the trailing edge.

  8. Boosting Web Intrusion Detection Systems by Inferring Positive Signatures

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, Sandro

    2008-01-01

    We present a new approach to anomaly-based network intrusion detection for web applications. This approach is based on dividing the input parameters of the monitored web application in two groups: the "regular" and the "irregular" ones, and applying a new method for anomaly detection on the

  9. Ionizing particle detection based on phononic crystals

    Energy Technology Data Exchange (ETDEWEB)

    Aly, Arafa H., E-mail: arafa16@yahoo.com, E-mail: arafa.hussien@science.bsu.edu.eg; Mehaney, Ahmed; Eissa, Mostafa F. [Physics Department, Faculty of Science, Beni-Suef University, Beni-Suef (Egypt)

    2015-08-14

    Most conventional radiation detectors are based on electronic or photon collections. In this work, we introduce a new and novel type of ionizing particle detector based on phonon collection. Helium ion radiation treats tumors with better precision. There are nine known isotopes of helium, but only helium-3 and helium-4 are stable. Helium-4 is formed in fusion reactor technology and in enormous quantities during Big Bang nucleo-synthesis. In this study, we introduce a technique for helium-4 ion detection (sensing) based on the innovative properties of the new composite materials known as phononic crystals (PnCs). PnCs can provide an easy and cheap technique for ion detection compared with conventional methods. PnC structures commonly consist of a periodic array of two or more materials with different elastic properties. The two materials are polymethyl-methacrylate and polyethylene polymers. The calculations showed that the energies lost to target phonons are maximized at 1 keV helium-4 ion energy. There is a correlation between the total phonon energies and the transmittance of PnC structures. The maximum transmission for phonons due to the passage of helium-4 ions was found in the case of making polyethylene as a first layer in the PnC structure. Therefore, the concept of ion detection based on PnC structure is achievable.

  10. Ionospheric earthquake effects detection based on Total Electron Content (TEC) GPS Correlation

    Science.gov (United States)

    Sunardi, Bambang; Muslim, Buldan; Eka Sakya, Andi; Rohadi, Supriyanto; Sulastri; Murjaya, Jaya

    2018-03-01

    Advances in science and technology showed that ground-based GPS receiver was able to detect ionospheric Total Electron Content (TEC) disturbances caused by various natural phenomena such as earthquakes. One study of Tohoku (Japan) earthquake, March 11, 2011, magnitude M 9.0 showed TEC fluctuations observed from GPS observation network spread around the disaster area. This paper discussed the ionospheric earthquake effects detection using TEC GPS data. The case studies taken were Kebumen earthquake, January 25, 2014, magnitude M 6.2, Sumba earthquake, February 12, 2016, M 6.2 and Halmahera earthquake, February 17, 2016, M 6.1. TEC-GIM (Global Ionosphere Map) correlation methods for 31 days were used to monitor TEC anomaly in ionosphere. To ensure the geomagnetic disturbances due to solar activity, we also compare with Dst index in the same time window. The results showed anomalous ratio of correlation coefficient deviation to its standard deviation upon occurrences of Kebumen and Sumba earthquake, but not detected a similar anomaly for the Halmahera earthquake. It was needed a continous monitoring of TEC GPS data to detect the earthquake effects in ionosphere. This study giving hope in strengthening the earthquake effect early warning system using TEC GPS data. The method development of continuous TEC GPS observation derived from GPS observation network that already exists in Indonesia is needed to support earthquake effects early warning systems.

  11. 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

  12. Tracheobronchial Branching Anomalies

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Min Ji; Kim, Young Tong; Jou, Sung Shick [Soonchunhyang University, Cheonan Hospital, Cheonan (Korea, Republic of); Park, A Young [Soonchunhyang University College of Medicine, Asan (Korea, Republic of)

    2010-04-15

    There are various congenital anomalies with respect to the number, length, diameter, and location of tracheobronchial branching patterns. The tracheobronchial anomalies are classified into two groups. The first one, anomalies of division, includes tracheal bronchus, cardiac bronchus, tracheal diverticulum, pulmonary isomerism, and minor variations. The second one, dysmorphic lung, includes lung agenesis-hypoplasia complex and lobar agenesis-aplasia complex

  13. Anomalies of nuclear criticality

    Energy Technology Data Exchange (ETDEWEB)

    Clayton, E.D.

    1979-06-01

    During the development of nuclear energy, a number of apparent anomalies have become evident in nuclear criticality. Some of these have appeared in the open literature and some have not. Yet, a naive extrapolation or application of existing data, without knowledge of the anomalies, could lead to potentially serious consequences. This report discusses several of these anomalies.

  14. An FPGA-Based People Detection System

    Directory of Open Access Journals (Sweden)

    James J. Clark

    2005-05-01

    Full Text Available This paper presents an FPGA-based system for detecting people from video. The system is designed to use JPEG-compressed frames from a network camera. Unlike previous approaches that use techniques such as background subtraction and motion detection, we use a machine-learning-based approach to train an accurate detector. We address the hardware design challenges involved in implementing such a detector, along with JPEG decompression, on an FPGA. We also present an algorithm that efficiently combines JPEG decompression with the detection process. This algorithm carries out the inverse DCT step of JPEG decompression only partially. Therefore, it is computationally more efficient and simpler to implement, and it takes up less space on the chip than the full inverse DCT algorithm. The system is demonstrated on an automated video surveillance application and the performance of both hardware and software implementations is analyzed. The results show that the system can detect people accurately at a rate of about 2.5 frames per second on a Virtex-II 2V1000 using a MicroBlaze processor running at 75 MHz, communicating with dedicated hardware over FSL links.

  15. Tensor-based spatiotemporal saliency detection

    Science.gov (United States)

    Dou, Hao; Li, Bin; Deng, Qianqian; Zhang, LiRui; Pan, Zhihong; Tian, Jinwen

    2018-03-01

    This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos. First, we construct the tensor representation of video frames. Then, the spatiotemporal saliency can be directly computed by the tensor distance between different tensors, which can preserve the complete temporal and spatial structure information of object in the spatiotemporal domain. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.

  16. Frequency-based Vehicle Idling Detection

    OpenAIRE

    Kai-Chao Yang; Chih-Ting Kuo; Chun-Yu Chen; Chih-Chyau Yang; Chien-Ming Wu; Chun-Ming Huang

    2014-01-01

    Continuous increases in fuel prices and environmental awareness have raised the importance of reducing vehicle emissions, with many national governments passing anti-idling laws. To reduce air pollution and fuel consumption, we propose a frequency-based vehicle idling detection method to remind drivers to turn off the engine vehicle idling exceeds a certain time threshold. The method is implemented in existing handheld devices without any modification to the car or engine, making the solution...

  17. Peripheral leukocyte anomaly detected with routine automated hematology analyzer sensitive to adipose triglyceride lipase deficiency manifesting neutral lipid storage disease with myopathy/triglyceride deposit cardiomyovasculopathy

    Directory of Open Access Journals (Sweden)

    Akira Suzuki

    2014-01-01

    Full Text Available Adipose triglyceride lipase (ATGL deficiency manifesting neutral lipid storage disease with myopathy/triglyceride deposit cardiomyovasculopathy presents distinct fat-containing vacuoles known as Jordans' anomaly in peripheral leucocytes. To develop an automatic notification system for Jordans' anomaly in ATGL-deficient patients, we analyzed circulatory leukocyte scattergrams on automated hematology analyzer XE-5000. The BASO-WX and BASO-WY values were found to be significantly higher in patients than those in non-affected subjects. The two parameters measured by automated hematology analyzer may be expected to provide an important diagnostic clue for homozygous ATGL deficiency.

  18. Model-based approach for cyber-physical attack detection in water distribution systems.

    Science.gov (United States)

    Housh, Mashor; Ohar, Ziv

    2018-03-17

    Modern Water Distribution Systems (WDSs) are often controlled by Supervisory Control and Data Acquisition (SCADA) systems and Programmable Logic Controllers (PLCs) which manage their operation and maintain a reliable water supply. As such, and with the cyber layer becoming a central component of WDS operations, these systems are at a greater risk of being subjected to cyberattacks. This paper offers a model-based methodology based on a detailed hydraulic understanding of WDSs combined with an anomaly detection algorithm for the identification of complex cyberattacks that cannot be fully identified by hydraulically based rules alone. The results show that the proposed algorithm is capable of achieving the best-known performance when tested on the data published in the BATtle of the Attack Detection ALgorithms (BATADAL) competition (http://www.batadal.net). Copyright © 2018. Published by Elsevier Ltd.

  19. Fluorescence quenching based alkaline phosphatase activity detection.

    Science.gov (United States)

    Mei, Yaqi; Hu, Qiong; Zhou, Baojing; Zhang, Yonghui; He, Minhui; Xu, Ting; Li, Feng; Kong, Jinming

    2018-01-01

    Simple and fast detection of alkaline phosphatase (ALP) activity is of great importance for diagnostic and analytical applications. In this work, we report a turn-off approach for the real-time detection of ALP activity on the basis of the charge transfer induced fluorescence quenching of the Cu(BCDS) 2 2- (BCDS = bathocuproine disulfonate) probe. Initially, ALP can enzymatically hydrolyze the substrate ascorbic acid 2-phosphate to release ascorbic acid (AA). Subsequently, the AA-mediated reduction of the Cu(BCDS) 2 2- probe, which displays an intense photoluminescence band at the wavelength of 402nm, leads to the static quenching of fluorescence of the probe as a result of charge transfer. The underlying mechanism of the fluorescence quenching was demonstrated by quantum mechanical calculations. The Cu(BCDS) 2 2- probe features a large Stokes shift (86nm) and is highly immune to photo bleaching. In addition, this approach is free of elaborately designed fluorescent probes and allows the detection of ALP activity in a real-time manner. Under optimal conditions, it provides a fast and sensitive detection of ALP activity within the dynamic range of 0-220mUmL -1 , with a detection limit down to 0.27mUmL -1 . Results demonstrate that it is highly selective, and applicable to the screening of ALP inhibitors in drug discovery. More importantly, it shows a good analytical performance for the direct detection of the endogenous ALP levels of undiluted human serum and even whole blood samples. Therefore, the proposed charge transfer based approach has great potential in diagnostic and analytical applications. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. An Audit of Second-Trimester Fetal Anomaly Scans Based on a Novel Image-Scoring Method in the Southwest Region of the Netherlands.

    Science.gov (United States)

    Ursem, Nicolette T C; Peters, Ingrid A; Kraan-van der Est, Mieke N; Reijerink-Verheij, Jacqueline C I Y; Knapen, Maarten F C M; Cohen-Overbeek, Titia E

    2017-06-01

    Since 2007 the second-trimester fetal anomaly scan is offered to all pregnant women as part of the national prenatal screening program in the Netherlands. Dutch population-based screening programs generally have a well-described system to achieve quality assurance. Because of the absence of a uniform system to monitor the actual performance of the fetal anomaly scan in 2012, we developed a standardized image-scoring method. The aim of this study was to evaluate the scanning performance of all sonographers in the southwestern region of the Netherlands using this image-scoring method. Each sonographer was requested to set up a digital portfolio. A portfolio consists of five logbooks from five different pregnant women, each containing 25 fetal anatomical structures and six biometric measures of randomly selected fetal anomaly scans. During the study period, 425 logbooks of 85 sonographers were assessed as part of the audit process. Seventy-three out of 85 sonographers (86%) met the criteria in the primary audit, and 12 sonographers required individual hands-on training. A successful assessment was achieved for 11 sonographers in the re-audit and one sonographer ceased her contract. Moreover, 2.1% of the required images were not digitally stored and therefore could not be reviewed. Quality assessment using the image-scoring method demonstrated that most of the sonographers met the expectations of the audit process, but those who had subpar performance met the expectations after retraining. © 2017 by the American Institute of Ultrasound in Medicine.