Cross-linguistic variation in the neurophysiological response to semantic processing: Evidence from anomalies at the borderline of awareness

PubMed Central

Tune, Sarah; Schlesewsky, Matthias; Small, Steven L.; Sanford, Anthony J.; Bohan, Jason; Sassenhagen, Jona; Bornkessel-Schlesewsky, Ina

2014-01-01

The N400 event-related brain potential (ERP) has played a major role in the examination of how the human brain processes meaning. For current theories of the N400, classes of semantic inconsistencies which do not elicit N400 effects have proven particularly influential. Semantic anomalies that are difficult to detect are a case in point (“borderline anomalies”, e.g. “After an air crash, where should the survivors be buried?”), engendering a late positive ERP response but no N400 effect in English (Sanford, Leuthold, Bohan, & Sanford, 2011). In three auditory ERP experiments, we demonstrate that this result is subject to cross-linguistic variation. In a German version of Sanford and colleagues' experiment (Experiment 1), detected borderline anomalies elicited both N400 and late positivity effects compared to control stimuli or to missed borderline anomalies. Classic easy-to-detect semantic (non-borderline) anomalies showed the same pattern as in English (N400 plus late positivity). The cross-linguistic difference in the response to borderline anomalies was replicated in two additional studies with a slightly modified task (Experiment 2a: German; Experiment 2b: English), with a reliable LANGUAGE × ANOMALY interaction for the borderline anomalies confirming that the N400 effect is subject to systematic cross-linguistic variation. We argue that this variation results from differences in the language-specific default weighting of top-down and bottom-up information, concluding that N400 amplitude reflects the interaction between the two information sources in the form-to-meaning mapping. PMID:24447768

Rocketdyne Safety Algorithm: Space Shuttle Main Engine Fault Detection

NASA Technical Reports Server (NTRS)

Norman, Arnold M., Jr.

1994-01-01

The Rocketdyne Safety Algorithm (RSA) has been developed to the point of use on the TTBE at MSFC on Task 4 of LeRC contract NAS3-25884. This document contains a description of the work performed, the results of the nominal test of the major anomaly test cases and a table of the resulting cutoff times, a plot of the RSA value vs. time for each anomaly case, a logic flow description of the algorithm, the algorithm code, and a development plan for future efforts.

Abnormal global and local event detection in compressive sensing domain

NASA Astrophysics Data System (ADS)

Wang, Tian; Qiao, Meina; Chen, Jie; Wang, Chuanyun; Zhang, Wenjia; Snoussi, Hichem

2018-05-01

Abnormal event detection, also known as anomaly detection, is one challenging task in security video surveillance. It is important to develop effective and robust movement representation models for global and local abnormal event detection to fight against factors such as occlusion and illumination change. In this paper, a new algorithm is proposed. It can locate the abnormal events on one frame, and detect the global abnormal frame. The proposed algorithm employs a sparse measurement matrix designed to represent the movement feature based on optical flow efficiently. Then, the abnormal detection mission is constructed as a one-class classification task via merely learning from the training normal samples. Experiments demonstrate that our algorithm performs well on the benchmark abnormal detection datasets against state-of-the-art methods.

Anomaly Detection for Next-Generation Space Launch Ground Operations

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

CSAX: Characterizing Systematic Anomalies in eXpression Data.

PubMed

Noto, Keith; Majidi, Saeed; Edlow, Andrea G; Wick, Heather C; Bianchi, Diana W; Slonim, Donna K

2015-05-01

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

Neural evidence for enhanced error detection in major depressive disorder.

PubMed

Chiu, Pearl H; Deldin, Patricia J

2007-04-01

Anomalies in error processing have been implicated in the etiology and maintenance of major depressive disorder. In particular, depressed individuals exhibit heightened sensitivity to error-related information and negative environmental cues, along with reduced responsivity to positive reinforcers. The authors examined the neural activation associated with error processing in individuals diagnosed with and without major depression and the sensitivity of these processes to modulation by monetary task contingencies. The error-related negativity and error-related positivity components of the event-related potential were used to characterize error monitoring in individuals with major depressive disorder and the degree to which these processes are sensitive to modulation by monetary reinforcement. Nondepressed comparison subjects (N=17) and depressed individuals (N=18) performed a flanker task under two external motivation conditions (i.e., monetary reward for correct responses and monetary loss for incorrect responses) and a nonmonetary condition. After each response, accuracy feedback was provided. The error-related negativity component assessed the degree of anomaly in initial error detection, and the error positivity component indexed recognition of errors. Across all conditions, the depressed participants exhibited greater amplitude of the error-related negativity component, relative to the comparison subjects, and equivalent error positivity amplitude. In addition, the two groups showed differential modulation by task incentives in both components. These data implicate exaggerated early error-detection processes in the etiology and maintenance of major depressive disorder. Such processes may then recruit excessive neural and cognitive resources that manifest as symptoms of depression.

Development of life prediction capabilities for liquid propellant rocket engines. Post-fire diagnostic system for the SSME system architecture study

NASA Technical Reports Server (NTRS)

Gage, Mark; Dehoff, Ronald

1991-01-01

This system architecture task (1) analyzed the current process used to make an assessment of engine and component health after each test or flight firing of an SSME, (2) developed an approach and a specific set of objectives and requirements for automated diagnostics during post fire health assessment, and (3) listed and described the software applications required to implement this system. The diagnostic system described is a distributed system with a database management system to store diagnostic information and test data, a CAE package for visual data analysis and preparation of plots of hot-fire data, a set of procedural applications for routine anomaly detection, and an expert system for the advanced anomaly detection and evaluation.

CSAX: Characterizing Systematic Anomalies in eXpression Data

PubMed Central

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

2015-01-01

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

Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy.

PubMed

Kittler, Josef; Christmas, William; de Campos, Teófilo; Windridge, David; Yan, Fei; Illingworth, John; Osman, Magda

2014-05-01

We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifaceted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature. To illustrate some of its distinguishing features, in here the domain anomaly detection methodology is applied to the problem of anomaly detection for a video annotation system.

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

DTIC Science & Technology

2009-03-01

viii 3.2.3 Sub7 ...from TaskInfo in Excel Format. 3.2.3 Sub7 Also known as SubSeven, this is one of the best known, most widely distributed backdoor programs on the...engineering the spread of viruses, worms, backdoors and other malware. The Sub7 Trojan establishes a server on the victim computer that

Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation

NASA Astrophysics Data System (ADS)

Karargyros, Alex; Syeda-Mahmood, Tanveer

2018-02-01

Deep learning networks are gaining popularity in many medical image analysis tasks due to their generalized ability to automatically extract relevant features from raw images. However, this can make the learning problem unnecessarily harder requiring network architectures of high complexity. In case of anomaly detection, in particular, there is often sufficient regional difference between the anomaly and the surrounding parenchyma that could be easily highlighted through bottom-up saliency operators. In this paper we propose a new hybrid deep learning network using a combination of raw image and such regional maps to more accurately learn the anomalies using simpler network architectures. Specifically, we modify a deep learning network called U-Net using both the raw and pre-segmented images as input to produce joint encoding (contraction) and expansion paths (decoding) in the U-Net. We present results of successfully delineating subdural and epidural hematomas in brain CT imaging and liver hemangioma in abdominal CT images using such network.

Anomaly Detection and Modeling of Trajectories

DTIC Science & Technology

2012-08-01

policies, either expressed or implied, of the Gates Millennium Scholars Program , or the Office of Naval Research. Report Documentation Page Form... PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Carnegie...thesis proposes several methods using statistics and machine learning (ML) that provide a deep understanding of trajectory datasets. In particular

Conditional anomaly detection methods for patient–management alert systems

PubMed Central

Valko, Michal; Cooper, Gregory; Seybert, Amy; Visweswaran, Shyam; Saul, Melissa; Hauskrecht, Milos

2010-01-01

Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses on instance–based methods for detecting conditional anomalies. The methods rely on the distance metric to identify examples in the dataset that are most critical for detecting the anomaly. We investigate various metrics and metric learning methods to optimize the performance of the instance–based anomaly detection methods. We show the benefits of the instance–based methods on two real–world detection problems: detection of unusual admission decisions for patients with the community–acquired pneumonia and detection of unusual orders of an HPF4 test that is used to confirm Heparin induced thrombocytopenia — a life–threatening condition caused by the Heparin therapy. PMID:25392850

Inductive System Monitors Tasks

NASA Technical Reports Server (NTRS)

2008-01-01

The Inductive Monitoring System (IMS) software developed at Ames Research Center uses artificial intelligence and data mining techniques to build system-monitoring knowledge bases from archived or simulated sensor data. This information is then used to detect unusual or anomalous behavior that may indicate an impending system failure. Currently helping analyze data from systems that help fly and maintain the space shuttle and the International Space Station (ISS), the IMS has also been employed by data classes are then used to build a monitoring knowledge base. In real time, IMS performs monitoring functions: determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Successful applications have ranged from real-time monitoring of aircraft engine and control systems to anomaly detection in space shuttle and ISS data. IMS was used on shuttle missions STS-121, STS-115, and STS-116 to search the Wing Leading Edge Impact Detection System (WLEIDS) data for signs of possible damaging impacts during launch. It independently verified findings of the WLEIDS Mission Evaluation Room (MER) analysts and indicated additional points of interest that were subsequently investigated by the MER team. In support of the Exploration Systems Mission Directorate, IMS is being deployed as an anomaly detection tool on ISS mission control consoles in the Johnson Space Center Mission Operations Directorate. IMS has been trained to detect faults in the ISS Control Moment Gyroscope (CMG) systems. In laboratory tests, it has already detected several minor anomalies in real-time CMG data. When tested on archived data, IMS was able to detect precursors of the CMG1 failure nearly 15 hours in advance of the actual failure event. In the Aeronautics Research Mission Directorate, IMS successfully performed real-time engine health analysis. IMS was able to detect simulated failures and actual engine anomalies in an F/A-18 aircraft during the course of 25 test flights. IMS is also being used in colla

Anomaly Detection Using an Ensemble of Feature Models

PubMed Central

Noto, Keith; Brodley, Carla; Slonim, Donna

2011-01-01

We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model that will be able to distinguish examples in the future that do not belong to the same class. Traditional approaches typically compare the position of a new data point to the set of “normal” training data points in a chosen representation of the feature space. For some data sets, the normal data may not have discernible positions in feature space, but do have consistent relationships among some features that fail to appear in the anomalous examples. Our approach learns to predict the values of training set features from the values of other features. After we have formed an ensemble of predictors, we apply this ensemble to new data points. To combine the contribution of each predictor in our ensemble, we have developed a novel, information-theoretic anomaly measure that our experimental results show selects against noisy and irrelevant features. Our results on 47 data sets show that for most data sets, this approach significantly improves performance over current state-of-the-art feature space distance and density-based approaches. PMID:22020249

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Solaimani, Mohiuddin; Iftekhar, Mohammed; Khan, Latifur

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

Challenges of using electrical resistivity method to locate karst conduits-A field case in the Inner Bluegrass Region, Kentucky

USGS Publications Warehouse

Zhu, J.; Currens, J.C.; Dinger, J.S.

2011-01-01

Conduits serve as major pathways for groundwater flow in karst aquifers. Locating them from the surface, however, is one of the most challenging tasks in karst research. Geophysical methods are often deployed to help locate voids by mapping variations of physical properties of the subsurface. Conduits can cause significant contrasts of some physical properties that can be detected; other subsurface features such as water-bearing fractures often yield similar contrasts, which are difficult to distinguish from the effects of the conduits. This study used electrical resistivity method to search for an unmapped karst conduit that recharges Royal Spring in the Inner Bluegrass karst region, Kentucky, USA. Three types of resistivity techniques (surface 2D survey, quasi-3D survey, and time-lapse survey) were used to map and characterize resistivity anomalies. Some of the major anomalies were selected as drilling targets to verify the existence of the conduits. Drilling near an anomaly identified by an electrical resistivity profile resulted in successful penetration of a major water-filled conduit. The drilling results also suggest that, in this study area, low resistivity anomalies in general are associated with water-bearing features. However, differences in the anomaly signals between the water-filled conduit and other water-bearing features such as water-filled fracture zones were undistinguishable. The electrical resistivity method is useful in conduit detection by providing potential drilling targets. Knowledge of geology and hydrogeology about the site and professional judgment also played important roles in locating the major conduit. ?? 2011 Elsevier B.V.

Engine Data Interpretation System (EDIS), phase 2

NASA Technical Reports Server (NTRS)

Cost, Thomas L.; Hofmann, Martin O.

1991-01-01

A prototype of an expert system was developed which applies qualitative constraint-based reasoning to the task of post-test analysis of data resulting from a rocket engine firing. Data anomalies are detected and corresponding faults are diagnosed. Engine behavior is reconstructed using measured data and knowledge about engine behavior. Knowledge about common faults guides but does not restrict the search for the best explanation in terms of hypothesized faults. The system contains domain knowledge about the behavior of common rocket engine components and was configured for use with the Space Shuttle Main Engine (SSME). A graphical user interface allows an expert user to intimately interact with the system during diagnosis. The system was applied to data taken during actual SSME tests where data anomalies were observed.

Imagining Counterfactual Worlds in Autism Spectrum Disorder.

PubMed

Black, Jo; Williams, David; Ferguson, Heather J

2018-02-01

Two experiments are presented that explore online counterfactual processing in autism spectrum disorder (ASD) using eye-tracking. Participants' eye movements were tracked while they read factual and counterfactual sentences in an anomaly detection task. In Experiment 1, the sentences depicted everyday counterfactual situations (e.g., If Joanne had remembered her umbrella, her hair would have been dry/wet when she arrived home). Sentences in Experiment 2 depicted counterfactual versions of real world events (e.g., If the Titanic had not hit an iceberg, it would have survived/sunk along with all the passengers). Results from both experiments suggest that counterfactual understanding is undiminished in adults with ASD. In fact, participants with ASD were faster than Typically Developing (TD) participants to detect anomalies within realistic, discourse-based counterfactuals (Experiment 1). Detection was comparable for TD and ASD groups when understanding could be grounded in knowledge about reality (Experiment 2), though the 2 groups used subtly different strategies for responding to and recovering from counterfactual inconsistent words. These data argue against general difficulties in global coherence and complex integration in ASD. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

INSPIRE Project (IoNospheric Sounding for Pre-seismic anomalies Identification REsearch): Main Results and Future Prospects

NASA Astrophysics Data System (ADS)

Pulinets, S. A.; Andrzej, K.; Hernandez-Pajares, M.; Cherniak, I.; Zakharenkova, I.; Rothkaehl, H.; Davidenko, D.

2017-12-01

The INSPIRE project is dedicated to the study of physical processes and their effects in ionosphere which could be determined as earthquake precursors together with detailed description of the methodology of ionospheric pre-seismic anomalies definition. It was initiated by ESA and carried out by international consortium. The physical mechanisms of the ionospheric pre-seismic anomalies generation from ground to the ionosphere altitudes were formulated within framework of the Lithosphere-Atmosphere-Ionosphere-Magnetosphere Coupling (LAIMC) model (Pulinets et al., 2015). The general algorithm for the identification of the ionospheric precursors was formalized which also takes into account the external Space Weather factors able to generate the false alarms. Importance of the special stable pattern called the "precursor mask" was highlighted which is based on self-similarity of pre-seismic ionospheric variations. The role of expert decision in pre-seismic anomalies interpretation for generation of seismic warning is important as well. The algorithm performance of the LAIMC seismo-ionospheric effect detection module has been demonstrated using the L'Aquila 2009 earthquake as a case study. The results of INSPIRE project have demonstrated that the ionospheric anomalies registered before the strong earthquakes could be used as reliable precursors. The detailed classification of the pre-seismic anomalies was presented in different regions of the ionosphere and signatures of the pre-seismic anomalies as detected by ground and satellite based instruments were described what clarified methodology of the precursor's identification from ionospheric multi-instrumental measurements. Configuration for the dedicated multi-observation experiment and satellite payload was proposed for the future implementation of the INSPIRE project results. In this regard the multi-instrument set can be divided by two groups: space equipment and ground-based support, which could be used for real-time monitoring. Together with scientific and technical tasks the set of political, logistic and administrative problems (including certification of approaches by seismological community, juridical procedures by the governmental authorities) should be resolved for the real earthquake forecast effectuation.

Building Intrusion Detection with a Wireless Sensor Network

NASA Astrophysics Data System (ADS)

Wälchli, Markus; Braun, Torsten

This paper addresses the detection and reporting of abnormal building access with a wireless sensor network. A common office room, offering space for two working persons, has been monitored with ten sensor nodes and a base station. The task of the system is to report suspicious office occupation such as office searching by thieves. On the other hand, normal office occupation should not throw alarms. In order to save energy for communication, the system provides all nodes with some adaptive short-term memory. Thus, a set of sensor activation patterns can be temporarily learned. The local memory is implemented as an Adaptive Resonance Theory (ART) neural network. Unknown event patterns detected on sensor node level are reported to the base station, where the system-wide anomaly detection is performed. The anomaly detector is lightweight and completely self-learning. The system can be run autonomously or it could be used as a triggering system to turn on an additional high-resolution system on demand. Our building monitoring system has proven to work reliably in different evaluated scenarios. Communication costs of up to 90% could be saved compared to a threshold-based approach without local memory.

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

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-04-01

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

A model for anomaly classification in intrusion detection systems

NASA Astrophysics Data System (ADS)

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.

An immunity-based anomaly detection system with sensor agents.

PubMed

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.

A universal reference sample derived from clone vector for improved detection of differential gene expression

PubMed Central

Khan, Rishi L; Gonye, Gregory E; Gao, Guang; Schwaber, James S

2006-01-01

Background Using microarrays by co-hybridizing two samples labeled with different dyes enables differential gene expression measurements and comparisons across slides while controlling for within-slide variability. Typically one dye produces weaker signal intensities than the other often causing signals to be undetectable. In addition, undetectable spots represent a large problem for two-color microarray designs and most arrays contain at least 40% undetectable spots even when labeled with reference samples such as Stratagene's Universal Reference RNAs™. Results We introduce a novel universal reference sample that produces strong signal for all spots on the array, increasing the average fraction of detectable spots to 97%. Maximizing detectable spots on the reference image channel also decreases the variability of microarray data allowing for reliable detection of smaller differential gene expression changes. The reference sample is derived from sequence contained in the parental EST clone vector pT7T3D-Pac and is called vector RNA (vRNA). We show that vRNA can also be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This reference sample can be made inexpensively in large quantities as a renewable resource that is consistent across experiments. Conclusion Results of this study show that vRNA provides a useful universal reference that yields high signal for almost all spots on a microarray, reduces variation and allows for comparisons between experiments and laboratories. Further, it can be used for quality control of microarray printing and PCR product quality, detection of hybridization anomalies, and simplification of spot finding and segmentation tasks. This type of reference allows for detection of small changes in differential expression while reference designs in general allow for large-scale multivariate experimental designs. vRNA in combination with reference designs enable systems biology microarray experiments of small physiologically relevant changes. PMID:16677381

Selective Attention to Semantic and Syntactic Features Modulates Sentence Processing Networks in Anterior Temporal Cortex

PubMed Central

Rogalsky, Corianne

2009-01-01

Numerous studies have identified an anterior temporal lobe (ATL) region that responds preferentially to sentence-level stimuli. It is unclear, however, whether this activity reflects a response to syntactic computations or some form of semantic integration. This distinction is difficult to investigate with the stimulus manipulations and anomaly detection paradigms traditionally implemented. The present functional magnetic resonance imaging study addresses this question via a selective attention paradigm. Subjects monitored for occasional semantic anomalies or occasional syntactic errors, thus directing their attention to semantic integration, or syntactic properties of the sentences. The hemodynamic response in the sentence-selective ATL region (defined with a localizer scan) was examined during anomaly/error-free sentences only, to avoid confounds due to error detection. The majority of the sentence-specific region of interest was equally modulated by attention to syntactic or compositional semantic features, whereas a smaller subregion was only modulated by the semantic task. We suggest that the sentence-specific ATL region is sensitive to both syntactic and integrative semantic functions during sentence processing, with a smaller portion of this area preferentially involved in the later. This study also suggests that selective attention paradigms may be effective tools to investigate the functional diversity of networks involved in sentence processing. PMID:18669589

Statistical Traffic Anomaly Detection in Time-Varying Communication Networks

DTIC Science & Technology

2015-02-01

methods perform better than their vanilla counterparts, which assume that normal traffic is stationary. Statistical Traffic Anomaly Detection in Time...our methods perform better than their vanilla counterparts, which assume that normal traffic is stationary. Index Terms—Statistical anomaly detection...anomaly detection but also for understanding the normal traffic in time-varying networks. C. Comparison with vanilla stochastic methods For both types

Statistical Traffic Anomaly Detection in Time Varying Communication Networks

DTIC Science & Technology

2015-02-01

methods perform better than their vanilla counterparts, which assume that normal traffic is stationary. Statistical Traffic Anomaly Detection in Time...our methods perform better than their vanilla counterparts, which assume that normal traffic is stationary. Index Terms—Statistical anomaly detection...anomaly detection but also for understanding the normal traffic in time-varying networks. C. Comparison with vanilla stochastic methods For both types

Non-supervised method for early forest fire detection and rapid mapping

NASA Astrophysics Data System (ADS)

Artés, Tomás; Boca, Roberto; Liberta, Giorgio; San-Miguel, Jesús

2017-09-01

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a non-supervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

A Survey on Anomaly Based Host Intrusion Detection System

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

NON-INVASIVE DETERMINATION OF THE LOCATION AND DISTRIBUTION OF FREE-PHASE DENSE NONAQUEOUS PHASE LIQUIDS (DNAPL) BY SEISMIC REFLECTION TECHNIQUES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michael G. Waddell; William J. Domoracki; Tom J. Temples

2001-12-01

This annual technical progress report is for part of Task 4 (site evaluation), Task 5 (2D seismic design, acquisition, and processing), and Task 6 (2D seismic reflection, interpretation, and AVO analysis) on DOE contact number DE-AR26-98FT40369. The project had planned one additional deployment to another site other than Savannah River Site (SRS) or DOE Hanford Site. After the SUBCON midyear review in Albuquerque, NM, it was decided that two additional deployments would be performed. The first deployment is to test the feasibility of using non-invasive seismic reflection and AVO analysis as a monitoring tool to assist in determining the effectivenessmore » of Dynamic Underground Stripping (DUS) in removal of DNAPL. The second deployment is to the Department of Defense (DOD) Charleston Naval Weapons Station Solid Waste Management Unit 12 (SWMU-12), Charleston, SC to further test the technique to detect high concentrations of DNAPL. The Charleston Naval Weapons Station SWMU-12 site was selected in consultation with National Energy Technology Laboratory (NETL) and DOD Naval Facilities Engineering Command Southern Division (NAVFAC) personnel. Based upon the review of existing data and due to the shallow target depth, the project team collected three Vertical Seismic Profiles (VSP) and an experimental P-wave seismic reflection line. After preliminary data analysis of the VSP data and the experimental reflection line data, it was decided to proceed with Task 5 and Task 6. Three high resolution P-wave reflection profiles were collected with two objectives; (1) design the reflection survey to image a target depth of 20 feet below land surface to assist in determining the geologic controls on the DNAPL plume geometry, and (2) apply AVO analysis to the seismic data to locate the zone of high concentration of DNAPL. Based upon the results of the data processing and interpretation of the seismic data, the project team was able to map the channel that is controlling the DNAPL plume geometry. The AVO analysis located a major amplitude anomaly, which was tested using a Geoprobe{trademark} direct push system. The Geoprobe{trademark} was equipped with a membrane interface probe (MIP) that was interfaced with a sorbent trap/gas chromatograph (GC) system. Both the Photo Ionization Detector (PID) and Electron Capture Detector (ECD) on the GC exceeded the maximum measurement values through the anomaly. A well was installed to collect a water sample. The concentration of chlorinated solvents in the water sample was in excess of 500 ppm. Other amplitude anomalies located directly under an asphalt road were also tested. Both the PID and ECD were zero. It appears that editing of poor quality near-offset traces during data processing caused these anomalies. Not having the full range of source to receiver offset traces in those areas resulted in a false anomaly during AVO analysis. This phenomenon was also observed at the beginning and end of each seismic profile also for the same reason. Based upon the water samples and MIP probes, it appears that surface seismic and AVO analysis were able to detect the area of highest concentration of DNAPL.« less

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

DTIC Science & Technology

2016-09-01

ARL-TR-7798 ● SEP 2016 US Army Research Laboratory Setup Instructions for the Applied Anomaly Detection Tool (AADT) Web Server...for the Applied Anomaly Detection Tool (AADT) Web Server by Christian D Schlesiger Computational and Information Sciences Directorate, ARL...SUBTITLE Setup Instructions for the Applied Anomaly Detection Tool (AADT) Web Server 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

Seismic data fusion anomaly detection

NASA Astrophysics Data System (ADS)

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

2014-06-01

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

Adiabatic Quantum Anomaly Detection and Machine Learning

NASA Astrophysics Data System (ADS)

Pudenz, Kristen; Lidar, Daniel

2012-02-01

We present methods of anomaly detection and machine learning using adiabatic quantum computing. The machine learning algorithm is a boosting approach which seeks to optimally combine somewhat accurate classification functions to create a unified classifier which is much more accurate than its components. This algorithm then becomes the first part of the larger anomaly detection algorithm. In the anomaly detection routine, we first use adiabatic quantum computing to train two classifiers which detect two sets, the overlap of which forms the anomaly class. We call this the learning phase. Then, in the testing phase, the two learned classification functions are combined to form the final Hamiltonian for an adiabatic quantum computation, the low energy states of which represent the anomalies in a binary vector space.

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

NASA Astrophysics Data System (ADS)

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 in practice.

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

PubMed

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).

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

PubMed Central

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

2016-01-01

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). PMID:27845717

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abedin, Ahmad Firdaus Zainal; Ibrahim, Noorddin; Zabidi, Noriza Ahmad

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 measuredmore » 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.« less

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound.

PubMed

Baumgartner, Christian F; Kamnitsas, Konstantinos; Matthew, Jacqueline; Fletcher, Tara P; Smith, Sandra; Koch, Lisa M; Kainz, Bernhard; Rueckert, Daniel

2017-11-01

Identifying and interpreting fetal standard scan planes during 2-D ultrasound mid-pregnancy examinations are highly complex tasks, which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks, which can automatically detect 13 fetal standard views in freehand 2-D ultrasound data as well as provide a localization of the fetal structures via a bounding box. An important contribution is that the network learns to localize the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localization task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localization on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modeling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localization task.

Quantum-state anomaly detection for arbitrary errors using a machine-learning technique

NASA Astrophysics Data System (ADS)

Hara, Satoshi; Ono, Takafumi; Okamoto, Ryo; Washio, Takashi; Takeuchi, Shigeki

2016-10-01

The accurate detection of small deviations in given density matrice is important for quantum information processing, which is a difficult task because of the intrinsic fluctuation in density matrices reconstructed using a limited number of experiments. We previously proposed a method for decoherence error detection using a machine-learning technique [S. Hara, T. Ono, R. Okamoto, T. Washio, and S. Takeuchi, Phys. Rev. A 89, 022104 (2014), 10.1103/PhysRevA.89.022104]. However, the previous method is not valid when the errors are just changes in phase. Here, we propose a method that is valid for arbitrary errors in density matrices. The performance of the proposed method is verified using both numerical simulation data and real experimental data.

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

NASA Technical Reports Server (NTRS)

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

Apparatus for detecting a magnetic anomaly contiguous to remote location by squid gradiometer and magnetometer systems

DOEpatents

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

1984-01-01

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

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

DOEpatents

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

1981-05-22

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

A Context-Driven Model for the Flat Roofs Construction Process through Sensing Systems, Internet-of-Things and Last Planner System

PubMed Central

Andújar-Montoya, María Dolores

2017-01-01

The main causes of building defects are errors in the design and the construction phases. These causes related to construction are mainly due to the general lack of control of construction work and represent approximately 75% of the anomalies. In particular, one of the main causes of such anomalies, which end in building defects, is the lack of control over the physical variables of the work environment during the execution of tasks. Therefore, the high percentage of defects detected in buildings that have the root cause in the construction phase could be avoidable with a more accurate and efficient control of the process. The present work proposes a novel integration model based on information and communications technologies for the automation of both construction work and its management at the execution phase, specifically focused on the flat roof construction process. Roofs represent the second area where more defects are claimed. The proposed model is based on a Web system, supported by a service oriented architecture, for the integral management of tasks through the Last Planner System methodology, but incorporating the management of task restrictions from the physical environment variables by designing specific sensing systems. Likewise, all workers are integrated into the management process by Internet-of-Things solutions that guide them throughout the execution process in a non-intrusive and transparent way. PMID:28737693

A Context-Driven Model for the Flat Roofs Construction Process through Sensing Systems, Internet-of-Things and Last Planner System.

PubMed

Andújar-Montoya, María Dolores; Marcos-Jorquera, Diego; García-Botella, Francisco Manuel; Gilart-Iglesias, Virgilio

2017-07-22

The main causes of building defects are errors in the design and the construction phases. These causes related to construction are mainly due to the general lack of control of construction work and represent approximately 75% of the anomalies. In particular, one of the main causes of such anomalies, which end in building defects, is the lack of control over the physical variables of the work environment during the execution of tasks. Therefore, the high percentage of defects detected in buildings that have the root cause in the construction phase could be avoidable with a more accurate and efficient control of the process. The present work proposes a novel integration model based on information and communications technologies for the automation of both construction work and its management at the execution phase, specifically focused on the flat roof construction process. Roofs represent the second area where more defects are claimed. The proposed model is based on a Web system, supported by a service oriented architecture, for the integral management of tasks through the Last Planner System methodology, but incorporating the management of task restrictions from the physical environment variables by designing specific sensing systems. Likewise, all workers are integrated into the management process by Internet-of-Things solutions that guide them throughout the execution process in a non-intrusive and transparent way.

Network anomaly detection system with optimized DS evidence theory.

PubMed

Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

2014-01-01

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

Network Anomaly Detection System with Optimized DS Evidence Theory

PubMed Central

Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

2014-01-01

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

Camouflage target detection via hyperspectral imaging plus information divergence measurement

NASA Astrophysics Data System (ADS)

Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Ji, Yiqun; Shen, Weimin

2016-01-01

Target detection is one of most important applications in remote sensing. Nowadays accurate camouflage target distinction is often resorted to spectral imaging technique due to its high-resolution spectral/spatial information acquisition ability as well as plenty of data processing methods. In this paper, hyper-spectral imaging technique together with spectral information divergence measure method is used to solve camouflage target detection problem. A self-developed visual-band hyper-spectral imaging device is adopted to collect data cubes of certain experimental scene before spectral information divergences are worked out so as to discriminate target camouflage and anomaly. Full-band information divergences are measured to evaluate target detection effect visually and quantitatively. Information divergence measurement is proved to be a low-cost and effective tool for target detection task and can be further developed to other target detection applications beyond spectral imaging technique.

Near Real-Time Optimal Prediction of Adverse Events in Aviation Data

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander; Das, Santanu

2010-01-01

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we demonstrate how to recast the anomaly prediction problem into a form whose solution is accessible as a level-crossing prediction problem. The level-crossing prediction problem has an elegant, optimal, yet untested solution under certain technical constraints, and only when the appropriate modeling assumptions are made. As such, we will thoroughly investigate the resilience of these modeling assumptions, and show how they affect final performance. Finally, the predictive capability of this method will be assessed by quantitative means, using both validation and test data containing anomalies or adverse events from real aviation data sets that have previously been identified as operationally significant by domain experts. It will be shown that the formulation proposed yields a lower false alarm rate on average than competing methods based on similarly advanced concepts, and a higher correct detection rate than a standard method based upon exceedances that is commonly used for prediction.

Real-time anomaly detection for very short-term load forecasting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Jian; Hong, Tao; Yue, Meng

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Real-time anomaly detection for very short-term load forecasting

DOE PAGES

Luo, Jian; Hong, Tao; Yue, Meng

2018-01-06

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonlymore » used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.« less

Magellan spacecraft and memory state tracking: Lessons learned, future thoughts

NASA Technical Reports Server (NTRS)

Bucher, Allen W.

1993-01-01

Numerous studies have been dedicated to improving the two main elements of Spacecraft Mission Operations: Command and Telemetry. As a result, not much attention has been given to other tasks that can become tedious, repetitive, and error prone. One such task is Spacecraft and Memory State Tracking, the process by which the status of critical spacecraft components, parameters, and the contents of on-board memory are managed on the ground to maintain knowledge of spacecraft and memory states for future testing, anomaly investigation, and on-board memory reconstruction. The task of Spacecraft and Memory State Tracking has traditionally been a manual task allocated to Mission Operations Procedures. During nominal Mission Operations this job is tedious and error prone. Because the task is not complex and can be accomplished manually, the worth of a sophisticated software tool is often questioned. However, in the event of an anomaly which alters spacecraft components autonomously or a memory anomaly such as a corrupt memory or flight software error, an accurate ground image that can be reconstructed quickly is a priceless commodity. This study explores the process of Spacecraft and Memory State Tracking used by the Magellan Spacecraft Team highlighting its strengths as well as identifying lessons learned during the primary and extended missions, two memory anomalies, and other hardships encountered due to incomplete knowledge of spacecraft states. Ideas for future state tracking tools that require minimal user interaction and are integrated into the Ground Data System will also be discussed.

Magellan spacecraft and memory state tracking: Lessons learned, future thoughts

NASA Astrophysics Data System (ADS)

Bucher, Allen W.

1993-03-01

Numerous studies have been dedicated to improving the two main elements of Spacecraft Mission Operations: Command and Telemetry. As a result, not much attention has been given to other tasks that can become tedious, repetitive, and error prone. One such task is Spacecraft and Memory State Tracking, the process by which the status of critical spacecraft components, parameters, and the contents of on-board memory are managed on the ground to maintain knowledge of spacecraft and memory states for future testing, anomaly investigation, and on-board memory reconstruction. The task of Spacecraft and Memory State Tracking has traditionally been a manual task allocated to Mission Operations Procedures. During nominal Mission Operations this job is tedious and error prone. Because the task is not complex and can be accomplished manually, the worth of a sophisticated software tool is often questioned. However, in the event of an anomaly which alters spacecraft components autonomously or a memory anomaly such as a corrupt memory or flight software error, an accurate ground image that can be reconstructed quickly is a priceless commodity. This study explores the process of Spacecraft and Memory State Tracking used by the Magellan Spacecraft Team highlighting its strengths as well as identifying lessons learned during the primary and extended missions, two memory anomalies, and other hardships encountered due to incomplete knowledge of spacecraft states. Ideas for future state tracking tools that require minimal user interaction and are integrated into the Ground Data System will also be discussed.

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

PubMed

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

2018-05-11

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

22nd Annual Logistics Conference and Exhibition

DTIC Science & Technology

2006-04-20

Prognostics & Health Management at GE Dr. Piero P.Bonissone Industrial AI Lab GE Global Research NCD Select detection model Anomaly detection results...Mode 213 x Failure mode histogram 2130014 Anomaly detection from event-log data Anomaly detection from event-log data Diagnostics/ Prognostics Using...Failure Monitoring & AssessmentTactical C4ISR Sense Respond 7 •Diagnostics, Prognostics and health management

Reply to Stone Et Al.: Human-Made Role in Local Temperature Extremes

NASA Technical Reports Server (NTRS)

Hansen, James; Sato, Makiko; Ruedy, Reto A.

2013-01-01

Stone et al. find that their analysis is unable to show a causal relation of local temperature anomalies, such as in Texas in 2011, with global warming. It was because of limitations in such local analyses that we reframed the problem in our report, separating the task of attribution of the causes of global warming from the task of quantifying changes in the likelihood of extreme local temperature anomalies.

Failure Control Techniques for the SSME

NASA Technical Reports Server (NTRS)

Taniguchi, M. H.

1987-01-01

Since ground testing of the Space Shuttle Main Engine (SSME) began in 1975, the detection of engine anomalies and the prevention of major damage have been achieved by a multi-faceted detection/shutdown system. The system continues the monitoring task today and consists of the following: sensors, automatic redline and other limit logic, redundant sensors and controller voting logic, conditional decision logic, and human monitoring. Typically, on the order of 300 to 500 measurements are sensed and recorded for each test, while on the order of 100 are used for control and monitoring. Despite extensive monitoring by the current detection system, twenty-seven (27) major incidents have occurred. This number would appear insignificant compared with over 1200 hot-fire tests which have taken place since 1976. However, the number suggests the requirement for and future benefits of a more advanced failure detection system.

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

PubMed Central

Li, Gang; He, Bin; Huang, Hongwei; Tang, Limin

2016-01-01

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. PMID:27690035

Pre-seismic anomalies from optical satellite observations: a review

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, X; Liu, S; Kalet, A

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 anomalymore » 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 automated plan checks. The senior author received research grants from ViewRay Inc. and Varian Medical System.« less

Automated Network Anomaly Detection with Learning, Control and Mitigation

ERIC Educational Resources Information Center

Ippoliti, Dennis

2014-01-01

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

Systematic Screening for Subtelomeric Anomalies in a Clinical Sample of Autism

ERIC Educational Resources Information Center

Wassink, Thomas H.; Losh, Molly; Piven, Joseph; Sheffield, Val C.; Ashley, Elizabeth; Westin, Erik R.; Patil, Shivanand R.

2007-01-01

High-resolution karyotyping detects cytogenetic anomalies in 5-10% of cases of autism. Karyotyping, however, may fail to detect abnormalities of chromosome subtelomeres, which are gene rich regions prone to anomalies. We assessed whether panels of FISH probes targeted for subtelomeres could detect abnormalities beyond those identified by…

Topological anomaly detection performance with multispectral polarimetric imagery

NASA Astrophysics Data System (ADS)

Gartley, M. G.; Basener, W.,

2009-05-01

Polarimetric imaging has demonstrated utility for increasing contrast of manmade targets above natural background clutter. Manual detection of manmade targets in multispectral polarimetric imagery can be challenging and a subjective process for large datasets. Analyst exploitation may be improved utilizing conventional anomaly detection algorithms such as RX. In this paper we examine the performance of a relatively new approach to anomaly detection, which leverages topology theory, applied to spectral polarimetric imagery. Detection results for manmade targets embedded in a complex natural background will be presented for both the RX and Topological Anomaly Detection (TAD) approaches. We will also present detailed results examining detection sensitivities relative to: (1) the number of spectral bands, (2) utilization of Stoke's images versus intensity images, and (3) airborne versus spaceborne measurements.

Quantum machine learning for quantum anomaly detection

NASA Astrophysics Data System (ADS)

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.

Systematic review and meta-analysis of isolated posterior fossa malformations on prenatal ultrasound imaging (part 1): nomenclature, diagnostic accuracy and associated anomalies.

PubMed

D'Antonio, F; Khalil, A; Garel, C; Pilu, G; Rizzo, G; Lerman-Sagie, T; Bhide, A; Thilaganathan, B; Manzoli, L; Papageorghiou, A T

2016-06-01

To explore the outcome in fetuses with prenatal diagnosis of posterior fossa anomalies apparently isolated on ultrasound imaging. MEDLINE and EMBASE were searched electronically utilizing combinations of relevant medical subject headings for 'posterior fossa' and 'outcome'. The posterior fossa anomalies analyzed were Dandy-Walker malformation (DWM), mega cisterna magna (MCM), Blake's pouch cyst (BPC) and vermian hypoplasia (VH). The outcomes observed were rate of chromosomal abnormalities, additional anomalies detected at prenatal magnetic resonance imaging (MRI), additional anomalies detected at postnatal imaging and concordance between prenatal and postnatal diagnoses. Only isolated cases of posterior fossa anomalies - defined as having no cerebral or extracerebral additional anomalies detected on ultrasound examination - were included in the analysis. Quality assessment of the included studies was performed using the Newcastle-Ottawa Scale for cohort studies. We used meta-analyses of proportions to combine data and fixed- or random-effects models according to the heterogeneity of the results. Twenty-two studies including 531 fetuses with posterior fossa anomalies were included in this systematic review. The prevalence of chromosomal abnormalities in fetuses with isolated DWM was 16.3% (95% CI, 8.7-25.7%). The prevalence of additional central nervous system (CNS) abnormalities that were missed at ultrasound examination and detected only at prenatal MRI was 13.7% (95% CI, 0.2-42.6%), and the prevalence of additional CNS anomalies that were missed at prenatal imaging and detected only after birth was 18.2% (95% CI, 6.2-34.6%). Prenatal diagnosis was not confirmed after birth in 28.2% (95% CI, 8.5-53.9%) of cases. MCM was not significantly associated with additional anomalies detected at prenatal MRI or detected after birth. Prenatal diagnosis was not confirmed postnatally in 7.1% (95% CI, 2.3-14.5%) of cases. The rate of chromosomal anomalies in fetuses with isolated BPC was 5.2% (95% CI, 0.9-12.7%) and there was no associated CNS anomaly detected at prenatal MRI or only after birth. Prenatal diagnosis of BPC was not confirmed after birth in 9.8% (95% CI, 2.9-20.1%) of cases. The rate of chromosomal anomalies in fetuses with isolated VH was 6.5% (95% CI, 0.8-17.1%) and there were no additional anomalies detected at prenatal MRI (0% (95% CI, 0.0-45.9%)). The proportions of cerebral anomalies detected only after birth was 14.2% (95% CI, 2.9-31.9%). Prenatal diagnosis was not confirmed after birth in 32.4% (95% CI, 18.3-48.4%) of cases. DWM apparently isolated on ultrasound imaging is a condition with a high risk for chromosomal and associated structural anomalies. Isolated MCM and BPC have a low risk for aneuploidy or associated structural anomalies. The small number of cases with isolated VH prevents robust conclusions regarding their management from being drawn. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd.

Relational databases for rare disease study: application to vascular anomalies.

PubMed

Perkins, Jonathan A; Coltrera, Marc D

2008-01-01

To design a relational database integrating clinical and basic science data needed for multidisciplinary treatment and research in the field of vascular anomalies. Based on data points agreed on by the American Society of Pediatric Otolaryngology (ASPO) Vascular Anomalies Task Force. The database design enables sharing of data subsets in a Health Insurance Portability and Accountability Act (HIPAA)-compliant manner for multisite collaborative trials. Vascular anomalies pose diagnostic and therapeutic challenges. Our understanding of these lesions and treatment improvement is limited by nonstandard terminology, severity assessment, and measures of treatment efficacy. The rarity of these lesions places a premium on coordinated studies among multiple participant sites. The relational database design is conceptually centered on subjects having 1 or more lesions. Each anomaly can be tracked individually along with their treatment outcomes. This design allows for differentiation between treatment responses and untreated lesions' natural course. The relational database design eliminates data entry redundancy and results in extremely flexible search and data export functionality. Vascular anomaly programs in the United States. A relational database correlating clinical findings and photographic, radiologic, histologic, and treatment data for vascular anomalies was created for stand-alone and multiuser networked systems. Proof of concept for independent site data gathering and HIPAA-compliant sharing of data subsets was demonstrated. The collaborative effort by the ASPO Vascular Anomalies Task Force to create the database helped define a common vascular anomaly data set. The resulting relational database software is a powerful tool to further the study of vascular anomalies and the development of evidence-based treatment innovation.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kotoku, J; Kumagai, S; Nakabayashi, S

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 depthmore » 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.« less

A modified anomaly detection method for capsule endoscopy images using non-linear color conversion and Higher-order Local Auto-Correlation (HLAC).

PubMed

Hu, Erzhong; Nosato, Hirokazu; Sakanashi, Hidenori; Murakawa, Masahiro

2013-01-01

Capsule endoscopy is a patient-friendly endoscopy broadly utilized in gastrointestinal examination. However, the efficacy of diagnosis is restricted by the large quantity of images. This paper presents a modified anomaly detection method, by which both known and unknown anomalies in capsule endoscopy images of small intestine are expected to be detected. To achieve this goal, this paper introduces feature extraction using a non-linear color conversion and Higher-order Local Auto Correlation (HLAC) Features, and makes use of image partition and subspace method for anomaly detection. Experiments are implemented among several major anomalies with combinations of proposed techniques. As the result, the proposed method achieved 91.7% and 100% detection accuracy for swelling and bleeding respectively, so that the effectiveness of proposed method is demonstrated.

Unsupervised Ensemble Anomaly Detection Using Time-Periodic Packet Sampling

NASA Astrophysics Data System (ADS)

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

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

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

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

Anomaly Detection in Power Quality at Data Centers

NASA Technical Reports Server (NTRS)

Grichine, Art; Solano, Wanda M.

2015-01-01

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

Support vector machines for TEC seismo-ionospheric anomalies detection

NASA Astrophysics Data System (ADS)

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 the novelty changes of a nonlinear time series such as variations of earthquake precursors.

Road Anomalies Detection System Evaluation.

PubMed

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

2018-06-21

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

Visualizing Uncertainty for Data Fusion Graphics: Review of Selected Literature and Industry Approaches

DTIC Science & Technology

2015-06-09

anomaly detection , which is generally considered part of high level information fusion (HLIF) involving temporal-geospatial data as well as meta-data... Anomaly detection in the Maritime defence and security domain typically focusses on trying to identify vessels that are behaving in an unusual...manner compared with lawful vessels operating in the area – an applied case of target detection among distractors. Anomaly detection is a complex problem

The Monitoring, Detection, Isolation and Assessment of Information Warfare Attacks Through Multi-Level, Multi-Scale System Modeling and Model Based Technology

DTIC Science & Technology

2004-01-01

login identity to the one under which the system call is executed, the parameters of the system call execution - file names including full path...Anomaly detection COAST-EIMDT Distributed on target hosts EMERALD Distributed on target hosts and security servers Signature recognition Anomaly...uses a centralized architecture, and employs an anomaly detection technique for intrusion detection. The EMERALD project [80] proposes a

Post-processing for improving hyperspectral anomaly detection accuracy

NASA Astrophysics Data System (ADS)

Wu, Jee-Cheng; Jiang, Chi-Ming; Huang, Chen-Liang

2015-10-01

Anomaly detection is an important topic in the exploitation of hyperspectral data. Based on the Reed-Xiaoli (RX) detector and a morphology operator, this research proposes a novel technique for improving the accuracy of hyperspectral anomaly detection. Firstly, the RX-based detector is used to process a given input scene. Then, a post-processing scheme using morphology operator is employed to detect those pixels around high-scoring anomaly pixels. Tests were conducted using two real hyperspectral images with ground truth information and the results based on receiver operating characteristic curves, illustrated that the proposed method reduced the false alarm rates of the RXbased detector.

An incremental anomaly detection model for virtual machines.

PubMed

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.

Novel Hyperspectral Anomaly Detection Methods Based on Unsupervised Nearest Regularized Subspace

NASA Astrophysics Data System (ADS)

Hou, Z.; Chen, Y.; Tan, K.; Du, P.

2018-04-01

Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.

An incremental anomaly detection model for virtual machines

PubMed Central

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

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

PubMed Central

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

2013-01-01

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

ISHM Anomaly Lexicon for Rocket Test

NASA Technical Reports Server (NTRS)

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

2007-01-01

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

Hyperspectral data collection for the assessment of target detection algorithms: the Viareggio 2013 trial

NASA Astrophysics Data System (ADS)

Rossi, Alessandro; Acito, Nicola; Diani, Marco; Corsini, Giovanni; De Ceglie, Sergio Ugo; Riccobono, Aldo; Chiarantini, Leandro

2014-10-01

Airborne hyperspectral imagery is valuable for military and civilian applications, such as target identification, detection of anomalies and changes within multiple acquisitions. In target detection (TD) applications, the performance assessment of different algorithms is an important and critical issue. In this context, the small number of public available hyperspectral data motivated us to perform an extensive measurement campaign including various operating scenarios. The campaign was organized by CISAM in cooperation with University of Pisa, Selex ES and CSSN-ITE, and it was conducted in Viareggio, Italy in May, 2013. The Selex ES airborne hyperspectral sensor SIM.GA was mounted on board of an airplane to collect images over different sites in the morning and afternoon of two subsequent days. This paper describes the hyperspectral data collection of the trial. Four different sites were set up, representing a complex urban scenario, two parking lots and a rural area. Targets with dimensions comparable to the sensor ground resolution were deployed in the sites to reproduce different operating situations. An extensive ground truth documentation completes the data collection. Experiments to test anomalous change detection techniques were set up changing the position of the deployed targets. Search and rescue scenarios were simulated to evaluate the performance of anomaly detection algorithms. Moreover, the reflectance signatures of the targets were measured on the ground to perform spectral matching in varying atmospheric and illumination conditions. The paper presents some preliminary results that show the effectiveness of hyperspectral data exploitation for the object detection tasks of interest in this work.

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

NASA Technical Reports Server (NTRS)

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

2001-01-01

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

Multi-Level Anomaly Detection on Time-Varying Graph Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bridges, Robert A; Collins, John P; Ferragut, Erik M

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, thismore » 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.« less

Very Large Graphs for Information Extraction (VLG) Detection and Inference in the Presence of Uncertainty

DTIC Science & Technology

2015-09-21

this framework, MIT LL carried out a one-year proof- of-concept study to determine the capabilities and challenges in the detection of anomalies in...extremely large graphs [5]. Under this effort, two real datasets were considered, and algorithms for data modeling and anomaly detection were developed...is required in a well-defined experimental framework for the detection of anomalies in very large graphs. This study is intended to inform future

Lidar detection algorithm for time and range anomalies.

PubMed

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

2007-10-10

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 t(1) to t(2)" is addressed, and for range anomaly where the question "is a target present at time t within ranges R(1) and R(2)" 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 CO(2) 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.

An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake of 11 August 2012

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-09-01

Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.

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

PubMed Central

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

2016-01-01

In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved. PMID:27136561

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

PubMed

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

2016-04-29

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

Variable Discretisation for Anomaly Detection using Bayesian Networks

DTIC Science & Technology

2017-01-01

UNCLASSIFIED DST- Group –TR–3328 1 Introduction Bayesian network implementations usually require each variable to take on a finite number of mutually...UNCLASSIFIED Variable Discretisation for Anomaly Detection using Bayesian Networks Jonathan Legg National Security and ISR Division Defence Science...and Technology Group DST- Group –TR–3328 ABSTRACT Anomaly detection is the process by which low probability events are automatically found against a

Enhanced detection and visualization of anomalies in spectral imagery

NASA Astrophysics Data System (ADS)

Basener, William F.; Messinger, David W.

2009-05-01

Anomaly detection algorithms applied to hyperspectral imagery are able to reliably identify man-made objects from a natural environment based on statistical/geometric likelyhood. The process is more robust than target identification, which requires precise prior knowledge of the object of interest, but has an inherently higher false alarm rate. Standard anomaly detection algorithms measure deviation of pixel spectra from a parametric model (either statistical or linear mixing) estimating the image background. The topological anomaly detector (TAD) creates a fully non-parametric, graph theory-based, topological model of the image background and measures deviation from this background using codensity. In this paper we present a large-scale comparative test of TAD against 80+ targets in four full HYDICE images using the entire canonical target set for generation of ROC curves. TAD will be compared against several statistics-based detectors including local RX and subspace RX. Even a perfect anomaly detection algorithm would have a high practical false alarm rate in most scenes simply because the user/analyst is not interested in every anomalous object. To assist the analyst in identifying and sorting objects of interest, we investigate coloring of the anomalies with principle components projections using statistics computed from the anomalies. This gives a very useful colorization of anomalies in which objects of similar material tend to have the same color, enabling an analyst to quickly sort and identify anomalies of highest interest.

Pediatric tinnitus: Incidence of imaging anomalies and the impact of hearing loss.

PubMed

Kerr, Rhorie; Kang, Elise; Hopkins, Brandon; Anne, Samantha

2017-12-01

Guidelines exist for evaluation and management of tinnitus in adults; however lack of evidence in children limits applicability of these guidelines to pediatric patients. Objective of this study is to determine the incidence of inner ear anomalies detected on imaging studies within the pediatric population with tinnitus and evaluate if presence of hearing loss increases the rate of detection of anomalies in comparison to normal hearing patients. Retrospective review of all children with diagnosis of tinnitus from 2010 to 2015 ;at a tertiary care academic center. 102 pediatric patients with tinnitus were identified. Overall, 53 patients had imaging studies with 6 abnormal findings (11.3%). 51/102 patients had hearing loss of which 33 had imaging studies demonstrating 6 inner ear anomalies detected. This is an incidence of 18.2% for inner ear anomalies identified in patients with hearing loss (95% confidence interval (CI) of 7.0-35.5%). 4 of these 6 inner ear anomalies detected were vestibular aqueduct abnormalities. The other two anomalies were cochlear hypoplasia and bilateral semicircular canal dysmorphism. 51 patients had no hearing loss and of these patients, 20 had imaging studies with no inner ear abnormalities detected. There was no statistical difference in incidence of abnormal imaging findings in patients with and without hearing loss (Fisher's exact test, p ;= ;0.072.) CONCLUSION: There is a high incidence of anomalies detected in imaging studies done in pediatric patients with tinnitus, especially in the presence of hearing loss. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatially-Aware Temporal Anomaly Mapping of Gamma Spectra

NASA Astrophysics Data System (ADS)

Reinhart, Alex; Athey, Alex; Biegalski, Steven

2014-06-01

For security, environmental, and regulatory purposes it is useful to continuously monitor wide areas for unexpected changes in radioactivity. We report on a temporal anomaly detection algorithm which uses mobile detectors to build a spatial map of background spectra, allowing sensitive detection of any anomalies through many days or months of monitoring. We adapt previously-developed anomaly detection methods, which compare spectral shape rather than count rate, to function with limited background data, allowing sensitive detection of small changes in spectral shape from day to day. To demonstrate this technique we collected daily observations over the period of six weeks on a 0.33 square mile research campus and performed source injection simulations.

Hyperspectral anomaly detection using Sony PlayStation 3

NASA Astrophysics Data System (ADS)

Rosario, Dalton; Romano, João; Sepulveda, Rene

2009-05-01

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

A novel approach for detection of anomalies using measurement data of the Ironton-Russell bridge

NASA Astrophysics Data System (ADS)

Zhang, Fan; Norouzi, Mehdi; Hunt, Victor; Helmicki, Arthur

2015-04-01

Data models have been increasingly used in recent years for documenting normal behavior of structures and hence detect and classify anomalies. Large numbers of machine learning algorithms were proposed by various researchers to model operational and functional changes in structures; however, a limited number of studies were applied to actual measurement data due to limited access to the long term measurement data of structures and lack of access to the damaged states of structures. By monitoring the structure during construction and reviewing the effect of construction events on the measurement data, this study introduces a new approach to detect and eventually classify anomalies during construction and after construction. First, the implementation procedure of the sensory network that develops while the bridge is being built and its current status will be detailed. Second, the proposed anomaly detection algorithm will be applied on the collected data and finally, detected anomalies will be validated against the archived construction events.

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

NASA Astrophysics Data System (ADS)

Sun, Hao; Zou, Huanxin; Zhou, Shilin

2016-03-01

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

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

PubMed

Jang, J; Seo, J K

2015-06-01

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

Multi-Level Modeling of Complex Socio-Technical Systems - Phase 1

DTIC Science & Technology

2013-06-06

is to detect anomalous organizational outcomes, diagnose the causes of these anomalies , and decide upon appropriate compensation schemes. All of...monitor process outcomes. The purpose of this monitoring is to detect anomalous process outcomes, diagnose the causes of these anomalies , and decide upon...monitor work outcomes in terms of performance. The purpose of this monitoring is to detect anomalous work outcomes, diagnose the causes of these anomalies

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ondrej Linda; Todd Vollmer; Milos Manic

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, thismore » 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.« less

Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound.

PubMed

Oh, Dong Yul; Yun, Il Dong

2018-04-24

Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection.

First trimester PAPP-A in the detection of non-Down syndrome aneuploidy.

PubMed

Ochshorn, Y; Kupferminc, M J; Wolman, I; Orr-Urtreger, A; Jaffa, A J; Yaron, Y

2001-07-01

Combined first trimester screening using pregnancy associated plasma protein-A (PAPP-A), free beta-human chorionic gonadotrophin, and nuchal translucency (NT), is currently accepted as probably the best combination for the detection of Down syndrome (DS). Current first trimester algorithms provide computed risks only for DS. However, low PAPP-A is also associated with other chromosome anomalies such as trisomy 13, 18, and sex chromosome aneuploidy. Thus, using currently available algorithms, some chromosome anomalies may not be detected. The purpose of the present study was to establish a low-end cut-off value for PAPP-A that would increase the detection rates for non-DS chromosome anomalies. The study included 1408 patients who underwent combined first trimester screening. To determine a low-end cut-off value for PAPP-A, a Receiver-Operator Characteristic (ROC) curve analysis was performed. In the entire study group there were 18 cases of chromosome anomalies (trisomy 21, 13, 18, sex chromosome anomalies), 14 of which were among screen-positive patients, a detection rate of 77.7% for all chromosome anomalies (95% CI: 55.7-99.7%). ROC curve analysis detected a statistically significant cut-off for PAPP-A at 0.25 MoM. If the definition of screen-positive were to also include patients with PAPP-A<0.25 MoM, the detection rate would increase to 88.8% for all chromosome anomalies (95% CI: 71.6-106%). This low cut-off value may be used until specific algorithms are implemented for non-Down syndrome aneuploidy. Copyright 2001 John Wiley & Sons, Ltd.

SUMO: operation and maintenance management web tool for astronomical observatories

NASA Astrophysics Data System (ADS)

Mujica-Alvarez, Emma; Pérez-Calpena, Ana; García-Vargas, María. Luisa

2014-08-01

SUMO is an Operation and Maintenance Management web tool, which allows managing the operation and maintenance activities and resources required for the exploitation of a complex facility. SUMO main capabilities are: information repository, assets and stock control, tasks scheduler, executed tasks archive, configuration and anomalies control and notification and users management. The information needed to operate and maintain the system must be initially stored at the tool database. SUMO shall automatically schedule the periodical tasks and facilitates the searching and programming of the non-periodical tasks. Tasks planning can be visualized in different formats and dynamically edited to be adjusted to the available resources, anomalies, dates and other constrains that can arise during daily operation. SUMO shall provide warnings to the users notifying potential conflicts related to the required personal availability or the spare stock for the scheduled tasks. To conclude, SUMO has been designed as a tool to help during the operation management of a scientific facility, and in particular an astronomical observatory. This is done by controlling all operating parameters: personal, assets, spare and supply stocks, tasks and time constrains.

Security inspection in ports by anomaly detection using hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Rivera, Javier; Valverde, Fernando; Saldaña, Manuel; Manian, Vidya

2013-05-01

Applying hyperspectral imaging technology in port security is crucial for the detection of possible threats or illegal activities. One of the most common problems that cargo suffers is tampering. This represents a danger to society because it creates a channel to smuggle illegal and hazardous products. If a cargo is altered, security inspections on that cargo should contain anomalies that reveal the nature of the tampering. Hyperspectral images can detect anomalies by gathering information through multiple electromagnetic bands. The spectrums extracted from these bands can be used to detect surface anomalies from different materials. Based on this technology, a scenario was built in which a hyperspectral camera was used to inspect the cargo for any surface anomalies and a user interface shows the results. The spectrum of items, altered by different materials that can be used to conceal illegal products, is analyzed and classified in order to provide information about the tampered cargo. The image is analyzed with a variety of techniques such as multiple features extracting algorithms, autonomous anomaly detection, and target spectrum detection. The results will be exported to a workstation or mobile device in order to show them in an easy -to-use interface. This process could enhance the current capabilities of security systems that are already implemented, providing a more complete approach to detect threats and illegal cargo.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

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

NASA Astrophysics Data System (ADS)

Zhang, Xing; Wen, Gongjian

2015-10-01

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

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bridges, Robert A.; Collins, John P.; Ferragut, Erik M.

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 node probabilities, and these 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 facilitatesmore » 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. Furthermore, 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.« less

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

PubMed

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.

An Optimized Method to Detect BDS Satellites’ Orbit Maneuvering and Anomalies in Real-Time

PubMed Central

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

2018-01-01

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. PMID:29495638

A multi-level anomaly detection algorithm for time-varying graph data with interactive visualization

DOE PAGES

Bridges, Robert A.; Collins, John P.; Ferragut, Erik M.; ...

2016-01-01

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 node probabilities, and these 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 facilitatesmore » 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. Furthermore, 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.« less

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

NASA Technical Reports Server (NTRS)

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.

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

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Application of the Augmented Operator Function Model for Developing Cognitive Metrics in Persistent Surveillance

DTIC Science & Technology

2013-09-26

vehicle-lengths between frames. The low specificity of object detectors in WAMI means all vehicle detections are treated equally. Motion clutter...timing of the anomaly . If an anomaly was detected , recent activity would have a priority over older activity. This is due to the reasoning that if the...this could be a potential anomaly detected . Other baseline activities include normal work hours, religious observance times and interactions between

Critical Infrastructure Protection and Resilience Literature Survey: Modeling and Simulation

DTIC Science & Technology

2014-11-01

2013 Page 34 of 63 Below the yellow set is a purple cluster bringing together detection , anomaly , intrusion, sensors, monitoring and alerting (early...hazards and threats to security56 Water ADWICE, PSS®SINCAL ADWICE for real-time anomaly detection in water management systems57 One tool that...Systems. Cybernetics and Information Technologies. 2008;8(4):57-68. 57. Raciti M, Cucurull J, Nadjm-Tehrani S. Anomaly detection in water management

Symbolic Time-Series Analysis for Anomaly Detection in Mechanical Systems

DTIC Science & Technology

2006-08-01

Amol Khatkhate, Asok Ray , Fellow, IEEE, Eric Keller, Shalabh Gupta, and Shin C. Chin Abstract—This paper examines the efficacy of a novel method for...recognition. KHATKHATE et al.: SYMBOLIC TIME-SERIES ANALYSIS FOR ANOMALY DETECTION 447 Asok Ray (F’02) received graduate degrees in electri- cal...anomaly detection has been pro- posed by Ray [6], where the underlying information on the dynamical behavior of complex systems is derived based on

Autonomous detection of crowd anomalies in multiple-camera surveillance feeds

NASA Astrophysics Data System (ADS)

Nordlöf, Jonas; Andersson, Maria

2016-10-01

A novel approach for autonomous detection of anomalies in crowded environments is presented in this paper. The proposed models uses a Gaussian mixture probability hypothesis density (GM-PHD) filter as feature extractor in conjunction with different Gaussian mixture hidden Markov models (GM-HMMs). Results, based on both simulated and recorded data, indicate that this method can track and detect anomalies on-line in individual crowds through multiple camera feeds in a crowded environment.

Deep learning on temporal-spectral data for anomaly detection

NASA Astrophysics Data System (ADS)

Ma, King; Leung, Henry; Jalilian, Ehsan; Huang, Daniel

2017-05-01

Detecting anomalies is important for continuous monitoring of sensor systems. One significant challenge is to use sensor data and autonomously detect changes that cause different conditions to occur. Using deep learning methods, we are able to monitor and detect changes as a result of some disturbance in the system. We utilize deep neural networks for sequence analysis of time series. We use a multi-step method for anomaly detection. We train the network to learn spectral and temporal features from the acoustic time series. We test our method using fiber-optic acoustic data from a pipeline.

Firefly Algorithm in detection of TEC seismo-ionospheric anomalies

NASA Astrophysics Data System (ADS)

Akhoondzadeh, Mehdi

2015-07-01

Anomaly detection in time series of different earthquake precursors is an essential introduction to create an early warning system with an allowable uncertainty. Since these time series are more often non linear, complex and massive, therefore the applied predictor method should be able to detect the discord patterns from a large data in a short time. This study acknowledges Firefly Algorithm (FA) as a simple and robust predictor to detect the TEC (Total Electron Content) seismo-ionospheric anomalies around the time of the some powerful earthquakes including Chile (27 February 2010), Varzeghan (11 August 2012) and Saravan (16 April 2013). Outstanding anomalies were observed 7 and 5 days before the Chile and Varzeghan earthquakes, respectively and also 3 and 8 days prior to the Saravan earthquake.

Latent Space Tracking from Heterogeneous Data with an Application for Anomaly Detection

DTIC Science & Technology

2015-11-01

specific, if the anomaly behaves as a sudden outlier after which the data stream goes back to normal state, then the anomalous data point should be...introduced three types of anomalies , all of them are sudden outliers . 438 J. Huang and X. Ning Table 2. Synthetic dataset: AUC and parameters method...Latent Space Tracking from Heterogeneous Data with an Application for Anomaly Detection Jiaji Huang1(B) and Xia Ning2 1 Department of Electrical

Dynamic analysis methods for detecting anomalies in asynchronously interacting systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 needmore » 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.« less

Disparity : scalable anomaly detection for clusters.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Desai, N.; Bradshaw, R.; Lusk, E.

2008-01-01

In this paper, we describe disparity, a tool that does parallel, scalable anomaly detection for clusters. Disparity uses basic statistical methods and scalable reduction operations to perform data reduction on client nodes and uses these results to locate node anomalies. We discuss the implementation of disparity and present results of its use on a SiCortex SC5832 system.

Integrated System Health Management (ISHM) for Test Stand and J-2X Engine: Core Implementation

NASA Technical Reports Server (NTRS)

Figueroa, Jorge F.; Schmalzel, John L.; Aguilar, Robert; Shwabacher, Mark; Morris, Jon

2008-01-01

ISHM capability enables a system to detect anomalies, determine causes and effects, predict future anomalies, and provides an integrated awareness of the health of the system to users (operators, customers, management, etc.). NASA Stennis Space Center, NASA Ames Research Center, and Pratt & Whitney Rocketdyne have implemented a core ISHM capability that encompasses the A1 Test Stand and the J-2X Engine. The implementation incorporates all aspects of ISHM; from anomaly detection (e.g. leaks) to root-cause-analysis based on failure mode and effects analysis (FMEA), to a user interface for an integrated visualization of the health of the system (Test Stand and Engine). The implementation provides a low functional capability level (FCL) in that it is populated with few algorithms and approaches for anomaly detection, and root-cause trees from a limited FMEA effort. However, it is a demonstration of a credible ISHM capability, and it is inherently designed for continuous and systematic augmentation of the capability. The ISHM capability is grounded on an integrating software environment used to create an ISHM model of the system. The ISHM model follows an object-oriented approach: includes all elements of the system (from schematics) and provides for compartmentalized storage of information associated with each element. For instance, a sensor object contains a transducer electronic data sheet (TEDS) with information that might be used by algorithms and approaches for anomaly detection, diagnostics, etc. Similarly, a component, such as a tank, contains a Component Electronic Data Sheet (CEDS). Each element also includes a Health Electronic Data Sheet (HEDS) that contains health-related information such as anomalies and health state. Some practical aspects of the implementation include: (1) near real-time data flow from the test stand data acquisition system through the ISHM model, for near real-time detection of anomalies and diagnostics, (2) insertion of the J-2X predictive model providing predicted sensor values for comparison with measured values and use in anomaly detection and diagnostics, and (3) insertion of third-party anomaly detection algorithms into the integrated ISHM model.

Robust and efficient anomaly detection using heterogeneous representations

NASA Astrophysics Data System (ADS)

Hu, Xing; Hu, Shiqiang; Xie, Jinhua; Zheng, Shiyou

2015-05-01

Various approaches have been proposed for video anomaly detection. Yet these approaches typically suffer from one or more limitations: they often characterize the pattern using its internal information, but ignore its external relationship which is important for local anomaly detection. Moreover, the high-dimensionality and the lack of robustness of pattern representation may lead to problems, including overfitting, increased computational cost and memory requirements, and high false alarm rate. We propose a video anomaly detection framework which relies on a heterogeneous representation to account for both the pattern's internal information and external relationship. The internal information is characterized by slow features learned by slow feature analysis from low-level representations, and the external relationship is characterized by the spatial contextual distances. The heterogeneous representation is compact, robust, efficient, and discriminative for anomaly detection. Moreover, both the pattern's internal information and external relationship can be taken into account in the proposed framework. Extensive experiments demonstrate the robustness and efficiency of our approach by comparison with the state-of-the-art approaches on the widely used benchmark datasets.

Final report for LDRD project 11-0029 : high-interest event detection in large-scale multi-modal data sets : proof of concept.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rohrer, Brandon Robinson

2011-09-01

Events of interest to data analysts are sometimes difficult to characterize in detail. Rather, they consist of anomalies, events that are unpredicted, unusual, or otherwise incongruent. The purpose of this LDRD was to test the hypothesis that a biologically-inspired anomaly detection algorithm could be used to detect contextual, multi-modal anomalies. There currently is no other solution to this problem, but the existence of a solution would have a great national security impact. The technical focus of this research was the application of a brain-emulating cognition and control architecture (BECCA) to the problem of anomaly detection. One aspect of BECCA inmore » particular was discovered to be critical to improved anomaly detection capabilities: it's feature creator. During the course of this project the feature creator was developed and tested against multiple data types. Development direction was drawn from psychological and neurophysiological measurements. Major technical achievements include the creation of hierarchical feature sets created from both audio and imagery data.« less

Randomized subspace-based robust principal component analysis for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Yang, Gang; Li, Jialin; Zhang, Dianfa

2018-01-01

A randomized subspace-based robust principal component analysis (RSRPCA) method for anomaly detection in hyperspectral imagery (HSI) is proposed. The RSRPCA combines advantages of randomized column subspace and robust principal component analysis (RPCA). It assumes that the background has low-rank properties, and the anomalies are sparse and do not lie in the column subspace of the background. First, RSRPCA implements random sampling to sketch the original HSI dataset from columns and to construct a randomized column subspace of the background. Structured random projections are also adopted to sketch the HSI dataset from rows. Sketching from columns and rows could greatly reduce the computational requirements of RSRPCA. Second, the RSRPCA adopts the columnwise RPCA (CWRPCA) to eliminate negative effects of sampled anomaly pixels and that purifies the previous randomized column subspace by removing sampled anomaly columns. The CWRPCA decomposes the submatrix of the HSI data into a low-rank matrix (i.e., background component), a noisy matrix (i.e., noise component), and a sparse anomaly matrix (i.e., anomaly component) with only a small proportion of nonzero columns. The algorithm of inexact augmented Lagrange multiplier is utilized to optimize the CWRPCA problem and estimate the sparse matrix. Nonzero columns of the sparse anomaly matrix point to sampled anomaly columns in the submatrix. Third, all the pixels are projected onto the complemental subspace of the purified randomized column subspace of the background and the anomaly pixels in the original HSI data are finally exactly located. Several experiments on three real hyperspectral images are carefully designed to investigate the detection performance of RSRPCA, and the results are compared with four state-of-the-art methods. Experimental results show that the proposed RSRPCA outperforms four comparison methods both in detection performance and in computational time.

Anomaly detection of turbopump vibration in Space Shuttle Main Engine using statistics and neural networks

NASA Technical Reports Server (NTRS)

Lo, C. F.; Wu, K.; Whitehead, B. A.

1993-01-01

The statistical and neural networks methods have been applied to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. The anomalies are detected based on the amplitude of peaks of fundamental and harmonic frequencies in the power spectral density. These data are reduced to the proper format from sensor data measured by strain gauges and accelerometers. Both methods are feasible to detect the vibration anomalies. The statistical method requires sufficient data points to establish a reasonable statistical distribution data bank. This method is applicable for on-line operation. The neural networks method also needs to have enough data basis to train the neural networks. The testing procedure can be utilized at any time so long as the characteristics of components remain unchanged.

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

PubMed Central

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

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

PubMed Central

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

2012-01-01

Purpose: This work uses repeat images of intensity modulated radiation therapy (IMRT) fields to quantify fluence anomalies (i.e., delivery errors) that can be reliably detected in electronic portal images used for IMRT pretreatment quality assurance. Methods: Repeat images of 11 clinical IMRT fields are acquired on a Varian Trilogy linear accelerator at energies of 6 MV and 18 MV. Acquired images are corrected for output variations and registered to minimize the impact of linear accelerator and electronic portal imaging device (EPID) positioning deviations. Detection studies are performed in which rectangular anomalies of various sizes are inserted into the images. The performance of detection strategies based on pixel intensity deviations (PIDs) and gamma indices is evaluated using receiver operating characteristic analysis. Results: Residual differences between registered images are due to interfraction positional deviations of jaws and multileaf collimator leaves, plus imager noise. Positional deviations produce large intensity differences that degrade anomaly detection. Gradient effects are suppressed in PIDs using gradient scaling. Background noise is suppressed using median filtering. In the majority of images, PID-based detection strategies can reliably detect fluence anomalies of ≥5% in ∼1 mm2 areas and ≥2% in ∼20 mm2 areas. Conclusions: The ability to detect small dose differences (≤2%) depends strongly on the level of background noise. This in turn depends on the accuracy of image registration, the quality of the reference image, and field properties. The longer term aim of this work is to develop accurate and reliable methods of detecting IMRT delivery errors and variations. The ability to resolve small anomalies will allow the accuracy of advanced treatment techniques, such as image guided, adaptive, and arc therapies, to be quantified. PMID:22894421

Implementing Classification on a Munitions Response Project

DTIC Science & Technology

2011-12-01

Detection Dig List  IVS/Seed Site Planning Decisions Dig All  Anomalies Site Characterization Implementing Classification on a Munitions Response...Details ● Seed emplacement ● EM61-MK2 detection survey  RTK GPS ● Select anomalies for further investigation ● Collect cued data using MetalMapper...5.2 mV in channel 2  938 anomalies selected ● All QC seeds detected using this threshold  Some just inside the 60-cm halo ● IVS reproducibility

Fuzzy Kernel k-Medoids algorithm for anomaly detection problems

NASA Astrophysics Data System (ADS)

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.

Anomaly detection in hyperspectral imagery: statistics vs. graph-based algorithms

NASA Astrophysics Data System (ADS)

Berkson, Emily E.; Messinger, David W.

2016-05-01

Anomaly detection (AD) algorithms are frequently applied to hyperspectral imagery, but different algorithms produce different outlier results depending on the image scene content and the assumed background model. This work provides the first comparison of anomaly score distributions between common statistics-based anomaly detection algorithms (RX and subspace-RX) and the graph-based Topological Anomaly Detector (TAD). Anomaly scores in statistical AD algorithms should theoretically approximate a chi-squared distribution; however, this is rarely the case with real hyperspectral imagery. The expected distribution of scores found with graph-based methods remains unclear. We also look for general trends in algorithm performance with varied scene content. Three separate scenes were extracted from the hyperspectral MegaScene image taken over downtown Rochester, NY with the VIS-NIR-SWIR ProSpecTIR instrument. In order of most to least cluttered, we study an urban, suburban, and rural scene. The three AD algorithms were applied to each scene, and the distributions of the most anomalous 5% of pixels were compared. We find that subspace-RX performs better than RX, because the data becomes more normal when the highest variance principal components are removed. We also see that compared to statistical detectors, anomalies detected by TAD are easier to separate from the background. Due to their different underlying assumptions, the statistical and graph-based algorithms highlighted different anomalies within the urban scene. These results will lead to a deeper understanding of these algorithms and their applicability across different types of imagery.

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

PubMed

Napoletano, Paolo; Piccoli, Flavio; Schettini, Raimondo

2018-01-12

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.

Modeling And Detecting Anomalies In Scada Systems

NASA Astrophysics Data System (ADS)

Svendsen, Nils; Wolthusen, Stephen

The detection of attacks and intrusions based on anomalies is hampered by the limits of specificity underlying the detection techniques. However, in the case of many critical infrastructure systems, domain-specific knowledge and models can impose constraints that potentially reduce error rates. At the same time, attackers can use their knowledge of system behavior to mask their manipulations, causing adverse effects to observed only after a significant period of time. This paper describes elementary statistical techniques that can be applied to detect anomalies in critical infrastructure networks. A SCADA system employed in liquefied natural gas (LNG) production is used as a case study.

First and second trimester screening for fetal structural anomalies.

PubMed

Edwards, Lindsay; Hui, Lisa

2018-04-01

Fetal structural anomalies are found in up to 3% of all pregnancies and ultrasound-based screening has been an integral part of routine prenatal care for decades. The prenatal detection of fetal anomalies allows for optimal perinatal management, providing expectant parents with opportunities for additional imaging, genetic testing, and the provision of information regarding prognosis and management options. Approximately one-half of all major structural anomalies can now be detected in the first trimester, including acrania/anencephaly, abdominal wall defects, holoprosencephaly and cystic hygromata. Due to the ongoing development of some organ systems however, some anomalies will not be evident until later in the pregnancy. To this extent, the second trimester anatomy is recommended by professional societies as the standard investigation for the detection of fetal structural anomalies. The reported detection rates of structural anomalies vary according to the organ system being examined, and are also dependent upon factors such as the equipment settings and sonographer experience. Technological advances over the past two decades continue to support the role of ultrasound as the primary imaging modality in pregnancy, and the safety of ultrasound for the developing fetus is well established. With increasing capabilities and experience, detailed examination of the central nervous system and cardiovascular system is possible, with dedicated examinations such as the fetal neurosonogram and the fetal echocardiogram now widely performed in tertiary centers. Magnetic resonance imaging (MRI) is well recognized for its role in the assessment of fetal brain anomalies; other potential indications for fetal MRI include lung volume measurement (in cases of congenital diaphragmatic hernia), and pre-surgical planning prior to fetal spina bifida repair. When a major structural abnormality is detected prenatally, genetic testing with chromosomal microarray is recommended over routine karyotype due to its higher genomic resolution. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garcia, Humberto E.; Simpson, Michael F.; Lin, Wen-Chiao

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

An incremental knowledge assimilation system (IKAS) for mine detection

NASA Astrophysics Data System (ADS)

Porway, Jake; Raju, Chaitanya; Varadarajan, Karthik Mahesh; Nguyen, Hieu; Yadegar, Joseph

2010-04-01

In this paper we present an adaptive incremental learning system for underwater mine detection and classification that utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector (BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this information, BAAN classifies the background type and updates its detection using background-specific parameters. To perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection accuracy by constantly learning from new samples.

Hyperspectral target detection using heavy-tailed distributions

NASA Astrophysics Data System (ADS)

Willis, Chris J.

2009-09-01

One promising approach to target detection in hyperspectral imagery exploits a statistical mixture model to represent scene content at a pixel level. The process then goes on to look for pixels which are rare, when judged against the model, and marks them as anomalies. It is assumed that military targets will themselves be rare and therefore likely to be detected amongst these anomalies. For the typical assumption of multivariate Gaussianity for the mixture components, the presence of the anomalous pixels within the training data will have a deleterious effect on the quality of the model. In particular, the derivation process itself is adversely affected by the attempt to accommodate the anomalies within the mixture components. This will bias the statistics of at least some of the components away from their true values and towards the anomalies. In many cases this will result in a reduction in the detection performance and an increased false alarm rate. This paper considers the use of heavy-tailed statistical distributions within the mixture model. Such distributions are better able to account for anomalies in the training data within the tails of their distributions, and the balance of the pixels within their central masses. This means that an improved model of the majority of the pixels in the scene may be produced, ultimately leading to a better anomaly detection result. The anomaly detection techniques are examined using both synthetic data and hyperspectral imagery with injected anomalous pixels. A range of results is presented for the baseline Gaussian mixture model and for models accommodating heavy-tailed distributions, for different parameterizations of the algorithms. These include scene understanding results, anomalous pixel maps at given significance levels and Receiver Operating Characteristic curves.

A hybrid approach for efficient anomaly detection using metaheuristic methods

PubMed Central

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

2014-01-01

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. PMID:26199752

Identifying Threats Using Graph-based Anomaly Detection

NASA Astrophysics Data System (ADS)

Eberle, William; Holder, Lawrence; Cook, Diane

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

A hybrid approach for efficient anomaly detection using metaheuristic methods.

PubMed

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.

LEADER - An integrated engine behavior and design analyses based real-time fault diagnostic expert system for Space Shuttle Main Engine (SSME)

NASA Technical Reports Server (NTRS)

Gupta, U. K.; Ali, M.

1989-01-01

The LEADER expert system has been developed for automatic learning tasks encompassing real-time detection, identification, verification, and correction of anomalous propulsion system operations, using a set of sensors to monitor engine component performance to ascertain anomalies in engine dynamics and behavior. Two diagnostic approaches are embodied in LEADER's architecture: (1) learning and identifying engine behavior patterns to generate novel hypotheses about possible abnormalities, and (2) the direction of engine sensor data processing to perform resoning based on engine design and functional knowledge, as well as the principles of the relevant mechanics and physics.

Real-time Bayesian anomaly detection in streaming environmental data

NASA Astrophysics Data System (ADS)

Hill, David J.; Minsker, Barbara S.; Amir, Eyal

2009-04-01

With large volumes of data arriving in near real time from environmental sensors, there is a need for automated detection of anomalous data caused by sensor or transmission errors or by infrequent system behaviors. This study develops and evaluates three automated anomaly detection methods using dynamic Bayesian networks (DBNs), which perform fast, incremental evaluation of data as they become available, scale to large quantities of data, and require no a priori information regarding process variables or types of anomalies that may be encountered. This study investigates these methods' abilities to identify anomalies in eight meteorological data streams from Corpus Christi, Texas. The results indicate that DBN-based detectors, using either robust Kalman filtering or Rao-Blackwellized particle filtering, outperform a DBN-based detector using Kalman filtering, with the former having false positive/negative rates of less than 2%. These methods were successful at identifying data anomalies caused by two real events: a sensor failure and a large storm.

Conditional Anomaly Detection with Soft Harmonic Functions

PubMed Central

Valko, Michal; Kveton, Branislav; Valizadegan, Hamed; Cooper, Gregory F.; Hauskrecht, Milos

2012-01-01

In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method on several synthetic and UCI ML datasets in detecting unusual labels when compared to several baseline approaches. We also evaluate the performance of our method on a real-world electronic health record dataset where we seek to identify unusual patient-management decisions. PMID:25309142

Conditional Anomaly Detection with Soft Harmonic Functions.

PubMed

Valko, Michal; Kveton, Branislav; Valizadegan, Hamed; Cooper, Gregory F; Hauskrecht, Milos

2011-01-01

In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method on several synthetic and UCI ML datasets in detecting unusual labels when compared to several baseline approaches. We also evaluate the performance of our method on a real-world electronic health record dataset where we seek to identify unusual patient-management decisions.

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

NASA Astrophysics Data System (ADS)

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

2011-07-01

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

CNNEDGEPOT: CNN based edge detection of 2D near surface potential field data

NASA Astrophysics Data System (ADS)

Aydogan, D.

2012-09-01

All anomalies are important in the interpretation of gravity and magnetic data because they indicate some important structural features. One of the advantages of using gravity or magnetic data for searching contacts is to be detected buried structures whose signs could not be seen on the surface. In this paper, a general view of the cellular neural network (CNN) method with a large scale nonlinear circuit is presented focusing on its image processing applications. The proposed CNN model is used consecutively in order to extract body and body edges. The algorithm is a stochastic image processing method based on close neighborhood relationship of the cells and optimization of A, B and I matrices entitled as cloning template operators. Setting up a CNN (continues time cellular neural network (CTCNN) or discrete time cellular neural network (DTCNN)) for a particular task needs a proper selection of cloning templates which determine the dynamics of the method. The proposed algorithm is used for image enhancement and edge detection. The proposed method is applied on synthetic and field data generated for edge detection of near-surface geological bodies that mask each other in various depths and dimensions. The program named as CNNEDGEPOT is a set of functions written in MATLAB software. The GUI helps the user to easily change all the required CNN model parameters. A visual evaluation of the outputs due to DTCNN and CTCNN are carried out and the results are compared with each other. These examples demonstrate that in detecting the geological features the CNN model can be used for visual interpretation of near surface gravity or magnetic anomaly maps.

Rate based failure detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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 datamore » paths may be adjusted in real-time to ensure that the power grid data network does not become overloaded and/or fail.« less

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

PubMed Central

Schettini, Raimondo

2018-01-01

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. PMID:29329268

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

PubMed

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

2013-07-12

In Norway almost all pregnant women attend one routine ultrasound examination. Detection of fetal structural anomalies triggers psychological stress responses in the women affected. Despite the frequent use of ultrasound examination in pregnancy, little attention has been devoted to the psychological response of the expectant father following the detection of fetal anomalies. This is important for later fatherhood and the psychological interaction within the couple. We aimed to describe paternal psychological responses shortly after detection of structural fetal anomalies by ultrasonography, and to compare paternal and maternal responses within the same couple. A prospective observational study was performed at a tertiary referral centre for fetal medicine. Pregnant women with a structural fetal anomaly detected by ultrasound and their partners (study group,n=155) and 100 with normal ultrasound findings (comparison group) were included shortly after sonographic examination (inclusion period: May 2006-February 2009). Gestational age was >12 weeks. We used psychometric questionnaires to assess self-reported social dysfunction, health perception, and psychological distress (intrusion, avoidance, arousal, anxiety, and depression): Impact of Event Scale. General Health Questionnaire and Edinburgh Postnatal Depression Scale. Fetal anomalies were classified according to severity and diagnostic or prognostic ambiguity at the time of assessment. Median (range) gestational age at inclusion in the study and comparison group was 19 (12-38) and 19 (13-22) weeks, respectively. Men and women in the study group had significantly higher levels of psychological distress than men and women in the comparison group on all psychometric endpoints. The lowest level of distress in the study group was associated with the least severe anomalies with no diagnostic or prognostic ambiguity (p < 0.033). Men had lower scores than women on all psychometric outcome variables. The correlation in distress scores between men and women was high in the fetal anomaly group (p < 0.001), but non-significant in the comparison group. Severity of the anomaly including ambiguity significantly influenced paternal response. Men reported lower scores on all psychometric outcomes than women. This knowledge may facilitate support for both expectant parents to reduce strain within the family after detectionof a fetal anomaly.

Apollo-Soyuz pamphlet no. 4: Gravitational field. [experimental design

NASA Technical Reports Server (NTRS)

Page, L. W.; From, T. P.

1977-01-01

Two Apollo Soyuz experiments designed to detect gravity anomalies from spacecraft motion are described. The geodynamics experiment (MA-128) measured large-scale gravity anomalies by detecting small accelerations of Apollo in the 222 km orbit, using Doppler tracking from the ATS-6 satellite. Experiment MA-089 measured 300 km anomalies on the earth's surface by detecting minute changes in the separation between Apollo and the docking module. Topics discussed in relation to these experiments include the Doppler effect, gravimeters, and the discovery of mascons on the moon.

Relationship of ocular accommodation and motor skills performance in developmental coordination disorder.

PubMed

Rafique, Sara A; Northway, Nadia

2015-08-01

Ocular accommodation provides a well-focussed image, feedback for accurate eye movement control, and cues for depth perception. To accurately perform visually guided motor tasks, integration of ocular motor systems is essential. Children with motor coordination impairment are established to be at higher risk of accommodation anomalies. The aim of the present study was to examine the relationship between ocular accommodation and motor tasks, which are often overlooked, in order to better understand the problems experienced by children with motor coordination impairment. Visual function, gross and fine motor skills were assessed in children with developmental coordination disorder (DCD) and typically developing control children. Children with DCD had significantly poorer accommodation facility and amplitude dynamics compared to controls. Results indicate a relationship between impaired accommodation and motor skills. Specifically, accommodation anomalies correlated with visual motor, upper limb and fine dexterity task performance. Consequently, we argue accommodation anomalies influence the ineffective coordination of action and perception in DCD. Furthermore, reading disabilities were related to poorer motor performance. We postulate the role of the fastigial nucleus as a common pathway for accommodation and motor deficits. Implications of the findings and recommended visual screening protocols are discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

Thermal wake/vessel detection technique

DOEpatents

Roskovensky, John K [Albuquerque, NM; Nandy, Prabal [Albuquerque, NM; Post, Brian N [Albuquerque, NM

2012-01-10

A computer-automated method for detecting a vessel in water based on an image of a portion of Earth includes generating a thermal anomaly mask. The thermal anomaly mask flags each pixel of the image initially deemed to be a wake pixel based on a comparison of a thermal value of each pixel against other thermal values of other pixels localized about each pixel. Contiguous pixels flagged by the thermal anomaly mask are grouped into pixel clusters. A shape of each of the pixel clusters is analyzed to determine whether each of the pixel clusters represents a possible vessel detection event. The possible vessel detection events are represented visually within the image.

Anomaly Detection in Large Sets of High-Dimensional Symbol Sequences

NASA Technical Reports Server (NTRS)

Budalakoti, Suratna; Srivastava, Ashok N.; Akella, Ram; Turkov, Eugene

2006-01-01

This paper addresses the problem of detecting and describing anomalies in large sets of high-dimensional symbol sequences. The approach taken uses unsupervised clustering of sequences using the normalized longest common subsequence (LCS) as a similarity measure, followed by detailed analysis of outliers to detect anomalies. As the LCS measure is expensive to compute, the first part of the paper discusses existing algorithms, such as the Hunt-Szymanski algorithm, that have low time-complexity. We then discuss why these algorithms often do not work well in practice and present a new hybrid algorithm for computing the LCS that, in our tests, outperforms the Hunt-Szymanski algorithm by a factor of five. The second part of the paper presents new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence was deemed to be an outlier. The algorithms provide a coherent description to an analyst of the anomalies in the sequence, compared to more normal sequences. The algorithms we present are general and domain-independent, so we discuss applications in related areas such as anomaly detection.

Anomaly Monitoring Method for Key Components of Satellite

PubMed Central

Fan, Linjun; Xiao, Weidong; Tang, Jun

2014-01-01

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 (R e) and the charge transfer resistance (R ct) 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 (R X) and healthy residual value (R L) of LIBs based on the state estimation of MSET, and then, through the residual values (R X and R L) 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). PMID:24587703

Prevalence and distribution of dental anomalies in orthodontic patients.

PubMed

Montasser, Mona A; Taha, Mahasen

2012-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Model selection for anomaly detection

NASA Astrophysics Data System (ADS)

Burnaev, E.; Erofeev, P.; Smolyakov, D.

2015-12-01

Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is "cancerous" or "healthy" from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a kernel, used to measure similarity in a feature space. The standard approaches (e.g. cross-validation) for kernel selection, used in two-class classification problems, can not be used directly due to the specific nature of a data (absence of a second, abnormal, class data). In this paper we generalize several kernel selection methods from binary-class case to the case of one-class classification and perform extensive comparison of these approaches using both synthetic and real-world data.

EMPACT 3D: an advanced EMI discrimination sensor for CONUS and OCONUS applications

NASA Astrophysics Data System (ADS)

Keranen, Joe; Miller, Jonathan S.; Schultz, Gregory; Sander-Olhoeft, Morgan; Laudato, Stephen

2018-04-01

We recently developed a new, man-portable, electromagnetic induction (EMI) sensor designed to detect and classify small, unexploded sub-munitions and discriminate them from non-hazardous debris. The ability to distinguish innocuous metal clutter from potentially hazardous unexploded ordnance (UXO) and other explosive remnants of war (ERW) before excavation can significantly accelerate land reclamation efforts by eliminating time spent removing harmless scrap metal. The EMI sensor employs a multi-axis transmitter and receiver configuration to produce data sufficient for anomaly discrimination. A real-time data inversion routine produces intrinsic and extrinsic anomaly features describing the polarizability, location, and orientation of the anomaly under test. We discuss data acquisition and post-processing software development, and results from laboratory and field tests demonstrating the discrimination capability of the system. Data acquisition and real-time processing emphasize ease-of-use, quality control (QC), and display of discrimination results. Integration of the QC and discrimination methods into the data acquisition software reduces the time required between sensor data collection and the final anomaly discrimination result. The system supports multiple concepts of operations (CONOPs) including: 1) a non-GPS cued configuration in which detected anomalies are discriminated and excavated immediately following the anomaly survey; 2) GPS integration to survey multiple anomalies to produce a prioritized dig list with global anomaly locations; and 3) a dynamic mapping configuration supporting detection followed by discrimination and excavation of targets of interest.

Detection of sinkholes or anomalies using full seismic wave fields.

DOT National Transportation Integrated Search

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, ...

A Testbed for Data Fusion for Engine Diagnostics and Prognostics1

DTIC Science & Technology

2002-03-01

detected ; too late to be useful for prognostics development. Table 1. Table of acronyms ACRONYM MEANING AD Anomaly detector...strictly defined points. Determining where we are on the engine health curve is the first step in prognostics . Fault detection / diagnostic reasoning... Detection As described above the ability of the monitoring system to detect an anomaly is especially important for knowledge-based systems, i.e.,

Integrated Multivariate Health Monitoring System for Helicopters Main Rotor Drives: Development and Validation with In-Service Data

DTIC Science & Technology

2014-10-02

potential advantages of using multi- variate classification/discrimination/ anomaly detection meth- ods on real world accelerometric condition monitoring ...case of false anomaly reports. A possible explanation of this phenomenon could be given 8 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT...of those helicopters. 1. Anomaly detection by means of a self-learning Shewhart control chart. A problem highlighted by the experts of Agusta- Westland

An intelligent crowdsourcing system for forensic analysis of surveillance video

NASA Astrophysics Data System (ADS)

Tahboub, Khalid; Gadgil, Neeraj; Ribera, Javier; Delgado, Blanca; Delp, Edward J.

2015-03-01

Video surveillance systems are of a great value for public safety. With an exponential increase in the number of cameras, videos obtained from surveillance systems are often archived for forensic purposes. Many automatic methods have been proposed to do video analytics such as anomaly detection and human activity recognition. However, such methods face significant challenges due to object occlusions, shadows and scene illumination changes. In recent years, crowdsourcing has become an effective tool that utilizes human intelligence to perform tasks that are challenging for machines. In this paper, we present an intelligent crowdsourcing system for forensic analysis of surveillance video that includes the video recorded as a part of search and rescue missions and large-scale investigation tasks. We describe a method to enhance crowdsourcing by incorporating human detection, re-identification and tracking. At the core of our system, we use a hierarchal pyramid model to distinguish the crowd members based on their ability, experience and performance record. Our proposed system operates in an autonomous fashion and produces a final output of the crowdsourcing analysis consisting of a set of video segments detailing the events of interest as one storyline.

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

NASA Astrophysics Data System (ADS)

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.

Analysis of SSEM Sensor Data Using BEAM

NASA Technical Reports Server (NTRS)

Zak, Michail; Park, Han; James, Mark

2004-01-01

A report describes analysis of space shuttle main engine (SSME) sensor data using Beacon-based Exception Analysis for Multimissions (BEAM) [NASA Tech Briefs articles, the two most relevant being Beacon-Based Exception Analysis for Multimissions (NPO- 20827), Vol. 26, No.9 (September 2002), page 32 and Integrated Formulation of Beacon-Based Exception Analysis for Multimissions (NPO- 21126), Vol. 27, No. 3 (March 2003), page 74] for automated detection of anomalies. A specific implementation of BEAM, using the Dynamical Invariant Anomaly Detector (DIAD), is used to find anomalies commonly encountered during SSME ground test firings. The DIAD detects anomalies by computing coefficients of an autoregressive model and comparing them to expected values extracted from previous training data. The DIAD was trained using nominal SSME test-firing data. DIAD detected all the major anomalies including blade failures, frozen sense lines, and deactivated sensors. The DIAD was particularly sensitive to anomalies caused by faulty sensors and unexpected transients. The system offers a way to reduce SSME analysis time and cost by automatically indicating specific time periods, signals, and features contributing to each anomaly. The software described here executes on a standard workstation and delivers analyses in seconds, a computing time comparable to or faster than the test duration itself, offering potential for real-time analysis.

Intelligent system for a remote diagnosis of a photovoltaic solar power plant

NASA Astrophysics Data System (ADS)

Sanz-Bobi, M. A.; Muñoz San Roque, A.; de Marcos, A.; Bada, M.

2012-05-01

Usually small and mid-sized photovoltaic solar power plants are located in rural areas and typically they operate unattended. Some technicians are in charge of the supervision of these plants and, if an alarm is automatically issued, they try to investigate the problem and correct it. Sometimes these anomalies are detected some hours or days after they begin. Also the analysis of the causes once the anomaly is detected can take some additional time. All these factors motivated the development of a methodology able to perform continuous and automatic monitoring of the basic parameters of a photovoltaic solar power plant in order to detect anomalies as soon as possible, to diagnose their causes, and to immediately inform the personnel in charge of the plant. The methodology proposed starts from the study of the most significant failure modes of a photovoltaic plant through a FMEA and using this information, its typical performance is characterized by the creation of its normal behaviour models. They are used to detect the presence of a failure in an incipient or current form. Once an anomaly is detected, an automatic and intelligent diagnosis process is started in order to investigate the possible causes. The paper will describe the main features of a software tool able to detect anomalies and to diagnose them in a photovoltaic solar power plant.

Method and system for monitoring environmental conditions

DOEpatents

Kulesz, James J [Oak Ridge, TN; Lee, Ronald W [Oak Ridge, TN

2010-11-16

A system for detecting the occurrence of anomalies includes a plurality of spaced apart nodes, with each node having adjacent nodes, each of the nodes having one or more sensors associated with the node and capable of detecting anomalies, and each of the nodes having a controller connected to the sensors associated with the node. The system also includes communication links between adjacent nodes, whereby the nodes form a network. At least one software agent is capable of changing the operation of at least one of the controllers in response to the detection of an anomaly by a sensor.

Method for locating underground anomalies by diffraction of electromagnetic waves passing between spaced boreholes

DOEpatents

Lytle, R. Jeffrey; Lager, Darrel L.; Laine, Edwin F.; Davis, Donald T.

1979-01-01

Underground anomalies or discontinuities, such as holes, tunnels, and caverns, are located by lowering an electromagnetic signal transmitting antenna down one borehole and a receiving antenna down another, the ground to be surveyed for anomalies being situated between the boreholes. Electronic transmitting and receiving equipment associated with the antennas is activated and the antennas are lowered in unison at the same rate down their respective boreholes a plurality of times, each time with the receiving antenna at a different level with respect to the transmitting antenna. The transmitted electromagnetic waves diffract at each edge of an anomaly. This causes minimal signal reception at the receiving antenna. Triangulation of the straight lines between the antennas for the depths at which the signal minimums are detected precisely locates the anomaly. Alternatively, phase shifts of the transmitted waves may be detected to locate an anomaly, the phase shift being distinctive for the waves directed at the anomaly.

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

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-08-01

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

A study of the effect of seasonal climatic factors on the electrical resistivity response of three experimental graves

NASA Astrophysics Data System (ADS)

Jervis, John R.; Pringle, Jamie K.

2014-09-01

Electrical resistivity surveys have proven useful for locating clandestine graves in a number of forensic searches. However, some aspects of grave detection with resistivity surveys remain imperfectly understood. One such aspect is the effect of seasonal changes in climate on the resistivity response of graves. In this study, resistivity survey data collected over three years over three simulated graves were analysed in order to assess how the graves' resistivity anomalies varied seasonally and when they could most easily be detected. Thresholds were used to identify anomalies, and the ‘residual volume' of grave-related anomalies was calculated as the area bounded by the relevant thresholds multiplied by the anomaly's average value above the threshold. The residual volume of a resistivity anomaly associated with a buried pig cadaver showed evidence of repeating annual patterns and was moderately correlated with the soil moisture budget. This anomaly was easiest to detect between January and April each year, after prolonged periods of high net gain in soil moisture. The resistivity response of a wrapped cadaver was more complex, although it also showed evidence of seasonal variation during the third year after burial. We suggest that the observed variation in the graves' resistivity anomalies was caused by seasonal change in survey data noise levels, which was in turn influenced by the soil moisture budget. It is possible that similar variations occur elsewhere for sites with seasonal climate variations and this could affect successful detection of other subsurface features. Further research to investigate how different climates and soil types affect seasonal variation in grave-related resistivity anomalies would be useful.

Gravity anomaly detection: Apollo/Soyuz

NASA Technical Reports Server (NTRS)

Vonbun, F. O.; Kahn, W. D.; Bryan, J. W.; Schmid, P. E.; Wells, W. T.; Conrad, D. T.

1976-01-01

The Goddard Apollo-Soyuz Geodynamics Experiment is described. It was performed to demonstrate the feasibility of tracking and recovering high frequency components of the earth's gravity field by utilizing a synchronous orbiting tracking station such as ATS-6. Gravity anomalies of 5 MGLS or larger having wavelengths of 300 to 1000 kilometers on the earth's surface are important for geologic studies of the upper layers of the earth's crust. Short wavelength Earth's gravity anomalies were detected from space. Two prime areas of data collection were selected for the experiment: (1) the center of the African continent and (2) the Indian Ocean Depression centered at 5% north latitude and 75% east longitude. Preliminary results show that the detectability objective of the experiment was met in both areas as well as at several additional anomalous areas around the globe. Gravity anomalies of the Karakoram and Himalayan mountain ranges, ocean trenches, as well as the Diamantina Depth, can be seen. Maps outlining the anomalies discovered are shown.

Interpretation of clinical relevance of X-chromosome copy number variations identified in a large cohort of individuals with cognitive disorders and/or congenital anomalies.

PubMed

Willemsen, Marjolein H; de Leeuw, Nicole; de Brouwer, Arjan P M; Pfundt, Rolph; Hehir-Kwa, Jayne Y; Yntema, Helger G; Nillesen, Willy M; de Vries, Bert B A; van Bokhoven, Hans; Kleefstra, Tjitske

2012-11-01

Genome-wide array studies are now routinely being used in the evaluation of patients with cognitive disorders (CD) and/or congenital anomalies (CA). Therefore, inevitably each clinician is confronted with the challenging task of the interpretation of copy number variations detected by genome-wide array platforms in a diagnostic setting. Clinical interpretation of autosomal copy number variations is already challenging, but assessment of the clinical relevance of copy number variations of the X-chromosome is even more complex. This study provides an overview of the X-Chromosome copy number variations that we have identified by genome-wide array analysis in a large cohort of 4407 male and female patients. We have made an interpretation of the clinical relevance of each of these copy number variations based on well-defined criteria and previous reports in literature and databases. The prevalence of X-chromosome copy number variations in this cohort was 57/4407 (∼1.3%), of which 15 (0.3%) were interpreted as (likely) pathogenic. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Network Anomaly Detection Based on Wavelet Analysis

NASA Astrophysics Data System (ADS)

Lu, Wei; Ghorbani, Ali A.

2008-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2011-06-01

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

An Investigation of State-Space Model Fidelity for SSME Data

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2008-01-01

In previous studies, a variety of unsupervised anomaly detection techniques for anomaly detection were applied to SSME (Space Shuttle Main Engine) data. The observed results indicated that the identification of certain anomalies were specific to the algorithmic method under consideration. This is the reason why one of the follow-on goals of these previous investigations was to build an architecture to support the best capabilities of all algorithms. We appeal to that goal here by investigating a cascade, serial architecture for the best performing and most suitable candidates from previous studies. As a precursor to a formal ROC (Receiver Operating Characteristic) curve analysis for validation of resulting anomaly detection algorithms, our primary focus here is to investigate the model fidelity as measured by variants of the AIC (Akaike Information Criterion) for state-space based models. We show that placing constraints on a state-space model during or after the training of the model introduces a modest level of suboptimality. Furthermore, we compare the fidelity of all candidate models including those embodying the cascade, serial architecture. We make recommendations on the most suitable candidates for application to subsequent anomaly detection studies as measured by AIC-based criteria.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network

PubMed Central

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish–Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection. PMID:26447696

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

PubMed

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

Detection and characterization of buried lunar craters with GRAIL data

NASA Astrophysics Data System (ADS)

Sood, Rohan; Chappaz, Loic; Melosh, Henry J.; Howell, Kathleen C.; Milbury, Colleen; Blair, David M.; Zuber, Maria T.

2017-06-01

We used gravity mapping observations from NASA's Gravity Recovery and Interior Laboratory (GRAIL) to detect, characterize and validate the presence of large impact craters buried beneath the lunar maria. In this paper we focus on two prominent anomalies detected in the GRAIL data using the gravity gradiometry technique. Our detection strategy is applied to both free-air and Bouguer gravity field observations to identify gravitational signatures that are similar to those observed over buried craters. The presence of buried craters is further supported by individual analysis of regional free-air gravity anomalies, Bouguer gravity anomaly maps, and forward modeling. Our best candidate, for which we propose the informal name of Earhart Crater, is approximately 200 km in diameter and forms part of the northwestern rim of Lacus Somniorum, The other candidate, for which we propose the informal name of Ashoka Anomaly, is approximately 160 km in diameter and lies completely buried beneath Mare Tranquillitatis. Other large, still unrecognized, craters undoubtedly underlie other portions of the Moon's vast mare lavas.

Anomaly Detection In Additively Manufactured Parts Using Laser Doppler Vibrometery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hernandez, Carlos A.

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.

Query-Based Outlier Detection in Heterogeneous Information Networks.

PubMed

Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

2015-03-01

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user's search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks.

Query-Based Outlier Detection in Heterogeneous Information Networks

PubMed Central

Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

2015-01-01

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user’s search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks. PMID:27064397

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

NASA Astrophysics Data System (ADS)

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.

Improved determination of vector lithospheric magnetic anomalies from MAGSAT data

NASA Technical Reports Server (NTRS)

Ravat, Dhananjay

1993-01-01

Scientific contributions made in developing new methods to isolate and map vector magnetic anomalies from measurements made by Magsat are described. In addition to the objective of the proposal, the isolation and mapping of equatorial vector lithospheric Magsat anomalies, isolation of polar ionospheric fields during the period were also studied. Significant progress was also made in isolation of polar delta(Z) component and scalar anomalies as well as integration and synthesis of various techniques of removing equatorial and polar ionospheric effects. The significant contributions of this research are: (1) development of empirical/analytical techniques in modeling ionospheric fields in Magsat data and their removal from uncorrected anomalies to obtain better estimates of lithospheric anomalies (this task was accomplished for equatorial delta(X), delta(Z), and delta(B) component and polar delta(Z) and delta(B) component measurements; (2) integration of important processing techniques developed during the last decade with the newly developed technologies of ionospheric field modeling into an optimum processing scheme; and (3) implementation of the above processing scheme to map the most robust magnetic anomalies of the lithosphere (components as well as scalar).

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

DTIC Science & Technology

2015-06-01

system accuracy. The AnRAD system was also generalized for the additional application of network intrusion detection . A self-structuring technique...to Host- based Intrusion Detection Systems using Contiguous and Discontiguous System Call Patterns,” IEEE Transactions on Computer, 63(4), pp. 807...square kilometer areas. The anomaly recognition and detection (AnRAD) system was built as a cogent confabulation network . It represented road

Department of Defense Fiscal Year (FY) 2005 Budget Estimates. Research, Development, Test and Evaluation, Defense-Wide. Volume 1 - Defense Advanced Research Projects Agency

DTIC Science & Technology

2004-02-01

UNCLASSIFIED − Conducted experiments to determine the usability of general-purpose anomaly detection algorithms to monitor a large, complex military...reaction and detection modules to perform tailored analysis sequences to monitor environmental conditions, health hazards and physiological states...scalability of lab proven anomaly detection techniques for intrusion detection in real world high volume environments. Narrative Title FY 2003

Intrusion-aware alert validation algorithm for cooperative distributed intrusion detection schemes of wireless sensor networks.

PubMed

Shaikh, Riaz Ahmed; Jameel, Hassan; d'Auriol, Brian J; Lee, Heejo; Lee, Sungyoung; Song, Young-Jae

2009-01-01

Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.

Intrusion-Aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks

PubMed Central

Shaikh, Riaz Ahmed; Jameel, Hassan; d’Auriol, Brian J.; Lee, Heejo; Lee, Sungyoung; Song, Young-Jae

2009-01-01

Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm. PMID:22454568

Routine screening for fetal anomalies: expectations.

PubMed

Goldberg, James D

2004-03-01

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

Transient ice mass variations over Greenland detected by the combination of GPS and GRACE data

NASA Astrophysics Data System (ADS)

Zhang, B.; Liu, L.; Khan, S. A.; van Dam, T. M.; Zhang, E.

2017-12-01

Over the past decade, the Greenland Ice Sheet (GrIS) has been undergoing significant warming and ice mass loss. Such mass loss was not always a steady process but had substantial temporal and spatial variabilities. Here we apply multi-channel singular spectral analysis to crustal deformation time series measured at about 50 Global Positioning System (GPS) stations mounted on bedrock around the Greenland coast and mass changes inferred from Gravity Recovery and Climate Experiment (GRACE) to detect transient changes in ice mass balance over the GrIS. We detect two transient anomalies: one is a negative melting anomaly (Anomaly 1) that peaked around 2010; the other is a positive melting anomaly (Anomaly 2) that peaked between 2012 and 2013. The GRACE data show that both anomalies caused significant mass changes south of 74°N but negligible changes north of 74°N. Both anomalies caused the maximum mass change in southeast GrIS, followed by in west GrIS near Jakobshavn. Our results also show that the mass change caused by Anomaly 1 first reached the maximum in late 2009 in the southeast GrIS and then migrated to west GrIS. However, in Anomaly 2, the southeast GrIS was the last place that reached the maximum mass change in early 2013 and the west GrIS near Jakobshavn was the second latest place that reached the maximum mass change. Most of the GPS data show similar spatiotemporal patterns as those obtained from the GRACE data. However, some GPS time series show discrepancies in either space or time, because of data gaps and different sensitivities of mass loading change. Namely, loading deformation measured by GPS can be significantly affected by local dynamical mass changes, which, yet, has little impact on GRACE observations.

Locality-constrained anomaly detection for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Liu, Jiabin; Li, Wei; Du, Qian; Liu, Kui

2015-12-01

Detecting a target with low-occurrence-probability from unknown background in a hyperspectral image, namely anomaly detection, is of practical significance. Reed-Xiaoli (RX) algorithm is considered as a classic anomaly detector, which calculates the Mahalanobis distance between local background and the pixel under test. Local RX, as an adaptive RX detector, employs a dual-window strategy to consider pixels within the frame between inner and outer windows as local background. However, the detector is sensitive if such a local region contains anomalous pixels (i.e., outliers). In this paper, a locality-constrained anomaly detector is proposed to remove outliers in the local background region before employing the RX algorithm. Specifically, a local linear representation is designed to exploit the internal relationship between linearly correlated pixels in the local background region and the pixel under test and its neighbors. Experimental results demonstrate that the proposed detector improves the original local RX algorithm.

Detection of anomalous events

DOEpatents

Ferragut, Erik M.; Laska, Jason A.; Bridges, Robert A.

2016-06-07

A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The system can include a plurality of anomaly detectors that together implement an algorithm to identify low-probability events and detect atypical traffic patterns. The anomaly detector provides for comparability of disparate sources of data (e.g., network flow data and firewall logs.) Additionally, the anomaly detector allows for regulatability, meaning that the algorithm can be user configurable to adjust a number of false alerts. The anomaly detector can be used for a variety of probability density functions, including normal Gaussian distributions, irregular distributions, as well as functions associated with continuous or discrete variables.

Quantifying Performance Bias in Label Fusion

DTIC Science & Technology

2012-08-21

detect ), may provide the end-user with the means to appropriately adjust the performance and optimal thresholds for performance by fusing legacy systems...boolean combination of classification systems in ROC space: An application to anomaly detection with HMMs. Pattern Recognition, 43(8), 2732-2752. 10...Shamsuddin, S. (2009). An overview of neural networks use in anomaly intrusion detection systems. Paper presented at the Research and Development (SCOReD

Anomaly detection for machine learning redshifts applied to SDSS galaxies

NASA Astrophysics Data System (ADS)

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

2015-10-01

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million `clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 `anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed `anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.

Road Traffic Anomaly Detection via Collaborative Path Inference from GPS Snippets

PubMed Central

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

Bio-Inspired Distributed Decision Algorithms for Anomaly Detection

DTIC Science & Technology

2017-03-01

TERMS DIAMoND, Local Anomaly Detector, Total Impact Estimation, Threat Level Estimator 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU...21 4.2 Performance of the DIAMoND Algorithm as a DNS-Server Level Attack Detection and Mitigation...with 6 Nodes ........................................................................................ 13 8 Hierarchical 2- Level Topology

Anomaly Detection in the Right Hemisphere: The Influence of Visuospatial Factors

ERIC Educational Resources Information Center

Smith, Stephen D.; Dixon, Michael J.; Tays, William J.; Bulman-Fleming, M. Barbara

2004-01-01

Previous research with both brain-damaged and neurologically intact populations has demonstrated that the right cerebral hemisphere (RH) is superior to the left cerebral hemisphere (LH) at detecting anomalies (or incongruities) in objects (Ramachandran, 1995; Smith, Tays, Dixon, & Bulman-Fleming, 2002). The current research assesses whether the RH…

A Semiparametric Model for Hyperspectral Anomaly Detection

DTIC Science & Technology

2012-01-01

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

A case study to detect the leakage of underground pressureless cement sewage water pipe using GPR, electrical, and chemical data.

PubMed

Liu, Guanqun; Jia, Yonggang; Liu, Hongjun; Qiu, Hanxue; Qiu, Dongling; Shan, Hongxian

2002-03-01

The exploration and determination of leakage of underground pressureless nonmetallic pipes is difficult to deal with. A comprehensive method combining Ground Penetrating Rader (GPR), electric potential survey and geochemical survey is introduced in the leakage detection of an underground pressureless nonmetallic sewage pipe in this paper. Theoretically, in the influencing scope of a leakage spot, the obvious changes of the electromagnetic properties and the physical-chemical properties of the underground media will be reflected as anomalies in GPR and electrical survey plots. The advantages of GPR and electrical survey are fast and accurate in detection of anomaly scope. In-situ analysis of the geophysical surveys can guide the geochemical survey. Then water and soil sampling and analyzing can be the evidence for judging the anomaly is caused by pipe leakage or not. On the basis of previous tests and practical surveys, the GPR waveforms, electric potential curves, contour maps, and chemical survey results are all classified into three types according to the extent or indexes of anomalies in orderto find out the leakage spots. When three survey methods all show their anomalies as type I in an anomalous spot, this spot is suspected as the most possible leakage location. Otherwise, it will be down grade suspected point. The suspect leakage spots should be confirmed by referring the site conditions because some anomalies are caused other factors. The excavation afterward proved that the method for determining the suspected location by anomaly type is effective and economic. Comprehensive method of GRP, electric potential survey, and geochemical survey is one of the effective methods in the leakage detection of underground nonmetallic pressureless pipe with its advantages of being fast and accurate.

Cost Analysis of Following Up Incomplete Low-Risk Fetal Anatomy Ultrasounds.

PubMed

O'Brien, Karen; Shainker, Scott A; Modest, Anna M; Spiel, Melissa H; Resetkova, Nina; Shah, Neel; Hacker, Michele R

2017-03-01

To examine the clinical utility and cost of follow-up ultrasounds performed as a result of suboptimal views at the time of initial second-trimester ultrasound in a cohort of low-risk pregnant women. We conducted a retrospective cohort study of women at low risk for fetal structural anomalies who had second-trimester ultrasounds at 16 to less than 24 weeks of gestation from 2011 to 2013. We determined the probability of women having follow-up ultrasounds as a result of suboptimal views at the time of the initial second-trimester ultrasound, and calculated the probability of detecting an anomaly on follow-up ultrasound. These probabilities were used to estimate the national cost of our current ultrasound practice, and the cost to identify one fetal anomaly on follow-up ultrasound. During the study period, 1,752 women met inclusion criteria. Four fetuses (0.23% [95% CI 0.06-0.58]) were found to have anomalies at the initial ultrasound. Because of suboptimal views, 205 women (11.7%) returned for a follow-up ultrasound, and one (0.49% [95% CI 0.01-2.7]) anomaly was detected. Two women (0.11%) still had suboptimal views and returned for an additional follow-up ultrasound, with no anomalies detected. When the incidence of incomplete ultrasounds was applied to a similar low-risk national cohort, the annual cost of these follow-up scans was estimated at $85,457,160. In our cohort, the cost to detect an anomaly on follow-up ultrasound was approximately $55,000. The clinical yield of performing follow-up ultrasounds because of suboptimal views on low-risk second-trimester ultrasounds is low. Since so few fetal abnormalities were identified on follow-up scans, this added cost and patient burden may not be warranted. © 2016 Wiley Periodicals, Inc.

Development of anomaly detection models for deep subsurface monitoring

NASA Astrophysics Data System (ADS)

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.

Machine intelligence-based decision-making (MIND) for automatic anomaly detection

NASA Astrophysics Data System (ADS)

Prasad, Nadipuram R.; King, Jason C.; Lu, Thomas

2007-04-01

Any event deemed as being out-of-the-ordinary may be called an anomaly. Anomalies by virtue of their definition are events that occur spontaneously with no prior indication of their existence or appearance. Effects of anomalies are typically unknown until they actually occur, and their effects aggregate in time to show noticeable change from the original behavior. An evolved behavior would in general be very difficult to correct unless the anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly. Substantial time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to abnormal behavior. There is a critical need therefore to automatically detect anomalous behavior as and when they may occur, and to do so with the operator in the loop. Human-machine interaction results in better machine learning and a better decision-support mechanism. This is the fundamental concept of intelligent control where machine learning is enhanced by interaction with human operators, and vice versa. The paper discusses a revolutionary framework for the characterization, detection, identification, learning, and modeling of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems.

OceanXtremes: Scalable Anomaly Detection in Oceanographic Time-Series

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

How do scientists respond to anomalies? Different strategies used in basic and applied science.

PubMed

Trickett, Susan Bell; Trafton, J Gregory; Schunn, Christian D

2009-10-01

We conducted two in vivo studies to explore how scientists respond to anomalies. Based on prior research, we identify three candidate strategies: mental simulation, mental manipulation of an image, and comparison between images. In Study 1, we compared experts in basic and applied domains (physics and meteorology). We found that the basic scientists used mental simulation to resolve an anomaly, whereas applied science practitioners mentally manipulated the image. In Study 2, we compared novice and expert meteorologists. We found that unlike experts, novices used comparison to address anomalies. We discuss the nature of expertise in the two kinds of science, the relationship between the type of science and the task performed, and the relationship of the strategies investigated to scientific creativity. Copyright © 2009 Cognitive Science Society, Inc.

Discrepancy of cytogenetic analysis in Western and eastern Taiwan.

PubMed

Chang, Yu-Hsun; Chen, Pui-Yi; Li, Tzu-Ying; Yeh, Chung-Nan; Li, Yi-Shian; Chu, Shao-Yin; Lee, Ming-Liang

2013-06-01

This study aimed at investigating the results of second-trimester amniocyte karyotyping in western and eastern Taiwan, and identifying any regional differences in the prevalence of fetal chromosomal anomalies. From 2004 to 2009, pregnant women who underwent amniocentesis in their second trimester at three hospitals in western Taiwan and at four hospitals in eastern Taiwan were included. All the cytogenetic analyses of cultured amniocytes were performed in the cytogenetics laboratory of the Genetic Counseling Center of Hualien Buddhist Tzu Chi General Hospital. We used the chi-square test, Student t test, and Mann-Whitney U test to evaluate the variants of clinical indications, amniocyte karyotyping results, and prevalence and types of chromosomal anomalies in western and eastern Taiwan. During the study period, 3573 samples, 1990 (55.7%) from western Taiwan and 1583 (44.3%) from eastern Taiwan, were collected and analyzed. The main indication for amniocyte karyotyping was advanced maternal age (69.0% in western Taiwan, 67.1% in eastern Taiwan). The detection rates of chromosomal anomalies by amniocyte karyotyping in eastern Taiwan (45/1582, 2.8%) did not differ significantly from that in western Taiwan (42/1989, 2.1%) (p = 1.58). Mothers who had abnormal ultrasound findings and histories of familial hereditary diseases or chromosomal anomalies had higher detection rates of chromosomal anomalies (9.3% and 7.2%, respectively). The detection rate of autosomal anomalies was higher in eastern Taiwan (93.3% vs. 78.6%, p = 0.046), but the detection rate of sex-linked chromosomal anomalies was higher in western Taiwan (21.4% vs. 6.7%, p = 0.046). We demonstrated regional differences in second-trimester amniocyte karyotyping results and established a database of common chromosomal anomalies that could be useful for genetic counseling, especially in eastern Taiwan. Copyright © 2012. Published by Elsevier B.V.

An investigation of thermal anomalies in the Central American volcanic chain and evaluation of the utility of thermal anomaly monitoring in the prediction of volcanic eruptions. [Central America

NASA Technical Reports Server (NTRS)

Stoiber, R. E. (Principal Investigator); Rose, W. I., Jr.

1975-01-01

The author has identified the following significant results. Ground truth data collection proves that significant anomalies exist at 13 volcanoes within the test site of Central America. The dimensions and temperature contrast of these ten anomalies are large enough to be detected by the Skylab 192 instrument. The dimensions and intensity of thermal anomalies have changed at most of these volcanoes during the Skylab mission.

System and method for anomaly detection

DOEpatents

Scherrer, Chad

2010-06-15

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

Survey of Anomaly Detection Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, B

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

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Effects of Sampling and Spatio/Temporal Granularity in Traffic Monitoring on Anomaly Detectability

NASA Astrophysics Data System (ADS)

Ishibashi, Keisuke; Kawahara, Ryoichi; Mori, Tatsuya; Kondoh, Tsuyoshi; Asano, Shoichiro

We quantitatively evaluate how sampling and spatio/temporal granularity in traffic monitoring affect the detectability of anomalous traffic. Those parameters also affect the monitoring burden, so network operators face a trade-off between the monitoring burden and detectability and need to know which are the optimal paramter values. We derive equations to calculate the false positive ratio and false negative ratio for given values of the sampling rate, granularity, statistics of normal traffic, and volume of anomalies to be detected. Specifically, assuming that the normal traffic has a Gaussian distribution, which is parameterized by its mean and standard deviation, we analyze how sampling and monitoring granularity change these distribution parameters. This analysis is based on observation of the backbone traffic, which exhibits spatially uncorrelated and temporally long-range dependence. Then we derive the equations for detectability. With those equations, we can answer the practical questions that arise in actual network operations: what sampling rate to set to find the given volume of anomaly, or, if the sampling is too high for actual operation, what granularity is optimal to find the anomaly for a given lower limit of sampling rate.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tardiff, Mark F.; Runkle, Robert C.; Anderson, K. K.

2006-01-23

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

Method for Real-Time Model Based Structural Anomaly Detection

NASA Technical Reports Server (NTRS)

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

2015-01-01

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

Spectral anomaly methods for aerial detection using KUT nuisance rejection

NASA Astrophysics Data System (ADS)

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

2015-06-01

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

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

PubMed

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

2016-01-01

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

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

PubMed Central

Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun

2016-01-01

Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively. PMID:27974882

Congenital aplastic-hypoplastic lumbar pedicle in infants and young children

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yousefzadeh, D.K.; El-Khoury, G.Y.; Lupetin, A.R.

1982-01-01

Nine cases of congenital aplastic-hypoplastic lumbar pedicle (mean age 27 months) are described. Their data are compared to those of 18 other reported cases (mean age 24.7 years) and the following conclusions are made: (1) Almost exclusively, the pedicular defect in infants and young children is due to developmental anomaly rather than destruction by malignancy or infectious processes. (2) This anomaly, we think, is more common than it is believed to be. (3) Unlike adults, infants and young children rarely develop hypertrophy and/or sclerosis of the contralateral pedicle. (4) Detection of pedicular anomaly is more than satisfying a radiographic curiositymore » and may lead to discovery of other coexisting anomalies. (5) Ultrasonic screening of the patients with congenital pedicular defects may detect the associated genitourinary anomalies, if present, and justify further studies in a selected group of patients.« less

Observed TEC Anomalies by GNSS Sites Preceding the Aegean Sea Earthquake of 2014

NASA Astrophysics Data System (ADS)

Ulukavak, Mustafa; Yal&ccedul; ınkaya, Mualla

2016-11-01

In recent years, Total Electron Content (TEC) data, obtained from Global Navigation Satellites Systems (GNSS) receivers, has been widely used to detect seismo-ionospheric anomalies. In this study, Global Positioning System - Total Electron Content (GPS-TEC) data were used to investigate ionospheric abnormal behaviors prior to the 2014 Aegean Sea earthquake (40.305°N 25.453°E, 24 May 2014, 09:25:03 UT, Mw:6.9). The data obtained from three Continuously Operating Reference Stations in Turkey (CORS-TR) and two International GNSS Service (IGS) sites near the epicenter of the earthquake is used to detect ionospheric anomalies before the earthquake. Solar activity index (F10.7) and geomagnetic activity index (Dst), which are both related to space weather conditions, were used to analyze these pre-earthquake ionospheric anomalies. An examination of these indices indicated high solar activity between May 8 and 15, 2014. The first significant increase (positive anomalies) in Vertical Total Electron Content (VTEC) was detected on May 14, 2014 or 10 days before the earthquake. This positive anomaly can be attributed to the high solar activity. The indices do not imply high solar or geomagnetic activity after May 15, 2014. Abnormal ionospheric TEC changes (negative anomaly) were observed at all stations one day before the earthquake. These changes were lower than the lower bound by approximately 10-20 TEC unit (TECU), and may be considered as the ionospheric precursor of the 2014 Aegean Sea earthquake

Eddy-Current Inspection of Ball Bearings

NASA Technical Reports Server (NTRS)

Bankston, B.

1985-01-01

Custom eddy-current probe locates surface anomalies. Low friction air cushion within cone allows ball to roll easily. Eddy current probe reliably detects surface and near-surface cracks, voids, and material anomalies in bearing balls or other spherical objects. Defects in ball surface detected by probe displayed on CRT and recorded on strip-chart recorder.

Anomaly Detection Techniques for Ad Hoc Networks

ERIC Educational Resources Information Center

Cai, Chaoli

2009-01-01

Anomaly detection is an important and indispensable aspect of any computer security mechanism. Ad hoc and mobile networks consist of a number of peer mobile nodes that are capable of communicating with each other absent a fixed infrastructure. Arbitrary node movements and lack of centralized control make them vulnerable to a wide variety of…

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

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2014-01-01

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.

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

NASA Technical Reports Server (NTRS)

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.

Archean Isotope Anomalies as a Window into the Differentiation History of the Earth

NASA Astrophysics Data System (ADS)

Wainwright, A. N.; Debaille, V.; Zincone, S. A.

2018-05-01

No resolvable µ142Nd anomaly was detected in Paleo- Mesoarchean rocks of São Francisco and West African cratons. The lack of µ142Nd anomalies outside of North America and Greenland implies the Earth differentiated into at least two distinct domains.

GBAS Ionospheric Anomaly Monitoring Based on a Two-Step Approach

PubMed Central

Zhao, Lin; Yang, Fuxin; Li, Liang; Ding, Jicheng; Zhao, Yuxin

2016-01-01

As one significant component of space environmental weather, the ionosphere has to be monitored using Global Positioning System (GPS) receivers for the Ground-Based Augmentation System (GBAS). This is because an ionospheric anomaly can pose a potential threat for GBAS to support safety-critical services. The traditional code-carrier divergence (CCD) methods, which have been widely used to detect the variants of the ionospheric gradient for GBAS, adopt a linear time-invariant low-pass filter to suppress the effect of high frequency noise on the detection of the ionospheric anomaly. However, there is a counterbalance between response time and estimation accuracy due to the fixed time constants. In order to release the limitation, a two-step approach (TSA) is proposed by integrating the cascaded linear time-invariant low-pass filters with the adaptive Kalman filter to detect the ionospheric gradient anomaly. The performance of the proposed method is tested by using simulated and real-world data, respectively. The simulation results show that the TSA can detect ionospheric gradient anomalies quickly, even when the noise is severer. Compared to the traditional CCD methods, the experiments from real-world GPS data indicate that the average estimation accuracy of the ionospheric gradient improves by more than 31.3%, and the average response time to the ionospheric gradient at a rate of 0.018 m/s improves by more than 59.3%, which demonstrates the ability of TSA to detect a small ionospheric gradient more rapidly. PMID:27240367

Advances in soil gas geochemical exploration for natural resources: Some current examples and practices

NASA Astrophysics Data System (ADS)

McCarthy, J. Howard, Jr.; Reimer, G. Michael

1986-11-01

Field studies have demonstrated that gas anomalies are found over buried mineral deposits. Abnormally high concentrations of sulfur gases and carbon dioxide and abnormally low concentrations of oxygen are commonly found over sulfide ore deposits. Helium anomalies are commonly associated with uranium deposits and geothermal areas. Helium and hydrocarbon gas anomalies have been detected over oil and gas deposits. Gases are sampled by extracting them from the pore space of soil, by degassing soil or rock, or by adsorbing them on artificial collectors. The two most widely used techniques for gas analysis are gas chromatography and mass spectrometry. The detection of gas anomalies at or near the surface may be an effective method to locate buried mineral deposits.

Pre-seismic anomalies in remotely sensed land surface temperature measurements: The case study of 2003 Boumerdes earthquake

NASA Astrophysics Data System (ADS)

Bellaoui, Mebrouk; Hassini, Abdelatif; Bouchouicha, Kada

2017-05-01

Detection of thermal anomaly prior to earthquake events has been widely confirmed by researchers over the past decade. One of the popular approaches for anomaly detection is the Robust Satellite Approach (RST). In this paper, we use this method on a collection of six years of MODIS satellite data, representing land surface temperature (LST) images to predict 21st May 2003 Boumerdes Algeria earthquake. The thermal anomalies results were compared with the ambient temperature variation measured in three meteorological stations of Algerian National Office of Meteorology (ONM) (DELLYS-AFIR, TIZI-OUZOU, and DAR-EL-BEIDA). The results confirm the importance of RST as an approach highly effective for monitoring the earthquakes.

Practical method to identify orbital anomaly as spacecraft breakup in the geostationary region

NASA Astrophysics Data System (ADS)

Hanada, Toshiya; Uetsuhara, Masahiko; Nakaniwa, Yoshitaka

2012-07-01

Identifying a spacecraft breakup is an essential issue to define the current orbital debris environment. This paper proposes a practical method to identify an orbital anomaly, which appears as a significant discontinuity in the observation data, as a spacecraft breakup. The proposed method is applicable to orbital anomalies in the geostationary region. Long-term orbital evolutions of breakup fragments may conclude that their orbital planes will converge into several corresponding regions in inertial space even if the breakup epoch is not specified. This empirical method combines the aforementioned conclusion with the search strategy developed at Kyushu University, which can identify origins of observed objects as fragments released from a specified spacecraft. This practical method starts with selecting a spacecraft that experienced an orbital anomaly, and formulates a hypothesis to generate fragments from the anomaly. Then, the search strategy is applied to predict the behavior of groups of fragments hypothetically generated. Outcome of this predictive analysis specifies effectively when, where and how we should conduct optical measurements using ground-based telescopes. Objects detected based on the outcome are supposed to be from the anomaly, so that we can confirm the anomaly as a spacecraft breakup to release the detected objects. This paper also demonstrates observation planning for a spacecraft anomaly in the geostationary region.

Topological characterization and early detection of bifurcations and chaos in complex systems using persistent homology.

PubMed

Mittal, Khushboo; Gupta, Shalabh

2017-05-01

Early detection of bifurcations and chaos and understanding their topological characteristics are essential for safe and reliable operation of various electrical, chemical, physical, and industrial processes. However, the presence of non-linearity and high-dimensionality in system behavior makes this analysis a challenging task. The existing methods for dynamical system analysis provide useful tools for anomaly detection (e.g., Bendixson-Dulac and Poincare-Bendixson criteria can detect the presence of limit cycles); however, they do not provide a detailed topological understanding about system evolution during bifurcations and chaos, such as the changes in the number of subcycles and their positions, lifetimes, and sizes. This paper addresses this research gap by using topological data analysis as a tool to study system evolution and develop a mathematical framework for detecting the topological changes in the underlying system using persistent homology. Using the proposed technique, topological features (e.g., number of relevant k-dimensional holes, etc.) are extracted from nonlinear time series data which are useful for deeper analysis of the system behavior and early detection of bifurcations and chaos. When applied to a Logistic map, a Duffing oscillator, and a real life Op-amp based Jerk circuit, these features are shown to accurately characterize the system dynamics and detect the onset of chaos.

Heimdall System for MSSS Sensor Tasking

NASA Astrophysics Data System (ADS)

Herz, A.; Jones, B.; Herz, E.; George, D.; Axelrad, P.; Gehly, S.

In Norse Mythology, Heimdall uses his foreknowledge and keen eyesight to keep watch for disaster from his home near the Rainbow Bridge. Orbit Logic and the Colorado Center for Astrodynamics Research (CCAR) at the University of Colorado (CU) have developed the Heimdall System to schedule observations of known and uncharacterized objects and search for new objects from the Maui Space Surveillance Site. Heimdall addresses the current need for automated and optimized SSA sensor tasking driven by factors associated with improved space object catalog maintenance. Orbit Logic and CU developed an initial baseline prototype SSA sensor tasking capability for select sensors at the Maui Space Surveillance Site (MSSS) using STK and STK Scheduler, and then added a new Track Prioritization Component for FiSST-inspired computations for predicted Information Gain and Probability of Detection, and a new SSA-specific Figure-of-Merit (FOM) for optimized SSA sensor tasking. While the baseline prototype addresses automation and some of the multi-sensor tasking optimization, the SSA-improved prototype addresses all of the key elements required for improved tasking leading to enhanced object catalog maintenance. The Heimdall proof-of-concept was demonstrated for MSSS SSA sensor tasking for a 24 hour period to attempt observations of all operational satellites in the unclassified NORAD catalog, observe a small set of high priority GEO targets every 30 minutes, make a sky survey of the GEO belt region accessible to MSSS sensors, and observe particular GEO regions that have a high probability of finding new objects with any excess sensor time. This Heimdall prototype software paves the way for further R&D that will integrate this technology into the MSSS systems for operational scheduling, improve the software's scalability, and further tune and enhance schedule optimization. The Heimdall software for SSA sensor tasking provides greatly improved performance over manual tasking, improved coordinated sensor usage, and tasking schedules driven by catalog improvement goals (reduced overall covariance, etc.). The improved performance also enables more responsive sensor tasking to address external events, newly detected objects, newly detected object activity, and sensor anomalies. Instead of having to wait until the next day's scheduling phase, events can be addressed with new tasking schedules immediately (within seconds or minutes). Perhaps the most important benefit is improved SSA based on an overall improvement to the quality of the space catalog. By driving sensor tasking and scheduling based on predicted Information Gain and other relevant factors, better decisions are made in the application of available sensor resources, leading to an improved catalog and better information about the objects of most interest. The Heimdall software solution provides a configurable, automated system to improve sensor tasking efficiency and responsiveness for SSA applications. The FISST algorithms for Track Prioritization, SSA specific task and resource attributes, Scheduler algorithms, and configurable SSA-specific Figure-of-Merit together provide optimized and tunable scheduling for the Maui Space Surveillance Site and possibly other sites and organizations across the U.S. military and for allies around the world.

Anomaly-based intrusion detection for SCADA systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

2006-07-01

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 willmore » 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)« less

Min-max hyperellipsoidal clustering for anomaly detection in network security.

PubMed

Sarasamma, Suseela T; Zhu, Qiuming A

2006-08-01

A novel hyperellipsoidal clustering technique is presented for an intrusion-detection system in network security. Hyperellipsoidal clusters toward maximum intracluster similarity and minimum intercluster similarity are generated from training data sets. The novelty of the technique lies in the fact that the parameters needed to construct higher order data models in general multivariate Gaussian functions are incrementally derived from the data sets using accretive processes. The technique is implemented in a feedforward neural network that uses a Gaussian radial basis function as the model generator. An evaluation based on the inclusiveness and exclusiveness of samples with respect to specific criteria is applied to accretively learn the output clusters of the neural network. One significant advantage of this is its ability to detect individual anomaly types that are hard to detect with other anomaly-detection schemes. Applying this technique, several feature subsets of the tcptrace network-connection records that give above 95% detection at false-positive rates below 5% were identified.

Detecting Pulsing Denial-of-Service Attacks with Nondeterministic Attack Intervals

NASA Astrophysics Data System (ADS)

Luo, Xiapu; Chan, Edmond W. W.; Chang, Rocky K. C.

2009-12-01

This paper addresses the important problem of detecting pulsing denial of service (PDoS) attacks which send a sequence of attack pulses to reduce TCP throughput. Unlike previous works which focused on a restricted form of attacks, we consider a very broad class of attacks. In particular, our attack model admits any attack interval between two adjacent pulses, whether deterministic or not. It also includes the traditional flooding-based attacks as a limiting case (i.e., zero attack interval). Our main contribution is Vanguard, a new anomaly-based detection scheme for this class of PDoS attacks. The Vanguard detection is based on three traffic anomalies induced by the attacks, and it detects them using a CUSUM algorithm. We have prototyped Vanguard and evaluated it on a testbed. The experiment results show that Vanguard is more effective than the previous methods that are based on other traffic anomalies (after a transformation using wavelet transform, Fourier transform, and autocorrelation) and detection algorithms (e.g., dynamic time warping).

A scalable architecture for online anomaly detection of WLCG batch jobs

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Detection of emerging sunspot regions in the solar interior.

PubMed

Ilonidis, Stathis; Zhao, Junwei; Kosovichev, Alexander

2011-08-19

Sunspots are regions where strong magnetic fields emerge from the solar interior and where major eruptive events occur. These energetic events can cause power outages, interrupt telecommunication and navigation services, and pose hazards to astronauts. We detected subsurface signatures of emerging sunspot regions before they appeared on the solar disc. Strong acoustic travel-time anomalies of an order of 12 to 16 seconds were detected as deep as 65,000 kilometers. These anomalies were associated with magnetic structures that emerged with an average speed of 0.3 to 0.6 kilometer per second and caused high peaks in the photospheric magnetic flux rate 1 to 2 days after the detection of the anomalies. Thus, synoptic imaging of subsurface magnetic activity may allow anticipation of large sunspot regions before they become visible, improving space weather forecast.

Comparison of outliers and novelty detection to identify ionospheric TEC irregularities during geomagnetic storm and substorm

NASA Astrophysics Data System (ADS)

Pattisahusiwa, Asis; Houw Liong, The; Purqon, Acep

2016-08-01

In this study, we compare two learning mechanisms: outliers and novelty detection in order to detect ionospheric TEC disturbance by November 2004 geomagnetic storm and January 2005 substorm. The mechanisms are applied by using v-SVR learning algorithm which is a regression version of SVM. Our results show that both mechanisms are quiet accurate in learning TEC data. However, novelty detection is more accurate than outliers detection in extracting anomalies related to geomagnetic events. The detected anomalies by outliers detection are mostly related to trend of data, while novelty detection are associated to geomagnetic events. Novelty detection also shows evidence of LSTID during geomagnetic events.

The role of noninvasive and invasive diagnostic imaging techniques for detection of extra-cranial venous system anomalies and developmental variants

PubMed Central

2013-01-01

The extra-cranial venous system is complex and not well studied in comparison to the peripheral venous system. A newly proposed vascular condition, named chronic cerebrospinal venous insufficiency (CCSVI), described initially in patients with multiple sclerosis (MS) has triggered intense interest in better understanding of the role of extra-cranial venous anomalies and developmental variants. So far, there is no established diagnostic imaging modality, non-invasive or invasive, that can serve as the “gold standard” for detection of these venous anomalies. However, consensus guidelines and standardized imaging protocols are emerging. Most likely, a multimodal imaging approach will ultimately be the most comprehensive means for screening, diagnostic and monitoring purposes. Further research is needed to determine the spectrum of extra-cranial venous pathology and to compare the imaging findings with pathological examinations. The ability to define and reliably detect noninvasively these anomalies is an essential step toward establishing their incidence and prevalence. The role for these anomalies in causing significant hemodynamic consequences for the intra-cranial venous drainage in MS patients and other neurologic disorders, and in aging, remains unproven. PMID:23806142

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

PubMed

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.

GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harshaw, Chris R; Bridges, Robert A; Iannacone, Michael D

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

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

DOEpatents

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.

Artificial intelligence techniques for ground test monitoring of rocket engines

NASA Technical Reports Server (NTRS)

Ali, Moonis; Gupta, U. K.

1990-01-01

An expert system is being developed which can detect anomalies in Space Shuttle Main Engine (SSME) sensor data significantly earlier than the redline algorithm currently in use. The training of such an expert system focuses on two approaches which are based on low frequency and high frequency analyses of sensor data. Both approaches are being tested on data from SSME tests and their results compared with the findings of NASA and Rocketdyne experts. Prototype implementations have detected the presence of anomalies earlier than the redline algorithms that are in use currently. It therefore appears that these approaches have the potential of detecting anomalies early eneough to shut down the engine or take other corrective action before severe damage to the engine occurs.

Model-Based Anomaly Detection for a Transparent Optical Transmission System

NASA Astrophysics Data System (ADS)

Bengtsson, Thomas; Salamon, Todd; Ho, Tin Kam; White, Christopher A.

In this chapter, we present an approach for anomaly detection at the physical layer of networks where detailed knowledge about the devices and their operations is available. The approach combines physics-based process models with observational data models to characterize the uncertainties and derive the alarm decision rules. We formulate and apply three different methods based on this approach for a well-defined problem in optical network monitoring that features many typical challenges for this methodology. Specifically, we address the problem of monitoring optically transparent transmission systems that use dynamically controlled Raman amplification systems. We use models of amplifier physics together with statistical estimation to derive alarm decision rules and use these rules to automatically discriminate between measurement errors, anomalous losses, and pump failures. Our approach has led to an efficient tool for systematically detecting anomalies in the system behavior of a deployed network, where pro-active measures to address such anomalies are key to preventing unnecessary disturbances to the system's continuous operation.

Unsupervised algorithms for intrusion detection and identification in wireless ad hoc sensor networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

2009-05-01

In previous work by the author, parameters across network protocol layers were selected as features in supervised algorithms that detect and identify certain intrusion attacks on wireless ad hoc sensor networks (WSNs) carrying multisensor data. The algorithms improved the residual performance of the intrusion prevention measures provided by any dynamic key-management schemes and trust models implemented among network nodes. The approach of this paper does not train algorithms on the signature of known attack traffic, but, instead, the approach is based on unsupervised anomaly detection techniques that learn the signature of normal network traffic. Unsupervised learning does not require the data to be labeled or to be purely of one type, i.e., normal or attack traffic. The approach can be augmented to add any security attributes and quantified trust levels, established during data exchanges among nodes, to the set of cross-layer features from the WSN protocols. A two-stage framework is introduced for the security algorithms to overcome the problems of input size and resource constraints. The first stage is an unsupervised clustering algorithm which reduces the payload of network data packets to a tractable size. The second stage is a traditional anomaly detection algorithm based on a variation of support vector machines (SVMs), whose efficiency is improved by the availability of data in the packet payload. In the first stage, selected algorithms are adapted to WSN platforms to meet system requirements for simple parallel distributed computation, distributed storage and data robustness. A set of mobile software agents, acting like an ant colony in securing the WSN, are distributed at the nodes to implement the algorithms. The agents move among the layers involved in the network response to the intrusions at each active node and trustworthy neighborhood, collecting parametric values and executing assigned decision tasks. This minimizes the need to move large amounts of audit-log data through resource-limited nodes and locates routines closer to that data. Performance of the unsupervised algorithms is evaluated against the network intrusions of black hole, flooding, Sybil and other denial-of-service attacks in simulations of published scenarios. Results for scenarios with intentionally malfunctioning sensors show the robustness of the two-stage approach to intrusion anomalies.

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

PubMed

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

2018-05-16

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

Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

NASA Technical Reports Server (NTRS)

Thompson, David R.; Majid, Walid A.; Reed, Colorado J.; Wagstaff, Kiri L.

2011-01-01

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses adaptive eigenbases to combine 1) prior knowledge about uninteresting signals with 2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply the method to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies.

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

PubMed

Bronshtein, Moshe; Solt, Ido; Blumenfeld, Zeev

2014-06-01

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

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

NASA Technical Reports Server (NTRS)

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.

MUSIC algorithm for location searching of dielectric anomalies from S-parameters using microwave imaging

NASA Astrophysics Data System (ADS)

Park, Won-Kwang; Kim, Hwa Pyung; Lee, Kwang-Jae; Son, Seong-Ho

2017-11-01

Motivated by the biomedical engineering used in early-stage breast cancer detection, we investigated the use of MUltiple SIgnal Classification (MUSIC) algorithm for location searching of small anomalies using S-parameters. We considered the application of MUSIC to functional imaging where a small number of dipole antennas are used. Our approach is based on the application of Born approximation or physical factorization. We analyzed cases in which the anomaly is respectively small and large in relation to the wavelength, and the structure of the left-singular vectors is linked to the nonzero singular values of a Multi-Static Response (MSR) matrix whose elements are the S-parameters. Using simulations, we demonstrated the strengths and weaknesses of the MUSIC algorithm in detecting both small and extended anomalies.

Integrity Verification for SCADA Devices Using Bloom Filters and Deep Packet Inspection

DTIC Science & Technology

2014-03-27

prevent intrusions in smart grids [PK12]. Parthasarathy proposed an anomaly detection based IDS that takes into account system state. In his implementation...Security, 25(7):498–506, 10 2006. [LMV12] O. Linda, M. Manic, and T. Vollmer. Improving cyber-security of smart grid systems via anomaly detection and...6 2012. 114 [PK12] S. Parthasarathy and D. Kundur. Bloom filter based intrusion detection for smart grid SCADA. In Electrical & Computer Engineering

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

PubMed

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.

Analysis of MAGSAT and surface data of the Indian region

NASA Technical Reports Server (NTRS)

Agarwal, G. C. (Principal Investigator)

1983-01-01

Techniques and significant results of an analysis of MAGSAT and surface data of the Indian region are described. Specific investigative tasks included: (1) use of the multilevel data at different altitudes to develop a model for variation of magnetic anomaly with altitude; (2) development of the regional model for the description of main geomagnetic field for the Indian sub-continent using MAGSAT and observatory data; (3) development of regional mathematical model of secular variations over the Indian sub-continent; and (4) downward continuation of the anomaly field obtained from MAGSAT and its combination with the existing observatory data to produce a regional anomaly map for elucidating tectonic features of the Indian sub-continent.

Integrated System for Autonomous Science

NASA Technical Reports Server (NTRS)

Chien, Steve; Sherwood, Robert; Tran, Daniel; Cichy, Benjamin; Davies, Ashley; Castano, Rebecca; Rabideau, Gregg; Frye, Stuart; Trout, Bruce; Shulman, Seth;

Wetware, Hardware, or Software Incapacitation: Observational Methods to Determine When Autonomy Should Assume Control

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Gregory, Irene M.

2014-01-01

Control-theoretic modeling of human operator's dynamic behavior in manual control tasks has a long, rich history. There has been significant work on techniques used to identify the pilot model of a given structure. This research attempts to go beyond pilot identification based on experimental data to develop a predictor of pilot behavior. Two methods for pre-dicting pilot stick input during changing aircraft dynamics and deducing changes in pilot behavior are presented This approach may also have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot. With this ability to detect changes in piloting behavior, the possibility now exists to mediate human adverse behaviors, hardware failures, and software anomalies with autono-my that may ameliorate these undesirable effects. However, appropriate timing of when au-tonomy should assume control is dependent on criticality of actions to safety, sensitivity of methods to accurately detect these adverse changes, and effects of changes in levels of auto-mation of the system as a whole.

Detection of immunocytological markers in photomicroscopic images

NASA Astrophysics Data System (ADS)

Friedrich, David; zur Jacobsmühlen, Joschka; Braunschweig, Till; Bell, André; Chaisaowong, Kraisorn; Knüchel-Clarke, Ruth; Aach, Til

2012-03-01

Early detection of cervical cancer can be achieved through visual analysis of cell anomalies. The established PAP smear achieves a sensitivity of 50-90%, most false negative results are caused by mistakes in the preparation of the specimen or reader variability in the subjective, visual investigation. Since cervical cancer is caused by human papillomavirus (HPV), the detection of HPV-infected cells opens new perspectives for screening of precancerous abnormalities. Immunocytochemical preparation marks HPV-positive cells in brush smears of the cervix with high sensitivity and specificity. The goal of this work is the automated detection of all marker-positive cells in microscopic images of a sample slide stained with an immunocytochemical marker. A color separation technique is used to estimate the concentrations of the immunocytochemical marker stain as well as of the counterstain used to color the nuclei. Segmentation methods based on Otsu's threshold selection method and Mean Shift are adapted to the task of segmenting marker-positive cells and their nuclei. The best detection performance of single marker-positive cells was achieved with the adapted thresholding method with a sensitivity of 95.9%. The contours differed by a modified Hausdorff Distance (MHD) of 2.8 μm. Nuclei of single marker positive cells were detected with a sensitivity of 95.9% and MHD = 1.02 μm.

Occurrence and Detectability of Thermal Anomalies on Europa

NASA Astrophysics Data System (ADS)

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 show that if Europa is resurfaced continuously by the processes considered, there is a >99% chance that E-THEMIS will detect a thermal anomaly due to endogenic activity. Therefore, if no anomalies are detected, these models can be ruled out, or revised.

Tactile sensor of hardness recognition based on magnetic anomaly detection

NASA Astrophysics Data System (ADS)

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.

Wolffian duct derivative anomalies: technical considerations when encountered during robot-assisted radical prostatectomy.

PubMed

Acharya, Sujeet S; Gundeti, Mohan S; Zagaja, Gregory P; Shalhav, Arieh L; Zorn, Kevin C

2009-04-01

Although malformations of the genitourinary tract are typically identified during childhood, they can remain silent until incidental detection in evaluation and treatment of other pathologies during adulthood. The advent of the minimally invasive era in urologic surgery has given rise to unique challenges in the surgical management of anomalies of the genitourinary tract. This article reviews the embryology of anomalies of Wolffian duct (WD) derivatives with specific attention to the seminal vesicles, vas deferens, ureter, and kidneys. This is followed by a discussion of the history of the laparoscopic approach to WD derivative anomalies. Finally, we present two cases to describe technical considerations when managing these anomalies when encountered during robotic-assisted radical prostatectomy. The University of Chicago Robotic Laparoscopic Radical Prostatectomy (RLRP) database was reviewed for cases where anomalies of WD derivatives were encountered. We describe how modifications in technique allowed for completion of the procedure without difficulty. None Of the 1230 RLRP procedures performed at our institution by three surgeons, only two cases (0.16%) have been noted to have a WD anomaly. These cases were able to be completed without difficulty by making simple modifications in technique. Although uncommon, it is important for the urologist to be familiar with the origin and surgical management of WD anomalies, particularly when detected incidentally during surgery. Simple modifications in technique allow for completion of RLRP without difficulty.

A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals.

PubMed

Gold, Nathan; Frasch, Martin G; Herry, Christophe L; Richardson, Bryan S; Wang, Xiaogang

2017-01-01

Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods. We propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labor, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing point-wise detection methods.

A lightweight network anomaly detection technique

DOE PAGES

Kim, Jinoh; Yoo, Wucherl; Sim, Alex; ...

2017-03-13

While the network anomaly detection is essential in network operations and management, it becomes further challenging to perform the first line of detection against the exponentially increasing volume of network traffic. In this paper, we develop a technique for the first line of online anomaly detection with two important considerations: (i) availability of traffic attributes during the monitoring time, and (ii) computational scalability for streaming data. The presented learning technique is lightweight and highly scalable with the beauty of approximation based on the grid partitioning of the given dimensional space. With the public traffic traces of KDD Cup 1999 andmore » NSL-KDD, we show that our technique yields 98.5% and 83% of detection accuracy, respectively, only with a couple of readily available traffic attributes that can be obtained without the help of post-processing. Finally, the results are at least comparable with the classical learning methods including decision tree and random forest, with approximately two orders of magnitude faster learning performance.« less

A lightweight network anomaly detection technique

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Jinoh; Yoo, Wucherl; Sim, Alex

While the network anomaly detection is essential in network operations and management, it becomes further challenging to perform the first line of detection against the exponentially increasing volume of network traffic. In this paper, we develop a technique for the first line of online anomaly detection with two important considerations: (i) availability of traffic attributes during the monitoring time, and (ii) computational scalability for streaming data. The presented learning technique is lightweight and highly scalable with the beauty of approximation based on the grid partitioning of the given dimensional space. With the public traffic traces of KDD Cup 1999 andmore » NSL-KDD, we show that our technique yields 98.5% and 83% of detection accuracy, respectively, only with a couple of readily available traffic attributes that can be obtained without the help of post-processing. Finally, the results are at least comparable with the classical learning methods including decision tree and random forest, with approximately two orders of magnitude faster learning performance.« less

AnRAD: A Neuromorphic Anomaly Detection Framework for Massive Concurrent Data Streams.

PubMed

Chen, Qiuwen; Luley, Ryan; Wu, Qing; Bishop, Morgan; Linderman, Richard W; Qiu, Qinru

2018-05-01

The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic. In this paper, we propose anomaly recognition and detection (AnRAD), a bioinspired detection framework that performs probabilistic inferences. We analyze the feature dependency and develop a self-structuring method that learns an efficient confabulation network using unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base using streaming data. Compared with several existing anomaly detection approaches, our method provides competitive detection quality. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementations of the detection algorithm on the graphic processing unit and the Xeon Phi coprocessor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor. The framework provides real-time service to concurrent data streams within diversified knowledge contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle behavior detection, the framework is able to monitor up to 16000 vehicles (data streams) and their interactions in real time with a single commodity coprocessor, and uses less than 0.2 ms for one testing subject. Finally, the detection network is ported to our spiking neural network simulator to show the potential of adapting to the emerging neuromorphic architectures.

Spatial-temporal event detection in climate parameter imagery.

DOE Office of Scientific and Technical Information (OSTI.GOV)

McKenna, Sean Andrew; Gutierrez, Karen A.

Previously developed techniques that comprise statistical parametric mapping, with applications focused on human brain imaging, are examined and tested here for new applications in anomaly detection within remotely-sensed imagery. Two approaches to analysis are developed: online, regression-based anomaly detection and conditional differences. These approaches are applied to two example spatial-temporal data sets: data simulated with a Gaussian field deformation approach and weekly NDVI images derived from global satellite coverage. Results indicate that anomalies can be identified in spatial temporal data with the regression-based approach. Additionally, la Nina and el Nino climatic conditions are used as different stimuli applied to themore » earth and this comparison shows that el Nino conditions lead to significant decreases in NDVI in both the Amazon Basin and in Southern India.« less

Identifying High-Risk Patients without Labeled Training Data: Anomaly Detection Methodologies to Predict Adverse Outcomes

PubMed Central

Syed, Zeeshan; Saeed, Mohammed; Rubinfeld, Ilan

2010-01-01

For many clinical conditions, only a small number of patients experience adverse outcomes. Developing risk stratification algorithms for these conditions typically requires collecting large volumes of data to capture enough positive and negative for training. This process is slow, expensive, and may not be appropriate for new phenomena. In this paper, we explore different anomaly detection approaches to identify high-risk patients as cases that lie in sparse regions of the feature space. We study three broad categories of anomaly detection methods: classification-based, nearest neighbor-based, and clustering-based techniques. When evaluated on data from the National Surgical Quality Improvement Program (NSQIP), these methods were able to successfully identify patients at an elevated risk of mortality and rare morbidities following inpatient surgical procedures. PMID:21347083

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Microwave and Millimeter Wave Nondestructive Evaluation of the Space Shuttle External Tank Insulating Foam

NASA Technical Reports Server (NTRS)

Shrestha, S.; Kharkovsky, S.; Zoughi, R.; Hepburn, F

2005-01-01

The Space Shuttle Columbia s catastrophic failure has been attributed to a piece of external fuel tank insulating SOFI (Spray On Foam Insulation) foam striking the leading edge of the left wing of the orbiter causing significant damage to some of the protecting heat tiles. The accident emphasizes the growing need to develop effective, robust and life-cycle oriented methods of nondestructive testing and evaluation (NDT&E) of complex conductor-backed insulating foam and protective acreage heat tiles used in the space shuttle fleet and in future multi-launch space vehicles. The insulating SOFI foam is constructed from closed-cell foam. In the microwave regime this foam is in the family of low permittivity and low loss dielectric materials. Near-field microwave and millimeter wave NDT methods were one of the techniques chosen for this purpose. To this end several flat and thick SOFI foam panels, two structurally complex panels similar to the external fuel tank and a "blind" panel were used in this investigation. Several anomalies such as voids and disbonds were embedded in these panels at various locations. The location and properties of the embedded anomalies in the "blind" panel were not disclosed to the investigating team prior to the investigation. Three frequency bands were used in this investigation covering a frequency range of 8-75 GHz. Moreover, the influence of signal polarization was also investigated. Overall the results of this investigation were very promising for detecting the presence of anomalies in different panels covered with relatively thick insulating SOFI foam. Different types of anomalies were detected in foam up to 9 in thick. Many of the anomalies in the more complex panels were also detected. When investigating the blind panel no false positives were detected. Anomalies in between and underneath bolt heads were not easily detected. This paper presents the results of this investigation along with a discussion of the capabilities of the method used.

Trust Management in Mobile Ad Hoc Networks for Bias Minimization and Application Performance Maximization

DTIC Science & Technology

2014-02-26

set of anomaly detection rules 62 I.-R. Chen et al. / Ad Hoc Networks 19 (2014) 59–74 Author’s personal copy including the interval rule (for...deficiencies in anomaly detection (e.g., imperfection of rules) by a false negative probability (PHfn) of misidentifying an unhealthy node as a...multimedia servers, Multimedia Syst. 8 (2) (2000) 83–91. [53] R. Mitchell, I.R. Chen, Adaptive intrusion detection for unmanned aircraft systems based on

Using Physical Models for Anomaly Detection in Control Systems

NASA Astrophysics Data System (ADS)

Svendsen, Nils; Wolthusen, Stephen

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

Comparison of U-spatial statistics and C-A fractal models for delineating anomaly patterns of porphyry-type Cu geochemical signatures in the Varzaghan district, NW Iran

NASA Astrophysics Data System (ADS)

Ghezelbash, Reza; Maghsoudi, Abbas

2018-05-01

The delineation of populations of stream sediment geochemical data is a crucial task in regional exploration surveys. In this contribution, uni-element stream sediment geochemical data of Cu, Au, Mo, and Bi have been subjected to two reliable anomaly-background separation methods, namely, the concentration-area (C-A) fractal and the U-spatial statistics methods to separate geochemical anomalies related to porphyry-type Cu mineralization in northwest Iran. The quantitative comparison of the delineated geochemical populations using the modified success-rate curves revealed the superiority of the U-spatial statistics method over the fractal model. Moreover, geochemical maps of investigated elements revealed strongly positive correlations between strong anomalies and Oligocene-Miocene intrusions in the study area. Therefore, follow-up exploration programs should focus on these areas.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Detection of Perlger-Huet anomaly based on augmented fast marching method and speeded up robust features.

PubMed

Sun, Minglei; Yang, Shaobao; Jiang, Jinling; Wang, Qiwei

2015-01-01

Pelger-Huet anomaly (PHA) and Pseudo Pelger-Huet anomaly (PPHA) are neutrophil with abnormal morphology. They have the bilobed or unilobed nucleus and excessive clumping chromatin. Currently, detection of this kind of cell mainly depends on the manual microscopic examination by a clinician, thus, the quality of detection is limited by the efficiency and a certain subjective consciousness of the clinician. In this paper, a detection method for PHA and PPHA is proposed based on karyomorphism and chromatin distribution features. Firstly, the skeleton of the nucleus is extracted using an augmented Fast Marching Method (AFMM) and width distribution is obtained through distance transform. Then, caryoplastin in the nucleus is extracted based on Speeded Up Robust Features (SURF) and a K-nearest-neighbor (KNN) classifier is constructed to analyze the features. Experiment shows that the sensitivity and specificity of this method achieved 87.5% and 83.33%, which means that the detection accuracy of PHA is acceptable. Meanwhile, the detection method should be helpful to the automatic morphological classification of blood cells.

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

PubMed

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

2016-01-01

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

A novel approach for pilot error detection using Dynamic Bayesian Networks.

PubMed

Saada, Mohamad; Meng, Qinggang; Huang, Tingwen

2014-06-01

In the last decade Dynamic Bayesian Networks (DBNs) have become one type of the most attractive probabilistic modelling framework extensions of Bayesian Networks (BNs) for working under uncertainties from a temporal perspective. Despite this popularity not many researchers have attempted to study the use of these networks in anomaly detection or the implications of data anomalies on the outcome of such models. An abnormal change in the modelled environment's data at a given time, will cause a trailing chain effect on data of all related environment variables in current and consecutive time slices. Albeit this effect fades with time, it still can have an ill effect on the outcome of such models. In this paper we propose an algorithm for pilot error detection, using DBNs as the modelling framework for learning and detecting anomalous data. We base our experiments on the actions of an aircraft pilot, and a flight simulator is created for running the experiments. The proposed anomaly detection algorithm has achieved good results in detecting pilot errors and effects on the whole system.

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

NASA Technical Reports Server (NTRS)

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.

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

PubMed

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yan

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 ismore » 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.« less

Discrimination between pre-seismic electromagnetic anomalies and solar activity effects

NASA Astrophysics Data System (ADS)

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

2009-04-01

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

Discrimination between preseismic electromagnetic anomalies and solar activity effects

NASA Astrophysics Data System (ADS)

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

2009-04-01

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

Acute maternal social dysfunction, health perception and psychological distress after ultrasonographic detection of a fetal structural anomaly.

PubMed

Kaasen, A; Helbig, A; Malt, U F; Naes, T; Skari, H; Haugen, G

2010-08-01

To predict acute psychological distress in pregnant women following detection of a fetal structural anomaly by ultrasonography, and to relate these findings to a comparison group. A prospective, observational study. Tertiary referral centre for fetal medicine. One hundred and eighty pregnant women with a fetal structural anomaly detected by ultrasound (study group) and 111 with normal ultrasound findings (comparison group) were included within a week following sonographic examination after gestational age 12 weeks (inclusion period: May 2006 to February 2009). Social dysfunction and health perception were assessed by the corresponding subscales of the General Health Questionnaire (GHQ-28). Psychological distress was assessed using the Impact of Events Scale (IES-22), Edinburgh Postnatal Depression Scale (EPDS) and the anxiety and depression subscales of the GHQ-28. Fetal anomalies were classified according to severity and diagnostic or prognostic ambiguity at the time of assessment. Social dysfunction, health perception and psychological distress (intrusion, avoidance, arousal, anxiety, depression). The least severe anomalies with no diagnostic or prognostic ambiguity induced the lowest levels of IES intrusive distress (P = 0.025). Women included after 22 weeks of gestation (24%) reported significantly higher GHQ distress than women included earlier in pregnancy (P = 0.003). The study group had significantly higher levels of psychosocial distress than the comparison group on all psychometric endpoints. Psychological distress was predicted by gestational age at the time of assessment, severity of the fetal anomaly, and ambiguity concerning diagnosis or prognosis.

Detecting errors and anomalies in computerized materials control and accountability databases

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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 thesemore » 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.« less

Value of brain MRI when sonography raises suspicion of agenesis of the corpus callosum in fetuses.

PubMed

Jarre, A; Llorens Salvador, R; Montoliu Fornas, G; Montoya Filardi, A

To evaluate the role of magnetic resonance imaging (MRI) in fetuses with a previous sonographic suspicion of agenesis of the corpus callosum (ACC) to confirm the diagnosis and to detect associated intracranial anomalies. Single-center retrospective and descriptive observational study of the brain MRI performed in 78 fetuses with ACC sonographic suspicion between January 2006 and December 2015. Two experts in fetal imaging reviewed the MRI findings to evaluate the presence and morphology of the corpus callosum. When ACC was detected the whole fetal brain anatomy was thoroughly studied to determine the presence of associated anomalies. Prenatal MR imaging findings were compared to postnatal brain MRI or necropsy findings when available. Fetal MRI diagnosed 45 cases of ACC, 12 were partial (26.7%) and 33 complete (73.3%). In 28 cases (62,2%) associated intracranial anomalies were identified. The most often abnormality was ventriculomegaly (78,6%), followed by cortical malformations (53,6%), posterior fossa (25%) and midline anomalies (10,7%). Fetal brain MRI has an important role in the diagnosis of ACC and detection of associated anomalies. To perform a fetal brain MRI is important in fetuses with sonographic suspicion of ACC. Copyright © 2017 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

IR Thermography of International Space Station Radiator Panels

NASA Technical Reports Server (NTRS)

Koshti, Ajay; Winfree, WIlliam; Morton, Richard; Howell, Patricia

2010-01-01

Several non-flight qualification test radiators were inspected using flash thermography. Flash thermography data analysis used raw and second derivative images to detect anomalies (Echotherm and Mosaic). Simple contrast evolutions were plotted for the detected anomalies to help in anomaly characterization. Many out-of-family indications were noted. Some out-of-family indications were classified as cold spot indications and are due to additional adhesive or adhesive layer behind the facesheet. Some out-of-family indications were classified as hot spot indications and are due to void, unbond or lack of adhesive behind the facesheet. The IR inspection helped in assessing expected manufacturing quality of the radiators.

How much does the MSW effect contribute to the reactor antineutrino anomaly?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valdiviesso, G. A.

2015-05-15

It has been pointed out that there is a 5.7 ± 2.3 discrepancy between the predicted and the observed reactor antineutrino flux in very short baseline experiments. Several causes for this anomaly have been discussed, including a possible non-standard forth sterile neutrino. In order to quantify how much non-standard this anomaly really is, the standard MSW effect is reviewed. Knowing that reactor antineutrinos are produced in a dense medium (the nuclear fuel) and is usually detected in a less dense one (water, or scintillator), non-adiabatic effects are expected to happen, creating a difference between the creation and detection mixing angles.

Radioactive anomaly discrimination from spectral ratios

DOEpatents

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

2013-08-20

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

Dataset of anomalies and malicious acts in a cyber-physical subsystem.

PubMed

Laso, Pedro Merino; Brosset, David; Puentes, John

2017-10-01

This article presents a dataset produced to investigate how data and information quality estimations enable to detect aNomalies and malicious acts in cyber-physical systems. Data were acquired making use of a cyber-physical subsystem consisting of liquid containers for fuel or water, along with its automated control and data acquisition infrastructure. Described data consist of temporal series representing five operational scenarios - Normal, aNomalies, breakdown, sabotages, and cyber-attacks - corresponding to 15 different real situations. The dataset is publicly available in the .zip file published with the article, to investigate and compare faulty operation detection and characterization methods for cyber-physical systems.

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

NASA Technical Reports Server (NTRS)

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.

Extending TOPS: Ontology-driven Anomaly Detection and Analysis System

NASA Astrophysics Data System (ADS)

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

2010-12-01

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 a capability for automated anomaly detection and analysis of both on-line (streaming) and off-line data. 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. We can query the knowledge base and answer questions about dataset compatibilities, similarities and dependencies so that we can, for example, automatically analyze similar datasets in order to verify a given anomaly occurrence in multiple data sources. We are further extending the system to go beyond anomaly detection towards reasoning about possible causes of anomalies that are also encoded in the knowledge base as either learned or implied knowledge. This enables us to scale up the analysis by eliminating a large number of anomalies early on during the processing by either failure to verify them from other sources, or matching them directly with other observable events without having to perform an extensive and time-consuming exploration and analysis. 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. The information is stored using Sesame server and is accessible through both Java API and web services using SeRQL and SPARQL query languages. Inference is provided using OWLIM component integrated with Sesame.

Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations.

PubMed

Cheng, Wei; Zhang, Kai; Chen, Haifeng; Jiang, Guofei; Chen, Zhengzhang; Wang, Wei

2016-08-01

Modern world has witnessed a dramatic increase in our ability to collect, transmit and distribute real-time monitoring and surveillance data from large-scale information systems and cyber-physical systems. Detecting system anomalies thus attracts significant amount of interest in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be a powerful way in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: 1) fault propagation in the network is ignored; 2) the root casual anomalies may not always be the nodes with a high-percentage of vanishing correlations; 3) temporal patterns of vanishing correlations are not exploited for robust detection. To address these limitations, in this paper we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network, and can perform joint inference on both the structural, and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations, and can compensate for unstructured measurement noise in the system. Extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets demonstrate the effectiveness of our approach.

Solving the muon g -2 anomaly in deflected anomaly mediated SUSY breaking with messenger-matter interactions

NASA Astrophysics Data System (ADS)

Wang, Fei; Wang, Wenyu; Yang, Jin Min

2017-10-01

We propose to introduce general messenger-matter interactions in the deflected anomaly mediated supersymmetry (SUSY) breaking (AMSB) scenario to explain the gμ-2 anomaly. Scenarios with complete or incomplete grand unified theory (GUT) multiplet messengers are discussed, respectively. The introduction of incomplete GUT mulitiplets can be advantageous in various aspects. We found that the gμ-2 anomaly can be solved in both scenarios under current constraints including the gluino mass bounds, while the scenarios with incomplete GUT representation messengers are more favored by the gμ-2 data. We also found that the gluino is upper bounded by about 2.5 TeV (2.0 TeV) in scenario A and 3.0 TeV (2.7 TeV) in scenario B if the generalized deflected AMSB scenarios are used to fully account for the gμ-2 anomaly at 3 σ (2 σ ) level. Such a gluino should be accessible in the future LHC searches. Dark matter (DM) constraints, including DM relic density and direct detection bounds, favor scenario B with incomplete GUT multiplets. Much of the allowed parameter space for scenario B could be covered by the future DM direct detection experiments.

Congenital anomalies of the left brachiocephalic vein detected in adults on computed tomography.

PubMed

Yamamuro, Hiroshi; Ichikawa, Tamaki; Hashimoto, Jun; Ono, Shun; Nagata, Yoshimi; Kawada, Shuichi; Kobayashi, Makiko; Koizumi, Jun; Shibata, Takeo; Imai, Yutaka

2017-10-01

Anomalous left brachiocephalic vein (BCV) is a rare and less known systemic venous anomaly. We evaluated congenital anomalies of the left BCV in adults detected during computed tomography (CT) examinations. This retrospective study included 81,425 patients without congenital heart disease who underwent chest CT. We reviewed the recorded reports and CT images for congenital anomalies of the left BCV including aberrant and supernumerary BCVs. The associated congenital aortic anomalies were assessed. Among 73,407 cases at a university hospital, 22 (16 males, 6 females; mean age, 59 years) with aberrant left BCVs were found using keyword research on recorded reports (0.03%). Among 8018 cases at the branch hospital, 5 (4 males, 1 female; mean age, 67 years) with aberrant left BCVs were found using CT image review (0.062%). There were no significant differences in incidences of aberrant left BCV between the two groups. Two cases had double left BCVs. Eleven cases showed high aortic arches. Two cases had the right aortic arch, one case had an incomplete double aortic arch, and one case was associated with coarctation. Aberrant left BCV on CT examination in adults was extremely rare. Some cases were associated with aortic arch anomalies.

CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm

NASA Astrophysics Data System (ADS)

Crist, Eric P.; Thelen, Brian J.; Carrara, David A.

1998-10-01

Anomaly detection offers a means by which to identify potentially important objects in a scene without prior knowledge of their spectral signatures. As such, this approach is less sensitive to variations in target class composition, atmospheric and illumination conditions, and sensor gain settings than would be a spectral matched filter or similar algorithm. The best existing anomaly detectors generally fall into one of two categories: those based on local Gaussian statistics, and those based on linear mixing moles. Unmixing-based approaches better represent the real distribution of data in a scene, but are typically derived and applied on a global or scene-wide basis. Locally adaptive approaches allow detection of more subtle anomalies by accommodating the spatial non-homogeneity of background classes in a typical scene, but provide a poorer representation of the true underlying background distribution. The CHAMP algorithm combines the best attributes of both approaches, applying a linear-mixing model approach in a spatially adaptive manner. The algorithm itself, and teste results on simulated and actual hyperspectral image data, are presented in this paper.

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

NASA Astrophysics Data System (ADS)

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.

Radiation anomaly detection algorithms for field-acquired gamma energy spectra

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

Microarray-based comparative genomic hybridization analysis in neonates with congenital anomalies: detection of chromosomal imbalances.

PubMed

Emy Dorfman, Luiza; Leite, Júlio César L; Giugliani, Roberto; Riegel, Mariluce

2015-01-01

To identify chromosomal imbalances by whole-genome microarray-based comparative genomic hybridization (array-CGH) in DNA samples of neonates with congenital anomalies of unknown cause from a birth defects monitoring program at a public maternity hospital. A blind genomic analysis was performed retrospectively in 35 stored DNA samples of neonates born between July of 2011 and December of 2012. All potential DNA copy number variations detected (CNVs) were matched with those reported in public genomic databases, and their clinical significance was evaluated. Out of a total of 35 samples tested, 13 genomic imbalances were detected in 12/35 cases (34.3%). In 4/35 cases (11.4%), chromosomal imbalances could be defined as pathogenic; in 5/35 (14.3%) cases, DNA CNVs of uncertain clinical significance were identified; and in 4/35 cases (11.4%), normal variants were detected. Among the four cases with results considered causally related to the clinical findings, two of the four (50%) showed causative alterations already associated with well-defined microdeletion syndromes. In two of the four samples (50%), the chromosomal imbalances found, although predicted as pathogenic, had not been previously associated with recognized clinical entities. Array-CGH analysis allowed for a higher rate of detection of chromosomal anomalies, and this determination is especially valuable in neonates with congenital anomalies of unknown etiology, or in cases in which karyotype results cannot be obtained. Moreover, although the interpretation of the results must be refined, this method is a robust and precise tool that can be used in the first-line investigation of congenital anomalies, and should be considered for prospective/retrospective analyses of DNA samples by birth defect monitoring programs. Copyright © 2014 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Choosing options for ultrasound screening in pregnancy and comparing cost effectiveness: a decision analysis approach.

PubMed

Roberts, T; Mugford, M; Piercy, J

1998-09-01

To compare the cost effectiveness of different programmes of routine antenatal ultrasound screening to detect four key fetal anomalies: serious cardiac anomalies, spina bifida, Down's syndrome and lethal anomalies, using existing evidence. Decision analysis was used based on the best data currently available, including expert opinion from the Royal College of Obstetricians and Gynaecologists, Working Party and secondary data from the literature, to predict the likely outcomes in terms of malformations detected by each screening programme. Results applicable in clinics, hospitals or GP practices delivering antenatal screening. The number of cases with a 'target' malformation correctly detected antenatally. There was substantial overlap between the cost ranges of each screening programme demonstrating considerable uncertainty about the relative economic efficiency of alternative programmes for ultrasound screening. The cheapest, but not the most effective, screening programme consisted of one second trimester ultrasound scan. The cost per target anomaly detected (cost effectiveness) for this programme was in the range 5,000 pound silver-109,000, pound silver but in any 1000 women it will also fail to detect between 3.6 and 4.7 target anomalies. The range of uncertainty in the costs did not allow selection of any one programme as a clear choice for NHS purchasers. The results suggested that the overall allocation of resources for routine ultrasound screening in the UK is not currently economically efficient, but that certain scenarios for ultrasound screening are potentially within the range of cost effectiveness reached by other, possibly competing, screening programmes. The model highlighted the weakness of available evidence and demonstrated the need for more information both about current practice and costs.

Launch and Commissioning of the Deep Space Climate Observatory

NASA Technical Reports Server (NTRS)

Frey, Nicholas P.; Davis, Edward P.

2016-01-01

The Deep Space Climate Observatory (DSCOVR), formerly known as Triana, successfully launched on February 11th, 2015. To date, each of the five space-craft attitude control system (ACS) modes have been operating as expected and meeting all guidance, navigation, and control (GN&C) requirements, although since launch, several anomalies were encountered. While unplanned, these anomalies have proven to be invaluable in developing a deeper understanding of the ACS, and drove the design of three alterations to the ACS task of the flight software (FSW). An overview of the GN&C subsystem hardware, including re-furbishment, and ACS architecture are introduced, followed by a chronological discussion of key events, flight performance, as well as anomalies encountered by the GN&C team.

The hydrocarbon accumulations mapping in crystalline rocks by mobile geophysical methods

NASA Astrophysics Data System (ADS)

Nesterenko, A.

2013-05-01

Sedimentary-migration origin theory of hydrocarbons dominates nowadays. However, a significant amount of hydrocarbon deposits were discovered in the crystalline rocks, which corroborates the theory of non-organic origin of hydrocarbons. During the solving of problems of oil and gas exploration in crystalline rocks and arrays so-called "direct" methods can be used. These methods include geoelectric methods of forming short-pulsed electromagnetic field (FSPEF) and vertical electric-resonance sounding (VERS) (FSPEF-VERS express-technology). Use of remote Earth sounding (RES) methods is also actual. These mobile technologies are extensively used during the exploration of hydrocarbon accumulations in crystalline rocks, including those within the Ukrainian crystalline shield. The results of explorations Four anomalous geoelectric zones of "gas condensate reservoir" type were quickly revealed as a result of reconnaissance prospecting works (Fig. 1). DTA "Obukhovychi". Anomaly was traced over a distance of 4 km. Approximate area is 12.0 km2. DTA"Korolevskaya". Preliminary established size of anomalous zone is 10.0 km2. The anomalous polarized layers of gas and gas-condensate type were determined. DTA "Olizarovskaya". Approximate size of anomaly is about 56.0 km2. This anomaly is the largest and the most intense. DTA "Druzhba". Preliminary estimated size of anomaly is 16.0 km2. Conclusions Long experience of a successful application of non-classical geoelectric methods for the solving of variety of practical tasks allow one to state their contribution to the development of a new paradigm of geophysical researches. Simultaneous usage of the remote sensing data processing and interpretation method and FSPEF and VERS technologies can essentially optimize and speed up geophysical work. References 1. S.P. Levashov. Detection and mapping of anomalies of "hydrocarbon deposit" type in the fault zones of crystalline arrays by geoelectric methods. / S.P. Levashov, N.A. Yakymchuk, I.N. Korchagin, V.V. Prilukov, J.N. Yakymchuk / / Oil. Gas. Novations. - 2011/4. - P. 10-17. Introduction. (in Russian); Fig. 1. The map of "gas condensate reservoir" type anomalous geoelectric zones on the area of human settlements Malin: 1 - a scale of the intensity of anomalous response, 2 - the zones of tectonic disturbances.

Visual analytics of anomaly detection in large data streams

NASA Astrophysics Data System (ADS)

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

2009-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2009-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

Methods for Finding Legacy Wells in Residential and Commercial Areas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hammack, Richard W.; Veloski, Garret A.

In 1919, the enthusiasm surrounding a short-lived gas play in Versailles Borough, Pennsylvania resulted in the drilling of many needless wells. The legacy of this activity exists today in the form of abandoned, unplugged gas wells that are a continuing source of fugitive methane in the midst of a residential and commercial area. Flammable concentrations of methane have been detected near building foundations, which have forced people from their homes and businesses until methane concentrations decreased. Despite mitigation efforts, methane problems persist and have caused some buildings to be permanently abandoned and demolished. This paper describes the use of magneticmore » and methane sensing methods by the National Energy Technology Laboratory (NETL) to locate abandoned gas wells in Versailles Borough where site access is limited and existing infrastructure can interfere. Here, wells are located between closely spaced houses and beneath buildings and parking lots. Wells are seldom visible, often because wellheads and internal casing strings have been removed, and external casing has been cut off below ground level. The magnetic survey of Versailles Borough identified 53 strong, monopole magnetic anomalies that are presumed to indicate the locations of steel-cased wells. This hypothesis was tested by excavating the location of one strong, monopole magnetic anomaly that was within an area of anomalous methane concentrations. The excavation uncovered an unplugged gas well that was within 0.2 m of the location of the maximum magnetic signal. Truck-mounted methane surveys of Versailles Borough detected numerous methane anomalies that were useful for narrowing search areas. Methane sources identified during truck-mounted surveys included strong methane sources such as sewers and methane mitigation vents. However, inconsistent wind direction and speed, especially between buildings, made locating weaker methane sources (such as leaking wells) difficult. Walking surveys with the methane detector mounted on a cart or wagon were more effective for detecting leaking wells because the instrument’s air inlet was near the ground where: 1) the methane concentration from subsurface sources (including wells) was a maximum, and 2) there was less displacement of methane anomalies from methane sources by air currents. The Versailles Borough survey found 15 methane anomalies that coincided with the location of well-type magnetic anomalies; the methane sources for these anomalies were assumed to be leaking wells. For abandoned well locations where the wellhead and all casing strings have been removed and there is no magnetic anomaly, leaking wellbores can sometimes be detected by methane surveys. Unlike magnetic anomalies, methane anomalies can be: 1) ephemeral, 2) significantly displaced from the well location, and 3) from non-well sources that cannot be discriminated without isotopic analysis. If methane surveys are used for well location, the air inlet to the instrument should be kept as close to the ground as possible to minimize the likelihood of detecting methane from distant, wind-blown sources.« less

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

PubMed Central

Liu, Datong; Peng, Yu; Peng, Xiyuan

2018-01-01

Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP), which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%). There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI) based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA) algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application. PMID:29587372

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Accurate mobile malware detection and classification in the cloud.

PubMed

Wang, Xiaolei; Yang, Yuexiang; Zeng, Yingzhi

2015-01-01

As the dominator of the Smartphone operating system market, consequently android has attracted the attention of s malware authors and researcher alike. The number of types of android malware is increasing rapidly regardless of the considerable number of proposed malware analysis systems. In this paper, by taking advantages of low false-positive rate of misuse detection and the ability of anomaly detection to detect zero-day malware, we propose a novel hybrid detection system based on a new open-source framework CuckooDroid, which enables the use of Cuckoo Sandbox's features to analyze Android malware through dynamic and static analysis. Our proposed system mainly consists of two parts: anomaly detection engine performing abnormal apps detection through dynamic analysis; signature detection engine performing known malware detection and classification with the combination of static and dynamic analysis. We evaluate our system using 5560 malware samples and 6000 benign samples. Experiments show that our anomaly detection engine with dynamic analysis is capable of detecting zero-day malware with a low false negative rate (1.16 %) and acceptable false positive rate (1.30 %); it is worth noting that our signature detection engine with hybrid analysis can accurately classify malware samples with an average positive rate 98.94 %. Considering the intensive computing resources required by the static and dynamic analysis, our proposed detection system should be deployed off-device, such as in the Cloud. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.

A transdiagnostic investigation of 'theory of mind' and 'jumping to conclusions' in patients with persecutory delusions.

PubMed

Corcoran, R; Rowse, G; Moore, R; Blackwood, N; Kinderman, P; Howard, R; Cummins, S; Bentall, R P

2008-11-01

A tendency to make hasty decisions on probabilistic reasoning tasks and a difficulty attributing mental states to others are key cognitive features of persecutory delusions (PDs) in the context of schizophrenia. This study examines whether these same psychological anomalies characterize PDs when they present in the context of psychotic depression. Performance on measures of probabilistic reasoning and theory of mind (ToM) was examined in five subgroups differing in diagnostic category and current illness status. The tendency to draw hasty decisions in probabilistic settings and poor ToM tested using story format feature in PDs irrespective of diagnosis. Furthermore, performance on the ToM story task correlated with the degree of distress caused by and preoccupation with the current PDs in the currently deluded groups. By contrast, performance on the non-verbal ToM task appears to be more sensitive to diagnosis, as patients with schizophrenia spectrum disorders perform worse on this task than those with depression irrespective of the presence of PDs. The psychological anomalies associated with PDs examined here are transdiagnostic but different measures of ToM may be more or less sensitive to indices of severity of the PDs, diagnosis and trait- or state-related cognitive effects.

Fiber Optic Bragg Grating Sensors for Thermographic Detection of Subsurface Anomalies

NASA Technical Reports Server (NTRS)

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.

An Unsupervised Deep Hyperspectral Anomaly Detector

PubMed Central

Ma, Ning; Peng, Yu; Wang, Shaojun

2018-01-01

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). PMID:29495410

The incidence of coronary anomalies on routine coronary computed tomography scans

PubMed Central

Karabay, Kanber Ocal; Yildiz, Abdulmelik; Bagirtan, Bayram; Geceer, Gurkan; Uysal, Ender

2013-01-01

Summary Objective This study aimed to assess the incidence of coronary anomalies using 64-multi-slice coronary computed tomography (MSCT). Methods The diagnostic MSCT scans of 745 consecutive patients were reviewed. Results The incidence of coronary anomalies was 4.96%. The detected coronary anomalies included the conus artery originating separately from the right coronary sinus (RCS) (n = 8, 1.07%), absence of the left main artery (n = 7, 0.93%), a superior right coronary artery (RCA) (n = 7, 0.93%), the circumflex artery (CFX) arising from the RCS (n = 4, 0.53%), the CFX originating from the RCA (n = 2, 0.26%), a posterior RCA (n = 1, 0.13%), a coronary fistula from the left anterior descending artery and RCA to the pulmonary artery (n = 1, 0.13%), and a coronary aneurysm (n = 1, 0.13%). Conclusions This study indicated that MSCT can be used to detect common coronary anomalies, and shows it has the potential to aid cardiologists and cardiac surgeons by revealing the origin and course of the coronary vessels. PMID:24042853

The relationship between the neural computations for speech and music perception is context-dependent: an activation likelihood estimate study.

PubMed

LaCroix, Arianna N; Diaz, Alvaro F; Rogalsky, Corianne

2015-01-01

The relationship between the neurobiology of speech and music has been investigated for more than a century. There remains no widespread agreement regarding how (or to what extent) music perception utilizes the neural circuitry that is engaged in speech processing, particularly at the cortical level. Prominent models such as Patel's Shared Syntactic Integration Resource Hypothesis (SSIRH) and Koelsch's neurocognitive model of music perception suggest a high degree of overlap, particularly in the frontal lobe, but also perhaps more distinct representations in the temporal lobe with hemispheric asymmetries. The present meta-analysis study used activation likelihood estimate analyses to identify the brain regions consistently activated for music as compared to speech across the functional neuroimaging (fMRI and PET) literature. Eighty music and 91 speech neuroimaging studies of healthy adult control subjects were analyzed. Peak activations reported in the music and speech studies were divided into four paradigm categories: passive listening, discrimination tasks, error/anomaly detection tasks and memory-related tasks. We then compared activation likelihood estimates within each category for music vs. speech, and each music condition with passive listening. We found that listening to music and to speech preferentially activate distinct temporo-parietal bilateral cortical networks. We also found music and speech to have shared resources in the left pars opercularis but speech-specific resources in the left pars triangularis. The extent to which music recruited speech-activated frontal resources was modulated by task. While there are certainly limitations to meta-analysis techniques particularly regarding sensitivity, this work suggests that the extent of shared resources between speech and music may be task-dependent and highlights the need to consider how task effects may be affecting conclusions regarding the neurobiology of speech and music.

The relationship between the neural computations for speech and music perception is context-dependent: an activation likelihood estimate study

PubMed Central

LaCroix, Arianna N.; Diaz, Alvaro F.; Rogalsky, Corianne

2015-01-01

The relationship between the neurobiology of speech and music has been investigated for more than a century. There remains no widespread agreement regarding how (or to what extent) music perception utilizes the neural circuitry that is engaged in speech processing, particularly at the cortical level. Prominent models such as Patel's Shared Syntactic Integration Resource Hypothesis (SSIRH) and Koelsch's neurocognitive model of music perception suggest a high degree of overlap, particularly in the frontal lobe, but also perhaps more distinct representations in the temporal lobe with hemispheric asymmetries. The present meta-analysis study used activation likelihood estimate analyses to identify the brain regions consistently activated for music as compared to speech across the functional neuroimaging (fMRI and PET) literature. Eighty music and 91 speech neuroimaging studies of healthy adult control subjects were analyzed. Peak activations reported in the music and speech studies were divided into four paradigm categories: passive listening, discrimination tasks, error/anomaly detection tasks and memory-related tasks. We then compared activation likelihood estimates within each category for music vs. speech, and each music condition with passive listening. We found that listening to music and to speech preferentially activate distinct temporo-parietal bilateral cortical networks. We also found music and speech to have shared resources in the left pars opercularis but speech-specific resources in the left pars triangularis. The extent to which music recruited speech-activated frontal resources was modulated by task. While there are certainly limitations to meta-analysis techniques particularly regarding sensitivity, this work suggests that the extent of shared resources between speech and music may be task-dependent and highlights the need to consider how task effects may be affecting conclusions regarding the neurobiology of speech and music. PMID:26321976

The Compact Environmental Anomaly Sensor (CEASE) III

NASA Astrophysics Data System (ADS)

Roddy, P.; Hilmer, R. V.; Ballenthin, J.; Lindstrom, C. D.; Barton, D. A.; Ignazio, J. M.; Coombs, J. M.; Johnston, W. R.; Wheelock, A. T.; Quigley, S.

2016-12-01

The Air Force Research Laboratory's Energetic Charged Particle (ECP) sensor project is a comprehensive effort to measure the charged particle environment that causes satellite anomalies. The project includes the Compact Environmental Anomaly Sensor (CEASE) III, building on the flight heritage of prior CEASE designs. CEASE III consists of multiple sensor modules. High energy particles are observed using independent unique silicon detector stacks. In addition CEASE III includes an electrostatic analyzer (ESA) assembly which uses charge multiplication for particle detection. The sensors cover a wide range of proton and electron energies that contribute to satellite anomalies.

A Comparative Study of Unsupervised Anomaly Detection Techniques Using Honeypot Data

NASA Astrophysics Data System (ADS)

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

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

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

PubMed Central

Sherwin, Jason; Sajda, Paul

2013-01-01

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 cognition, with a model of the action for generating the anomaly playing a role in its detection. PMID:24056235

Detecting Anomalies from End-to-End Internet Performance Measurements (PingER) Using Cluster Based Local Outlier Factor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ali, Saqib; Wang, Guojun; Cottrell, Roger Leslie

PingER (Ping End-to-End Reporting) is a worldwide end-to-end Internet performance measurement framework. It was developed by the SLAC National Accelerator Laboratory, Stanford, USA and running from the last 20 years. It has more than 700 monitoring agents and remote sites which monitor the performance of Internet links around 170 countries of the world. At present, the size of the compressed PingER data set is about 60 GB comprising of 100,000 flat files. The data is publicly available for valuable Internet performance analyses. However, the data sets suffer from missing values and anomalies due to congestion, bottleneck links, queuing overflow, networkmore » software misconfiguration, hardware failure, cable cuts, and social upheavals. Therefore, the objective of this paper is to detect such performance drops or spikes labeled as anomalies or outliers for the PingER data set. In the proposed approach, the raw text files of the data set are transformed into a PingER dimensional model. The missing values are imputed using the k-NN algorithm. The data is partitioned into similar instances using the k-means clustering algorithm. Afterward, clustering is integrated with the Local Outlier Factor (LOF) using the Cluster Based Local Outlier Factor (CBLOF) algorithm to detect the anomalies or outliers from the PingER data. Lastly, anomalies are further analyzed to identify the time frame and location of the hosts generating the major percentage of the anomalies in the PingER data set ranging from 1998 to 2016.« less

Detecting Anomalies from End-to-End Internet Performance Measurements (PingER) Using Cluster Based Local Outlier Factor

DOE PAGES

Ali, Saqib; Wang, Guojun; Cottrell, Roger Leslie; ...

2018-05-28

PingER (Ping End-to-End Reporting) is a worldwide end-to-end Internet performance measurement framework. It was developed by the SLAC National Accelerator Laboratory, Stanford, USA and running from the last 20 years. It has more than 700 monitoring agents and remote sites which monitor the performance of Internet links around 170 countries of the world. At present, the size of the compressed PingER data set is about 60 GB comprising of 100,000 flat files. The data is publicly available for valuable Internet performance analyses. However, the data sets suffer from missing values and anomalies due to congestion, bottleneck links, queuing overflow, networkmore » software misconfiguration, hardware failure, cable cuts, and social upheavals. Therefore, the objective of this paper is to detect such performance drops or spikes labeled as anomalies or outliers for the PingER data set. In the proposed approach, the raw text files of the data set are transformed into a PingER dimensional model. The missing values are imputed using the k-NN algorithm. The data is partitioned into similar instances using the k-means clustering algorithm. Afterward, clustering is integrated with the Local Outlier Factor (LOF) using the Cluster Based Local Outlier Factor (CBLOF) algorithm to detect the anomalies or outliers from the PingER data. Lastly, anomalies are further analyzed to identify the time frame and location of the hosts generating the major percentage of the anomalies in the PingER data set ranging from 1998 to 2016.« less

Added Value of Fetal MRI in the Evaluation of Fetal Anomalies of the Corpus Callosum: A Retrospective Analysis of 78 Cases.

PubMed

Manevich-Mazor, Mirra; Weissmann-Brenner, Alina; Bar Yosef, Omer; Hoffmann, Chen; Mazor, Roei David; Mosheva, Mariela; Achiron, Reuven Ryszard; Katorza, Eldad

2018-06-07

 To evaluate the added value of fetal MRI to ultrasound in detecting and specifying callosal anomalies, and its impact on clinical decision making.  Fetuses with a sonographic diagnosis of an anomalous corpus callosum (CC) who underwent a subsequent fetal brain MRI between 2010 and 2015 were retrospectively evaluated and classified according to the severity of the findings. The findings detected on ultrasound were compared to those detected on MRI. An analysis was performed to assess whether fetal MRI altered the group classification, and thus the management of these pregnancies.  78 women were recruited following sonographic diagnoses of either complete or partial callosal agenesis, short, thin or thick CC. Normal MRI studies were obtained inµ19 cases (24 %). Among these, all children available for follow-up received an adequate adaptive score in their Vineland II adaptive behavior scale assessment. Analysis of the concordance between US and MRI demonstrated a substantial level of agreement for complete callosal agenesis (kappa: 0.742), moderate agreement for thin CC (kappa: 0.418) and fair agreement for all other callosal anomalies. Comparison between US and MRI-based mild/severe findings classifications revealed that MRI contributed to a change in the management for 28 fetuses (35.9 %), mostly (25 fetuses, 32.1 %) in favor of pregnancy preservation.  Fetal MRI effectively detects callosal anomalies and enables satisfactory validation of the presence or absence of callosal anomalies identified by ultrasound and adds valuable data that improves clinical decision making. © Georg Thieme Verlag KG Stuttgart · New York.

Dental anomalies: prevalence and associations between them in a large sample of non-orthodontic subjects, a cross-sectional study.

PubMed

Laganà, G; Venza, N; Borzabadi-Farahani, A; Fabi, F; Danesi, C; Cozza, P

2017-03-11

To analyze the prevalence and associations between dental anomalies detectable on panoramic radiographs in a sample of non-orthodontic growing subjects. For this cross-sectional study, digital panoramic radiographs of 5005 subjects were initially screened from a single radiographic center in Rome. Inclusion criteria were: subjects who were aged 8-12 years, Caucasian, and had good diagnostic quality radiographs. Syndromic subjects, those with craniofacial malformation, or orthodontic patients were excluded and this led to a sample of 4706 subjects [mean (SD) age = 9.6 (1.2) years, 2366 males and 2340 females]. Sample was subsequently divided into four subgroups (8, 9, 10, and 11-12 year-old groups). Two operators examined panoramic radiographs to observe the presence of common dental anomalies. The prevalence and associations between dental anomalies were also investigated. The overall prevalence of dental anomalies was 20.9%. Approximately, 17.9% showed only one anomaly, 2.7% two anomalies, while only 0.3% had more than two anomalies. The most frequent anomalies were the displacement of maxillary canine (7.5%), hypodontia (7.1%), impacted teeth (3.9%), tooth ankylosis (2.8%), and tooth transposition (1.4%). The lower right second premolar was the most frequent missing teeth; 3.7% had only one tooth agenesis, and 0.08% had six or more missing tooth (Oligodontia). Mesiodens was the most common type of supernumerary tooth (0.66%). Two subjects had taurodontic tooth (0.04%). Tooth transpositions and displacement of maxillary canine were seen in 1.4 and 7.5%, retrospectively (approximately 69 and 58% were in the 8 and 9 year-old groups, retrospectively). Significant associations were detected between the different dental anomalies (P < .05). The results of our study revealed significant associations among different dental anomalies and provide further evidences to support common etiological factors.

Listening to Limericks: A Pupillometry Investigation of Perceivers’ Expectancy

PubMed Central

Scheepers, Christoph; Mohr, Sibylle; Fischer, Martin H.; Roberts, Andrew M.

2013-01-01

What features of a poem make it captivating, and which cognitive mechanisms are sensitive to these features? We addressed these questions experimentally by measuring pupillary responses of 40 participants who listened to a series of Limericks. The Limericks ended with either a semantic, syntactic, rhyme or metric violation. Compared to a control condition without violations, only the rhyme violation condition induced a reliable pupillary response. An anomaly-rating study on the same stimuli showed that all violations were reliably detectable relative to the control condition, but the anomaly induced by rhyme violations was perceived as most severe. Together, our data suggest that rhyme violations in Limericks may induce an emotional response beyond mere anomaly detection. PMID:24086417

Toward Continuous GPS Carrier-Phase Time Transfer: Eliminating the Time Discontinuity at an Anomaly

PubMed Central

Yao, Jian; Levine, Judah; Weiss, Marc

2015-01-01

The wide application of Global Positioning System (GPS) carrier-phase (CP) time transfer is limited by the problem of boundary discontinuity (BD). The discontinuity has two categories. One is “day boundary discontinuity,” which has been studied extensively and can be solved by multiple methods [1–8]. The other category of discontinuity, called “anomaly boundary discontinuity (anomaly-BD),” comes from a GPS data anomaly. The anomaly can be a data gap (i.e., missing data), a GPS measurement error (i.e., bad data), or a cycle slip. Initial study of the anomaly-BD shows that we can fix the discontinuity if the anomaly lasts no more than 20 min, using the polynomial curve-fitting strategy to repair the anomaly [9]. However, sometimes, the data anomaly lasts longer than 20 min. Thus, a better curve-fitting strategy is in need. Besides, a cycle slip, as another type of data anomaly, can occur and lead to an anomaly-BD. To solve these problems, this paper proposes a new strategy, i.e., the satellite-clock-aided curve fitting strategy with the function of cycle slip detection. Basically, this new strategy applies the satellite clock correction to the GPS data. After that, we do the polynomial curve fitting for the code and phase data, as before. Our study shows that the phase-data residual is only ~3 mm for all GPS satellites. The new strategy also detects and finds the number of cycle slips by searching the minimum curve-fitting residual. Extensive examples show that this new strategy enables us to repair up to a 40-min GPS data anomaly, regardless of whether the anomaly is due to a data gap, a cycle slip, or a combination of the two. We also find that interference of the GPS signal, known as “jamming”, can possibly lead to a time-transfer error, and that this new strategy can compensate for jamming outages. Thus, the new strategy can eliminate the impact of jamming on time transfer. As a whole, we greatly improve the robustness of the GPS CP time transfer. PMID:26958451

Diagnostic value of perinatal autopsies: analysis of 486 cases.

PubMed

Neşe, Nalan; Bülbül, Yeşim

2018-02-23

Autopsy is a beneficial procedure to determine the cause of death and the frequency of anomalies in perinatal losses. Even in the event of an autopsy not providing any additional information, completion of the procedure confirming the clinical diagnoses gives reassurance to both clinicians and parents. Here we present a 15-year archival study based on findings of perinatal autopsies. Four hundred and eighty-six cases from our archive were reviewed and according to the findings they were divided into three subcategories; (1) miscarriages (MCF); (2) fetuses terminated (FTA) for vital anomalies detected by prenatal ultrasonography; (3) premature or term newborns died within first month of life (neonates: NN). Autopsies were documented and classified according to week/age of cases, anomalies and causes of abortion or death. Two hundred and twenty-six of 486 cases (46.5%) were in MCF group while 227 (46.7%) and 33 (6.8%) were of them in FTA and NN groups, respectively. In FTA group, the most frequent anomaly detected was neural tube defects. In NN group, prematurity related complications were the most common cause of death. The autopsy process was found valuable in 39.7% of all cases. We suggest that autopsy procedure is diagnostically valuable even in situations when there is USG findings that are confirming FTAs or there is no important major fetal or placental anomaly detected in MCFs.

It is not “Just Circumcision”

PubMed Central

Yesildag, Ebru

2015-01-01

Objective: Circumcision is one of the most commonly performed operations during childhood. The procedure is often underestimated in areas where it is frequently executed due to social and religion-based indications. In fact it might be an opportunity to detect and to correct any existing penile anomaly. The aim of the study was to retrospectively evaluate the boys who were admitted to a hospital for circumcision and the outcome of the procedure. Methods: The boys who were brought to outpatient clinics for circumcision between 2009-2015, were retrospectively evaluated. The indications for hospital admission and the presence of associated penile anomalies were searched. All the boys were examined and operated by a single surgeon of the institution. Results: Nine hundred forty four boys were brought to pediatric surgery outpatient clinics in order to be circumcised. The operation was performed in 318 of them. The physical examination revealed penile anomalies in 29 of the 318 cases. The detected anomalies were webbed penis, penile torsion, hypospadias, chordee without hypospadias and meatal stenosis. Conclusions: The proper examination of the boys by a physician prior to circumcision provides the detection of penile anomalies which can be corrected at the same session. The arrangements for performing circumcision in hospitals by the medical staff should be favored. The misleading perception of underestimation of the procedure where it is ritually performed, should be corrected. PMID:26430441

The characteristics of hydrothermal plumes observed in the Precious Stone Mountain hydrothermal field, the Galapagos spreading center

NASA Astrophysics Data System (ADS)

Chen, S.; Tao, C.; Li, H.; Zhou, J.; Deng, X.; Tao, W.; Zhang, G.; Liu, W.; He, Y.

2014-12-01

The Precious Stone Mountain hydrothermal field (PSMHF) is located on the southern rim of the Galapagos Microplate. It was found at the 3rd leg of the 2009 Chinese DY115-21 expedition on board R/V Dayangyihao. It is efficient to learn the distribution of hydrothermal plumes and locate the hydrothermal vents by detecting the anomalies of turbidity and temperature. Detecting seawater turbidity by MAPR based on deep-tow technology is established and improved during our cruises. We collected data recorded by MAPR and information from geological sampling, yielding the following results: (1)Strong hydrothermal turbidity and temperature anomalies were recorded at 1.23°N, southeast and northwest of PSMHF. According to the CTD data on the mooring system, significant temperature anomalies were observed over PSMHF at the depth of 1,470 m, with anomalies range from 0.2℃ to 0.4℃, which gave another evidence of the existence of hydrothermal plume. (2)At 1.23°N (101.4802°W/1.2305°N), the nose-shaped particle plume was concentrated at a depth interval of 1,400-1,600 m, with 200 m thickness and an east-west diffusion range of 500 m. The maximum turbidity anomaly (0.045 △NTU) was recorded at the depth of 1,500 m, while the background anomaly was about 0.01△NTU. A distinct temperature anomaly was also detected at the seafloor near 1.23°N. Deep-tow camera showed the area was piled up by hydrothermal sulfide sediments. (3) In the southeast (101.49°W/1.21°N), the thickness of hydrothermal plume was 300 m and it was spreading laterally at a depth of 1,500-1,800 m, for a distance about 800 m. The maximum turbidity anomaly of nose-shaped plume is about 0.04 △NTU at the depth of 1,600 m. Distinct temperature anomaly was also detected in the northwest (101.515°W/1.235°N). (4) Terrain and bottom current were the main factors controlling the distribution of hydrothermal plume. Different from the distribution of hydrothermal plumes on the mid-ocean ridges, which was mostly effected by seafloor topography, the terrain of the PSMHF was relatively flat, so the impact was negligible. Southwest direction bottom current at the speed of 0.05 m/s in PSMHF had a great influence on the distribution and spreading direction of hydrothermal plume. Keyword: hydrothermal plume, Precious Stone Mountain hydrothermal field, turbidity

Enhanced situational awareness in the maritime domain: an agent-based approach for situation management

NASA Astrophysics Data System (ADS)

Brax, Christoffer; Niklasson, Lars

2009-05-01

Maritime Domain Awareness is important for both civilian and military applications. An important part of MDA is detection of unusual vessel activities such as piracy, smuggling, poaching, collisions, etc. Today's interconnected sensorsystems provide us with huge amounts of information over large geographical areas which can make the operators reach their cognitive capacity and start to miss important events. We propose and agent-based situation management system that automatically analyse sensor information to detect unusual activity and anomalies. The system combines knowledge-based detection with data-driven anomaly detection. The system is evaluated using information from both radar and AIS sensors.

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

PubMed Central

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

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

PubMed

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.

Magnetic Anomaly Detection by Remote Means

DTIC Science & Technology

2016-09-21

REFERENCES 1. W. Happer, "Laser Remote Sensing of Magnetic Fields in the Atmosphere by Two-Photon Optical Pumping of Xe 129,” , NADC Report N62269-78-M...by Remote Means 5b. GRANT NUMBER NOOO 14-13-1-0282 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER Miles , Richard and Dogariu...unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT Research on the possibility of detecting magnetic anomalies remotely using laser excitation of a

Detecting Anomalies in Process Control Networks

NASA Astrophysics Data System (ADS)

Rrushi, Julian; Kang, Kyoung-Don

This paper presents the estimation-inspection algorithm, a statistical algorithm for anomaly detection in process control networks. The algorithm determines if the payload of a network packet that is about to be processed by a control system is normal or abnormal based on the effect that the packet will have on a variable stored in control system memory. The estimation part of the algorithm uses logistic regression integrated with maximum likelihood estimation in an inductive machine learning process to estimate a series of statistical parameters; these parameters are used in conjunction with logistic regression formulas to form a probability mass function for each variable stored in control system memory. The inspection part of the algorithm uses the probability mass functions to estimate the normalcy probability of a specific value that a network packet writes to a variable. Experimental results demonstrate that the algorithm is very effective at detecting anomalies in process control networks.

Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

NASA Astrophysics Data System (ADS)

Nawir, Mukrimah; Amir, Amiza; Lynn, Ong Bi; Yaakob, Naimah; Badlishah Ahmad, R.

2018-05-01

The rapid growth of technologies might endanger them to various network attacks due to the nature of data which are frequently exchange their data through Internet and large-scale data that need to be handle. Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. Several issues regarding these available labelled network datasets are discussed in this paper. The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. The finding showed that AODE algorithm is performed well in term of accuracy and processing time for binary classification towards UNSW-NB15 dataset.

Thermal surveillance of active volcanoes

NASA Technical Reports Server (NTRS)

Friedman, J. D. (Principal Investigator)

1973-01-01

The author has identified the following significant results. There are three significant scientific results of the discovery of 48 pinpoint anomalies on the upper flanks of Mt. Rainier: (1) Many of these points may actually be the location of fumarolic vapor emission or warm ground considerably below the summit crater. (2) Discovery of these small anomalies required specific V/H scanner settings for precise elevation on Mt. Rainier's flank, to avoid smearing the anomalies to the point of nonrecognition. Several past missions flown to map the thermal anomalies of the summit area did not/detect the flank anomalies. (3) This illustrates the value of the aerial IR scanner as a geophysical tool suited to specific problem-oriented missions, in contrast to its more general value in a regional or reconnaissance anomaly-mapping role.

Time Series Discord Detection in Medical Data using a Parallel Relational Database

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodbridge, Diane; Rintoul, Mark Daniel; Wilson, Andrew T.

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithmsmore » on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.« less

Time Series Discord Detection in Medical Data using a Parallel Relational Database [PowerPoint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodbridge, Diane; Wilson, Andrew T.; Rintoul, Mark Daniel

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithmsmore » on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.« less

Continental and oceanic magnetic anomalies: Enhancement through GRM

NASA Technical Reports Server (NTRS)

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

1985-01-01

In contrast to the POGO and MAGSAT satellites, the Geopotential Research Mission (GRM) satellite system will orbit at a minimum elevation to provide significantly better resolved lithospheric magnetic anomalies for more detailed and improved geologic analysis. In addition, GRM will measure corresponding gravity anomalies to enhance our understanding of the gravity field for vast regions of the Earth which are largely inaccessible to more conventional surface mapping. Crustal studies will greatly benefit from the dual data sets as modeling has shown that lithospheric sources of long wavelength magnetic anomalies frequently involve density variations which may produce detectable gravity anomalies at satellite elevations. Furthermore, GRM will provide an important replication of lithospheric magnetic anomalies as an aid to identifying and extracting these anomalies from satellite magnetic measurements. The potential benefits to the study of the origin and characterization of the continents and oceans, that may result from the increased GRM resolution are examined.

Maternal psychological responses during pregnancy after ultrasonographic detection of structural fetal anomalies: A prospective longitudinal observational study

PubMed Central

Kaasen, Anne; Helbig, Anne; Malt, Ulrik F.; Næs, Tormod; Skari, Hans; Haugen, Guttorm

2017-01-01

In this longitudinal prospective observational study performed at a tertiary perinatal referral centre, we aimed to assess maternal distress in pregnancy in women with ultrasound findings of fetal anomaly and compare this with distress in pregnant women with normal ultrasound findings. Pregnant women with a structural fetal anomaly (n = 48) and normal ultrasound (n = 105) were included. We administered self-report questionnaires (General Health Questionnaire-28, Impact of Event Scale-22 [IES], and Edinburgh Postnatal Depression Scale) a few days following ultrasound detection of a fetal anomaly or a normal ultrasound (T1), 3 weeks post-ultrasound (T2), and at 30 (T3) and 36 weeks gestation (T4). Social dysfunction, health perception, and psychological distress (intrusion, avoidance, arousal, anxiety, and depression) were the main outcome measures. The median gestational age at T1 was 20 and 19 weeks in the group with and without fetal anomaly, respectively. In the fetal anomaly group, all psychological distress scores were highest at T1. In the group with a normal scan, distress scores were stable throughout pregnancy. At all assessments, the fetal anomaly group scored significantly higher (especially on depression-related questions) compared to the normal scan group, except on the IES Intrusion and Arousal subscales at T4, although with large individual differences. In conclusion, women with a known fetal anomaly initially had high stress scores, which gradually decreased, resembling those in women with a normal pregnancy. Psychological stress levels were stable and low during the latter half of gestation in women with a normal pregnancy. PMID:28350879

Prevalence of dental anomalies in Saudi orthodontic patients.

PubMed

Al-Jabaa, Aljazi H; Aldrees, Abdullah M

2013-07-01

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

Detection of Anomalous Insiders in Collaborative Environments via Relational Analysis of Access Logs

PubMed Central

Chen, You; Malin, Bradley

2014-01-01

Collaborative information systems (CIS) are deployed within a diverse array of environments, ranging from the Internet to intelligence agencies to healthcare. It is increasingly the case that such systems are applied to manage sensitive information, making them targets for malicious insiders. While sophisticated security mechanisms have been developed to detect insider threats in various file systems, they are neither designed to model nor to monitor collaborative environments in which users function in dynamic teams with complex behavior. In this paper, we introduce a community-based anomaly detection system (CADS), an unsupervised learning framework to detect insider threats based on information recorded in the access logs of collaborative environments. CADS is based on the observation that typical users tend to form community structures, such that users with low a nity to such communities are indicative of anomalous and potentially illicit behavior. The model consists of two primary components: relational pattern extraction and anomaly detection. For relational pattern extraction, CADS infers community structures from CIS access logs, and subsequently derives communities, which serve as the CADS pattern core. CADS then uses a formal statistical model to measure the deviation of users from the inferred communities to predict which users are anomalies. To empirically evaluate the threat detection model, we perform an analysis with six months of access logs from a real electronic health record system in a large medical center, as well as a publicly-available dataset for replication purposes. The results illustrate that CADS can distinguish simulated anomalous users in the context of real user behavior with a high degree of certainty and with significant performance gains in comparison to several competing anomaly detection models. PMID:25485309

Magnetic Resonance Imaging of Developmental Anomalies of the Uterus and the Vagina in Pediatric Patients.

PubMed

Gould, Sharon W; Epelman, Monica

2015-08-01

Developmental anomalies of the uterus and the vagina are associated with infertility and miscarriage and are most commonly detected in the postpubertal age-group. These conditions may also present in younger patients as a mass or pain owing to obstruction of the uterus or the vagina. Associated urinary tract anomalies are common, as well. Accurate diagnosis and thorough description of these anomalies is essential for appropriate management; however, evaluation may be difficult in an immature reproductive tract. Magnetic resonance imaging technique pertinent to imaging of the pediatric female reproductive tract is presented and illustrated along with the findings associated with various anomalies. Copyright © 2015 Elsevier Inc. All rights reserved.

An Unsupervised Anomalous Event Detection and Interactive Analysis Framework for Large-scale Satellite Data

NASA Astrophysics Data System (ADS)

LIU, Q.; Lv, Q.; Klucik, R.; Chen, C.; Gallaher, D. W.; Grant, G.; Shang, L.

2016-12-01

Due to the high volume and complexity of satellite data, computer-aided tools for fast quality assessments and scientific discovery are indispensable for scientists in the era of Big Data. In this work, we have developed a framework for automated anomalous event detection in massive satellite data. The framework consists of a clustering-based anomaly detection algorithm and a cloud-based tool for interactive analysis of detected anomalies. The algorithm is unsupervised and requires no prior knowledge of the data (e.g., expected normal pattern or known anomalies). As such, it works for diverse data sets, and performs well even in the presence of missing and noisy data. The cloud-based tool provides an intuitive mapping interface that allows users to interactively analyze anomalies using multiple features. As a whole, our framework can (1) identify outliers in a spatio-temporal context, (2) recognize and distinguish meaningful anomalous events from individual outliers, (3) rank those events based on "interestingness" (e.g., rareness or total number of outliers) defined by users, and (4) enable interactively query, exploration, and analysis of those anomalous events. In this presentation, we will demonstrate the effectiveness and efficiency of our framework in the application of detecting data quality issues and unusual natural events using two satellite datasets. The techniques and tools developed in this project are applicable for a diverse set of satellite data and will be made publicly available for scientists in early 2017.

Classification of SD-OCT volumes for DME detection: an anomaly detection approach

NASA Astrophysics Data System (ADS)

Sankar, S.; Sidibé, D.; Cheung, Y.; Wong, T. Y.; Lamoureux, E.; Milea, D.; Meriaudeau, F.

2016-03-01

Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binary Pattern (LBP) features are considered. The dimensionality of the extracted features is reduced using PCA. As the last stage, a GMM is fitted with features from normal volumes. During testing, features extracted from the test volume are evaluated with the fitted model for anomaly and classification is made based on the number of B-scans detected as outliers. The proposed method is tested on two OCT datasets achieving a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, experiments show that the proposed method achieves better classification performances than other recently published works.

Radon anomalies: When are they possible to be detected?

NASA Astrophysics Data System (ADS)

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 rate, but the results are strongly dependent on the length of the time window and/or type of frequency filtering. More importantly, when raw time-series contain cyclic components (e.g. seasonal or diurnal variation), the quest of anomalies related to transients becomes meaningless. We conclude that an objective identification of transient changes can be performed only after filtering the raw time-series for the physically meaningful frequency content.

Inflight and Preflight Detection of Pitot Tube Anomalies

NASA Technical Reports Server (NTRS)

Mitchell, Darrell W.

2014-01-01

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

Infrasonic Influences of Tornados and Cyclonic Weather Systems

NASA Astrophysics Data System (ADS)

Cook, Tessa

2014-03-01

Infrasound waves travel through the air at approximately 340 m/s at sea level, while experiencing low levels of friction, allowing the waves to travel over larger distances. When seismic waves travel through unconsolidated soil, the waves slow down to approximately 340 m/s. Because the speeds of waves in the air and ground are similar, a more effective transfer of energy from the atmosphere to the ground can occur. Large ring lasers can be utilized for detecting sources of infrasound traveling through the ground by measuring anomalies in the frequency difference between their two counter-rotating beams. Sources of infrasound include tornados and other cyclonic weather systems. The way systems create waves that transfer to the ground is unknown and will be continued in further research; this research has focused on attempting to isolate the time that the ring laser detected anomalies in order to investigate if these anomalies may be contributed to isolatable weather systems. Furthermore, this research analyzed the frequencies detected in each of the anomalies and compared the frequencies with various characteristics of each weather system, such as tornado width, wind speeds, and system development. This research may be beneficial for monitoring gravity waves and weather systems.

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

PubMed

Yassin, Syed M

2016-12-01

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

Infrared Contrast Analysis Technique for Flash Thermography Nondestructive Evaluation

NASA Technical Reports Server (NTRS)

Koshti, Ajay

2014-01-01

The paper deals with the infrared flash thermography inspection to detect and analyze delamination-like anomalies in nonmetallic materials. It provides information on an IR Contrast technique that involves extracting normalized contrast verses time evolutions from the flash thermography infrared video data. The paper provides the analytical model used in the simulation of infrared image contrast. The contrast evolution simulation is achieved through calibration on measured contrast evolutions from many flat bottom holes in the subject material. The paper also provides formulas to calculate values of the thermal measurement features from the measured contrast evolution curve. Many thermal measurement features of the contrast evolution that relate to the anomaly characteristics are calculated. The measurement features and the contrast simulation are used to evaluate flash thermography inspection data in order to characterize the delamination-like anomalies. In addition, the contrast evolution prediction is matched to the measured anomaly contrast evolution to provide an assessment of the anomaly depth and width in terms of depth and diameter of the corresponding equivalent flat-bottom hole (EFBH) or equivalent uniform gap (EUG). The paper provides anomaly edge detection technique called the half-max technique which is also used to estimate width of an indication. The EFBH/EUG and half-max width estimations are used to assess anomaly size. The paper also provides some information on the "IR Contrast" software application, half-max technique and IR Contrast feature imaging application, which are based on models provided in this paper.

Hyperspectral processing in graphical processing units

NASA Astrophysics Data System (ADS)

Winter, Michael E.; Winter, Edwin M.

2011-06-01

With the advent of the commercial 3D video card in the mid 1990s, we have seen an order of magnitude performance increase with each generation of new video cards. While these cards were designed primarily for visualization and video games, it became apparent after a short while that they could be used for scientific purposes. These Graphical Processing Units (GPUs) are rapidly being incorporated into data processing tasks usually reserved for general purpose computers. It has been found that many image processing problems scale well to modern GPU systems. We have implemented four popular hyperspectral processing algorithms (N-FINDR, linear unmixing, Principal Components, and the RX anomaly detection algorithm). These algorithms show an across the board speedup of at least a factor of 10, with some special cases showing extreme speedups of a hundred times or more.

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

NASA Astrophysics Data System (ADS)

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.

Visual saliency detection based on modeling the spatial Gaussianity

NASA Astrophysics Data System (ADS)

Ju, Hongbin

2015-04-01

In this paper, a novel salient object detection method based on modeling the spatial anomalies is presented. The proposed framework is inspired by the biological mechanism that human eyes are sensitive to the unusual and anomalous objects among complex background. It is supposed that a natural image can be seen as a combination of some similar or dissimilar basic patches, and there is a direct relationship between its saliency and anomaly. Some patches share high degree of similarity and have a vast number of quantity. They usually make up the background of an image. On the other hand, some patches present strong rarity and specificity. We name these patches "anomalies". Generally, anomalous patch is a reflection of the edge or some special colors and textures in an image, and these pattern cannot be well "explained" by their surroundings. Human eyes show great interests in these anomalous patterns, and will automatically pick out the anomalous parts of an image as the salient regions. To better evaluate the anomaly degree of the basic patches and exploit their nonlinear statistical characteristics, a multivariate Gaussian distribution saliency evaluation model is proposed. In this way, objects with anomalous patterns usually appear as the outliers in the Gaussian distribution, and we identify these anomalous objects as salient ones. Experiments are conducted on the well-known MSRA saliency detection dataset. Compared with other recent developed visual saliency detection methods, our method suggests significant advantages.

Hot spots of multivariate extreme anomalies in Earth observations

NASA Astrophysics Data System (ADS)

Flach, M.; Sippel, S.; Bodesheim, P.; Brenning, A.; Denzler, J.; Gans, F.; Guanche, Y.; Reichstein, M.; Rodner, E.; Mahecha, M. D.

2016-12-01

Anomalies in Earth observations might indicate data quality issues, extremes or the change of underlying processes within a highly multivariate system. Thus, considering the multivariate constellation of variables for extreme detection yields crucial additional information over conventional univariate approaches. We highlight areas in which multivariate extreme anomalies are more likely to occur, i.e. hot spots of extremes in global atmospheric Earth observations that impact the Biosphere. In addition, we present the year of the most unusual multivariate extreme between 2001 and 2013 and show that these coincide with well known high impact extremes. Technically speaking, we account for multivariate extremes by using three sophisticated algorithms adapted from computer science applications. Namely an ensemble of the k-nearest neighbours mean distance, a kernel density estimation and an approach based on recurrences is used. However, the impact of atmosphere extremes on the Biosphere might largely depend on what is considered to be normal, i.e. the shape of the mean seasonal cycle and its inter-annual variability. We identify regions with similar mean seasonality by means of dimensionality reduction in order to estimate in each region both the `normal' variance and robust thresholds for detecting the extremes. In addition, we account for challenges like heteroscedasticity in Northern latitudes. Apart from hot spot areas, those anomalies in the atmosphere time series are of particular interest, which can only be detected by a multivariate approach but not by a simple univariate approach. Such an anomalous constellation of atmosphere variables is of interest if it impacts the Biosphere. The multivariate constellation of such an anomalous part of a time series is shown in one case study indicating that multivariate anomaly detection can provide novel insights into Earth observations.

A Healthcare Utilization Analysis Framework for Hot Spotting and Contextual Anomaly Detection

PubMed Central

Hu, Jianying; Wang, Fei; Sun, Jimeng; Sorrentino, Robert; Ebadollahi, Shahram

2012-01-01

Patient medical records today contain vast amount of information regarding patient conditions along with treatment and procedure records. Systematic healthcare resource utilization analysis leveraging such observational data can provide critical insights to guide resource planning and improve the quality of care delivery while reducing cost. Of particular interest to providers are hot spotting: the ability to identify in a timely manner heavy users of the systems and their patterns of utilization so that targeted intervention programs can be instituted, and anomaly detection: the ability to identify anomalous utilization cases where the patients incurred levels of utilization that are unexpected given their clinical characteristics which may require corrective actions. Past work on medical utilization pattern analysis has focused on disease specific studies. We present a framework for utilization analysis that can be easily applied to any patient population. The framework includes two main components: utilization profiling and hot spotting, where we use a vector space model to represent patient utilization profiles, and apply clustering techniques to identify utilization groups within a given population and isolate high utilizers of different types; and contextual anomaly detection for utilization, where models that map patient’s clinical characteristics to the utilization level are built in order to quantify the deviation between the expected and actual utilization levels and identify anomalies. We demonstrate the effectiveness of the framework using claims data collected from a population of 7667 diabetes patients. Our analysis demonstrates the usefulness of the proposed approaches in identifying clinically meaningful instances for both hot spotting and anomaly detection. In future work we plan to incorporate additional sources of observational data including EMRs and disease registries, and develop analytics models to leverage temporal relationships among medical encounters to provide more in-depth insights. PMID:23304306

A healthcare utilization analysis framework for hot spotting and contextual anomaly detection.

PubMed

Hu, Jianying; Wang, Fei; Sun, Jimeng; Sorrentino, Robert; Ebadollahi, Shahram

2012-01-01

Patient medical records today contain vast amount of information regarding patient conditions along with treatment and procedure records. Systematic healthcare resource utilization analysis leveraging such observational data can provide critical insights to guide resource planning and improve the quality of care delivery while reducing cost. Of particular interest to providers are hot spotting: the ability to identify in a timely manner heavy users of the systems and their patterns of utilization so that targeted intervention programs can be instituted, and anomaly detection: the ability to identify anomalous utilization cases where the patients incurred levels of utilization that are unexpected given their clinical characteristics which may require corrective actions. Past work on medical utilization pattern analysis has focused on disease specific studies. We present a framework for utilization analysis that can be easily applied to any patient population. The framework includes two main components: utilization profiling and hot spotting, where we use a vector space model to represent patient utilization profiles, and apply clustering techniques to identify utilization groups within a given population and isolate high utilizers of different types; and contextual anomaly detection for utilization, where models that map patient's clinical characteristics to the utilization level are built in order to quantify the deviation between the expected and actual utilization levels and identify anomalies. We demonstrate the effectiveness of the framework using claims data collected from a population of 7667 diabetes patients. Our analysis demonstrates the usefulness of the proposed approaches in identifying clinically meaningful instances for both hot spotting and anomaly detection. In future work we plan to incorporate additional sources of observational data including EMRs and disease registries, and develop analytics models to leverage temporal relationships among medical encounters to provide more in-depth insights.

Dispersive Phase in the L-band InSAR Image Associated with Heavy Rain Episodes

NASA Astrophysics Data System (ADS)

Furuya, M.; Kinoshita, Y.

2017-12-01

Interferometric synthetic aperture radar (InSAR) is a powerful geodetic technique that allows us to detect ground displacements with unprecedented spatial resolution, and has been used to detect displacements due to earthquakes, volcanic eruptions, and glacier motion. In the meantime, due to the microwave propagation through ionosphere and troposphere, we often encounter non-negligible phase anomaly in InSAR data. Correcting for the ionsphere and troposphere is therefore a long-standing issue for high-precision geodetic measurements. However, if ground displacements are negligible, InSAR image can tell us the details of the atmosphere.Kinoshita and Furuya (2017, SOLA) detected phase anomaly in ALOS/PALSAR InSAR data associated with heavy rain over Niigata area, Japan, and performed numerical weathr model simulation to reproduce the anomaly; ALOS/PALSAR is a satellite-based L-band SAR sensor launched by JAXA in 2006 and terminated in 2011. The phase anomaly could be largely reproduced, using the output data from the weather model. However, we should note that numerical weather model outputs can only account for the non-dispersive effect in the phase anomaly. In case of severe weather event, we may expect dispersive effect that could be caused by the presence of free-electrons.In Global Navigation Satellite System (GNSS) positioning, dual frequency measurements allow us to separate the ionospheric dispersive component from tropospheric non-dispersive components. In contrast, SAR imaging is based on a single carrier frequency, and thus no operational ionospheric corrections have been performed in InSAR data analyses. Recently, Gomba et al (2016) detailed the processing strategy of split spectrum method (SSM) for InSAR, which splits the finite bandwidth of the range spectrum and virtually allows for dual-frequency measurements.We apply the L-band InSAR SSM to the heavy rain episodes, in which more than 50 mm/hour precipitations were reported. We report the presence of phase anomaly in both dispersive and non-dispersive components. While the original phase anomaly turns out to be mostly due to the non-dispersive effect, we could recognize local anomalies in the dispersive component as well. We will discuss its geophysical implications, and may show several case studies.

Magnetic anomalies in the Cosmonauts Sea, off East Antarctica

NASA Astrophysics Data System (ADS)

Nogi, Y.; Hanyu, T.; Fujii, M.

2017-12-01

Identification of magnetic anomaly lineations and fracture zone trends in the Southern Indian Ocean, are vital to understanding the breakup of Gondwana. However, the magnetic spreading anomalies and fracture zones are not clear in the Southern Indian Ocean. Magnetic anomaly lineations in the Cosmonauts Sea, off East Antarctica, are key to elucidation of separation between Sri Lanka/India and Antarctica. No obvious magnetic anomaly lineations are observed from a Japanese/German aerogeophysical survey in the Cosmonauts Sea, and this area is considered to be created by seafloor spreading during the Cretaceous Normal Superchron. Vector magnetic anomaly measurements have been conducted on board the Icebreaker Shirase mainly to understand the process of Gondwana fragmentation in the Indian Ocean. Magnetic boundary strikes are derived from vector magnetic anomalies obtained in the Cosmonauts Sea. NE-SW trending magnetic boundary strikes are mainly observed along the several NW-SE oriented observation lines with magnetic anomaly amplitudes of about 200 nT. These NE-SW trending magnetic boundary strikes possibly indicate M-series magnetic anomalies that can not be detected from the aerogeophysical survey with nearly N-S observation lines. We will discuss the magnetic spreading anomalies and breakup process between Sri Lanka/India and Antarctica in the Cosmonauts Sea.

Computing Critical Properties with Yang-Yang Anomalies

NASA Astrophysics Data System (ADS)

Orkoulas, Gerassimos; Cerdeirina, Claudio; Fisher, Michael

2017-01-01

Computation of the thermodynamics of fluids in the critical region is a challenging task owing to divergence of the correlation length and lack of particle-hole symmetries found in Ising or lattice-gas models. In addition, analysis of experiments and simulations reveals a Yang-Yang (YY) anomaly which entails sharing of the specific heat singularity between the pressure and the chemical potential. The size of the YY anomaly is measured by the YY ratio Rμ =C μ /CV of the amplitudes of C μ = - T d2 μ /dT2 and of the total specific heat CV. A ``complete scaling'' theory, in which the pressure mixes into the scaling fields, accounts for the YY anomaly. In Phys. Rev. Lett. 116, 040601 (2016), compressible cell gas (CCG) models which exhibit YY and singular diameter anomalies, have been advanced for near-critical fluids. In such models, the individual cell volumes are allowed to fluctuate. The thermodynamics of CCGs can be computed through mapping onto the Ising model via the seldom-used great grand canonical ensemble. The computations indicate that local free volume fluctuations are the origins of the YY effects. Furthermore, local energy-volume coupling (to model water) is another crucial factor underlying the phenomena.

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

DOT National Transportation Integrated Search

2016-08-01

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

A Case of Multiple Cardiovascular and Tracheal Anomalies Presented with Wolff-Parkinson-White Syndrome in a Middle-aged Adult.

PubMed

Shi, Hyejin; Sohn, Sungmin; Wang, SungHo; Park, Sungrock; Lee, SangKi; Kim, Song Yi; Jeong, Sun Young; Kim, Changhwan

2017-12-01

Congenital cardiovascular anomalies, such as dextrocardia, persistent left superior vena cava (SVC), and pulmonary artery (PA) sling, are rare disorders. These congenital anomalies can occur alone, or coincide with other congenital malformations. In the majority of cases, congenital anomalies are detected early in life by certain signs and symptoms. A 56-year-old man with no previous medical history was admitted due to recurrent wide QRS complex tachycardia with hemodynamic collapse. A chest radiograph showed dextrocardia. After synchronized cardioversion, an electrocardiogram revealed Wolff-Parkinson-White (WPW) syndrome. Persistent left SVC, PA sling, and right tracheal bronchus were also detected by a chest computed tomography (CT) scan. He was diagnosed with paroxysmal supraventricular tachycardia (PSVT) associated with WPW syndrome, and underwent radiofrequency ablation. We reported the first case of situs solitus dextrocardia coexisting with persistent left SVC, PA sling and right tracheal bronchus presented with WPW and PSVT in a middle-aged adult. In patients with a cardiovascular anomaly, clinicians should consider thorough evaluation of possibly combined cardiovascular and airway malformations and cardiac dysrhythmia. © 2017 The Korean Academy of Medical Sciences.

Investigation of Axial Electric Field Measurements with Grounded-Wire TEM Surveys

NASA Astrophysics Data System (ADS)

Zhou, Nan-nan; Xue, Guo-qiang; Li, Hai; Hou, Dong-yang

2018-01-01

The grounded-wire transient electromagnetic (TEM) surveying is often performed along the equatorial direction with its observation lines paralleling to the transmitting wire with a certain transmitter-receiver distance. However, such method takes into account only the equatorial component of the electromagnetic field, and a little effort has been made on incorporating the other major component along the transmitting wire, here denoted as axial field. To obtain a comprehensive understanding of its fundamental characteristics and guide the designing of the corresponding observation system for reliable anomaly detection, this study for the first time investigates the axial electric field from three crucial aspects, including its decay curve, plane distribution, and anomaly sensitivity, through both synthetic modeling and real application to one major coal field in China. The results demonstrate a higher sensitivity to both high- and low-resistivity anomalies by the electric field in axial direction and confirm its great potentials for robust anomaly detection in the subsurface.

Innovativ Airborne Sensors for Disaster Management

NASA Astrophysics Data System (ADS)

Altan, M. O.; Kemper, G.

2016-06-01

Modern Disaster Management Systems are based on 3 columns, crisis preparedness, early warning and the final crisis management. In all parts, special data are needed in order to analyze existing structures, assist in the early warning system and in the updating after a disaster happens to assist the crises management organizations. How can new and innovative sensors assist in these tasks? Aerial images have been frequently used in the past for generating spatial data, however in urban structures not all information can be extracted easily. Modern Oblique camera systems already assist in the evaluation of building structures to define rescue paths, analyze building structures and give also information of the stability of the urban fabric. For this application there is no need of a high geometric accurate sensor, also SLC Camera based Oblique Camera system as the OI X5, which uses Nikon Cameras, do a proper job. Such a camera also delivers worth full information after a Disaster happens to validate the degree of deformation in order to estimate stability and usability for the population. Thermal data in combination with RGB give further information of the building structure, damages and potential water intrusion. Under development is an oblique thermal sensor with 9 heads which enables nadir and oblique thermal data acquisition. Beside the application for searching people, thermal anomalies can be created out of humidity in constructions (transpiration effects), damaged power lines, burning gas tubes and many other dangerous facts. A big task is in the data analysis which should be made automatically and fast. This requires a good initial orientation and a proper relative adjustment of the single sensors. Like that, many modern software tools enable a rapid data extraction. Automated analysis of the data before and after a disaster can highlight areas of significant changes. Detecting anomalies are the way to get the focus on the prior area. Also Lidar supports Disaster management by analyzing changes in the DSM before and after the "event". Advantage of Lidar is that beside rain and clouds, no other weather conditions limit their use. As an active sensor, missions in the nighttime are possible. The new mid-format cameras that make use CMOS sensors (e.g. Phase One IXU1000) can capture data also under poor and difficult light conditions and might will be the first choice for remotely sensed data acquisition in aircrafts and UAVs. UAVs will surely be more and more part of the disaster management on the detailed level. Today equipped with video live cams using RGB and Thermal IR, they assist in looking inside buildings and behind. Thus, they can continue with the aerial survey where airborne anomalies have been detected.

RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection.

PubMed

Wu, Ke; Zhang, Kun; Fan, Wei; Edwards, Andrea; Yu, Philip S

Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS-Forest to systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request.

RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection

PubMed Central

Wu, Ke; Zhang, Kun; Fan, Wei; Edwards, Andrea; Yu, Philip S.

2015-01-01

Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS-Forest to systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request. PMID:25685112

Prevalence of dental developmental anomalies: a radiographic study.

PubMed

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

2007-09-01

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

Hierarchical Kohonenen net for anomaly detection in network security.

PubMed

Sarasamma, Suseela T; Zhu, Qiuming A; Huff, Julie

2005-04-01

A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data set in detecting anomalies. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark data are used to train the hierarchical net. We use a confidence measure to label the clusters. Then we use the test set from the same KDD Cup 1999 benchmark to test the hierarchical net. We show that a hierarchical K-Map in which each layer operates on a small subset of the feature space is superior to a single-layer K-Map operating on the whole feature space in detecting a variety of attacks in terms of detection rate as well as false positive rate.

Detecting misinformation and knowledge conflicts in relational data

NASA Astrophysics Data System (ADS)

Levchuk, Georgiy; Jackobsen, Matthew; Riordan, Brian

2014-06-01

Information fusion is required for many mission-critical intelligence analysis tasks. Using knowledge extracted from various sources, including entities, relations, and events, intelligence analysts respond to commander's information requests, integrate facts into summaries about current situations, augment existing knowledge with inferred information, make predictions about the future, and develop action plans. However, information fusion solutions often fail because of conflicting and redundant knowledge contained in multiple sources. Most knowledge conflicts in the past were due to translation errors and reporter bias, and thus could be managed. Current and future intelligence analysis, especially in denied areas, must deal with open source data processing, where there is much greater presence of intentional misinformation. In this paper, we describe a model for detecting conflicts in multi-source textual knowledge. Our model is based on constructing semantic graphs representing patterns of multi-source knowledge conflicts and anomalies, and detecting these conflicts by matching pattern graphs against the data graph constructed using soft co-reference between entities and events in multiple sources. The conflict detection process maintains the uncertainty throughout all phases, providing full traceability and enabling incremental updates of the detection results as new knowledge or modification to previously analyzed information are obtained. Detected conflicts are presented to analysts for further investigation. In the experimental study with SYNCOIN dataset, our algorithms achieved perfect conflict detection in ideal situation (no missing data) while producing 82% recall and 90% precision in realistic noise situation (15% of missing attributes).

Invesigation of prevalence of dental anomalies by using digital panoramic radiographs.

PubMed

Bilge, Nebiha Hilal; Yeşiltepe, Selin; Törenek Ağırman, Kübra; Çağlayan, Fatma; Bilge, Osman Murat

2017-09-21

This study was performed to evaluate the prevalence of all types and subtypes of dental anomalies among 6 to 40 year-old patients by using panoramic radiographs. This cross-sectional study was conducted by analyzing digital panoramic radiographs of 1200 patients admitted to our clinic in 2014. Dental anomalies were examined under 5 types and 16 subtypes. Dental anomalies were divided into five types: (a) number (including hypodontia, oligodontia and hyperdontia); (b) size (including microdontia and macrodontia); (c) structure (including amelogenesis imperfecta, dentinogenesis imperfecta and dentin dysplasia); (d) position (including transposition, ectopia, displacement, impaction and inversion); (e) shape (including fusion-gemination, dilaceration and taurodontism); RESULTS: The prevalence of dental anomalies diagnosed by panoramic radiographs was 39.2% (men (46%), women (54%)). Anomalies of position (60.8%) and shape (27.8%) were the most common types of abnormalities and anomalies of size (8.2%), structure (0.2%) and number (17%) were the least in both genders. Anomalies of impaction (45.5%), dilacerations (16.3%), hypodontia (13.8%) and taurodontism (11.2%) were the most common subtypes of dental anomalies. Taurodontism was more common in the age groups of 13-19 years. The age range of the most frequent of all other anomalies was 20-29. Anomalies of tooth position were the most common type of dental anomalies and structure anomalies were the least in this Turkish dental population. The frequency and type of dental anomalies vary within and between populations, confirming the role of racial factors in the prevalence of dental anomalies. Digital panoramic radiography is a very useful method for the detection of dental anomalies.

Tropical Forest Backscatter Anomaly Evident in SeaWinds Scatterometer Morning Overpass Data During 2005 Drought in Amazonia

NASA Astrophysics Data System (ADS)

Frolking, S. E.; Milliman, T.; Palace, M. W.; Wisser, D.; Lammers, R. B.; Fahnestock, M. A.

2010-12-01

A severe drought occurred in many portions of Amazonia in the dry season (June-September) of 2005. We analyzed ten years (7/99-10/09) of SeaWinds active microwave Ku-band backscatter data collected over the Amazon Basin, developing a monthly climatology and monthly anomalies from that climatology in an effort to detect landscape responses to this drought. We compared these to seasonal accumulating water deficit anomalies generated using Tropical Rainfall Monitoring Mission (TRMM) precipitation data (1999-2009) and 100 mm/mo evapotranspirative demand as a water deficit threshold. There was significant interannual variability in monthly mean backscatter only for ascending (early morning) overpass data, and little interannual variability in monthly mean backscatter for descending (late afternoon) overpass data. Strong negative anomalies in both ascending-overpass backscatter and accumulating water deficit developed during July-October 2005, centered on the southwestern Amazon Basin (Acre and western Amazonas states in Brazil; Madre de Dios state in Peru; Pando state in Bolivia). During the 2005 drought, there was a strong spatial correlation between morning overpass backscatter anomalies and water deficit anomalies. We hypothesize that as the drought persisted over several months, the forest canopy was increasingly unable to recover full leaf moisture content over night, and the early morning overpass backscatter data became anomalously low. This is the first reporting of tropical wet forest seasonal drought detection by active microwave scatterometry.

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

PubMed Central

2016-01-01

Background Dental anomalies are not an unusual finding in routine dental examination. The effect of dental anomalies can lead to functional, esthetic and occlusal problems. The Purpose of the study was to determine the prevalence and distribution of selected developmental dental anomalies in Saudi children. Material and Methods The study was based on clinical examination and Panoramic radiographs of children who visited the Pediatric dentistry clinics at King Khalid University College of Dentistry, Saudi Arabia. These patients were examined for dental anomalies in size, shape, number, structure and position. Data collected were entered and analyzed using statistical package for social sciences version. Results Of the 1252 children (638 Boys, 614 girls) examined, 318 subjects (25.39%) presented with selected dental anomalies. The distribution by gender was 175 boys (27.42%) and 143 girls (23.28%). On intergroup comparison, number anomalies was the most common anomaly with Hypodontia (9.7%) being the most common anomaly in Saudi children, followed by hyperdontia (3.5%). The Prevalence of size anomalies were Microdontia (2.6%) and Macrodontia (1.8%). The prevalence of Shape anomalies were Talon cusp (1.4%), Taurodontism (1.4%), Fusion (0.8%).The prevalence of Positional anomalies were Ectopic eruption (2.3%) and Rotation (0.4%). The prevalence of structural anomalies were Amelogenesis imperfecta (0.3%) Dentinogenesis imperfecta (0.1%). Conclusions A significant number of children had dental anomaly with Hypodontia being the most common anomaly and Dentinogenesis imperfecta being the rare anomaly in the study. Early detection and management of these anomalies can avoid potential orthodontic and esthetic problems in a child. Key words:Dental anomalies, children, Saudi Arabia. PMID:27957258

Deep belief networks for false alarm rejection in forward-looking ground-penetrating radar

NASA Astrophysics Data System (ADS)

Becker, John; Havens, Timothy C.; Pinar, Anthony; Schulz, Timothy J.

2015-05-01

Explosive hazards are one of the most deadly threats in modern conflicts. The U.S. Army is interested in a reliable way to detect these hazards at range. A promising way of accomplishing this task is using a forward-looking ground-penetrating radar (FLGPR) system. Recently, the Army has been testing a system that utilizes both L-band and X-band radar arrays on a vehicle mounted platform. Using data from this system, we sought to improve the performance of a constant false-alarm-rate (CFAR) prescreener through the use of a deep belief network (DBN). DBNs have also been shown to perform exceptionally well at generalized anomaly detection. They combine unsupervised pre-training with supervised fine-tuning to generate low-dimensional representations of high-dimensional input data. We seek to take advantage of these two properties by training a DBN on the features of the CFAR prescreener's false alarms (FAs) and then use that DBN to separate FAs from true positives. Our analysis shows that this method improves the detection statistics significantly. By training the DBN on a combination of image features, we were able to significantly increase the probability of detection while maintaining a nominal number of false alarms per square meter. Our research shows that DBNs are a good candidate for improving detection rates in FLGPR systems.

[Significance of bad habits in orthodontics].

PubMed

Tarján, Ildikó

2002-08-01

The author is concerned with the etiological role of bad habits in the development. Disturbances caused by pacifier habits, finger sucking, various forms of swallowing habits and their therapeutical possibilities are discussed. The role of mouth breathing, nail biting, bruxism and self-mutilation in development of anomalies and their therapy are also mentioned. The attention is called to the fact that dentists have responsibility and task to diagnose as early as can be the oral bad habits and that the adequate therapy in time in co-operation with other specialists helping the child get out of bad habits, preventing the development of severe anomaly.

Steganography anomaly detection using simple one-class classification

NASA Astrophysics Data System (ADS)

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.

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

DTIC Science & Technology

2014-03-27

20 Intrusion Detection...alarms ( Rem ). ............................................................................................................. 86 Figure 25. TP% for...literature concerning the focus areas of this research. The focus areas include SCADA vulnerabilities, information theory, and intrusion detection

Development of an Intelligent Monitoring System for Geological Carbon Sequestration (GCS) Systems

NASA Astrophysics Data System (ADS)

Sun, A. Y.; Jeong, H.; Xu, W.; Hovorka, S. D.; Zhu, T.; Templeton, T.; Arctur, D. K.

2016-12-01

To provide stakeholders timely evidence that GCS repositories are operating safely and efficiently requires integrated monitoring to assess the performance of the storage reservoir as the CO2 plume moves within it. As a result, GCS projects can be data intensive, as a result of proliferation of digital instrumentation and smart-sensing technologies. GCS projects are also resource intensive, often requiring multidisciplinary teams performing different monitoring, verification, and accounting (MVA) tasks throughout the lifecycle of a project to ensure secure containment of injected CO2. How to correlate anomaly detected by a certain sensor to events observed by other devices to verify leakage incidents? How to optimally allocate resources for task-oriented monitoring if reservoir integrity is in question? These are issues that warrant further investigation before real integration can take place. In this work, we are building a web-based, data integration, assimilation, and learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS will be an intelligent monitoring system (IMS) for automating GCS closed-loop management by leveraging recent developments in high-throughput database, complex event processing, data assimilation, and machine learning technologies. Results will be demonstrated using realistic data and model derived from a GCS site.

A recurrent neural network for classification of unevenly sampled variable stars

NASA Astrophysics Data System (ADS)

Naul, Brett; Bloom, Joshua S.; Pérez, Fernando; van der Walt, Stéfan

2018-02-01

Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time (`light curves'). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally due to intranight cadence choices as well as diurnal and seasonal constraints1-5. With nightly observations of millions of variable stars and transients from upcoming surveys4,6, efficient and accurate discovery and classification techniques on noisy, irregularly sampled data must be employed with minimal human-in-the-loop involvement. Machine learning for inference tasks on such data traditionally requires the laborious hand-coding of domain-specific numerical summaries of raw data (`features')7. Here, we present a novel unsupervised autoencoding recurrent neural network8 that makes explicit use of sampling times and known heteroskedastic noise properties. When trained on optical variable star catalogues, this network produces supervised classification models that rival other best-in-class approaches. We find that autoencoded features learned in one time-domain survey perform nearly as well when applied to another survey. These networks can continue to learn from new unlabelled observations and may be used in other unsupervised tasks, such as forecasting and anomaly detection.

Frequency of developmental dental anomalies in the Indian population.

PubMed

Guttal, Kruthika S; Naikmasur, Venkatesh G; Bhargava, Puneet; Bathi, Renuka J

2010-07-01

To evaluate the frequency of developmental dental anomalies in the Indian population. This prospective study was conducted over a period of 1 year and comprised both clinical and radiographic examinations in oral medicine and radiology outpatient department. Adult patients were screened for the presence of dental anomalies with appropriate radiographs. A comprehensive clinical examination was performed to detect hyperdontia, talon cusp, fused teeth, gemination, concrescence, hypodontia, dens invaginatus, dens evaginatus, macro- and microdontia and taurodontism. Patients with syndromes were not included in the study. Of the 20,182 patients screened, 350 had dental anomalies. Of these, 57.43% of anomalies occurred in male patients and 42.57% occurred in females. Hyperdontia, root dilaceration, peg-shaped laterals (microdontia), and hypodontia were more frequent compared to other dental anomalies of size and shape. Dental anomalies are clinically evident abnormalities. They may be the cause of various dental problems. Careful observation and appropriate investigations are required to diagnose the condition and institute treatment.

Multimodal noninvasive and invasive imaging of extracranial venous abnormalities indicative of CCSVI: Results of the PREMiSe pilot study

PubMed Central

2013-01-01

Background There is no established noninvasive or invasive diagnostic imaging modality at present that can serve as a ‘gold standard’ or “benchmark” for the detection of the venous anomalies, indicative of chronic cerebrospinal venous insufficiency (CCSVI). We investigated the sensitivity and specificity of 2 invasive vs. 2 noninvasive imaging techniques for the detection of extracranial venous anomalies in the internal jugular veins (IJVs) and azygos vein/vertebral veins (VVs) in patients with multiple sclerosis (MS). Methods The data for this multimodal imaging comparison pilot study was collected in phase 2 of the “Prospective Randomized Endovascular therapy in Multiple Sclerosis” (PREMiSe) study using standardized imaging techniques. Thirty MS subjects were screened initially with Doppler sonography (DS), out of which 10 did not fulfill noninvasive screening procedure requirements on DS that consisted of ≥2 venous hemodynamic extracranial criteria. Accordingly, 20 MS patients with relapsing MS were enrolled into the multimodal diagnostic imaging study. For magnetic resonance venography (MRV), IJVs abnormal findings were considered absent or pinpoint flow, whereas abnormal VVs flow was classified as absent. Abnormalities of the VVs were determined only using non-invasive testing. Catheter venography (CV) was considered abnormal when ≥50% lumen restriction was detected, while intravascular ultrasound (IVUS) was considered abnormal when ≥50% restriction of the lumen or intra-luminal defects or reduced pulsatility was found. Non-invasive and invasive imaging modality comparisons between left, right and total IJVs and between the VVs and azygos vein were performed. Because there is no reliable way of non-invasively assessing the azygos vein, the VVs abnormalities detected by the non-invasive testing were compared to the azygos abnormalities detected by the invasive testing. All image modalities were analyzed in a blinded manner by more than one viewer, upon which consensus was reached. The sensitivity and specificity were calculated using contingency tables denoting the presence or absence of vein-specific abnormality findings between all imaging modalities used individually as the benchmark. Results The sensitivity of CV + IVUS was 68.4% for the right and 90% for the left IJV and 85.7% for the azygos vein/VVs, compared to venous anomalies detected on DS. Compared to the venous anomalies detected on MRV, the sensitivity of CV + IVUS was 71.4% in right and 100% in left IJVs and 100% in the azygos vein/VVs; however, the specificity was 38.5%, 38.9% and 11.8%, respectively. The sensitivity between the two invasive imaging techniques, used as benchmarks, ranged from 72.7% for the right IJV to 90% for the azygos vein but the IVUS showed a higher rate of venous anomalies than the CV. There was excellent correspondence between identifying collateral veins on MRV and CV. Conclusions Noninvasive DS screening for the detection of venous anomalies indicative of CCSVI may be a reliable approach for identifying patients eligible for further multimodal invasive imaging testing of the IJVs. However, the noninvasive screening methods were inadequate to depict the total amount of azygos vein/VVs anomalies identified with invasive testing. This pilot study, with limited sample size, shows that both a non-invasive and invasive multimodal imaging diagnostic approach should be recommended to depict a range of extracranial venous anomalies indicative of CCSVI. However, lack of invasive testing on the study subjects whose results were negative on the DS screening and of healthy controls, limits further generalizibility of our findings. In addition, the findings from the 2 invasive techniques confirmed the existence of severe extracranial venous anomalies that significantly impaired normal blood outflow from the brain in this group of MS patients. PMID:24139135

Optimization of Archeological Anomalies using GIS method for Magnetic and Resistivity Study at Sungai Batu, Lembah Bujang, Kedah (Malaysia)

NASA Astrophysics Data System (ADS)

Yusoh, R.; Saad, R.; Saidin, M.; Anda, S. T.; Muhammad, S. B.; Ashraf, M. I. M.; Hazreek, Z. A. M.

2018-04-01

Magnetic and resistivity method has become a reliable option in archeological exploration. The use of both method has become popular these day. However, both method gives different type of sensing in detecting anomalies and direct interpret from the anomalies will result large coverage area for excavation. Therefore, to overcome this issue, both anomalies can be extracted using ArcGIS software to reduce excavated coverage area. The case study located at Sungai Batu, Lembah Bujang near SB2ZZ lot expected buried clay brick monument which will be a perfect case to apply this technique. Magnetic and resistivity method was implemented at the study area where the anomalies coverage area for magnetic and resistivity is 531.5 m2 and 636 m2 respectively which total area of both anomalies was 764 m2. By applying combine technique, the anomalies area reduce to 403.7 m2 which reduce the suspected anomalies by 47.16 %. The unsuspected clay brick monument area was increase from 15.86% to 55.54% which improve the cost and labor work for excavation.

Congenital basis of posterior fossa anomalies

PubMed Central

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

Methods and apparatus for rotor blade ice detection

DOEpatents

LeMieux, David Lawrence

2006-08-08

A method for detecting ice on a wind turbine having a rotor and one or more rotor blades each having blade roots includes monitoring meteorological conditions relating to icing conditions and monitoring one or more physical characteristics of the wind turbine in operation that vary in accordance with at least one of the mass of the one or more rotor blades or a mass imbalance between the rotor blades. The method also includes using the one or more monitored physical characteristics to determine whether a blade mass anomaly exists, determining whether the monitored meteorological conditions are consistent with blade icing; and signaling an icing-related blade mass anomaly when a blade mass anomaly is determined to exist and the monitored meteorological conditions are determined to be consistent with icing.

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

NASA Astrophysics Data System (ADS)

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 defects/anomalies have been developed inside at the weldment. The results from the inspection test showed that the signatures of magnetic leakage field gradient distribution prove that the peak is found on the location of defect/anomaly in the reference block. It is in agreement with the evidence of anomaly that seen in the radiography test film (RT).

Limb anomalies in DiGeorge and CHARGE syndromes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prasad, C.; Quackenbush, E.J.; Whiteman, D.

1997-01-20

Limb anomalies are not common in the DiGeorge or CHARGE syndromes. We describe limb anomalies in two children, one with DiGeorge and the other with CHARGE syndrome. Our first patient had a bifid left thumb, Tetralogy of Fallot, absent thymus, right facial palsy, and a reduced number of T-cells. A deletion of 22q11 was detected by fluorescence in situ hybridization (FISH). The second patient, with CHARGE syndrome, had asymmetric findings that included right fifth finger clinodactyly, camptodactyly, tibial hemimelia and dimpling, and severe club-foot. The expanded spectrum of the DiGeorge and CHARGE syndromes includes limb anomalies. 14 refs., 4 figs.

Surveying the South Pole-Aitken basin magnetic anomaly for remnant impactor metallic iron

USGS Publications Warehouse

Cahill, Joshua T.S.; Hagerty, Justin J.; Lawrence, David M.; Klima, Rachel L.; Blewett, David T.

2014-01-01

The Moon has areas of magnetized crust ("magnetic anomalies"), the origins of which are poorly constrained. A magnetic anomaly near the northern rim of South Pole-Aitken (SPA) basin was recently postulated to originate from remnant metallic iron emplaced by the SPA basin-forming impactor. Here, we remotely examine the regolith of this SPA magnetic anomaly with a combination of Clementine and Lunar Prospector derived iron maps for any evidence of enhanced metallic iron content. We find that these data sets do not definitively detect the hypothesized remnant metallic iron within the upper tens of centimeters of the lunar regolith.

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

PubMed

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.

Autonomous software: Myth or magic?

NASA Astrophysics Data System (ADS)

Allan, A.; Naylor, T.; Saunders, E. S.

2008-03-01

We discuss work by the eSTAR project which demonstrates a fully closed loop autonomous system for the follow up of possible micro-lensing anomalies. Not only are the initial micro-lensing detections followed up in real time, but ongoing events are prioritised and continually monitored, with the returned data being analysed automatically. If the ``smart software'' running the observing campaign detects a planet-like anomaly, further follow-up will be scheduled autonomously and other telescopes and telescope networks alerted to the possible planetary detection. We further discuss the implications of this, and how such projects can be used to build more general autonomous observing and control systems.

Identification and detection of anomalies through SSME data analysis

NASA Technical Reports Server (NTRS)

Pereira, Lisa; Ali, Moonis

1990-01-01

The goal of the ongoing research described in this paper is to analyze real-time ground test data in order to identify patterns associated with the anomalous engine behavior, and on the basis of this analysis to develop an expert system which detects anomalous engine behavior in the early stages of fault development. A prototype of the expert system has been developed and tested on the high frequency data of two SSME tests, namely Test #901-0516 and Test #904-044. The comparison of our results with the post-test analyses indicates that the expert system detected the presence of the anomalies in a significantly early stage of fault development.

Communications and tracking expert systems study

NASA Technical Reports Server (NTRS)

Leibfried, T. F.; Feagin, Terry; Overland, David

1987-01-01

The original objectives of the study consisted of five broad areas of investigation: criteria and issues for explanation of communication and tracking system anomaly detection, isolation, and recovery; data storage simplification issues for fault detection expert systems; data selection procedures for decision tree pruning and optimization to enhance the abstraction of pertinent information for clear explanation; criteria for establishing levels of explanation suited to needs; and analysis of expert system interaction and modularization. Progress was made in all areas, but to a lesser extent in the criteria for establishing levels of explanation suited to needs. Among the types of expert systems studied were those related to anomaly or fault detection, isolation, and recovery.

Modeling of electrical impedance tomography to detect breast cancer by finite volume methods

NASA Astrophysics Data System (ADS)

Ain, K.; Wibowo, R. A.; Soelistiono, S.

2017-05-01

The properties of the electrical impedance of tissue are an interesting study, because changes of the electrical impedance of organs are related to physiological and pathological. Both physiological and pathological properties are strongly associated with disease information. Several experiments shown that the breast cancer has a lower impedance than the normal breast tissue. Thus, the imaging based on impedance can be used as an alternative equipment to detect the breast cancer. This research carries out by modelling of Electrical Impedance Tomography to detect the breast cancer by finite volume methods. The research includes development of a mathematical model of the electric potential field by 2D Finite Volume Method, solving the forward problem and inverse problem by linear reconstruction method. The scanning is done by 16 channel electrode with neighbors method to collect data. The scanning is performed at a frequency of 10 kHz and 100 kHz with three objects numeric includes an anomaly at the surface, an anomaly at the depth and an anomaly at the surface and at depth. The simulation has been successfully to reconstruct image of functional anomalies of the breast cancer at the surface position, the depth position or a combination of surface and the depth.

Standardized Analysis for UXO Demonstration Sites

DTIC Science & Technology

2008-04-01

is a time-domain electromagnetic instrument designed to detect shallow ferrous and nonferrous metallic objects. The applicability of the EM61 for UXO...or spots show EMI field anomalies caused by buried metal objects, both UXO and clutter. Anomaly maps for APG are shown in Figure 5. The Blind Grid

Linking entanglement and discrete anomaly

NASA Astrophysics Data System (ADS)

Hung, Ling-Yan; Wu, Yong-Shi; Zhou, Yang

2018-05-01

In 3 d Chern-Simons theory, there is a discrete one-form symmetry, whose symmetry group is isomorphic to the center of the gauge group. We study the `t Hooft anomaly associated to this discrete one-form symmetry in theories with generic gauge groups, A, B, C, D-types. We propose to detect the discrete anomaly by computing the Hopf state entanglement in the subspace spanned by the symmetry generators and develop a systematical way based on the truncated modular S matrix. We check our proposal for many examples.

Neutrino scattering and the reactor antineutrino anomaly

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Mosaic tetraploidy in a liveborn infant with features of the DiGeorge anomaly.

PubMed

Wullich, B; Henn, W; Groterath, E; Ermis, A; Fuchs, S; Zankl, M

1991-11-01

We report on a liveborn male infant with mosaic tetraploidy who presented with multiple congenital anomalies including features of the DiGeorge anomaly (type I truncus arteriosus with other cardiovascular malformations, thymic hypoplasia, hypocalcemia). No structural chromosome aberrations, namely of chromosome 22, were detected. These findings contribute to the variability of symptoms of the polyploid phenotype. Additionally, the cytogenetic studies in our case emphasize the necessity of investigating fibroblasts in order to evaluate the relevant proportion of aberrant cells in mosaicism.

Develop Advanced Nonlinear Signal Analysis Topographical Mapping System

NASA Technical Reports Server (NTRS)

Jong, Jen-Yi

1997-01-01

During the development of the SSME, a hierarchy of advanced signal analysis techniques for mechanical signature analysis has been developed by NASA and AI Signal Research Inc. (ASRI) to improve the safety and reliability for Space Shuttle operations. These techniques can process and identify intelligent information hidden in a measured signal which is often unidentifiable using conventional signal analysis methods. Currently, due to the highly interactive processing requirements and the volume of dynamic data involved, detailed diagnostic analysis is being performed manually which requires immense man-hours with extensive human interface. To overcome this manual process, NASA implemented this program to develop an Advanced nonlinear signal Analysis Topographical Mapping System (ATMS) to provide automatic/unsupervised engine diagnostic capabilities. The ATMS will utilize a rule-based Clips expert system to supervise a hierarchy of diagnostic signature analysis techniques in the Advanced Signal Analysis Library (ASAL). ASAL will perform automatic signal processing, archiving, and anomaly detection/identification tasks in order to provide an intelligent and fully automated engine diagnostic capability. The ATMS has been successfully developed under this contract. In summary, the program objectives to design, develop, test and conduct performance evaluation for an automated engine diagnostic system have been successfully achieved. Software implementation of the entire ATMS system on MSFC's OISPS computer has been completed. The significance of the ATMS developed under this program is attributed to the fully automated coherence analysis capability for anomaly detection and identification which can greatly enhance the power and reliability of engine diagnostic evaluation. The results have demonstrated that ATMS can significantly save time and man-hours in performing engine test/flight data analysis and performance evaluation of large volumes of dynamic test data.

Using Satellite Data to Characterize the Temporal Thermal Behavior of an Active Volcano: Mount St. Helens, WA

NASA Technical Reports Server (NTRS)

Vaughan, R. Greg; Hook, Simon J.

2006-01-01

ASTER thermal infrared data over Mt. St Helens were used to characterize its thermal behavior from Jun 2000 to Feb 2006. Prior to the Oct 2004 eruption, the average crater temperature varied seasonally between -12 and 6 C. After the eruption, maximum single-pixel temperature increased from 10 C (Oct 2004) to 96 C (Aug 2005), then showed a decrease to Feb 2006. The initial increase in temperature was correlated with dome morphology and growth rate and the subsequent decrease was interpreted to relate to both seasonal trends and a decreased growth rate/increased cooling rate, possibly suggesting a significant change in the volcanic system. A single-pixel ASTER thermal anomaly first appeared on Oct 1, 2004, eleven hours after the first eruption - 10 days before new lava was exposed at the surface. By contrast, an automated algorithm for detecting thermal anomalies in MODIS data did not trigger an alert until Dec 18. However, a single-pixel thermal anomaly first appeared in MODIS channel 23 (4 um) on Oct 13, 12 days after the first eruption - 2 days after lava was exposed. The earlier thermal anomaly detected with ASTER data is attributed to the higher spatial resolution (90 m) compared with MODIS (1 m) and the earlier visual observation of anomalous pixels compared to the automated detection method suggests that local spatial statistics and background radiance data could improve automated detection methods.

Structural Hippocampal Anomalies in a Schizophrenia Population Correlate with Navigation Performance on a Wayfinding Task

PubMed Central

Ledoux, Andrée-Anne; Boyer, Patrice; Phillips, Jennifer L.; Labelle, Alain; Smith, Andra; Bohbot, Véronique D.

2014-01-01

Episodic memory, related to the hippocampus, has been found to be impaired in schizophrenia. Further, hippocampal anomalies have also been observed in schizophrenia. This study investigated whether average hippocampal gray matter (GM) would differentiate performance on a hippocampus-dependent memory task in patients with schizophrenia and healthy controls. Twenty-one patients with schizophrenia and 22 control participants were scanned with an MRI while being tested on a wayfinding task in a virtual town (e.g., find the grocery store from the school). Regressions were performed for both groups individually and together using GM and performance on the wayfinding task. Results indicate that controls successfully completed the task more often than patients, took less time, and made fewer errors. Additionally, controls had significantly more hippocampal GM than patients. Poor performance was associated with a GM decrease in the right hippocampus for both groups. Within group regressions found an association between right hippocampi GM and performance in controls and an association between the left hippocampi GM and performance in patients. A second analysis revealed that different anatomical GM regions, known to be associated with the hippocampus, such as the parahippocampal cortex, amygdala, medial, and orbital prefrontal cortices, covaried with the hippocampus in the control group. Interestingly, the cuneus and cingulate gyrus also covaried with the hippocampus in the patient group but the orbital frontal cortex did not, supporting the hypothesis of impaired connectivity between the hippocampus and the frontal cortex in schizophrenia. These results present important implications for creating intervention programs aimed at measuring functional and structural changes in the hippocampus in schizophrenia. PMID:24672451

Structural hippocampal anomalies in a schizophrenia population correlate with navigation performance on a wayfinding task.

PubMed

Ledoux, Andrée-Anne; Boyer, Patrice; Phillips, Jennifer L; Labelle, Alain; Smith, Andra; Bohbot, Véronique D

2014-01-01

Episodic memory, related to the hippocampus, has been found to be impaired in schizophrenia. Further, hippocampal anomalies have also been observed in schizophrenia. This study investigated whether average hippocampal gray matter (GM) would differentiate performance on a hippocampus-dependent memory task in patients with schizophrenia and healthy controls. Twenty-one patients with schizophrenia and 22 control participants were scanned with an MRI while being tested on a wayfinding task in a virtual town (e.g., find the grocery store from the school). Regressions were performed for both groups individually and together using GM and performance on the wayfinding task. Results indicate that controls successfully completed the task more often than patients, took less time, and made fewer errors. Additionally, controls had significantly more hippocampal GM than patients. Poor performance was associated with a GM decrease in the right hippocampus for both groups. Within group regressions found an association between right hippocampi GM and performance in controls and an association between the left hippocampi GM and performance in patients. A second analysis revealed that different anatomical GM regions, known to be associated with the hippocampus, such as the parahippocampal cortex, amygdala, medial, and orbital prefrontal cortices, covaried with the hippocampus in the control group. Interestingly, the cuneus and cingulate gyrus also covaried with the hippocampus in the patient group but the orbital frontal cortex did not, supporting the hypothesis of impaired connectivity between the hippocampus and the frontal cortex in schizophrenia. These results present important implications for creating intervention programs aimed at measuring functional and structural changes in the hippocampus in schizophrenia.

Rules for Optical Testing

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2014-01-01

Based on 30 years of optical testing experience, a lot of mistakes, a lot of learning and a lot of experience, I have defined seven guiding principles for optical testing - regardless of how small or how large the optical testing or metrology task: Fully Understand the Task, Develop an Error Budget, Continuous Metrology Coverage, Know where you are, Test like you fly, Independent Cross-Checks, Understand All Anomalies. These rules have been applied with great success to the inprocess optical testing and final specification compliance testing of the JWST mirrors.

Relationships between Rwandan seasonal rainfall anomalies and ENSO events

NASA Astrophysics Data System (ADS)

Muhire, I.; Ahmed, F.; Abutaleb, K.

2015-10-01

This study aims primarily at investigating the relationships between Rwandan seasonal rainfall anomalies and El Niño-South Oscillation phenomenon (ENSO) events. The study is useful for early warning of negative effects associated with extreme rainfall anomalies across the country. It covers the period 1935-1992, using long and short rains data from 28 weather stations in Rwanda and ENSO events resourced from Glantz (2001). The mean standardized anomaly indices were calculated to investigate their associations with ENSO events. One-way analysis of variance was applied on the mean standardized anomaly index values per ENSO event to explore the spatial correlation of rainfall anomalies per ENSO event. A geographical information system was used to present spatially the variations in mean standardized anomaly indices per ENSO event. The results showed approximately three climatic periods, namely, dry period (1935-1960), semi-humid period (1961-1976) and wet period (1977-1992). Though positive and negative correlations were detected between extreme short rains anomalies and El Niño events, La Niña events were mostly linked to negative rainfall anomalies while El Niño events were associated with positive rainfall anomalies. The occurrence of El Niño and La Niña in the same year does not show any clear association with rainfall anomalies. However, the phenomenon was more linked with positive long rains anomalies and negative short rains anomalies. The normal years were largely linked with negative long rains anomalies and positive short rains anomalies, which is a pointer to the influence of other factors other than ENSO events. This makes projection of seasonal rainfall anomalies in the country by merely predicting ENSO events difficult.

Mining IP to Domain Name Interactions to Detect DNS Flood Attacks on Recursive DNS Servers.

PubMed

Alonso, Roberto; Monroy, Raúl; Trejo, Luis A

2016-08-17

The Domain Name System (DNS) is a critical infrastructure of any network, and, not surprisingly a common target of cybercrime. There are numerous works that analyse higher level DNS traffic to detect anomalies in the DNS or any other network service. By contrast, few efforts have been made to study and protect the recursive DNS level. In this paper, we introduce a novel abstraction of the recursive DNS traffic to detect a flooding attack, a kind of Distributed Denial of Service (DDoS). The crux of our abstraction lies on a simple observation: Recursive DNS queries, from IP addresses to domain names, form social groups; hence, a DDoS attack should result in drastic changes on DNS social structure. We have built an anomaly-based detection mechanism, which, given a time window of DNS usage, makes use of features that attempt to capture the DNS social structure, including a heuristic that estimates group composition. Our detection mechanism has been successfully validated (in a simulated and controlled setting) and with it the suitability of our abstraction to detect flooding attacks. To the best of our knowledge, this is the first time that work is successful in using this abstraction to detect these kinds of attacks at the recursive level. Before concluding the paper, we motivate further research directions considering this new abstraction, so we have designed and tested two additional experiments which exhibit promising results to detect other types of anomalies in recursive DNS servers.

Recombinant Temporal Aberration Detection Algorithms for Enhanced Biosurveillance

PubMed Central

Murphy, Sean Patrick; Burkom, Howard

2008-01-01

Objective Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors. Methods This study decomposes existing temporal aberration detection algorithms into two sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (Stage 1) generates predictions of time series values a certain number of time steps in the future based on historical data. The anomaly measure stage (Stage 2) compares features of this prediction to corresponding features of the actual time series to compute a statistical anomaly measure. A Monte Carlo simulation procedure is then used to examine the recombinant algorithms’ ability to detect synthetic aberrations injected into authentic syndromic time series. Results New methods obtained with procedural components of published, sometimes widely used, algorithms were compared to the known methods using authentic datasets with plausible stochastic injected signals. Performance improvements were found for some of the recombinant methods, and these improvements were consistent over a range of data types, outbreak types, and outbreak sizes. For gradual outbreaks, the WEWD MovAvg7+WEWD Z-Score recombinant algorithm performed best; for sudden outbreaks, the HW+WEWD Z-Score performed best. Conclusion This decomposition was found not only to yield valuable insight into the effects of the aberration detection algorithms but also to produce novel combinations of data forecasters and anomaly measures with enhanced detection performance. PMID:17947614

Mining IP to Domain Name Interactions to Detect DNS Flood Attacks on Recursive DNS Servers

PubMed Central

Alonso, Roberto; Monroy, Raúl; Trejo, Luis A.

2016-01-01

The Domain Name System (DNS) is a critical infrastructure of any network, and, not surprisingly a common target of cybercrime. There are numerous works that analyse higher level DNS traffic to detect anomalies in the DNS or any other network service. By contrast, few efforts have been made to study and protect the recursive DNS level. In this paper, we introduce a novel abstraction of the recursive DNS traffic to detect a flooding attack, a kind of Distributed Denial of Service (DDoS). The crux of our abstraction lies on a simple observation: Recursive DNS queries, from IP addresses to domain names, form social groups; hence, a DDoS attack should result in drastic changes on DNS social structure. We have built an anomaly-based detection mechanism, which, given a time window of DNS usage, makes use of features that attempt to capture the DNS social structure, including a heuristic that estimates group composition. Our detection mechanism has been successfully validated (in a simulated and controlled setting) and with it the suitability of our abstraction to detect flooding attacks. To the best of our knowledge, this is the first time that work is successful in using this abstraction to detect these kinds of attacks at the recursive level. Before concluding the paper, we motivate further research directions considering this new abstraction, so we have designed and tested two additional experiments which exhibit promising results to detect other types of anomalies in recursive DNS servers. PMID:27548169

Detection of Low Temperature Volcanogenic Thermal Anomalies with ASTER

NASA Astrophysics Data System (ADS)

Pieri, D. C.; Baxter, S.

2009-12-01

Predicting volcanic eruptions is a thorny problem, as volcanoes typically exhibit idiosyncratic waxing and/or waning pre-eruption emission, geodetic, and seismic behavior. It is no surprise that increasing our accuracy and precision in eruption prediction depends on assessing the time-progressions of all relevant precursor geophysical, geochemical, and geological phenomena, and on more frequently observing volcanoes when they become restless. The ASTER instrument on the NASA Terra Earth Observing System satellite in low earth orbit provides important capabilities in the area of detection of volcanogenic anomalies such as thermal precursors and increased passive gas emissions. Its unique high spatial resolution multi-spectral thermal IR imaging data (90m/pixel; 5 bands in the 8-12um region), bore-sighted with visible and near-IR imaging data, and combined with off-nadir pointing and stereo-photogrammetric capabilities make ASTER a potentially important volcanic precursor detection tool. We are utilizing the JPL ASTER Volcano Archive (http://ava.jpl.nasa.gov) to systematically examine 80,000+ ASTER volcano images to analyze (a) thermal emission baseline behavior for over 1500 volcanoes worldwide, (b) the form and magnitude of time-dependent thermal emission variability for these volcanoes, and (c) the spatio-temporal limits of detection of pre-eruption temporal changes in thermal emission in the context of eruption precursor behavior. We are creating and analyzing a catalog of the magnitude, frequency, and distribution of volcano thermal signatures worldwide as observed from ASTER since 2000 at 90m/pixel. Of particular interest as eruption precursors are small low contrast thermal anomalies of low apparent absolute temperature (e.g., melt-water lakes, fumaroles, geysers, grossly sub-pixel hotspots), for which the signal-to-noise ratio may be marginal (e.g., scene confusion due to clouds, water and water vapor, fumarolic emissions, variegated ground emissivity, and their combinations). To systematically detect such intrinsically difficult anomalies within our large archive, we are exploring a four step approach: (a) the recursive application of a GPU-accelerated, edge-preserving bilateral filter prepares a thermal image by removing noise and fine detail; (b) the resulting stylized filtered image is segmented by a path-independent region-growing algorithm, (c) the resulting segments are fused based on thermal affinity, and (d) fused segments are subjected to thermal and geographical tests for hotspot detection and classification, to eliminate false alarms or non-volcanogenic anomalies. We will discuss our progress in creating the general thermal anomaly catalog as well as algorithm approach and results. This work was carried out at the Jet Propulsion Laboratory of the California Institute of Technology under contract to NASA.

A Spatially Constrained Multi-autoencoder Approach for Multivariate Geochemical Anomaly Recognition

NASA Astrophysics Data System (ADS)

Lirong, C.; Qingfeng, G.; Renguang, Z.; Yihui, X.

2017-12-01

Separating and recognizing geochemical anomalies from the geochemical background is one of the key tasks in geochemical exploration. Many methods have been developed, such as calculating the mean ±2 standard deviation, and fractal/multifractal models. In recent years, deep autoencoder, a deep learning approach, have been used for multivariate geochemical anomaly recognition. While being able to deal with the non-normal distributions of geochemical concentrations and the non-linear relationships among them, this self-supervised learning method does not take into account the spatial heterogeneity of geochemical background and the uncertainty induced by the randomly initialized weights of neurons, leading to ineffective recognition of weak anomalies. In this paper, we introduce a spatially constrained multi-autoencoder (SCMA) approach for multivariate geochemical anomaly recognition, which includes two steps: spatial partitioning and anomaly score computation. The first step divides the study area into multiple sub-regions to segregate the geochemical background, by grouping the geochemical samples through K-means clustering, spatial filtering, and spatial constraining rules. In the second step, for each sub-region, a group of autoencoder neural networks are constructed with an identical structure but different initial weights on neurons. Each autoencoder is trained using the geochemical samples within the corresponding sub-region to learn the sub-regional geochemical background. The best autoencoder of a group is chosen as the final model for the corresponding sub-region. The anomaly score at each location can then be calculated as the euclidean distance between the observed concentrations and reconstructed concentrations of geochemical elements.The experiments using the geochemical data and Fe deposits in the southwestern Fujian province of China showed that our SCMA approach greatly improved the recognition of weak anomalies, achieving the AUC of 0.89, compared with the AUC of 0.77 using a single deep autoencoder approach.

Unsupervised Sequential Outlier Detection With Deep Architectures.

PubMed

Lu, Weining; Cheng, Yu; Xiao, Cao; Chang, Shiyu; Huang, Shuai; Liang, Bin; Huang, Thomas

2017-09-01

Unsupervised outlier detection is a vital task and has high impact on a wide variety of applications domains, such as image analysis and video surveillance. It also gains long-standing attentions and has been extensively studied in multiple research areas. Detecting and taking action on outliers as quickly as possible are imperative in order to protect network and related stakeholders or to maintain the reliability of critical systems. However, outlier detection is difficult due to the one class nature and challenges in feature construction. Sequential anomaly detection is even harder with more challenges from temporal correlation in data, as well as the presence of noise and high dimensionality. In this paper, we introduce a novel deep structured framework to solve the challenging sequential outlier detection problem. We use autoencoder models to capture the intrinsic difference between outliers and normal instances and integrate the models to recurrent neural networks that allow the learning to make use of previous context as well as make the learners more robust to warp along the time axis. Furthermore, we propose to use a layerwise training procedure, which significantly simplifies the training procedure and hence helps achieve efficient and scalable training. In addition, we investigate a fine-tuning step to update all parameters set by incorporating the temporal correlation in the sequence. We further apply our proposed models to conduct systematic experiments on five real-world benchmark data sets. Experimental results demonstrate the effectiveness of our model, compared with other state-of-the-art approaches.

Simulations and phantom evaluations of magnetic resonance electrical impedance tomography (MREIT) for breast cancer detection

NASA Astrophysics Data System (ADS)

Sadleir, Rosalind J.; Sajib, Saurav Z. K.; Kim, Hyung Joong; Kwon, Oh In; Woo, Eung Je

2013-05-01

MREIT is a new imaging modality that can be used to reconstruct high-resolution conductivity images of the human body. Since conductivity values of cancerous tissues in the breast are significantly higher than those of surrounding normal tissues, breast imaging using MREIT may provide a new noninvasive way of detecting early stage of cancer. In this paper, we present results of experimental and numerical simulation studies of breast MREIT. We built a realistic three-dimensional model of the human breast connected to a simplified model of the chest including the heart and evaluated the ability of MREIT to detect cancerous anomalies in a background material with similar electrical properties to breast tissue. We performed numerical simulations of various scenarios in breast MREIT including assessment of the effects of fat inclusions and effects related to noise levels, such as changing the amplitude of injected currents, effect of added noise and number of averages. Phantom results showed straightforward detection of cancerous anomalies in a background was possible with low currents and few averages. The simulation results showed it should be possible to detect a cancerous anomaly in the breast, while restricting the maximal current density in the heart below published levels for nerve excitation.

Application of data cubes for improving detection of water cycle extreme events

NASA Astrophysics Data System (ADS)

Teng, W. L.; Albayrak, A.

2015-12-01

As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case for our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme (WCE) events, a specific case of anomaly detection, requiring time series data. We investigate the use of the sequential probability ratio test (SPRT) for anomaly detection and support vector machines (SVM) for anomaly classification. We show an example of detection of WCE events, using the Global Land Data Assimilation Systems (GLDAS) data set.

Rule-based expert system for maritime anomaly detection

NASA Astrophysics Data System (ADS)

Roy, Jean

2010-04-01

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

A magnetoelectric flux gate: new approach for weak DC magnetic field detection.

PubMed

Chu, Zhaoqiang; Shi, Huaduo; PourhosseiniAsl, Mohammad Javad; Wu, Jingen; Shi, Weiliang; Gao, Xiangyu; Yuan, Xiaoting; Dong, Shuxiang

2017-08-17

The magnetic flux gate sensors based on Faraday's Law of Induction are widely used for DC or extremely low frequency magnetic field detection. Recently, as the fast development of multiferroics and magnetoelectric (ME) composite materials, a new technology based on ME coupling effect is emerging for potential devices application. Here, we report a magnetoelectric flux gate sensor (MEFGS) for weak DC magnetic field detection for the first time, which works on a similar magnetic flux gate principle, but based on ME coupling effect. The proposed MEFGS has a shuttle-shaped configuration made of amorphous FeBSi alloy (Metglas) serving as both magnetic and magnetostrictive cores for producing a closed-loop high-frequency magnetic flux and also a longitudinal vibration, and one pair of embedded piezoelectric PMN-PT fibers ([011]-oriented Pb(Mg,Nb)O 3 -PbTiO 3 single crystal) serving as ME flux gate in a differential mode for detecting magnetic anomaly. In this way, the relative change in output signal of the MEFGS under an applied DC magnetic anomaly of 1 nT was greatly enhanced by a factor of 4 to 5 in comparison with the previous reports. The proposed ME flux gate shows a great potential for magnetic anomaly detections, such as magnetic navigation, magnetic based medical diagnosis, etc.

Deep-cascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes.

PubMed

Sabokrou, Mohammad; Fayyaz, Mohsen; Fathy, Mahmood; Klette, Reinhard

2017-02-17

This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubicpatch- based method, characterised by a cascade of classifiers, which makes use of an advanced feature-learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of "many" normal cubic patches. This deep network operates on small cubic patches as being the first stage, before carefully resizing remaining candidates of interest, and evaluating those at the second stage using a more complex and deeper 3D convolutional neural network (CNN). We divide the deep autoencoder and the CNN into multiple sub-stages which operate as cascaded classifiers. Shallow layers of the cascaded deep networks (designed as Gaussian classifiers, acting as weak single-class classifiers) detect "simple" normal patches such as background patches, and more complex normal patches are detected at deeper layers. It is shown that the proposed novel technique (a cascade of two cascaded classifiers) performs comparable to current top-performing detection and localization methods on standard benchmarks, but outperforms those in general with respect to required computation time.

Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

PubMed Central

Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

2016-01-01

Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. PMID:27092509

Magnetic anomalies in the Imbrium and Schrödinger impact basins: Orbital evidence for persistence of the lunar core dynamo into the Imbrian epoch

NASA Astrophysics Data System (ADS)

Hood, L. L.; Spudis, P. D.

2016-11-01

Approximate maps of the lunar crustal magnetic field at low altitudes in the vicinities of the three Imbrian-aged impact basins, Orientale, Schrödinger, and Imbrium, have been constructed using Lunar Prospector and Kaguya orbital magnetometer data. Detectable anomalies are confirmed to be present well within the rims of Imbrium and Schrödinger. Anomalies in Schrödinger are asymmetrically distributed about the basin center, while a single isolated anomaly is most clearly detected within Imbrium northwest of Timocharis crater. The subsurface within these basins was heated to high temperatures at the time of impact and required long time periods (up to 1 Myr) to cool below the Curie temperature for metallic iron remanence carriers (1043 K). Therefore, consistent with laboratory analyses of returned samples, a steady, long-lived magnetizing field, i.e., a former core dynamo, is inferred to have existed when these basins formed. The asymmetrical distribution within Schrödinger suggests partial demagnetization by later volcanic activity when the dynamo field was much weaker or nonexistent. However, it remains true that anomalies within Imbrian-aged basins are much weaker than those within most Nectarian-aged basins. The virtual absence of anomalies within Orientale where impact melt rocks (the Maunder Formation) are exposed at the surface is difficult to explain unless the dynamo field was much weaker during the Imbrian period.

The prevalence, pattern and clinical presentation of developmental dental hard-tissue anomalies in children with primary and mix dentition from Ile-Ife, Nigeria.

PubMed

Temilola, Dada Oluwaseyi; Folayan, Morenike Oluwatoyin; Fatusi, Olawunmi; Chukwumah, Nneka Maureen; Onyejaka, Nneka; Oziegbe, Elizabeth; Oyedele, Titus; Kolawole, Kikelomo Adebanke; Agbaje, Hakeem

2014-10-16

The study of dental anomalies is important because it generates information that is important for both the anthropological and clinical management of patients. The objective of this study is to determine the prevalence and pattern of presentation of dental hard-tissue developmental anomalies in the mix dentition of children residing in Ile-Ife, a suburban region of Nigeria. Information on age, sex and socioeconomic status was collected from 1,036 children aged four months to 12 years through a household survey. Clinical examination was conducted to assess the presence of dental anomalies. Associations between age, sex, socioeconomic status, prevalence, and pattern of presentation of the developmental hard-tissue dental anomalies were determined. Two hundred and seventy six (26.6%) children had dental anomalies. Of these, 23.8% had one anomaly, 2.5% had two anomalies, and 0.3% had more than two anomalies. Of the children with anomalies, 49.3%were male, 50.7%were female, and 47.8%, 28.6% and 23.6% were children from low, middle and high socioeconomic classes, respectively. More anomalies were seen in permanent than primary dentition. Anomalies of tooth structure were most prevalent (16.1%); anomalies which affect tooth number were least prevalent (1.3%). Dens evaginatus, peg-shaped lateral, macrodontia, and talon cusp were more prevalent in the permanent dentition, and dens evaginatus peg-shaped lateral and macrodontia were more prevalent in the maxilla. There were significantly more macrodontia anomalies in males and in children of high socioeconomic status. This large survey of dental hard-tissue anomalies found in the primary dentition and mixed dentition of children in Nigeria provides anthropological and clinical data that may aid the detection and management of dental problems of children in Nigeria.

A very rare venous anomaly in a living liver donor: left hepatic venous connection to the right atrium.

PubMed

Uraz, S; Duran, C; Balci, D; Akin, B; Dayangac, M; Kurt, Z; Ayanoglu, O H; Killi, R; Yuzer, Y; Tokat, Y

2007-06-01

In humans, three main hepatic veins drain the liver into the inferior vena cava below the diaphragm. This report represents the first living donor liver that had a rare anatomic variation of the left hepatic vein draining directly to the right atrium, which was detected preoperatively by routine investigations of the living donor transplantation. This type of anomaly may present potentially fatal challenges to a donor operation if not detected preoperatively, especially when the left lobe is the choice for explantation.

Embryo with XYY syndrome presenting with clubfoot: a case report.

PubMed

Athanatos, Dimitrios; Tsakalidis, Christos; Tampakoudis, George P; Papastergiou, Maria N; Tzevelekis, Fillipos; Pados, George; Assimakopoulos, Efstratios A

2009-09-01

Talipes equinovarus (clubfoot) is a skeletal anomaly of the embryo's legs, with a frequency of 1-3:1000 living born babies. It may occur as an independent anomaly, or as part of a syndrome with concomitant chromosomal abnormalities.XYY syndrome is a quite rare sex chromosomal abnormality with 47, XYY karyotype. Prenatal diagnosis is usually accidental because the syndrome is not associated with increased prevalence of sonographically detectable defects. The possibility of co-existence of skeletal anomalies in embryos with 47, XYY karyotype is scant, with only a few cases reported in the literature.An amniocentesis was performed in an embryo at the 21(st) week of gestation because clubfoot was detected in the 2(nd) trimester scan, and the embryo was found to have abnormal karyotype of 47, XYY. Current opinions and management dilemmas are discussed.

Embryo with XYY syndrome presenting with clubfoot: a case report

PubMed Central

Tsakalidis, Christos; Tampakoudis, George P; Papastergiou, Maria N; Tzevelekis, Fillipos; Pados, George; Assimakopoulos, Efstratios A

2009-01-01

Talipes equinovarus (clubfoot) is a skeletal anomaly of the embryo’s legs, with a frequency of 1-3:1000 living born babies. It may occur as an independent anomaly, or as part of a syndrome with concomitant chromosomal abnormalities. XYY syndrome is a quite rare sex chromosomal abnormality with 47, XYY karyotype. Prenatal diagnosis is usually accidental because the syndrome is not associated with increased prevalence of sonographically detectable defects. The possibility of co-existence of skeletal anomalies in embryos with 47, XYY karyotype is scant, with only a few cases reported in the literature. An amniocentesis was performed in an embryo at the 21st week of gestation because clubfoot was detected in the 2nd trimester scan, and the embryo was found to have abnormal karyotype of 47, XYY. Current opinions and management dilemmas are discussed. PMID:19918427

Anomaly clustering in hyperspectral images

NASA Astrophysics Data System (ADS)

Doster, Timothy J.; Ross, David S.; Messinger, David W.; Basener, William F.

2009-05-01

The topological anomaly detection algorithm (TAD) differs from other anomaly detection algorithms in that it uses a topological/graph-theoretic model for the image background instead of modeling the image with a Gaussian normal distribution. In the construction of the model, TAD produces a hard threshold separating anomalous pixels from background in the image. We build on this feature of TAD by extending the algorithm so that it gives a measure of the number of anomalous objects, rather than the number of anomalous pixels, in a hyperspectral image. This is done by identifying, and integrating, clusters of anomalous pixels via a graph theoretical method combining spatial and spectral information. The method is applied to a cluttered HyMap image and combines small groups of pixels containing like materials, such as those corresponding to rooftops and cars, into individual clusters. This improves visualization and interpretation of objects.

Intelligence Surveillance And Reconnaissance Full Motion Video Automatic Anomaly Detection Of Crowd Movements: System Requirements For Airborne Application

DTIC Science & Technology

The collection of Intelligence , Surveillance, and Reconnaissance (ISR) Full Motion Video (FMV) is growing at an exponential rate, and the manual... intelligence for the warfighter. This paper will address the question of how can automatic pattern extraction, based on computer vision, extract anomalies in

Obstetric audit: the Bradford way.

PubMed

Lodge, Virginia; Lomas, Karen; Jaworskyj, Suzanne; Thomson, Heidi

2014-08-01

Ultrasound is widely used as a screening tool in obstetrics with the aim of reducing maternal and foetal morbidity. However, to be effective it is recommended that scanning services follow standard protocols based on national guidelines and that scanning practice is audited to ensure consistency. Bradford has a multi-ethnic population with one of the highest rates of birth defects in the UK and it requires an effective foetal anomaly screening service. We implemented a rolling programme of audits of dating scans, foetal anomaly scans and growth scans carried out by sonographers in Bradford. All three categories of scan were audited using measurable parameters based on national guidelines. Following feedback and re-training to address issues identified, re-audits of dating and foetal anomaly scans were carried out. In both cases, sonographers being re-audited had a marked improvement in their practice. Analysis of foetal abnormality detection rates showed that as a department, we were reaching the nationally agreed detection rates for the Fetal Anomaly Screening Programme auditable conditions. Audit has been shown to be a useful and essential process in achieving consistent scanning practices and high quality images and measurements.

Volcanic activity and satellite-detected thermal anomalies at Central American volcanoes

NASA Technical Reports Server (NTRS)

Stoiber, R. E. (Principal Investigator); Rose, W. I., Jr.

1973-01-01

The author has identified the following significant results. A large nuee ardente eruption occurred at Santiaguito volcano, within the test area on 16 September 1973. Through a system of local observers, the eruption has been described, reported to the international scientific community, extent of affected area mapped, and the new ash sampled. A more extensive report on this event will be prepared. The eruption is an excellent example of the kind of volcanic situation in which satellite thermal imagery might be useful. The Santiaguito dome is a complex mass with a whole series of historically active vents. It's location makes access difficult, yet its activity is of great concern to large agricultural populations who live downslope. Santiaguito has produced a number of large eruptions with little apparent warning. In the earlier ground survey large thermal anomalies were identified at Santiaguito. There is no way of knowing whether satellite monitoring could have detected changes in thermal anomaly patterns related to this recent event, but the position of thermal anomalies on Santiaguito and any changes in their character would be relevant information.

Estimation of anomaly location and size using electrical impedance tomography.

PubMed

Kwon, Ohin; Yoon, Jeong Rock; Seo, Jin Keun; Woo, Eung Je; Cho, Young Gu

2003-01-01

We developed a new algorithm that estimates locations and sizes of anomalies in electrically conducting medium based on electrical impedance tomography (EIT) technique. When only the boundary current and voltage measurements are available, it is not practically feasible to reconstruct accurate high-resolution cross-sectional conductivity or resistivity images of a subject. In this paper, we focus our attention on the estimation of locations and sizes of anomalies with different conductivity values compared with the background tissues. We showed the performance of the algorithm from experimental results using a 32-channel EIT system and saline phantom. With about 1.73% measurement error in boundary current-voltage data, we found that the minimal size (area) of the detectable anomaly is about 0.72% of the size (area) of the phantom. Potential applications include the monitoring of impedance related physiological events and bubble detection in two-phase flow. Since this new algorithm requires neither any forward solver nor time-consuming minimization process, it is fast enough for various real-time applications in medicine and nondestructive testing.

SOME GEOCHEMICAL METHODS OF URANIUM EXPLORATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Illsley, C.T.; Bills, C.W.; Pollock, J.W.

Geochemical research and development projects were carried on to provide basic information which may be applied to exploration or general studies of uranium geology. The applications and limitations of various aspects of geochemistry to uranium geological problems are considerd. Modifications of existing analytical techniques were made and tested in the laboratory and in the field. These include rapid quantitative determination of unranium in water, soil and peat, and of trace amounts of sulfate and phosphate in water. Geochemical anomaly'' has been defined as a significant departure from the average abundance background of an element where the distribution has not beenmore » disturbed by mineralization. The detection and significance of geocthemical anomalies are directly related to the mobility of the element being sought in the zone of weathering. Mobility of uranium is governed by complex physical, chemical, and biological factors. For uranium anomalies in surface materils, the chemicaly factors affecting mobility are the most sigificant. The effects of pH, solubility, coprecipitution, adsorption complexion, or compound formation are discussed in relation to anomalies detected in water, soil, and stream sediments. (auth)« less

Interplay between the b →s l l anomalies and dark matter physics

NASA Astrophysics Data System (ADS)

Kawamura, Junichiro; Okawa, Shohei; Omura, Yuji

2017-10-01

Recently, the LHCb Collaboration has reported the excesses in the b →s l l processes. One of the promising candidates for new physics to explain the anomalies is the extended Standard Model (SM) with vectorlike quarks and leptons. In that model, Yukawa couplings between the extra fermions and SM fermions are introduced, adding extra scalars. Then, the box diagrams involving the extra fields achieve the b →s l l anomalies. It has been known that the excesses require the large Yukawa couplings of leptons, so that this kind of model can be tested by studying correlations with other observables. In this paper, we consider the extra scalar to be a dark matter (DM) candidate, and investigate DM physics as well as the flavor physics and the LHC physics. The DM relic density and the direct-detection cross section are also dominantly given by the Yukawa couplings, so that we find some explicit correlations between DM physics and the flavor physics. In particular, we find the predictions of the b →s l l anomalies against the direct detection of DM.

Obstetric audit: the Bradford way

PubMed Central

Lomas, Karen; Jaworskyj, Suzanne; Thomson, Heidi

2014-01-01

Ultrasound is widely used as a screening tool in obstetrics with the aim of reducing maternal and foetal morbidity. However, to be effective it is recommended that scanning services follow standard protocols based on national guidelines and that scanning practice is audited to ensure consistency. Bradford has a multi-ethnic population with one of the highest rates of birth defects in the UK and it requires an effective foetal anomaly screening service. We implemented a rolling programme of audits of dating scans, foetal anomaly scans and growth scans carried out by sonographers in Bradford. All three categories of scan were audited using measurable parameters based on national guidelines. Following feedback and re-training to address issues identified, re-audits of dating and foetal anomaly scans were carried out. In both cases, sonographers being re-audited had a marked improvement in their practice. Analysis of foetal abnormality detection rates showed that as a department, we were reaching the nationally agreed detection rates for the Fetal Anomaly Screening Programme auditable conditions. Audit has been shown to be a useful and essential process in achieving consistent scanning practices and high quality images and measurements. PMID:27433213

A function approximation approach to anomaly detection in propulsion system test data

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Hoyt, W. A.

1993-01-01

Ground test data from propulsion systems such as the Space Shuttle Main Engine (SSME) can be automatically screened for anomalies by a neural network. The neural network screens data after being trained with nominal data only. Given the values of 14 measurements reflecting external influences on the SSME at a given time, the neural network predicts the expected nominal value of a desired engine parameter at that time. We compared the ability of three different function-approximation techniques to perform this nominal value prediction: a novel neural network architecture based on Gaussian bar basis functions, a conventional back propagation neural network, and linear regression. These three techniques were tested with real data from six SSME ground tests containing two anomalies. The basis function network trained more rapidly than back propagation. It yielded nominal predictions with, a tight enough confidence interval to distinguish anomalous deviations from the nominal fluctuations in an engine parameter. Since the function-approximation approach requires nominal training data only, it is capable of detecting unknown classes of anomalies for which training data is not available.

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

NASA Technical Reports Server (NTRS)

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.

Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data

PubMed Central

Dobra, Adrian; Williams, Nathalie E.; Eagle, Nathan

2015-01-01

With the aim to contribute to humanitarian response to disasters and violent events, scientists have proposed the development of analytical tools that could identify emergency events in real-time, using mobile phone data. The assumption is that dramatic and discrete changes in behavior, measured with mobile phone data, will indicate extreme events. In this study, we propose an efficient system for spatiotemporal detection of behavioral anomalies from mobile phone data and compare sites with behavioral anomalies to an extensive database of emergency and non-emergency events in Rwanda. Our methodology successfully captures anomalous behavioral patterns associated with a broad range of events, from religious and official holidays to earthquakes, floods, violence against civilians and protests. Our results suggest that human behavioral responses to extreme events are complex and multi-dimensional, including extreme increases and decreases in both calling and movement behaviors. We also find significant temporal and spatial variance in responses to extreme events. Our behavioral anomaly detection system and extensive discussion of results are a significant contribution to the long-term project of creating an effective real-time event detection system with mobile phone data and we discuss the implications of our findings for future research to this end. PMID:25806954

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

PubMed Central

Hu, Shiqiang; Zhang, Huanlong; Luo, Lingkun

2014-01-01

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. PMID:25105164

Application of Array Comparative Genomic Hybridization in Newborns with Multiple Congenital Anomalies.

PubMed

Szczałuba, Krzysztof; Nowakowska, Beata; Sobecka, Katarzyna; Smyk, Marta; Castaneda, Jennifer; Klapecki, Jakub; Kutkowska-Kaźmierczak, Anna; Śmigiel, Robert; Bocian, Ewa; Radkowski, Marek; Demkow, Urszula

2016-01-01

Major congenital anomalies are detectable in 2-3 % of the newborn population. Some of their genetic causes are attributable to copy number variations identified by array comparative genomic hybridization (aCGH). The value of aCGH screening as a first-tier test in children with multiple congenital anomalies has been studied and consensus adopted. However, array resolution has not been agreed upon, specifically in the newborn or infant population. Moreover, most array studies have been focused on mixed populations of intellectual disability/developmental delay with or without multiple congenital anomalies, making it difficult to assess the value of microarrays in newborns. The aim of the study was to determine the optimal quality and clinical sensitivity of high-resolution array comparative genomic hybridization in neonates with multiple congenital anomalies. We investigated a group of 54 newborns with multiple congenital anomalies defined as two or more birth defects from more than one organ system. Cytogenetic studies were performed using OGT CytoSure 8 × 60 K microarray. We found ten rearrangements in ten newborns. Of these, one recurrent syndromic microduplication was observed, whereas all other changes were unique. Six rearrangements were definitely pathogenic, including one submicroscopic and five that could be seen on routine karyotype analysis. Four other copy number variants were likely pathogenic. The candidate genes that may explain the phenotype were discussed. In conclusion, high-resolution array comparative hybridization can be applied successfully in newborns with multiple congenital anomalies as the method detects a significant number of pathogenic changes, resulting in early diagnoses. We hypothesize that small changes previously considered benign or even inherited rearrangements should be classified as potentially pathogenic at least until a subsequent clinical assessment would exclude a developmental delay or dysmorphism.

Exploring a Potential Bias in Dark Matter Investigations Using Strongly Lensed Quasars

NASA Astrophysics Data System (ADS)

Hsueh, Jen-Wei; Fassnacht, Christopher; Vegetti, Simona; Springola, Cristiana; Oldham, Lindsay; Despali, Giulia; Auger, Matthew; Xu, Dandan; Metcalf, Benton; McKean, John; Koopmans, Leon; Lagattuta, David

2018-01-01

Simulations based on ΛCDM cosmology predict thousands of substructures under galactic scale have not been detected in the local universe. One hypothesis proposes that most of these substructures are dark for various astrophysical reasons. Gravitational lensing provides a powerful alternative way to probe dark substructures in distant galaxies by detecting their gravitational perturbations and therefore provides insights into the nature of dark matter. Lensed quasars with certain image configurations are especially promising for probing substructure abundance in lens galaxy halos. When the observed flux ratios of the lensed quasar images deviate from the smooth mass model predictions, these “flux-ratio anomalies” are considered to be the evidence of gravitational perturbations. While the standard analysis of flux-ratio anomalies assumes that substructures are the only cause of anomalies, we found that in two edge-on disk lenses, B1555+375 and B0712+472, their flux anomalies can be explained by including disk components into their mass models. Our results bring up a concern with a potential bias in the previous analyses of flux-ratio anomalies. To further investigate the baryonic effects in flux-ratio anomalies, we create mock quasar lenses by selecting disk and elliptical galaxies in the Illustris simulation. Our analysis shows that baryon-induced flux anomalies can be found in all morphological types of lens galaxies. The baryonic effects increase the probability of finding lenses with strong anomalies by 8% in ellipticals and 10~20% in disk lenses, showing that the baryonic effects are unneglectable in the analysis. As future large-scale surveys are expected to bring numerous lensed quasar samples, further investigations on baryonic effects should be done in order to achieve precise constraints on dark matter in the future.

Decision-making on terminating pregnancy for Muslim Arab women pregnant with fetuses with congenital anomalies: maternal affect and doctor-patient communication.

PubMed

Gesser-Edelsburg, Anat; Shahbari, Nour Abed Elhadi

2017-04-04

This study focused on decision-making on terminating pregnancy for Arab Muslim women in Israel who were pregnant with fetuses diagnosed with congenital anomalies. It examined the impact of the doctor-patient interaction on the women's decision, especially in light of social and religious pressures not to terminate under any circumstances. Our goal was to identify perceptions and attitudes of Muslim Arab women who choose to continue their pregnancy following the detection of congenital anomalies in prenatal tests. Specific objectives included (1) To examine the Muslim Arab women's perceptions on genetic testing, and ascertain the reasons for their decision to continue the pregnancy following the detection of a congenital anomaly in the fetus; and (2) To examine risk communication of gynecologists regarding genetic testing and abortions, and regarding the decision of continuing or terminating a pregnancy following detection of a congenital anomaly. The research framework used the constructivist classical qualitative method to understand the experience of women at high risk for congenital anomalies and their experience of how doctors communicate the risk. It showed that the emotional element is no less dominant than religious and social elements. The findings emphasized the disparities between doctors and women regarding emotional involvement (non-directive counselling). The women interviewees (N = 24) felt that this expressed insensitivity. As far as we know, the emotional component has not been raised in previous studies of Muslim women at high risk for congenital defects in their fetus, and therefore comprises a significant contribution of the present study. To mitigate gaps, doctors should take affect into consideration in their communication with patients. It is important for doctors to understand the emotional element in risk communication, both in how they respect women's emotions and in creating an emotional interaction between themselves and the women.

Determinants of parental decisions to abort for chromosome abnormalities.

PubMed

Drugan, A; Greb, A; Johnson, M P; Krivchenia, E L; Uhlmann, W R; Moghissi, K S; Evans, M I

1990-08-01

Parental decisions concerning the continuation of pregnancy following prenatal detection of abnormal chromosomes were evaluated for 80 patients whose diagnosis and prenatal counselling were performed in our centre. Twenty-two anomalies were diagnosed by chorionic villus sampling (CVS) and 58 by amniocentesis. The severity of the chromosome anomaly and associated ultrasound findings in the first vs. second trimester were correlated with patients' decisions. No difference was found in the likelihood of parental decisions to interrupt or continue a pregnancy between CVS and amniocentesis for either the 'severe' or the 'questionable' group of chromosome anomalies. Ninety-three per cent of patients with severe prognosis and 27 per cent with questionable prognosis opted for pregnancy termination (p less than 0.0001). The association of ultrasound anomalies and termination was highly significant (p less than 0.001). The severity of the chromosome anomaly, and, to a lesser extent, the visualization of anomalies on ultrasound were the major determinants of parental decisions to terminate the pregnancy. The diagnosis of an anomaly in the first trimester was no more likely ito lead to a termination of pregnancy than in the second trimester.

Detecting Biosphere anomalies hotspots

NASA Astrophysics Data System (ADS)

Guanche-Garcia, Yanira; Mahecha, Miguel; Flach, Milan; Denzler, Joachim

2017-04-01

The current amount of satellite remote sensing measurements available allow for applying data-driven methods to investigate environmental processes. The detection of anomalies or abnormal events is crucial to monitor the Earth system and to analyze their impacts on ecosystems and society. By means of a combination of statistical methods, this study proposes an intuitive and efficient methodology to detect those areas that present hotspots of anomalies, i.e. higher levels of abnormal or extreme events or more severe phases during our historical records. Biosphere variables from a preliminary version of the Earth System Data Cube developed within the CAB-LAB project (http://earthsystemdatacube.net/) have been used in this study. This database comprises several atmosphere and biosphere variables expanding 11 years (2001-2011) with 8-day of temporal resolution and 0.25° of global spatial resolution. In this study, we have used 10 variables that measure the biosphere. The methodology applied to detect abnormal events follows the intuitive idea that anomalies are assumed to be time steps that are not well represented by a previously estimated statistical model [1].We combine the use of Autoregressive Moving Average (ARMA) models with a distance metric like Mahalanobis distance to detect abnormal events in multiple biosphere variables. In a first step we pre-treat the variables by removing the seasonality and normalizing them locally (μ=0,σ=1). Additionally we have regionalized the area of study into subregions of similar climate conditions, by using the Köppen climate classification. For each climate region and variable we have selected the best ARMA parameters by means of a Bayesian Criteria. Then we have obtained the residuals by comparing the fitted models with the original data. To detect the extreme residuals from the 10 variables, we have computed the Mahalanobis distance to the data's mean (Hotelling's T^2), which considers the covariance matrix of the joint distribution. The proposed methodology has been applied to different areas around the globe. The results show that the method is able to detect historic events and also provides a useful tool to define sensitive regions. This method and results have been developed within the framework of the project BACI (http://baci-h2020.eu/), which aims to integrate Earth Observation data to monitor the earth system and assessing the impacts of terrestrial changes. [1] V. Chandola, A., Banerjee and v., Kumar. Anomaly detection: a survey. ACM computing surveys (CSUR), vol. 41, n. 3, 2009. [2] P. Mahalanobis. On the generalised distance in statistics. Proceedings National Institute of Science, vol. 2, pp 49-55, 1936.

Discrimination Report: ESTCP UXO Discrimination Study, ESTCPProject #MM-0437

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gasperikova, Erika; Smith, J. Torquil; Morrison, H. Frank

2007-12-21

The FY06 Defense Appropriation contains funding for the 'Development of Advanced, Sophisticated, Discrimination Technologies for UXO Cleanup' in the Environmental Security Technology Certification Program. In 2003, the Defense Science Board observed: 'The problem is that instruments that can detect the buried UXOs also detect numerous scrap metal objects and other artifacts, which leads to an enormous amount of expensive digging. Typically 100 holes may be dug before a real UXO is unearthed! The Task Force assessment is that much of this wasteful digging can be eliminated by the use of more advanced technology instruments that exploit modern digital processing andmore » advanced multi-mode sensors to achieve an improved level of discrimination of scrap from UXOs.' Significant progress has been made in discrimination technology. To date, testing of these approaches has been primarily limited to test sites with only limited application at live sites. Acceptance of discrimination technologies requires demonstration of system capabilities at real UXO sites under real world conditions. Any attempt to declare detected anomalies to be harmless and requiring no further investigation require demonstration to regulators of not only individual technologies, but of an entire decision making process. This discrimination study was be the first phase in what is expected to be a continuing effort that will span several years.« less

Effectiveness of Natural Field Induced Polarization for Detecting Polymetallic Deposits

NASA Astrophysics Data System (ADS)

YANG, Jin; LIU, Zhaoping; WANG, Long

To validate the effect of Natural Field Induced Polarization (NFIP), a certain polymetallic deposit was chosen as the test site, where Induced Polarization (IP) using gradient array and the Magnetotelluric (MT) sounding were conducted simultaneously. Analysis and comparison of the data indicated that the anomaly of the Relative Percent Frequency Effect (RPFE) from the MT data and the anomaly of IP coincided well with each other in the extents of the anomalous site and anomaly magnitudes. The results showed that NFIP was effective in the exploration of polymetallic deposits, under certain conditions.

Gravity anomalies on Venus

NASA Technical Reports Server (NTRS)

Sjogren, W. L.; Phillips, R. J.; Birkeland, P. W.; Wimberly, R. N.

1980-01-01

Doppler radio tracking of the Pioneer Venus orbiter has provided gravity measures over a significant portion of Venus. Feature resolution is approximately 300-1000 km within an area extending from 10 deg S to 40 deg N latitude and from 70 deg W to 130 deg E longitude (approximately equal to 200 deg). Many anomalies were detected, and there is considerable correlation with radar altimetry topography (Pettengill et al., 1980). The amplitudes of the anomalies are relatively mild and similar to those on earth at this resolution. Calculations for isostatic adjustment reveal that significant compensation has occurred.

Volcanomagnetic signals during the recent Popocatépetl (México) eruptions and their relation to eruptive activity

NASA Astrophysics Data System (ADS)

Martin-Del Pozzo, A. L.; Cifuentes-Nava, G.; Cabral-Cano, E.; Sánchez-Rubio, G.; Reyes, M.; Martínez-Bringas, Alicia; Garcia, E.; Arango-Galvan, C.

2002-03-01

An interdisciplinary approach correlating magnetic anomalies with composition of the ejecta in each eruption, as well as with seismicity, was used to study the effect of magmatic activity on the local magnetic record at Popocatépetl Volcano located 65 km southeast of México City. Eruptions began on December, 1994, and have continued with dome growth and ash emissions since then. The Tlamacas (TLA) geomagnetic total field monitoring station, located 5 km away from Popocatépetl's crater, was installed in December, 1997, in order to detect magnetic anomalies induced by this activity. Spatial correlation and weighted difference methods were applied to detect temporal geomagnetic anomalies using TLA's record and the Teoloyucan Magnetic Observatory as a reference station. Weighted differences were applied to cancel the effects of non-vulcanogenic external field variations. Magnetic anomalies over a 2-year time span were classified into four types correlating them with geochemical, seismic and visual monitoring of the volcanic activity. Magnetic anomalies are believed to be caused by magma injection and gas pressure build-up, which is sensitive to vent morphology and clearing during eruption, although some anomalies appear to be thermally related, changes in the stress field are very important. Most magnetic anomalies are short time signals that reverse to baseline level. Decreasing anomalies (-0.5 to -6.8 nT) precede eruptions by 1-8 days. The presence of a mafic magmatic component was determined by mineral examination and silica and magnesium analyses on the ejecta from the 1997-1999 eruptions. Whole rock analyses ranged from dacitic (65% SiO 2) to andesitic (57% SiO 2) with 2-6.6% MgO. The higher MgO, lower silica samples contain forsteritic olivine (Fo90). SiO 2 does not increase and MgO does not increase with time, suggesting ascent of small magma pulses which are consistent with the magnetic data.

On-line condition monitoring applications in nuclear power plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hastiemian, H. M.; Feltus, M. A.

2006-07-01

Existing signals from process instruments in nuclear power plants can be sampled while the plant is operating and analyzed to verify the static and dynamic performance of process sensors, identify process-to-sensor problems, detect instrument anomalies such as venturi fouling, measure the vibration of the reactor vessel and its internals, or detect thermal hydraulic anomalies within the reactor coolant system. These applications are important in nuclear plants to satisfy a variety of objectives such as: 1) meeting the plant technical specification requirements; 2) complying with regulatory regulations; 3) guarding against equipment and process degradation; 4) providing a means for incipient failuremore » detection and predictive maintenance; or 5) identifying the root cause of anomalies in equipment and plant processes. The technologies that are used to achieve these objectives are collectively referred to as 'on-line condition monitoring.' This paper presents a review of key elements of these technologies, provides examples of their use in nuclear power plants, and illustrates how they can be integrated into an on-line condition monitoring system for nuclear power plants. (authors)« less

Interactions between Cytokines, Congenital Anomalies of Kidney and Urinary Tract and Chronic Kidney Disease

PubMed Central

Simões e Silva, Ana Cristina; Valério, Flávia Cordeiro; Vasconcelos, Mariana Affonso; Miranda, Débora Marques; Oliveira, Eduardo Araújo

2013-01-01

Fetal hydronephrosis is the most common anomaly detected on antenatal ultrasound, affecting 1–5% of pregnancies. Postnatal investigation has the major aim in detecting infants with severe urinary tract obstruction and clinically significant urinary tract anomalies among the heterogeneous universe of patients. Congenital uropathies are frequent causes of pediatric chronic kidney disease (CKD). Imaging techniques clearly contribute to this purpose; however, sometimes, these exams are invasive, very expensive, and not sufficient to precisely define the best approach as well as the prognosis. Recently, biomarkers have become a focus of clinical research as potentially useful diagnostic tools in pediatric urological diseases. In this regard, recent studies suggest a role for cytokines and chemokines in the pathophysiology of CAKUT and for the progression to CKD. Some authors proposed that the evaluation of these inflammatory mediators might help the management of postnatal uropathies and the detection of patients with high risk to developed chronic kidney disease. Therefore, the aim of this paper is to revise general aspects of cytokines and the link between cytokines, CAKUT, and CKD by including experimental and clinical evidence. PMID:24066006

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

NASA Astrophysics Data System (ADS)

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.

Integrated System Health Management Development Toolkit

NASA Technical Reports Server (NTRS)

Figueroa, Jorge; Smith, Harvey; Morris, Jon

2009-01-01

This software toolkit is designed to model complex systems for the implementation of embedded Integrated System Health Management (ISHM) capability, which focuses on determining the condition (health) of every element in a complex system (detect anomalies, diagnose causes, and predict future anomalies), and to provide data, information, and knowledge (DIaK) to control systems for safe and effective operation.

Design, fabrication and acceptance testing of a zero gravity whole body shower

NASA Technical Reports Server (NTRS)

Schumacher, E. A.; Lenda, J. A.

1974-01-01

Recent research and development programs have established the ability of the zero gravity whole body shower to maintain a comfortable environment in which the crewman can safely cleanse and dry the body. The purpose of this program was to further advance the technology of whole body bathing and to demonstrate technological readiness including in-flight maintenance by component replacement for flight applications. Three task efforts of this program are discussed. Conceptual designs and system tradeoffs were accomplished in task 1. Task 2 involved the formulation of preliminary and final designs for the shower, while task 3 included the fabrication and test of the shower assembly. Particular attention is paid to the evaluation and correction of test anomalies during the final phase of the program.

Geochemical and Visual Indicators of Hydrothermal Fluid Flow through a Sediment-Hosted Volcanic Ridge in the Central Bransfield Basin (Antarctica)

PubMed Central

Aquilina, Alfred; Connelly, Douglas P.; Copley, Jon T.; Green, Darryl R. H.; Hawkes, Jeffrey A.; Hepburn, Laura E.; Huvenne, Veerle A. I.; Marsh, Leigh; Mills, Rachel A.; Tyler, Paul A.

2013-01-01

In the austral summer of 2011 we undertook an investigation of three volcanic highs in the Central Bransfield Basin, Antarctica, in search of hydrothermal activity and associated fauna to assess changes since previous surveys and to evaluate the extent of hydrothermalism in this basin. At Hook Ridge, a submarine volcanic edifice at the eastern end of the basin, anomalies in water column redox potential (Eh) were detected close to the seafloor, unaccompanied by temperature or turbidity anomalies, indicating low-temperature hydrothermal discharge. Seepage was manifested as shimmering water emanating from the sediment and from mineralised structures on the seafloor; recognisable vent endemic fauna were not observed. Pore fluids extracted from Hook Ridge sediment were depleted in chloride, sulfate and magnesium by up to 8% relative to seawater, enriched in lithium, boron and calcium, and had a distinct strontium isotope composition (87Sr/86Sr  = 0.708776 at core base) compared with modern seawater (87Sr/86Sr ≈0.70918), indicating advection of hydrothermal fluid through sediment at this site. Biogeochemical zonation of redox active species implies significant moderation of the hydrothermal fluid with in situ diagenetic processes. At Middle Sister, the central ridge of the Three Sisters complex located about 100 km southwest of Hook Ridge, small water column Eh anomalies were detected but visual observations of the seafloor and pore fluid profiles provided no evidence of active hydrothermal circulation. At The Axe, located about 50 km southwest of Three Sisters, no water column anomalies in Eh, temperature or turbidity were detected. These observations demonstrate that the temperature anomalies observed in previous surveys are episodic features, and suggest that hydrothermal circulation in the Bransfield Strait is ephemeral in nature and therefore may not support vent biota. PMID:23359806

Contribution of ground surface altitude difference to thermal anomaly detection using satellite images: Application to volcanic/geothermal complexes in the Andes of Central Chile

NASA Astrophysics Data System (ADS)

Gutiérrez, Francisco J.; Lemus, Martín; Parada, Miguel A.; Benavente, Oscar M.; Aguilera, Felipe A.

2012-09-01

Detection of thermal anomalies in volcanic-geothermal areas using remote sensing methodologies requires the subtraction of temperatures, not provided by geothermal manifestations (e.g. hot springs, fumaroles, active craters), from satellite image kinetic temperature, which is assumed to correspond to the ground surface temperature. Temperatures that have been subtracted in current models include those derived from the atmospheric transmittance, reflectance of the Earth's surface (albedo), topography effect, thermal inertia and geographic position effect. We propose a model that includes a new parameter (K) that accounts for the variation of temperature with ground surface altitude difference in areas where steep relief exists. The proposed model was developed and applied, using ASTER satellite images, in two Andean volcanic/geothermal complexes (Descabezado Grande-Cerro Azul Volcanic Complex and Planchón-Peteroa-Azufre Volcanic Complex) where field data of atmosphere and ground surface temperature as well as radiation for albedo calibration were obtained in 10 selected sites. The study area was divided into three zones (Northern, Central and Southern zones) where the thermal anomalies were obtained independently. K value calculated for night images of the three zones are better constrained and resulted to be very similar to the Environmental Lapse Rate (ELR) determined for a stable atmosphere (ELR > 7 °C/km). Using the proposed model, numerous thermal anomalies in areas of ≥ 90 m × 90 m were identified that were successfully cross-checked in the field. Night images provide more reliable information for thermal anomaly detection than day images because they record higher temperature contrast between geothermal areas and its surroundings and correspond to more stable atmospheric condition at the time of image acquisition.

Dental anomalies associated with cleft lip and palate in Northern Finland.

PubMed

Lehtonen, V; Anttonen, V; Ylikontiola, L P; Koskinen, S; Pesonen, P; Sándor, G K

2015-12-01

Despite the reported occurrence of dental anomalies of cleft lip and palate, little is known about their prevalence in children from Northern Finland with cleft lip and palate. The aim was to investigate the prevalence of dental anomalies among patients with different types of clefts in Northern Finland. Design and Statistics: patient records of 139 subjects aged three years and older (with clefts treated in Oulu University Hospital, Finland during the period 1996-2010 (total n. 183) were analysed for dental anomalies including the number of teeth, morphological and developmental anomalies and their association with the cleft type. The analyses were carried out using Chi-square test and Fisher's exact test. Differences between the groups were considered statistically significant at p values < 0.05. More than half of the patients had clefts of the hard palate, 18% of the lip and palate, and 13% of the lip. At least one dental anomaly was detected in 47% of the study population. Almost one in three (26.6%) subjects had at least one anomaly and 17.9% had two or three anomalies. The most common type of anomaly in permanent teeth were missing teeth followed by supernumerary teeth. Supernumerary teeth were significantly more apparent when the lip was involved in the cleft compared with palatal clefts. Missing teeth were less prevalent among those 5 years or younger. The prevalence of different anomalies was significantly associated with the cleft type in both age groups. Dental anomalies are more prevalent among cleft children than in the general population in Finland. The most prevalent anomalies associated with cleft were missing and supernumerary teeth.

Monitoring System for Storm Readiness and Recovery of Test Facilities: Integrated System Health Management (ISHM) Approach

NASA Technical Reports Server (NTRS)

Figueroa, Fernando; Morris, Jon; Turowski, Mark; Franzl, Richard; Walker, Mark; Kapadia, Ravi; Venkatesh, Meera; Schmalzel, John

2010-01-01

Severe weather events are likely occurrences on the Mississippi Gulf Coast. It is important to rapidly diagnose and mitigate the effects of storms on Stennis Space Center's rocket engine test complex to avoid delays to critical test article programs, reduce costs, and maintain safety. An Integrated Systems Health Management (ISHM) approach and technologies are employed to integrate environmental (weather) monitoring, structural modeling, and the suite of available facility instrumentation to provide information for readiness before storms, rapid initial damage assessment to guide mitigation planning, and then support on-going assurance as repairs are effected and finally support recertification. The system is denominated Katrina Storm Monitoring System (KStorMS). Integrated Systems Health Management (ISHM) describes a comprehensive set of capabilities that provide insight into the behavior the health of a system. Knowing the status of a system allows decision makers to effectively plan and execute their mission. For example, early insight into component degradation and impending failures provides more time to develop work around strategies and more effectively plan for maintenance. Failures of system elements generally occur over time. Information extracted from sensor data, combined with system-wide knowledge bases and methods for information extraction and fusion, inference, and decision making, can be used to detect incipient failures. If failures do occur, it is critical to detect and isolate them, and suggest an appropriate course of action. ISHM enables determining the condition (health) of every element in a complex system-of-systems or SoS (detect anomalies, diagnose causes, predict future anomalies), and provide data, information, and knowledge (DIaK) to control systems for safe and effective operation. ISHM capability is achieved by using a wide range of technologies that enable anomaly detection, diagnostics, prognostics, and advise for control: (1) anomaly detection algorithms and strategies, (2) fusion of DIaK for anomaly detection (model-based, numerical, statistical, empirical, expert-based, qualitative, etc.), (3) diagnostics/prognostics strategies and methods, (4) user interface, (5) advanced control strategies, (6) integration architectures/frameworks, (7) embedding of intelligence. Many of these technologies are mature, and they are being used in the KStorMS. The paper will describe the design, implementation, and operation of the KStorMS; and discuss further evolution to support other needs such as condition-based maintenance (CBM).

The intermediate wavelength magnetic anomaly field of the north Pacific and possible source distributions

NASA Technical Reports Server (NTRS)

Labrecque, J. L.; Cande, S. C.; Jarrard, R. D. (Principal Investigator)

1983-01-01

A technique that eliminates external field sources and the effects of strike aliasing was used to extract from marine survey data the intermediate wavelength magnetic anomaly field for (B) in the North Pacific. A strong correlation exists between this field and the MAGSAT field although a directional sensitivity in the MAGSAT field can be detected. The intermediate wavelength field is correlated to tectonic features. Island arcs appear as positive anomalies of induced origin likely due to variations in crustal thickness. Seamount chains and oceanic plateaus also are manifested by strong anomalies. The primary contribution to many of these anomalies appears to be due to a remanent magnetization. The source parameters for the remainder of these features are presently unidentified ambiguous. Results indicate that the sea surface field is a valuable source of information for secular variation analysis and the resolution of intermediate wavelength source parameters.

Remote detection of geobotanical anomalies associated with hydrocarbon microseepage

NASA Technical Reports Server (NTRS)

Rock, B. N.

1985-01-01

As part of the continuing study of the Lost River, West Virginia NASA/Geosat Test Case Site, an extensive soil gas survey of the site was conducted during the summer of 1983. This soil gas survey has identified an order of magnitude methane, ethane, propane, and butane anomaly that is precisely coincident with the linear maple anomaly reported previously. This and other maple anomalies were previously suggested to be indicative of anaerobic soil conditions associated with hydrocarbon microseepage. In vitro studies support the view that anomalous distributions of native tree species tolerant of anaerobic soil conditions may be useful indicators of methane microseepage in heavily vegetated areas of the United States characterized by deciduous forest cover. Remote sensing systems which allow discrimination and mapping of native tree species and/or species associations will provide the exploration community with a means of identifying vegetation distributional anomalies indicative of microseepage.

Brain anomalies in velo-cardio-facial syndrome

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitnick, R.J.; Bello, J.A.; Shprintzen, R.J.

Magnetic resonance imaging of the brain in 11 consecutively referred patients with velo-cardio-facial syndrome (VCF) showed anomalies in nine cases including small vermis, cysts adjacent to the frontal horns, and small posterior fossa. Focal signal hyperintensities in the white matter on long TR images were also noted. The nine patients showed a variety of behavioral abnormalities including mild development delay, learning disabilities, and characteristic personality traits typical of this common multiple anomaly syndrome which has been related to a microdeletion at 22q11. Analysis of the behavorial findings showed no specific pattern related to the brain anomalies, and the patients withmore » VCF who did not have detectable brain lesions also had behavioral abnormalities consistent with VCF. The significance of the lesions is not yet known, but the high prevalence of anomalies in this sample suggests that structural brain abnormalities are probably common in VCF. 25 refs.« less

Enabling the Discovery of Recurring Anomalies in Aerospace System Problem Reports using High-Dimensional Clustering Techniques

NASA Technical Reports Server (NTRS)

Srivastava, Ashok, N.; Akella, Ram; Diev, Vesselin; Kumaresan, Sakthi Preethi; McIntosh, Dawn M.; Pontikakis, Emmanuel D.; Xu, Zuobing; Zhang, Yi

2006-01-01

This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining techniques to discover anomalies in free-text reports regarding system health and safety of two aerospace systems. We discuss two problems of significant importance in the aviation industry. The first problem is that of automatic anomaly discovery about an aerospace system through the analysis of tens of thousands of free-text problem reports that are written about the system. The second problem that we address is that of automatic discovery of recurring anomalies, i.e., anomalies that may be described m different ways by different authors, at varying times and under varying conditions, but that are truly about the same part of the system. The intent of recurring anomaly identification is to determine project or system weakness or high-risk issues. The discovery of recurring anomalies is a key goal in building safe, reliable, and cost-effective aerospace systems. We address the anomaly discovery problem on thousands of free-text reports using two strategies: (1) as an unsupervised learning problem where an algorithm takes free-text reports as input and automatically groups them into different bins, where each bin corresponds to a different unknown anomaly category; and (2) as a supervised learning problem where the algorithm classifies the free-text reports into one of a number of known anomaly categories. We then discuss the application of these methods to the problem of discovering recurring anomalies. In fact the special nature of recurring anomalies (very small cluster sizes) requires incorporating new methods and measures to enhance the original approach for anomaly detection. ?& pant 0-

Prevalence of developmental dental hard-tissue anomalies and association with caries and oral hygiene status of children in Southwestern, Nigeria.

PubMed

Popoola, Bamidele O; Onyejaka, Nneka; Folayan, Morenike O

2016-07-07

Developmental dental hard tissue anomalies are often associated with oral health problems. This study determined the clinical prevalence of developmental dental hard tissue anomalies in the permanent dentition of children resident in southwestern Nigeria and its association with dental caries and poor oral hygiene status. This was a cross-sectional study recruiting 1565 school children, 12 to 15 year old attending schools in Ibadan, Oyo State and Ile-Ife, Osun State. All eligible study participants had oral examinations conducted to determine presence of developmental hard dental tissue anomalies, caries and oral hygiene status. The prevalence of developmental dental hard tissue anomalies was determined. Logistic Poisson regression was used to determine the association of between developmental dental hard tissue anomalies, caries and oral hygiene status. Only 65 (4.2 %) children had clinically diagnosed developmental dental hard tissue anomalies. The most prevalent anomaly was enamel hypoplasia (2.2 %). More females (p = 0.003) and more children with middle socioeconomic class (p = 0.001) had enamel hypoplasia. The probability of having poor oral hygiene was significantly increased for children with developmental dental anomalies (APR: 0.07; 95 % CI: 0.03 - 0.12; p = 0.002). The probability of having caries was insignificantly increased for children with developmental dental hard tissue anomalies (APR: 0.005; 95 % CI: -0.03 - 0.04; p = 0.08). The most prevalence clinically detectable developmental dental hard tissue anomalies for the study population was enamel hypoplasia. The presence of developmental dental hard tissue anomalies significantly increased the chances of having poor oral hygiene but not caries. Further studies are required to understand if poor oral hygiene is associated with dental caries in children with developmental dental hard tissue anomalies.

Seismo-ionospheric anomalies in total electron content of the GIM and electron density of DEMETER before the 27 February 2010 M8.8 Chile earthquake

NASA Astrophysics Data System (ADS)

Ho, Yi-Ying; Jhuang, Hau-Kun; Su, Yung-Chih; Liu, Jann-Yenq

2013-06-01

In this paper we examine the pre-earthquake ionospheric anomalies by the total electron content (TEC) extracted from GIM (global ionospheric map) and the electron density (Ne) observed by the DEMETER (Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions) satellite during the 2010 M8.8 Chile earthquake. Temporal variations show the nighttime TEC and Ne simultaneously increase 9-19 days before the earthquake. A cross-comparison of data recorded during the period of 1 February to 3 March in 2006-2010 confirms the above temporal anomalies specifically appear in 2010. The spatial analyses show that the anomalies tend to appear over the epicenter.

Contrast enhancing solution for use in confocal microscopy

DOEpatents

Tannous, Zeina; Torres, Abel; Gonzalez, Salvador

2006-10-31

A method of optically detecting a tumor during surgery. The method includes imaging at least one test point defined on the tumor using a first optical imaging system to provide a first tumor image. The method further includes excising a first predetermined layer of the tumor for forming an in-vivo defect area. A predetermined contrast enhancing solution is disposed on the in-vivo defect area, which is adapted to interact with at least one cell anomaly, such as basal cell carcinoma, located on the in-vivo defect area for optically enhancing the cell anomaly. Thereafter the defect area can be optically imaged to provide a clear and bright representation of the cell anomaly to aid a surgeon while surgically removing the cell anomaly.

Towards Reliable Evaluation of Anomaly-Based Intrusion Detection Performance

NASA Technical Reports Server (NTRS)

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 variability in training data for anomaly detectors, but has ignored variability in the attack signal that will necessarily affect the evaluation results for such detectors. We posit that current evaluation strategies implicitly assume that attacks always manifest in a stable manner; we show that this assumption is wrong. We describe a simple experiment to demonstrate the effects of environmental noise on the manifestation of attacks in data and introduce the notion of attack manifestation stability. Finally, we argue that conclusions about detector performance will be unreliable and incomplete if the stability of attack manifestation is not accounted for in the evaluation strategy.

Detection of anomalies in ocean acoustic velocity structure and their effect in sea-bottom crustal deformation measurement: synthetic test and future suggestion

NASA Astrophysics Data System (ADS)

Nagai, S.; Eto, S.; Tadokoro, K.; Watanabe, T.

2011-12-01

On-land geodetic observations are not enough to monitor crustal activities in and around the subduction zone, so seafloor geodetic observations have been required. However, present accuracy of seafloor geodetic observation is an order of 1 cm or larger, which is difficult to detect differences from plate motion in short time interval, which means a plate coupling rate and its spatio-temporal variation. Our group has been developed observation system and methodology for seafloor geodesy, which is combined kinematic GPS and ocean acoustic ranging. One of influence factors is acoustic velocity change in ocean, due to change in temperature, ocean currents in different scale, and so on. A typical perturbation of acoustic velocity makes an order of 1 ms difference in travel time, which corresponds to 1 m difference in ray length. We have investigated this effect in seafloor geodesy using both observed and synthetic data to reduce estimation error of benchmarker (transponder) positions and to develop our strategy for observation and its analyses. In this paper, we focus on forward modeling of travel times of acoustic ranging data and recovery tests using synthetic data comparing with observed results [Eto et al., 2011; in this meeting]. Estimation procedure for benchmarker positions is similar to those used in earthquake location method and seismic tomography. So we have applied methods in seismic study, especially in tomographic inversion. First, we use method of a one-dimensional velocity inversion with station corrections, proposed by Kissling et al. [1994], to detect spatio-temporal change in ocean acoustic velocity from observed data in the Suruga-Nankai Trough, Japan. From these analyses, some important information has been clarified in travel time data [Eto et al., 2011]. Most of them can explain small velocity anomaly at a depth of 300m or shallower, through forward modeling of travel time data using simple velocity structure with velocity anomaly. However, due to simple data acquisition procedure, we cannot detect velocity anomaly(s) in space and time precisely, that is size of anomaly and its (their) movement. As a next step, we demonstrate recovery of benchmarker positions in tomographic inversion using synthetic data including anomalous travel time data to develop idea to calculate benchmarker positions with high-accuracy. In the tomographic inversion, we introduce some constraints corresponding to realistic conditions. This step gives us new developed system to detect crustal deformation in seafloor geodesy and new findings for understanding these in and around plate boundaries.

Epidemiology and structure of congenital anomalies of the newborns in the region of Novi Sad (Vojvodina, Serbia) in 1996 and 2006.

PubMed

Ristivojević, Andjelka; Djokić, Petra Lukić; Katanić, Dragan; Dobanovacki, Dušanka; Privrodski, Jadranka Jovanović

2016-05-01

According to the World Health Organization (WHO) definition, congenital anomalies are all disorders of the organs or tissues, regardless of whether they are visible at birth or manifest in life, and are registered in the International Classification of Diseases. The aim of this study was to compare the incidence and structure of prenatally detected and clinically manifested congenital anomalies in the newborns in the region of Novi Sad (Province of Vojvodina, Serbia) in the two distant years (1996 and 2006). This retrospective cohort study included all the children born at the Clinic for Gynecology and Obstetrics (Clinical Center of Vojvodina) in Novi Sad during 1996 and 2006. The incidence and the structure of congenital anomalies were analyzed. During 1996 there were 6,099 births and major congenital anomalies were found in 215 infants, representing 3.5%. In 2006 there were 6,628 births and major congenital anomalies were noted in 201 newborns, which is 3%. During 1996 there were more children with anomalies of musculoskeletal system, urogenital tract, with anomalies of the central nervous system and chromosomal abnormalities. During the year 2006 there were more children with cardiovascular anomalies, followed by urogenital anomalies, with significant decline in musculoskeletal anomalies. The distribution of the newborns with major congenital anomalies, regarding perinatal outcome, showed the difference between the studied years. In 2006 the increasing number of children required further investigation and treatment. There is no national registry of congenital anomalies in Serbia so the aim of this study was to enlight this topic. In the span of ten years, covering the period of the NATO campaign in Novi Sad and Serbia, the frequency of major congenital anomalies in the newborns was not increased. The most frequent anomalies observed during both years implied the musculosketelal, cardiovascular, urogenital and central nervous system. In the year 2006 there was a significant eruption of cardiovascular anomalies and a significant decrease of musculoskeletal anomalies, chromosomal abnormalities and central nervous system anomalies, while the number of urogenital anomalies declined compared to the year 1996.

International confederation for cleft lip and palate and related craniofacial anomalies task force report: holistic outcomes.

PubMed

Broder, Hillary L

2014-11-01

Objective : This paper describes the process and outcomes of the 2013 American Cleft Palate-Craniofacial Association task force on Holistic Outcomes. The goals and membership of the task force are presented. Methods : Using internet communication, the group introduced themselves, shared ideas and information related to holistic assessment and implementation of using a validated holistic measure, the Child Oral Health Impact Profile (COHIP) at participating international sites. Results : Data from the sites were analyzed using descriptive statistics. Administration of the COHIP was successful. It varied from self-completion as well as verbal presentation due to language differences and a function of the short time period to complete collection. Additionally qualitative comments were reported by the task force site directors. Conclusions : Future directions for holistic assessment and communication among task force members and sites were discussed at the Congress and are presented in this report.

Experiments on Adaptive Techniques for Host-Based Intrusion Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

DRAELOS, TIMOTHY J.; COLLINS, MICHAEL J.; DUGGAN, DAVID P.

2001-09-01

This research explores four experiments of adaptive host-based intrusion detection (ID) techniques in an attempt to develop systems that can detect novel exploits. The technique considered to have the most potential is adaptive critic designs (ACDs) because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Preliminary results of ID using an ACD, an Elman recurrent neural network, and a statistical anomaly detection technique demonstrate an ability to learn to distinguish between clean and exploit data. We used the Solaris Basic Security Module (BSM) as a data source and performed considerablemore » preprocessing on the raw data. A detection approach called generalized signature-based ID is recommended as a middle ground between signature-based ID, which has an inability to detect novel exploits, and anomaly detection, which detects too many events including events that are not exploits. The primary results of the ID experiments demonstrate the use of custom data for generalized signature-based intrusion detection and the ability of neural network-based systems to learn in this application environment.« less

Children of the Philippines: attitudes toward visible physical impairment.

PubMed

Harper, D C; Peterson, D B

2001-11-01

This pilot study was designed to evaluate children's attitudes and understanding of physical disabilities with special reference to those with craniofacial anomalies in the Philippines. Children with and without craniofacial anomalies were studied. This was a two-group correlational design with additional statistical assessment of subgroup differences. Each group was interviewed and information obtained on a standard disability preference task, attributions for playmate choice, and frequency of contact with disabilities. Parents completed a structured interview. Participants were 122 children recruited from Negros, Philippines. Fifty-four children with craniofacial anomalies (aged 7 to 12 years) were enrolled in the study, and 68 children without any disabilities were recruited from a local school in Bacolod City, Negros, Philippines. Participants completed a picture-ranking interview of specific physical disabilities and provided their reasons for their play choices and their contact with physical disabilities. The Kendall W correlation was significant for the children with craniofacial anomalies and for those without physical disabilities. Both groups reported lower preferences for disabilities that interfere with play and social interactions. Children depicted with facial anomalies received lower preference, compared with other physical disabilities. Children with craniofacial anomalies who have experienced surgical repair reported more positive rankings for the child depicted with a facial cleft. Sex differences in disability preference were noted. Children in the Philippines with and without craniofacial differences revealed similarities in preferences to children in several Western (United States) and non-Western countries. Children depicted with facial anomalies received lower preference than other visible physical differences. Children reported both positive and negative explanations for their disability play preferences. Facial differences may result in illogical and negative explanations for social avoidance among children. Similar reactions are noted in other parts of the world.

Target detection using the background model from the topological anomaly detection algorithm

NASA Astrophysics Data System (ADS)

Dorado Munoz, Leidy P.; Messinger, David W.; Ziemann, Amanda K.

2013-05-01

The Topological Anomaly Detection (TAD) algorithm has been used as an anomaly detector in hyperspectral and multispectral images. TAD is an algorithm based on graph theory that constructs a topological model of the background in a scene, and computes an anomalousness ranking for all of the pixels in the image with respect to the background in order to identify pixels with uncommon or strange spectral signatures. The pixels that are modeled as background are clustered into groups or connected components, which could be representative of spectral signatures of materials present in the background. Therefore, the idea of using the background components given by TAD in target detection is explored in this paper. In this way, these connected components are characterized in three different approaches, where the mean signature and endmembers for each component are calculated and used as background basis vectors in Orthogonal Subspace Projection (OSP) and Adaptive Subspace Detector (ASD). Likewise, the covariance matrix of those connected components is estimated and used in detectors: Constrained Energy Minimization (CEM) and Adaptive Coherence Estimator (ACE). The performance of these approaches and the different detectors is compared with a global approach, where the background characterization is derived directly from the image. Experiments and results using self-test data set provided as part of the RIT blind test target detection project are shown.

Mapping Subsurface Structure at Guar Kepah by using Ground Penetrating Radar

NASA Astrophysics Data System (ADS)

Mansor, Hafizuddin; Rosli, Najmiah; Ismail, N. A.; Saidin, M.; Masnan, S. S. K.

2018-04-01

A Ground Penetrating Radar (GPR) survey was conducted at Guar Kepah to detect buried object before commencement of archaeological gallery construction. The study area covered around 20 m length and 14 m width. 15 GPR lines were constructed from north to south with 20 m length, 1 m spacing and parallel to each other. The 500 MHz closed antenna had been used in this study. The surface findings were noticed before started GPR survey. The data was analysed and interpreted by using Groundvision software and several filters were applied to radargrams to enhance the data. Based on the result, several anomalies were detected. The surface findings also detected by GPR which cause hyperbolic curve in radargrams. The subsurface layer was detected by GPR survey. The anomalies are assigned to several classes based on the pattern of signals obtained in radargrams.

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

NASA Astrophysics Data System (ADS)

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.

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

PubMed

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

2017-11-01

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

Visualizing the chiral anomaly in Dirac and Weyl semimetals with photoemission spectroscopy

NASA Astrophysics Data System (ADS)

Behrends, Jan; Grushin, Adolfo G.; Ojanen, Teemu; Bardarson, Jens H.

2016-02-01

Quantum anomalies are the breaking of a classical symmetry by quantum fluctuations. They dictate how physical systems of diverse nature, ranging from fundamental particles to crystalline materials, respond topologically to external perturbations, insensitive to local details. The anomaly paradigm was triggered by the discovery of the chiral anomaly that contributes to the decay of pions into photons and influences the motion of superfluid vortices in 3He-A. In the solid state, it also fundamentally affects the properties of topological Weyl and Dirac semimetals, recently realized experimentally. In this work we propose that the most identifying consequence of the chiral anomaly, the charge density imbalance between fermions of different chirality induced by nonorthogonal electric and magnetic fields, can be directly observed in these materials with the existing technology of photoemission spectroscopy. With angle resolution, the chiral anomaly is identified by a characteristic note-shaped pattern of the emission spectra, originating from the imbalanced occupation of the bulk states and a previously unreported momentum dependent energy shift of the surface state Fermi arcs. We further demonstrate that the chiral anomaly likewise leaves an imprint in angle averaged emission spectra, facilitating its experimental detection. Thereby, our work provides essential theoretical input to foster the direct visualization of the chiral anomaly in condensed matter, in contrast to transport properties, such as negative magnetoresistance, which can also be obtained in the absence of a chiral anomaly.

Microgravity and Electrical Resistivity Techniques for Detection of Caves and Clandestine Tunnels

NASA Astrophysics Data System (ADS)

Crawford, N. C.; Croft, L. A.; Cesin, G. L.; Wilson, S.

2006-05-01

The Center for Cave and Karst Studies, CCKS, has been using microgravity to locate caves from the ground's surface since 1985. The geophysical subsurface investigations began during a period when explosive and toxic vapors were rising from the karst aquifer under Bowling Green into homes, businesses, and schools. The USEPA provided the funding for this Superfund Emergency, and the CCKS was able to drill numerous wells into low-gravity anomalies to confirm and even map the route of caves in the underlying limestone bedrock. In every case, a low-gravity anomaly indicated a bedrock cave, a cave with a collapsed roof or locations where a bedrock cave had collapsed and filled with alluvium. At numerous locations, several wells were cored into microgravity anomalies and in every case, additional wells were drilled on both sides of the anomalies to confirm that the technique was in fact reliable. The wells cored on both sides of the anomalies did not intersect caves but instead intersected virtually solid limestone. Microgravity also easily detected storm sewers and even sanitary sewers, sometimes six meters (twenty feet) beneath the surface. Microgravity has also been used on many occasions to investigate sinkhole collapses. It identified potential collapse areas by detecting voids in the unconsolidated material above bedrock. The system will soon be tested over known tunnels and then during a blind test along a section of the U.S. border at Nogales, Arizona. The CCKS has experimented with other geophysical techniques, particularly ground penetrating radar, seismic and electrical resistivity. In the late 1990s the CCKS started using the Swift/Sting resistivity meter to perform karst geophysical subsurface investigations. The system provides good depth to bedrock data, but it is often difficult to interpret bedrock caves from the modeled data. The system typically used now by the CCKS to perform karst subsurface investigations is to use electrical resistivity traverses followed by microgravity over suspect areas identified on the modeled resistivity data. Some areas of high resistivity indicate caves, but others simply indicate pockets of dry limestone, and the signatures looks virtually identical. Therefore, the CCKS performs microgravity over all suspect areas along the resistivity traverses. A low-gravity anomaly that corresponds with a high-resistivity anomaly indicates a cave location. A high-resistivity anomaly that does not also have a low- gravity anomaly indicates a pocket of dry limestone. Numerous cored wells have been drilled both into the anomalies and on both sides to confirm the cave locations and to establish that the technique is accurate. The September 11, 2001 World Trade Center catastrophe was the catalyst for the formation of a program within the CCKS to use the techniques for locating bedrock caves and voids in unconsolidated materials for search and rescue and for locating clandestine tunnels. We are now into our third year of a grant from the Kentucky Science and Technology Center to develop a robot that will measure microgravity and other geophysical techniques. The robot has the potential for detecting clandestine tunnels under the U.S. border as well as military applications. The system will soon be tested over known tunnels and then during a blind test along a section of the U.S. border at Nogales, Arizona.

Weather Satellite Thermal IR Responses Prior to Earthquakes

NASA Technical Reports Server (NTRS)

OConnor, Daniel P.

2005-01-01

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

Behavior Analysis Based on Coordinates of Body Tags

NASA Astrophysics Data System (ADS)

Luštrek, Mitja; Kaluža, Boštjan; Dovgan, Erik; Pogorelc, Bogdan; Gams, Matjaž

This paper describes fall detection, activity recognition and the detection of anomalous gait in the Confidence project. The project aims to prolong the independence of the elderly by detecting falls and other types of behavior indicating a health problem. The behavior will be analyzed based on the coordinates of tags worn on the body. The coordinates will be detected with radio sensors. We describe two Confidence modules. The first one classifies the user's activity into one of six classes, including falling. The second one detects walking anomalies, such as limping, dizziness and hemiplegia. The walking analysis can automatically adapt to each person by using only the examples of normal walking of that person. Both modules employ machine learning: the paper focuses on the features they use and the effect of tag placement and sensor noise on the classification accuracy. Four tags were enough for activity recognition accuracy of over 93% at moderate sensor noise, while six were needed to detect walking anomalies with the accuracy of over 90%.

Are the U.S. Navy’s Current Procedures for Responding to Homeland Defense and Security Tasking Adequately Designed?

DTIC Science & Technology

2009-12-01

Amphibious Transport Dock LSD Landing Ship Dock MACA Military Assistance to Civil Authorities MAD Magnetic Anomaly Detector MARFORNORTH Marine...as Military Assistance to Civil Authorities ( MACA ), is DoD support for domestic emergencies.38 DoD support includes anything from providing experts

Intact Spectral but Abnormal Temporal Processing of Auditory Stimuli in Autism

ERIC Educational Resources Information Center

Groen, Wouter B.; van Orsouw, Linda; ter Huurne, Niels; Swinkels, Sophie; van der Gaag, Rutger-Jan; Buitelaar, Jan K.; Zwiers, Marcel P.

2009-01-01

The perceptual pattern in autism has been related to either a specific localized processing deficit or a pathway-independent, complexity-specific anomaly. We examined auditory perception in autism using an auditory disembedding task that required spectral and temporal integration. 23 children with high-functioning-autism and 23 matched controls…

Measurement of forest condition and response along the Pennsylvania atmospheric deposition gradent

Treesearch

D.D. David; J.M. Skelly; J.A. Lynch; L.H. McCormick; B.L. Nash; M. Simini; E.A. Cameron; J.R. McClenahen; R.P. Long

1991-01-01

Research in the oak-hickory forest of northcentral Pennsylvania is being conducted to detect anomalies in forest condition that may be due to atmospheric deposition, with the intent that such anomalies will be further studied to determine the role, if any, of atmospheric deposition. This paper presents the status of research along a 160-km gradient of sulfate/nitrate...

Using the Landsat Thematic Mapper to detect and monitor active volcanoes - An example from Lascar volcano, northern Chile

NASA Technical Reports Server (NTRS)

Francis, P. W.; Rothery, D. A.

1987-01-01

The Landsat Thematic Mapper (TM) offers a means of detecting and monitoring thermal features of active volcanoes. Using the TM, a prominent thermal anomaly has been discovered on Lascar volcano, northern Chile. Data from two short-wavelength infrared channels of the TM show that material within a 300-m-diameter pit crater was at a temperature of at least 380 C on two dates in 1985. The thermal anomaly closely resembles in size and radiant temperature the anomaly over the active lava lake at Erta'ale in Ethiopia. An eruption took place at Lascar on Sept. 16, 1986. TM data acquired on Oct. 27, 1986, revealed significant changes within the crater area. Lascar is in a much more active state than any other volcano in the central Andes, and for this reason it merits further careful monitoring. Studies show that the TM is capable of confidently identifying thermal anomalies less than 100 m in size, at temperatures of above 150 C, and thus it offers a valuable means of monitoring the conditions of active or potentially active volcanoes, particularly those in remote regions.

A novel water quality data analysis framework based on time-series data mining.

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Aeromagnetic anomalies over faulted strata

USGS Publications Warehouse

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

2011-01-01

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

Dental Anomalies in Permanent Teeth after Trauma in Primary Dentition.

PubMed

Bardellini, Elena; Amadori, Francesca; Pasini, Stefania; Majorana, Alessandra

This retrospective study aims to evaluate the prevalence of dental anomalies in permanent teeth as a result of a trauma concerning the predecessor primary teeth. A total of 241 records of children (118 males and 123 females, mean age 3.62 ± 1.40) affected by trauma on primary teeth were analyzed. All patients were recalled to evaluate the status of the permanent successor teeth by clinical and radiographic investigations. Out of 241 patients, 106 patients (for a total of 179 traumatized primary teeth) presented at the recall. Dental anomalies on successor permanent teeth were detected in 21 patients (19.8%), for a total of 26 teeth (14.5%) and 28 anomalies. Anomalies of the eruptive process were the most observed disturbances (60.7%), followed by enamel hypoplasia (25%) and white spots (14.3%). A higher percentage of anomalies on permanent teeth was observed when trauma occurred at an age less than 36 months (38.5% of cases). Intrusive and extrusive luxation were related with the most cases of clinical disturbances in the successor permanent teeth. The results of this study highlight the risk of dental anomalies after a trauma in primary dentition, especially in early-aged children and in case of intrusive luxation.

Constraints on lithospheric structure from satellite potential field data: Africa and Asia. Analysis and interpretation of MAGSAT anomalies over North Africa

NASA Technical Reports Server (NTRS)

Phillips, R. J.

1986-01-01

Crustal anomaly detection with MAGSAT data is frustrated by the inherent resolving power of the data and by contamination from the external and core fields. The quality of the data might be tested by modeling specific tectonic features which produce anomalies that fall within the proposed resolution and crustal amplitude capabilities of the MAGSAT fields. To test this hypothesis, the north African hotspots associated with Ahaggar, Tibestia and Darfur have been modeled as magnetic induction anomalies due solely to shallower depth to the Curie isotherm surface beneath these features. The MAGSAT data were reduced by subtracting the external and core fields to isolate the scalar and vertical component crustal signals. The predicted model magnetic signal arising from the surface topography of the uplift and the Curie isotherm surface was calculated at MAGSAT altitudes by the Fourier transform technique modified to allow for variable magnetization. In summary it is suggested that the region beneath Ahaggar is associated with a strong thermal anomaly and the predicted anomaly best fits the associated MAGSAT anomaly if the African plate is moving in a northeasterly direction.

Correction of Stahl ear deformity using a suture technique.

PubMed

Khan, Muhammad Adil Abbas; Jose, Rajive M; Ali, Syed Nadir; Yap, Lok Huei

2010-09-01

Correction of partial ear deformities can be a challenging task for the plastic surgeon. There are no standard techniques for correcting many of these deformities, and several different techniques are described in literature. Stahl ear is one such anomaly, characterized by an accessory third crus in the ear cartilage, giving rise to an irregular helical rim. The conventional techniques of correcting this deformity include either excision of the cartilage, repositioning of the cartilage, or scoring techniques. We recently encountered a case of Stahl ear deformity and undertook correction using internal sutures with very good results. The technical details of the surgery are described along with a review of literature on correcting similar anomalies.

Identification of mineral resources in Afghanistan-Detecting and mapping resource anomalies in prioritized areas using geophysical and remote sensing (ASTER and HyMap) data

USGS Publications Warehouse

King, Trude V.V.; Johnson, Michaela R.; Hubbard, Bernard E.; Drenth, Benjamin J.

2011-01-01

During the independent analysis of the geophysical, ASTER, and imaging spectrometer (HyMap) data by USGS scientists, previously unrecognized targets of potential mineralization were identified using evaluation criteria most suitable to the individual dataset. These anomalous zones offer targets of opportunity that warrant additional field verification. This report describes the standards used to define the anomalies, summarizes the results of the evaluations for each type of data, and discusses the importance and implications of regions of anomaly overlap between two or three of the datasets.

Detection of Landmines by Neutron Backscattering: Effects of Soil Moisture on the Detection System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baysoy, D. Y.; Subasi, M.

2010-01-21

Detection of buried land mines by using neutron backscattering technique (NBS) is a well established method. It depends on detecting a hydrogen anomaly in dry soil. Since a landmine and its plastic casing contain much more hydrogen atoms than the dry soil, this anomaly can be detected by observing a rise in the number of neutrons moderated to thermal or epithermal energy. But, the presence of moisture in the soil limits the effectiveness of the measurements. In this work, a landmine detection system using the NBS technique was designed. A series of Monte Carlo calculations was carried out to determinemore » the limits of the system due to the moisture content of the soil. In the simulations, an isotropic fast neutron source ({sup 252}Cf, 100 mug) and a neutron detection system which consists of five {sup 3}He detectors were used in a practicable geometry. In order to see the effects of soil moisture on the efficiency of the detection system, soils with different water contents were tested.« less

Prevalence, prenatal diagnosis and clinical features of oculo-auriculo-vertebral spectrum: a registry-based study in Europe

PubMed Central

Barisic, Ingeborg; Odak, Ljubica; Loane, Maria; Garne, Ester; Wellesley, Diana; Calzolari, Elisa; Dolk, Helen; Addor, Marie-Claude; Arriola, Larraitz; Bergman, Jorieke; Bianca, Sebastiano; Doray, Berenice; Khoshnood, Babak; Klungsoyr, Kari; McDonnell, Bob; Pierini, Anna; Rankin, Judith; Rissmann, Anke; Rounding, Catherine; Queisser-Luft, Annette; Scarano, Gioacchino; Tucker, David

2014-01-01

Oculo-auriculo-vertebral spectrum is a complex developmental disorder characterised mainly by anomalies of the ear, hemifacial microsomia, epibulbar dermoids and vertebral anomalies. The aetiology is largely unknown, and the epidemiological data are limited and inconsistent. We present the largest population-based epidemiological study to date, using data provided by the large network of congenital anomalies registries in Europe. The study population included infants diagnosed with oculo-auriculo-vertebral spectrum during the 1990–2009 period from 34 registries active in 16 European countries. Of the 355 infants diagnosed with oculo-auriculo-vertebral spectrum, there were 95.8% (340/355) live born, 0.8% (3/355) fetal deaths, 3.4% (12/355) terminations of pregnancy for fetal anomaly and 1.5% (5/340) neonatal deaths. In 18.9%, there was prenatal detection of anomaly/anomalies associated with oculo-auriculo-vertebral spectrum, 69.7% were diagnosed at birth, 3.9% in the first week of life and 6.1% within 1 year of life. Microtia (88.8%), hemifacial microsomia (49.0%) and ear tags (44.4%) were the most frequent anomalies, followed by atresia/stenosis of external auditory canal (25.1%), diverse vertebral (24.3%) and eye (24.3%) anomalies. There was a high rate (69.5%) of associated anomalies of other organs/systems. The most common were congenital heart defects present in 27.8% of patients. The prevalence of oculo-auriculo-vertebral spectrum, defined as microtia/ear anomalies and at least one major characteristic anomaly, was 3.8 per 100 000 births. Twinning, assisted reproductive techniques and maternal pre-pregnancy diabetes were confirmed as risk factors. The high rate of different associated anomalies points to the need of performing an early ultrasound screening in all infants born with this disorder. PMID:24398798

Using a combination of MLPA kits to detect chromosomal imbalances in patients with multiple congenital anomalies and mental retardation is a valuable choice for developing countries.

PubMed

Jehee, Fernanda Sarquis; Takamori, Jean Tetsuo; Medeiros, Paula F Vasconcelos; Pordeus, Ana Carolina B; Latini, Flavia Roche M; Bertola, Débora Romeo; Kim, Chong Ae; Passos-Bueno, Maria Rita

2011-01-01

Conventional karyotyping detects anomalies in 3-15% of patients with multiple congenital anomalies and mental retardation (MCA/MR). Whole-genome array screening (WGAS) has been consistently suggested as the first choice diagnostic test for this group of patients, but it is very costly for large-scale use in developing countries. We evaluated the use of a combination of Multiplex Ligation-dependent Probe Amplification (MLPA) kits to increase the detection rate of chromosomal abnormalities in MCA/MR patients. We screened 261 MCA/MR patients with two subtelomeric and one microdeletion kits. This would theoretically detect up to 70% of all submicroscopic abnormalities. Additionally we scored the de Vries score for 209 patients in an effort to find a suitable cut-off for MLPA screening. Our results reveal that chromosomal abnormalities were present in 87 (33.3%) patients, but only 57 (21.8%) were considered causative. Karyotyping detected 15 abnormalities (6.9%), while MLPA identified 54 (20.7%). Our combined MLPA screening raised the total detection number of pathogenic imbalances more than three times when compared to conventional karyotyping. We also show that using the de Vries score as a cut-off for this screening would only be suitable under financial restrictions. A decision analytic model was constructed with three possible strategies: karyotype, karyotype + MLPA and karyotype + WGAS. Karyotype + MLPA strategy detected anomalies in 19.8% of cases which account for 76.45% of the expected yield for karyotype + WGAS. Incremental Cost Effectiveness Ratio (ICER) of MLPA is three times lower than that of WGAS, which means that, for the same costs, we have three additional diagnoses with MLPA but only one with WGAS. We list all causative alterations found, including rare findings, such as reciprocal duplications of regions deleted in Sotos and Williams-Beuren syndromes. We also describe imbalances that were considered polymorphisms or rare variants, such as the new SNP that confounded the analysis of the 22q13.3 deletion syndrome. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Autonomous Aeromagnetic Surveys Using a Fluxgate Magnetometer

PubMed Central

Macharet, Douglas G.; Perez-Imaz, Héctor I. A.; Rezeck, Paulo A. F.; Potje, Guilherme A.; Benyosef, Luiz C. C.; Wiermann, André; Freitas, Gustavo M.; Garcia, Luis G. U.; Campos, Mario F. M.

2016-01-01

Recent advances in the research of autonomous vehicles have showed a vast range of applications, such as exploration, surveillance and environmental monitoring. Considering the mining industry, it is possible to use such vehicles in the prospection of minerals of commercial interest beneath the ground. However, tasks such as geophysical surveys are highly dependent on specific sensors, which mostly are not designed to be used in these new range of autonomous vehicles. In this work, we propose a novel magnetic survey pipeline that aims to increase versatility, speed and robustness by using autonomous rotary-wing Unmanned Aerial Vehicles (UAVs). We also discuss the development of a state-of-the-art three-axis fluxgate, where our goal in this work was to refine and adjust the sensor topology and coupled electronics specifically for this type of vehicle and application. The sensor was built with two ring-cores using a specially developed stress-annealed CoFeSiB amorphous ribbon, in order to get sufficient resolution to detect concentrations of small ferrous minerals. Finally, we report on the results of experiments performed with a real UAV in an outdoor environment, showing the efficacy of the methodology in detecting an artificial ferrous anomaly. PMID:27999307

Autonomous Aeromagnetic Surveys Using a Fluxgate Magnetometer.

PubMed

Macharet, Douglas G; Perez-Imaz, Héctor I A; Rezeck, Paulo A F; Potje, Guilherme A; Benyosef, Luiz C C; Wiermann, André; Freitas, Gustavo M; Garcia, Luis G U; Campos, Mario F M

2016-12-17

Recent advances in the research of autonomous vehicles have showed a vast range of applications, such as exploration, surveillance and environmental monitoring. Considering the mining industry, it is possible to use such vehicles in the prospection of minerals of commercial interest beneath the ground. However, tasks such as geophysical surveys are highly dependent on specific sensors, which mostly are not designed to be used in these new range of autonomous vehicles. In this work, we propose a novel magnetic survey pipeline that aims to increase versatility, speed and robustness by using autonomous rotary-wing Unmanned Aerial Vehicles (UAVs). We also discuss the development of a state-of-the-art three-axis fluxgate, where our goal in this work was to refine and adjust the sensor topology and coupled electronics specifically for this type of vehicle and application. The sensor was built with two ring-cores using a specially developed stress-annealed CoFeSiB amorphous ribbon, in order to get sufficient resolution to detect concentrations of small ferrous minerals. Finally, we report on the results of experiments performed with a real UAV in an outdoor environment, showing the efficacy of the methodology in detecting an artificial ferrous anomaly.

Volcanic hotspots of the central and southern Andes as seen from space by ASTER and MODVOLC between the years 2000-2011

NASA Astrophysics Data System (ADS)

Jay, J.; Pritchard, M. E.; Mares, P. J.; Mnich, M. E.; Welch, M. D.; Melkonian, A. K.; Aguilera, F.; Naranjo, J.; Sunagua, M.; Clavero, J. E.

2011-12-01

We examine 153 volcanoes and geothermal areas in the central, southern, and austral Andes for temperature anomalies between 2000-2011 from two different spacebourne sensors: 1) those automatically detected by the MODVOLC algorithm (Wright et al., 2004) from MODIS and 2) manually identified hotspots in nighttime images from ASTER. Based on previous work, we expected to find 8 thermal anomalies (volcanoes: Ubinas, Villarrica, Copahue, Láscar, Llaima, Chaitén, Puyehue-Cordón Caulle, Chiliques). We document 31 volcanic areas with pixel integrated temperatures of 4 to more than 100 K above background in at least two images, and another 29 areas that have questionable hotspots with either smaller anomalies or a hotspot in only one image. Most of the thermal anomalies are related to known activity (lava and pyroclastic flows, growing lava domes, fumaroles, and lakes) while others are of unknown origin or reflect activity at volcanoes that were not thought to be active. A handful of volcanoes exhibit temporal variations in the magnitude and location of their temperature anomaly that can be related to both documented and undocumented pulses of activity. Our survey reveals that low amplitude volcanic hotspots detectable from space are more common than expected (based on lower resolution data) and that these features could be more widely used to monitor changes in the activity of remote volcanoes. We find that the shape, size, magnitude, and location on the volcano of the thermal anomaly vary significantly from volcano to volcano, and these variations should be considered when developing algorithms for hotspot identification and detection. We compare our thermal results to satellite InSAR measurements of volcanic deformation and find that there is no simple relationship between deformation and thermal anomalies - while 31 volcanoes have continuous hotspots, at least 17 volcanoes in the same area have exhibited deformation, and these lists do not completely overlap. In order to investigate the relationship between seismic and thermal volcanic activity, we examine seismic data for 5 of the volcanoes (Uturuncu, Olca-Paruma, Ollague, Irruputuncu, and Sol de Mañana) as well as seismological reports from the Chilean geological survey SERNAGEOMIN for 11 additional volcanoes. Although there were 7 earthquakes with Mw > 7 in our study area from 2000-2010, there is essentially no evidence from ASTER or MODVOLC that the thermal anomalies were affected by seismic shaking.

SSME Post Test Diagnostic System: Systems Section

NASA Technical Reports Server (NTRS)

Bickmore, Timothy

1995-01-01

An assessment of engine and component health is routinely made after each test firing or flight firing of a Space Shuttle Main Engine (SSME). Currently, this health assessment is done by teams of engineers who manually review sensor data, performance data, and engine and component operating histories. Based on review of information from these various sources, an evaluation is made as to the health of each component of the SSME and the preparedness of the engine for another test or flight. The objective of this project - the SSME Post Test Diagnostic System (PTDS) - is to develop a computer program which automates the analysis of test data from the SSME in order to detect and diagnose anomalies. This report primarily covers work on the Systems Section of the PTDS, which automates the analyses performed by the systems/performance group at the Propulsion Branch of NASA Marshall Space Flight Center (MSFC). This group is responsible for assessing the overall health and performance of the engine, and detecting and diagnosing anomalies which involve multiple components (other groups are responsible for analyzing the behavior of specific components). The PTDS utilizes several advanced software technologies to perform its analyses. Raw test data is analyzed using signal processing routines which detect features in the data, such as spikes, shifts, peaks, and drifts. Component analyses are performed by expert systems, which use 'rules-of-thumb' obtained from interviews with the MSFC data analysts to detect and diagnose anomalies. The systems analysis is performed using case-based reasoning. Results of all analyses are stored in a relational database and displayed via an X-window-based graphical user interface which provides ranked lists of anomalies and observations by engine component, along with supporting data plots for each.

Improving detection of low SNR targets using moment-based detection

NASA Astrophysics Data System (ADS)

Young, Shannon R.; Steward, Bryan J.; Hawks, Michael; Gross, Kevin C.

2016-05-01

Increases in the number of cameras deployed, frame rate, and detector array sizes have led to a dramatic increase in the volume of motion imagery data that is collected. Without a corresponding increase in analytical manpower, much of the data is not analyzed to full potential. This creates a need for fast, automated, and robust methods for detecting signals of interest. Current approaches fall into two categories: detect-before-track (DBT), which are fast but often poor at detecting dim targets, and track-before-detect (TBD) methods which can offer better performance but are typically much slower. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallelizable. It was found that intelligent selection of parameters can improve probability of detection by as much as 25% compared to earlier work with higherorder moments. The present method can reduce detection thresholds by 40% compared to the Reed-Xiaoli anomaly detector for low SNR targets (for a given probability of detection and false alarm).

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

PubMed

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 control chart method. The time complexity of the Bayesian algorithm is linear in the number of the observed events being monitored, due to a novel, closed-form derivation that is introduced in the paper. This paper introduces a novel prior probability for Bayesian outbreak detection that is expressive, easy-to-apply, computationally efficient, and performs as well or better than a standard frequentist method.

Genetics of Congenital Heart Disease: Past and Present.

PubMed

Muntean, Iolanda; Togănel, Rodica; Benedek, Theodora

2017-04-01

Congenital heart disease is the most common congenital anomaly, representing an important cause of infant morbidity and mortality. Congenital heart disease represents a group of heart anomalies that include septal defects, valve defects, and outflow tract anomalies. The exact genetic, epigenetic, or environmental basis of congenital heart disease remains poorly understood, although the exact mechanism is likely multifactorial. However, the development of new technologies including copy number variants, single-nucleotide polymorphism, next-generation sequencing are accelerating the detection of genetic causes of heart anomalies. Recent studies suggest a role of small non-coding RNAs, micro RNA, in congenital heart disease. The recently described epigenetic factors have also been found to contribute to cardiac morphogenesis. In this review, we present past and recent genetic discoveries in congenital heart disease.

A review on remotely sensed land surface temperature anomaly as an earthquake precursor

NASA Astrophysics Data System (ADS)

Bhardwaj, Anshuman; Singh, Shaktiman; Sam, Lydia; Joshi, P. K.; Bhardwaj, Akanksha; Martín-Torres, F. Javier; Kumar, Rajesh

2017-12-01

The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors.

Quantitative subsurface analysis using frequency modulated thermal wave imaging

NASA Astrophysics Data System (ADS)

Subhani, S. K.; Suresh, B.; Ghali, V. S.

2018-01-01

Quantitative depth analysis of the anomaly with an enhanced depth resolution is a challenging task towards the estimation of depth of the subsurface anomaly using thermography. Frequency modulated thermal wave imaging introduced earlier provides a complete depth scanning of the object by stimulating it with a suitable band of frequencies and further analyzing the subsequent thermal response using a suitable post processing approach to resolve subsurface details. But conventional Fourier transform based methods used for post processing unscramble the frequencies with a limited frequency resolution and contribute for a finite depth resolution. Spectral zooming provided by chirp z transform facilitates enhanced frequency resolution which can further improves the depth resolution to axially explore finest subsurface features. Quantitative depth analysis with this augmented depth resolution is proposed to provide a closest estimate to the actual depth of subsurface anomaly. This manuscript experimentally validates this enhanced depth resolution using non stationary thermal wave imaging and offers an ever first and unique solution for quantitative depth estimation in frequency modulated thermal wave imaging.

Processing the Bouguer anomaly map of Biga and the surrounding area by the cellular neural network: application to the southwestern Marmara region

NASA Astrophysics Data System (ADS)

Aydogan, D.

2007-04-01

An image processing technique called the cellular neural network (CNN) approach is used in this study to locate geological features giving rise to gravity anomalies such as faults or the boundary of two geologic zones. CNN is a stochastic image processing technique based on template optimization using the neighborhood relationships of cells. These cells can be characterized by a functional block diagram that is typical of neural network theory. The functionality of CNN is described in its entirety by a number of small matrices (A, B and I) called the cloning template. CNN can also be considered to be a nonlinear convolution of these matrices. This template describes the strength of the nearest neighbor interconnections in the network. The recurrent perceptron learning algorithm (RPLA) is used in optimization of cloning template. The CNN and standard Canny algorithms were first tested on two sets of synthetic gravity data with the aim of checking the reliability of the proposed approach. The CNN method was compared with classical derivative techniques by applying the cross-correlation method (CC) to the same anomaly map as this latter approach can detect some features that are difficult to identify on the Bouguer anomaly maps. This approach was then applied to the Bouguer anomaly map of Biga and its surrounding area, in Turkey. Structural features in the area between Bandirma, Biga, Yenice and Gonen in the southwest Marmara region are investigated by applying the CNN and CC to the Bouguer anomaly map. Faults identified by these algorithms are generally in accordance with previously mapped surface faults. These examples show that the geologic boundaries can be detected from Bouguer anomaly maps using the cloning template approach. A visual evaluation of the outputs of the CNN and CC approaches is carried out, and the results are compared with each other. This approach provides quantitative solutions based on just a few assumptions, which makes the method more powerful than the classical methods.

Geotechnical Applications of the Self-Potential (SP) Method. Report 2. The Use of Self Potential to Detect Ground-Water Flow in Karst

DTIC Science & Technology

1989-05-01

with Kilauea Volcano in Hawaii . 14. The streaming potentials produced by the motion of ground water under a pressure gradient through a porous media...topography. An i.xtreme example is the -2,693-mv anomaly found on Agadak Volcano in Alaska (Corwin and Hoover 1978) and the -1,600-mv anomaly associated