WorldWideScience

Sample records for event detection systems

  1. Generalized Detectability for Discrete Event Systems

    Science.gov (United States)

    Shu, Shaolong; Lin, Feng

    2011-01-01

    In our previous work, we investigated detectability of discrete event systems, which is defined as the ability to determine the current and subsequent states of a system based on observation. For different applications, we defined four types of detectabilities: (weak) detectability, strong detectability, (weak) periodic detectability, and strong periodic detectability. In this paper, we extend our results in three aspects. (1) We extend detectability from deterministic systems to nondeterministic systems. Such a generalization is necessary because there are many systems that need to be modeled as nondeterministic discrete event systems. (2) We develop polynomial algorithms to check strong detectability. The previous algorithms are based on observer whose construction is of exponential complexity, while the new algorithms are based on a new automaton called detector. (3) We extend detectability to D-detectability. While detectability requires determining the exact state of a system, D-detectability relaxes this requirement by asking only to distinguish certain pairs of states. With these extensions, the theory on detectability of discrete event systems becomes more applicable in solving many practical problems. PMID:21691432

  2. Event storm detection and identification in communication systems

    International Nuclear Information System (INIS)

    Albaghdadi, Mouayad; Briley, Bruce; Evens, Martha

    2006-01-01

    Event storms are the manifestation of an important class of abnormal behaviors in communication systems. They occur when a large number of nodes throughout the system generate a set of events within a small period of time. It is essential for network management systems to detect every event storm and identify its cause, in order to prevent and repair potential system faults. This paper presents a set of techniques for the effective detection and identification of event storms in communication systems. First, we introduce a new algorithm to synchronize events to a single node in the system. Second, the system's event log is modeled as a normally distributed random process. This is achieved by using data analysis techniques to explore and then model the statistical behavior of the event log. Third, event storm detection is proposed using a simple test statistic combined with an exponential smoothing technique to overcome the non-stationary behavior of event logs. Fourth, the system is divided into non-overlapping regions to locate the main contributing regions of a storm. We show that this technique provides us with a method for event storm identification. Finally, experimental results from a commercially deployed multimedia communication system that uses these techniques demonstrate their effectiveness

  3. Event-Triggered Fault Detection of Nonlinear Networked Systems.

    Science.gov (United States)

    Li, Hongyi; Chen, Ziran; Wu, Ligang; Lam, Hak-Keung; Du, Haiping

    2017-04-01

    This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

  4. An integrated logit model for contamination event detection in water distribution systems.

    Science.gov (United States)

    Housh, Mashor; Ostfeld, Avi

    2015-05-15

    The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. LAN attack detection using Discrete Event Systems.

    Science.gov (United States)

    Hubballi, Neminath; Biswas, Santosh; Roopa, S; Ratti, Ritesh; Nandi, Sukumar

    2011-01-01

    Address Resolution Protocol (ARP) is used for determining the link layer or Medium Access Control (MAC) address of a network host, given its Internet Layer (IP) or Network Layer address. ARP is a stateless protocol and any IP-MAC pairing sent by a host is accepted without verification. This weakness in the ARP may be exploited by malicious hosts in a Local Area Network (LAN) by spoofing IP-MAC pairs. Several schemes have been proposed in the literature to circumvent these attacks; however, these techniques either make IP-MAC pairing static, modify the existing ARP, patch operating systems of all the hosts etc. In this paper we propose a Discrete Event System (DES) approach for Intrusion Detection System (IDS) for LAN specific attacks which do not require any extra constraint like static IP-MAC, changing the ARP etc. A DES model is built for the LAN under both a normal and compromised (i.e., spoofed request/response) situation based on the sequences of ARP related packets. Sequences of ARP events in normal and spoofed scenarios are similar thereby rendering the same DES models for both the cases. To create different ARP events under normal and spoofed conditions the proposed technique uses active ARP probing. However, this probing adds extra ARP traffic in the LAN. Following that a DES detector is built to determine from observed ARP related events, whether the LAN is operating under a normal or compromised situation. The scheme also minimizes extra ARP traffic by probing the source IP-MAC pair of only those ARP packets which are yet to be determined as genuine/spoofed by the detector. Also, spoofed IP-MAC pairs determined by the detector are stored in tables to detect other LAN attacks triggered by spoofing namely, man-in-the-middle (MiTM), denial of service etc. The scheme is successfully validated in a test bed. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Complexity of deciding detectability in discrete event systems

    Czech Academy of Sciences Publication Activity Database

    Masopust, Tomáš

    2018-01-01

    Roč. 93, July (2018), s. 257-261 ISSN 0005-1098 Institutional support: RVO:67985840 Keywords : discrete event systems * finite automata * detectability Subject RIV: BA - General Mathematics OBOR OECD: Computer science s, information science , bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 5.451, year: 2016 https://www. science direct.com/ science /article/pii/S0005109818301730

  7. Complexity of deciding detectability in discrete event systems

    Czech Academy of Sciences Publication Activity Database

    Masopust, Tomáš

    2018-01-01

    Roč. 93, July (2018), s. 257-261 ISSN 0005-1098 Institutional support: RVO:67985840 Keywords : discrete event systems * finite automata * detectability Subject RIV: BA - General Mathematics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 5.451, year: 2016 https://www.sciencedirect.com/science/article/pii/S0005109818301730

  8. A coupled classification - evolutionary optimization model for contamination event detection in water distribution systems.

    Science.gov (United States)

    Oliker, Nurit; Ostfeld, Avi

    2014-03-15

    This study describes a decision support system, alerts for contamination events in water distribution systems. The developed model comprises a weighted support vector machine (SVM) for the detection of outliers, and a following sequence analysis for the classification of contamination events. The contribution of this study is an improvement of contamination events detection ability and a multi-dimensional analysis of the data, differing from the parallel one-dimensional analysis conducted so far. The multivariate analysis examines the relationships between water quality parameters and detects changes in their mutual patterns. The weights of the SVM model accomplish two goals: blurring the difference between sizes of the two classes' data sets (as there are much more normal/regular than event time measurements), and adhering the time factor attribute by a time decay coefficient, ascribing higher importance to recent observations when classifying a time step measurement. All model parameters were determined by data driven optimization so the calibration of the model was completely autonomic. The model was trained and tested on a real water distribution system (WDS) data set with randomly simulated events superimposed on the original measurements. The model is prominent in its ability to detect events that were only partly expressed in the data (i.e., affecting only some of the measured parameters). The model showed high accuracy and better detection ability as compared to previous modeling attempts of contamination event detection. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

    Science.gov (United States)

    Housh, Mashor; Ohar, Ziv

    2017-03-01

    The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Developing Fluorescence Sensor Systems for Early Detection of Nitrification Events in Chloraminated Drinking Water Distribution Systems

    Science.gov (United States)

    Detection of nitrification events in chloraminated drinking water distribution systems remains an ongoing challenge for many drinking water utilities, including Dallas Water Utilities (DWU) and the City of Houston (CoH). Each year, these utilities experience nitrification events ...

  11. Event detection and exception handling strategies in the ASDEX Upgrade discharge control system

    International Nuclear Information System (INIS)

    Treutterer, W.; Neu, G.; Rapson, C.; Raupp, G.; Zasche, D.; Zehetbauer, T.

    2013-01-01

    Highlights: •Event detection and exception handling is integrated in control system architecture. •Pulse control with local exception handling and pulse supervision with central exception handling are strictly separated. •Local exception handling limits the effect of an exception to a minimal part of the controlled system. •Central Exception Handling solves problems requiring coordinated action of multiple control components. -- Abstract: Thermonuclear plasmas are governed by nonlinear characteristics: plasma operation can be classified into scenarios with pronounced features like L and H-mode, ELMs or MHD activity. Transitions between them may be treated as events. Similarly, technical systems are also subject to events such as failure of measurement sensors, actuator saturation or violation of machine and plant operation limits. Such situations often are handled with a mixture of pulse abortion and iteratively improved pulse schedule reference programming. In case of protection-relevant events, however, the complexity of even a medium-sized device as ASDEX Upgrade requires a sophisticated and coordinated shutdown procedure rather than a simple stop of the pulse. The detection of events and their intelligent handling by the control system has been shown to be valuable also in terms of saving experiment time and cost. This paper outlines how ASDEX Upgrade's discharge control system (DCS) detects events and handles exceptions in two stages: locally and centrally. The goal of local exception handling is to limit the effect of an unexpected or asynchronous event to a minimal part of the controlled system. Thus, local exception handling facilitates robustness to failures but keeps the decision structures lean. A central state machine deals with exceptions requiring coordinated action of multiple control components. DCS implements the state machine by means of pulse schedule segments containing pre-programmed waveforms to define discharge goal and control

  12. Event detection and exception handling strategies in the ASDEX Upgrade discharge control system

    Energy Technology Data Exchange (ETDEWEB)

    Treutterer, W., E-mail: Wolfgang.Treutterer@ipp.mpg.de; Neu, G.; Rapson, C.; Raupp, G.; Zasche, D.; Zehetbauer, T.

    2013-10-15

    Highlights: •Event detection and exception handling is integrated in control system architecture. •Pulse control with local exception handling and pulse supervision with central exception handling are strictly separated. •Local exception handling limits the effect of an exception to a minimal part of the controlled system. •Central Exception Handling solves problems requiring coordinated action of multiple control components. -- Abstract: Thermonuclear plasmas are governed by nonlinear characteristics: plasma operation can be classified into scenarios with pronounced features like L and H-mode, ELMs or MHD activity. Transitions between them may be treated as events. Similarly, technical systems are also subject to events such as failure of measurement sensors, actuator saturation or violation of machine and plant operation limits. Such situations often are handled with a mixture of pulse abortion and iteratively improved pulse schedule reference programming. In case of protection-relevant events, however, the complexity of even a medium-sized device as ASDEX Upgrade requires a sophisticated and coordinated shutdown procedure rather than a simple stop of the pulse. The detection of events and their intelligent handling by the control system has been shown to be valuable also in terms of saving experiment time and cost. This paper outlines how ASDEX Upgrade's discharge control system (DCS) detects events and handles exceptions in two stages: locally and centrally. The goal of local exception handling is to limit the effect of an unexpected or asynchronous event to a minimal part of the controlled system. Thus, local exception handling facilitates robustness to failures but keeps the decision structures lean. A central state machine deals with exceptions requiring coordinated action of multiple control components. DCS implements the state machine by means of pulse schedule segments containing pre-programmed waveforms to define discharge goal and control

  13. Impact of sensor detection limits on protecting water distribution systems from contamination events

    International Nuclear Information System (INIS)

    McKenna, Sean Andrew; Hart, David Blaine; Yarrington, Lane

    2006-01-01

    Real-time water quality sensors are becoming commonplace in water distribution systems. However, field deployable, contaminant-specific sensors are still in the development stage. As development proceeds, the necessary operating parameters of these sensors must be determined to protect consumers from accidental and malevolent contamination events. This objective can be quantified in several different ways including minimization of: the time necessary to detect a contamination event, the population exposed to contaminated water, the extent of the contamination within the network, and others. We examine the ability of a sensor set to meet these objectives as a function of both the detection limit of the sensors and the number of sensors in the network. A moderately sized distribution network is used as an example and different sized sets of randomly placed sensors are considered. For each combination of a certain number of sensors and a detection limit, the mean values of the different objectives across multiple random sensor placements are calculated. The tradeoff between the necessary detection limit in a sensor and the number of sensors is evaluated. Results show that for the example problem examined here, a sensor detection limit of 0.01 of the average source concentration is adequate for maximum protection. Detection of events is dependent on the detection limit of the sensors, but for those events that are detected, the values of the performance measures are not a function of the sensor detection limit. The results of replacing a single sensor in a network with a sensor having a much lower detection limit show that while this replacement can improve results, the majority of the additional events detected had performance measures of relatively low consequence.

  14. Detection of anomalous events

    Science.gov (United States)

    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.

  15. Ultra-Low Power Sensor System for Disaster Event Detection in Metro Tunnel Systems

    Directory of Open Access Journals (Sweden)

    Jonah VINCKE

    2017-05-01

    Full Text Available In this extended paper, the concept for an ultra-low power wireless sensor network (WSN for underground tunnel systems is presented highlighting the chosen sensors. Its objectives are the detection of emergency events either from natural disasters, such as flooding or fire, or from terrorist attacks using explosives. Earlier works have demonstrated that the power consumption for the communication can be reduced such that the data acquisition (i.e. sensor sub-system becomes the most significant energy consumer. By using ultra-low power components for the smoke detector, a hydrostatic pressure sensor for water ingress detection and a passive acoustic emission sensor for explosion detection, all considered threats are covered while the energy consumption can be kept very low in relation to the data acquisition. In addition to 1 the sensor system is integrated into a sensor board. The total average power consumption for operating the sensor sub-system is measured to be 35.9 µW for lower and 7.8 µW for upper nodes.

  16. Metrics for Polyphonic Sound Event Detection

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    Annamaria Mesaros

    2016-05-01

    Full Text Available This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics.

  17. Simultaneous Event-Triggered Fault Detection and Estimation for Stochastic Systems Subject to Deception Attacks.

    Science.gov (United States)

    Li, Yunji; Wu, QingE; Peng, Li

    2018-01-23

    In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring deception attacks. To achieve a fault-detection residual is only sensitive to faults while robust to disturbances, a coordinate transformation approach is exploited. This approach can transform the considered system into two subsystems and the unknown disturbances are removed from one of the subsystems. The gain of fault-detection filter is derived by minimizing an upper bound of filter error covariance. Meanwhile, system faults can be reconstructed by the remote fault estimator. An recursive approach is developed to obtain fault estimator gains as well as guarantee the fault estimator performance. Furthermore, the corresponding event-triggered sensor data transmission scheme is also presented for improving working-life of the wireless sensor node when measurement information are aperiodically transmitted. Finally, a scaled version of an industrial system consisting of local PC, remote estimator and wireless sensor node is used to experimentally evaluate the proposed theoretical results. In particular, a novel fault-alarming strategy is proposed so that the real-time capacity of fault-detection is guaranteed when the event condition is triggered.

  18. Surface Management System Departure Event Data Analysis

    Science.gov (United States)

    Monroe, Gilena A.

    2010-01-01

    This paper presents a data analysis of the Surface Management System (SMS) performance of departure events, including push-back and runway departure events.The paper focuses on the detection performance, or the ability to detect departure events, as well as the prediction performance of SMS. The results detail a modest overall detection performance of push-back events and a significantly high overall detection performance of runway departure events. The overall detection performance of SMS for push-back events is approximately 55%.The overall detection performance of SMS for runway departure events nears 100%. This paper also presents the overall SMS prediction performance for runway departure events as well as the timeliness of the Aircraft Situation Display for Industry data source for SMS predictions.

  19. Multilingual event extraction for epidemic detection.

    Science.gov (United States)

    Lejeune, Gaël; Brixtel, Romain; Doucet, Antoine; Lucas, Nadine

    2015-10-01

    This paper presents a multilingual news surveillance system applied to tele-epidemiology. It has been shown that multilingual approaches improve timeliness in detection of epidemic events across the globe, eliminating the wait for local news to be translated into major languages. We present here a system to extract epidemic events in potentially any language, provided a Wikipedia seed for common disease names exists. The Daniel system presented herein relies on properties that are common to news writing (the journalistic genre), the most useful being repetition and saliency. Wikipedia is used to screen common disease names to be matched with repeated characters strings. Language variations, such as declensions, are handled by processing text at the character-level, rather than at the word level. This additionally makes it possible to handle various writing systems in a similar fashion. As no multilingual ground truth existed to evaluate the Daniel system, we built a multilingual corpus from the Web, and collected annotations from native speakers of Chinese, English, Greek, Polish and Russian, with no connection or interest in the Daniel system. This data set is available online freely, and can be used for the evaluation of other event extraction systems. Experiments for 5 languages out of 17 tested are detailed in this paper: Chinese, English, Greek, Polish and Russian. The Daniel system achieves an average F-measure of 82% in these 5 languages. It reaches 87% on BEcorpus, the state-of-the-art corpus in English, slightly below top-performing systems, which are tailored with numerous language-specific resources. The consistent performance of Daniel on multiple languages is an important contribution to the reactivity and the coverage of epidemiological event detection systems. Most event extraction systems rely on extensive resources that are language-specific. While their sophistication induces excellent results (over 90% precision and recall), it restricts their

  20. Automatic Detection and Classification of Audio Events for Road Surveillance Applications

    Directory of Open Access Journals (Sweden)

    Noor Almaadeed

    2018-06-01

    Full Text Available This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time-domain, frequency-domain, and joint time-frequency features extracted from a class of quadratic time-frequency distributions (QTFDs to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features.

  1. Event Detection Challenges, Methods, and Applications in Natural and Artificial Systems

    Science.gov (United States)

    2009-03-01

    Sauvageon, Agogino, Mehr, and Tumer [2006], for instance, use a fourth degree polynomial within an event detection algorithm to sense high... cancer , and coronary artery disease. His study examines the age at which to begin screening exams, the intervals between the exams, and (possibly...AM, Mehr AF, and Tumer IY. 2006. “Comparison of Event Detection Methods for Centralized Sensor Networks.” IEEE Sensors Applications Symposium 2006

  2. On Event/Time Triggered and Distributed Analysis of a WSN System for Event Detection, Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Sofia Maria Dima

    2016-01-01

    Full Text Available Event detection in realistic WSN environments is a critical research domain, while the environmental monitoring comprises one of its most pronounced applications. Although efforts related to the environmental applications have been presented in the current literature, there is a significant lack of investigation on the performance of such systems, when applied in wireless environments. Aiming at addressing this shortage, in this paper an advanced multimodal approach is followed based on fuzzy logic. The proposed fuzzy inference system (FIS is implemented on TelosB motes and evaluates the probability of fire detection while aiming towards power conservation. Additionally to a straightforward centralized approach, a distributed implementation of the above FIS is also proposed, aiming towards network congestion reduction while optimally distributing the energy consumption among network nodes so as to maximize network lifetime. Moreover this work proposes an event based execution of the aforementioned FIS aiming to further reduce the computational as well as the communication cost, compared to a periodical time triggered FIS execution. As a final contribution, performance metrics acquired from all the proposed FIS implementation techniques are thoroughly compared and analyzed with respect to critical network conditions aiming to offer realistic evaluation and thus objective conclusions’ extraction.

  3. Integrated hydraulic and organophosphate pesticide injection simulations for enhancing event detection in water distribution systems.

    Science.gov (United States)

    Schwartz, Rafi; Lahav, Ori; Ostfeld, Avi

    2014-10-15

    As a complementary step towards solving the general event detection problem of water distribution systems, injection of the organophosphate pesticides, chlorpyrifos (CP) and parathion (PA), were simulated at various locations within example networks and hydraulic parameters were calculated over 24-h duration. The uniqueness of this study is that the chemical reactions and byproducts of the contaminants' oxidation were also simulated, as well as other indicative water quality parameters such as alkalinity, acidity, pH and the total concentration of free chlorine species. The information on the change in water quality parameters induced by the contaminant injection may facilitate on-line detection of an actual event involving this specific substance and pave the way to development of a generic methodology for detecting events involving introduction of pesticides into water distribution systems. Simulation of the contaminant injection was performed at several nodes within two different networks. For each injection, concentrations of the relevant contaminants' mother and daughter species, free chlorine species and water quality parameters, were simulated at nodes downstream of the injection location. The results indicate that injection of these substances can be detected at certain conditions by a very rapid drop in Cl2, functioning as the indicative parameter, as well as a drop in alkalinity concentration and a small decrease in pH, both functioning as supporting parameters, whose usage may reduce false positive alarms. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. On Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Suresh, Mahima Agumbe

    2013-05-01

    Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.

  5. Detection of Visual Events in Underwater Video Using a Neuromorphic Saliency-based Attention System

    Science.gov (United States)

    Edgington, D. R.; Walther, D.; Cline, D. E.; Sherlock, R.; Salamy, K. A.; Wilson, A.; Koch, C.

    2003-12-01

    The Monterey Bay Aquarium Research Institute (MBARI) uses high-resolution video equipment on remotely operated vehicles (ROV) to obtain quantitative data on the distribution and abundance of oceanic animals. High-quality video data supplants the traditional approach of assessing the kinds and numbers of animals in the oceanic water column through towing collection nets behind ships. Tow nets are limited in spatial resolution, and often destroy abundant gelatinous animals resulting in species undersampling. Video camera-based quantitative video transects (QVT) are taken through the ocean midwater, from 50m to 4000m, and provide high-resolution data at the scale of the individual animals and their natural aggregation patterns. However, the current manual method of analyzing QVT video by trained scientists is labor intensive and poses a serious limitation to the amount of information that can be analyzed from ROV dives. Presented here is an automated system for detecting marine animals (events) visible in the videos. Automated detection is difficult due to the low contrast of many translucent animals and due to debris ("marine snow") cluttering the scene. Video frames are processed with an artificial intelligence attention selection algorithm that has proven a robust means of target detection in a variety of natural terrestrial scenes. The candidate locations identified by the attention selection module are tracked across video frames using linear Kalman filters. Typically, the occurrence of visible animals in the video footage is sparse in space and time. A notion of "boring" video frames is developed by detecting whether or not there is an interesting candidate object for an animal present in a particular sequence of underwater video -- video frames that do not contain any "interesting" events. If objects can be tracked successfully over several frames, they are stored as potentially "interesting" events. Based on low-level properties, interesting events are

  6. Detection of goal events in soccer videos

    Science.gov (United States)

    Kim, Hyoung-Gook; Roeber, Steffen; Samour, Amjad; Sikora, Thomas

    2005-01-01

    In this paper, we present an automatic extraction of goal events in soccer videos by using audio track features alone without relying on expensive-to-compute video track features. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The detection of soccer video highlights using audio contents comprises three steps: 1) extraction of audio features from a video sequence, 2) event candidate detection of highlight events based on the information provided by the feature extraction Methods and the Hidden Markov Model (HMM), 3) goal event selection to finally determine the video intervals to be included in the summary. For this purpose we compared the performance of the well known Mel-scale Frequency Cepstral Coefficients (MFCC) feature extraction method vs. MPEG-7 Audio Spectrum Projection feature (ASP) extraction method based on three different decomposition methods namely Principal Component Analysis( PCA), Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). To evaluate our system we collected five soccer game videos from various sources. In total we have seven hours of soccer games consisting of eight gigabytes of data. One of five soccer games is used as the training data (e.g., announcers' excited speech, audience ambient speech noise, audience clapping, environmental sounds). Our goal event detection results are encouraging.

  7. Piecing together the puzzle: Improving event content coverage for real-time sub-event detection using adaptive microblog crawling.

    Science.gov (United States)

    Tokarchuk, Laurissa; Wang, Xinyue; Poslad, Stefan

    2017-01-01

    In an age when people are predisposed to report real-world events through their social media accounts, many researchers value the benefits of mining user generated content from social media. Compared with the traditional news media, social media services, such as Twitter, can provide more complete and timely information about the real-world events. However events are often like a puzzle and in order to solve the puzzle/understand the event, we must identify all the sub-events or pieces. Existing Twitter event monitoring systems for sub-event detection and summarization currently typically analyse events based on partial data as conventional data collection methodologies are unable to collect comprehensive event data. This results in existing systems often being unable to report sub-events in real-time and often in completely missing sub-events or pieces in the broader event puzzle. This paper proposes a Sub-event detection by real-TIme Microblog monitoring (STRIM) framework that leverages the temporal feature of an expanded set of news-worthy event content. In order to more comprehensively and accurately identify sub-events this framework first proposes the use of adaptive microblog crawling. Our adaptive microblog crawler is capable of increasing the coverage of events while minimizing the amount of non-relevant content. We then propose a stream division methodology that can be accomplished in real time so that the temporal features of the expanded event streams can be analysed by a burst detection algorithm. In the final steps of the framework, the content features are extracted from each divided stream and recombined to provide a final summarization of the sub-events. The proposed framework is evaluated against traditional event detection using event recall and event precision metrics. Results show that improving the quality and coverage of event contents contribute to better event detection by identifying additional valid sub-events. The novel combination of

  8. Piecing together the puzzle: Improving event content coverage for real-time sub-event detection using adaptive microblog crawling.

    Directory of Open Access Journals (Sweden)

    Laurissa Tokarchuk

    Full Text Available In an age when people are predisposed to report real-world events through their social media accounts, many researchers value the benefits of mining user generated content from social media. Compared with the traditional news media, social media services, such as Twitter, can provide more complete and timely information about the real-world events. However events are often like a puzzle and in order to solve the puzzle/understand the event, we must identify all the sub-events or pieces. Existing Twitter event monitoring systems for sub-event detection and summarization currently typically analyse events based on partial data as conventional data collection methodologies are unable to collect comprehensive event data. This results in existing systems often being unable to report sub-events in real-time and often in completely missing sub-events or pieces in the broader event puzzle. This paper proposes a Sub-event detection by real-TIme Microblog monitoring (STRIM framework that leverages the temporal feature of an expanded set of news-worthy event content. In order to more comprehensively and accurately identify sub-events this framework first proposes the use of adaptive microblog crawling. Our adaptive microblog crawler is capable of increasing the coverage of events while minimizing the amount of non-relevant content. We then propose a stream division methodology that can be accomplished in real time so that the temporal features of the expanded event streams can be analysed by a burst detection algorithm. In the final steps of the framework, the content features are extracted from each divided stream and recombined to provide a final summarization of the sub-events. The proposed framework is evaluated against traditional event detection using event recall and event precision metrics. Results show that improving the quality and coverage of event contents contribute to better event detection by identifying additional valid sub-events. The

  9. A simple strategy for fall events detection

    KAUST Repository

    Harrou, Fouzi

    2017-01-20

    The paper concerns the detection of fall events based on human silhouette shape variations. The detection of fall events is addressed from the statistical point of view as an anomaly detection problem. Specifically, the paper investigates the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart. The MEWMA fall detection scheme has been successfully applied to two publicly available fall detection databases, the UR fall detection dataset (URFD) and the fall detection dataset (FDD). The monitoring strategy developed was able to provide early alert mechanisms in the event of fall situations.

  10. TACKLING EVENT DETECTION IN THE CONTEXT OF VIDEO SURVEILLANCE

    Directory of Open Access Journals (Sweden)

    Raducu DUMITRESCU

    2011-11-01

    Full Text Available In this paper we address the problem of event detection in the context of video surveillance systems. First we deal with background extraction. Three methods are being tested, namely: frame differencing, running average and an estimate of median filtering technique. This provides information about changing contents. Further, we use this information to address human presence detection in the scene. This is carried out thought a contour-based approach. Contours are extracted from moving regions and parameterized. Human silhouettes show particular signatures of these parameters. Experimental results prove the potential of this approach to event detection. However, these are our first preliminary results to this application.

  11. Towards Optimal Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Agumbe Suresh, Mahima

    2012-01-03

    Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.

  12. Neutron detector for detecting rare events of spontaneous fission

    International Nuclear Information System (INIS)

    Ter-Akop'yan, G.M.; Popeko, A.G.; Sokol, E.A.; Chelnokov, L.P.; Smirnov, V.I.; Gorshkov, V.A.

    1981-01-01

    The neutron detector for registering rare events of spontaneous fission by detecting multiple neutron emission is described. The detector represents a block of plexiglas of 550 mm diameter and 700 mm height in the centre of which there is a through 160 mm diameter channel for the sample under investigation. The detector comprises 56 3 He filled counters (up to 7 atm pressure) with 1% CO 2 addition. The counters have a 500 mm length and a 32 mm diameter. The sampling of fission events is realized by an electron system which allows determining the number of detected neutrons, numbers of operated counters, signal amplitude and time for fission event detecting. A block diagram of a neutron detector electron system is presented and its operation principle is considered. For protection against cosmic radiation the detector is surronded by a system of plastic scintillators and placed behind the concrete shield of 6 m thickness. The results of measurements of background radiation are given. It has been found that the background radiation of single neutron constitutes about 150 counts per hour, the detecting efficiency of single neutron equals 0.483 +- 0.005, for a 10l detector sensitive volume. By means of the detector described the parameters of multiplicity distribution of prompt neutrons for 256 Fm spontaneous fission are measured. The average multiplicity equals 3.59+-0.06 the dispersion being 2.30+-0.65

  13. Network hydraulics inclusion in water quality event detection using multiple sensor stations data.

    Science.gov (United States)

    Oliker, Nurit; Ostfeld, Avi

    2015-09-01

    Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  14. Joint Attributes and Event Analysis for Multimedia Event Detection.

    Science.gov (United States)

    Ma, Zhigang; Chang, Xiaojun; Xu, Zhongwen; Sebe, Nicu; Hauptmann, Alexander G

    2017-06-15

    Semantic attributes have been increasingly used the past few years for multimedia event detection (MED) with promising results. The motivation is that multimedia events generally consist of lower level components such as objects, scenes, and actions. By characterizing multimedia event videos with semantic attributes, one could exploit more informative cues for improved detection results. Much existing work obtains semantic attributes from images, which may be suboptimal for video analysis since these image-inferred attributes do not carry dynamic information that is essential for videos. To address this issue, we propose to learn semantic attributes from external videos using their semantic labels. We name them video attributes in this paper. In contrast with multimedia event videos, these external videos depict lower level contents such as objects, scenes, and actions. To harness video attributes, we propose an algorithm established on a correlation vector that correlates them to a target event. Consequently, we could incorporate video attributes latently as extra information into the event detector learnt from multimedia event videos in a joint framework. To validate our method, we perform experiments on the real-world large-scale TRECVID MED 2013 and 2014 data sets and compare our method with several state-of-the-art algorithms. The experiments show that our method is advantageous for MED.

  15. Review of nuclear power reactor coolant system leakage events and leak detection requirements

    International Nuclear Information System (INIS)

    Chokshi, N.C.; Srinivasan, M.; Kupperman, D.S.; Krishnaswamy, P.

    2005-01-01

    In response to the vessel head event at the Davis-Besse reactor, the U.S. Nuclear Regulatory Commission (NRC) formed a Lessons Learned Task Force (LLTF). Four action plans were formulated to respond to the recommendations of the LLTF. The action plans involved efforts on barrier integrity, stress corrosion cracking (SCC), operating experience, and inspection and program management. One part of the action plan on barrier integrity was an assessment to identify potential safety benefits from changes in requirements pertaining to leakage in the reactor coolant system (RCS). In this effort, experiments and models were reviewed to identify correlations between crack size, crack-tip-opening displacement (CTOD), and leak rate in the RCS. Sensitivity studies using the Seepage Quantification of Upsets In Reactor Tubes (SQUIRT) code were carried out to correlate crack parameters, such as crack size, with leak rate for various types of crack configurations in RCS components. A database that identifies the leakage source, leakage rate, and resulting actions from RCS leaks discovered in U.S. light water reactors was developed. Humidity monitoring systems for detecting leakage and acoustic emission crack monitoring systems for the detection of crack initiation and growth before a leak occurs were also considered. New approaches to the detection of a leak in the reactor head region by monitoring boric-acid aerosols were also considered. (authors)

  16. Detection of planets in extremely weak central perturbation microlensing events via next-generation ground-based surveys

    International Nuclear Information System (INIS)

    Chung, Sun-Ju; Lee, Chung-Uk; Koo, Jae-Rim

    2014-01-01

    Even though the recently discovered high-magnification event MOA-2010-BLG-311 had complete coverage over its peak, confident planet detection did not happen due to extremely weak central perturbations (EWCPs, fractional deviations of ≲ 2%). For confident detection of planets in EWCP events, it is necessary to have both high cadence monitoring and high photometric accuracy better than those of current follow-up observation systems. The next-generation ground-based observation project, Korea Microlensing Telescope Network (KMTNet), satisfies these conditions. We estimate the probability of occurrence of EWCP events with fractional deviations of ≤2% in high-magnification events and the efficiency of detecting planets in the EWCP events using the KMTNet. From this study, we find that the EWCP events occur with a frequency of >50% in the case of ≲ 100 M E planets with separations of 0.2 AU ≲ d ≲ 20 AU. We find that for main-sequence and sub-giant source stars, ≳ 1 M E planets in EWCP events with deviations ≤2% can be detected with frequency >50% in a certain range that changes with the planet mass. However, it is difficult to detect planets in EWCP events of bright stars like giant stars because it is easy for KMTNet to be saturated around the peak of the events because of its constant exposure time. EWCP events are caused by close, intermediate, and wide planetary systems with low-mass planets and close and wide planetary systems with massive planets. Therefore, we expect that a much greater variety of planetary systems than those already detected, which are mostly intermediate planetary systems, regardless of the planet mass, will be significantly detected in the near future.

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

    International Nuclear Information System (INIS)

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

    2017-01-01

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

  18. Detecting Seismic Events Using a Supervised Hidden Markov Model

    Science.gov (United States)

    Burks, L.; Forrest, R.; Ray, J.; Young, C.

    2017-12-01

    We explore the use of supervised hidden Markov models (HMMs) to detect seismic events in streaming seismogram data. Current methods for seismic event detection include simple triggering algorithms, such as STA/LTA and the Z-statistic, which can lead to large numbers of false positives that must be investigated by an analyst. The hypothesis of this study is that more advanced detection methods, such as HMMs, may decreases false positives while maintaining accuracy similar to current methods. We train a binary HMM classifier using 2 weeks of 3-component waveform data from the International Monitoring System (IMS) that was carefully reviewed by an expert analyst to pick all seismic events. Using an ensemble of simple and discrete features, such as the triggering of STA/LTA, the HMM predicts the time at which transition occurs from noise to signal. Compared to the STA/LTA detection algorithm, the HMM detects more true events, but the false positive rate remains unacceptably high. Future work to potentially decrease the false positive rate may include using continuous features, a Gaussian HMM, and multi-class HMMs to distinguish between types of seismic waves (e.g., P-waves and S-waves). Acknowledgement: Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.SAND No: SAND2017-8154 A

  19. Machine learning for the automatic detection of anomalous events

    Science.gov (United States)

    Fisher, Wendy D.

    In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97

  20. Real-time detection and classification of anomalous events in streaming data

    Science.gov (United States)

    Ferragut, Erik M.; Goodall, John R.; Iannacone, Michael D.; Laska, Jason A.; Harrison, Lane T.

    2016-04-19

    A system is described for receiving a stream of events and scoring the events based on anomalousness and maliciousness (or other classification). The events can be displayed to a user in user-defined groupings in an animated fashion. 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 atypical traffic patterns can then be classified as being of interest or not. In one particular example, in a network environment, the classification can be whether the network traffic is malicious or not.

  1. Cartan invariants and event horizon detection

    Science.gov (United States)

    Brooks, D.; Chavy-Waddy, P. C.; Coley, A. A.; Forget, A.; Gregoris, D.; MacCallum, M. A. H.; McNutt, D. D.

    2018-04-01

    We show that it is possible to locate the event horizon of a black hole (in arbitrary dimensions) by the zeros of certain Cartan invariants. This approach accounts for the recent results on the detection of stationary horizons using scalar polynomial curvature invariants, and improves upon them since the proposed method is computationally less expensive. As an application, we produce Cartan invariants that locate the event horizons for various exact four-dimensional and five-dimensional stationary, asymptotically flat (or (anti) de Sitter), black hole solutions and compare the Cartan invariants with the corresponding scalar curvature invariants that detect the event horizon.

  2. Signal Detection of Imipenem Compared to Other Drugs from Korea Adverse Event Reporting System Database.

    Science.gov (United States)

    Park, Kyounghoon; Soukavong, Mick; Kim, Jungmee; Kwon, Kyoung Eun; Jin, Xue Mei; Lee, Joongyub; Yang, Bo Ram; Park, Byung Joo

    2017-05-01

    To detect signals of adverse drug events after imipenem treatment using the Korea Institute of Drug Safety & Risk Management-Korea adverse event reporting system database (KIDS-KD). We performed data mining using KIDS-KD, which was constructed using spontaneously reported adverse event (AE) reports between December 1988 and June 2014. We detected signals calculated the proportional reporting ratio, reporting odds ratio, and information component of imipenem. We defined a signal as any AE that satisfied all three indices. The signals were compared with drug labels of nine countries. There were 807582 spontaneous AEs reports in the KIDS-KD. Among those, the number of antibiotics related AEs was 192510; 3382 reports were associated with imipenem. The most common imipenem-associated AE was the drug eruption; 353 times. We calculated the signal by comparing with all other antibiotics and drugs; 58 and 53 signals satisfied the three methods. We compared the drug labelling information of nine countries, including the USA, the UK, Japan, Italy, Switzerland, Germany, France, Canada, and South Korea, and discovered that the following signals were currently not included in drug labels: hypokalemia, cardiac arrest, cardiac failure, Parkinson's syndrome, myocardial infarction, and prostate enlargement. Hypokalemia was an additional signal compared with all other antibiotics, and the other signals were not different compared with all other antibiotics and all other drugs. We detected new signals that were not listed on the drug labels of nine countries. However, further pharmacoepidemiologic research is needed to evaluate the causality of these signals. © Copyright: Yonsei University College of Medicine 2017

  3. Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhangbing Zhou

    2015-12-01

    Full Text Available With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s. When sensory data are collected at sink node(s, the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady.

  4. Power System Extreme Event Detection: The VulnerabilityFrontier

    Energy Technology Data Exchange (ETDEWEB)

    Lesieutre, Bernard C.; Pinar, Ali; Roy, Sandip

    2007-10-17

    In this work we apply graph theoretic tools to provide aclose bound on a frontier relating the number of line outages in a gridto the power disrupted by the outages. This frontier describes theboundary of a space relating the possible severity of a disturbance interms of power disruption, from zero to some maximum on the boundary, tothe number line outages involved in the event. We present the usefulnessof this analysis with a complete analysis of a 30 bus system, and presentresults for larger systems.

  5. Subsurface event detection and classification using Wireless Signal Networks.

    Science.gov (United States)

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

    2012-11-05

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  6. Event detection for car park entries by video-surveillance

    Science.gov (United States)

    Coquin, Didier; Tailland, Johan; Cintract, Michel

    2007-10-01

    Intelligent surveillance has become an important research issue due to the high cost and low efficiency of human supervisors, and machine intelligence is required to provide a solution for automated event detection. In this paper we describe a real-time system that has been used for detecting car park entries, using an adaptive background learning algorithm and two indicators representing activity and identity to overcome the difficulty of tracking objects.

  7. Subsurface Event Detection and Classification Using Wireless Signal Networks

    Directory of Open Access Journals (Sweden)

    Muhannad T. Suleiman

    2012-11-01

    Full Text Available Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs. The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  8. Full-waveform detection of non-impulsive seismic events based on time-reversal methods

    Science.gov (United States)

    Solano, Ericka Alinne; Hjörleifsdóttir, Vala; Liu, Qinya

    2017-12-01

    We present a full-waveform detection method for non-impulsive seismic events, based on time-reversal principles. We use the strain Green's tensor as a matched filter, correlating it with continuous observed seismograms, to detect non-impulsive seismic events. We show that this is mathematically equivalent to an adjoint method for detecting earthquakes. We define the detection function, a scalar valued function, which depends on the stacked correlations for a group of stations. Event detections are given by the times at which the amplitude of the detection function exceeds a given value relative to the noise level. The method can make use of the whole seismic waveform or any combination of time-windows with different filters. It is expected to have an advantage compared to traditional detection methods for events that do not produce energetic and impulsive P waves, for example glacial events, landslides, volcanic events and transform-fault earthquakes for events which velocity structure along the path is relatively well known. Furthermore, the method has advantages over empirical Greens functions template matching methods, as it does not depend on records from previously detected events, and therefore is not limited to events occurring in similar regions and with similar focal mechanisms as these events. The method is not specific to any particular way of calculating the synthetic seismograms, and therefore complicated structural models can be used. This is particularly beneficial for intermediate size events that are registered on regional networks, for which the effect of lateral structure on the waveforms can be significant. To demonstrate the feasibility of the method, we apply it to two different areas located along the mid-oceanic ridge system west of Mexico where non-impulsive events have been reported. The first study area is between Clipperton and Siqueiros transform faults (9°N), during the time of two earthquake swarms, occurring in March 2012 and May

  9. TNO at TRECVID 2013: Multimedia Event Detection and Instance Search

    NARCIS (Netherlands)

    Bouma, H.; Azzopardi, G.; Spitters, M.M.; Wit, J.J. de; Versloot, C.A.; Zon, R.W.L. van der; Eendebak, P.T.; Baan, J.; Hove, R.J.M. ten; Eekeren, A.W.M. van; Haar, F.B. ter; Hollander, R.J.M. den; Huis, R.J. van; Boer, M.H.T. de; Antwerpen, G. van; Broekhuijsen, B.J.; Daniele, L.M.; Brandt, P.; Schavemaker, J.G.M.; Kraaij, W.; Schutte, K.

    2013-01-01

    We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level

  10. Research and development of a high-temperature helium-leak detection system (joint research). Part 1 survey on leakage events and current leak detection technology

    Energy Technology Data Exchange (ETDEWEB)

    Sakaba, Nariaki; Nakazawa, Toshio; Kawasaki, Kozo [Japan Atomic Energy Research Inst., Oarai, Ibaraki (Japan). Oarai Research Establishment; Urakami, Masao; Saisyu, Sadanori [Japan Atomic Power Co., Tokyo (Japan)

    2003-03-01

    In High Temperature Gas-cooled Reactors (HTGR), the detection of leakage of helium at an early stage is very important for the safety and stability of operations. Since helium is a colourless gas, it is generally difficult to identify the location and the amount of leakage when very little leakage has occurred. The purpose of this R and D is to develop a helium leak detection system for the high temperature environment appropriate to the HTGR. As the first step in the development, this paper describes the result of surveying leakage events at nuclear facilities inside and outside Japan and current gas leakage detection technology to adapt optical-fibre detection technology to HTGRs. (author)

  11. EDICAM (Event Detection Intelligent Camera)

    Energy Technology Data Exchange (ETDEWEB)

    Zoletnik, S. [Wigner RCP RMI, EURATOM Association, Budapest (Hungary); Szabolics, T., E-mail: szabolics.tamas@wigner.mta.hu [Wigner RCP RMI, EURATOM Association, Budapest (Hungary); Kocsis, G.; Szepesi, T.; Dunai, D. [Wigner RCP RMI, EURATOM Association, Budapest (Hungary)

    2013-10-15

    Highlights: ► We present EDICAM's hardware modules. ► We present EDICAM's main design concepts. ► This paper will describe EDICAM firmware architecture. ► Operation principles description. ► Further developments. -- Abstract: A new type of fast framing camera has been developed for fusion applications by the Wigner Research Centre for Physics during the last few years. A new concept was designed for intelligent event driven imaging which is capable of focusing image readout to Regions of Interests (ROIs) where and when predefined events occur. At present these events mean intensity changes and external triggers but in the future more sophisticated methods might also be defined. The camera provides 444 Hz frame rate at full resolution of 1280 × 1024 pixels, but monitoring of smaller ROIs can be done in the 1–116 kHz range even during exposure of the full image. Keeping space limitations and the harsh environment in mind the camera is divided into a small Sensor Module and a processing card interconnected by a fast 10 Gbit optical link. This camera hardware has been used for passive monitoring of the plasma in different devices for example at ASDEX Upgrade and COMPASS with the first version of its firmware. The new firmware and software package is now available and ready for testing the new event processing features. This paper will present the operation principle and features of the Event Detection Intelligent Camera (EDICAM). The device is intended to be the central element in the 10-camera monitoring system of the Wendelstein 7-X stellarator.

  12. Screening DNA chip and event-specific multiplex PCR detection methods for biotech crops.

    Science.gov (United States)

    Lee, Seong-Hun

    2014-11-01

    There are about 80 biotech crop events that have been approved by safety assessment in Korea. They have been controlled by genetically modified organism (GMO) and living modified organism (LMO) labeling systems. The DNA-based detection method has been used as an efficient scientific management tool. Recently, the multiplex polymerase chain reaction (PCR) and DNA chip have been developed as simultaneous detection methods for several biotech crops' events. The event-specific multiplex PCR method was developed to detect five biotech maize events: MIR604, Event 3272, LY 038, MON 88017 and DAS-59122-7. The specificity was confirmed and the sensitivity was 0.5%. The screening DNA chip was developed from four endogenous genes of soybean, maize, cotton and canola respectively along with two regulatory elements and seven genes: P35S, tNOS, pat, bar, epsps1, epsps2, pmi, cry1Ac and cry3B. The specificity was confirmed and the sensitivity was 0.5% for four crops' 12 events: one soybean, six maize, three cotton and two canola events. The multiplex PCR and DNA chip can be available for screening, gene-specific and event-specific analysis of biotech crops as efficient detection methods by saving on workload and time. © 2014 Society of Chemical Industry. © 2014 Society of Chemical Industry.

  13. Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data.

    Science.gov (United States)

    Zhang, Jian; Hou, Dibo; Wang, Ke; Huang, Pingjie; Zhang, Guangxin; Loáiciga, Hugo

    2017-05-01

    The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T 2 statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

  14. Distributed Event Detection in Wireless Sensor Networks for Disaster Management

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Poel, Mannes; Taghikhaki, Zahra; Havinga, Paul J.M.

    2010-01-01

    Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and become one of the enabling technologies for disaster early-warning systems. Event detection functionality of WSNs can be of great help and importance for

  15. Quench detection of fast plasma events for the JT-60SA central solenoid

    International Nuclear Information System (INIS)

    Murakami, Haruyuki; Kizu, Kaname; Tsuchiya, Katsuhiko; Kamiya, Koji; Takahashi, Yoshikazu; Yoshida, Kiyoshi

    2012-01-01

    Highlights: ► Pick-up coil method is used for the quench detection of JT-60SA magnet system. ► Disk-shaped pick-up coils are inserted in CS module to compensate inductive voltage. ► Applicability of pick-up coil is evaluated by two dimensional analysis. ► Pick-up coil is applicable whenever disruption, mini collapse and other plasma event. - Abstract: The JT-60 is planned to be modified to a full-superconducting tokamak referred to as the JT-60 Super Advanced (JT-60SA). The maximum temperature of the magnet during its quench might reach the temperature of higher than several hundreds Kelvin that will damage the superconducting magnet itself. The high precision quench detection system, therefore, is one of the key technologies in the superconducting magnet protection system. The pick-up coil method, which is using voltage taps to detect the normal voltage, is used for the quench detection of the JT-60SA superconducting magnet system. The disk-shaped pick-up coils are inserted in the central solenoid (CS) module to compensate the inductive voltage. In the previous study, the quench detection system requires a large number of pick-up coils. The reliability of quench detection system would be higher by simplifying the detection system such as reducing the number of pick-up coils. Simplifying the quench detection system is also important to reduce the total cost of the protection system. Hence the design method is improved by increasing optimizing parameters. The improved design method can reduce the number of pick-up coils without reducing the sensitivity of detection; consequently the protection system can be designed with higher reliability and lower cost. The applicability of the disk-shaped pick-up coil for quench detection system is evaluated by the two dimensional analysis. In the previous study, however, the analysis model only took into account the CS, EF (equilibrium field) coils and plasma. Therefore, applicability of the disk-shaped pick-up coil for

  16. Abnormal Event Detection in Wireless Sensor Networks Based on Multiattribute Correlation

    Directory of Open Access Journals (Sweden)

    Mengdi Wang

    2017-01-01

    Full Text Available Abnormal event detection is one of the vital tasks in wireless sensor networks. However, the faults of nodes and the poor deployment environment have brought great challenges to abnormal event detection. In a typical event detection technique, spatiotemporal correlations are collected to detect an event, which is susceptible to noises and errors. To improve the quality of detection results, we propose a novel approach for abnormal event detection in wireless sensor networks. This approach considers not only spatiotemporal correlations but also the correlations among observed attributes. A dependency model of observed attributes is constructed based on Bayesian network. In this model, the dependency structure of observed attributes is obtained by structure learning, and the conditional probability table of each node is calculated by parameter learning. We propose a new concept named attribute correlation confidence to evaluate the fitting degree between the sensor reading and the abnormal event pattern. On the basis of time correlation detection and space correlation detection, the abnormal events are identified. Experimental results show that the proposed algorithm can reduce the impact of interference factors and the rate of the false alarm effectively; it can also improve the accuracy of event detection.

  17. Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

    Directory of Open Access Journals (Sweden)

    Shaonian Huang

    2018-01-01

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

  18. Real-time distributed fiber optic sensor for security systems: Performance, event classification and nuisance mitigation

    Science.gov (United States)

    Mahmoud, Seedahmed S.; Visagathilagar, Yuvaraja; Katsifolis, Jim

    2012-09-01

    The success of any perimeter intrusion detection system depends on three important performance parameters: the probability of detection (POD), the nuisance alarm rate (NAR), and the false alarm rate (FAR). The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system, and the reliability of the sensing equipment. The suppression of nuisance alarms without degrading sensitivity in fiber optic intrusion detection systems is key to maintaining acceptable performance. Signal processing algorithms that maintain the POD and eliminate nuisance alarms are crucial for achieving this. In this paper, a robust event classification system using supervised neural networks together with a level crossings (LCs) based feature extraction algorithm is presented for the detection and recognition of intrusion and non-intrusion events in a fence-based fiber-optic intrusion detection system. A level crossings algorithm is also used with a dynamic threshold to suppress torrential rain-induced nuisance alarms in a fence system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100 mm/hr with the simultaneous detection of intrusion events. The use of a level crossing based detection and novel classification algorithm is also presented for a buried pipeline fiber optic intrusion detection system for the suppression of nuisance events and discrimination of intrusion events. The sensor employed for both types of systems is a distributed bidirectional fiber-optic Mach-Zehnder (MZ) interferometer.

  19. A novel CUSUM-based approach for event detection in smart metering

    Science.gov (United States)

    Zhu, Zhicheng; Zhang, Shuai; Wei, Zhiqiang; Yin, Bo; Huang, Xianqing

    2018-03-01

    Non-intrusive load monitoring (NILM) plays such a significant role in raising consumer awareness on household electricity use to reduce overall energy consumption in the society. With regard to monitoring low power load, many researchers have introduced CUSUM into the NILM system, since the traditional event detection method is not as effective as expected. Due to the fact that the original CUSUM faces limitations given the small shift is below threshold, we therefore improve the test statistic which allows permissible deviation to gradually rise as the data size increases. This paper proposes a novel event detection and corresponding criterion that could be used in NILM systems to recognize transient states and to help the labelling task. Its performance has been tested in a real scenario where eight different appliances are connected to main line of electric power.

  20. π0 detection system

    International Nuclear Information System (INIS)

    Suzuki, Yoichiro

    1977-01-01

    A π-zero meson detection system used for the measurement of charge exchange reaction is described. The detection of π-zero is made by observing the coincidence events of two gamma-ray emission following the decay of π-zero meson. The angles of the emitted gamma-rays are detected with a wire spark chamber system, and the energies of the gamma-rays are measured with hodoscope type lead glass Cherenkov counters. In front of the π-zero counter system, a lead converter is set, and the incident gamma-rays convert to electron positron pairs, which can be detected with the wire spark chambers. The system is a multi-track detection system. The high voltage pulser of the wire spark chamber system is a charge line thyratron pulser, and the chamber itself is a transmission line type. Read-out can be made by a mag-line system. Wave forms and efficiencies were measured. The three-track efficiency was about 90% by the condenser method and 95% by the charge line method. (Kato, T.)

  1. Flow detection via sparse frame analysis for suspicious event recognition in infrared imagery

    Science.gov (United States)

    Fernandes, Henrique C.; Batista, Marcos A.; Barcelos, Celia A. Z.; Maldague, Xavier P. V.

    2013-05-01

    It is becoming increasingly evident that intelligent systems are very bene¯cial for society and that the further development of such systems is necessary to continue to improve society's quality of life. One area that has drawn the attention of recent research is the development of automatic surveillance systems. In our work we outline a system capable of monitoring an uncontrolled area (an outside parking lot) using infrared imagery and recognizing suspicious events in this area. The ¯rst step is to identify moving objects and segment them from the scene's background. Our approach is based on a dynamic background-subtraction technique which robustly adapts detection to illumination changes. It is analyzed only regions where movement is occurring, ignoring in°uence of pixels from regions where there is no movement, to segment moving objects. Regions where movement is occurring are identi¯ed using °ow detection via sparse frame analysis. During the tracking process the objects are classi¯ed into two categories: Persons and Vehicles, based on features such as size and velocity. The last step is to recognize suspicious events that may occur in the scene. Since the objects are correctly segmented and classi¯ed it is possible to identify those events using features such as velocity and time spent motionless in one spot. In this paper we recognize the suspicious event suspicion of object(s) theft from inside a parked vehicle at spot X by a person" and results show that the use of °ow detection increases the recognition of this suspicious event from 78:57% to 92:85%.

  2. Event detection intelligent camera development

    International Nuclear Information System (INIS)

    Szappanos, A.; Kocsis, G.; Molnar, A.; Sarkozi, J.; Zoletnik, S.

    2008-01-01

    A new camera system 'event detection intelligent camera' (EDICAM) is being developed for the video diagnostics of W-7X stellarator, which consists of 10 distinct and standalone measurement channels each holding a camera. Different operation modes will be implemented for continuous and for triggered readout as well. Hardware level trigger signals will be generated from real time image processing algorithms optimized for digital signal processor (DSP) and field programmable gate array (FPGA) architectures. At full resolution a camera sends 12 bit sampled 1280 x 1024 pixels with 444 fps which means 1.43 Terabyte over half an hour. To analyse such a huge amount of data is time consuming and has a high computational complexity. We plan to overcome this problem by EDICAM's preprocessing concepts. EDICAM camera system integrates all the advantages of CMOS sensor chip technology and fast network connections. EDICAM is built up from three different modules with two interfaces. A sensor module (SM) with reduced hardware and functional elements to reach a small and compact size and robust action in harmful environment as well. An image processing and control unit (IPCU) module handles the entire user predefined events and runs image processing algorithms to generate trigger signals. Finally a 10 Gigabit Ethernet compatible image readout card functions as the network interface for the PC. In this contribution all the concepts of EDICAM and the functions of the distinct modules are described

  3. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

    Energy Technology Data Exchange (ETDEWEB)

    Talathi, S. S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-06-05

    Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.

  4. Non-Linguistic Vocal Event Detection Using Online Random

    DEFF Research Database (Denmark)

    Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll

    2014-01-01

    areas such as object detection, face recognition, and audio event detection. This paper proposes to use online random forest technique for detecting laughter and filler and for analyzing the importance of various features for non-linguistic vocal event classification through permutation. The results...... show that according to the Area Under Curve measure the online random forest achieved 88.1% compared to 82.9% obtained by the baseline support vector machines for laughter classification and 86.8% to 83.6% for filler classification....

  5. Semantic Context Detection Using Audio Event Fusion

    Directory of Open Access Journals (Sweden)

    Cheng Wen-Huang

    2006-01-01

    Full Text Available Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models audio events over a time series in order to accomplish semantic context detection. Two levels of modeling, audio event and semantic context modeling, are devised to bridge the gap between physical audio features and semantic concepts. In this work, hidden Markov models (HMMs are used to model four representative audio events, that is, gunshot, explosion, engine, and car braking, in action movies. At the semantic context level, generative (ergodic hidden Markov model and discriminative (support vector machine (SVM approaches are investigated to fuse the characteristics and correlations among audio events, which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and provide a preliminary framework for information mining by using audio characteristics.

  6. An Examination of Three Spatial Event Cluster Detection Methods

    Directory of Open Access Journals (Sweden)

    Hensley H. Mariathas

    2015-03-01

    Full Text Available In spatial disease surveillance, geographic areas with large numbers of disease cases are to be identified, so that targeted investigations can be pursued. Geographic areas with high disease rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. In some situations, disease-related events rather than individuals are of interest for geographical surveillance, and methods to detect clusters of disease-related events are called event cluster detection methods. In this paper, we examine three distributional assumptions for the events in cluster detection: compound Poisson, approximate normal and multiple hypergeometric (exact. The methods differ on the choice of distributional assumption for the potentially multiple correlated events per individual. The methods are illustrated on emergency department (ED presentations by children and youth (age < 18 years because of substance use in the province of Alberta, Canada, during 1 April 2007, to 31 March 2008. Simulation studies are conducted to investigate Type I error and the power of the clustering methods.

  7. Nuisance alarm suppression techniques for fibre-optic intrusion detection systems

    Science.gov (United States)

    Mahmoud, Seedahmed S.; Visagathilagar, Yuvaraja; Katsifolis, Jim

    2012-02-01

    The suppression of nuisance alarms without degrading sensitivity in fibre-optic intrusion detection systems is important for maintaining acceptable performance. Signal processing algorithms that maintain the POD and minimize nuisance alarms are crucial for achieving this. A level crossings algorithm is presented for suppressing torrential rain-induced nuisance alarms in a fibre-optic fence-based perimeter intrusion detection system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100 mm/hr, and intrusion events can be detected simultaneously during rain periods. The use of a level crossing based detection and novel classification algorithm is also presented demonstrating the suppression of nuisance events and discrimination of nuisance and intrusion events in a buried pipeline fibre-optic intrusion detection system. The sensor employed for both types of systems is a distributed bidirectional fibre-optic Mach Zehnder interferometer.

  8. Ontology-based knowledge management for personalized adverse drug events detection.

    Science.gov (United States)

    Cao, Feng; Sun, Xingzhi; Wang, Xiaoyuan; Li, Bo; Li, Jing; Pan, Yue

    2011-01-01

    Since Adverse Drug Event (ADE) has become a leading cause of death around the world, there arises high demand for helping clinicians or patients to identify possible hazards from drug effects. Motivated by this, we present a personalized ADE detection system, with the focus on applying ontology-based knowledge management techniques to enhance ADE detection services. The development of electronic health records makes it possible to automate the personalized ADE detection, i.e., to take patient clinical conditions into account during ADE detection. Specifically, we define the ADE ontology to uniformly manage the ADE knowledge from multiple sources. We take advantage of the rich semantics from the terminology SNOMED-CT and apply it to ADE detection via the semantic query and reasoning.

  9. Why conventional detection methods fail in identifying the existence of contamination events.

    Science.gov (United States)

    Liu, Shuming; Li, Ruonan; Smith, Kate; Che, Han

    2016-04-15

    Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

    Directory of Open Access Journals (Sweden)

    Angel Mur

    2016-04-01

    Full Text Available In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.

  11. Spatial-Temporal Event Detection from Geo-Tagged Tweets

    Directory of Open Access Journals (Sweden)

    Yuqian Huang

    2018-04-01

    Full Text Available As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events.

  12. Abnormal global and local event detection in compressive sensing domain

    Science.gov (United States)

    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.

  13. A novel seizure detection algorithm informed by hidden Markov model event states

    Science.gov (United States)

    Baldassano, Steven; Wulsin, Drausin; Ung, Hoameng; Blevins, Tyler; Brown, Mesha-Gay; Fox, Emily; Litt, Brian

    2016-06-01

    Objective. Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. Approach. We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. Main results. Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h-1). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). Significance. This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.

  14. NIST Special Publication on Intrusion Detection Systems

    National Research Council Canada - National Science Library

    Bace, Rebecca Gurley

    2001-01-01

    Intrusion detection systems (IDSs) are software or hardware systems that automate the process of monitoring the events occurring in a computer system or network, analyzing them for signs of security problems...

  15. A Multiple-Channel Sub-Band Transient Detection System

    Energy Technology Data Exchange (ETDEWEB)

    David A. Smith

    1998-11-01

    We have developed a unique multiple-channel sub-band transient detection system to record transient electromagnetic signals in carrier-dominated radio environments; the system has been used to make unique observations of weak, transient HF signals. The detection system has made these observations possible through improved sensitivity compared to conventional broadband transient detection systems; the sensitivity improvement is estimated to be at least 20 dB. The increase in sensitivity has been achieved through subdivision of the band of interest (an 18 MHz tunable bandwidth) into eight sub-band independent detection channels, each with a 400 kHz bandwidth and its own criteria. The system generates a system trigger signal when a predetermined number of channels (typically five) trigger within a predetermined window of time (typically 100 ~s). Events are recorded with a broadband data acquisition system sampling at 50 or 100 Msample/s, so despite the fact that the detection system operates on portions of the signal confined to narrow bands, data acquisition is broadband. Between May and September of 1994, the system was used to detect and record over six thousand transient events in the frequency band from 3 to 30 MHz. Approximately 500 of the events have been characterized as paired bursts of radio noise with individual durations of 2 to 10 ps and separations between the bursts of 5 to 160 ps. The paired transients are typically 5 to 40 dB brighter than the background electromagnetic spectrum between carrier signals. We have termed these events SubIonospheric Pulse Pairs (SIPPS) and presently have no explanation as to their source. Our observations of SIPPS resemble observations of TransIonospheric Pulse Pairs (TIPPs) recorded by the Blackboard instrument on the ALEXIS satellite; the source of TIPP events is also unknown. Most of the recorded SIPP events do not exhibit frequency dispersion, implying propagation along a line-of-sight (groundwave) path; but seven of

  16. Abnormal global and local event detection in compressive sensing domain

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2018-05-01

    Full Text Available 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.

  17. Multivariate algorithms for initiating event detection and identification in nuclear power plants

    International Nuclear Information System (INIS)

    Wu, Shun-Chi; Chen, Kuang-You; Lin, Ting-Han; Chou, Hwai-Pwu

    2018-01-01

    Highlights: •Multivariate algorithms for NPP initiating event detection and identification. •Recordings from multiple sensors are simultaneously considered for detection. •Both spatial and temporal information is used for event identification. •Untrained event isolation avoids falsely relating an untrained event. •Efficacy of the algorithms is verified with data from the Maanshan NPP simulator. -- Abstract: To prevent escalation of an initiating event into a severe accident, promptly detecting its occurrence and precisely identifying its type are essential. In this study, several multivariate algorithms for initiating event detection and identification are proposed to help maintain safe operations of nuclear power plants (NPPs). By monitoring changes in the NPP sensing variables, an event is detected when the preset thresholds are exceeded. Unlike existing approaches, recordings from sensors of the same type are simultaneously considered for detection, and no subjective reasoning is involved in setting these thresholds. To facilitate efficient event identification, a spatiotemporal feature extractor is proposed. The extracted features consist of the temporal traits used by existing techniques and the spatial signature of an event. Through an F-score-based feature ranking, only those that are most discriminant in classifying the events under consideration will be retained for identification. Moreover, an untrained event isolation scheme is introduced to avoid relating an untrained event to those in the event dataset so that improper recovery actions can be prevented. Results from experiments containing data of 12 event classes and a total of 125 events generated using a Taiwan’s Maanshan NPP simulator are provided to illustrate the efficacy of the proposed algorithms.

  18. Detecting impacts of extreme events with ecological in situ monitoring networks

    Directory of Open Access Journals (Sweden)

    M. D. Mahecha

    2017-09-01

    Full Text Available Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR, identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with  ≈  100 randomly placed sites in Europe yield a  ≥  90 % chance of detecting the eight largest (typically very large extreme events; but only a  ≥  50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON reliably detect the largest extremes, but that the extreme event detection rates are not higher than would

  19. Online Detection of Abnormal Events in Video Streams

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2013-01-01

    an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image intensity as a descriptor encoding moving information. The nonlinear online support vector machine (SVM firstly learns a limited set of the training frames to provide a basic reference model then updates the model and detects abnormal events in the current frame. We finally apply the method to detect abnormal events on a benchmark video surveillance dataset to demonstrate the effectiveness of the proposed technique.

  20. Nuclear facility safeguards systems modeling using discrete event simulation

    International Nuclear Information System (INIS)

    Engi, D.

    1977-01-01

    The threat of theft or dispersal of special nuclear material at a nuclear facility is treated by studying the temporal relationships between adversaries having authorized access to the facility (insiders) and safeguards system events by using a GASP IV discrete event simulation. The safeguards system events--detection, assessment, delay, communications, and neutralization--are modeled for the general insider adversary strategy which includes degradation of the safeguards system elements followed by an attempt to steal or disperse special nuclear material. The performance measure used in the analysis is the estimated probability of safeguards system success in countering the adversary based upon a predetermined set of adversary actions. An exemplary problem which includes generated results is presented for a hypothetical nuclear facility. The results illustrate representative information that could be utilized by safeguards decision-makers

  1. A Risk Assessment System with Automatic Extraction of Event Types

    Science.gov (United States)

    Capet, Philippe; Delavallade, Thomas; Nakamura, Takuya; Sandor, Agnes; Tarsitano, Cedric; Voyatzi, Stavroula

    In this article we describe the joint effort of experts in linguistics, information extraction and risk assessment to integrate EventSpotter, an automatic event extraction engine, into ADAC, an automated early warning system. By detecting as early as possible weak signals of emerging risks ADAC provides a dynamic synthetic picture of situations involving risk. The ADAC system calculates risk on the basis of fuzzy logic rules operated on a template graph whose leaves are event types. EventSpotter is based on a general purpose natural language dependency parser, XIP, enhanced with domain-specific lexical resources (Lexicon-Grammar). Its role is to automatically feed the leaves with input data.

  2. Detection of rain events in radiological early warning networks with spectro-dosimetric systems

    Science.gov (United States)

    Dąbrowski, R.; Dombrowski, H.; Kessler, P.; Röttger, A.; Neumaier, S.

    2017-10-01

    Short-term pronounced increases of the ambient dose equivalent rate, due to rainfall are a well-known phenomenon. Increases in the same order of magnitude or even below may also be caused by a nuclear or radiological event, i.e. by artificial radiation. Hence, it is important to be able to identify natural rain events in dosimetric early warning networks and to distinguish them from radiological events. Novel spectrometric systems based on scintillators may be used to differentiate between the two scenarios, because the measured gamma spectra provide significant nuclide-specific information. This paper describes three simple, automatic methods to check whether an dot H*(10) increase is caused by a rain event or by artificial radiation. These methods were applied to measurements of three spectrometric systems based on CeBr3, LaBr3 and SrI2 scintillation crystals, investigated and tested for their practicability at a free-field reference site of PTB.

  3. Multi-sensor explosive detection system

    International Nuclear Information System (INIS)

    Gozani, T.; Shea, P.M.; Sawa, Z.P.

    1992-01-01

    This patent describes an explosive detection system. It comprises a source of neutrons; a detector array comprising a plurality of gamma ray detectors, each of the gamma ray detectors providing a detection signal in the event a gamma ray is captured by the detector, and at least one neutron detector, the neutron detector providing a neutron detection signal in the event a neutron is captured by the neutron detector; means for irradiating an object being examined with neutrons from the neutron source and for positioning the detector array relative to the object so that gamma rays emitted from the elements within the object as a result of the neutron irradiation are detected by the gamma ray detectors of the detector array; and parallel distributed processing means responsive to the detection signals of the detector array for discriminating between objects carrying explosives and objects not carrying explosives, the parallel distributed processing means including an artificial neural system (ANS), the ANS having a parallel network of processors, each processor of the parallel network of processors, each processor of the parallel network of processors including means for receiving at least one input signal, and means for generating an output signal as a function of the at least one input signal

  4. Signal detection to identify serious adverse events (neuropsychiatric events in travelers taking mefloquine for chemoprophylaxis of malaria

    Directory of Open Access Journals (Sweden)

    Naing C

    2012-08-01

    Full Text Available Cho Naing,1,3 Kyan Aung,1 Syed Imran Ahmed,2 Joon Wah Mak31School of Medical Sciences, 2School of Pharmacy and Health Sciences, 3School of Postgraduate Studies and Research, International Medical University, Kuala Lumpur, MalaysiaBackground: For all medications, there is a trade-off between benefits and potential for harm. It is important for patient safety to detect drug-event combinations and analyze by appropriate statistical methods. Mefloquine is used as chemoprophylaxis for travelers going to regions with known chloroquine-resistant Plasmodium falciparum malaria. As such, there is a concern about serious adverse events associated with mefloquine chemoprophylaxis. The objective of the present study was to assess whether any signal would be detected for the serious adverse events of mefloquine, based on data in clinicoepidemiological studies.Materials and methods: We extracted data on adverse events related to mefloquine chemoprophylaxis from the two published datasets. Disproportionality reporting of adverse events such as neuropsychiatric events and other adverse events was presented in the 2 × 2 contingency table. Reporting odds ratio and corresponding 95% confidence interval [CI] data-mining algorithm was applied for the signal detection. The safety signals are considered significant when the ROR estimates and the lower limits of the corresponding 95% CI are ≥2.Results: Two datasets addressing adverse events of mefloquine chemoprophylaxis (one from a published article and one from a Cochrane systematic review were included for analyses. Reporting odds ratio 1.58, 95% CI: 1.49–1.68 based on published data in the selected article, and 1.195, 95% CI: 0.94–1.44 based on data in the selected Cochrane review. Overall, in both datasets, the reporting odds ratio values of lower 95% CI were less than 2.Conclusion: Based on available data, findings suggested that signals for serious adverse events pertinent to neuropsychiatric event were

  5. Event Index - a LHCb Event Search System

    CERN Document Server

    INSPIRE-00392208; Kazeev, Nikita; Redkin, Artem

    2015-12-23

    LHC experiments generate up to $10^{12}$ events per year. This paper describes Event Index - an event search system. Event Index's primary function is quickly selecting subsets of events from a combination of conditions, such as the estimated decay channel or stripping lines output. Event Index is essentially Apache Lucene optimized for read-only indexes distributed over independent shards on independent nodes.

  6. Process variant comparison: using event logs to detect differences in behavior and business rules

    NARCIS (Netherlands)

    Bolt, A.; de Leoni, M.; van der Aalst, W.M.P.

    2018-01-01

    This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was recorded in event logs. We use transition systems to model behavior and to highlight differences. Transition systems are annotated with

  7. A computer aided treatment event recognition system in radiation therapy

    International Nuclear Information System (INIS)

    Xia, Junyi; Mart, Christopher; Bayouth, John

    2014-01-01

    Purpose: To develop an automated system to safeguard radiation therapy treatments by analyzing electronic treatment records and reporting treatment events. Methods: CATERS (Computer Aided Treatment Event Recognition System) was developed to detect treatment events by retrieving and analyzing electronic treatment records. CATERS is designed to make the treatment monitoring process more efficient by automating the search of the electronic record for possible deviations from physician's intention, such as logical inconsistencies as well as aberrant treatment parameters (e.g., beam energy, dose, table position, prescription change, treatment overrides, etc). Over a 5 month period (July 2012–November 2012), physicists were assisted by the CATERS software in conducting normal weekly chart checks with the aims of (a) determining the relative frequency of particular events in the authors’ clinic and (b) incorporating these checks into the CATERS. During this study period, 491 patients were treated at the University of Iowa Hospitals and Clinics for a total of 7692 fractions. Results: All treatment records from the 5 month analysis period were evaluated using all the checks incorporated into CATERS after the training period. About 553 events were detected as being exceptions, although none of them had significant dosimetric impact on patient treatments. These events included every known event type that was discovered during the trial period. A frequency analysis of the events showed that the top three types of detected events were couch position override (3.2%), extra cone beam imaging (1.85%), and significant couch position deviation (1.31%). The significant couch deviation is defined as the number of treatments where couch vertical exceeded two times standard deviation of all couch verticals, or couch lateral/longitudinal exceeded three times standard deviation of all couch laterals and longitudinals. On average, the application takes about 1 s per patient when

  8. A computer aided treatment event recognition system in radiation therapy

    Energy Technology Data Exchange (ETDEWEB)

    Xia, Junyi, E-mail: junyi-xia@uiowa.edu; Mart, Christopher [Department of Radiation Oncology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, Iowa 52242 (United States); Bayouth, John [Department of Radiation Oncology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, Iowa 52242 and Department of Human Oncology, University of Wisconsin - Madison, 600 Highland Avenue, K4/B55, Madison, Wisconsin 53792-0600 (United States)

    2014-01-15

    Purpose: To develop an automated system to safeguard radiation therapy treatments by analyzing electronic treatment records and reporting treatment events. Methods: CATERS (Computer Aided Treatment Event Recognition System) was developed to detect treatment events by retrieving and analyzing electronic treatment records. CATERS is designed to make the treatment monitoring process more efficient by automating the search of the electronic record for possible deviations from physician's intention, such as logical inconsistencies as well as aberrant treatment parameters (e.g., beam energy, dose, table position, prescription change, treatment overrides, etc). Over a 5 month period (July 2012–November 2012), physicists were assisted by the CATERS software in conducting normal weekly chart checks with the aims of (a) determining the relative frequency of particular events in the authors’ clinic and (b) incorporating these checks into the CATERS. During this study period, 491 patients were treated at the University of Iowa Hospitals and Clinics for a total of 7692 fractions. Results: All treatment records from the 5 month analysis period were evaluated using all the checks incorporated into CATERS after the training period. About 553 events were detected as being exceptions, although none of them had significant dosimetric impact on patient treatments. These events included every known event type that was discovered during the trial period. A frequency analysis of the events showed that the top three types of detected events were couch position override (3.2%), extra cone beam imaging (1.85%), and significant couch position deviation (1.31%). The significant couch deviation is defined as the number of treatments where couch vertical exceeded two times standard deviation of all couch verticals, or couch lateral/longitudinal exceeded three times standard deviation of all couch laterals and longitudinals. On average, the application takes about 1 s per patient when

  9. Effect of parameters in moving average method for event detection enhancement using phase sensitive OTDR

    Science.gov (United States)

    Kwon, Yong-Seok; Naeem, Khurram; Jeon, Min Yong; Kwon, Il-bum

    2017-04-01

    We analyze the relations of parameters in moving average method to enhance the event detectability of phase sensitive optical time domain reflectometer (OTDR). If the external events have unique frequency of vibration, then the control parameters of moving average method should be optimized in order to detect these events efficiently. A phase sensitive OTDR was implemented by a pulsed light source, which is composed of a laser diode, a semiconductor optical amplifier, an erbium-doped fiber amplifier, a fiber Bragg grating filter, and a light receiving part, which has a photo-detector and high speed data acquisition system. The moving average method is operated with the control parameters: total number of raw traces, M, number of averaged traces, N, and step size of moving, n. The raw traces are obtained by the phase sensitive OTDR with sound signals generated by a speaker. Using these trace data, the relation of the control parameters is analyzed. In the result, if the event signal has one frequency, then the optimal values of N, n are existed to detect the event efficiently.

  10. Automated Feature and Event Detection with SDO AIA and HMI Data

    Science.gov (United States)

    Davey, Alisdair; Martens, P. C. H.; Attrill, G. D. R.; Engell, A.; Farid, S.; Grigis, P. C.; Kasper, J.; Korreck, K.; Saar, S. H.; Su, Y.; Testa, P.; Wills-Davey, M.; Savcheva, A.; Bernasconi, P. N.; Raouafi, N.-E.; Delouille, V. A.; Hochedez, J. F..; Cirtain, J. W.; Deforest, C. E.; Angryk, R. A.; de Moortel, I.; Wiegelmann, T.; Georgouli, M. K.; McAteer, R. T. J.; Hurlburt, N.; Timmons, R.

    The Solar Dynamics Observatory (SDO) represents a new frontier in quantity and quality of solar data. At about 1.5 TB/day, the data will not be easily digestible by solar physicists using the same methods that have been employed for images from previous missions. In order for solar scientists to use the SDO data effectively they need meta-data that will allow them to identify and retrieve data sets that address their particular science questions. We are building a comprehensive computer vision pipeline for SDO, abstracting complete metadata on many of the features and events detectable on the Sun without human intervention. Our project unites more than a dozen individual, existing codes into a systematic tool that can be used by the entire solar community. The feature finding codes will run as part of the SDO Event Detection System (EDS) at the Joint Science Operations Center (JSOC; joint between Stanford and LMSAL). The metadata produced will be stored in the Heliophysics Event Knowledgebase (HEK), which will be accessible on-line for the rest of the world directly or via the Virtual Solar Observatory (VSO) . Solar scientists will be able to use the HEK to select event and feature data to download for science studies.

  11. Detection of Abnormal Events via Optical Flow Feature Analysis

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2015-03-01

    Full Text Available In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm.

  12. Detection of Abnormal Events via Optical Flow Feature Analysis

    Science.gov (United States)

    Wang, Tian; Snoussi, Hichem

    2015-01-01

    In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. PMID:25811227

  13. Real-time monitoring of clinical processes using complex event processing and transition systems.

    Science.gov (United States)

    Meinecke, Sebastian

    2014-01-01

    Dependencies between tasks in clinical processes are often complex and error-prone. Our aim is to describe a new approach for the automatic derivation of clinical events identified via the behaviour of IT systems using Complex Event Processing. Furthermore we map these events on transition systems to monitor crucial clinical processes in real-time for preventing and detecting erroneous situations.

  14. Microfluidic Arrayed Lab-On-A-Chip for Electrochemical Capacitive Detection of DNA Hybridization Events.

    Science.gov (United States)

    Ben-Yoav, Hadar; Dykstra, Peter H; Bentley, William E; Ghodssi, Reza

    2017-01-01

    A microfluidic electrochemical lab-on-a-chip (LOC) device for DNA hybridization detection has been developed. The device comprises a 3 × 3 array of microelectrodes integrated with a dual layer microfluidic valved manipulation system that provides controlled and automated capabilities for high throughput analysis of microliter volume samples. The surface of the microelectrodes is functionalized with single-stranded DNA (ssDNA) probes which enable specific detection of complementary ssDNA targets. These targets are detected by a capacitive technique which measures dielectric variation at the microelectrode-electrolyte interface due to DNA hybridization events. A quantitative analysis of the hybridization events is carried out based on a sensing modeling that includes detailed analysis of energy storage and dissipation components. By calculating these components during hybridization events the device is able to demonstrate specific and dose response sensing characteristics. The developed microfluidic LOC for DNA hybridization detection offers a technology for real-time and label-free assessment of genetic markers outside of laboratory settings, such as at the point-of-care or in-field environmental monitoring.

  15. DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

    Science.gov (United States)

    Lawhern, Vernon; Hairston, W David; Robbins, Kay

    2013-01-01

    Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.

  16. DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

    Directory of Open Access Journals (Sweden)

    Vernon Lawhern

    Full Text Available Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG data as an additional illustration.

  17. A robust neural network-based approach for microseismic event detection

    KAUST Repository

    Akram, Jubran

    2017-08-17

    We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.

  18. Event Detection Intelligent Camera: Demonstration of flexible, real-time data taking and processing

    Energy Technology Data Exchange (ETDEWEB)

    Szabolics, Tamás, E-mail: szabolics.tamas@wigner.mta.hu; Cseh, Gábor; Kocsis, Gábor; Szepesi, Tamás; Zoletnik, Sándor

    2015-10-15

    Highlights: • We present EDICAM's operation principles description. • Firmware tests results. • Software test results. • Further developments. - Abstract: An innovative fast camera (EDICAM – Event Detection Intelligent CAMera) was developed by MTA Wigner RCP in the last few years. This new concept was designed for intelligent event driven processing to be able to detect predefined events and track objects in the plasma. The camera provides a moderate frame rate of 400 Hz at full frame resolution (1280 × 1024), and readout of smaller region of interests can be done in the 1–140 kHz range even during exposure of the full image. One of the most important advantages of this hardware is a 10 Gbit/s optical link which ensures very fast communication and data transfer between the PC and the camera, enabling two level of processing: primitive algorithms in the camera hardware and high-level processing in the PC. This camera hardware has successfully proven to be able to monitoring the plasma in several fusion devices for example at ASDEX Upgrade, KSTAR and COMPASS with the first version of firmware. A new firmware and software package is under development. It allows to detect predefined events in real time and therefore the camera is capable to change its own operation or to give warnings e.g. to the safety system of the experiment. The EDICAM system can handle a huge amount of data (up to TBs) with high data rate (950 MB/s) and will be used as the central element of the 10 camera overview video diagnostic system of Wendenstein 7-X (W7-X) stellarator. This paper presents key elements of the newly developed built-in intelligence stressing the revolutionary new features and the results of the test of the different software elements.

  19. Automatic detection of esophageal pressure events. Is there an alternative to rule-based criteria?

    DEFF Research Database (Denmark)

    Kruse-Andersen, S; Rütz, K; Kolberg, Jens Godsk

    1995-01-01

    of relevant pressure peaks at the various recording levels. Until now, this selection has been performed entirely by rule-based systems, requiring each pressure deflection to fit within predefined rigid numerical limits in order to be detected. However, due to great variations in the shapes of the pressure...... curves generated by muscular contractions, rule-based criteria do not always select the pressure events most relevant for further analysis. We have therefore been searching for a new concept for automatic event recognition. The present study describes a new system, based on the method of neurocomputing.......79-0.99 and accuracies of 0.89-0.98, depending on the recording level within the esophageal lumen. The neural networks often recognized peaks that clearly represented true contractions but that had been rejected by a rule-based system. We conclude that neural networks have potentials for automatic detections...

  20. COMPARISON OF FOUR METHODS TO DETECT ADVERSE EVENTS IN HOSPITAL

    Directory of Open Access Journals (Sweden)

    Inge Dhamanti

    2015-09-01

    Full Text Available AbstrakDeteksi terjadinya kejadian yang tidak diharapkan (KTD telah menjadi salah satu tantangan dalam keselamatan pasien oleh karena itu metode untuk mendeteksi terjadinya KTD sangatlah penting untuk meningkatkan keselamatan pasien. Tujuan dari artikel ini adalah untuk membandingkan kelebihan dan kekurangan dari beberapa metode untuk mendeteksi terjadinya KTD di rumah sakit, meliputi review rekam medis, pelaporan insiden secara mandiri, teknologi informasi, dan pelaporan oleh pasien. Studi ini merupakan kajian literatur untuk membandingkan dan menganalisa metode terbaik untuk mendeteksi KTD yang dapat diimplementasikan oleh rumah sakit. Semua dari empat metode telah terbukti mampu untuk mendeteksi terjadinya KTD di rumah sakit, tetapi masing-masing metode mempunyai kelebihan dan kekurangan yang perlu diatasi. Tidak ada satu metode terbaik yang akan memberikan hasil terbaik untuk mendeteksi KTD di rumah sakit. Sehingga untuk mendeteksi lebih banyak KTD yang seharusnya dapat dicegah, atau KTD yang telah terjadi, rumah sakit seharusnya mengkombinasikan lebih dari satu metode untuk mendeteksi, karena masing-masing metode mempunyai sensitivitas berbeda-beda.AbstractDetecting adverse events has become one of the challenges in patient safety thus methods to detect adverse events become critical for improving patient safety. The purpose of this paper is to compare the strengths and weaknesses of several methods of identifying adverse events in hospital, including medical records reviews, self-reported incidents, information technology, and patient self-reports. This study is a literature review to compared and analyzed to determine the best method implemented by the hospital. All of four methods have been proved in their ability in detecting adverse events in hospitals, but each method had strengths and limitations to be overcome. There is no ‘best’ single method that will give the best results for adverse events detection in hospital. Thus to

  1. Portable reconfigurable detection and assessment system

    International Nuclear Information System (INIS)

    Blattman, D.A.

    1991-01-01

    Rapidly changing geopolitical issues throughout the world have made the ability to effectively respond to political, military, terrorist and peace-keeping requirements increasingly important. Recent Middle East events indicate a continuing escalation in these activities. These activities are defining the requirements for a rapidly deployable, portable, real-time detection and assessment operational security system that is reconfigurable to site specific threats. This paper describes such a system Mobile Operational Detection and Assessment system (MODAS); a commercially-off-the shelf (COTS) integrated and reconfigurable hardware/software system solution for the ever-changing geopolitical security issues of the Nineties

  2. The global event system

    International Nuclear Information System (INIS)

    Winans, J.

    1994-01-01

    The support for the global event system has been designed to allow an application developer to control the APS event generator and receiver boards. This is done by the use of four new record types. These records are customized and are only supported by the device support modules for the APS event generator and receiver boards. The use of the global event system and its associated records should not be confused with the vanilla EPICS events and the associated event records. They are very different

  3. Object-Oriented Query Language For Events Detection From Images Sequences

    Science.gov (United States)

    Ganea, Ion Eugen

    2015-09-01

    In this paper is presented a method to represent the events extracted from images sequences and the query language used for events detection. Using an object oriented model the spatial and temporal relationships between salient objects and also between events are stored and queried. This works aims to unify the storing and querying phases for video events processing. The object oriented language syntax used for events processing allow the instantiation of the indexes classes in order to improve the accuracy of the query results. The experiments were performed on images sequences provided from sport domain and it shows the reliability and the robustness of the proposed language. To extend the language will be added a specific syntax for constructing the templates for abnormal events and for detection of the incidents as the final goal of the research.

  4. An intrusion detection system based on fiber hydrophone

    Science.gov (United States)

    Liu, Junrong; Qiu, Xiufen; Shen, Heping

    2017-10-01

    This paper provides a new intrusion detection system based on fiber hydrophone, focusing beam forming figure positioning according to the near field and high precision sound source location algorithm which can accurately position the intrusion; obtaining its behavior path , obtaining the intrusion events related information such as speed form tracking intrusion trace; And analyze identification the detected intrusion behavior. If the monitor area is larger, the algorithm will take too much time once, and influence the system response time, for reduce the calculating time. This paper provides way that coarse location first, and then scanned for accuracy, so as to realize the intrusion events (such as car, etc.) the remote monitoring of positioning. The system makes up the blank in process capture of the fiber optic intrusion detection technology, and improves the understanding of the invasion. Through the capture of the process of intrusion behavior, and the fusion detection of intrusion behavior itself, thus analysis, judgment, identification of the intrusion information can greatly reduce the rate of false positives, greatly improved the reliability and practicability of the perimeter security system.

  5. Secure access control and large scale robust representation for online multimedia event detection.

    Science.gov (United States)

    Liu, Changyu; Lu, Bin; Li, Huiling

    2014-01-01

    We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.

  6. Secure Access Control and Large Scale Robust Representation for Online Multimedia Event Detection

    Directory of Open Access Journals (Sweden)

    Changyu Liu

    2014-01-01

    Full Text Available We developed an online multimedia event detection (MED system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.

  7. Detections of Planets in Binaries Through the Channel of Chang–Refsdal Gravitational Lensing Events

    Energy Technology Data Exchange (ETDEWEB)

    Han, Cheongho [Department of Physics, Chungbuk National University, Cheongju 361-763 (Korea, Republic of); Shin, In-Gu; Jung, Youn Kil [Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA 02138 (United States)

    2017-02-01

    Chang–Refsdal (C–R) lensing, which refers to the gravitational lensing of a point mass perturbed by a constant external shear, provides a good approximation in describing lensing behaviors of either a very wide or a very close binary lens. C–R lensing events, which are identified by short-term anomalies near the peak of high-magnification lensing light curves, are routinely detected from lensing surveys, but not much attention is paid to them. In this paper, we point out that C–R lensing events provide an important channel to detect planets in binaries, both in close and wide binary systems. Detecting planets through the C–R lensing event channel is possible because the planet-induced perturbation occurs in the same region of the C–R lensing-induced anomaly and thus the existence of the planet can be identified by the additional deviation in the central perturbation. By presenting the analysis of the actually observed C–R lensing event OGLE-2015-BLG-1319, we demonstrate that dense and high-precision coverage of a C–R lensing-induced perturbation can provide a strong constraint on the existence of a planet in a wide range of planet parameters. The sample of an increased number of microlensing planets in binary systems will provide important observational constraints in giving shape to the details of planet formation, which have been restricted to the case of single stars to date.

  8. Use of wireless sensor networks for distributed event detection in disaster management applications

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Poel, Mannes; Taghikhaki, Zahra; Havinga, Paul J.M.

    Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster systems. Event detection functionality of WSNs can be of great help and importance

  9. Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

    Science.gov (United States)

    Barboza, Philippe; Vaillant, Laetitia; Mawudeku, Abla; Nelson, Noele P.; Hartley, David M.; Madoff, Lawrence C.; Linge, Jens P.; Collier, Nigel; Brownstein, John S.; Yangarber, Roman; Astagneau, Pascal; on behalf of the Early Alerting, Reporting Project of the Global Health Security Initiative

    2013-01-01

    The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7–13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users. PMID:23472077

  10. Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events.

    Directory of Open Access Journals (Sweden)

    Philippe Barboza

    Full Text Available The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI Early Alerting and Reporting (EAR project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7-13.8. This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.

  11. Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events.

    Science.gov (United States)

    Barboza, Philippe; Vaillant, Laetitia; Mawudeku, Abla; Nelson, Noele P; Hartley, David M; Madoff, Lawrence C; Linge, Jens P; Collier, Nigel; Brownstein, John S; Yangarber, Roman; Astagneau, Pascal

    2013-01-01

    The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7-13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.

  12. Analytic 3D image reconstruction using all detected events

    International Nuclear Information System (INIS)

    Kinahan, P.E.; Rogers, J.G.

    1988-11-01

    We present the results of testing a previously presented algorithm for three-dimensional image reconstruction that uses all gamma-ray coincidence events detected by a PET volume-imaging scanner. By using two iterations of an analytic filter-backprojection method, the algorithm is not constrained by the requirement of a spatially invariant detector point spread function, which limits normal analytic techniques. Removing this constraint allows the incorporation of all detected events, regardless of orientation, which improves the statistical quality of the final reconstructed image

  13. DEVS representation of dynamical systems - Event-based intelligent control. [Discrete Event System Specification

    Science.gov (United States)

    Zeigler, Bernard P.

    1989-01-01

    It is shown how systems can be advantageously represented as discrete-event models by using DEVS (discrete-event system specification), a set-theoretic formalism. Such DEVS models provide a basis for the design of event-based logic control. In this control paradigm, the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by its DEVS model of the system under control. The event-based contral paradigm is applied in advanced robotic and intelligent automation, showing how classical process control can be readily interfaced with rule-based symbolic reasoning systems.

  14. Adaptive Sensor Tuning for Seismic Event Detection in Environment with Electromagnetic Noise

    Science.gov (United States)

    Ziegler, Abra E.

    The goal of this research is to detect possible microseismic events at a carbon sequestration site. Data recorded on a continuous downhole microseismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project, were evaluated using machine learning and reinforcement learning techniques to determine their effectiveness at seismic event detection on a dataset with electromagnetic noise. The data were recorded from a passive vertical monitoring array consisting of 16 levels of 3-component 15 Hz geophones installed in the field and continuously recording since January 2014. Electromagnetic and other noise recorded on the array has significantly impacted the utility of the data and it was necessary to characterize and filter the noise in order to attempt event detection. Traditional detection methods using short-term average/long-term average (STA/LTA) algorithms were evaluated and determined to be ineffective because of changing noise levels. To improve the performance of event detection and automatically and dynamically detect seismic events using effective data processing parameters, an adaptive sensor tuning (AST) algorithm developed by Sandia National Laboratories was utilized. AST exploits neuro-dynamic programming (reinforcement learning) trained with historic event data to automatically self-tune and determine optimal detection parameter settings. The key metric that guides the AST algorithm is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The effects that changes in neighborhood configuration have on signal detection were explored, as it was determined that neighborhood-based detections significantly reduce the number of both missed and false detections in ground-truthed data. The performance of the AST algorithm was

  15. Towards Detecting the Crowd Involved in Social Events

    Directory of Open Access Journals (Sweden)

    Wei Huang

    2017-10-01

    Full Text Available Knowing how people interact with urban environments is fundamental for a variety of fields, ranging from transportation to social science. Despite the fact that human mobility patterns have been a major topic of study in recent years, a challenge to understand large-scale human behavior when a certain event occurs remains due to a lack of either relevant data or suitable approaches. Psychological crowd refers to a group of people who are usually located at different places and show different behaviors, but who are very sensitively driven to take the same act (gather together by a certain event, which has been theoretically studied by social psychologists since the 19th century. This study aims to propose a computational approach using a machine learning method to model psychological crowds, contributing to the better understanding of human activity patterns under events. Psychological features and mental unity of the crowd are computed to detect the involved individuals. A national event happening across the USA in April, 2015 is analyzed using geotagged tweets as a case study to test our approach. The result shows that 81% of individuals in the crowd can be successfully detected. Through investigating the geospatial pattern of the involved users, not only can the event related users be identified but also those unobserved users before the event can be uncovered. The proposed approach can effectively represent the psychological feature and measure the mental unity of the psychological crowd, which sheds light on the study of large-scale psychological crowd and provides an innovative way to understanding human behavior under events.

  16. A simple strategy for fall events detection

    KAUST Repository

    Harrou, Fouzi; Zerrouki, Nabil; Sun, Ying; Houacine, Amrane

    2017-01-01

    the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy areas of the human body for each frame are collected and used as the input data to MEWMA chart

  17. Traffic Congestion Detection System through Connected Vehicles and Big Data

    Directory of Open Access Journals (Sweden)

    Néstor Cárdenas-Benítez

    2016-04-01

    Full Text Available This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO2 and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur.

  18. Traffic Congestion Detection System through Connected Vehicles and Big Data.

    Science.gov (United States)

    Cárdenas-Benítez, Néstor; Aquino-Santos, Raúl; Magaña-Espinoza, Pedro; Aguilar-Velazco, José; Edwards-Block, Arthur; Medina Cass, Aldo

    2016-04-28

    This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO₂ and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility) traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur.

  19. A Macro-Observation Scheme for Abnormal Event Detection in Daily-Life Video Sequences

    Directory of Open Access Journals (Sweden)

    Chiu Wei-Yao

    2010-01-01

    Full Text Available Abstract We propose a macro-observation scheme for abnormal event detection in daily life. The proposed macro-observation representation records the time-space energy of motions of all moving objects in a scene without segmenting individual object parts. The energy history of each pixel in the scene is instantly updated with exponential weights without explicitly specifying the duration of each activity. Since possible activities in daily life are numerous and distinct from each other and not all abnormal events can be foreseen, images from a video sequence that spans sufficient repetition of normal day-to-day activities are first randomly sampled. A constrained clustering model is proposed to partition the sampled images into groups. The new observed event that has distinct distance from any of the cluster centroids is then classified as an anomaly. The proposed method has been evaluated in daily work of a laboratory and BEHAVE benchmark dataset. The experimental results reveal that it can well detect abnormal events such as burglary and fighting as long as they last for a sufficient duration of time. The proposed method can be used as a support system for the scene that requires full time monitoring personnel.

  20. Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring

    Directory of Open Access Journals (Sweden)

    Yingchi Mao

    2017-12-01

    Full Text Available Time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the agricultural water supply network. The accurate and efficient detection and warning of contamination events to prevent pollution from spreading is one of the most important issues when pollution occurs. In order to comprehensively reduce the event detection deviation, a spatial–temporal-based event detection approach with multivariate time-series data for water quality monitoring (M-STED was proposed. The M-STED approach includes three parts. The first part is that M-STED adopts a Rule K algorithm to select backbone nodes as the nodes in the CDS, and forward the sensed data of multiple water parameters. The second part is to determine the state of each backbone node with back propagation neural network models and the sequential Bayesian analysis in the current timestamp. The third part is to establish a spatial model with Bayesian networks to estimate the state of the backbones in the next timestamp and trace the “outlier” node to its neighborhoods to detect a contamination event. The experimental results indicate that the average detection rate is more than 80% with M-STED and the false detection rate is lower than 9%, respectively. The M-STED approach can improve the rate of detection by about 40% and reduce the false alarm rate by about 45%, compared with the event detection with a single water parameter algorithm, S-STED. Moreover, the proposed M-STED can exhibit better performance in terms of detection delay and scalability.

  1. Energy-Efficient Fault-Tolerant Dynamic Event Region Detection in Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Enemark, Hans-Jacob; Zhang, Yue; Dragoni, Nicola

    2015-01-01

    to a hybrid algorithm for dynamic event region detection, such as real-time tracking of chemical leakage regions. Considering the characteristics of the moving away dynamic events, we propose a return back condition for the hybrid algorithm from distributed neighborhood collaboration, in which a node makes......Fault-tolerant event detection is fundamental to wireless sensor network applications. Existing approaches usually adopt neighborhood collaboration for better detection accuracy, while need more energy consumption due to communication. Focusing on energy efficiency, this paper makes an improvement...... its detection decision based on decisions received from its spatial and temporal neighbors, to local non-communicative decision making. The simulation results demonstrate that the improved algorithm does not degrade the detection accuracy of the original algorithm, while it has better energy...

  2. Statin-associated muscular and renal adverse events: data mining of the public version of the FDA adverse event reporting system.

    Directory of Open Access Journals (Sweden)

    Toshiyuki Sakaeda

    Full Text Available OBJECTIVE: Adverse event reports (AERs submitted to the US Food and Drug Administration (FDA were reviewed to assess the muscular and renal adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA reductase inhibitors (statins and to attempt to determine the rank-order of the association. METHODS: After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving pravastatin, simvastatin, atorvastatin, or rosuvastatin were analyzed. Authorized pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Myalgia, rhabdomyolysis and an increase in creatine phosphokinase level were focused on as the muscular adverse events, and acute renal failure, non-acute renal failure, and an increase in blood creatinine level as the renal adverse events. RESULTS: Based on 1,644,220 AERs from 2004 to 2009, signals were detected for 4 statins with respect to myalgia, rhabdomyolysis, and an increase in creatine phosphokinase level, but these signals were stronger for rosuvastatin than pravastatin and atorvastatin. Signals were also detected for acute renal failure, though in the case of atorvastatin, the association was marginal, and furthermore, a signal was not detected for non-acute renal failure or for an increase in blood creatinine level. CONCLUSIONS: Data mining of the FDA's adverse event reporting system, AERS, is useful for examining statin-associated muscular and renal adverse events. The data strongly suggest the necessity of well-organized clinical studies with respect to statin-associated adverse events.

  3. Statin-associated muscular and renal adverse events: data mining of the public version of the FDA adverse event reporting system.

    Science.gov (United States)

    Sakaeda, Toshiyuki; Kadoyama, Kaori; Okuno, Yasushi

    2011-01-01

    Adverse event reports (AERs) submitted to the US Food and Drug Administration (FDA) were reviewed to assess the muscular and renal adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) and to attempt to determine the rank-order of the association. After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving pravastatin, simvastatin, atorvastatin, or rosuvastatin were analyzed. Authorized pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Myalgia, rhabdomyolysis and an increase in creatine phosphokinase level were focused on as the muscular adverse events, and acute renal failure, non-acute renal failure, and an increase in blood creatinine level as the renal adverse events. Based on 1,644,220 AERs from 2004 to 2009, signals were detected for 4 statins with respect to myalgia, rhabdomyolysis, and an increase in creatine phosphokinase level, but these signals were stronger for rosuvastatin than pravastatin and atorvastatin. Signals were also detected for acute renal failure, though in the case of atorvastatin, the association was marginal, and furthermore, a signal was not detected for non-acute renal failure or for an increase in blood creatinine level. Data mining of the FDA's adverse event reporting system, AERS, is useful for examining statin-associated muscular and renal adverse events. The data strongly suggest the necessity of well-organized clinical studies with respect to statin-associated adverse events.

  4. Detecting failure events in buildings: a numerical and experimental analysis

    OpenAIRE

    Heckman, V. M.; Kohler, M. D.; Heaton, T. H.

    2010-01-01

    A numerical method is used to investigate an approach for detecting the brittle fracture of welds associated with beam -column connections in instrumented buildings in real time through the use of time-reversed Green’s functions and wave propagation reciprocity. The approach makes use of a prerecorded catalog of Green’s functions for an instrumented building to detect failure events in the building during a later seismic event by screening continuous data for the presence of wavef...

  5. Abnormal Event Detection Using Local Sparse Representation

    DEFF Research Database (Denmark)

    Ren, Huamin; Moeslund, Thomas B.

    2014-01-01

    We propose to detect abnormal events via a sparse subspace clustering algorithm. Unlike most existing approaches, which search for optimized normal bases and detect abnormality based on least square error or reconstruction error from the learned normal patterns, we propose an abnormality measurem...... is found that satisfies: the distance between its local space and the normal space is large. We evaluate our method on two public benchmark datasets: UCSD and Subway Entrance datasets. The comparison to the state-of-the-art methods validate our method's effectiveness....

  6. Rapid and reliable detection and identification of GM events using multiplex PCR coupled with oligonucleotide microarray.

    Science.gov (United States)

    Xu, Xiaodan; Li, Yingcong; Zhao, Heng; Wen, Si-yuan; Wang, Sheng-qi; Huang, Jian; Huang, Kun-lun; Luo, Yun-bo

    2005-05-18

    To devise a rapid and reliable method for the detection and identification of genetically modified (GM) events, we developed a multiplex polymerase chain reaction (PCR) coupled with a DNA microarray system simultaneously aiming at many targets in a single reaction. The system included probes for screening gene, species reference gene, specific gene, construct-specific gene, event-specific gene, and internal and negative control genes. 18S rRNA was combined with species reference genes as internal controls to assess the efficiency of all reactions and to eliminate false negatives. Two sets of the multiplex PCR system were used to amplify four and five targets, respectively. Eight different structure genes could be detected and identified simultaneously for Roundup Ready soybean in a single microarray. The microarray specificity was validated by its ability to discriminate two GM maizes Bt176 and Bt11. The advantages of this method are its high specificity and greatly reduced false-positives and -negatives. The multiplex PCR coupled with microarray technology presented here is a rapid and reliable tool for the simultaneous detection of GM organism ingredients.

  7. Adaptive prediction applied to seismic event detection

    International Nuclear Information System (INIS)

    Clark, G.A.; Rodgers, P.W.

    1981-01-01

    Adaptive prediction was applied to the problem of detecting small seismic events in microseismic background noise. The Widrow-Hoff LMS adaptive filter used in a prediction configuration is compared with two standard seismic filters as an onset indicator. Examples demonstrate the technique's usefulness with both synthetic and actual seismic data

  8. Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration's Adverse Event Reporting System Narratives.

    Science.gov (United States)

    Polepalli Ramesh, Balaji; Belknap, Steven M; Li, Zuofeng; Frid, Nadya; West, Dennis P; Yu, Hong

    2014-06-27

    The Food and Drug Administration's (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs. FAERS reports include both structured reports and unstructured narratives. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem. The objective of this study was to develop an annotated corpus of FAERS narratives and biomedical named entity tagger to automatically identify ADE related information in the FAERS narratives. We developed an annotation guideline and annotate medication information and adverse event related entities on 122 FAERS narratives comprising approximately 23,000 word tokens. A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features. The annotated corpus had an agreement of over .9 Cohen's kappa for medication and adverse event entities. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities. In this study, we developed an annotated corpus of FAERS narratives and machine learning based models for automatically extracting medication and adverse event information from the FAERS narratives. Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance.

  9. Evaluation of advanced and current leak detection systems

    International Nuclear Information System (INIS)

    Kupperman, D.S.

    1988-01-01

    U.S. Nuclear Regulatory Commission Guide 1.45 recommends the use of at least three different detection methods in reactors to detect leakage. Monitoring of both sump-flow and airborne particulate radioactivity is mandatory. A third method can involve either monitoring of condensate flow rate from air coolers or monitoring of airborne gaseous radioactivity. Although the methods currently used for leak detection reflect the state of the art, other techniques may be developed and used. Since the recommendations of Regulatory Guide 1.45 are not mandatory, Licensee Event Report Compilations have been reviewed to help establish actual capabilities for leak detection. The review of event reports, which had previously covered the period of June 1985 to August 1986 has been extended, and now covers events to June 1987. The total number of significant events is now 83. These reports have provided documented, sometimes detailed, summaries of reactor leaks. They have helped establish the capabilities of existing systems to detect and locate leaks. Differences between PWRs and BWRs with regard to leak detection have now been analyzed. With regard to detection methods, the greatest differences between reactor types are as follows: (a) The sump pump is reported as the detection method more frequently in BWRs than in PWRs (64% vs. 35%). (b) The radiation monitor is reported as the detection method (excluding false alarms) more frequently in PWRs. Current efforts at Argonne National Laboratory (ANL) to evaluate advanced acoustic leak detection methods are directed toward the generation and analysis of acoustic data from large (0.5 to 10 gal/min) leaks and modification of the software of the GARD/ANL advanced acoustic leak detection system. In order to reach the goal of 10 gal/min leaks, the Steam Generator Test Facility at ANL has been modified to carry out the leak testing. Tests were carried out with water at 525 deg. F and 1100 psi leaking through a fatigue crack in a 4-in

  10. National Earthquake Information Center Seismic Event Detections on Multiple Scales

    Science.gov (United States)

    Patton, J.; Yeck, W. L.; Benz, H.; Earle, P. S.; Soto-Cordero, L.; Johnson, C. E.

    2017-12-01

    The U.S. Geological Survey National Earthquake Information Center (NEIC) monitors seismicity on local, regional, and global scales using automatic picks from more than 2,000 near-real time seismic stations. This presents unique challenges in automated event detection due to the high variability in data quality, network geometries and density, and distance-dependent variability in observed seismic signals. To lower the overall detection threshold while minimizing false detection rates, NEIC has begun to test the incorporation of new detection and picking algorithms, including multiband (Lomax et al., 2012) and kurtosis (Baillard et al., 2014) pickers, and a new bayesian associator (Glass 3.0). The Glass 3.0 associator allows for simultaneous processing of variably scaled detection grids, each with a unique set of nucleation criteria (e.g., nucleation threshold, minimum associated picks, nucleation phases) to meet specific monitoring goals. We test the efficacy of these new tools on event detection in networks of various scales and geometries, compare our results with previous catalogs, and discuss lessons learned. For example, we find that on local and regional scales, rapid nucleation of small events may require event nucleation with both P and higher-amplitude secondary phases (e.g., S or Lg). We provide examples of the implementation of a scale-independent associator for an induced seismicity sequence (local-scale), a large aftershock sequence (regional-scale), and for monitoring global seismicity. Baillard, C., Crawford, W. C., Ballu, V., Hibert, C., & Mangeney, A. (2014). An automatic kurtosis-based P-and S-phase picker designed for local seismic networks. Bulletin of the Seismological Society of America, 104(1), 394-409. Lomax, A., Satriano, C., & Vassallo, M. (2012). Automatic picker developments and optimization: FilterPicker - a robust, broadband picker for real-time seismic monitoring and earthquake early-warning, Seism. Res. Lett. , 83, 531-540, doi: 10

  11. Adaptive prediction applied to seismic event detection

    Energy Technology Data Exchange (ETDEWEB)

    Clark, G.A.; Rodgers, P.W.

    1981-09-01

    Adaptive prediction was applied to the problem of detecting small seismic events in microseismic background noise. The Widrow-Hoff LMS adaptive filter used in a prediction configuration is compared with two standard seismic filters as an onset indicator. Examples demonstrate the technique's usefulness with both synthetic and actual seismic data.

  12. First CNGS events detected by LVD

    International Nuclear Information System (INIS)

    Selvi, M.

    2007-01-01

    The Cern Neutrino to Gran Sasso (CNGS) project aims to produce a high energy, wide band ν μ beam at Cern and send it towards the INFN Gran Sasso National Laboratory (LNGS), 732 km away. Its main goal is the observation of the ν τ appearance, through neutrino flavour oscillation. The beam started its operation in August 2006 for about 12 days: a total amount of 7.6 10 17 protons were delivered to the target. The LVD detector, installed in hall A of the LNGS and mainly dedicated to the study of supernova neutrinos, was fully operating during the whole CNGS running time. A total number of 569 events were detected in coincidence with the beam spill time. This is in good agreement with the expected number of events from Montecarlo simulations

  13. myBlackBox: Blackbox Mobile Cloud Systems for Personalized Unusual Event Detection

    Directory of Open Access Journals (Sweden)

    Junho Ahn

    2016-05-01

    Full Text Available We demonstrate the feasibility of constructing a novel and practical real-world mobile cloud system, called myBlackBox, that efficiently fuses multimodal smartphone sensor data to identify and log unusual personal events in mobile users’ daily lives. The system incorporates a hybrid architectural design that combines unsupervised classification of audio, accelerometer and location data with supervised joint fusion classification to achieve high accuracy, customization, convenience and scalability. We show the feasibility of myBlackBox by implementing and evaluating this end-to-end system that combines Android smartphones with cloud servers, deployed for 15 users over a one-month period.

  14. Power System Event Ranking Using a New Linear Parameter-Varying Modeling with a Wide Area Measurement System-Based Approach

    Directory of Open Access Journals (Sweden)

    Mohammad Bagher Abolhasani Jabali

    2017-07-01

    Full Text Available Detecting critical power system events for Dynamic Security Assessment (DSA is required for reliability improvement. The approach proposed in this paper investigates the effects of events on dynamic behavior during nonlinear system response while common approaches use steady-state conditions after events. This paper presents some new and enhanced indices for event ranking based on time-domain simulation and polytopic linear parameter-varying (LPV modeling of a power system. In the proposed approach, a polytopic LPV representation is generated via linearization about some points of the nonlinear dynamic behavior of power system using wide-area measurement system (WAMS concepts and then event ranking is done based on the frequency response of the system models on the vertices. Therefore, the nonlinear behaviors of the system in the time of fault occurrence are considered for events ranking. The proposed algorithm is applied to a power system using nonlinear simulation. The comparison of the results especially in different fault conditions shows the advantages of the proposed approach and indices.

  15. Context-aware event detection smartphone application for first responders

    Science.gov (United States)

    Boddhu, Sanjay K.; Dave, Rakesh P.; McCartney, Matt; West, James A.; Williams, Robert L.

    2013-05-01

    The rise of social networking platforms like Twitter, Facebook, etc…, have provided seamless sharing of information (as chat, video and other media) among its user community on a global scale. Further, the proliferation of the smartphones and their connectivity networks has powered the ordinary individuals to share and acquire information regarding the events happening in his/her immediate vicinity in a real-time fashion. This human-centric sensed data being generated in "human-as-sensor" approach is tremendously valuable as it delivered mostly with apt annotations and ground truth that would be missing in traditional machine-centric sensors, besides high redundancy factor (same data thru multiple users). Further, when appropriately employed this real-time data can support in detecting localized events like fire, accidents, shooting, etc…, as they unfold and pin-point individuals being affected by those events. This spatiotemporal information, when made available for first responders in the event vicinity (or approaching it) can greatly assist them to make effective decisions to protect property and life in a timely fashion. In this vein, under SATE and YATE programs, the research team at AFRL Tec^Edge Discovery labs had demonstrated the feasibility of developing Smartphone applications, that can provide a augmented reality view of the appropriate detected events in a given geographical location (localized) and also provide an event search capability over a large geographic extent. In its current state, the application thru its backend connectivity utilizes a data (Text & Image) processing framework, which deals with data challenges like; identifying and aggregating important events, analyzing and correlating the events temporally and spatially and building a search enabled event database. Further, the smartphone application with its backend data processing workflow has been successfully field tested with live user generated feeds.

  16. Real Time Robot Soccer Game Event Detection Using Finite State Machines with Multiple Fuzzy Logic Probability Evaluators

    Directory of Open Access Journals (Sweden)

    Elmer P. Dadios

    2009-01-01

    Full Text Available This paper presents a new algorithm for real time event detection using Finite State Machines with multiple Fuzzy Logic Probability Evaluators (FLPEs. A machine referee for a robot soccer game is developed and is used as the platform to test the proposed algorithm. A novel technique to detect collisions and other events in microrobot soccer game under inaccurate and insufficient information is presented. The robots' collision is used to determine goalkeeper charging and goal score events which are crucial for the machine referee's decisions. The Main State Machine (MSM handles the schedule of event activation. The FLPE calculates the probabilities of the true occurrence of the events. Final decisions about the occurrences of events are evaluated and compared through threshold crisp probability values. The outputs of FLPEs can be combined to calculate the probability of an event composed of subevents. Using multiple fuzzy logic system, the FLPE utilizes minimal number of rules and can be tuned individually. Experimental results show the accuracy and robustness of the proposed algorithm.

  17. Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis

    Science.gov (United States)

    Vilar, Santiago; Harpaz, Rave; Chase, Herbert S; Costanzi, Stefano; Rabadan, Raul

    2011-01-01

    Background Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals identifying associations between drugs and ADE. The signals must then be monitored further by domain experts, which is a time-consuming task. Objective To develop a new methodology that combines existing data mining algorithms with chemical information by analysis of molecular fingerprints to enhance initial ADE signals generated from AERS, and to provide a decision support mechanism to facilitate the identification of novel adverse events. Results The method achieved a significant improvement in precision in identifying known ADE, and a more than twofold signal enhancement when applied to the ADE rhabdomyolysis. The simplicity of the method assists in highlighting the etiology of the ADE by identifying structurally similar drugs. A set of drugs with strong evidence from both AERS and molecular fingerprint-based modeling is constructed for further analysis. Conclusion The results demonstrate that the proposed methodology could be used as a pharmacovigilance decision support tool to facilitate ADE detection. PMID:21946238

  18. Event-by-event simulation of quantum phenomena

    NARCIS (Netherlands)

    De Raedt, Hans; Michielsen, Kristel

    A discrete-event simulation approach is reviewed that does not require the knowledge of the solution of the wave equation of the whole system, yet reproduces the statistical distributions of wave theory by generating detection events one-by-one. The simulation approach is illustrated by applications

  19. Data-mining for detecting signals of adverse drug reactions of fluoxetine using the Korea Adverse Event Reporting System (KAERS) database.

    Science.gov (United States)

    Kim, Seonji; Park, Kyounghoon; Kim, Mi-Sook; Yang, Bo Ram; Choi, Hyun Jin; Park, Byung-Joo

    2017-10-01

    Selective serotonin reuptake inhibitors (SSRIs) have become one of the most broadly used medications in psychiatry. Fluoxetine is the first representative antidepressant SSRI drug approved by the Food and Drug Administration (FDA) in 1987. Safety information on fluoxetine use alone was less reported than its combined use with other drugs. There were no published papers on adverse drug reactions (ADRs) of fluoxetine analyzing spontaneous adverse events reports. We detected signals of the adverse drug reactions of fluoxetine by data mining using the Korea Adverse Events Reporting System (KAERS) database. We defined signals in this study by the reporting odds ratios (ROR), proportional reporting ratios (PRR), and information components (IC) indices. The KAERS database included 860,224 AE reports, among which 866 reports contained fluoxetine. We compared the labels of fluoxetine among the United States, UK, Germany, France, China, and Korea. Some of the signals, including emotional lability, myositis, spinal stenosis, paradoxical drug reaction, drug dependence, extrapyramidal disorder, adrenal insufficiency, and intracranial hemorrhage, were not labeled in the six countries. In conclusion, we identified new signals that were not known at the time of market approval. However, certain factors should be required for signal evaluation, such as clinical significance, preventability, and causality of the detected signals. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Revised licensee event report system

    International Nuclear Information System (INIS)

    Mays, G.T.; Poore, W.P.

    1985-01-01

    Licensee Event Reports (LERs) provide the basis for evaluating and assessing operating experience information from nuclear power plants. The reporting requirements for submitting LERs to the Nuclear Regulatory Commission have been revised. Effective Jan. 1, 1984, all events were to be submitted in accordance with 10 CFR 50.73 of the Code of Federal Regulations. Report NUREG-1022, Licensee Event Report System-Description of System and Guidelines for Reporting, describes the guidelines on reportability of events. This article summarizes the reporting requirements as presented in NUREG-1022, high-lights differences in data reported between the revised and previous LER systems, and presents results from a preliminary assessment of LERs submitted under the revised LER reporting system

  1. First CNGS events detected by LVD

    International Nuclear Information System (INIS)

    Agafonova, N.Yu.; Boyarkin, V.V.; Kuznetsov, V.V.; Kuznetsov, V.A.; Malguin, A.S.; Ryasny, V.G.; Ryazhskaya, O.G.; Yakushev, V.F.; Zatsepin, G.T.; Aglietta, M.; Bonardi, A.; Fulgione, W.; Galeotti, P.; Porta, A.; Saavedra, O.; Vigorito, C.; Antonioli, P.; Bari, G.; Giusti, P.; Menghetti, H.; Persiani, R.; Pesci, A.; Sartorelli, G.; Selvi, M.; Zichichi, A.; Bruno, G.; Ghia, P.L.; Garbini, M.; Kemp, E.; Pless, I.A.; Votano, L.

    2007-01-01

    The CERN Neutrino to Gran Sasso (CNGS) project aims to produce a high energy, wide band ν μ beam at CERN and send it toward the INFN Gran Sasso National Laboratory (LNGS), 732 km away. Its main goal is the observation of the ν τ appearance, through neutrino flavour oscillation. The beam started its operation in August 2006 for about 12 days: a total amount of 7.6 x 10 17 protons were delivered to the target. The LVD detector, installed in hall A of the LNGS and mainly dedicated to the study of supernova neutrinos, was fully operating during the whole CNGS running time. A total number of 569 events were detected in coincidence with the beam spill time. This is in good agreement with the expected number of events from Monte Carlo simulations. (orig.)

  2. The Advanced Photon Source event system

    International Nuclear Information System (INIS)

    Lenkszus, F.R.; Laird, R.

    1995-01-01

    The Advanced Photon Source, like many other facilities, requires a means of transmitting timing information to distributed control system 1/0 controllers. The APS event system provides the means of distributing medium resolution/accuracy timing events throughout the facility. It consists of VME event generators and event receivers which are interconnected with 10OMbit/sec fiber optic links at distances of up to 650m in either a star or a daisy chain configuration. The systems event throughput rate is 1OMevents/sec with a peak-to-peak timing jitter down to lOOns depending on the source of the event. It is integrated into the EPICS-based A.PS control system through record and device support. Event generators broadcast timing events over fiber optic links to event receivers which are programmed to decode specific events. Event generators generate events in response to external inputs, from internal programmable event sequence RAMS, and from VME bus writes. The event receivers can be programmed to generate both pulse and set/reset level outputs to synchronize hardware, and to generate interrupts to initiate EPICS record processing. In addition, each event receiver contains a time stamp counter which is used to provide synchronized time stamps to EPICS records

  3. Event Registration System for INR Linac

    International Nuclear Information System (INIS)

    Grekhov, O.V.; Drugakov, A.N.; Kiselev, Yu.V.

    2006-01-01

    The software of the Event registration system for the linear accelerators is described. This system allows receiving of the information on changes of operating modes of the accelerator and supervising of hundreds of key parameters of various systems of the accelerator. The Event registration system consists of the source and listeners of events. The sources of events are subroutines built in existing ACS Linac. The listeners of events are software Supervisor and Client ERS. They are used for warning the operator about change controlled parameter of the accelerator

  4. Distributed event-triggered consensus tracking of second-order multi-agent systems with a virtual leader

    International Nuclear Information System (INIS)

    Cao Jie; Wu Zhi-Hai; Peng Li

    2016-01-01

    This paper investigates the consensus tracking problems of second-order multi-agent systems with a virtual leader via event-triggered control. A novel distributed event-triggered transmission scheme is proposed, which is intermittently examined at constant sampling instants. Only partial neighbor information and local measurements are required for event detection. Then the corresponding event-triggered consensus tracking protocol is presented to guarantee second-order multi-agent systems to achieve consensus tracking. Numerical simulations are given to illustrate the effectiveness of the proposed strategy. (paper)

  5. Detection and interpretation of seismoacoustic events at German infrasound stations

    Science.gov (United States)

    Pilger, Christoph; Koch, Karl; Ceranna, Lars

    2016-04-01

    Three infrasound arrays with collocated or nearby installed seismometers are operated by the Federal Institute for Geosciences and Natural Resources (BGR) as the German National Data Center (NDC) for the verification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Infrasound generated by seismoacoustic events is routinely detected at these infrasound arrays, but air-to-ground coupled acoustic waves occasionally show up in seismometer recordings as well. Different natural and artificial sources like meteoroids as well as industrial and mining activity generate infrasonic signatures that are simultaneously detected at microbarometers and seismometers. Furthermore, many near-surface sources like earthquakes and explosions generate both seismic and infrasonic waves that can be detected successively with both technologies. The combined interpretation of seismic and acoustic signatures provides additional information about the origin time and location of remote infrasound events or about the characterization of seismic events distinguishing man-made and natural origins. Furthermore, seismoacoustic studies help to improve the modelling of infrasound propagation and ducting in the atmosphere and allow quantifying the portion of energy coupled into ground and into air by seismoacoustic sources. An overview of different seismoacoustic sources and their detection by German infrasound stations as well as some conclusions on the benefit of a combined seismoacoustic analysis are presented within this study.

  6. TED: a novel man portable infrared detection and situation awareness system

    Science.gov (United States)

    Tidhar, Gil; Manor, Ran

    2007-04-01

    Infrared Search and Track (IRST) and threat warning systems are used in vehicle mounted or in fixed land positions. Migration of this technology to the man portable applications proves to be difficult due to the tight constraints of power consumption, dimensions, weight and due to the high video rate requirements. In this report we provide design details of a novel transient event detection (TED) system, capable of detection of blasts and gun shot events in a very wide field of view, while used by an operator in motion

  7. Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster–Shafer method

    International Nuclear Information System (INIS)

    Hou, Dibo; He, Huimei; Huang, Pingjie; Zhang, Guangxin; Loaiciga, Hugo

    2013-01-01

    This study presents a method for detecting contamination events of sources of drinking water based on the Dempster–Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events. (paper)

  8. On-line detection of apnea/hypopnea events using SpO2 signal: a rule-based approach employing binary classifier models.

    Science.gov (United States)

    Koley, Bijoy Laxmi; Dey, Debangshu

    2014-01-01

    This paper presents an online method for automatic detection of apnea/hypopnea events, with the help of oxygen saturation (SpO2) signal, measured at fingertip by Bluetooth nocturnal pulse oximeter. Event detection is performed by identifying abnormal data segments from the recorded SpO2 signal, employing a binary classifier model based on a support vector machine (SVM). Thereafter the abnormal segment is further analyzed to detect different states within the segment, i.e., steady, desaturation, and resaturation, with the help of another SVM-based binary ensemble classifier model. Finally, a heuristically obtained rule-based system is used to identify the apnea/hypopnea events from the time-sequenced decisions of these classifier models. In the developmental phase, a set of 34 time domain-based features was extracted from the segmented SpO2 signal using an overlapped windowing technique. Later, an optimal set of features was selected on the basis of recursive feature elimination technique. A total of 34 subjects were included in the study. The results show average event detection accuracies of 96.7% and 93.8% for the offline and the online tests, respectively. The proposed system provides direct estimation of the apnea/hypopnea index with the help of a relatively inexpensive and widely available pulse oximeter. Moreover, the system can be monitored and accessed by physicians through LAN/WAN/Internet and can be extended to deploy in Bluetooth-enabled mobile phones.

  9. Method for detecting binding events using micro-X-ray fluorescence spectrometry

    Science.gov (United States)

    Warner, Benjamin P.; Havrilla, George J.; Mann, Grace

    2010-12-28

    Method for detecting binding events using micro-X-ray fluorescence spectrometry. Receptors are exposed to at least one potential binder and arrayed on a substrate support. Each member of the array is exposed to X-ray radiation. The magnitude of a detectable X-ray fluorescence signal for at least one element can be used to determine whether a binding event between a binder and a receptor has occurred, and can provide information related to the extent of binding between the binder and receptor.

  10. An analog cell to detect single event transients in voltage references

    Energy Technology Data Exchange (ETDEWEB)

    Franco, F.J., E-mail: fjfranco@fis.ucm.es [Departamento de Física Aplicada III, Facultad de Físicas, Universidad Complutense de Madrid (UCM), 28040 Madrid (Spain); Palomar, C. [Departamento de Física Aplicada III, Facultad de Físicas, Universidad Complutense de Madrid (UCM), 28040 Madrid (Spain); Izquierdo, J.G. [Centro de Láseres Ultrarrápidos, Facultad de Químicas, Universidad Complutense de Madrid (UCM), 28040 Madrid (Spain); Agapito, J.A. [Departamento de Física Aplicada III, Facultad de Físicas, Universidad Complutense de Madrid (UCM), 28040 Madrid (Spain)

    2015-01-11

    A reliable voltage reference is mandatory in mixed-signal systems. However, this family of components can undergo very long single event transients when operating in radiation environments such as space and nuclear facilities due to the impact of heavy ions. The purpose of the present paper is to demonstrate how a simple cell can be used to detect these transients. The cell was implemented with typical COTS components and its behavior was verified by SPICE simulations and in a laser facility. Different applications of the cell are explored as well.

  11. Automatic Emergence Detection in Complex Systems

    Directory of Open Access Journals (Sweden)

    Eugene Santos

    2017-01-01

    Full Text Available Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis results. In this paper, we propose a quantitative definition of emergence for complex systems. We also propose a framework to detect emergent properties given observations of its subsystems. This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs, learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. Fusion is the central element of our approach to account for situations when a common variable may have different probabilistic distributions in different subsystems. We evaluate our detection performance against a baseline approach (Bayesian Network ensemble on synthetic testbeds from UCI datasets. To do so, we also introduce a method to simulate and a metric to measure discrepancies that occur with shared/common variables. Experiments demonstrate that our framework outperforms the baseline. In addition, we demonstrate that this framework has uniform polynomial time complexity across all three learning, fusion, and reasoning procedures.

  12. Early snowmelt events: detection, distribution, and significance in a major sub-arctic watershed

    International Nuclear Information System (INIS)

    Semmens, Kathryn Alese; Ramage, Joan; Bartsch, Annett; Liston, Glen E

    2013-01-01

    High latitude drainage basins are experiencing higher average temperatures, earlier snowmelt onset in spring, and an increase in rain on snow (ROS) events in winter, trends that climate models project into the future. Snowmelt-dominated basins are most sensitive to winter temperature increases that influence the frequency of ROS events and the timing and duration of snowmelt, resulting in changes to spring runoff. Of specific interest in this study are early melt events that occur in late winter preceding melt onset in the spring. The study focuses on satellite determination and characterization of these early melt events using the Yukon River Basin (Canada/USA) as a test domain. The timing of these events was estimated using data from passive (Advanced Microwave Scanning Radiometer—EOS (AMSR-E)) and active (SeaWinds on Quick Scatterometer (QuikSCAT)) microwave remote sensors, employing detection algorithms for brightness temperature (AMSR-E) and radar backscatter (QuikSCAT). The satellite detected events were validated with ground station meteorological and hydrological data, and the spatial and temporal variability of the events across the entire river basin was characterized. Possible causative factors for the detected events, including ROS, fog, and positive air temperatures, were determined by comparing the timing of the events to parameters from SnowModel and National Centers for Environmental Prediction North American Regional Reanalysis (NARR) outputs, and weather station data. All melt events coincided with above freezing temperatures, while a limited number corresponded to ROS (determined from SnowModel and ground data) and a majority to fog occurrence (determined from NARR). The results underscore the significant influence that warm air intrusions have on melt in some areas and demonstrate the large temporal and spatial variability over years and regions. The study provides a method for melt detection and a baseline from which to assess future change

  13. DESIGNING AN EVENT EXTRACTION SYSTEM

    Directory of Open Access Journals (Sweden)

    Botond BENEDEK

    2017-06-01

    Full Text Available In the Internet world, the amount of information available reaches very high quotas. In order to find specific information, some tools were created that automatically scroll through the existing web pages and update their databases with the latest information on the Internet. In order to systematize the search and achieve a result in a concrete form, another step is needed for processing the information returned by the search engine and generating the response in a more organized form. Centralizing events of a certain type is useful first of all for creating a news service. Through this system we are pursuing a knowledge - events from the Internet documents - extraction system. The system will recognize events of a certain type (weather, sports, politics, text data mining, etc. depending on how it will be trained (the concept it has in the dictionary. These events can be provided to the user, or it can also extract the context in which the event occurred, to indicate the initial form in which the event was embedded.

  14. Sudden Event Recognition: A Survey

    Directory of Open Access Journals (Sweden)

    Mohd Asyraf Zulkifley

    2013-08-01

    Full Text Available Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1 the importance of a sudden event over a general anomalous event; (2 frameworks used in sudden event recognition; (3 the requirements and comparative studies of a sudden event recognition system and (4 various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

  15. ALGORITHMS FOR OPTIMIZATION OF SYSYTEM PERFORMANCE IN LAYERED DETECTION SYSTEMS UNDER DETECTOR COORELATION

    International Nuclear Information System (INIS)

    Wood, Thomas W.; Heasler, Patrick G.; Daly, Don S.

    2010-01-01

    Almost all of the 'architectures' for radiation detection systems in Department of Energy (DOE) and other USG programs rely on some version of layered detector deployment. Efficacy analyses of layered (or more generally extended) detection systems in many contexts often assume statistical independence among detection events and thus predict monotonically increasing system performance with the addition of detection layers. We show this to be a false conclusion for the ROC curves typical of most current technology gamma detectors, and more generally show that statistical independence is often an unwarranted assumption for systems in which there is ambiguity about the objects to be detected. In such systems, a model of correlation among detection events allows optimization of system algorithms for interpretation of detector signals. These algorithms are framed as optimal discriminant functions in joint signal space, and may be applied to gross counting or spectroscopic detector systems. We have shown how system algorithms derived from this model dramatically improve detection probabilities compared to the standard serial detection operating paradigm for these systems. These results would not surprise anyone who has confronted the problem of correlated errors (or failure rates) in the analogous contexts, but is seems to be largely underappreciated among those analyzing the radiation detection problem - independence is widely assumed and experimental studies typical fail to measure correlation. This situation, if not rectified, will lead to several unfortunate results. Including overconfidence in system efficacy, overinvestment in layers of similar technology, and underinvestment in diversity among detection assets.

  16. The sequentially discounting autoregressive (SDAR) method for on-line automatic seismic event detecting on long term observation

    Science.gov (United States)

    Wang, L.; Toshioka, T.; Nakajima, T.; Narita, A.; Xue, Z.

    2017-12-01

    In recent years, more and more Carbon Capture and Storage (CCS) studies focus on seismicity monitoring. For the safety management of geological CO2 storage at Tomakomai, Hokkaido, Japan, an Advanced Traffic Light System (ATLS) combined different seismic messages (magnitudes, phases, distributions et al.) is proposed for injection controlling. The primary task for ATLS is the seismic events detection in a long-term sustained time series record. Considering the time-varying characteristics of Signal to Noise Ratio (SNR) of a long-term record and the uneven energy distributions of seismic event waveforms will increase the difficulty in automatic seismic detecting, in this work, an improved probability autoregressive (AR) method for automatic seismic event detecting is applied. This algorithm, called sequentially discounting AR learning (SDAR), can identify the effective seismic event in the time series through the Change Point detection (CPD) of the seismic record. In this method, an anomaly signal (seismic event) can be designed as a change point on the time series (seismic record). The statistical model of the signal in the neighborhood of event point will change, because of the seismic event occurrence. This means the SDAR aims to find the statistical irregularities of the record thought CPD. There are 3 advantages of SDAR. 1. Anti-noise ability. The SDAR does not use waveform messages (such as amplitude, energy, polarization) for signal detecting. Therefore, it is an appropriate technique for low SNR data. 2. Real-time estimation. When new data appears in the record, the probability distribution models can be automatic updated by SDAR for on-line processing. 3. Discounting property. the SDAR introduces a discounting parameter to decrease the influence of present statistic value on future data. It makes SDAR as a robust algorithm for non-stationary signal processing. Within these 3 advantages, the SDAR method can handle the non-stationary time-varying long

  17. Microseismic Events Detection on Xishancun Landslide, Sichuan Province, China

    Science.gov (United States)

    Sheng, M.; Chu, R.; Wei, Z.

    2016-12-01

    On landslide, the slope movement and the fracturing of the rock mass often lead to microearthquakes, which are recorded as weak signals on seismographs. The distribution characteristics of temporal and spatial regional unstability as well as the impact of external factors on the unstable regions can be understand and analyzed by monitoring those microseismic events. Microseismic method can provide some information inside the landslide, which can be used as supplementary of geodetic methods for monitoring the movement of landslide surface. Compared to drilling on landslide, microseismic method is more economical and safe. Xishancun Landslide is located about 60km northwest of Wenchuan earthquake centroid, it keep deforming after the earthquake, which greatly increases the probability of disasters. In the autumn of 2015, 30 seismometers were deployed on the landslide for 3 months with intervals of 200 500 meters. First, we used regional earthquakes for time correction of seismometers to eliminate the influence of inaccuracy GPS clocks and the subsurface structure of stations. Due to low velocity of the loose medium, the travel time difference of microseismic events on the landslide up to 5s. According to travel time and waveform characteristics, we found many microseismic events and converted them into envelopes as templates, then we used a sliding-window cross-correlation technique based on waveform envelope to detect the other microseismic events. Consequently, 100 microseismic events were detected with the waveforms recorded on all seismometers. Based on the location, we found most of them located on the front of the landslide while the others located on the back end. The bottom and top of the landslide accumulated considerable energy and deformed largely, radiated waves could be recorded by all stations. What's more, the bottom with more events seemed very active. In addition, there were many smaller events happened in middle part of the landslide where released

  18. High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

    Science.gov (United States)

    Maly, K.

    1998-01-01

    Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated

  19. Vision-based Event Detection of the Sit-to-Stand Transition

    Directory of Open Access Journals (Sweden)

    Victor Shia

    2015-12-01

    Full Text Available Sit-to-stand (STS motions are one of the most important activities of daily living as they serve as a precursor to mobility and walking. However, there exist no standard method of segmenting STS motions. This is partially due to the variety of different sensors and modalities used to study the STS motion such as force plate, vision, and accelerometers, each providing different types of data, and the variability of the STS motion in video data. In this work, we present a method using motion capture to detect events in the STS motion by estimating ground reaction forces, thereby eliminating the variability in joint angles from visual data. We illustrate the accuracy of this method with 10 subjects with an average difference of 16.5ms in event times obtained via motion capture vs force plate. This method serves as a proof of concept for detecting events in the STS motion via video which are comparable to those obtained via force plate.

  20. Automatic, ECG-based detection of autonomic arousals and their association with cortical arousals, leg movements, and respiratory events in sleep

    DEFF Research Database (Denmark)

    Olsen, Mads; Schneider, Logan Douglas; Cheung, Joseph

    2018-01-01

    The current definition of sleep arousals neglects to address the diversity of arousals and their systemic cohesion. Autonomic arousals (AA) are autonomic activations often associated with cortical arousals (CA), but they may also occur in isolation in relation to a respiratory event, a leg movement...... event or spontaneously, without any other physiological associations. AA should be acknowledged as essential events to understand and explore the systemic implications of arousals. We developed an automatic AA detection algorithm based on intelligent feature selection and advanced machine learning using...... or respiratory events. This indicates that most FP constitute autonomic activations that are indistinguishable from those with cortical cohesion. The proposed algorithm provides an automatic system trained in a clinical environment, which can be utilized to analyse the systemic and clinical impacts of arousals....

  1. Halftone display, particularly for a high resolution radioactivity distribution detection system

    International Nuclear Information System (INIS)

    Grenier, R.P.

    1977-01-01

    A device is described for presenting a halftone pictorial presentation composed of dot picture elements by selectively controlling the number of dot picture elements per unit area at locations on a display. In a high resolution radioactivity distribution detection system, the number of detected radioactive elements at XY locations of an array of sensing devices are fed to a computer and stored at corresponding address locations. The number of radioactive events detected at each address location is normalized into Gray scale coded signals as a function of the greatest number of radioactive events detected at any one address location. The normalized Gray scale coded signals are applied to a display for controlling the number of dot picture elements per unit area presented at corresponding XY locations on the display. The number of radioactive events detected at XY locations of the array are presented on the display as a halftone pictorial representation; the greatest number of picture dot elements per unit are being presented as a brighter image

  2. An Autonomous System for Grouping Events in a Developing Aftershock Sequence

    Energy Technology Data Exchange (ETDEWEB)

    Harris, D. B. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Dodge, D. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2011-03-22

    We describe a prototype detection framework that automatically clusters events in real time from a rapidly unfolding aftershock sequence. We use the fact that many aftershocks are repetitive, producing similar waveforms. By clustering events based on correlation measures of waveform similarity, the number of independent event instances that must be examined in detail by analysts may be reduced. Our system processes array data and acquires waveform templates with a short-term average (STA)/long-term average (LTA) detector operating on a beam directed at the P phases of the aftershock sequence. The templates are used to create correlation-type (subspace) detectors that sweep the subsequent data stream for occurrences of the same waveform pattern. Events are clustered by association with a particular detector. Hundreds of subspace detectors can run in this framework a hundred times faster than in real time. Nonetheless, to check the growth in the number of detectors, the framework pauses periodically and reclusters detections to reduce the number of event groups. These groups define new subspace detectors that replace the older generation of detectors. Because low-magnitude occurrences of a particular signal template may be missed by the STA/LTA detector, we advocate restarting the framework from the beginning of the sequence periodically to reprocess the entire data stream with the existing detectors. We tested the framework on 10 days of data from the Nevada Seismic Array (NVAR) covering the 2003 San Simeon earthquake. One hundred eighty-four automatically generated detectors produced 676 detections resulting in a potential reduction in analyst workload of up to 73%.

  3. Perancangan dan Implementasi Instrusion Detection System di Jaringan Universitas Diponegoro

    Directory of Open Access Journals (Sweden)

    Dyakso Anindito Nugroho

    2015-04-01

    Full Text Available The use of information technology gives the advantage of open access for its users, but a new problem arises that there is a threat from unauthorized users. Intrusion Detection System (IDS is applied to assist administrator to monitoring network security. IDS displays illegal access information in a raw form which is require more time to read the detected threats. This final project aims to design an IDS with web application which is made for pulling information on IDS sensor database, then processing and representing them in tables and graphs that are easy to understand. The web application also has IpTables firewall module to block attacker's IP address. The hardware used is Cisco IPS 4240, two computers Compaq Presario 4010F as client and gateway, and Cisco Catalyst 2960 switch. The software used is Ubuntu 12.0 LTS Precise operating system, BackTrack 5 R1 operating system, PHP 5.4 programming language, MySQL 5 database, and web-based system configuration tool Webmin. Testing is done using several BackTrack applications with the aim of Cisco IPS 4240 is capable of detecting accordance with the applicable rules. Each events of any attack attempt or threat was obtained from IDS sensor database in XML form. XML file is sent using Security Device Event Exchange (SDEE protocol. The web application is tested by looking at the output tables and graphs that displays the appropriate results of sensor detection. This study generated an intrusion detection system that is easier to monitor. Network packets copied by the Cisco 2960 switch and then forwarded to the sensor. Intruder detection is done by Cisco IPS 4240 sensor. Log detection processed by the web application into tables and graphs. Intrusion detection systems are intended to improve network security.

  4. Analysis of arrhythmic events is useful to detect lead failure earlier in patients followed by remote monitoring.

    Science.gov (United States)

    Nishii, Nobuhiro; Miyoshi, Akihito; Kubo, Motoki; Miyamoto, Masakazu; Morimoto, Yoshimasa; Kawada, Satoshi; Nakagawa, Koji; Watanabe, Atsuyuki; Nakamura, Kazufumi; Morita, Hiroshi; Ito, Hiroshi

    2018-03-01

    Remote monitoring (RM) has been advocated as the new standard of care for patients with cardiovascular implantable electronic devices (CIEDs). RM has allowed the early detection of adverse clinical events, such as arrhythmia, lead failure, and battery depletion. However, lead failure was often identified only by arrhythmic events, but not impedance abnormalities. To compare the usefulness of arrhythmic events with conventional impedance abnormalities for identifying lead failure in CIED patients followed by RM. CIED patients in 12 hospitals have been followed by the RM center in Okayama University Hospital. All transmitted data have been analyzed and summarized. From April 2009 to March 2016, 1,873 patients have been followed by the RM center. During the mean follow-up period of 775 days, 42 lead failure events (atrial lead 22, right ventricular pacemaker lead 5, implantable cardioverter defibrillator [ICD] lead 15) were detected. The proportion of lead failures detected only by arrhythmic events, which were not detected by conventional impedance abnormalities, was significantly higher than that detected by impedance abnormalities (arrhythmic event 76.2%, 95% CI: 60.5-87.9%; impedance abnormalities 23.8%, 95% CI: 12.1-39.5%). Twenty-seven events (64.7%) were detected without any alert. Of 15 patients with ICD lead failure, none has experienced inappropriate therapy. RM can detect lead failure earlier, before clinical adverse events. However, CIEDs often diagnose lead failure as just arrhythmic events without any warning. Thus, to detect lead failure earlier, careful human analysis of arrhythmic events is useful. © 2017 Wiley Periodicals, Inc.

  5. Technical note: Efficient online source identification algorithm for integration within a contamination event management system

    Science.gov (United States)

    Deuerlein, Jochen; Meyer-Harries, Lea; Guth, Nicolai

    2017-07-01

    Drinking water distribution networks are part of critical infrastructures and are exposed to a number of different risks. One of them is the risk of unintended or deliberate contamination of the drinking water within the pipe network. Over the past decade research has focused on the development of new sensors that are able to detect malicious substances in the network and early warning systems for contamination. In addition to the optimal placement of sensors, the automatic identification of the source of a contamination is an important component of an early warning and event management system for security enhancement of water supply networks. Many publications deal with the algorithmic development; however, only little information exists about the integration within a comprehensive real-time event detection and management system. In the following the analytical solution and the software implementation of a real-time source identification module and its integration within a web-based event management system are described. The development was part of the SAFEWATER project, which was funded under FP 7 of the European Commission.

  6. Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

    Science.gov (United States)

    Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

    2013-11-01

    The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

  7. EVENT DETECTION USING MOBILE PHONE MASS GPS DATA AND THEIR RELIAVILITY VERIFICATION BY DMSP/OLS NIGHT LIGHT IMAGE

    Directory of Open Access Journals (Sweden)

    A. Yuki

    2016-06-01

    Full Text Available In this study, we developed a method to detect sudden population concentration on a certain day and area, that is, an “Event,” all over Japan in 2012 using mass GPS data provided from mobile phone users. First, stay locations of all phone users were detected using existing methods. Second, areas and days where Events occurred were detected by aggregation of mass stay locations into 1-km-square grid polygons. Finally, the proposed method could detect Events with an especially large number of visitors in the year by removing the influences of Events that occurred continuously throughout the year. In addition, we demonstrated reasonable reliability of the proposed Event detection method by comparing the results of Event detection with light intensities obtained from the night light images from the DMSP/OLS night light images. Our method can detect not only positive events such as festivals but also negative events such as natural disasters and road accidents. These results are expected to support policy development of urban planning, disaster prevention, and transportation management.

  8. Building a Global Catalog of Nonvolcanic Tremor Events Using an Automatic Detection Algorithm

    Science.gov (United States)

    Bagley, B. C.; Revenaugh, J.

    2009-12-01

    Nonvolcanic tremor is characterized by a long-period seismic event containing a series of low-frequency earthquakes (LFEs). Tremor has been detected in regions of subduction (e.g. Kao et. al. 2007, 2008; Shelly 2006) and beneath the San Andreas fault near Cholame, California (e.g. Nadeau and Dolenc, 2005). In some cases tremor events seem to have periodicity, and these are often referred to as episodic tremor and slip (ETS). The origin of nonvolcanic tremor has been ascribed to shear slip along plate boundaries and/or high pore-fluid pressure. The apparent periodicity and tectonic setting associated with ETS has led to the suggestion that there may be a link between ETS and megathrust earthquakes. Until recently tremor detection has been a manual process requiring visual inspection of seismic data. In areas that have dense seismic arrays (e.g. Japan) waveform cross correlation techniques have been successfully employed (e.g. Obara, 2002). Kao et al. (2007) developed an algorithm for automatic detection of seismic tremor that can be used in regions without dense arrays. This method has been used to create the Tremor Activity Monitoring System (TAMS), which is used by the Geologic Survey of Canada to monitor northern Cascadia. So far the study of nonvolcanic tremor has been limited to regions of subduction or along major transform faults. It is unknown if tremor events occur in other tectonic settings, or if the current detection schemes will be useful for finding them. We propose to look for tremor events in non-subduction regions. It is possible that if tremor exists in other regions it will have different characteristics and may not trigger the TAMS system or be amenable to other existing detection schemes. We are developing algorithms for searching sparse array data sets for quasi-harmonic energy bursts in hopes of recognizing and cataloging nonvolcanic tremor in an expanded tectonic setting. Statistical comparisons against the TAMS algorithm will be made if

  9. ISOMER: Informative Segment Observations for Multimedia Event Recounting

    NARCIS (Netherlands)

    Sun, C.; Burns, B.; Nevatia, R.; Snoek, C.; Bolles, B.; Myers, G.; Wang, W.; Yeh, E.

    2014-01-01

    This paper describes a system for multimedia event detection and recounting. The goal is to detect a high level event class in unconstrained web videos and generate event oriented summarization for display to users. For this purpose, we detect informative segments and collect observations for them,

  10. Very low frequency earthquakes (VLFEs) detected during episodic tremor and slip (ETS) events in Cascadia using a match filter method indicate repeating events

    Science.gov (United States)

    Hutchison, A. A.; Ghosh, A.

    2016-12-01

    Very low frequency earthquakes (VLFEs) occur in transitional zones of faults, releasing seismic energy in the 0.02-0.05 Hz frequency band over a 90 s duration and typically have magntitudes within the range of Mw 3.0-4.0. VLFEs can occur down-dip of the seismogenic zone, where they can transfer stress up-dip potentially bringing the locked zone closer to a critical failure stress. VLFEs also occur up-dip of the seismogenic zone in a region along the plate interface that can rupture coseismically during large megathrust events, such as the 2011 Tohoku-Oki earthquake [Ide et al., 2011]. VLFEs were first detected in Cascadia during the 2011 episodic tremor and slip (ETS) event, occurring coincidentally with tremor [Ghosh et al., 2015]. However, during the 2014 ETS event, VLFEs were spatially and temporally asynchronous with tremor activity [Hutchison and Ghosh, 2016]. Such contrasting behaviors remind us that the mechanics behind such events remain elusive, yet they are responsible for the largest portion of the moment release during an ETS event. Here, we apply a match filter method using known VLFEs as template events to detect additional VLFEs. Using a grid-search centroid moment tensor inversion method, we invert stacks of the resulting match filter detections to ensure moment tensor solutions are similar to that of the respective template events. Our ability to successfully employ a match filter method to VLFE detection in Cascadia intrinsically indicates that these events can be repeating, implying that the same asperities are likely responsible for generating multiple VLFEs.

  11. Negated bio-events: analysis and identification

    Science.gov (United States)

    2013-01-01

    Background Negation occurs frequently in scientific literature, especially in biomedical literature. It has previously been reported that around 13% of sentences found in biomedical research articles contain negation. Historically, the main motivation for identifying negated events has been to ensure their exclusion from lists of extracted interactions. However, recently, there has been a growing interest in negative results, which has resulted in negation detection being identified as a key challenge in biomedical relation extraction. In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations. Results We have conducted a detailed analysis of three open access bio-event corpora containing negation information (i.e., GENIA Event, BioInfer and BioNLP’09 ST), and have identified the main types of negated bio-events. We have analysed the key aspects of a machine learning solution to the problem of detecting negated events, including selection of negation cues, feature engineering and the choice of learning algorithm. Combining the best solutions for each aspect of the problem, we propose a novel framework for the identification of negated bio-events. We have evaluated our system on each of the three open access corpora mentioned above. The performance of the system significantly surpasses the best results previously reported on the BioNLP’09 ST corpus, and achieves even better results on the GENIA Event and BioInfer corpora, both of which contain more varied and complex events. Conclusions Recently, in the field of biomedical text mining, the development and enhancement of event-based systems has received significant interest. The ability to identify negated events is a key performance element for these systems. We have conducted the first detailed study on the analysis and identification of negated bio-events. Our proposed framework can be integrated with state-of-the-art event extraction systems. The

  12. A New Protection System for Islanding Detection in LV Distribution Systems

    Directory of Open Access Journals (Sweden)

    Anna Rita Di Fazio

    2015-04-01

    Full Text Available The growth of penetration of Distributed Generators (DGs is increasing the risk of unwanted islanded operation in Low Voltage (LV distribution systems. In this scenario, the existing anti-islanding protection systems, installed at the DG premises and based on classical voltage and frequency relays, are no longer effective, especially in the cases of islands characterized by a close match between generation and load. In this paper, a new protection system for islanding detection in LV distribution systems is proposed. The classical voltage and frequency relays in the DG interface protections are enriched with an innovative Smart Islanding Detector, which adopts a new passive islanding detection method. The aim is to keep the advantages of the classical relays while overcoming the problem of their limited sensitivity in detecting balanced islands. In the paper, to define the requirements of the anti-islanding protection system, the events causing the islanded operation of the LV distribution systems are firstly identified and classified. Then, referring to proposed protection system, its architecture and operation are described and, eventually, its performance is analyzed and validated by experimental laboratory tests, carried out with a hardware-in-the-loop technique.

  13. Description and detection of burst events in turbulent flows

    Science.gov (United States)

    Schmid, P. J.; García-Gutierrez, A.; Jiménez, J.

    2018-04-01

    A mathematical and computational framework is developed for the detection and identification of coherent structures in turbulent wall-bounded shear flows. In a first step, this data-based technique will use an embedding methodology to formulate the fluid motion as a phase-space trajectory, from which state-transition probabilities can be computed. Within this formalism, a second step then applies repeated clustering and graph-community techniques to determine a hierarchy of coherent structures ranked by their persistencies. This latter information will be used to detect highly transitory states that act as precursors to violent and intermittent events in turbulent fluid motion (e.g., bursts). Used as an analysis tool, this technique allows the objective identification of intermittent (but important) events in turbulent fluid motion; however, it also lays the foundation for advanced control strategies for their manipulation. The techniques are applied to low-dimensional model equations for turbulent transport, such as the self-sustaining process (SSP), for varying levels of complexity.

  14. [Comparison of the "Trigger" tool with the minimum basic data set for detecting adverse events in general surgery].

    Science.gov (United States)

    Pérez Zapata, A I; Gutiérrez Samaniego, M; Rodríguez Cuéllar, E; Gómez de la Cámara, A; Ruiz López, P

    Surgery is a high risk for the occurrence of adverse events (AE). The main objective of this study is to compare the effectiveness of the Trigger tool with the Hospital National Health System registration of Discharges, the minimum basic data set (MBDS), in detecting adverse events in patients admitted to General Surgery and undergoing surgery. Observational and descriptive retrospective study of patients admitted to general surgery of a tertiary hospital, and undergoing surgery in 2012. The identification of adverse events was made by reviewing the medical records, using an adaptation of "Global Trigger Tool" methodology, as well as the (MBDS) registered on the same patients. Once the AE were identified, they were classified according to damage and to the extent to which these could have been avoided. The area under the curve (ROC) were used to determine the discriminatory power of the tools. The Hanley and Mcneil test was used to compare both tools. AE prevalence was 36.8%. The TT detected 89.9% of all AE, while the MBDS detected 28.48%. The TT provides more information on the nature and characteristics of the AE. The area under the curve was 0.89 for the TT and 0.66 for the MBDS. These differences were statistically significant (P<.001). The Trigger tool detects three times more adverse events than the MBDS registry. The prevalence of adverse events in General Surgery is higher than that estimated in other studies. Copyright © 2017 SECA. Publicado por Elsevier España, S.L.U. All rights reserved.

  15. Solar Power Ramp Events Detection Using an Optimized Swinging Door Algorithm: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Mingjian; Zhang, Jie; Florita, Anthony; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-07

    Solar power ramp events (SPREs) are those that significantly influence the integration of solar power on non-clear days and threaten the reliable and economic operation of power systems. Accurately extracting solar power ramps becomes more important with increasing levels of solar power penetrations in power systems. In this paper, we develop an optimized swinging door algorithm (OpSDA) to detection. First, the swinging door algorithm (SDA) is utilized to segregate measured solar power generation into consecutive segments in a piecewise linear fashion. Then we use a dynamic programming approach to combine adjacent segments into significant ramps when the decision thresholds are met. In addition, the expected SPREs occurring in clear-sky solar power conditions are removed. Measured solar power data from Tucson Electric Power is used to assess the performance of the proposed methodology. OpSDA is compared to two other ramp detection methods: the SDA and the L1-Ramp Detect with Sliding Window (L1-SW) method. The statistical results show the validity and effectiveness of the proposed method. OpSDA can significantly improve the performance of the SDA, and it can perform as well as or better than L1-SW with substantially less computation time.

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

    Directory of Open Access Journals (Sweden)

    Wenqiang Cui

    2017-11-01

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

  17. Decision support methods for the detection of adverse events in post-marketing data.

    Science.gov (United States)

    Hauben, M; Bate, A

    2009-04-01

    Spontaneous reporting is a crucial component of post-marketing drug safety surveillance despite its significant limitations. The size and complexity of some spontaneous reporting system databases represent a challenge for drug safety professionals who traditionally have relied heavily on the scientific and clinical acumen of the prepared mind. Computer algorithms that calculate statistical measures of reporting frequency for huge numbers of drug-event combinations are increasingly used to support pharamcovigilance analysts screening large spontaneous reporting system databases. After an overview of pharmacovigilance and spontaneous reporting systems, we discuss the theory and application of contemporary computer algorithms in regular use, those under development, and the practical considerations involved in the implementation of computer algorithms within a comprehensive and holistic drug safety signal detection program.

  18. Developing nucleic acid-based electrical detection systems

    Directory of Open Access Journals (Sweden)

    Gabig-Ciminska Magdalena

    2006-03-01

    Full Text Available Abstract Development of nucleic acid-based detection systems is the main focus of many research groups and high technology companies. The enormous work done in this field is particularly due to the broad versatility and variety of these sensing devices. From optical to electrical systems, from label-dependent to label-free approaches, from single to multi-analyte and array formats, this wide range of possibilities makes the research field very diversified and competitive. New challenges and requirements for an ideal detector suitable for nucleic acid analysis include high sensitivity and high specificity protocol that can be completed in a relatively short time offering at the same time low detection limit. Moreover, systems that can be miniaturized and automated present a significant advantage over conventional technology, especially if detection is needed in the field. Electrical system technology for nucleic acid-based detection is an enabling mode for making miniaturized to micro- and nanometer scale bio-monitoring devices via the fusion of modern micro- and nanofabrication technology and molecular biotechnology. The electrical biosensors that rely on the conversion of the Watson-Crick base-pair recognition event into a useful electrical signal are advancing rapidly, and recently are receiving much attention as a valuable tool for microbial pathogen detection. Pathogens may pose a serious threat to humans, animal and plants, thus their detection and analysis is a significant element of public health. Although different conventional methods for detection of pathogenic microorganisms and their toxins exist and are currently being applied, improvements of molecular-based detection methodologies have changed these traditional detection techniques and introduced a new era of rapid, miniaturized and automated electrical chip detection technologies into pathogen identification sector. In this review some developments and current directions in

  19. Detection of red tide events in the Ariake Sound, Japan

    Science.gov (United States)

    Ishizaka, Joji

    2003-05-01

    High resolution SeaWiFS data was used to detect a red tide event occurred in the Ariake Sound, Japan, in winter of 2000 to 2001. The area is small embayment surrounding by tidal flat, and it is known as one of the most productive areas in coast of Japan. The red tide event damaged to seaweed (Nori) culture, and the relation to the reclamation at the Isahaya Bay in the Sound has been discussed. SeaWiFS chlorophyll data showed the red tide started early December 2000, from the Isahaya Bay, although direct relationship to the reclamation was not clear. The red tide persisted to the end of February. Monthly average of SeaWiFS data from May 1998 to December 2001 indicated that the chlorophyll increased twice a year, early summer and fall after the rain. The red tide event was part of the fall bloom which started later and continued longer than other years. Ocean color is useful to detect the red tide; however, it is required to improve the algorithms to accurately estimate chlorophyll in high turbid water and to discriminate toxic flagellates.

  20. Technologies, Methodologies and Challenges in Network Intrusion Detection and Prevention Systems

    Directory of Open Access Journals (Sweden)

    Nicoleta STANCIU

    2013-01-01

    Full Text Available This paper presents an overview of the technologies and the methodologies used in Network Intrusion Detection and Prevention Systems (NIDPS. Intrusion Detection and Prevention System (IDPS technologies are differentiated by types of events that IDPSs can recognize, by types of devices that IDPSs monitor and by activity. NIDPSs monitor and analyze the streams of network packets in order to detect security incidents. The main methodology used by NIDPSs is protocol analysis. Protocol analysis requires good knowledge of the theory of the main protocols, their definition, how each protocol works.

  1. Detecting Specific Health-Related Events Using an Integrated Sensor System for Vital Sign Monitoring

    Directory of Open Access Journals (Sweden)

    Mourad Adnane

    2009-09-01

    Full Text Available In this paper, a new method for the detection of apnea/hypopnea periods in physiological data is presented. The method is based on the intelligent combination of an integrated sensor system for long-time cardiorespiratory signal monitoring and dedicated signal-processing packages. Integrated sensors are a PVDF film and conductive fabric sheets. The signal processing package includes dedicated respiratory cycle (RC and QRS complex detection algorithms and a new method using the respiratory cycle variability (RCV for detecting apnea/hypopnea periods in physiological data. Results show that our method is suitable for online analysis of long time series data.

  2. A software Event Summation System for MDSplus

    International Nuclear Information System (INIS)

    Davis, W.M.; Mastrovito, D.M.; Roney, P.G.; Sichta, P.

    2008-01-01

    The MDSplus data acquisition and management system uses software events for communication among interdependent processes anywhere on the network. Actions can then be triggered, such as a data-acquisition routine, or analysis or display programs waiting for data. A small amount of data, such as a shot number, can be passed with these events. Since programs sometimes need more than one data set, we developed a system on NSTX to declare composite events using logical AND and OR operations. The system is written in the IDL language, so it can be run on Linux, Macintosh or Windows platforms. Like MDSplus, the Experimental Physics and Industrial Control System (EPICS) is a core component of the NSTX software environment. The Event Summation System provides an IDL-based interface to EPICS. This permits EPICS-aware processes to be synchronized with MDSplus-aware processes, to provide, for example, engineering operators information about physics data acquisition and analysis. Reliability was a more important design consideration than performance for this system; the system's architecture includes features to support this. The system has run for weeks at a time without requiring manual intervention. Hundreds of incoming events per second can be handled reliably. All incoming and declared events are logged with a timestamp. The system can be configured easily through a single, easy-to-read text file

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

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

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

  4. Event-specific qualitative and quantitative detection of five genetically modified rice events using a single standard reference molecule.

    Science.gov (United States)

    Kim, Jae-Hwan; Park, Saet-Byul; Roh, Hyo-Jeong; Shin, Min-Ki; Moon, Gui-Im; Hong, Jin-Hwan; Kim, Hae-Yeong

    2017-07-01

    One novel standard reference plasmid, namely pUC-RICE5, was constructed as a positive control and calibrator for event-specific qualitative and quantitative detection of genetically modified (GM) rice (Bt63, Kemingdao1, Kefeng6, Kefeng8, and LLRice62). pUC-RICE5 contained fragments of a rice-specific endogenous reference gene (sucrose phosphate synthase) as well as the five GM rice events. An existing qualitative PCR assay approach was modified using pUC-RICE5 to create a quantitative method with limits of detection correlating to approximately 1-10 copies of rice haploid genomes. In this quantitative PCR assay, the square regression coefficients ranged from 0.993 to 1.000. The standard deviation and relative standard deviation values for repeatability ranged from 0.02 to 0.22 and 0.10% to 0.67%, respectively. The Ministry of Food and Drug Safety (Korea) validated the method and the results suggest it could be used routinely to identify five GM rice events. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. A robust neural network-based approach for microseismic event detection

    KAUST Repository

    Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel

    2017-01-01

    We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset

  6. Complex Event Detection via Multi Source Video Attributes (Open Access)

    Science.gov (United States)

    2013-10-03

    Complex Event Detection via Multi-Source Video Attributes Zhigang Ma† Yi Yang‡ Zhongwen Xu‡§ Shuicheng Yan Nicu Sebe† Alexander G. Hauptmann...under its International Research Centre @ Singapore Fund- ing Initiative and administered by the IDM Programme Of- fice, and the Intelligence Advanced

  7. Energy Reconstruction for Events Detected in TES X-ray Detectors

    Science.gov (United States)

    Ceballos, M. T.; Cardiel, N.; Cobo, B.

    2015-09-01

    The processing of the X-ray events detected by a TES (Transition Edge Sensor) device (such as the one that will be proposed in the ESA AO call for instruments for the Athena mission (Nandra et al. 2013) as a high spectral resolution instrument, X-IFU (Barret et al. 2013)), is a several step procedure that starts with the detection of the current pulses in a noisy signal and ends up with their energy reconstruction. For this last stage, an energy calibration process is required to convert the pseudo energies measured in the detector to the real energies of the incoming photons, accounting for possible nonlinearity effects in the detector. We present the details of the energy calibration algorithm we implemented as the last part of the Event Processing software that we are developing for the X-IFU instrument, that permits the calculation of the calibration constants in an analytical way.

  8. Study of the Convergence in State Estimators for LTI Systems with Event Detection

    Directory of Open Access Journals (Sweden)

    Juan C. Posada

    2016-01-01

    Full Text Available The methods frequently used to estimate the state of an LTI system require that the precise value of the output variable is known at all times, or at equidistant sampling times. In LTI systems, in which the output signal is measured through binary sensors (detectors, the traditional way of state observers design is not applicable even though the system has a complete observability matrix. This type of state observers design is known as passive. It is necessary, then, to introduce a new state estimation technique, which allows reckoning the state from the information of the variable’s crossing through a detector’s action threshold (switch. This paper seeks, therefore, to study the convergence in this type of estimators in finite time, allowing establishing, theoretically, whether some family of the proposed models can be estimated in a convergent way through the use of the estimation technique based on events.

  9. Insertable cardiac event recorder in detection of atrial fibrillation after cryptogenic stroke: an audit report.

    Science.gov (United States)

    Etgen, Thorleif; Hochreiter, Manfred; Mundel, Markus; Freudenberger, Thomas

    2013-07-01

    Atrial fibrillation (AF) is the most frequent risk factor in ischemic stroke but often remains undetected. We analyzed the value of insertable cardiac event recorder in detection of AF in a 1-year cohort of patients with cryptogenic ischemic stroke. All patients with cryptogenic stroke and eligibility for oral anticoagulation were offered the insertion of a cardiac event recorder. Regular follow-up for 1 year recorded the incidence of AF. Of the 393 patients with ischemic stroke, 65 (16.5%) had a cryptogenic stroke, and in 22 eligible patients, an event recorder was inserted. After 1 year, in 6 of 22 patients (27.3%), AF was detected. These preliminary data show that insertion of cardiac event recorder was eligible in approximately one third of patients with cryptogenic stroke and detected in approximately one quarter of these patients new AF.

  10. Detection of Cardiopulmonary Activity and Related Abnormal Events Using Microsoft Kinect Sensor.

    Science.gov (United States)

    Al-Naji, Ali; Chahl, Javaan

    2018-03-20

    Monitoring of cardiopulmonary activity is a challenge when attempted under adverse conditions, including different sleeping postures, environmental settings, and an unclear region of interest (ROI). This study proposes an efficient remote imaging system based on a Microsoft Kinect v2 sensor for the observation of cardiopulmonary-signal-and-detection-related abnormal cardiopulmonary events (e.g., tachycardia, bradycardia, tachypnea, bradypnea, and central apnoea) in many possible sleeping postures within varying environmental settings including in total darkness and whether the subject is covered by a blanket or not. The proposed system extracts the signal from the abdominal-thoracic region where cardiopulmonary activity is most pronounced, using a real-time image sequence captured by Kinect v2 sensor. The proposed system shows promising results in any sleep posture, regardless of illumination conditions and unclear ROI even in the presence of a blanket, whilst being reliable, safe, and cost-effective.

  11. Detection of Cardiopulmonary Activity and Related Abnormal Events Using Microsoft Kinect Sensor

    Directory of Open Access Journals (Sweden)

    Ali Al-Naji

    2018-03-01

    Full Text Available Monitoring of cardiopulmonary activity is a challenge when attempted under adverse conditions, including different sleeping postures, environmental settings, and an unclear region of interest (ROI. This study proposes an efficient remote imaging system based on a Microsoft Kinect v2 sensor for the observation of cardiopulmonary-signal-and-detection-related abnormal cardiopulmonary events (e.g., tachycardia, bradycardia, tachypnea, bradypnea, and central apnoea in many possible sleeping postures within varying environmental settings including in total darkness and whether the subject is covered by a blanket or not. The proposed system extracts the signal from the abdominal-thoracic region where cardiopulmonary activity is most pronounced, using a real-time image sequence captured by Kinect v2 sensor. The proposed system shows promising results in any sleep posture, regardless of illumination conditions and unclear ROI even in the presence of a blanket, whilst being reliable, safe, and cost-effective.

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

    Science.gov (United States)

    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.

  13. Discrete event systems diagnosis and diagnosability

    CERN Document Server

    Sayed-Mouchaweh, Moamar

    2014-01-01

    Discrete Event Systems: Diagnosis and Diagnosability addresses the problem of fault diagnosis of Discrete Event Systems (DES). This book provides the basic techniques and approaches necessary for the design of an efficient fault diagnosis system for a wide range of modern engineering applications. The different techniques and approaches are classified according to several criteria such as: modeling tools (Automata, Petri nets) that is used to construct the model; the information (qualitative based on events occurrences and/or states outputs, quantitative based on signal processing and data analysis) that is needed to analyze and achieve the diagnosis; the decision structure (centralized, decentralized) that is required to achieve the diagnosis. The goal of this classification is to select the efficient method to achieve the fault diagnosis according to the application constraints. This book focuses on the centralized and decentralized event based diagnosis approaches using formal language and automata as mode...

  14. Detecting Micro-seismicity and Long-duration Tremor-like Events from the Oklahoma Wavefield Experiment

    Science.gov (United States)

    Li, C.; Li, Z.; Peng, Z.; Zhang, C.; Nakata, N.

    2017-12-01

    Oklahoma has experienced abrupt increase of induced seismicity in the last decade. An important way to fully understand seismic activities in Oklahoma is to obtain more complete earthquake catalogs and detect different types of seismic events. The IRIS Community Wavefield Demonstration Experiment was deployed near Enid, Oklahoma in Summer of 2016. The dataset from this ultra-dense array provides an excellent opportunity for detecting microseismicity in that region with wavefield approaches. Here we examine continuous waveforms recorded by 3 seismic lines using local coherence for ultra-dense arrays (Li et al., 2017), which is a measure of cross-correlation of waveform at each station with its nearby stations. So far we have detected more than 5,000 events from 06/22/2016 to 07/20/2016, and majority of them are not listed on the regional catalog of Oklahoma or global catalogs, indicating that they are local events. We also identify 15-20 long-period long-duration events, some of them lasting for more than 500 s. Such events have been found at major plate-boundary faults (also known as deep tectonic tremor), as well as during hydraulic fracturing, slow-moving landslides and glaciers. Our next step is to locate these possible tremor-like events with their relative arrival times across the array and compare their occurrence times with solid-earth tides and injection histories to better understand their driving mechanisms.

  15. Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.

    Science.gov (United States)

    Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

    2016-10-01

    Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

  16. Nova Event Logging System

    International Nuclear Information System (INIS)

    Calliger, R.J.; Suski, G.J.

    1981-01-01

    Nova is a 200 terawatt, 10-beam High Energy Glass Laser currently under construction at LLNL. This facility, designed to demonstrate the feasibility of laser driven inertial confinement fusion, contains over 5000 elements requiring coordinated control, data acquisition, and analysis functions. The large amounts of data that will be generated must be maintained over the life of the facility. Often the most useful but inaccessible data is that related to time dependent events associated with, for example, operator actions or experiment activity. We have developed an Event Logging System to synchronously record, maintain, and analyze, in part, this data. We see the system as being particularly useful to the physics and engineering staffs of medium and large facilities in that it is entirely separate from experimental apparatus and control devices. The design criteria, implementation, use, and benefits of such a system will be discussed

  17. Assessment of the performance of a conceptual acoustic surveillance system for anomalous events in LMFBRs

    International Nuclear Information System (INIS)

    Doolittle, R.D.; Carey, W.M.

    1977-01-01

    A method is developed for calculating the detectability of anomalous acoustic events. The example used is the sodium vapor bubble collapse in the subcooled regions of a Liquid Metal Fast Breeder Reactor (LMFBR). This method provides a range of estimates for detection and false alarm probabilities in acoustic surveillance systems for sodium boiling and voiding detection, as well as any other impulsive events such as loose-parts monitoring. The signal excess at the receiver array from an impulsive source is computed by an extension of methods introduced by W. Carey. Assuming an exponential pulse form for the signal (or its envelope) the equivalent source level is determined from the energy flux spectral density for inclusion in the sonar equation. The signal excess (SE) is then given by the source level (SL) minus the noise level (NL) minus the transmission loss (TL) minus the detection threshold (DT) plus the receiving array gain

  18. Event notification system with a PLC

    International Nuclear Information System (INIS)

    Kawase, M.; Yoshikawa, Hiroshi; Sakaki, Hironao; Takahashi, Hiroki; Sako, Hiroyuki; Kamiya, Junichiro; Takayanagi, Tomohiro

    2004-01-01

    When an interlock occurs in the equipment, it is required to notify the upper rank control system of the Interlock and receive information for apparatus information in the upper rank control system as at high speed as possible. In the apparatus using FA-M3, it can respond to this by using the notice function of an event. This report shows the event notification system with a PLC based Kicker electromagnet power supply for 3GeV RCS. (author)

  19. On the event detected by the Mont Blanc underground neutrino detector on February 23, 1987

    Energy Technology Data Exchange (ETDEWEB)

    Dadykin, V L; Zatsepin, G T; Korchagin, V B

    1988-02-01

    The event detected by the Mont Balnc Soviet -Italian scintillation detector on February 23, 1987 at 2:52:37 are discussed. The corrected energies of the pulases of the event and the probability of the event imitation by the background are presented.

  20. Single-Trial Event-Related Potential Based Rapid Image Triage System

    Directory of Open Access Journals (Sweden)

    Ke Yu

    2011-06-01

    Full Text Available Searching for points of interest (POI in large-volume imagery is a challenging problem with few good solutions. In this work, a neural engineering approach called rapid image triage (RIT which could offer about a ten-fold speed up in POI searching is developed. It is essentially a cortically-coupled computer vision technique, whereby the user is presented bursts of images at a speed of 6–15 images per second and then neural signals called event-related potential (ERP is used as the ‘cue’ for user seeing images of high relevance likelihood. Compared to past efforts, the implemented system has several unique features: (1 it applies overlapping frames in image chip preparation, to ensure rapid image triage performance; (2 a novel common spatial-temporal pattern (CSTP algorithm that makes use of both spatial and temporal patterns of ERP topography is proposed for high-accuracy single-trial ERP detection; (3 a weighted version of probabilistic support-vector-machine (SVM is used to address the inherent unbalanced nature of single-trial ERP detection for RIT. High accuracy, fast learning, and real-time capability of the developed system shown on 20 subjects demonstrate the feasibility of a brainmachine integrated rapid image triage system for fast detection of POI from large-volume imagery.

  1. Monitoring of pipeline oil spill fire events using Geographical Information System and Remote Sensing

    Science.gov (United States)

    Ogungbuyi, M. G.; Eckardt, F. D.; Martinez, P.

    2016-12-01

    Nigeria, the largest producer of crude oil in Africa occupies sixth position in the world. Despite such huge oil revenue potentials, its pipeline network system is consistently susceptible to leaks causing oil spills. We investigate ground based spill events which are caused by operational error, equipment failure and most importantly by deliberate attacks along the major pipeline transport system. Sometimes, these spills are accompanied with fire explosion caused by accidental discharge, natural or illegal refineries in the creeds, etc. MODIS satellites fires data corresponding to the times and spill events (i.e. ground based data) of the Area of Interest (AOI) show significant correlation. The open source Quantum Geographical Information System (QGIS) was used to validate the dataset and the spatiotemporal analyses of the oil spill fires were expressed. We demonstrate that through QGIS and Google Earth (using the time sliders), we can identify and monitor oil spills when they are attended with fire events along the pipeline transport system accordingly. This is shown through the spatiotemporal images of the fires. Evidence of such fire cases resulting from bunt vegetation as different from industrial and domestic fire is also presented. Detecting oil spill fires in the study location may not require an enormous terabyte of image processing: we can however rely on a near-real-time (NRT) MODIS data that is readily available twice daily to detect oil spill fire as early warning signal for those hotspots areas where cases of oil seepage is significant in Nigeria.

  2. DMIA: A MALWARE DETECTION SYSTEM ON IOS PLATFORM

    OpenAIRE

    Hongliang Liang; Yilun Xie; Yan Song

    2016-01-01

    iOS is a popular operating system on Apple’s smartphones, and recent security events have shown the possibility of stealing the users' privacy in iOS without being detected, such as XcodeGhost. So, we present the design and implementation of a malware vetting system, called DMIA. DMIA first collects runtime information of an app and then distinguish between malicious and normal apps by a novel machine learning model. We evaluated DMIA with 1000 apps from the official App Store. The results of...

  3. A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines

    Science.gov (United States)

    Turso, James A.; Litt, Jonathan S.

    2004-01-01

    A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.

  4. Predictive modeling of structured electronic health records for adverse drug event detection.

    Science.gov (United States)

    Zhao, Jing; Henriksson, Aron; Asker, Lars; Boström, Henrik

    2015-01-01

    The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and

  5. Event detection and localization for small mobile robots using reservoir computing.

    Science.gov (United States)

    Antonelo, E A; Schrauwen, B; Stroobandt, D

    2008-08-01

    Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.

  6. Detection of visual events along the apparent motion trace in patients with paranoid schizophrenia.

    Science.gov (United States)

    Sanders, Lia Lira Olivier; Muckli, Lars; de Millas, Walter; Lautenschlager, Marion; Heinz, Andreas; Kathmann, Norbert; Sterzer, Philipp

    2012-07-30

    Dysfunctional prediction in sensory processing has been suggested as a possible causal mechanism in the development of delusions in patients with schizophrenia. Previous studies in healthy subjects have shown that while the perception of apparent motion can mask visual events along the illusory motion trace, such motion masking is reduced when events are spatio-temporally compatible with the illusion, and, therefore, predictable. Here we tested the hypothesis that this specific detection advantage for predictable target stimuli on the apparent motion trace is reduced in patients with paranoid schizophrenia. Our data show that, although target detection along the illusory motion trace is generally impaired, both patients and healthy control participants detect predictable targets more often than unpredictable targets. Patients had a stronger motion masking effect when compared to controls. However, patients showed the same advantage in the detection of predictable targets as healthy control subjects. Our findings reveal stronger motion masking but intact prediction of visual events along the apparent motion trace in patients with paranoid schizophrenia and suggest that the sensory prediction mechanism underlying apparent motion is not impaired in paranoid schizophrenia. Copyright © 2012. Published by Elsevier Ireland Ltd.

  7. Detection of unusual events and trends in complex non-stationary data streams

    International Nuclear Information System (INIS)

    Charlton-Perez, C.; Perez, R.B.; Protopopescu, V.; Worley, B.A.

    2011-01-01

    The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for diverse applications, ranging from power plant operation to homeland security. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden events inside intermittent signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method.

  8. Acoustic Event Detection in Multichannel Audio Using Gated Recurrent Neural Networks with High‐Resolution Spectral Features

    Directory of Open Access Journals (Sweden)

    Hyoung‐Gook Kim

    2017-12-01

    Full Text Available Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception‐based spatial and spectral‐domain noise‐reduced harmonic features are extracted from multichannel audio and used as high‐resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short‐term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.

  9. Final Scientific Report, Integrated Seismic Event Detection and Location by Advanced Array Processing

    Energy Technology Data Exchange (ETDEWEB)

    Kvaerna, T.; Gibbons. S.J.; Ringdal, F; Harris, D.B.

    2007-01-30

    In the field of nuclear explosion monitoring, it has become a priority to detect, locate, and identify seismic events down to increasingly small magnitudes. The consideration of smaller seismic events has implications for a reliable monitoring regime. Firstly, the number of events to be considered increases greatly; an exponential increase in naturally occurring seismicity is compounded by large numbers of seismic signals generated by human activity. Secondly, the signals from smaller events become more difficult to detect above the background noise and estimates of parameters required for locating the events may be subject to greater errors. Thirdly, events are likely to be observed by a far smaller number of seismic stations, and the reliability of event detection and location using a very limited set of observations needs to be quantified. For many key seismic stations, detection lists may be dominated by signals from routine industrial explosions which should be ascribed, automatically and with a high level of confidence, to known sources. This means that expensive analyst time is not spent locating routine events from repeating seismic sources and that events from unknown sources, which could be of concern in an explosion monitoring context, are more easily identified and can be examined with due care. We have obtained extensive lists of confirmed seismic events from mining and other artificial sources which have provided an excellent opportunity to assess the quality of existing fully-automatic event bulletins and to guide the development of new techniques for online seismic processing. Comparing the times and locations of confirmed events from sources in Fennoscandia and NW Russia with the corresponding time and location estimates reported in existing automatic bulletins has revealed substantial mislocation errors which preclude a confident association of detected signals with known industrial sources. The causes of the errors are well understood and are

  10. Final Scientific Report, Integrated Seismic Event Detection and Location by Advanced Array Processing

    International Nuclear Information System (INIS)

    Kvaerna, T.; Gibbons. S.J.; Ringdal, F; Harris, D.B.

    2007-01-01

    In the field of nuclear explosion monitoring, it has become a priority to detect, locate, and identify seismic events down to increasingly small magnitudes. The consideration of smaller seismic events has implications for a reliable monitoring regime. Firstly, the number of events to be considered increases greatly; an exponential increase in naturally occurring seismicity is compounded by large numbers of seismic signals generated by human activity. Secondly, the signals from smaller events become more difficult to detect above the background noise and estimates of parameters required for locating the events may be subject to greater errors. Thirdly, events are likely to be observed by a far smaller number of seismic stations, and the reliability of event detection and location using a very limited set of observations needs to be quantified. For many key seismic stations, detection lists may be dominated by signals from routine industrial explosions which should be ascribed, automatically and with a high level of confidence, to known sources. This means that expensive analyst time is not spent locating routine events from repeating seismic sources and that events from unknown sources, which could be of concern in an explosion monitoring context, are more easily identified and can be examined with due care. We have obtained extensive lists of confirmed seismic events from mining and other artificial sources which have provided an excellent opportunity to assess the quality of existing fully-automatic event bulletins and to guide the development of new techniques for online seismic processing. Comparing the times and locations of confirmed events from sources in Fennoscandia and NW Russia with the corresponding time and location estimates reported in existing automatic bulletins has revealed substantial mislocation errors which preclude a confident association of detected signals with known industrial sources. The causes of the errors are well understood and are

  11. Latency and mode of error detection as reflected in Swedish licensee event reports

    Energy Technology Data Exchange (ETDEWEB)

    Svenson, Ola; Salo, Ilkka [Stockholm Univ., (Sweden). Dept. of Psychology

    2002-03-01

    Licensee event reports (LERs) from an industry provide important information feedback about safety to the industry itself, the regulators and to the public. LERs from four nuclear power reactors were analyzed to find out about detection times, mode of detection and qualitative differences in reports from different reactors. The reliability of the coding was satisfactory and measured as the covariance between the ratings from two independent judges. The results showed differences in detection time across the reactors. On the average about ten percent of the errors remained undetected for 100 weeks or more, but the great majority of errors were detected soon after their first appearance in the plant. On the average 40 percent of the errors were detected in regular tests and 40 per cent through alarms. Operators found about 10 per cent of the errors through noticing something abnormal in the plant. The remaining errors were detected in various other ways. There were qualitative differences between the LERs from the different reactors reflecting the different conditions in the plants. The number of reports differed by a magnitude 1:2 between the different plants. However, a greater number of LERs can indicate both higher safety standards (e.g., a greater willingness to report all possible events to be able to learn from them) and lower safety standards (e.g., reporting as few events as possible to make a good impression). It was pointed out that LERs are indispensable in order to maintain safety of an industry and that the differences between plants found in the analyses of this study indicate how error reports can be used to initiate further investigations for improved safety.

  12. Latency and mode of error detection as reflected in Swedish licensee event reports

    International Nuclear Information System (INIS)

    Svenson, Ola; Salo, Ilkka

    2002-03-01

    Licensee event reports (LERs) from an industry provide important information feedback about safety to the industry itself, the regulators and to the public. LERs from four nuclear power reactors were analyzed to find out about detection times, mode of detection and qualitative differences in reports from different reactors. The reliability of the coding was satisfactory and measured as the covariance between the ratings from two independent judges. The results showed differences in detection time across the reactors. On the average about ten percent of the errors remained undetected for 100 weeks or more, but the great majority of errors were detected soon after their first appearance in the plant. On the average 40 percent of the errors were detected in regular tests and 40 per cent through alarms. Operators found about 10 per cent of the errors through noticing something abnormal in the plant. The remaining errors were detected in various other ways. There were qualitative differences between the LERs from the different reactors reflecting the different conditions in the plants. The number of reports differed by a magnitude 1:2 between the different plants. However, a greater number of LERs can indicate both higher safety standards (e.g., a greater willingness to report all possible events to be able to learn from them) and lower safety standards (e.g., reporting as few events as possible to make a good impression). It was pointed out that LERs are indispensable in order to maintain safety of an industry and that the differences between plants found in the analyses of this study indicate how error reports can be used to initiate further investigations for improved safety

  13. Convolutional neural networks for event-related potential detection: impact of the architecture.

    Science.gov (United States)

    Cecotti, H

    2017-07-01

    The detection of brain responses at the single-trial level in the electroencephalogram (EEG) such as event-related potentials (ERPs) is a difficult problem that requires different processing steps to extract relevant discriminant features. While most of the signal and classification techniques for the detection of brain responses are based on linear algebra, different pattern recognition techniques such as convolutional neural network (CNN), as a type of deep learning technique, have shown some interests as they are able to process the signal after limited pre-processing. In this study, we propose to investigate the performance of CNNs in relation of their architecture and in relation to how they are evaluated: a single system for each subject, or a system for all the subjects. More particularly, we want to address the change of performance that can be observed between specifying a neural network to a subject, or by considering a neural network for a group of subjects, taking advantage of a larger number of trials from different subjects. The results support the conclusion that a convolutional neural network trained on different subjects can lead to an AUC above 0.9 by using an appropriate architecture using spatial filtering and shift invariant layers.

  14. The necessity of recognizing all events in x-ray detection

    International Nuclear Information System (INIS)

    Papp, T.; Maxwell, J.A.; Papp, A.T.

    2008-01-01

    -ray detection. Examples will be given in detection of x-rays in nuclear backgrounds, and in industrial measurements for ROHS and WEEE compliance with input rates of up to several hundred thousands counts per seconds. The availability of all the events allows one to see the other part of the spectrum, and thus offer explanations why the basic parameters are in such a bad shape

  15. Second-Order Multiagent Systems with Event-Driven Consensus Control

    Directory of Open Access Journals (Sweden)

    Jiangping Hu

    2013-01-01

    Full Text Available Event-driven control scheduling strategies for multiagent systems play a key role in future use of embedded microprocessors of limited resources that gather information and actuate the agent control updates. In this paper, a distributed event-driven consensus problem is considered for a multi-agent system with second-order dynamics. Firstly, two kinds of event-driven control laws are, respectively, designed for both leaderless and leader-follower systems. Then, the input-to-state stability of the closed-loop multi-agent system with the proposed event-driven consensus control is analyzed and the bound of the inter-event times is ensured. Finally, some numerical examples are presented to validate the proposed event-driven consensus control.

  16. Fundamental aspects of seismic event detection, magnitude estimation and their interrelation

    International Nuclear Information System (INIS)

    Ringdal, F.

    1977-01-01

    The main common subject of the papers forming this thesis is statistical model development within the seismological disciplines of seismic event detection and event magnitude estimation. As more high quality seismic data become available as a result of recent seismic network developments, the opportunity will exist for large scale application and further refinement of these models. It is hoped that the work presented here will facilitate improved understanding of the basic issues, both within earthquake-explosion discrimination, in the framework of which most of this work originated, and in seismology in general. (Auth.)

  17. Early notification of the environmental radiation monitoring system to a radioactive event

    International Nuclear Information System (INIS)

    Haquin, G.; Ne'eman, F; Brenner, S.

    1997-01-01

    The National Environmental Radiation Monitoring System managed by the Radiation Safety Division of the Ministry of tile Environment has been completed and is composed of a network of 10 stations; 6 terrestrial stations, 3 waterside stations and one mobile. The system was built by Rotem Co. and the control center is located at the Unit of Environmental Resources of the Ministry of the Environment in Tel Aviv University. Each station consists of a wide range Geiger Mueller detector and ambient dose rate meter that provides the level of the environmental dose rate. Low level radioactive particles are detected by air sampling with devices that collect suspended and settling particles . Each station is connected to the control center through telephone lines and RF communication system providing 24 hour a day the level of the environmental radiation. The background radiation dose rate level depends on the location of the station and varies from 8 - 16 μR/h. The system has proved its efficiency in a 'simulation like event' early detecting an unregistered gamma radiography work in the proximity of two stations performed in June 96 in Ashdod port and in December 96 at Maspenot Israel in Haifa. During the events the radiation level increased up to 20 times above the background level. Survey teams of the Ashdod port and Maspenot Israel were sent to place to check the sources for the radiation level increase. These teams found workers performing radiography work in the area of the stations. (authors)

  18. Networked Estimation for Event-Based Sampling Systems with Packet Dropouts

    Directory of Open Access Journals (Sweden)

    Young Soo Suh

    2009-04-01

    Full Text Available This paper is concerned with a networked estimation problem in which sensor data are transmitted over the network. In the event-based sampling scheme known as level-crossing or send-on-delta (SOD, sensor data are transmitted to the estimator node if the difference between the current sensor value and the last transmitted one is greater than a given threshold. Event-based sampling has been shown to be more efficient than the time-triggered one in some situations, especially in network bandwidth improvement. However, it cannot detect packet dropout situations because data transmission and reception do not use a periodical time-stamp mechanism as found in time-triggered sampling systems. Motivated by this issue, we propose a modified event-based sampling scheme called modified SOD in which sensor data are sent when either the change of sensor output exceeds a given threshold or the time elapses more than a given interval. Through simulation results, we show that the proposed modified SOD sampling significantly improves estimation performance when packet dropouts happen.

  19. Individual differences in event-based prospective memory: Evidence for multiple processes supporting cue detection.

    Science.gov (United States)

    Brewer, Gene A; Knight, Justin B; Marsh, Richard L; Unsworth, Nash

    2010-04-01

    The multiprocess view proposes that different processes can be used to detect event-based prospective memory cues, depending in part on the specificity of the cue. According to this theory, attentional processes are not necessary to detect focal cues, whereas detection of nonfocal cues requires some form of controlled attention. This notion was tested using a design in which we compared performance on a focal and on a nonfocal prospective memory task by participants with high or low working memory capacity. An interaction was found, such that participants with high and low working memory performed equally well on the focal task, whereas the participants with high working memory performed significantly better on the nonfocal task than did their counterparts with low working memory. Thus, controlled attention was only necessary for detecting event-based prospective memory cues in the nonfocal task. These results have implications for theories of prospective memory, the processes necessary for cue detection, and the successful fulfillment of intentions.

  20. Temporal and spatial predictability of an irrelevant event differently affect detection and memory of items in a visual sequence

    Directory of Open Access Journals (Sweden)

    Junji eOhyama

    2016-02-01

    Full Text Available We examined how the temporal and spatial predictability of a task-irrelevant visual event affects the detection and memory of a visual item embedded in a continuously changing sequence. Participants observed 11 sequentially presented letters, during which a task-irrelevant visual event was either present or absent. Predictabilities of spatial location and temporal position of the event were controlled in 2 × 2 conditions. In the spatially predictable conditions, the event occurred at the same location within the stimulus sequence or at another location, while, in the spatially unpredictable conditions, it occurred at random locations. In the temporally predictable conditions, the event timing was fixed relative to the order of the letters, while in the temporally unpredictable condition, it could not be predicted from the letter order. Participants performed a working memory task and a target detection reaction time task. Memory accuracy was higher for a letter simultaneously presented at the same location as the event in the temporally unpredictable conditions, irrespective of the spatial predictability of the event. On the other hand, the detection reaction times were only faster for a letter simultaneously presented at the same location as the event when the event was both temporally and spatially predictable. Thus, to facilitate ongoing detection processes, an event must be predictable both in space and time, while memory processes are enhanced by temporally unpredictable (i.e., surprising events. Evidently, temporal predictability has differential effects on detection and memory of a visual item embedded in a sequence of images.

  1. Transient pattern analysis for fault detection and diagnosis of HVAC systems

    International Nuclear Information System (INIS)

    Cho, Sung-Hwan; Yang, Hoon-Cheol; Zaheer-uddin, M.; Ahn, Byung-Cheon

    2005-01-01

    Modern building HVAC systems are complex and consist of a large number of interconnected sub-systems and components. In the event of a fault, it becomes very difficult for the operator to locate and isolate the faulty component in such large systems using conventional fault detection methods. In this study, transient pattern analysis is explored as a tool for fault detection and diagnosis of an HVAC system. Several tests involving different fault replications were conducted in an environmental chamber test facility. The results show that the evolution of fault residuals forms clear and distinct patterns that can be used to isolate faults. It was found that the time needed to reach steady state for a typical building HVAC system is at least 50-60 min. This means incorrect diagnosis of faults can happen during online monitoring if the transient pattern responses are not considered in the fault detection and diagnosis analysis

  2. Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback.

    Science.gov (United States)

    Kreilinger, Alex; Hiebel, Hannah; Müller-Putz, Gernot R

    2016-03-01

    This work aimed to find and evaluate a new method for detecting errors in continuous brain-computer interface (BCI) applications. Instead of classifying errors on a single-trial basis, the new method was based on multiple events (MEs) analysis to increase the accuracy of error detection. In a BCI-driven car game, based on motor imagery (MI), discrete events were triggered whenever subjects collided with coins and/or barriers. Coins counted as correct events, whereas barriers were errors. This new method, termed ME method, combined and averaged the classification results of single events (SEs) and determined the correctness of MI trials, which consisted of event sequences instead of SEs. The benefit of this method was evaluated in an offline simulation. In an online experiment, the new method was used to detect erroneous MI trials. Such MI trials were discarded and could be repeated by the users. We found that, even with low SE error potential (ErrP) detection rates, feasible accuracies can be achieved when combining MEs to distinguish erroneous from correct MI trials. Online, all subjects reached higher scores with error detection than without, at the cost of longer times needed for completing the game. Findings suggest that ErrP detection may become a reliable tool for monitoring continuous states in BCI applications when combining MEs. This paper demonstrates a novel technique for detecting errors in online continuous BCI applications, which yields promising results even with low single-trial detection rates.

  3. A Key Event Path Analysis Approach for Integrated Systems

    Directory of Open Access Journals (Sweden)

    Jingjing Liao

    2012-01-01

    Full Text Available By studying the key event paths of probabilistic event structure graphs (PESGs, a key event path analysis approach for integrated system models is proposed. According to translation rules concluded from integrated system architecture descriptions, the corresponding PESGs are constructed from the colored Petri Net (CPN models. Then the definitions of cycle event paths, sequence event paths, and key event paths are given. Whereafter based on the statistic results after the simulation of CPN models, key event paths are found out by the sensitive analysis approach. This approach focuses on the logic structures of CPN models, which is reliable and could be the basis of structured analysis for discrete event systems. An example of radar model is given to characterize the application of this approach, and the results are worthy of trust.

  4. Analysis hierarchical model for discrete event systems

    Science.gov (United States)

    Ciortea, E. M.

    2015-11-01

    The This paper presents the hierarchical model based on discrete event network for robotic systems. Based on the hierarchical approach, Petri network is analysed as a network of the highest conceptual level and the lowest level of local control. For modelling and control of complex robotic systems using extended Petri nets. Such a system is structured, controlled and analysed in this paper by using Visual Object Net ++ package that is relatively simple and easy to use, and the results are shown as representations easy to interpret. The hierarchical structure of the robotic system is implemented on computers analysed using specialized programs. Implementation of hierarchical model discrete event systems, as a real-time operating system on a computer network connected via a serial bus is possible, where each computer is dedicated to local and Petri model of a subsystem global robotic system. Since Petri models are simplified to apply general computers, analysis, modelling, complex manufacturing systems control can be achieved using Petri nets. Discrete event systems is a pragmatic tool for modelling industrial systems. For system modelling using Petri nets because we have our system where discrete event. To highlight the auxiliary time Petri model using transport stream divided into hierarchical levels and sections are analysed successively. Proposed robotic system simulation using timed Petri, offers the opportunity to view the robotic time. Application of goods or robotic and transmission times obtained by measuring spot is obtained graphics showing the average time for transport activity, using the parameters sets of finished products. individually.

  5. On the feasibility of using satellite gravity observations for detecting large-scale solid mass transfer events

    Science.gov (United States)

    Peidou, Athina C.; Fotopoulos, Georgia; Pagiatakis, Spiros

    2017-10-01

    The main focus of this paper is to assess the feasibility of utilizing dedicated satellite gravity missions in order to detect large-scale solid mass transfer events (e.g. landslides). Specifically, a sensitivity analysis of Gravity Recovery and Climate Experiment (GRACE) gravity field solutions in conjunction with simulated case studies is employed to predict gravity changes due to past subaerial and submarine mass transfer events, namely the Agulhas slump in southeastern Africa and the Heart Mountain Landslide in northwestern Wyoming. The detectability of these events is evaluated by taking into account the expected noise level in the GRACE gravity field solutions and simulating their impact on the gravity field through forward modelling of the mass transfer. The spectral content of the estimated gravity changes induced by a simulated large-scale landslide event is estimated for the known spatial resolution of the GRACE observations using wavelet multiresolution analysis. The results indicate that both the Agulhas slump and the Heart Mountain Landslide could have been detected by GRACE, resulting in {\\vert }0.4{\\vert } and {\\vert }0.18{\\vert } mGal change on GRACE solutions, respectively. The suggested methodology is further extended to the case studies of the submarine landslide in Tohoku, Japan, and the Grand Banks landslide in Newfoundland, Canada. The detectability of these events using GRACE solutions is assessed through their impact on the gravity field.

  6. Polygraph lie detection on real events in a laboratory setting.

    Science.gov (United States)

    Bradley, M T; Cullen, M C

    1993-06-01

    This laboratory study dealt with real-life intense emotional events. Subjects generated embarrassing stories from their experience, then submitted to polygraph testing and, by lying, denied their stories and, by telling the truth, denied a randomly assigned story. Money was given as an incentive to be judged innocent on each story. An interrogator, blind to the stories, used Control Question Tests and found subjects more deceptive when lying than when truthful. Stories interacted with order such that lying on the second story was more easily detected than lying on the first. Embarrassing stories provide an alternative to the use of mock crimes to study lie detection in the laboratory.

  7. ATLAS EventIndex General Dataflow and Monitoring Infrastructure

    CERN Document Server

    Fernandez Casani, Alvaro; The ATLAS collaboration

    2016-01-01

    The ATLAS EventIndex has been running in production since mid-2015, reliably collecting information worldwide about all produced events and storing them in a central Hadoop infrastructure at CERN. A subset of this information is copied to an Oracle relational database for fast access. The system design and its optimization is serving event picking from requests of a few events up to scales of tens of thousand of events, and in addition, data consistency checks are performed for large production campaigns. Detecting duplicate events with a scope of physics collections has recently arisen as an important use case. This paper describes the general architecture of the project and the data flow and operation issues, which are addressed by recent developments to improve the throughput of the overall system. In this direction, the data collection system is reducing the usage of the messaging infrastructure to overcome the performance shortcomings detected during production peaks; an object storage approach is instea...

  8. Synchronous Parallel System for Emulation and Discrete Event Simulation

    Science.gov (United States)

    Steinman, Jeffrey S. (Inventor)

    2001-01-01

    A synchronous parallel system for emulation and discrete event simulation having parallel nodes responds to received messages at each node by generating event objects having individual time stamps, stores only the changes to the state variables of the simulation object attributable to the event object and produces corresponding messages. The system refrains from transmitting the messages and changing the state variables while it determines whether the changes are superseded, and then stores the unchanged state variables in the event object for later restoral to the simulation object if called for. This determination preferably includes sensing the time stamp of each new event object and determining which new event object has the earliest time stamp as the local event horizon, determining the earliest local event horizon of the nodes as the global event horizon, and ignoring events whose time stamps are less than the global event horizon. Host processing between the system and external terminals enables such a terminal to query, monitor, command or participate with a simulation object during the simulation process.

  9. Hypersensitivity reactions to anticancer agents: Data mining of the public version of the FDA adverse event reporting system, AERS

    Directory of Open Access Journals (Sweden)

    Sakaeda Toshiyuki

    2011-10-01

    Full Text Available Abstract Background Previously, adverse event reports (AERs submitted to the US Food and Drug Administration (FDA database were reviewed to confirm platinum agent-associated hypersensitivity reactions. The present study was performed to confirm whether the database could suggest the hypersensitivity reactions caused by anticancer agents, paclitaxel, docetaxel, procarbazine, asparaginase, teniposide, and etoposide. Methods After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving candidate agents were analyzed. The National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0 was applied to evaluate the susceptibility to hypersensitivity reactions, and standardized official pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Results Based on 1,644,220 AERs from 2004 to 2009, the signals were detected for paclitaxel-associated mild, severe, and lethal hypersensitivity reactions, and docetaxel-associated lethal reactions. However, the total number of adverse events occurring with procarbazine, asparaginase, teniposide, or etoposide was not large enough to detect signals. Conclusions The FDA's adverse event reporting system, AERS, and the data mining methods used herein are useful for confirming drug-associated adverse events, but the number of co-occurrences is an important factor in signal detection.

  10. Tamper Detection for Active Surveillance Systems

    DEFF Research Database (Denmark)

    Theodore, Tsesmelis; Christensen, Lars; Fihl, Preben

    2013-01-01

    If surveillance data are corrupted they are of no use to neither manually post-investigation nor automatic video analysis. It is therefore critical to automatically be able to detect tampering events such as defocusing, occlusion and displacement. In this work we for the first time ad- dress...... of different tampering events. In order to assess the developed methods we have collected a large data set, which contains sequences from different active cameras at different scenarios. We evaluate our sys- tem on these data and the results are encouraging with a very high detecting rate and relatively few...

  11. CDC Wonder Vaccine Adverse Event Reporting System

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Vaccine Adverse Event Reporting System (VAERS) online database on CDC WONDER provides counts and percentages of adverse event case reports after vaccination,...

  12. Detection of Unusual Events and Trends in Complex Non-Stationary Data Streams

    International Nuclear Information System (INIS)

    Perez, Rafael B.; Protopopescu, Vladimir A.; Worley, Brian Addison; Perez, Cristina

    2006-01-01

    The search for unusual events and trends hidden in multi-component, nonlinear, non-stationary, noisy signals is extremely important for a host of different applications, ranging from nuclear power plant and electric grid operation to internet traffic and implementation of non-proliferation protocols. In the context of this work, we define an unusual event as a local signal disturbance and a trend as a continuous carrier of information added to and different from the underlying baseline dynamics. The goal of this paper is to investigate the feasibility of detecting hidden intermittent events inside non-stationary signal data sets corrupted by high levels of noise, by using the Hilbert-Huang empirical mode decomposition method

  13. Development of a Premature Stop Codon-detection method based on a bacterial two-hybrid system

    Directory of Open Access Journals (Sweden)

    Mayorga Luis S

    2006-09-01

    Full Text Available Abstract Background The detection of Premature Stop Codons (PSCs in human genes is very useful for the genetic diagnosis of different hereditary cancers, e.g. Familial Breast Cancer and Hereditary Non-Polyposis Colorectal Cancer (HNPCC. The products of these PSCs are truncated proteins, detectable in vitro by the Protein Truncation Test and in vivo by using the living translation machinery of yeast or bacteria. These living strategies are based on the construction of recombinant plasmids where the human sequence of interest is inserted upstream of a reporter gene. Although simple, these assays have their limitations. The yeast system requires extensive work to enhance its specificity, and the bacterial systems yield many false results due to translation re-initiation events occurring post PSCs. Our aim was to design a recombinant plasmid useful for detecting PSCs in human genes and resistant to bacterial translation re-initiation interferences. Results A functional recombinant plasmid (pREAL was designed based on a bacterial two-hybrid system. In our design, the in vivo translation of fused fragments of the Bordetella pertussis adenylate cyclase triggers the production of cAMP giving rise to a selectable bacterial phenotype. When a gene of interest is inserted between the two fragments, any PSC inhibits the enzymatic activity of the product, and translation re-initiation events post-PSC yield separated inactive fragments. We demonstrated that the system can accurately detect PSCs in human genes by inserting mutated fragments of the brca1 and msh2 gene. Western Blot assays revealed translation re-initiation events in all the tested colonies, implying that a simpler plasmid would not be resistant to this source of false negative results. The application of the system to a HNPCC family with a nonsense mutation in the msh2 gene correctly diagnosed wild type homozygous and heterozygous patients. Conclusion The developed pREAL is applicable to the

  14. Detection of criticality accidents. The Intertechnique EDAC II system

    International Nuclear Information System (INIS)

    Prigent, R.

    1991-01-01

    The chief aim of the new generation of EDAC II criticality accidents detection system is to reduce the risks associated to the handling of fissile material by providing a swift and safe warning of the development of any criticality accident. To this function already devolving on the EDAC system of the previous generation, the EDAC II adds the possibility of storing in memory the characteristics of the accident, providing a daily follow-up of the striking events in the system through the print-out of a log book and providing assistance to the operators during the periodical tests. (Author)

  15. Client and event driven data hub system at CDF

    International Nuclear Information System (INIS)

    Kilminster, Ben; McFarland, Kevin; Vaiciulis, Tony; Matsunaga, Hiroyuki; Shimojima, Makoto

    2001-01-01

    The Consumer-Server Logger (CSL) system at the Collider Detector at Fermilab is a client and event driven data hub capable of receiving physics events from multiple connections, and logging them to multiple streams while distributing them to multiple online analysis programs (consumers). Its multiple-partitioned design allows data flowing through different paths of the detector sub-systems to be processed separately. The CSL system, using a set of internal memory buffers and message queues mapped to the location of events within its programs, and running on an SGI 2200 Server, is able to process at least the required 20 MB/s of constant event logging (75 Hz of 250 KB events) while also filtering up to 10 MB/s to consumers requesting specific types of events

  16. The LCLS Timing Event System

    Energy Technology Data Exchange (ETDEWEB)

    Dusatko, John; Allison, S.; Browne, M.; Krejcik, P.; /SLAC

    2012-07-23

    The Linac Coherent Light Source requires precision timing trigger signals for various accelerator diagnostics and controls at SLAC-NAL. A new timing system has been developed that meets these requirements. This system is based on COTS hardware with a mixture of custom-designed units. An added challenge has been the requirement that the LCLS Timing System must co-exist and 'know' about the existing SLC Timing System. This paper describes the architecture, construction and performance of the LCLS timing event system.

  17. Online surveillance of media health event reporting in Nepal: digital disease detection from a One Health perspective.

    Science.gov (United States)

    Schwind, Jessica S; Norman, Stephanie A; Karmacharya, Dibesh; Wolking, David J; Dixit, Sameer M; Rajbhandari, Rajesh M; Mekaru, Sumiko R; Brownstein, John S

    2017-09-21

    Traditional media and the internet are crucial sources of health information. Media can significantly shape public opinion, knowledge and understanding of emerging and endemic health threats. As digital communication rapidly progresses, local access and dissemination of health information contribute significantly to global disease detection and reporting. Health event reports in Nepal (October 2013-December 2014) were used to characterize Nepal's media environment from a One Health perspective using HealthMap - a global online disease surveillance and mapping tool. Event variables (location, media source type, disease or risk factor of interest, and affected species) were extracted from HealthMap. A total of 179 health reports were captured from various sources including newspapers, inter-government agency bulletins, individual reports, and trade websites, yielding 108 (60%) unique articles. Human health events were reported most often (n = 85; 79%), followed by animal health events (n = 23; 21%), with no reports focused solely on environmental health. By expanding event coverage across all of the health sectors, media in developing countries could play a crucial role in national risk communication efforts and could enhance early warning systems for disasters and disease outbreaks.

  18. Adverse Event Reporting System (AERS)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Adverse Event Reporting System (AERS) is a computerized information database designed to support the FDA's post-marketing safety surveillance program for all...

  19. Sensor-enabled chem/bio contamination detection system dedicated to situational awareness of water distribution security status

    Science.gov (United States)

    Ginsberg, Mark D.; Smith, Eddy D.; VanBlaricum, Vicki; Hock, Vincent F.; Kroll, Dan; Russell, Kevin J.

    2010-04-01

    Both real events and models have proven that drinking water systems are vulnerable to deliberate and/or accidental contamination. Additionally, homeland security initiatives and modeling efforts have determined that it is relatively easy to orchestrate the contamination of potable water supplies. Such contamination can be accomplished with classic and non-traditional chemical agents, toxic industrial chemicals (TICs), and/or toxic industrial materials (TIMs). Subsequent research and testing has developed a proven network for detection and response to these threats. The method uses offthe- shelf, broad-spectrum analytical instruments coupled with advanced interpretive algorithms. The system detects and characterizes any backflow events involving toxic contaminants by employing unique chemical signature (fingerprint) response data. This instrumentation has been certified by the Office of Homeland Security for detecting deliberate and/or accidental contamination of critical water infrastructure. The system involves integration of several mature technologies (sensors, SCADA, dynamic models, and the HACH HST Guardian Blue instrumentation) into a complete, real-time, management system that also can be used to address other water distribution concerns, such as corrosion. This paper summarizes the reasons and results for installing such a distribution-based detection and protection system.

  20. Implementation of a ferromagnetic detection system in a clinical MRI setting

    International Nuclear Information System (INIS)

    Orchard, L.J.

    2015-01-01

    Purpose: To evaluate the implementation of a ferromagnetic detection system (FMDS) into a clinical MRI setting. Materials and methods: One thousand patients were considered for MRI safety screening using an FMDS. Equipment used was a Ferroguard ® Screener (Metrasens Ltd, Malvern, Worcestershire, UK). Fully gowned patients rotated 360° in front of the FMDS in a standardized manner following traditional MRI screening methods (the use of a written questionnaire (Fig. B.1) and verbal interview. Results: Final results included 1032 individual screening events performed in 977 patients. There were 922 (94%) initial passes using the FMDS; 34 (4%) failed initial screens but passed a subsequent screen; 21 (2%) failed the initial and subsequent screens. Thus, including all screening events (n = 1032), there were 956 (93%) true negatives (TN); 21 (2%) false positives (FP) and 55 (5%) true positives (TP). No false negatives (FN) were recorded. Therefore, sensitivity was 100% and specificity was 98%. Conclusion: Implementation and correct usage of an FMDS proved to increase safety within a clinical MRI environment by alerting staff to ferromagnetic items or implants not identified using traditional MRI screening methods. An FMDS should be used as an adjunct to these methods. The information in this study pertains to the specific equipment used in this investigation. - Highlights: • Ferromagnetic detection system sensitivity in this study was 100%. • Ferromagnetic detection system specificity in this study was 98%. • The additional screening procedure had little impact on throughput ie additional time taken was minimal. • Staff training, technique and compliance is important in implementing the screening procedures. • The ferromagnetic detection system identified objects that may have demonstrated projectile, heating or artefact effects

  1. SQL injection detection system

    OpenAIRE

    Vargonas, Vytautas

    2017-01-01

    SQL injection detection system Programmers do not always ensure security of developed systems. That is why it is important to look for solutions outside being reliant on developers. In this work SQL injection detection system is proposed. The system analyzes HTTP request parameters and detects intrusions. It is based on unsupervised machine learning. Trained by regular request data system detects outlier user parameters. Since training is not reliant on previous knowledge of SQL injections, t...

  2. Automatic detection of lexical change: an auditory event-related potential study.

    Science.gov (United States)

    Muller-Gass, Alexandra; Roye, Anja; Kirmse, Ursula; Saupe, Katja; Jacobsen, Thomas; Schröger, Erich

    2007-10-29

    We investigated the detection of rare task-irrelevant changes in the lexical status of speech stimuli. Participants performed a nonlinguistic task on word and pseudoword stimuli that occurred, in separate conditions, rarely or frequently. Task performance for pseudowords was deteriorated relative to words, suggesting unintentional lexical analysis. Furthermore, rare word and pseudoword changes had a similar effect on the event-related potentials, starting as early as 165 ms. This is the first demonstration of the automatic detection of change in lexical status that is not based on a co-occurring acoustic change. We propose that, following lexical analysis of the incoming stimuli, a mental representation of the lexical regularity is formed and used as a template against which lexical change can be detected.

  3. On causality of extreme events

    Directory of Open Access Journals (Sweden)

    Massimiliano Zanin

    2016-06-01

    Full Text Available Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available.

  4. Contribution of Infrasound to IDC Reviewed Event Bulletin

    Science.gov (United States)

    Bittner, Paulina; Polich, Paul; Gore, Jane; Ali, Sherif Mohamed; Medinskaya, Tatiana; Mialle, Pierrick

    2016-04-01

    Until 2003 two waveform technologies, i.e. seismic and hydroacoustic were used to detect and locate events included in the International Data Centre (IDC) Reviewed Event Bulletin (REB). The first atmospheric event was published in the REB in 2003 but infrasound detections could not be used by the Global Association (GA) Software due to the unmanageable high number of spurious associations. Offline improvements of the automatic processing took place to reduce the number of false detections to a reasonable level. In February 2010 the infrasound technology was reintroduced to the IDC operations and has contributed to both automatic and reviewed IDC bulletins. The primary contribution of infrasound technology is to detect atmospheric events. These events may also be observed at seismic stations, which will significantly improve event location. Examples of REB events, which were detected by the International Monitoring System (IMS) infrasound network were fireballs (e.g. Bangkok fireball, 2015), volcanic eruptions (e.g. Calbuco, Chile 2015) and large surface explosions (e.g. Tjanjin, China 2015). Query blasts and large earthquakes belong to events primarily recorded at seismic stations of the IMS network but often detected at the infrasound stations. Presence of infrasound detection associated to an event from a mining area indicates a surface explosion. Satellite imaging and a database of active mines can be used to confirm the origin of such events. This presentation will summarize the contribution of 6 years of infrasound data to IDC bulletins and provide examples of events recorded at the IMS infrasound network. Results of this study may help to improve location of small events with observations on infrasound stations.

  5. Accuracy and precision of equine gait event detection during walking with limb and trunk mounted inertial sensors

    DEFF Research Database (Denmark)

    Olsen, Emil; Andersen, Pia Haubro; Pfau, Thilo

    2012-01-01

    The increased variations of temporal gait events when pathology is present are good candidate features for objective diagnostic tests. We hypothesised that the gait events hoof-on/off and stance can be detected accurately and precisely using features from trunk and distal limb-mounted Inertial....... Accuracy (bias) and precision (SD of bias) was calculated to compare force plate and IMU timings for gait events. Data were collected from seven horses. One hundred and twenty three (123) front limb steps were analysed; hoof-on was detected with a bias (SD) of -7 (23) ms, hoof-off with 0.7 (37) ms...... and front limb stance with -0.02 (37) ms. A total of 119 hind limb steps were analysed; hoof-on was found with a bias (SD) of -4 (25) ms, hoof-off with 6 (21) ms and hind limb stance with 0.2 (28) ms. IMUs mounted on the distal limbs and sacrum can detect gait events accurately and precisely....

  6. Discrete Event Simulation Model of the Polaris 2.1 Gamma Ray Imaging Radiation Detection Device

    Science.gov (United States)

    2016-06-01

    release; distribution is unlimited DISCRETE EVENT SIMULATION MODEL OF THE POLARIS 2.1 GAMMA RAY IMAGING RADIATION DETECTION DEVICE by Andres T...ONLY (Leave blank) 2. REPORT DATE June 2016 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE DISCRETE EVENT SIMULATION MODEL...modeled. The platform, Simkit, was utilized to create a discrete event simulation (DES) model of the Polaris. After carefully constructing the DES

  7. Unsupervised behaviour-specific dictionary learning for abnormal event detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2015-01-01

    the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from...... potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms...

  8. Modeling and simulation of discrete event systems

    CERN Document Server

    Choi, Byoung Kyu

    2013-01-01

    Computer modeling and simulation (M&S) allows engineers to study and analyze complex systems. Discrete-event system (DES)-M&S is used in modern management, industrial engineering, computer science, and the military. As computer speeds and memory capacity increase, so DES-M&S tools become more powerful and more widely used in solving real-life problems. Based on over 20 years of evolution within a classroom environment, as well as on decades-long experience in developing simulation-based solutions for high-tech industries, Modeling and Simulation of Discrete-Event Systems is the only book on

  9. Automatic Multi-sensor Data Quality Checking and Event Detection for Environmental Sensing

    Science.gov (United States)

    LIU, Q.; Zhang, Y.; Zhao, Y.; Gao, D.; Gallaher, D. W.; Lv, Q.; Shang, L.

    2017-12-01

    With the advances in sensing technologies, large-scale environmental sensing infrastructures are pervasively deployed to continuously collect data for various research and application fields, such as air quality study and weather condition monitoring. In such infrastructures, many sensor nodes are distributed in a specific area and each individual sensor node is capable of measuring several parameters (e.g., humidity, temperature, and pressure), providing massive data for natural event detection and analysis. However, due to the dynamics of the ambient environment, sensor data can be contaminated by errors or noise. Thus, data quality is still a primary concern for scientists before drawing any reliable scientific conclusions. To help researchers identify potential data quality issues and detect meaningful natural events, this work proposes a novel algorithm to automatically identify and rank anomalous time windows from multiple sensor data streams. More specifically, (1) the algorithm adaptively learns the characteristics of normal evolving time series and (2) models the spatial-temporal relationship among multiple sensor nodes to infer the anomaly likelihood of a time series window for a particular parameter in a sensor node. Case studies using different data sets are presented and the experimental results demonstrate that the proposed algorithm can effectively identify anomalous time windows, which may resulted from data quality issues and natural events.

  10. A Novel Event-Based Incipient Slip Detection Using Dynamic Active-Pixel Vision Sensor (DAVIS).

    Science.gov (United States)

    Rigi, Amin; Baghaei Naeini, Fariborz; Makris, Dimitrios; Zweiri, Yahya

    2018-01-24

    In this paper, a novel approach to detect incipient slip based on the contact area between a transparent silicone medium and different objects using a neuromorphic event-based vision sensor (DAVIS) is proposed. Event-based algorithms are developed to detect incipient slip, slip, stress distribution and object vibration. Thirty-seven experiments were performed on five objects with different sizes, shapes, materials and weights to compare precision and response time of the proposed approach. The proposed approach is validated by using a high speed constitutional camera (1000 FPS). The results indicate that the sensor can detect incipient slippage with an average of 44.1 ms latency in unstructured environment for various objects. It is worth mentioning that the experiments were conducted in an uncontrolled experimental environment, therefore adding high noise levels that affected results significantly. However, eleven of the experiments had a detection latency below 10 ms which shows the capability of this method. The results are very promising and show a high potential of the sensor being used for manipulation applications especially in dynamic environments.

  11. Corpuscular event-by-event simulation of quantum optics experiments : application to a quantum-controlled delayed-choice experiment

    NARCIS (Netherlands)

    De Raedt, Hans; Delina, M; Jin, Fengping; Michielsen, Kristel

    2012-01-01

    A corpuscular simulation model of optical phenomena that does not require knowledge of the solution of a wave equation of the whole system and reproduces the results of Maxwell's theory by generating detection events one by one is discussed. The event-based corpuscular model gives a unified

  12. How does structured sparsity work in abnormal event detection?

    DEFF Research Database (Denmark)

    Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

    the training, which is the due to the fact that abnormal videos are limited or even unavailable in advance in most video surveillance applications. As a result, there could be only one label in the training data which hampers supervised learning; 2) Even though there are multiple types of normal behaviors, how...... many normal patterns lie in the whole surveillance data is still unknown. This is because there is huge amount of video surveillance data and only a small proportion is used in algorithm learning, consequently, the normal patterns in the training data could be incomplete. As a result, any sparse...... structure learned from the training data could have a high bias and ruin the precision of abnormal event detection. Therefore, we in the paper propose an algorithm to solve the abnormality detection problem by sparse representation, in which local structured sparsity is preserved in coefficients. To better...

  13. Analysis of system and of course of events

    International Nuclear Information System (INIS)

    Hoertner, H.; Kersting, E.J.; Puetter, B.M.

    1986-01-01

    The analysis of the system and of the course of events is used to determine the frequency of core melt-out accidents and to describe the safety-related boundary conditions of appropriate accidents. The lecture is concerned with the effect of system changes in the reference plant and the effect of triggering events not assessed in detail or not sufficiently assessed in detail in phase A of the German Risk Study on the frequency of core melt-out accidents, the minimum requirements for system functions for controlling triggering events, i.e. to prevent core melt-out accidents, the reliability data important for reliability investigations and frequency assessments. (orig./DG) [de

  14. Zero Time of Transitory Nuclear Events Derived by Parent-Daughter Systems

    International Nuclear Information System (INIS)

    Nir-El, Y.

    2014-01-01

    The detection and identification of a nuclear event that results in the dissemination of radioactive products into the environment can be realized by dating the age of the event. In order to correct observed activities for the decay since the occurrence of the event, the age must be known to a high level of confidence. Previous papers. described the method to date the age of a nuclear event by measuring the activity of two fission products, which constitute the clock in this application. Within the proficiency test programme for radionuclide laboratories supporting the CTBT, a simulated gamma spectrum with the characteristics of an atmospheric test of a Chinese thermonuclear device, was used to determine the zero time by calculating the theoretical peak area ratio of 95Nb/95Zr. Their approach used only the main gamma lines at 766 and 757 keV and assigned the same detection efficiency to both these close lines. Their methodology of calculating the uncertainty of zero time is subject to comments because it takes the sum of two components (nuclide ratio and activity ratio as function of time) in quadrature. In another paper, the activity of 95Nb as a function of time was presented without any development or expression for the zero time. Analytical equations for zero time and the associated uncertainty calculations were derived in a recent paper using a measured activity ratio of two nuclides and illustrating the procedure by data from the Chinese test. The evaluation of the zero time uncertainty was performed by a very large set of very complicated analytical equations. The present paper aims at developing a procedure to determine the zero time and its uncertainty in a transitory nuclear event by treating a parent-daughter system of 3 nuclides, where one daughter feeds the other one, in addition to its direct feeding by the decay of the parent

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

    CERN Document Server

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

    2018-01-01

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

  16. Control of Discrete-Event Systems Automata and Petri Net Perspectives

    CERN Document Server

    Silva, Manuel; Schuppen, Jan

    2013-01-01

    Control of Discrete-event Systems provides a survey of the most important topics in the discrete-event systems theory with particular focus on finite-state automata, Petri nets and max-plus algebra. Coverage ranges from introductory material on the basic notions and definitions of discrete-event systems to more recent results. Special attention is given to results on supervisory control, state estimation and fault diagnosis of both centralized and distributed/decentralized systems developed in the framework of the Distributed Supervisory Control of Large Plants (DISC) project. Later parts of the text are devoted to the study of congested systems though fluidization, an over approximation allowing a much more efficient study of observation and control problems of timed Petri nets. Finally, the max-plus algebraic approach to the analysis and control of choice-free systems is also considered. Control of Discrete-event Systems provides an introduction to discrete-event systems for readers that are not familiar wi...

  17. Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lei Jiang

    2012-01-01

    Full Text Available Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.

  18. Developing an EEG based On-line Closed-loop Lapse Detection and Mitigation System

    Directory of Open Access Journals (Sweden)

    Yu-Te eWang

    2014-10-01

    Full Text Available In America, sixty percent of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-realty environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory feedback was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing feedback to subjects suffering momentary cognitive lapses, and assess the efficacy of the feedback in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.

  19. Developing an EEG-based on-line closed-loop lapse detection and mitigation system.

    Science.gov (United States)

    Wang, Yu-Te; Huang, Kuan-Chih; Wei, Chun-Shu; Huang, Teng-Yi; Ko, Li-Wei; Lin, Chin-Teng; Cheng, Chung-Kuan; Jung, Tzyy-Ping

    2014-01-01

    In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.

  20. Detection of genetically modified maize events in Brazilian maize-derived food products

    Directory of Open Access Journals (Sweden)

    Maria Regina Branquinho

    2013-09-01

    Full Text Available The Brazilian government has approved many transgenic maize lines for commercialization and has established a threshold of 1% for food labeling, which underscores need for monitoring programs. Thirty four samples including flours and different types of nacho chips were analyzed by conventional and real-time PCR in 2011 and 2012. The events MON810, Bt11, and TC1507 were detected in most of the samples, and NK603 was present only in the samples analyzed in 2012. The authorized lines GA21, T25, and the unauthorized Bt176 were not detected. All positive samples in the qualitative tests collected in 2011 showed a transgenic content higher than 1%, and none of them was correctly labeled. Regarding the samples collected in 2012, all positive samples were quantified higher than the threshold, and 47.0% were not correctly labeled. The overall results indicated that the major genetically modified organisms detected were MON810, TC1507, Bt11, and NK603 events. Some industries that had failed to label their products in 2011 started labeling them in 2012, demonstrating compliance with the current legislation observing the consumer rights. Although these results are encouraging, it has been clearly demonstrated the need for continuous monitoring programs to ensure consumers that food products are labeled properly.

  1. Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

    Science.gov (United States)

    Albayrak, Arif; Teng, William

    2015-01-01

    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 of 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 events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

  2. Detection and location of multiple events by MARS. Final report

    International Nuclear Information System (INIS)

    Wang, J.; Masso, J.F.; Archambeau, C.B.; Savino, J.M.

    1980-09-01

    Seismic data from two explosions was processed using the Systems Science and Software MARS (Multiple Arrival Recognition System) seismic event detector in an effort to determine their relative spatial and temporal separation on the basis of seismic data alone. The explosions were less than 1.0 kilometer apart and were separated by less than 0.5 sec in origin times. The seismic data consisted of nine local accelerograms (r < 1.0 km) and four regional (240 through 400 km) seismograms. The MARS processing clearly indicates the presence of multiple explosions, but the restricted frequency range of the data inhibits accurate time picks and hence limits the precision of the event location

  3. Rendering visual events as sounds: Spatial attention capture by auditory augmented reality.

    Science.gov (United States)

    Stone, Scott A; Tata, Matthew S

    2017-01-01

    Many salient visual events tend to coincide with auditory events, such as seeing and hearing a car pass by. Information from the visual and auditory senses can be used to create a stable percept of the stimulus. Having access to related coincident visual and auditory information can help for spatial tasks such as localization. However not all visual information has analogous auditory percepts, such as viewing a computer monitor. Here, we describe a system capable of detecting and augmenting visual salient events into localizable auditory events. The system uses a neuromorphic camera (DAVIS 240B) to detect logarithmic changes of brightness intensity in the scene, which can be interpreted as salient visual events. Participants were blindfolded and asked to use the device to detect new objects in the scene, as well as determine direction of motion for a moving visual object. Results suggest the system is robust enough to allow for the simple detection of new salient stimuli, as well accurately encoding direction of visual motion. Future successes are probable as neuromorphic devices are likely to become faster and smaller in the future, making this system much more feasible.

  4. Rendering visual events as sounds: Spatial attention capture by auditory augmented reality.

    Directory of Open Access Journals (Sweden)

    Scott A Stone

    Full Text Available Many salient visual events tend to coincide with auditory events, such as seeing and hearing a car pass by. Information from the visual and auditory senses can be used to create a stable percept of the stimulus. Having access to related coincident visual and auditory information can help for spatial tasks such as localization. However not all visual information has analogous auditory percepts, such as viewing a computer monitor. Here, we describe a system capable of detecting and augmenting visual salient events into localizable auditory events. The system uses a neuromorphic camera (DAVIS 240B to detect logarithmic changes of brightness intensity in the scene, which can be interpreted as salient visual events. Participants were blindfolded and asked to use the device to detect new objects in the scene, as well as determine direction of motion for a moving visual object. Results suggest the system is robust enough to allow for the simple detection of new salient stimuli, as well accurately encoding direction of visual motion. Future successes are probable as neuromorphic devices are likely to become faster and smaller in the future, making this system much more feasible.

  5. An expert system for prevention of abnormal event recurrence

    International Nuclear Information System (INIS)

    Nishiyama, Takuya

    1990-01-01

    A huge amount of information related to abnormal events occurring in nuclear power plants in Japan and abroad is collected and accumulated in the Nuclear Information Center at CRIEPI. This information contains a variety of knowledge which may be useful for prevention of similar trouble. An expert system named, 'Consultation System for Prevention of Abnormal-Event Recurrence (CSPAR) is being developed with the objective of preventing recurrence of similar abnormal events by offering an effective means of utilizing such knowledge. This paper presents the key points in designing and constructing the system, the system functional outline, and some demonstration examples. (author)

  6. Development and Application of an Alert System to Detect Cases of Food Poisoning in Japan.

    Directory of Open Access Journals (Sweden)

    Akie Maeyashiki

    Full Text Available Recent public health concerns regarding commercial food products have increased the need to develop an automated method to detect food product-related health events. We developed and verified a method for the early detection of potentially harmful events caused by commercial food products. We collected data from daily internet-based questionnaires examining the presence or absence of symptoms and information about food purchased by the respondents. Using these data, we developed a method to detect possible health concerns regarding commercialized food products. To achieve this, we combined the signal detection method used in the reporting system of adverse effects of pharmaceutical products and the Early Aberration Reporting System (EARS used by the United States Centers for Disease Control. Whiteleg shrimp (Litopenaeus vannamei, which had odds ratio and Odds(- of 8.99 and 4.13, respectively, was identified as a possible causative food product for diarrhea and vomiting. In conclusion, this study demonstrated that food distributors can implement post-marketing monitoring of the safety of food products purchased via the internet.

  7. Development and Application of an Alert System to Detect Cases of Food Poisoning in Japan

    Science.gov (United States)

    Akahane, Manabu; Sugiura, Hiroaki; Ohkusa, Yasushi; Okabe, Nobuhiko; Imamura, Tomoaki

    2016-01-01

    Recent public health concerns regarding commercial food products have increased the need to develop an automated method to detect food product-related health events. We developed and verified a method for the early detection of potentially harmful events caused by commercial food products. We collected data from daily internet-based questionnaires examining the presence or absence of symptoms and information about food purchased by the respondents. Using these data, we developed a method to detect possible health concerns regarding commercialized food products. To achieve this, we combined the signal detection method used in the reporting system of adverse effects of pharmaceutical products and the Early Aberration Reporting System (EARS) used by the United States Centers for Disease Control. Whiteleg shrimp (Litopenaeus vannamei), which had odds ratio and Odds(−) of 8.99 and 4.13, respectively, was identified as a possible causative food product for diarrhea and vomiting. In conclusion, this study demonstrated that food distributors can implement post-marketing monitoring of the safety of food products purchased via the internet. PMID:27231884

  8. Intrusion detection system elements

    International Nuclear Information System (INIS)

    Eaton, M.J.; Mangan, D.L.

    1980-09-01

    This report highlights elements required for an intrusion detection system and discusses problems which can be encountered in attempting to make the elements effective. Topics discussed include: sensors, both for exterior detection and interior detection; alarm assessment systems, with the discussion focused on video assessment; and alarm reporting systems, including alarm communication systems and dislay/console considerations. Guidance on careful planning and design of a new or to-be-improved system is presented

  9. System risk evolution analysis and risk critical event identification based on event sequence diagram

    International Nuclear Information System (INIS)

    Luo, Pengcheng; Hu, Yang

    2013-01-01

    During system operation, the environmental, operational and usage conditions are time-varying, which causes the fluctuations of the system state variables (SSVs). These fluctuations change the accidents’ probabilities and then result in the system risk evolution (SRE). This inherent relation makes it feasible to realize risk control by monitoring the SSVs in real time, herein, the quantitative analysis of SRE is essential. Besides, some events in the process of SRE are critical to system risk, because they act like the “demarcative points” of safety and accident, and this characteristic makes each of them a key point of risk control. Therefore, analysis of SRE and identification of risk critical events (RCEs) are remarkably meaningful to ensure the system to operate safely. In this context, an event sequence diagram (ESD) based method of SRE analysis and the related Monte Carlo solution are presented; RCE and risk sensitive variable (RSV) are defined, and the corresponding identification methods are also proposed. Finally, the proposed approaches are exemplified with an accident scenario of an aircraft getting into the icing region

  10. Targeting safety improvements through identification of incident origination and detection in a near-miss incident learning system

    International Nuclear Information System (INIS)

    Novak, Avrey; Nyflot, Matthew J.; Ermoian, Ralph P.; Jordan, Loucille E.; Sponseller, Patricia A.; Kane, Gabrielle M.; Ford, Eric C.; Zeng, Jing

    2016-01-01

    Purpose: Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity. Methods: From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared. Results: Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically

  11. Targeting safety improvements through identification of incident origination and detection in a near-miss incident learning system

    Energy Technology Data Exchange (ETDEWEB)

    Novak, Avrey; Nyflot, Matthew J.; Ermoian, Ralph P.; Jordan, Loucille E.; Sponseller, Patricia A.; Kane, Gabrielle M.; Ford, Eric C.; Zeng, Jing, E-mail: jzeng13@uw.edu [Department of Radiation Oncology, University of Washington Medical Center, 1959 NE Pacific Street, Campus Box 356043, Seattle, Washington 98195 (United States)

    2016-05-15

    Purpose: Radiation treatment planning involves a complex workflow that has multiple potential points of vulnerability. This study utilizes an incident reporting system to identify the origination and detection points of near-miss errors, in order to guide their departmental safety improvement efforts. Previous studies have examined where errors arise, but not where they are detected or applied a near-miss risk index (NMRI) to gauge severity. Methods: From 3/2012 to 3/2014, 1897 incidents were analyzed from a departmental incident learning system. All incidents were prospectively reviewed weekly by a multidisciplinary team and assigned a NMRI score ranging from 0 to 4 reflecting potential harm to the patient (no potential harm to potential critical harm). Incidents were classified by point of incident origination and detection based on a 103-step workflow. The individual steps were divided among nine broad workflow categories (patient assessment, imaging for radiation therapy (RT) planning, treatment planning, pretreatment plan review, treatment delivery, on-treatment quality management, post-treatment completion, equipment/software quality management, and other). The average NMRI scores of incidents originating or detected within each broad workflow area were calculated. Additionally, out of 103 individual process steps, 35 were classified as safety barriers, the process steps whose primary function is to catch errors. The safety barriers which most frequently detected incidents were identified and analyzed. Finally, the distance between event origination and detection was explored by grouping events by the number of broad workflow area events passed through before detection, and average NMRI scores were compared. Results: Near-miss incidents most commonly originated within treatment planning (33%). However, the incidents with the highest average NMRI scores originated during imaging for RT planning (NMRI = 2.0, average NMRI of all events = 1.5), specifically

  12. In-situ trainable intrusion detection system

    Energy Technology Data Exchange (ETDEWEB)

    Symons, Christopher T.; Beaver, Justin M.; Gillen, Rob; Potok, Thomas E.

    2016-11-15

    A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.

  13. CISN ShakeAlert: Faster Warning Information Through Multiple Threshold Event Detection in the Virtual Seismologist (VS) Early Warning Algorithm

    Science.gov (United States)

    Cua, G. B.; Fischer, M.; Caprio, M.; Heaton, T. H.; Cisn Earthquake Early Warning Project Team

    2010-12-01

    The Virtual Seismologist (VS) earthquake early warning (EEW) algorithm is one of 3 EEW approaches being incorporated into the California Integrated Seismic Network (CISN) ShakeAlert system, a prototype EEW system that could potentially be implemented in California. The VS algorithm, implemented by the Swiss Seismological Service at ETH Zurich, is a Bayesian approach to EEW, wherein the most probable source estimate at any given time is a combination of contributions from a likehihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS codes have been running in real-time at the Southern California Seismic Network since July 2008, and at the Northern California Seismic Network since February 2009. We discuss recent enhancements to the VS EEW algorithm that are being integrated into CISN ShakeAlert. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to initiate an event declaration, with the goal of reducing false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and the requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) into an on-site/regional approach capable of providing a continuously evolving stream of EEW information starting from the first P-detection. Real-time and offline analysis on Swiss and California waveform datasets indicate that the

  14. Detection of systemic hypersensitivity to drugs using standard guinea pig assays.

    Science.gov (United States)

    Weaver, James L; Staten, David; Swann, Joslyn; Armstrong, George; Bates, Melissa; Hastings, Kenneth L

    2003-12-01

    The most commonly used assays designed to detect either skin or systemic immune-based hypersensitivity reactions are those using guinea pigs (GP). We obtained data from various FDA records to evaluate the correlation between GP assay results and reported post-marketing systemic hypersensitivity reactions. We examined the new drug application (NDA) reviews of approved drugs for the results of GP assays. Post-marketing human data were extracted from the FDA adverse event reporting system (AERS). Drug usage data were obtained from a commercial database maintained by IMS Health Inc. We found 83 (21%) of 396 drugs approved between 1978 and 1998 had reported GP test results. Among these 83 drugs, 14 (17%) were found to have positive results in at least one GP assay. Simple reporting index (RI) values for systemic hypersensitivity reactions were calculated from AERS data and usage to produce the index of adverse event reports per million shipping units of drug. A variety of definitions of positive human response were examined. A statistically significant association was seen for rash between post-marketing and clinical trials adverse event reports. No statistically significant associations between human data and GP test results were observed. These data suggest that standard GP assays have limited ability to predict human systemic hypersensitivity potential for pharmaceuticals.

  15. CDC WONDER: Vaccine Adverse Event Reporting System (VAERS)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Vaccine Adverse Event Reporting System (VAERS) online database on CDC WONDER provides counts and percentages of adverse event case reports after vaccination, by...

  16. A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

    Directory of Open Access Journals (Sweden)

    Gang Li

    2015-08-01

    Full Text Available Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness.

  17. Automatic detection of whole night snoring events using non-contact microphone.

    Directory of Open Access Journals (Sweden)

    Eliran Dafna

    Full Text Available OBJECTIVE: Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology. DESIGN: Sounds during polysomnography (PSG were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events. PATIENTS: Sixty-seven subjects (age 52.5 ± 13.5 years, BMI 30.8 ± 4.7 kg/m(2, m/f 40/27 referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects. MEASUREMENTS AND RESULTS: To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental. A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore and specificity of 98.3% (noise as noise. CONCLUSIONS: Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.

  18. Studies on switch-based event building systems in RD13

    International Nuclear Information System (INIS)

    Bee, C.P.; Eshghi, S.; Jones, R.

    1996-01-01

    One of the goals of the RD13 project at CERN is to investigate the feasibility of parallel event building system for detectors at the LHC. Studies were performed by building a prototype based on the HiPPI standard and by modeling this prototype and extended architectures with MODSIM II. The prototype used commercially available VME-HiPPI interfaces and a HiPPI switch together with a modular software. The setup was tested successfully as a parallel event building system in different configurations and with different data flow control schemes. The simulation program was used with realistic parameters from the prototype measurements to simulate large-scale event building systems. This includes simulations of a realistic setup of the ATLAS event building system. The influence of different parameters and scaling behavior were investigated. The influence of realistic event size distributions was checked with data from off-line simulations. Different control schemes for destination assignment and traffic shaping were investigated as well as a two-stage event building system. (author)

  19. RHIC beam permit and quench detection communications system

    International Nuclear Information System (INIS)

    Conkling, C.R. Jr.

    1997-01-01

    A beam permit module has been developed to concentrate RHIC, subsystem sensor outputs, permit beam, and initiate emergency shutdowns. The modules accept inputs from the vacuum, cryogenic, power supply, beam loss, and superconducting magnet quench detection systems. Modules are located at equipment locations around the RHIC ring. The modules are connected by three fiberoptic communications links; a beam permit link, and two magnet power supply interlock links. During operation, carrier presence allows beam. If a RHIC subsystem detects a fault, the beam permit carrier terminates - initiating a beam dump. If the fault was a superconducting magnet quench, a power supply interlock carrier terminates - initiating an emergency magnet power dump. In addition, the master module triggers an event to cause remote sensors to log and hold data at the time-of-failure

  20. Open-Phase Condition Detecting System for Transformers in Nuclear Power Plant

    International Nuclear Information System (INIS)

    Ha, Che-Wung; Lee, Do-Hwan

    2015-01-01

    Recently, several events involving the loss of one of the three phases of the offsite power circuit occurred in the US nuclear power plants (NPPs).. In some cases, the open-phase condition existed undetected for an extended period and in other case, was not properly responded to. Accordingly, the Nuclear Regulatory Commission (NRC) requested all license holders to take corrective actions to address the open-phase condition. It was also requested that all holders or applicant for a standard design certification (DC) include a description of a protection system to detect and separate the open circuit into design control document (DCD). Currently, NPPs including Duke Energy, Exelon, and institutes including Electric Power Research Institute (EPRI) are working together to resolve issues associated with detecting an open-phase condition. This paper, using Electromagnetic Transients Program (EMTP), presents a system to detect and address the loss of one of three phases of the offsite power circuit connected to main, auxiliary and standby transformers, which is hard to be detected in the current protection system. This paper, using EMTP, presents a system to detect and address the loss of one of three phases of the offsite power circuit running to MT, UAT or SAT which is hard to be detected in the current protection system. The system presented in this paper will be useful not only for the KHNP to meet the NRC requirement, but also for nuclear power plants at home and abroad to take corrective actions to provide protection from a single phase open circuit condition for offsite power sources

  1. Open-Phase Condition Detecting System for Transformers in Nuclear Power Plant

    Energy Technology Data Exchange (ETDEWEB)

    Ha, Che-Wung; Lee, Do-Hwan [KHNP Central Research Institute, Daejeon (Korea, Republic of)

    2015-05-15

    Recently, several events involving the loss of one of the three phases of the offsite power circuit occurred in the US nuclear power plants (NPPs).. In some cases, the open-phase condition existed undetected for an extended period and in other case, was not properly responded to. Accordingly, the Nuclear Regulatory Commission (NRC) requested all license holders to take corrective actions to address the open-phase condition. It was also requested that all holders or applicant for a standard design certification (DC) include a description of a protection system to detect and separate the open circuit into design control document (DCD). Currently, NPPs including Duke Energy, Exelon, and institutes including Electric Power Research Institute (EPRI) are working together to resolve issues associated with detecting an open-phase condition. This paper, using Electromagnetic Transients Program (EMTP), presents a system to detect and address the loss of one of three phases of the offsite power circuit connected to main, auxiliary and standby transformers, which is hard to be detected in the current protection system. This paper, using EMTP, presents a system to detect and address the loss of one of three phases of the offsite power circuit running to MT, UAT or SAT which is hard to be detected in the current protection system. The system presented in this paper will be useful not only for the KHNP to meet the NRC requirement, but also for nuclear power plants at home and abroad to take corrective actions to provide protection from a single phase open circuit condition for offsite power sources.

  2. Nucleic acid detection system and method for detecting influenza

    Science.gov (United States)

    Cai, Hong; Song, Jian

    2015-03-17

    The invention provides a rapid, sensitive and specific nucleic acid detection system which utilizes isothermal nucleic acid amplification in combination with a lateral flow chromatographic device, or DNA dipstick, for DNA-hybridization detection. The system of the invention requires no complex instrumentation or electronic hardware, and provides a low cost nucleic acid detection system suitable for highly sensitive pathogen detection. Hybridization to single-stranded DNA amplification products using the system of the invention provides a sensitive and specific means by which assays can be multiplexed for the detection of multiple target sequences.

  3. An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMs

    Institute of Scientific and Technical Information of China (English)

    Ruifeng Xu; Jiannan Hu; Qin Lu; Dongyin Wu; Lin Gui

    2017-01-01

    In this paper,we present a new challenging task for emotion analysis,namely emotion cause extraction.In this task,we focus on the detection of emotion cause a.k.a the reason or the stimulant of an emotion,rather than the regular emotion classification or emotion component extraction.Since there is no open dataset for this task available,we first designed and annotated an emotion cause dataset which follows the scheme of W3C Emotion Markup Language.We then present an emotion cause detection method by using event extraction framework,where a tree structure-based representation method is used to represent the events.Since the distribution of events is imbalanced in the training data,we propose an under-sampling-based bagging algorithm to solve this problem.Even with a limited training set,the proposed approach may still extract sufficient features for analysis by a bagging of multi-kernel based SVMs method.Evaluations show that our approach achieves an F-measure 7.04% higher than the state-of-the-art methods.

  4. Detecting adverse events in surgery: comparing events detected by the Veterans Health Administration Surgical Quality Improvement Program and the Patient Safety Indicators.

    Science.gov (United States)

    Mull, Hillary J; Borzecki, Ann M; Loveland, Susan; Hickson, Kathleen; Chen, Qi; MacDonald, Sally; Shin, Marlena H; Cevasco, Marisa; Itani, Kamal M F; Rosen, Amy K

    2014-04-01

    The Patient Safety Indicators (PSIs) use administrative data to screen for select adverse events (AEs). In this study, VA Surgical Quality Improvement Program (VASQIP) chart review data were used as the gold standard to measure the criterion validity of 5 surgical PSIs. Independent chart review was also used to determine reasons for PSI errors. The sensitivity, specificity, and positive predictive value of PSI software version 4.1a were calculated among Veterans Health Administration hospitalizations (2003-2007) reviewed by VASQIP (n = 268,771). Nurses re-reviewed a sample of hospitalizations for which PSI and VASQIP AE detection disagreed. Sensitivities ranged from 31% to 68%, specificities from 99.1% to 99.8%, and positive predictive values from 31% to 72%. Reviewers found that coding errors accounted for some PSI-VASQIP disagreement; some disagreement was also the result of differences in AE definitions. These results suggest that the PSIs have moderate criterion validity; however, some surgical PSIs detect different AEs than VASQIP. Future research should explore using both methods to evaluate surgical quality. Published by Elsevier Inc.

  5. Multiple-Threshold Event Detection and Other Enhancements to the Virtual Seismologist (VS) Earthquake Early Warning Algorithm

    Science.gov (United States)

    Fischer, M.; Caprio, M.; Cua, G. B.; Heaton, T. H.; Clinton, J. F.; Wiemer, S.

    2009-12-01

    The Virtual Seismologist (VS) algorithm is a Bayesian approach to earthquake early warning (EEW) being implemented by the Swiss Seismological Service at ETH Zurich. The application of Bayes’ theorem in earthquake early warning states that the most probable source estimate at any given time is a combination of contributions from a likelihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS algorithm was one of three EEW algorithms involved in the California Integrated Seismic Network (CISN) real-time EEW testing and performance evaluation effort. Its compelling real-time performance in California over the last three years has led to its inclusion in the new USGS-funded effort to develop key components of CISN ShakeAlert, a prototype EEW system that could potentially be implemented in California. A significant portion of VS code development was supported by the SAFER EEW project in Europe. We discuss recent enhancements to the VS EEW algorithm. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to be declared an event to reduce false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and it requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) to a hybrid on-site/regional approach capable of providing a continuously evolving stream of EEW

  6. Event streaming in the online system

    CERN Document Server

    Klous, S; The ATLAS collaboration

    2010-01-01

    The Large Hadron Collider (LHC), currently in operation at CERN in Geneva, is a circular 27-kilometer-circumference machine, accelerating bunches of protons in opposite directions. The bunches will cross at four different interaction points with a bunch-crossing frequency of 40MHz. ATLAS, the largest LHC experiment, registers the signals induced by particles traversing the detector components on each bunch crossing. When this happens a total of around 1.5MB of data are collected. This results in a data rate of around 60 TB/s flowing out of the detector. Note that the available event storage space is limited to about 6 PB per year. With an operational period of about 20 million seconds per year, this requires a data reduction factor of 200:000 in the trigger and data acquisition (TDAQ) system. Events included in the recording rate budget are already subdivided and organized by ATLAS during data acquisition. So, the TDAQ system does not only take care of data reduction, but also organizes the collected events. ...

  7. Presentation of the results of a Bayesian automatic event detection and localization program to human analysts

    Science.gov (United States)

    Kushida, N.; Kebede, F.; Feitio, P.; Le Bras, R.

    2016-12-01

    The Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) has been developing and testing NET-VISA (Arora et al., 2013), a Bayesian automatic event detection and localization program, and evaluating its performance in a realistic operational mode. In our preliminary testing at the CTBTO, NET-VISA shows better performance than its currently operating automatic localization program. However, given CTBTO's role and its international context, a new technology should be introduced cautiously when it replaces a key piece of the automatic processing. We integrated the results of NET-VISA into the Analyst Review Station, extensively used by the analysts so that they can check the accuracy and robustness of the Bayesian approach. We expect the workload of the analysts to be reduced because of the better performance of NET-VISA in finding missed events and getting a more complete set of stations than the current system which has been operating for nearly twenty years. The results of a series of tests indicate that the expectations born from the automatic tests, which show an overall overlap improvement of 11%, meaning that the missed events rate is cut by 42%, hold for the integrated interactive module as well. New events are found by analysts, which qualify for the CTBTO Reviewed Event Bulletin, beyond the ones analyzed through the standard procedures. Arora, N., Russell, S., and Sudderth, E., NET-VISA: Network Processing Vertically Integrated Seismic Analysis, 2013, Bull. Seismol. Soc. Am., 103, 709-729.

  8. Portable modular detection system

    Science.gov (United States)

    Brennan, James S [Rodeo, CA; Singh, Anup [Danville, CA; Throckmorton, Daniel J [Tracy, CA; Stamps, James F [Livermore, CA

    2009-10-13

    Disclosed herein are portable and modular detection devices and systems for detecting electromagnetic radiation, such as fluorescence, from an analyte which comprises at least one optical element removably attached to at least one alignment rail. Also disclosed are modular detection devices and systems having an integrated lock-in amplifier and spatial filter and assay methods using the portable and modular detection devices.

  9. Fluorescence Sensors for Early Detection of Nitrification in Drinking Water Distribution Systems - Interference Corrections and Feasibility Assessment

    Science.gov (United States)

    Do, T. D.; Pifer, A.; Chowdhury, Z.; Wahman, D.; Zhang, W.; Fairey, J.

    2017-12-01

    Detection of nitrification events in chloraminated drinking water distribution systems remains an ongoing challenge for many drinking water utilities, including Dallas Water Utilities (DWU) and the City of Houston (CoH). Each year, these utilities experience nitrification events that necessitate extensive flushing, resulting in the loss of billions of gallons of finished water. Biological techniques used to quantify the activity of nitrifying bacteria are impractical for real-time monitoring because they require significant laboratory efforts and/or lengthy incubation times. At present, DWU and CoH regularly rely on physicochemical parameters including total chlorine and monochloramine residual, and free ammonia, nitrite, and nitrate as indicators of nitrification, but these metrics lack specificity to nitrifying bacteria. To improve detection of nitrification in chloraminated drinking water distribution systems, we seek to develop a real-time fluorescence-based sensor system to detect the early onset of nitrification events by measuring the fluorescence of soluble microbial products (SMPs) specific to nitrifying bacteria. Preliminary data indicates that fluorescence-based metrics have the sensitivity to detect these SMPs in the early stages of nitrification, but several remaining challenges will be explored in this presentation. We will focus on benchtop and sensor results from ongoing batch and annular reactor experiments designed to (1) identify fluorescence wavelength pairs and data processing techniques suitable for measurement of SMPs from nitrification and (2) assess and correct potential interferences, such as those from monochloramine, pH, iron, nitrite, nitrate and humic substances. This work will serve as the basis for developing fluorescence sensor packages for full-scale testing and validation in the DWU and CoH systems. Findings from this research could be leveraged to identify nitrification events in their early stages, facilitating proactive

  10. Radionuclide data analysis in connection of DPRK event in May 2009

    Science.gov (United States)

    Nikkinen, Mika; Becker, Andreas; Zähringer, Matthias; Polphong, Pornsri; Pires, Carla; Assef, Thierry; Han, Dongmei

    2010-05-01

    The seismic event detected in DPRK on 25.5.2009 was triggering a series of actions within CTBTO/PTS to ensure its preparedness to detect any radionuclide emissions possibly linked with the event. Despite meticulous work to detect and verify, traces linked to the DPRK event were not found. After three weeks of high alert the PTS resumed back to normal operational routine. This case illuminates the importance of objectivity and procedural approach in the data evaluation. All the data coming from particulate and noble gas stations were evaluated daily, some of the samples even outside of office hours and during the weekends. Standard procedures were used to determine the network detection thresholds of the key (CTBT relevant) radionuclides achieved across the DPRK event area and for the assessment of radionuclides typically occurring at IMS stations (background history). Noble gas system has sometimes detections that are typical for the sites due to legitimate non-nuclear test related activities. Therefore, set of hypothesis were used to see if the detection is consistent with event time and location through atmospheric transport modelling. Also the consistency of event timing and isotopic ratios was used in the evaluation work. As a result it was concluded that if even 1/1000 of noble gasses from a nuclear detonation would had leaked, the IMS system would not had problems to detect it. This case also showed the importance of on-site inspections to verify the nuclear traces of possible tests.

  11. Leveraging KVM Events to Detect Cache-Based Side Channel Attacks in a Virtualization Environment

    Directory of Open Access Journals (Sweden)

    Ady Wahyudi Paundu

    2018-01-01

    Full Text Available Cache-based side channel attack (CSCa techniques in virtualization systems are becoming more advanced, while defense methods against them are still perceived as nonpractical. The most recent CSCa variant called Flush + Flush has showed that the current detection methods can be easily bypassed. Within this work, we introduce a novel monitoring approach to detect CSCa operations inside a virtualization environment. We utilize the Kernel Virtual Machine (KVM event data in the kernel and process this data using a machine learning technique to identify any CSCa operation in the guest Virtual Machine (VM. We evaluate our approach using Receiver Operating Characteristic (ROC diagram of multiple attack and benign operation scenarios. Our method successfully separate the CSCa datasets from the non-CSCa datasets, on both trained and nontrained data scenarios. The successful classification also include the Flush + Flush attack scenario. We are also able to explain the classification results by extracting the set of most important features that separate both classes using their Fisher scores and show that our monitoring approach can work to detect CSCa in general. Finally, we evaluate the overhead impact of our CSCa monitoring method and show that it has a negligible computation overhead on the host and the guest VM.

  12. Performance and system flexibility of the CDF Hardware Event Builder

    Energy Technology Data Exchange (ETDEWEB)

    Shaw, T.M.; Schurecht, K. (Fermi National Accelerator Lab., Batavia, IL (United States)); Sinervo, P. (Toronto Univ., ON (Canada). Dept. of Physics)

    1991-11-01

    The CDF Hardware Event Builder (1) is a flexible system which is built from a combination of three different 68020-based single width Fastbus modules. The system may contain as few as three boards or as many as fifteen, depending on the specific application. Functionally, the boards receive a command to read out the raw event data from a set of Fastbus based data buffers ( scanners''), reformat data and then write the data to a Level 3 trigger/processing farm which will decide to throw the event away or to write it to tape. The data acquisition system at CDF will utilize two nine board systems which will allow an event rate of up to 35 Hz into the Level 3 trigger. This paper will present detailed performance factors, system and individual board architecture, and possible system configurations.

  13. ATLAS EventIndex General Dataflow and Monitoring Infrastructure

    CERN Document Server

    Barberis, Dario; The ATLAS collaboration

    2016-01-01

    The ATLAS EventIndex has been running in production since mid-2015, reliably collecting information worldwide about all produced events and storing them in a central Hadoop infrastructure at CERN. A subset of this information is copied to an Oracle relational database for fast datasets discovery, event-picking, crosschecks with other ATLAS systems and checks for event duplication. The system design and its optimization is serving event picking from requests of a few events up to scales of tens of thousand of events, and in addition, data consistency checks are performed for large production campaigns. Detecting duplicate events with a scope of physics collections has recently arisen as an important use case. This paper describes the general architecture of the project and the data flow and operation issues, which are addressed by recent developments to improve the throughput of the overall system. In this direction, the data collection system is reducing the usage of the messaging infrastructure to overcome t...

  14. ATLAS EventIndex general dataflow and monitoring infrastructure

    CERN Document Server

    AUTHOR|(SzGeCERN)638886; The ATLAS collaboration; Barberis, Dario; Favareto, Andrea; Garcia Montoro, Carlos; Gonzalez de la Hoz, Santiago; Hrivnac, Julius; Prokoshin, Fedor; Salt, Jose; Sanchez, Javier; Toebbicke, Rainer; Yuan, Ruijun

    2017-01-01

    The ATLAS EventIndex has been running in production since mid-2015, reliably collecting information worldwide about all produced events and storing them in a central Hadoop infrastructure at CERN. A subset of this information is copied to an Oracle relational database for fast dataset discovery, event-picking, crosschecks with other ATLAS systems and checks for event duplication. The system design and its optimization is serving event picking from requests of a few events up to scales of tens of thousand of events, and in addition, data consistency checks are performed for large production campaigns. Detecting duplicate events with a scope of physics collections has recently arisen as an important use case. This paper describes the general architecture of the project and the data flow and operation issues, which are addressed by recent developments to improve the throughput of the overall system. In this direction, the data collection system is reducing the usage of the messaging infrastructure to overcome th...

  15. Microcontroller based driver alertness detection systems to detect drowsiness

    Science.gov (United States)

    Adenin, Hasibah; Zahari, Rahimi; Lim, Tiong Hoo

    2018-04-01

    The advancement of embedded system for detecting and preventing drowsiness in a vehicle is a major challenge for road traffic accident systems. To prevent drowsiness while driving, it is necessary to have an alert system that can detect a decline in driver concentration and send a signal to the driver. Studies have shown that traffc accidents usually occur when the driver is distracted while driving. In this paper, we have reviewed a number of detection systems to monitor the concentration of a car driver and propose a portable Driver Alertness Detection System (DADS) to determine the level of concentration of the driver based on pixelated coloration detection technique using facial recognition. A portable camera will be placed at the front visor to capture facial expression and the eye activities. We evaluate DADS using 26 participants and have achieved 100% detection rate with good lighting condition and a low detection rate at night.

  16. 123I-MIBG imaging detects cardiac involvement and predicts cardiac events in Churg-Strauss syndrome

    International Nuclear Information System (INIS)

    Horiguchi, Yoriko; Morita, Yukiko; Tsurikisawa, Naomi; Akiyama, Kazuo

    2011-01-01

    In Churg-Strauss syndrome (CSS) it is important to detect cardiac involvement, which predicts poor prognosis. This study evaluated whether 123 I-metaiodobenzylguanidine (MIBG) scintigraphy could detect cardiac damage and predict cardiac events in CSS. 123 I-MIBG scintigraphy was performed in 28 patients with CSS, 12 of whom had cardiac involvement. The early and delayed heart to mediastinum ratio (early H/M and delayed H/M) and washout rate were calculated by using 123 I-MIBG scintigraphy and compared with those in control subjects. Early H/M and delayed H/M were significantly lower and the washout rate was significantly higher in patients with cardiac involvement than in those without and in controls (early H/M, p = 0.0024, p = 0.0001; delayed H/M, p = 0.0002, p = 0.0001; washout rate, p = 0.0012, p = 0.0052 vs those without and vs controls, respectively). Accuracy for detecting cardiac involvement was 86% for delayed H/M and washout rate and 79% for early H/M and B-type natriuretic peptide (BNP). Kaplan-Meier analysis showed significantly lower cardiac event-free rates in patients with early H/M ≤ 2.18 and BNP > 21.8 pg/ml than those with early H/M > 2.18 and BNP ≤ 21.8 pg/ml (log-rank test p = 0.006). Cardiac sympathetic nerve function was damaged in CSS patients with cardiac involvement. 123 I-MIBG scintigraphy was useful in detecting cardiac involvement and in predicting cardiac events. (orig.)

  17. Corrective actions to gas accumulation in safety injection system pipings of PWRs and gas void detection method

    International Nuclear Information System (INIS)

    Maki, Nobuo

    2000-01-01

    In the US, gas accumulation events of safety injection systems of PWRs during plant operation are continuously reported. As the events may result in loss of safety function, the USNRC is alerting licensees by Information Notices. The cause of the events is coolant leakage to interfacing systems with lower pressure, or gas dissolution of primary coolant by partial pressure drop. In this study, it was clarified by the evaluation of the cause of the events of US plants, gas accumulation in piping between an accumulator and Residual Heat Removal System should be quantitatively investigated regarding Japanese plants. Also, effectiveness of ultrasonic testing which is used for monthly gas accumulation surveillance in US plants was demonstrated using a model loop. In addition, the method was confirmed applicable by an experiment carried out at INSS to detect cavitation voids in piping systems. (author)

  18. A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

    Science.gov (United States)

    Chu, Catherine J; Chan, Arthur; Song, Dan; Staley, Kevin J; Stufflebeam, Steven M; Kramer, Mark A

    2017-02-01

    High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Consideration on developing of leaked inflammable gas detection system for HTGR hydrogen production system

    International Nuclear Information System (INIS)

    Nishihara, Tetsuo; Nakamura, Masashi

    1999-09-01

    One of most important safety design issues for High Temperature Gas-cooled Reactor (HTGR) - Hydrogen Production System (HTGR-HPS) is to ensure reactor safety against fire and explosion at the hydrogen production plant. The inflammable gas mixture in the HTGR-HPS does not use oxygen in any condition and are kept in high pressure in the normal operation. The piping system and/or heat transfer tubes which have the potential possibility of combustible materials ingress into the Reactor Building (R/B) due to the failure are designed to prevent the failure against any events. Then, it is not necessary to consider their self-combustion in vessels nor leakage in the R/B. The only one case which we must consider is the ex-building fire or explosion caused by their leakage from piping or vessel. And it is important to mitigate their effects by means of early detection of gas leakage. We investigated our domestic standards on gas detection, applications of gas detectors, their detection principles, performance, sensitivity, reliability, their technical trends, and so on. We proposed three gas detection systems which may be applied in HTGR-HPS. The first one is the universal solid sensor system; it may be applied when there is no necessity to request their safety credits. The second is the combination of the improved solid sensor system and enhanced beam detector system; it may be applied when it is necessary to request their safety credit. And the third is the combination of the universal solid sensor system and the existing beam detector system; it may be applied when the plant owner request higher detector sensitivity than usual, from the view point of public acceptance, though there is not necessity to request their safety credits. To reduce the plant cost by refusing of safety credits to the gas leakage detection system, we proposed that the equipment required to isolate from others should be installed in the inertrized compartments. (author)

  20. Balloon-Borne Infrasound Detection of Energetic Bolide Events

    Science.gov (United States)

    Young, Eliot F.; Ballard, Courtney; Klein, Viliam; Bowman, Daniel; Boslough, Mark

    2016-10-01

    Infrasound is usually defined as sound waves below 20 Hz, the nominal limit of human hearing. Infrasound waves propagate over vast distances through the Earth's atmosphere: the CTBTO (Comprehensive Nuclear-Test-Ban Treaty Organization) has 48 installed infrasound-sensing stations around the world to detect nuclear detonations and other disturbances. In February 2013, several CTBTO infrasound stations detected infrasound signals from a large bolide that exploded over Chelyabinsk, Russia. Some stations recorded signals that had circumnavigated the Earth, over a day after the original event. The goal of this project is to improve upon the sensitivity of the CTBTO network by putting microphones on small, long-duration super-pressure balloons, with the overarching goal of studying the small end of the NEO population by using the Earth's atmosphere as a witness plate.A balloon-borne infrasound sensor is expected to have two advantages over ground-based stations: a lack of wind noise and a concentration of infrasound energy in the "stratospheric duct" between roughly 5 - 50 km altitude. To test these advantages, we have built a small balloon payload with five calibrated microphones. We plan to fly this payload on a NASA high-altitude balloon from Ft Sumner, NM in August 2016. We have arranged for three large explosions to take place in Socorro, NM while the balloon is aloft to assess the sensitivity of balloon-borne vs. ground-based infrasound sensors. We will report on the results from this test flight and the prospects for detecting/characterizing small bolides in the stratosphere.

  1. Lessons derived from two high-frequency sea level events in the Atlantic: implications for coastal risk analysis and tsunami detection

    Directory of Open Access Journals (Sweden)

    Begoña Pérez-Gómez

    2016-11-01

    Full Text Available The upgrade and enhancement of sea level networks worldwide for integration in sea level hazard warning systems have significantly increased the possibilities for measuring and analyzing high frequency sea level oscillations, with typical periods ranging from a few minutes to a few hours. Many tide gauges now afford 1 min or more frequent sampling and have shown such events to be a common occurrence. Their origins and spatial distribution are diverse and must be well understood in order to correctly design and interpret, for example, the automatic detection algorithms used by tsunami warning centers. Two events recorded recently in European Atlantic waters are analyzed here: possible wave-induced seiches that occurred along the North coast of Spain during the storms of January and February of 2014, and oscillations detected after an earthquake in the mid-Atlantic the 13th of February of 2015. The former caused significant flooding in towns and villages and a huge increase in wave-induced coastal damage that was reported in the media for weeks. The second was a smaller signal present in several tide gauges along the Atlantic coast that, that coincided with the occurrence of this earthquake, leading to a debate on the potential detection of a very small tsunami and how it might yield significant information for tsunami wave modelers and for the development of tsunami detection software. These kinds of events inform us about the limitations of automatic algorithms for tsunami warning and help to improve the information provided to tsunami warning centers, whilst also emphasizing the importance of other forcings in generating extreme sea levels and their associated potential for causing damage to infrastructure.

  2. Optimizing Systems of Threshold Detection Sensors

    National Research Council Canada - National Science Library

    Banschbach, David C

    2008-01-01

    .... Below the threshold all signals are ignored. We develop a mathematical model for setting individual sensor thresholds to obtain optimal probability of detecting a significant event, given a limit on the total number of false positives allowed...

  3. Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

    Science.gov (United States)

    Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

    2015-08-01

    In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

  4. Detection of Healthcare-Related Extended-Spectrum Beta-Lactamase-Producing Escherichia coli Transmission Events Using Combined Genetic and Phenotypic Epidemiology.

    Directory of Open Access Journals (Sweden)

    Anne F Voor In 't Holt

    Full Text Available Since the year 2000 there has been a sharp increase in the prevalence of healthcare-related infections caused by extended-spectrum beta-lactamase (ESBL-producing Escherichia coli. However, the high community prevalence of ESBL-producing E. coli isolates means that many E. coli typing techniques may not be suitable for detecting E. coli transmission events. Therefore, we investigated if High-throughput MultiLocus Sequence Typing (HiMLST and/or Raman spectroscopy were suitable techniques for detecting recent E. coli transmission events.This study was conducted from January until December 2010 at Erasmus University Medical Center, Rotterdam, the Netherlands. Isolates were typed using HiMLST and Raman spectroscopy. A genetic cluster was defined as two or more patients carrying identical isolates. We used predefined definitions for epidemiological relatedness to assess healthcare-related transmission.We included 194 patients; strains of 112 patients were typed using HiMLST and strains of 194 patients were typed using Raman spectroscopy. Raman spectroscopy identified 16 clusters while HiMLST identified 10 clusters. However, no healthcare-related transmission events were detected. When combining data from both typing techniques, we identified eight clusters (n = 34 patients, as well as 78 patients with a non-cluster isolate. However, we could not detect any healthcare-related transmission in these 8 clusters.Although clusters were genetically detected using HiMLST and Raman spectroscopy, no definite epidemiological relationships could be demonstrated which makes the possibility of healthcare-related transmission events highly unlikely. Our results suggest that typing of ESBL-producing E. coli using HiMLST and/or Raman spectroscopy is not helpful in detecting E. coli healthcare-related transmission events.

  5. Prescription-event monitoring: developments in signal detection.

    Science.gov (United States)

    Ferreira, Germano

    2007-01-01

    Prescription-event monitoring (PEM) is a non-interventional intensive method for post-marketing drug safety monitoring of newly licensed medicines. PEM studies are cohort studies where exposure is obtained from a centralised service and outcomes from simple questionnaires completed by general practitioners. Follow-up forms are sent for selected events. Because PEM captures all events and not only the suspected adverse drug reactions, PEM cohorts potentially differ in respect to the distribution of number of events per person depending on the nature of the drug under study. This variance can be related either with the condition for which the drug is prescribed (e.g. a condition causing high morbidity will have, in average, a higher number of events per person compared with a condition with lower morbidity) or with the drug effect itself. This paper describes an exploratory investigation of the distortion caused by product-related variations of the number of events to the interpretation of the proportional reporting ratio (PRR) values ("the higher the PRR, the greater the strength of the signal") computed using drug-cohort data. We studied this effect by assessing the agreement between the PRR based on events (event of interest vs all other events) and PRR based on cases (cases with the event of interest vs cases with any other events). PRR were calculated for all combinations reported to ten selected drugs against a comparator of 81 other drugs. Three of the ten drugs had a cohort with an apparent higher proportion of patients with lower number of events. The PRRs based on events were systematically higher than the PRR based on cases for the combinations reported to these three drugs. Additionally, when applying the threshold criteria for signal screening (n > or =3, PRR > or =1.5 and Chi-squared > or =4), the binary agreement was generally high but apparently lower for these three drugs. In conclusion, the distribution of events per patient in drug cohorts shall be

  6. Optimized Swinging Door Algorithm for Wind Power Ramp Event Detection: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Mingjian; Zhang, Jie; Florita, Anthony R.; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang

    2015-08-06

    Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA.

  7. CESAS: Computerized event sequence abstracting system outlines and applications

    International Nuclear Information System (INIS)

    Watanabe, N.; Kobayashi, K.; Fujiki, K.

    1990-01-01

    For the purpose of efficient utilization of the safety-related event information on the nuclear power plants, a new computer software package CESAS has been under development. CESAS is to systematically abstract the event sequence, that is a series of sequential and causal relationships between occurrences, from the event description written in natural language of English. This system is designed to be based on the knowledge engineering technique utilized in the field of natural language processing. The analytical process in this system consists of morphemic, syntactic, semantic, and syntagmatic analyses. At this moment, the first version of CESAS has been developed and applied to several real event descriptions for studying its feasibility. This paper describes the outlines of CESAS and one of analytical results in comparison with a manually-extracted event sequence

  8. The event notification and alarm system for the Open Science Grid operations center

    Science.gov (United States)

    Hayashi, S.; Teige and, S.; Quick, R.

    2012-12-01

    The Open Science Grid Operations (OSG) Team operates a distributed set of services and tools that enable the utilization of the OSG by several HEP projects. Without these services users of the OSG would not be able to run jobs, locate resources, obtain information about the status of systems or generally use the OSG. For this reason these services must be highly available. This paper describes the automated monitoring and notification systems used to diagnose and report problems. Described here are the means used by OSG Operations to monitor systems such as physical facilities, network operations, server health, service availability and software error events. Once detected, an error condition generates a message sent to, for example, Email, SMS, Twitter, an Instant Message Server, etc. The mechanism being developed to integrate these monitoring systems into a prioritized and configurable alarming system is emphasized.

  9. The event notification and alarm system for the Open Science Grid operations center

    Energy Technology Data Exchange (ETDEWEB)

    Hayashi, S; Teige and, S; Quick, R [Indiana University, University Information Technology Services (United States)

    2012-12-13

    The Open Science Grid Operations (OSG) Team operates a distributed set of services and tools that enable the utilization of the OSG by several HEP projects. Without these services users of the OSG would not be able to run jobs, locate resources, obtain information about the status of systems or generally use the OSG. For this reason these services must be highly available. This paper describes the automated monitoring and notification systems used to diagnose and report problems. Described here are the means used by OSG Operations to monitor systems such as physical facilities, network operations, server health, service availability and software error events. Once detected, an error condition generates a message sent to, for example, Email, SMS, Twitter, an Instant Message Server, etc. The mechanism being developed to integrate these monitoring systems into a prioritized and configurable alarming system is emphasized.

  10. The event notification and alarm system for the Open Science Grid operations center

    International Nuclear Information System (INIS)

    Hayashi, S; Teige and, S; Quick, R

    2012-01-01

    The Open Science Grid Operations (OSG) Team operates a distributed set of services and tools that enable the utilization of the OSG by several HEP projects. Without these services users of the OSG would not be able to run jobs, locate resources, obtain information about the status of systems or generally use the OSG. For this reason these services must be highly available. This paper describes the automated monitoring and notification systems used to diagnose and report problems. Described here are the means used by OSG Operations to monitor systems such as physical facilities, network operations, server health, service availability and software error events. Once detected, an error condition generates a message sent to, for example, Email, SMS, Twitter, an Instant Message Server, etc. The mechanism being developed to integrate these monitoring systems into a prioritized and configurable alarming system is emphasized.

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

    KAUST Repository

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

    2015-01-01

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

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

    KAUST Repository

    Kadri, Farid

    2015-10-22

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

  13. Event tree analysis for the system of hybrid reactor

    International Nuclear Information System (INIS)

    Yang Yongwei; Qiu Lijian

    1993-01-01

    The application of probabilistic risk assessment for fusion-fission hybrid reactor is introduced. A hybrid reactor system has been analysed using event trees. According to the character of the conceptual design of Hefei Fusion-fission Experimental Hybrid Breeding Reactor, the probabilities of the event tree series induced by 4 typical initiating events were calculated. The results showed that the conceptual design is safe and reasonable. through this paper, the safety character of hybrid reactor system has been understood more deeply. Some suggestions valuable to safety design for hybrid reactor have been proposed

  14. Detection of Traveling Ionospheric Disturbances (TIDs) from various man-made sources using Global Navigation Satellite System (GNSS)

    Science.gov (United States)

    Helmboldt, J.; Park, J.; von Frese, R. R. B.; Grejner-Brzezinska, D. A.

    2016-12-01

    Traveling ionospheric disturbance (TID) is generated by various sources and detectable by observing the spatial and temporal change of electron contents in the ionosphere. This study focused on detecting and analyzing TIDs generated by acoustic-gravity waves from man-made events including underground nuclear explosions (UNEs), mine collapses, mine blasts, and large chemical explosions (LCEs) using Global Navigation Satellite System (GNSS). In this study we selected different types of events for case study which covers two US and three North Korean UNEs, two large US mine collapses, three large US mine blasts, and a LCE in northern China and a second LCE at the Nevada Test Site. In most cases, we successfully detected the TIDs as array signatures from the multiple nearby GNSS stations. The array-based TID signatures from these studies were found to yield event-appropriate TID propagation speeds ranging from about a few hundred m/s to roughly a km/s. In addition, the event TID waveforms, and propagation angles and directions were established. The TID waveforms and the maximum angle between each event and the IPP of its TID with the longest travel distance from the source may help differentiate UNEs and LCEs, but the uneven distributions of the observing GNSS stations complicates these results. Thus, further analysis is required of the utility of the apertures of event signatures in the ionosphere for discriminating these events. In general, the results of this study show the potential utility of GNSS observations for detecting and mapping the ionospheric signatures of large-energy anthropological explosions and subsurface collapses.

  15. Smart sensor systems for outdoor intrusion detection

    International Nuclear Information System (INIS)

    Lynn, J.K.

    1988-01-01

    A major improvement in outdoor perimeter security system probability of detection (PD) and reduction in false alarm rate (FAR) and nuisance alarm rate (NAR) may be obtained by analyzing the indications immediately preceding an event which might be interpreted as an intrusion. Existing systems go into alarm after crossing a threshold. Very slow changes, which accumulate until the threshold is reached, may be assessed falsely as an intrusion. A hierarchial program has begun at Stellar to develop a modular, expandable Smart Sensor system which may be interfaced to most types of sensor and alarm reporting systems. A major upgrade to the SSI Test Site is in progress so that intrusions may be simulated in a controlled and repeatable manner. A test platform is being constructed which will operate in conduction with a mobile instrumentation center with CCTVB, lighting control, weather and data monitoring and remote control of the test platform and intrusion simulators. Additional testing was contracted with an independent test facility to assess the effects of severe winter weather conditions

  16. Design a Learning-Oriented Fall Event Reporting System Based on Kirkpatrick Model.

    Science.gov (United States)

    Zhou, Sicheng; Kang, Hong; Gong, Yang

    2017-01-01

    Patient fall has been a severe problem in healthcare facilities around the world due to its prevalence and cost. Routine fall prevention training programs are not as effective as expected. Using event reporting systems is the trend for reducing patient safety events such as falls, although some limitations of the systems exist at current stage. We summarized these limitations through literature review, and developed an improved web-based fall event reporting system. The Kirkpatrick model, widely used in the business area for training program evaluation, has been integrated during the design of our system. Different from traditional event reporting systems that only collect and store the reports, our system automatically annotates and analyzes the reported events, and provides users with timely knowledge support specific to the reported event. The paper illustrates the design of our system and how its features are intended to reduce patient falls by learning from previous errors.

  17. Interior intrusion detection systems

    Energy Technology Data Exchange (ETDEWEB)

    Rodriguez, J.R.; Matter, J.C. (Sandia National Labs., Albuquerque, NM (United States)); Dry, B. (BE, Inc., Barnwell, SC (United States))

    1991-10-01

    The purpose of this NUREG is to present technical information that should be useful to NRC licensees in designing interior intrusion detection systems. Interior intrusion sensors are discussed according to their primary application: boundary-penetration detection, volumetric detection, and point protection. Information necessary for implementation of an effective interior intrusion detection system is presented, including principles of operation, performance characteristics and guidelines for design, procurement, installation, testing, and maintenance. A glossary of sensor data terms is included. 36 figs., 6 tabs.

  18. Interior intrusion detection systems

    International Nuclear Information System (INIS)

    Rodriguez, J.R.; Matter, J.C.; Dry, B.

    1991-10-01

    The purpose of this NUREG is to present technical information that should be useful to NRC licensees in designing interior intrusion detection systems. Interior intrusion sensors are discussed according to their primary application: boundary-penetration detection, volumetric detection, and point protection. Information necessary for implementation of an effective interior intrusion detection system is presented, including principles of operation, performance characteristics and guidelines for design, procurement, installation, testing, and maintenance. A glossary of sensor data terms is included. 36 figs., 6 tabs

  19. Asynchronous sampled-data approach for event-triggered systems

    Science.gov (United States)

    Mahmoud, Magdi S.; Memon, Azhar M.

    2017-11-01

    While aperiodically triggered network control systems save a considerable amount of communication bandwidth, they also pose challenges such as coupling between control and event-condition design, optimisation of the available resources such as control, communication and computation power, and time-delays due to computation and communication network. With this motivation, the paper presents separate designs of control and event-triggering mechanism, thus simplifying the overall analysis, asynchronous linear quadratic Gaussian controller which tackles delays and aperiodic nature of transmissions, and a novel event mechanism which compares the cost of the aperiodic system against a reference periodic implementation. The proposed scheme is simulated on a linearised wind turbine model for pitch angle control and the results show significant improvement against the periodic counterpart.

  20. Rule-Based Event Processing and Reaction Rules

    Science.gov (United States)

    Paschke, Adrian; Kozlenkov, Alexander

    Reaction rules and event processing technologies play a key role in making business and IT / Internet infrastructures more agile and active. While event processing is concerned with detecting events from large event clouds or streams in almost real-time, reaction rules are concerned with the invocation of actions in response to events and actionable situations. They state the conditions under which actions must be taken. In the last decades various reaction rule and event processing approaches have been developed, which for the most part have been advanced separately. In this paper we survey reaction rule approaches and rule-based event processing systems and languages.

  1. Single particle detecting telescope system

    International Nuclear Information System (INIS)

    Yamamoto, I.; Tomiyama, T.; Iga, Y.; Komatsubara, T.; Kanada, M.; Yamashita, Y.; Wada, T.; Furukawa, S.

    1981-01-01

    We constructed the single particle detecting telescope system for detecting a fractionally charged particle. The telescope consists of position detecting counters, wall-less multi-cell chambers, single detecting circuits and microcomputer system as data I/0 processor. Especially, a frequency of double particle is compared the case of the single particle detecting with the case of an ordinary measurement

  2. Event detection in athletics for personalized sports content delivery

    DEFF Research Database (Denmark)

    Katsarakis, N.; Pnevmatikakis, A.

    2009-01-01

    Broadcasting of athletics is nowadays biased towards running (sprint and longer distances) sports. Personalized content delivery can change that for users that wish to focus on different content. Using a combination of video signal processing algorithms and live information that accompanies the v...... algorithms needed for the extraction of the events that trigger both between and within sport camera selection, and describes a system that handles user preferences, live information andvideo-generated events to offer personalized content to the users.......Broadcasting of athletics is nowadays biased towards running (sprint and longer distances) sports. Personalized content delivery can change that for users that wish to focus on different content. Using a combination of video signal processing algorithms and live information that accompanies...... the video of large-scale sports like the Olympics, a system can attend to the preferences of users by selecting the most suitable camera view for them.There are two types of camera selection for personalized content delivery. According to the between sport camera selection, the view is changed between two...

  3. Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge Into Time-Frequency Analysis.

    Science.gov (United States)

    Khandelwal, Siddhartha; Wickstrom, Nicholas

    2016-12-01

    Detecting gait events is the key to many gait analysis applications that would benefit from continuous monitoring or long-term analysis. Most gait event detection algorithms using wearable sensors that offer a potential for use in daily living have been developed from data collected in controlled indoor experiments. However, for real-word applications, it is essential that the analysis is carried out in humans' natural environment; that involves different gait speeds, changing walking terrains, varying surface inclinations and regular turns among other factors. Existing domain knowledge in the form of principles or underlying fundamental gait relationships can be utilized to drive and support the data analysis in order to develop robust algorithms that can tackle real-world challenges in gait analysis. This paper presents a novel approach that exhibits how domain knowledge about human gait can be incorporated into time-frequency analysis to detect gait events from long-term accelerometer signals. The accuracy and robustness of the proposed algorithm are validated by experiments done in indoor and outdoor environments with approximately 93 600 gait events in total. The proposed algorithm exhibits consistently high performance scores across all datasets in both, indoor and outdoor environments.

  4. Architecture design of the multi-functional wavelet-based ECG microprocessor for realtime detection of abnormal cardiac events.

    Science.gov (United States)

    Cheng, Li-Fang; Chen, Tung-Chien; Chen, Liang-Gee

    2012-01-01

    Most of the abnormal cardiac events such as myocardial ischemia, acute myocardial infarction (AMI) and fatal arrhythmia can be diagnosed through continuous electrocardiogram (ECG) analysis. According to recent clinical research, early detection and alarming of such cardiac events can reduce the time delay to the hospital, and the clinical outcomes of these individuals can be greatly improved. Therefore, it would be helpful if there is a long-term ECG monitoring system with the ability to identify abnormal cardiac events and provide realtime warning for the users. The combination of the wireless body area sensor network (BASN) and the on-sensor ECG processor is a possible solution for this application. In this paper, we aim to design and implement a digital signal processor that is suitable for continuous ECG monitoring and alarming based on the continuous wavelet transform (CWT) through the proposed architectures--using both programmable RISC processor and application specific integrated circuits (ASIC) for performance optimization. According to the implementation results, the power consumption of the proposed processor integrated with an ASIC for CWT computation is only 79.4 mW. Compared with the single-RISC processor, about 91.6% of the power reduction is achieved.

  5. Rapid deployment intrusion detection system

    International Nuclear Information System (INIS)

    Graham, R.H.

    1997-01-01

    A rapidly deployable security system is one that provides intrusion detection, assessment, communications, and annunciation capabilities; is easy to install and configure; can be rapidly deployed, and is reusable. A rapidly deployable intrusion detection system (RADIDS) has many potential applications within the DOE Complex: back-up protection for failed zones in a perimeter intrusion detection and assessment system, intrusion detection and assessment capabilities in temporary locations, protection of assets during Complex reconfiguration, and protection in hazardous locations, protection of assets during Complex reconfiguration, and protection in hazardous locations. Many DOE user-need documents have indicated an interest in a rapidly deployable intrusion detection system. The purpose of the RADIDS project is to design, develop, and implement such a system. 2 figs

  6. Fusion events

    International Nuclear Information System (INIS)

    Aboufirassi, M; Angelique, J.C.; Bizard, G.; Bougault, R.; Brou, R.; Buta, A.; Colin, J.; Cussol, D.; Durand, D.; Genoux-Lubain, A.; Horn, D.; Kerambrun, A.; Laville, J.L.; Le Brun, C.; Lecolley, J.F.; Lefebvres, F.; Lopez, O.; Louvel, M.; Meslin, C.; Metivier, V.; Nakagawa, T.; Peter, J.; Popescu, R.; Regimbart, R.; Steckmeyer, J.C.; Tamain, B.; Vient, E.; Wieloch, A.; Yuasa-Nakagawa, K.

    1998-01-01

    The fusion reactions between low energy heavy ions have a very high cross section. First measurements at energies around 30-40 MeV/nucleon indicated no residue of either complete or incomplete fusion, thus demonstrating the disappearance of this process. This is explained as being due to the high amount o energies transferred to the nucleus, what leads to its total dislocation in light fragments and particles. Exclusive analyses have permitted to mark clearly the presence of fusion processes in heavy systems at energies above 30-40 MeV/nucleon. Among the complete events of the Kr + Au reaction at 60 MeV/nucleon the majority correspond to binary collisions. Nevertheless, for the most considerable energy losses, a class of events do occur for which the detected fragments appears to be emitted from a unique source. These events correspond to an incomplete projectile-target fusion followed by a multifragmentation. Such events were singled out also in the reaction Xe + Sn at 50 MeV/nucleon. For the events in which the energy dissipation was maximal it was possible to isolate an isotropic group of events showing all the characteristics of fusion nuclei. The fusion is said to be incomplete as pre-equilibrium Z = 1 and Z = 2 particles are emitted. The cross section is of the order of 25 mb. Similar conclusions were drown for the systems 36 Ar + 27 Al and 64 Zn + nat Ti. A cross section value of ∼ 20 mb was determined at 55 MeV/nucleon in the first case, while the measurement of evaporation light residues in the last system gave an upper limit of 20-30 mb for the cross section at 50 MeV/nucleon

  7. Event-based Simulation Model for Quantum Optics Experiments

    NARCIS (Netherlands)

    De Raedt, H.; Michielsen, K.; Jaeger, G; Khrennikov, A; Schlosshauer, M; Weihs, G

    2011-01-01

    We present a corpuscular simulation model of optical phenomena that does not require the knowledge of the solution of a wave equation of the whole system and reproduces the results of Maxwell's theory by generating detection events one-by-one. The event-based corpuscular model gives a unified

  8. Event generators for address event representation transmitters

    Science.gov (United States)

    Serrano-Gotarredona, Rafael; Serrano-Gotarredona, Teresa; Linares Barranco, Bernabe

    2005-06-01

    freezed to transmit any further events during this time window. This limited the maximum transmission speed. In order to improve this speed, Boahen proposed an improved 'burst mode' scheme. In this scheme after the row arbitration, a complete row of events is pipelined out of the array and arbitered out of the chip at higher speed. During this single row event arbitration, the array is free to generate new events and communicate to the row arbiter, in a pipelined mode. This scheme significantly improves maximum event transmission speed, specially for high traffic situations were speed is more critical. We have analyzed and studied this approach and have detected some shortcomings in the circuits reported by Boahen, which may render some false situations under some statistical conditions. The present paper proposes some improvements to overcome such situations. The improved "AER Generator" has been implemented in an AER transmitter system

  9. Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection

    Directory of Open Access Journals (Sweden)

    Tian Wang

    2013-12-01

    Full Text Available The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM, combined with its sparsified version (sparse online LS-OC-SVM. LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.

  10. Discrete event systems in dioid algebra and conventional algebra

    CERN Document Server

    Declerck, Philippe

    2013-01-01

    This book concerns the use of dioid algebra as (max, +) algebra to treat the synchronization of tasks expressed by the maximum of the ends of the tasks conditioning the beginning of another task - a criterion of linear programming. A classical example is the departure time of a train which should wait for the arrival of other trains in order to allow for the changeover of passengers.The content focuses on the modeling of a class of dynamic systems usually called "discrete event systems" where the timing of the events is crucial. Events are viewed as sudden changes in a process which i

  11. Fluorescence Sensors for Early Detection of Nitrification in Drinking Water Distribution Systems – Interference Corrections (Poster)

    Science.gov (United States)

    Nitrification event detection in chloraminated drinking water distribution systems (DWDSs) remains an ongoing challenge for many drinking water utilities, including Dallas Water Utilities (DWU) and the City of Houston (CoH). Each year, these utilities experience nitrification eve...

  12. Fluorescence Sensors for Early Detection of Nitrification in Drinking Water Distribution Systems – Interference Corrections (Abstract)

    Science.gov (United States)

    Nitrification event detection in chloraminated drinking water distribution systems (DWDSs) remains an ongoing challenge for many drinking water utilities, including Dallas Water Utilities (DWU) and the City of Houston (CoH). Each year, these utilities experience nitrification eve...

  13. The effect of cosmic rays on biological systems - an investigation during GLE events

    Science.gov (United States)

    Belisheva, N. K.; Lammer, H.; Biernat, H. K.; Vashenuyk, E. V.

    2012-01-01

    In this study, first direct and circumstantial evidences of the effects of cosmic rays (CR) on biological systems are presented. A direct evidence of biological effects of CR is demonstrated in experiments with three cellular lines growing in culture during three events of Ground Level Enhancement (GLEs) in the neutron count rate detected by ground-based neutron monitor in October 1989. Various phenomena associated with DNA lesion on the cellular level demonstrate coherent dynamics of radiation effects in all cellular lines coincident with the time of arrival of high-energy solar particles to the near-Earth space and with the main peak in GLE. These results were obtained in the course of six separate experiments, with partial overlapping of the time of previous and subsequent experiments, which started and finished in the quiet period of solar activity (SA). A significant difference between the values of multinuclear cells in all cellular lines in the quiet period and during GLE events indicates that the cause of radiation effects in the cell cultures is an exposure of cells to the secondary solar CR near the Earth's surface. The circumstantial evidence was obtained by statistical analysis of cases of congenital malformations (CM) at two sites in the Murmansk region. The number of cases of all classes of CM reveals a significant correlation with the number of GLE events. The number of cases of CM with pronounced chromosomal abnormalities clearly correlates with the GLE events that occurred a year before the birth of a child. We have found a significant correlation between modulations of the water properties and daily background variations of CR intensity. We believe that the effects of CR on biological systems can be also mediated by fluctuations in water properties, considered as one of possible mechanisms controlling the effects of CRs on biological systems.

  14. Test-bed of a real time detection system of L/H and H/L transitions implemented with the ITMS platform

    Energy Technology Data Exchange (ETDEWEB)

    Ruiz, M.; Barrera, E.; Gonzalez, J.; Melendez, R. [Grupo de Investigacion en Instrumentacion y Acustica Aplicada - Universidad Politecnica de Madrid (Spain); Vega, J.; Ratta, G.; Gonzalez, S. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Murari, A. [Consorzio RFX - Associazione EURATOM ENEA per la Fusione, Padova (Italy)

    2009-07-01

    A basic requirement of the data acquisition systems used in long pulse fusion experiments is to detect events of interest in the acquired signals in real time. Developing such applications is usually a complex task, so it is necessary to develop a set of hardware and software tools that simplify their implementation. An example of these tools is the Intelligent Test and Measurement System (ITMS), which offers distributed data acquisition, distribution and real time processing capabilities with advanced, but easy to use, software tools that simplify application development and system setup. This poster presents the application of the ITMS platform to solve the problem of detecting L/H and H/L transitions in real time based on the use of efficient pattern recognition algorithms. The system architecture used to implement this solution and its performance evaluation are discussed. The system consists of the following elements: a) the ITMS, which is used both to implement the data acquisition and real time LH-HL detection, and to simulate the working environment by reproducing real signals obtained from JET database; b) an event detector, developed using advanced pattern recognition algorithms, that will continuously analyze the information of the acquired signals in order to detect the L/H and H/L transitions; and c) a host computer with several software tools developed with LabVIEW, JAVA and JINI to simplify the development of these complex experiments. These tools allow the user to describe data acquisition and processing tasks using state machines defined in SCXML. These state machines become part of the system setup information, so the behaviour of the system can be changed subsequently by simply modifying these state machines. All the events and key detected are stored by the host computer. This document is a poster. (authors)

  15. Address-event-based platform for bioinspired spiking systems

    Science.gov (United States)

    Jiménez-Fernández, A.; Luján, C. D.; Linares-Barranco, A.; Gómez-Rodríguez, F.; Rivas, M.; Jiménez, G.; Civit, A.

    2007-05-01

    Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows a real-time virtual massive connectivity between huge number neurons, located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate "events" according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems, it is absolutely necessary to have a computer interface that allows (a) reading AER interchip traffic into the computer and visualizing it on the screen, and (b) converting conventional frame-based video stream in the computer into AER and injecting it at some point of the AER structure. This is necessary for test and debugging of complex AER systems. In the other hand, the use of a commercial personal computer implies to depend on software tools and operating systems that can make the system slower and un-robust. This paper addresses the problem of communicating several AER based chips to compose a powerful processing system. The problem was discussed in the Neuromorphic Engineering Workshop of 2006. The platform is based basically on an embedded computer, a powerful FPGA and serial links, to make the system faster and be stand alone (independent from a PC). A new platform is presented that allow to connect up to eight AER based chips to a Spartan 3 4000 FPGA. The FPGA is responsible of the network communication based in Address-Event and, at the same time, to map and transform the address space of the traffic to implement a pre-processing. A MMU microprocessor (Intel XScale 400MHz Gumstix Connex computer) is also connected to the FPGA

  16. An environmental monitoring detection system

    International Nuclear Information System (INIS)

    Leli Yuniarsari; Istofa; Sukandar

    2015-01-01

    Is part of radiation detection of the nuclear facilities engineering activities within nuclear facilities. The system comprised of gamma-ray radiation detector and weather detection which includes anemometer to detect the wind direction and speed, as well as rain gauge to measure the rainfall in a period of time. Data acquisition of the output is processed by Arduino Uno system which transformed the data into a particular standard and then displayed online in the website. The radiation detection system uses gamma-ray detector of NaI(Tl) and GM which convert the radiation detected into electric pulse to be fed into a pre-amp and amplifier and modified into square pulse. The weather detection system on the other hand works based on switch principle. For example, the wind with a certain speed could turn on a switch in the system and produce a voltage or pulse which can be measured. This value will then be interpreted as the wind direction and speed. Likewise for the rainfall gauge, the volume of water entering the bucket will turn the switch on, at the same time producing 1 pulse. The result of the experiment shows that for radiation detection system the output is a square pulse 4 volts by using detector NaI(Tl) and 4.4 volts by using detector GM. For weather detection system, basically was able to detect the wind direction, wind speed and rainfall just to find out further research is needed accuracy and the results compared with the standard tools available in BMKG. (author)

  17. Hospital staff should use more than one method to detect adverse events and potential adverse events: incident reporting, pharmacist surveillance and local real‐time record review may all have a place

    Science.gov (United States)

    Olsen, Sisse; Neale, Graham; Schwab, Kat; Psaila, Beth; Patel, Tejal; Chapman, E Jane; Vincent, Charles

    2007-01-01

    Background Over the past five years, in most hospitals in England and Wales, incident reporting has become well established but it remains unclear how well reports match clinical adverse events. International epidemiological studies of adverse events are based on retrospective, multi‐hospital case record review. In this paper the authors describe the use of incident reporting, pharmacist surveillance and local real‐time record review for the recognition of clinical risks associated with hospital inpatient care. Methodology Data on adverse events were collected prospectively on 288 patients discharged from adult acute medical and surgical units in an NHS district general hospital using incident reports, active surveillance of prescription charts by pharmacists and record review at time of discharge. Results Record review detected 26 adverse events (AEs) and 40 potential adverse events (PAEs) occurring during the index admission. In contrast, in the same patient group, incident reporting detected 11 PAEs and no AEs. Pharmacy surveillance found 10 medication errors all of which were PAEs. There was little overlap in the nature of events detected by the three methods. Conclusion The findings suggest that incident reporting does not provide an adequate assessment of clinical adverse events and that this method needs to be supplemented with other more systematic forms of data collection. Structured record review, carried out by clinicians, provides an important component of an integrated approach to identifying risk in the context of developing a safety and quality improvement programme. PMID:17301203

  18. Event-Based Corpuscular Model for Quantum Optics Experiments

    NARCIS (Netherlands)

    Michielsen, K.; Jin, F.; Raedt, H. De

    A corpuscular simulation model of optical phenomena that does not require the knowledge of the solution of a wave equation of the whole system and reproduces the results of Maxwell's theory by generating detection events one-by-one is presented. The event-based corpuscular model is shown to give a

  19. Femtomolar detection of single mismatches by discriminant analysis of DNA hybridization events using gold nanoparticles.

    Science.gov (United States)

    Ma, Xingyi; Sim, Sang Jun

    2013-03-21

    Even though DNA-based nanosensors have been demonstrated for quantitative detection of analytes and diseases, hybridization events have never been numerically investigated for further understanding of DNA mediated interactions. Here, we developed a nanoscale platform with well-designed capture and detection gold nanoprobes to precisely evaluate the hybridization events. The capture gold nanoprobes were mono-laid on glass and the detection probes were fabricated via a novel competitive conjugation method. The two kinds of probes combined in a suitable orientation following the hybridization with the target. We found that hybridization efficiency was markedly dependent on electrostatic interactions between DNA strands, which can be tailored by adjusting the salt concentration of the incubation solution. Due to the much lower stability of the double helix formed by mismatches, the hybridization efficiencies of single mismatched (MMT) and perfectly matched DNA (PMT) were different. Therefore, we obtained an optimized salt concentration that allowed for discrimination of MMT from PMT without stringent control of temperature or pH. The results indicated this to be an ultrasensitive and precise nanosensor for the diagnosis of genetic diseases.

  20. Early detection of drillstring washouts reduces fishing jobs

    Energy Technology Data Exchange (ETDEWEB)

    Dudleson, B. (Dubai Petroleum Co. (FR)); Arnold, M.; McCann, D. (Sedco Forex (FR))

    1990-10-01

    Rapid detection of unexpected drilling events requires continuous monitoring of drilling parameters. A major R and D program by a drilling contractor has led to the introduction of a computerized monitoring system on its offshore rigs. System includes advanced color graphics displays and new smart alarms to help both contractor and operator personnel detect and observe drilling events before they would normally be apparent with conventional rig instrumentation. This article describes a module of this monitoring system, which uses expert system technology to detect the earliest stages of drillstring washouts. Field results demonstrate the effectiveness of the smart alarm incorporated in the system. Early detection allows the driller to react before a twist-off results in expensive fishing operations.

  1. Performance Evaluation of Wireless Sensor Networks for Event-Detection with Shadowing-Induced Radio Irregularities

    Directory of Open Access Journals (Sweden)

    Giuseppe De Marco

    2007-01-01

    Full Text Available In this paper, we study a particular application of wireless sensor networks for event-detection and tracking. In this kind of application, the transport of data is simplified, and guaranteeing a minimum number of packets at the monitoring node is the only constraint on the performance of the sensor network. This minimum number of packets is called event-reliability. Contrary to other studies on the subject, here we consider the behavior of such a network in presence of a realistic radio model, such as the shadowing of the radio signal. With this setting, we extend our previous analysis of the event-reliability approach for the transport of data. In particular, both regular and random networks are considered. The contribute of this work is to show via simulations that, in the presence of randomness or irregularities in the radio channel, the event-reliability can be jeopardized, that is the constraint on the minimum number of packets at the sink node could not be satisfied.

  2. Detection and discrimination of maintenance and de novo CpG methylation events using MethylBreak.

    Science.gov (United States)

    Hsu, William; Mercado, Augustus T; Hsiao, George; Yeh, Jui-Ming; Chen, Chung-Yung

    2017-05-15

    Understanding the principles governing the establishment and maintenance activities of DNA methyltransferases (DNMTs) can help in the development of predictive biomarkers associated with genetic disorders and diseases. A detection system was developed that distinguishes and quantifies methylation events using methylation-sensitive endonucleases and molecular beacon technology. MethylBreak (MB) is a 22-mer oligonucleotide with one hemimethylated and two unmethylated CpG sites, which are also recognition sites for Sau96I and SacII, and is attached to a fluorophore and a quencher. Maintenance methylation was quantified by fluorescence emission due to the digestion of SacII when the hemimethylated CpG site is methylated, which inhibits Sau96I cleavage. The signal difference between SacII digestion of both MB substrate and maintenance methylated MB corresponds to de novo methylation event. Our technology successfully discriminated and measured both methylation activities at different concentrations of MB and achieved a high correlation coefficient of R 2 =0.997. Additionally, MB was effectively applied to normal and cancer cell lines and in the analysis of enzymatic kinetics and RNA inhibition of recombinant human DNMT1. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

    Science.gov (United States)

    He, Jian; Bai, Shuang; Wang, Xiaoyi

    2017-06-16

    Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.

  4. A Wearable Gait Phase Detection System Based on Force Myography Techniques

    Directory of Open Access Journals (Sweden)

    Xianta Jiang

    2018-04-01

    Full Text Available (1 Background: Quantitative evaluation of gait parameters can provide useful information for constructing individuals’ gait profile, diagnosing gait abnormalities, and better planning of rehabilitation schemes to restore normal gait pattern. Objective determination of gait phases in a gait cycle is a key requirement in gait analysis applications; (2 Methods: In this study, the feasibility of using a force myography-based technique for a wearable gait phase detection system is explored. In this regard, a force myography band is developed and tested with nine participants walking on a treadmill. The collected force myography data are first examined sample-by-sample and classified into four phases using Linear Discriminant Analysis. The gait phase events are then detected from these classified samples using a set of supervisory rules; (3 Results: The results show that the force myography band can correctly detect more than 99.9% of gait phases with zero insertions and only four deletions over 12,965 gait phase segments. The average temporal error of gait phase detection is 55.2 ms, which translates into 2.1% error with respect to the corresponding labelled stride duration; (4 Conclusions: This proof-of-concept study demonstrates the feasibility of force myography techniques as viable solutions in developing wearable gait phase detection systems.

  5. News and Events - Nanodelivery Systems and Devices Branch

    Science.gov (United States)

    The latest news from the Nanodelivery Systems and Devices Branch and the Alliance, as well as upcoming and past events attended by the Nanodelivery Systems and Devices Branchstaff, and relevant upcoming scientific meetings.

  6. Atomic-scale nanoindentation: detection and identification of single glide events in three dimensions by force microscopy

    International Nuclear Information System (INIS)

    Egberts, P; Bennewitz, R

    2011-01-01

    Indentation experiments on the nanometre scale have been performed by means of atomic force microscopy in ultra-high vacuum on KBr(100) surfaces. The surfaces yield in the form of discrete surface displacements with a typical length scale of 1 A. These surface displacements are detected in both normal and lateral directions. Measurement of the lateral tip displacement requires a load-dependent calibration due to the load dependence of the effective lateral compliance. Correlation of the lateral and normal displacements for each glide event allow identification of the activated slip system. The results are discussed in terms of the resolved shear stress in indentation experiments and of typical results in atomistic simulations of nanometre-scale indentation.

  7. Modelling machine ensembles with discrete event dynamical system theory

    Science.gov (United States)

    Hunter, Dan

    1990-01-01

    Discrete Event Dynamical System (DEDS) theory can be utilized as a control strategy for future complex machine ensembles that will be required for in-space construction. The control strategy involves orchestrating a set of interactive submachines to perform a set of tasks for a given set of constraints such as minimum time, minimum energy, or maximum machine utilization. Machine ensembles can be hierarchically modeled as a global model that combines the operations of the individual submachines. These submachines are represented in the global model as local models. Local models, from the perspective of DEDS theory , are described by the following: a set of system and transition states, an event alphabet that portrays actions that takes a submachine from one state to another, an initial system state, a partial function that maps the current state and event alphabet to the next state, and the time required for the event to occur. Each submachine in the machine ensemble is presented by a unique local model. The global model combines the local models such that the local models can operate in parallel under the additional logistic and physical constraints due to submachine interactions. The global model is constructed from the states, events, event functions, and timing requirements of the local models. Supervisory control can be implemented in the global model by various methods such as task scheduling (open-loop control) or implementing a feedback DEDS controller (closed-loop control).

  8. Logical Discrete Event Systems in a trace theory based setting

    NARCIS (Netherlands)

    Smedinga, R.

    1993-01-01

    Discrete event systems can be modelled using a triple consisting of some alphabet (representing the events that might occur), and two trace sets (sets of possible strings) denoting the possible behaviour and the completed tasks of the system. Using this definition we are able to formulate and solve

  9. Vision-based fall detection system for improving safety of elderly people

    KAUST Repository

    Harrou, Fouzi; Zerrouki, Nabil; Sun, Ying; Houacine, Amrane

    2017-01-01

    Recognition of human movements is very useful for several applications, such as smart rooms, interactive virtual reality systems, human detection and environment modeling. The objective of this work focuses on the detection and classification of falls based on variations in human silhouette shape, a key challenge in computer vision. Falls are a major health concern, specifically for the elderly. In this study, the detection is achieved with a multivariate exponentially weighted moving average (MEWMA) monitoring scheme, which is effective in detecting falls because it is sensitive to small changes. Unfortunately, an MEWMA statistic fails to differentiate real falls from some fall-like gestures. To remedy this limitation, a classification stage based on a support vector machine (SVM) is applied on detected sequences. To validate this methodology, two fall detection datasets have been tested: the University of Rzeszow fall detection dataset (URFD) and the fall detection dataset (FDD). The results of the MEWMA-based SVM are compared with three other classifiers: neural network (NN), naïve Bayes and K-nearest neighbor (KNN). These results show the capability of the developed strategy to distinguish fall events, suggesting that it can raise an early alert in the fall incidents.

  10. Vision-based fall detection system for improving safety of elderly people

    KAUST Repository

    Harrou, Fouzi

    2017-12-06

    Recognition of human movements is very useful for several applications, such as smart rooms, interactive virtual reality systems, human detection and environment modeling. The objective of this work focuses on the detection and classification of falls based on variations in human silhouette shape, a key challenge in computer vision. Falls are a major health concern, specifically for the elderly. In this study, the detection is achieved with a multivariate exponentially weighted moving average (MEWMA) monitoring scheme, which is effective in detecting falls because it is sensitive to small changes. Unfortunately, an MEWMA statistic fails to differentiate real falls from some fall-like gestures. To remedy this limitation, a classification stage based on a support vector machine (SVM) is applied on detected sequences. To validate this methodology, two fall detection datasets have been tested: the University of Rzeszow fall detection dataset (URFD) and the fall detection dataset (FDD). The results of the MEWMA-based SVM are compared with three other classifiers: neural network (NN), naïve Bayes and K-nearest neighbor (KNN). These results show the capability of the developed strategy to distinguish fall events, suggesting that it can raise an early alert in the fall incidents.

  11. Web-based online system for recording and examing of events in power plants

    International Nuclear Information System (INIS)

    Seyd Farshi, S.; Dehghani, M.

    2004-01-01

    Occurrence of events in power plants could results in serious drawbacks in generation of power. This suggests high degree of importance for online recording and examing of events. In this paper an online web-based system is introduced, which records and examines events in power plants. Throughout the paper, procedures for design and implementation of this system, its features and results gained are explained. this system provides predefined level of online access to all data of events for all its users in power plants, dispatching, regional utilities and top-level managers. By implementation of electric power industry intranet, an expandable modular system to be used in different sectors of industry is offered. Web-based online recording and examing system for events offers the following advantages: - Online recording of events in power plants. - Examing of events in regional utilities. - Access to event' data. - Preparing managerial reports

  12. Parallelized event chain algorithm for dense hard sphere and polymer systems

    International Nuclear Information System (INIS)

    Kampmann, Tobias A.; Boltz, Horst-Holger; Kierfeld, Jan

    2015-01-01

    We combine parallelization and cluster Monte Carlo for hard sphere systems and present a parallelized event chain algorithm for the hard disk system in two dimensions. For parallelization we use a spatial partitioning approach into simulation cells. We find that it is crucial for correctness to ensure detailed balance on the level of Monte Carlo sweeps by drawing the starting sphere of event chains within each simulation cell with replacement. We analyze the performance gains for the parallelized event chain and find a criterion for an optimal degree of parallelization. Because of the cluster nature of event chain moves massive parallelization will not be optimal. Finally, we discuss first applications of the event chain algorithm to dense polymer systems, i.e., bundle-forming solutions of attractive semiflexible polymers

  13. High-Performance Signal Detection for Adverse Drug Events using MapReduce Paradigm.

    Science.gov (United States)

    Fan, Kai; Sun, Xingzhi; Tao, Ying; Xu, Linhao; Wang, Chen; Mao, Xianling; Peng, Bo; Pan, Yue

    2010-11-13

    Post-marketing pharmacovigilance is important for public health, as many Adverse Drug Events (ADEs) are unknown when those drugs were approved for marketing. However, due to the large number of reported drugs and drug combinations, detecting ADE signals by mining these reports is becoming a challenging task in terms of computational complexity. Recently, a parallel programming model, MapReduce has been introduced by Google to support large-scale data intensive applications. In this study, we proposed a MapReduce-based algorithm, for common ADE detection approach, Proportional Reporting Ratio (PRR), and tested it in mining spontaneous ADE reports from FDA. The purpose is to investigate the possibility of using MapReduce principle to speed up biomedical data mining tasks using this pharmacovigilance case as one specific example. The results demonstrated that MapReduce programming model could improve the performance of common signal detection algorithm for pharmacovigilance in a distributed computation environment at approximately liner speedup rates.

  14. Discrete event simulation versus conventional system reliability analysis approaches

    DEFF Research Database (Denmark)

    Kozine, Igor

    2010-01-01

    Discrete Event Simulation (DES) environments are rapidly developing and appear to be promising tools for building reliability and risk analysis models of safety-critical systems and human operators. If properly developed, they are an alternative to the conventional human reliability analysis models...... and systems analysis methods such as fault and event trees and Bayesian networks. As one part, the paper describes briefly the author’s experience in applying DES models to the analysis of safety-critical systems in different domains. The other part of the paper is devoted to comparing conventional approaches...

  15. Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO

    Directory of Open Access Journals (Sweden)

    Lixin Yan

    2016-07-01

    Full Text Available The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1 the Markov blanket (MB algorithm is employed to extract the main factors associated with hazardous traffic events; (2 a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G have significant influences on hazardous traffic events. The sequential minimal optimization (SMO algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles.

  16. {sup 123}I-MIBG imaging detects cardiac involvement and predicts cardiac events in Churg-Strauss syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Horiguchi, Yoriko; Morita, Yukiko [National Hospital Organization Sagamihara National Hospital, Department of Cardiology, Sagamihara City, Kanagawa (Japan); Tsurikisawa, Naomi; Akiyama, Kazuo [National Hospital Organization Sagamihara National Hospital, Clinical Research Centre for Allergy and Rheumatology, Sagamihara City, Kanagawa (Japan)

    2011-02-15

    In Churg-Strauss syndrome (CSS) it is important to detect cardiac involvement, which predicts poor prognosis. This study evaluated whether {sup 123}I-metaiodobenzylguanidine (MIBG) scintigraphy could detect cardiac damage and predict cardiac events in CSS. {sup 123}I-MIBG scintigraphy was performed in 28 patients with CSS, 12 of whom had cardiac involvement. The early and delayed heart to mediastinum ratio (early H/M and delayed H/M) and washout rate were calculated by using {sup 123}I-MIBG scintigraphy and compared with those in control subjects. Early H/M and delayed H/M were significantly lower and the washout rate was significantly higher in patients with cardiac involvement than in those without and in controls (early H/M, p = 0.0024, p = 0.0001; delayed H/M, p = 0.0002, p = 0.0001; washout rate, p = 0.0012, p = 0.0052 vs those without and vs controls, respectively). Accuracy for detecting cardiac involvement was 86% for delayed H/M and washout rate and 79% for early H/M and B-type natriuretic peptide (BNP). Kaplan-Meier analysis showed significantly lower cardiac event-free rates in patients with early H/M {<=} 2.18 and BNP > 21.8 pg/ml than those with early H/M > 2.18 and BNP {<=} 21.8 pg/ml (log-rank test p = 0.006). Cardiac sympathetic nerve function was damaged in CSS patients with cardiac involvement. {sup 123}I-MIBG scintigraphy was useful in detecting cardiac involvement and in predicting cardiac events. (orig.)

  17. System Detects Vibrational Instabilities

    Science.gov (United States)

    Bozeman, Richard J., Jr.

    1990-01-01

    Sustained vibrations at two critical frequencies trigger diagnostic response or shutdown. Vibration-analyzing electronic system detects instabilities of combustion in rocket engine. Controls pulse-mode firing of engine and identifies vibrations above threshold amplitude at 5.9 and/or 12kHz. Adapted to other detection and/or control schemes involving simultaneous real-time detection of signals above or below preset amplitudes at two or more specified frequencies. Potential applications include rotating machinery and encoders and decoders in security systems.

  18. Detecting regular sound changes in linguistics as events of concerted evolution.

    Science.gov (United States)

    Hruschka, Daniel J; Branford, Simon; Smith, Eric D; Wilkins, Jon; Meade, Andrew; Pagel, Mark; Bhattacharya, Tanmoy

    2015-01-05

    Concerted evolution is normally used to describe parallel changes at different sites in a genome, but it is also observed in languages where a specific phoneme changes to the same other phoneme in many words in the lexicon—a phenomenon known as regular sound change. We develop a general statistical model that can detect concerted changes in aligned sequence data and apply it to study regular sound changes in the Turkic language family. Linguistic evolution, unlike the genetic substitutional process, is dominated by events of concerted evolutionary change. Our model identified more than 70 historical events of regular sound change that occurred throughout the evolution of the Turkic language family, while simultaneously inferring a dated phylogenetic tree. Including regular sound changes yielded an approximately 4-fold improvement in the characterization of linguistic change over a simpler model of sporadic change, improved phylogenetic inference, and returned more reliable and plausible dates for events on the phylogenies. The historical timings of the concerted changes closely follow a Poisson process model, and the sound transition networks derived from our model mirror linguistic expectations. We demonstrate that a model with no prior knowledge of complex concerted or regular changes can nevertheless infer the historical timings and genealogical placements of events of concerted change from the signals left in contemporary data. Our model can be applied wherever discrete elements—such as genes, words, cultural trends, technologies, or morphological traits—can change in parallel within an organism or other evolving group. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  19. [Detection of adverse events in hospitalized adult patients by using the Global Trigger Tool method].

    Science.gov (United States)

    Guzmán-Ruiz, O; Ruiz-López, P; Gómez-Cámara, A; Ramírez-Martín, M

    2015-01-01

    To identify and characterize adverse events (AE) in an Internal Medicine Department of a district hospital using an extension of the Global Trigger Tool (GTT), analyzing the diagnostic validity of the tool. An observational, analytical, descriptive and retrospective study was conducted on 2013 clinical charts from an Internal Medicine Department in order to detect EA through the identification of 'triggers' (an event often related to an AE). The 'triggers' and AE were located by systematic review of clinical documentation. The AE were characterized after they were identified. A total of 149 AE were detected in 291 clinical charts during 2013, of which 75.3% were detected directly by the tool, while the rest were not associated with a trigger. The percentage of charts that had at least one AE was 35.4%. The most frequent AE found was pressure ulcer (12%), followed by delirium, constipation, nosocomial respiratory infection and altered level of consciousness by drugs. Almost half (47.6%) of the AE were related to drug use, and 32.2% of all AE were considered preventable. The tool demonstrated a sensitivity of 91.3% (95%CI: 88.9-93.2) and a specificity of 32.5% (95%CI: 29.9-35.1). It had a positive predictive value of 42.5% (95%CI: 40.1-45.1) and a negative predictive value of 87.1% (95%CI: 83.8-89.9). The tool used in this study is valid, useful and reproducible for the detection of AE. It also serves to determine rates of injury and to observe their progression over time. A high frequency of both AE and preventable events were observed in this study. Copyright © 2014 SECA. Published by Elsevier Espana. All rights reserved.

  20. Constraint on Additional Planets in Planetary Systems Discovered Through the Channel of High-magnification Gravitational Microlensing Events

    Science.gov (United States)

    Shin, I.-G.; Han, C.; Choi, J.-Y.; Hwang, K.-H.; Jung, Y.-K.; Park, H.

    2015-04-01

    High-magnification gravitational microlensing events provide an important channel of detecting planetary systems with multiple giants located at their birth places. In order to investigate the potential existence of additional planets, we reanalyze the light curves of the eight high-magnification microlensing events, for each of which a single planet was previously detected. The analyzed events include OGLE-2005-BLG-071, OGLE-2005-BLG-169, MOA-2007-BLG-400, MOA-2008-BLG-310, MOA-2009-BLG-319, MOA-2009-BLG-387, MOA-2010-BLG-477, and MOA-2011-BLG-293. We find that including an additional planet improves fits with {Δ }{{χ }2}\\lt 80 for seven out of eight analyzed events. For MOA-2009-BLG-319, the improvement is relatively big with {Δ }{{χ }2}∼ 143. From inspection of the fits, we find that the improvement of the fits is attributed to systematics in data. Although no clear evidence of additional planets is found, it is still possible to constrain the existence of additional planets in the parameter space. For this purpose, we construct exclusion diagrams showing the confidence levels excluding the existence of an additional planet as a function of its separation and mass ratio. We also present the exclusion ranges of additional planets with 90% confidence level for Jupiter-, Saturn-, and Uranus-mass planets.

  1. Control of discrete-event systems with modular or distributed structure

    Czech Academy of Sciences Publication Activity Database

    Komenda, Jan; van Schuppen, J. H.

    2007-01-01

    Roč. 388, č. 3 (2007), s. 199-226 ISSN 0304-3975 R&D Projects: GA AV ČR(CZ) KJB100190609 Institutional research plan: CEZ:AV0Z10190503 Keywords : supervisory control * modular discrete-event system * distributed discrete-event system Subject RIV: BA - General Mathematics Impact factor: 0.735, year: 2007

  2. Motion Pattern Extraction and Event Detection for Automatic Visual Surveillance

    Directory of Open Access Journals (Sweden)

    Benabbas Yassine

    2011-01-01

    Full Text Available Efficient analysis of human behavior in video surveillance scenes is a very challenging problem. Most traditional approaches fail when applied in real conditions and contexts like amounts of persons, appearance ambiguity, and occlusion. In this work, we propose to deal with this problem by modeling the global motion information obtained from optical flow vectors. The obtained direction and magnitude models learn the dominant motion orientations and magnitudes at each spatial location of the scene and are used to detect the major motion patterns. The applied region-based segmentation algorithm groups local blocks that share the same motion direction and speed and allows a subregion of the scene to appear in different patterns. The second part of the approach consists in the detection of events related to groups of people which are merge, split, walk, run, local dispersion, and evacuation by analyzing the instantaneous optical flow vectors and comparing the learned models. The approach is validated and experimented on standard datasets of the computer vision community. The qualitative and quantitative results are discussed.

  3. Hierarchical Discrete Event Supervisory Control of Aircraft Propulsion Systems

    Science.gov (United States)

    Yasar, Murat; Tolani, Devendra; Ray, Asok; Shah, Neerav; Litt, Jonathan S.

    2004-01-01

    This paper presents a hierarchical application of Discrete Event Supervisory (DES) control theory for intelligent decision and control of a twin-engine aircraft propulsion system. A dual layer hierarchical DES controller is designed to supervise and coordinate the operation of two engines of the propulsion system. The two engines are individually controlled to achieve enhanced performance and reliability, necessary for fulfilling the mission objectives. Each engine is operated under a continuously varying control system that maintains the specified performance and a local discrete-event supervisor for condition monitoring and life extending control. A global upper level DES controller is designed for load balancing and overall health management of the propulsion system.

  4. System on chip module configured for event-driven architecture

    Science.gov (United States)

    Robbins, Kevin; Brady, Charles E.; Ashlock, Tad A.

    2017-10-17

    A system on chip (SoC) module is described herein, wherein the SoC modules comprise a processor subsystem and a hardware logic subsystem. The processor subsystem and hardware logic subsystem are in communication with one another, and transmit event messages between one another. The processor subsystem executes software actors, while the hardware logic subsystem includes hardware actors, the software actors and hardware actors conform to an event-driven architecture, such that the software actors receive and generate event messages and the hardware actors receive and generate event messages.

  5. Physics-based, Bayesian sequential detection method and system for radioactive contraband

    Science.gov (United States)

    Candy, James V; Axelrod, Michael C; Breitfeller, Eric F; Chambers, David H; Guidry, Brian L; Manatt, Douglas R; Meyer, Alan W; Sale, Kenneth E

    2014-03-18

    A distributed sequential method and system for detecting and identifying radioactive contraband from highly uncertain (noisy) low-count, radionuclide measurements, i.e. an event mode sequence (EMS), using a statistical approach based on Bayesian inference and physics-model-based signal processing based on the representation of a radionuclide as a monoenergetic decomposition of monoenergetic sources. For a given photon event of the EMS, the appropriate monoenergy processing channel is determined using a confidence interval condition-based discriminator for the energy amplitude and interarrival time and parameter estimates are used to update a measured probability density function estimate for a target radionuclide. A sequential likelihood ratio test is then used to determine one of two threshold conditions signifying that the EMS is either identified as the target radionuclide or not, and if not, then repeating the process for the next sequential photon event of the EMS until one of the two threshold conditions is satisfied.

  6. Fine grained event processing on HPCs with the ATLAS Yoda system

    CERN Document Server

    Calafiura, Paolo; The ATLAS collaboration; Guan, Wen; Maeno, Tadashi; Nilsson, Paul; Oleynik, Danila; Panitkin, Sergey; Tsulaia, Vakhtang; van Gemmeren, Peter; Wenaus, Torre

    2015-01-01

    High performance computing facilities present unique challenges and opportunities for HENP event processing. The massive scale of many HPC systems means that fractionally small utilizations can yield large returns in processing throughput. Parallel applications which can dynamically and efficiently fill any scheduling opportunities the resource presents benefit both the facility (maximal utilization) and the (compute-limited) science. The ATLAS Yoda system provides this capability to HENP-like event processing applications by implementing event-level processing in an MPI-based master-client model that integrates seamlessly with the more broadly scoped ATLAS Event Service. Fine grained, event level work assignments are intelligently dispatched to parallel workers to sustain full utilization on all cores, with outputs streamed off to destination object stores in near real time with similarly fine granularity, such that processing can proceed until termination with full utilization. The system offers the efficie...

  7. Detecting Forest Disturbance Events from MODIS and Landsat Time Series for the Conterminous United States

    Science.gov (United States)

    Zhang, G.; Ganguly, S.; Saatchi, S. S.; Hagen, S. C.; Harris, N.; Yu, Y.; Nemani, R. R.

    2013-12-01

    Spatial and temporal patterns of forest disturbance and regrowth processes are key for understanding aboveground terrestrial vegetation biomass and carbon stocks at regional-to-continental scales. The NASA Carbon Monitoring System (CMS) program seeks key input datasets, especially information related to impacts due to natural/man-made disturbances in forested landscapes of Conterminous U.S. (CONUS), that would reduce uncertainties in current carbon stock estimation and emission models. This study provides a end-to-end forest disturbance detection framework based on pixel time series analysis from MODIS (Moderate Resolution Imaging Spectroradiometer) and Landsat surface spectral reflectance data. We applied the BFAST (Breaks for Additive Seasonal and Trend) algorithm to the Normalized Difference Vegetation Index (NDVI) data for the time period from 2000 to 2011. A harmonic seasonal model was implemented in BFAST to decompose the time series to seasonal and interannual trend components in order to detect abrupt changes in magnitude and direction of these components. To apply the BFAST for whole CONUS, we built a parallel computing setup for processing massive time-series data using the high performance computing facility of the NASA Earth Exchange (NEX). In the implementation process, we extracted the dominant deforestation events from the magnitude of abrupt changes in both seasonal and interannual components, and estimated dates for corresponding deforestation events. We estimated the recovery rate for deforested regions through regression models developed between NDVI values and time since disturbance for all pixels. A similar implementation of the BFAST algorithm was performed over selected Landsat scenes (all Landsat cloud free data was used to generate NDVI from atmospherically corrected spectral reflectances) to demonstrate the spatial coherence in retrieval layers between MODIS and Landsat. In future, the application of this largely parallel disturbance

  8. Design and implementation of quench detection instrumentation for TF magnet system of SST-1

    International Nuclear Information System (INIS)

    Khristi, Y.; Sharma, A.N.; Doshi, K.; Banaudha, M.; Prasad, U.; Varmora, P.; Patel, D.; Pradhan, S.

    2014-01-01

    Steady State Superconducting Tokamak-1 (SST-1) at Institute for Plasma Research (IPR), India is now in engineering validation phase. The assembled Toroidal Field (TF) magnet system of SST-1 will be operated at 10 kA of nominal current at helium cooled condition of 4.5 K. A reliable and fail proof quench detection (QD) system is essential for the safety and the investment protection requirements of the magnets. This QD system needs to continuously monitor all the superconducting coils, which include 16 TF magnets, return-loop, bus bars and current leads. In case of any event initiating the normal resistive zone and reaching thermal run-away, the QD system needs to trigger the magnet protection circuits. Precision instrumentation and control system with 204 signal channels had been developed for detection of quench anywhere in the entire TF magnet system. In the present configuration of quench detection scheme, the voltage drop across each double pancake (DP) of each TF coil are compared with its two adjacent DPs for the detection of normal zone and cancelation of inductive couples. Two identical redundant systems with one out of two configurations are successfully commissioned and tested at IPR. This paper describes the design and implementation of the QD system, Installation experience, validation test and initial results from the recent SST-1 magnet system charging

  9. Ventilator-Related Adverse Events: A Taxonomy and Findings From 3 Incident Reporting Systems.

    Science.gov (United States)

    Pham, Julius Cuong; Williams, Tamara L; Sparnon, Erin M; Cillie, Tam K; Scharen, Hilda F; Marella, William M

    2016-05-01

    In 2009, researchers from Johns Hopkins University's Armstrong Institute for Patient Safety and Quality; public agencies, including the FDA; and private partners, including the Emergency Care Research Institute and the University HealthSystem Consortium (UHC) Safety Intelligence Patient Safety Organization, sought to form a public-private partnership for the promotion of patient safety (P5S) to advance patient safety through voluntary partnerships. The study objective was to test the concept of the P5S to advance our understanding of safety issues related to ventilator events, to develop a common classification system for categorizing adverse events related to mechanical ventilators, and to perform a comparison of adverse events across different adverse event reporting systems. We performed a cross-sectional analysis of ventilator-related adverse events reported in 2012 from the following incident reporting systems: the Pennsylvania Patient Safety Authority's Patient Safety Reporting System, UHC's Safety Intelligence Patient Safety Organization database, and the FDA's Manufacturer and User Facility Device Experience database. Once each organization had its dataset of ventilator-related adverse events, reviewers read the narrative descriptions of each event and classified it according to the developed common taxonomy. A Pennsylvania Patient Safety Authority, FDA, and UHC search provided 252, 274, and 700 relevant reports, respectively. The 3 event types most commonly reported to the UHC and the Pennsylvania Patient Safety Authority's Patient Safety Reporting System databases were airway/breathing circuit issue, human factor issues, and ventilator malfunction events. The top 3 event types reported to the FDA were ventilator malfunction, power source issue, and alarm failure. Overall, we found that (1) through the development of a common taxonomy, adverse events from 3 reporting systems can be evaluated, (2) the types of events reported in each database were related

  10. Ventilator-Related Adverse Events: A Taxonomy and Findings From 3 Incident Reporting Systems

    Science.gov (United States)

    Pham, Julius Cuong; Williams, Tamara L; Sparnon, Erin M; Cillie, Tam K; Scharen, Hilda F; Marella, William M

    2016-01-01

    BACKGROUND: In 2009, researchers from Johns Hopkins University's Armstrong Institute for Patient Safety and Quality; public agencies, including the FDA; and private partners, including the Emergency Care Research Institute and the University HealthSystem Consortium (UHC) Safety Intelligence Patient Safety Organization, sought to form a public-private partnership for the promotion of patient safety (P5S) to advance patient safety through voluntary partnerships. The study objective was to test the concept of the P5S to advance our understanding of safety issues related to ventilator events, to develop a common classification system for categorizing adverse events related to mechanical ventilators, and to perform a comparison of adverse events across different adverse event reporting systems. METHODS: We performed a cross-sectional analysis of ventilator-related adverse events reported in 2012 from the following incident reporting systems: the Pennsylvania Patient Safety Authority's Patient Safety Reporting System, UHC's Safety Intelligence Patient Safety Organization database, and the FDA's Manufacturer and User Facility Device Experience database. Once each organization had its dataset of ventilator-related adverse events, reviewers read the narrative descriptions of each event and classified it according to the developed common taxonomy. RESULTS: A Pennsylvania Patient Safety Authority, FDA, and UHC search provided 252, 274, and 700 relevant reports, respectively. The 3 event types most commonly reported to the UHC and the Pennsylvania Patient Safety Authority's Patient Safety Reporting System databases were airway/breathing circuit issue, human factor issues, and ventilator malfunction events. The top 3 event types reported to the FDA were ventilator malfunction, power source issue, and alarm failure. CONCLUSIONS: Overall, we found that (1) through the development of a common taxonomy, adverse events from 3 reporting systems can be evaluated, (2) the types of

  11. INTEGRAL Detection of the First Prompt Gamma-Ray Signal Coincident with the Gravitational-wave Event GW170817

    Energy Technology Data Exchange (ETDEWEB)

    Savchenko, V.; Ferrigno, C.; Bozzo, E.; Courvoisier, T. J.-L. [ISDC, Department of Astronomy, University of Geneva, Chemin d’Écogia, 16 CH-1290 Versoix (Switzerland); Kuulkers, E. [European Space Research and Technology Centre (ESA/ESTEC), Keplerlaan 1, 2201 AZ Noordwijk (Netherlands); Bazzano, A.; Natalucci, L.; Rodi, J. [INAF-Institute for Space Astrophysics and Planetology, Via Fosso del Cavaliere 100, I-00133-Rome (Italy); Brandt, S.; Chenevez, J. [DTU Space, National Space Institute Elektrovej, Building 327 DK-2800 Kongens Lyngby (Denmark); Diehl, R.; Von Kienlin, A. [Max-Planck-Institut für Extraterrestrische Physik, Garching (Germany); Domingo, A. [Centro de Astrobiología (CAB-CSIC/INTA, ESAC Campus), Camino bajo del Castillo S/N, E-28692 Villanueva de la Cañada, Madrid (Spain); Hanlon, L.; Martin-Carrillo, A. [Space Science Group, School of Physics, University College Dublin, Belfield, Dublin 4 (Ireland); Jourdain, E. [IRAP, Université de Toulouse, CNRS, UPS, CNES, 9 Av. Roche, F-31028 Toulouse (France); Laurent, P.; Lebrun, F. [APC, AstroParticule et Cosmologie, Université Paris Diderot, CNRS/IN2P3, CEA/Irfu, Observatoire de Paris Sorbonne Paris Cité, 10 rue Alice Domont et Léonie Duquet, F-75205 Paris Cedex 13 (France); Lutovinov, A. [Space Research Institute of Russian Academy of Sciences, Profsoyuznaya 84/32, 117997 Moscow (Russian Federation); Mereghetti, S. [INAF, IASF-Milano, via E.Bassini 15, I-20133 Milano (Italy); and others

    2017-10-20

    We report the INTernational Gamma-ray Astrophysics Laboratory ( INTEGRAL ) detection of the short gamma-ray burst GRB 170817A (discovered by Fermi -GBM) with a signal-to-noise ratio of 4.6, and, for the first time, its association with the gravitational waves (GWs) from binary neutron star (BNS) merging event GW170817 detected by the LIGO and Virgo observatories. The significance of association between the gamma-ray burst observed by INTEGRAL and GW170817 is 3.2σ, while the association between the Fermi -GBM and INTEGRAL detections is 4.2σ. GRB 170817A was detected by the SPI-ACS instrument about 2 s after the end of the GW event. We measure a fluence of (1.4 ± 0.4 ± 0.6) × 10{sup −7} erg cm{sup −2} (75–2000 keV), where, respectively, the statistical error is given at the 1σ confidence level, and the systematic error corresponds to the uncertainty in the spectral model and instrument response. We also report on the pointed follow-up observations carried out by INTEGRAL , starting 19.5 hr after the event, and lasting for 5.4 days. We provide a stringent upper limit on any electromagnetic signal in a very broad energy range, from 3 keV to 8 MeV, constraining the soft gamma-ray afterglow flux to <7.1 × 10{sup −11} erg cm{sup −2} s{sup −1} (80–300 keV). Exploiting the unique capabilities of INTEGRAL , we constrained the gamma-ray line emission from radioactive decays that are expected to be the principal source of the energy behind a kilonova event following a BNS coalescence. Finally, we put a stringent upper limit on any delayed bursting activity, for example, from a newly formed magnetar.

  12. Using Indexed and Synchronous Events to Model and Validate Cyber-Physical Systems

    Directory of Open Access Journals (Sweden)

    Chen-Wei Wang

    2015-06-01

    Full Text Available Timed Transition Models (TTMs are event-based descriptions for modelling, specifying, and verifying discrete real-time systems. An event can be spontaneous, fair, or timed with specified bounds. TTMs have a textual syntax, an operational semantics, and an automated tool supporting linear-time temporal logic. We extend TTMs and its tool with two novel modelling features for writing high-level specifications: indexed events and synchronous events. Indexed events allow for concise description of behaviour common to a set of actors. The indexing construct allows us to select a specific actor and to specify a temporal property for that actor. We use indexed events to validate the requirements of a train control system. Synchronous events allow developers to decompose simultaneous state updates into actions of separate events. To specify the intended data flow among synchronized actions, we use primed variables to reference the post-state (i.e., one resulted from taking the synchronized actions. The TTM tool automatically infers the data flow from synchronous events, and reports errors on inconsistencies due to circular data flow. We use synchronous events to validate part of the requirements of a nuclear shutdown system. In both case studies, we show how the new notation facilitates the formal validation of system requirements, and use the TTM tool to verify safety, liveness, and real-time properties.

  13. Identification of unusual events in multi-channel bridge monitoring data

    Science.gov (United States)

    Omenzetter, Piotr; Brownjohn, James Mark William; Moyo, Pilate

    2004-03-01

    Continuously operating instrumented structural health monitoring (SHM) systems are becoming a practical alternative to replace visual inspection for assessment of condition and soundness of civil infrastructure such as bridges. However, converting large amounts of data from an SHM system into usable information is a great challenge to which special signal processing techniques must be applied. This study is devoted to identification of abrupt, anomalous and potentially onerous events in the time histories of static, hourly sampled strains recorded by a multi-sensor SHM system installed in a major bridge structure and operating continuously for a long time. Such events may result, among other causes, from sudden settlement of foundation, ground movement, excessive traffic load or failure of post-tensioning cables. A method of outlier detection in multivariate data has been applied to the problem of finding and localising sudden events in the strain data. For sharp discrimination of abrupt strain changes from slowly varying ones wavelet transform has been used. The proposed method has been successfully tested using known events recorded during construction of the bridge, and later effectively used for detection of anomalous post-construction events.

  14. An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection.

    Science.gov (United States)

    Putra, I Putu Edy Suardiyana; Brusey, James; Gaura, Elena; Vesilo, Rein

    2017-12-22

    The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k -nearest neighbor ( k -NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

  15. Particle detection systems and methods

    Science.gov (United States)

    Morris, Christopher L.; Makela, Mark F.

    2010-05-11

    Techniques, apparatus and systems for detecting particles such as muons and neutrons. In one implementation, a particle detection system employs a plurality of drift cells, which can be for example sealed gas-filled drift tubes, arranged on sides of a volume to be scanned to track incoming and outgoing charged particles, such as cosmic ray-produced muons. The drift cells can include a neutron sensitive medium to enable concurrent counting of neutrons. The system can selectively detect devices or materials, such as iron, lead, gold, uranium, plutonium, and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can concurrently detect any unshielded neutron sources occupying the volume from neutrons emitted therefrom. If necessary, the drift cells can be used to also detect gamma rays. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

  16. GRAVITATIONAL MICROLENSING EVENTS AS A TARGET FOR THE SETI PROJECT

    Energy Technology Data Exchange (ETDEWEB)

    Rahvar, Sohrab, E-mail: rahvar@sharif.edu [Department of Physics, Sharif University of Technology, P.O. Box 11365–9161, Tehran (Iran, Islamic Republic of)

    2016-09-01

    The detection of signals from a possible extrasolar technological civilization is one of the most challenging efforts of science. In this work, we propose using natural telescopes made of single or binary gravitational lensing systems to magnify leakage of electromagnetic signals from a remote planet that harbors Extraterrestrial Intelligent (ETI) technology. Currently, gravitational microlensing surveys are monitoring a large area of the Galactic bulge to search for microlensing events, finding more than 2000 events per year. These lenses are capable of playing the role of natural telescopes, and, in some instances, they can magnify radio band signals from planets orbiting around the source stars in gravitational microlensing systems. Assuming that the frequency of electromagnetic waves used for telecommunication in ETIs is similar to ours, we propose follow-up observation of microlensing events with radio telescopes such as the Square Kilometre Array (SKA), the Low Frequency Demonstrators, and the Mileura Wide-Field Array. Amplifying signals from the leakage of broadcasting by an Earth-like civilization will allow us to detect them as far as the center of the Milky Way galaxy. Our analysis shows that in binary microlensing systems, the probability of amplification of signals from ETIs is more than that in single microlensing events. Finally, we propose the use of the target of opportunity mode for follow-up observations of binary microlensing events with SKA as a new observational program for searching ETIs. Using optimistic values for the factors of the Drake equation provides detection of about one event per year.

  17. Ferret Workflow Anomaly Detection System

    National Research Council Canada - National Science Library

    Smith, Timothy J; Bryant, Stephany

    2005-01-01

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

  18. Possible Evidence for an Event Horizon in Cyg XR-1

    Science.gov (United States)

    Dolan, Joseph F.; Fisher, Richard R. (Technical Monitor)

    2001-01-01

    The X-ray emitting component in the Cyg XR-1/HDE226868 system is a leading candidate for identification as a stellar-mass sized black hole. The positive identification of a black hole as predicted by general relativity requires the detection of an event horizon surrounding the point singularity. One signature of such an event horizon would be the existence of dying pulse trains emitted by material spiraling into the event horizon from the last stable orbit around the black hole. We observed the Cyg XR-1 system at three different epochs in a 1400 - 3000 A bandpass with 0.1 ms time resolution using the Hubble Space Telescope's High Speed Photometer. Repeated excursions of the detected flux by more than three standard deviations above the mean are present in the UV flux with FWHM 1 - 10 ms. If any of these excursions are pulses of radiation produced in the system (and not just stochastic variability associated with the Poisson distribution of detected photon arrival times), then this short a timescale requires that the pulses originate in the accretion disk around Cyg XR-1. Two series of pulses with characteristics similar to those expected from dying pulse trains were detected in three hours of observation.

  19. On Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Suresh, Mahima Agumbe; Stoleru, Radu; Zechman, Emily M.; Shihada, Basem

    2013-01-01

    Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against

  20. FOREWORD: 3rd Symposium on Large TPCs for Low Energy Event Detection

    Science.gov (United States)

    Irastorza, Igor G.; Colas, Paul; Gorodetzky, Phillippe

    2007-05-01

    The Third International Symposium on large TPCs for low-energy rare-event detection was held at Carré des sciences, Poincaré auditorium, 25 rue de la Montagne Ste Geneviève in Paris on 11 12 December 2006. This prestigious location belonging to the Ministry of Research is hosted in the former Ecole Polytechnique. The meeting, held in Paris every two years, gathers a significant community of physicists involved in rare event detection. Its purpose is an extensive discussion of present and future projects using large TPCs for low energy, low background detection of rare events (low-energy neutrinos, dark matter, solar axions). The use of a new generation of Micro-Pattern Gaseous Detectors (MPGD) appears to be a promising way to reach this goal. The program this year was enriched by a new session devoted to the detection challenge of polarized gamma rays, relevant novel experimental techniques and the impact on particle physics, astrophysics and astronomy. A very particular feature of this conference is the large variety of talks ranging from purely theoretical to purely experimental subjects including novel technological aspects. This allows discussion and exchange of useful information and new ideas that are emerging to address particle physics experimental challenges. The scientific highlights at the Symposium came on many fronts: Status of low-energy neutrino physics and double-beta decay New ideas on double-beta decay experiments Gamma ray polarization measurement combining high-precision TPCs with MPGD read-out Dark Matter challenges in both axion and WIMP search with new emerging ideas for detection improvements Progress in gaseous and liquid TPCs for rare event detection Georges Charpak opened the meeting with a talk on gaseous detectors for applications in the bio-medical field. He also underlined the importance of new MPGD detectors for both physics and applications. There were about 100 registered participants at the symposium. The successful

  1. Solar system fault detection

    Science.gov (United States)

    Farrington, R.B.; Pruett, J.C. Jr.

    1984-05-14

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  2. Evaluation of a Broad-Spectrum Partially Automated Adverse Event Surveillance System: A Potential Tool for Patient Safety Improvement in Hospitals With Limited Resources.

    Science.gov (United States)

    Saikali, Melody; Tanios, Alain; Saab, Antoine

    2017-11-21

    The aim of the study was to evaluate the sensitivity and resource efficiency of a partially automated adverse event (AE) surveillance system for routine patient safety efforts in hospitals with limited resources. Twenty-eight automated triggers from the hospital information system's clinical and administrative databases identified cases that were then filtered by exclusion criteria per trigger and then reviewed by an interdisciplinary team. The system, developed and implemented using in-house resources, was applied for 45 days of surveillance, for all hospital inpatient admissions (N = 1107). Each trigger was evaluated for its positive predictive value (PPV). Furthermore, the sensitivity of the surveillance system (overall and by AE category) was estimated relative to incidence ranges in the literature. The surveillance system identified a total of 123 AEs among 283 reviewed medical records, yielding an overall PPV of 52%. The tool showed variable levels of sensitivity across and within AE categories when compared with the literature, with a relatively low overall sensitivity estimated between 21% and 44%. Adverse events were detected in 23 of the 36 AE categories defined by an established harm classification system. Furthermore, none of the detected AEs were voluntarily reported. The surveillance system showed variable sensitivity levels across a broad range of AE categories with an acceptable PPV, overcoming certain limitations associated with other harm detection methods. The number of cases captured was substantial, and none had been previously detected or voluntarily reported. For hospitals with limited resources, this methodology provides valuable safety information from which interventions for quality improvement can be formulated.

  3. Station Set Residual: Event Classification Using Historical Distribution of Observing Stations

    Science.gov (United States)

    Procopio, Mike; Lewis, Jennifer; Young, Chris

    2010-05-01

    Analysts working at the International Data Centre in support of treaty monitoring through the Comprehensive Nuclear-Test-Ban Treaty Organization spend a significant amount of time reviewing hypothesized seismic events produced by an automatic processing system. When reviewing these events to determine their legitimacy, analysts take a variety of approaches that rely heavily on training and past experience. One method used by analysts to gauge the validity of an event involves examining the set of stations involved in the detection of an event. In particular, leveraging past experience, an analyst can say that an event located in a certain part of the world is expected to be detected by Stations A, B, and C. Implicit in this statement is that such an event would usually not be detected by Stations X, Y, or Z. For some well understood parts of the world, the absence of one or more "expected" stations—or the presence of one or more "unexpected" stations—is correlated with a hypothesized event's legitimacy and to its survival to the event bulletin. The primary objective of this research is to formalize and quantify the difference between the observed set of stations detecting some hypothesized event, versus the expected set of stations historically associated with detecting similar nearby events close in magnitude. This Station Set Residual can be quantified in many ways, some of which are correlated with the analysts' determination of whether or not the event is valid. We propose that this Station Set Residual score can be used to screen out certain classes of "false" events produced by automatic processing with a high degree of confidence, reducing the analyst burden. Moreover, we propose that the visualization of the historically expected distribution of detecting stations can be immediately useful as an analyst aid during their review process.

  4. Nuclear fuel element leak detection system

    International Nuclear Information System (INIS)

    John, C.D. Jr.

    1978-01-01

    Disclosed is a leak detection system integral with a wall of a building used to fabricate nuclear fuel elements for detecting radiation leakage from the nuclear fuel elements as the fuel elements exit the building. The leak detecting system comprises a shielded compartment constructed to withstand environmental hazards extending into a similarly constructed building and having sealed doors on both ends along with leak detecting apparatus connected to the compartment. The leak detecting system provides a system for removing a nuclear fuel element from its fabrication building while testing for radiation leaks in the fuel element

  5. Event-Triggered Output-Feedback Control for Disturbed Linear Systems

    Directory of Open Access Journals (Sweden)

    Hao Jiang

    2018-01-01

    Full Text Available In the last few decades, event-triggered control received considerable attention, because of advantages in reducing the resource utilization, such as communication load and processor. In this paper, we propose an event-triggered output-feedback controller for disturbed linear systems, in order to achieve both better resource utilization and disturbance attenuation properties at the same time. Based on our prior work on state-feedback H∞ control for disturbed systems, we propose an approach to design an output-feedback H∞ controller for the system whose states are not completely observable, and a sufficient condition guaranteeing the asymptotic stability and robustness of the system is given in the form of LMIs (Linear Matrix Inequalities.

  6. A broad-application microchannel-plate detector system for advanced particle or photon detection tasks large area imaging, precise multi-hit timing information and high detection rate

    CERN Document Server

    Jagutzki, O; Mergel, V; Schmidt-Böcking, H; Spielberger, L; Spillmann, U; Ullmann-Pfleger, K

    2002-01-01

    New applications for single particle and photon detection in many fields require both large area imaging performance and precise time information on each detected particle. Moreover, a very high data acquisition rate is desirable for most applications and eventually the detection and imaging of more than one particle arriving within a microsecond is required. Commercial CCD systems lack the timing information whereas other electronic microchannel plate (MCP) read-out schemes usually suffer from a low acquisition rate and complicated and sometimes costly read-out electronics. We have designed and tested a complete imaging system consisting of an MCP position readout with helical wire delay-lines, single-unit amplifier box and PC-controlled time-to-digital converter (TDC) readout. The system is very flexible and can detect and analyse position and timing information at single particle rates beyond 1 MHz. Alternatively, multi-hit events can be collected and analysed at about 20 kHz rate. We discuss the advantage...

  7. A fast large-area position-sensitive time-of-flight neutron detection system

    International Nuclear Information System (INIS)

    Crawford, R.K.; Haumann, J.R.

    1989-01-01

    A new position-sensitive time-of-flight neutron detection and histograming system has been developed for use at the Intense Pulsed Neutron Source. Spatial resolution of roughly 1 cm x 1 cm and time-of-flight resolution of ∼1 μsec are combined in a detection system which can ultimately be expanded to cover several square meters of active detector area. This system is based on the use of arrays of cylindrical one-dimensional position-sensitive proportional counters, and is capable of collecting the x-y-t data and sorting them into histograms at time-averaged data rates up to ∼300,000 events/sec over the full detector area and with instantaneous data rates up to more than fifty times that. Numerous hardware features have been incorporated to facilitate initial tuning of the position encoding, absolute calibration of the encoded positions, and automatic testing for drifts. 7 refs., 11 figs., 1 tabs

  8. Rule-based expert system for maritime anomaly detection

    Science.gov (United States)

    Roy, Jean

    2010-04-01

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

  9. A Novel Chewing Detection System Based on PPG, Audio, and Accelerometry.

    Science.gov (United States)

    Papapanagiotou, Vasileios; Diou, Christos; Zhou, Lingchuan; van den Boer, Janet; Mars, Monica; Delopoulos, Anastasios

    2017-05-01

    In the context of dietary management, accurate monitoring of eating habits is receiving increased attention. Wearable sensors, combined with the connectivity and processing of modern smartphones, can be used to robustly extract objective and real-time measurements of human behavior. In particular, for the task of chewing detection, several approaches based on an in-ear microphone can be found in the literature, while other types of sensors have also been reported, such as strain sensors. In this paper, performed in the context of the SPLENDID project, we propose to combine an in-ear microphone with a photoplethysmography (PPG) sensor placed in the ear concha, in a new high accuracy and low sampling rate prototype chewing detection system. We propose a pipeline that initially processes each sensor signal separately, and then fuses both to perform the final detection. Features are extracted from each modality, and support vector machine (SVM) classifiers are used separately to perform snacking detection. Finally, we combine the SVM scores from both signals in a late-fusion scheme, which leads to increased eating detection accuracy. We evaluate the proposed eating monitoring system on a challenging, semifree living dataset of 14 subjects, which includes more than 60 h of audio and PPG signal recordings. Results show that fusing the audio and PPG signals significantly improves the effectiveness of eating event detection, achieving accuracy up to 0.938 and class-weighted accuracy up to 0.892.

  10. 10 CFR 50.73 - Licensee event report system.

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 1 2010-01-01 2010-01-01 false Licensee event report system. 50.73 Section 50.73 Energy NUCLEAR REGULATORY COMMISSION DOMESTIC LICENSING OF PRODUCTION AND UTILIZATION FACILITIES Inspections... systems, including: emergency diesel generators (EDGs); hydroelectric facilities used in lieu of EDGs at...

  11. Demonstration of a Novel Synchrophasor-based Situational Awareness System: Wide Area Power System Visualization, On-line Event Replay and Early Warning of Grid Problems

    Energy Technology Data Exchange (ETDEWEB)

    Rosso, A.

    2012-12-31

    Since the large North Eastern power system blackout on August 14, 2003, U.S. electric utilities have spent lot of effort on preventing power system cascading outages. Two of the main causes of the August 14, 2003 blackout were inadequate situational awareness and inadequate operator training In addition to the enhancements of the infrastructure of the interconnected power systems, more research and development of advanced power system applications are required for improving the wide-area security monitoring, operation and planning in order to prevent large- scale cascading outages of interconnected power systems. It is critically important for improving the wide-area situation awareness of the operators or operational engineers and regional reliability coordinators of large interconnected systems. With the installation of large number of phasor measurement units (PMU) and the related communication infrastructure, it will be possible to improve the operators’ situation awareness and to quickly identify the sequence of events during a large system disturbance for the post-event analysis using the real-time or historical synchrophasor data. The purpose of this project was to develop and demonstrate a novel synchrophasor-based comprehensive situational awareness system for control centers of power transmission systems. The developed system named WASA (Wide Area Situation Awareness) is intended to improve situational awareness at control centers of the power system operators and regional reliability coordinators. It consists of following main software modules: • Wide-area visualizations of real-time frequency, voltage, and phase angle measurements and their contour displays for security monitoring. • Online detection and location of a major event (location, time, size, and type, such as generator or line outage). • Near-real-time event replay (in seconds) after a major event occurs. • Early warning of potential wide-area stability problems. The system has been

  12. Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

    Science.gov (United States)

    Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

    2012-04-01

    Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

  13. FDA Adverse Event Reporting System (FAERS): Latest Quartely Data Files

    Data.gov (United States)

    U.S. Department of Health & Human Services — The FDA Adverse Event Reporting System (FAERS) is a database that contains information on adverse event and medication error reports submitted to FDA. The database...

  14. 46 CFR 108.405 - Fire detection system.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Fire detection system. 108.405 Section 108.405 Shipping... EQUIPMENT Fire Extinguishing Systems § 108.405 Fire detection system. (a) Each fire detection system and each smoke detection system on a unit must— (1) Be approved by the Commandant; and (2) Have a visual...

  15. Proximity detection system underground

    Energy Technology Data Exchange (ETDEWEB)

    Denis Kent [Mine Site Technologies (Australia)

    2008-04-15

    Mine Site Technologies (MST) with the support ACARP and Xstrata Coal NSW, as well as assistance from Centennial Coal, has developed a Proximity Detection System to proof of concept stage as per plan. The basic aim of the project was to develop a system to reduce the risk of the people coming into contact with vehicles in an uncontrolled manner (i.e. being 'run over'). The potential to extend the developed technology into other areas, such as controls for vehicle-vehicle collisions and restricting access of vehicle or people into certain zones (e.g. non FLP vehicles into Hazardous Zones/ERZ) was also assessed. The project leveraged off MST's existing Intellectual Property and experience gained with our ImPact TRACKER tagging technology, allowing the development to be fast tracked. The basic concept developed uses active RFID Tags worn by miners underground to be detected by vehicle mounted Readers. These Readers in turn provide outputs that can be used to alert a driver (e.g. by light and/or audible alarm) that a person (Tag) approaching within their vicinity. The prototype/test kit developed proved the concept and technology, the four main components being: Active RFID Tags to send out signals for detection by vehicle mounted receivers; Receiver electronics to detect RFID Tags approaching within the vicinity of the unit to create a long range detection system (60 m to 120 m); A transmitting/exciter device to enable inner detection zone (within 5 m to 20 m); and A software/hardware device to process & log incoming Tags reads and create certain outputs. Tests undertaken in the laboratory and at a number of mine sites, confirmed the technology path taken could form the basis of a reliable Proximity Detection/Alert System.

  16. Development of electrochemical biosensor for detection of pathogenic microorganism in Asian dust events.

    Science.gov (United States)

    Yoo, Min-Sang; Shin, Minguk; Kim, Younghun; Jang, Min; Choi, Yoon-E; Park, Si Jae; Choi, Jonghoon; Lee, Jinyoung; Park, Chulhwan

    2017-05-01

    We developed a single-walled carbon nanotubes (SWCNTs)-based electrochemical biosensor for the detection of Bacillus subtilis, one of the microorganisms observed in Asian dust events, which causes respiratory diseases such as asthma and pneumonia. SWCNTs plays the role of a transducer in biological antigen/antibody reaction for the electrical signal while 1-pyrenebutanoic acid succinimidyl ester (1-PBSE) and ant-B. subtilis were performed as a chemical linker and an acceptor, respectively, for the adhesion of target microorganism in the developed biosensor. The detection range (10 2 -10 10  CFU/mL) and the detection limit (10 2  CFU/mL) of the developed biosensor were identified while the response time was 10 min. The amount of target B. subtilis was the highest in the specificity test of the developed biosensor, compared with the other tested microorganisms (Staphylococcus aureus, Flavobacterium psychrolimnae, and Aquabacterium commune). In addition, target B. subtilis detected by the developed biosensor was observed by scanning electron microscope (SEM) analysis. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Event-triggered control systems under packet losses

    NARCIS (Netherlands)

    Dolk, V.S.; Heemels, W.P.M.H.

    2017-01-01

    Networked control systems (NCSs) offer many benefits in terms of increased flexibility and maintainability but might also suffer from inevitable imperfections such as packet dropouts and limited communications resources. In this paper, (static and dynamic) event-triggered control (ETC) strategies

  18. Selection of initial events of accelerator driven subcritical system

    International Nuclear Information System (INIS)

    Wang Qianglong; Hu Liqin; Wang Jiaqun; Li Yazhou; Yang Zhiyi

    2013-01-01

    The Probabilistic Safety Assessment (PSA) is an important tool in reactor safety analysis and a significant reference to the design and operation of reactor. It is the origin and foundation of the PSA for a reactor to select the initial events. Accelerator Driven Subcritical System (ADS) has advanced design characteristics, complicated subsystems and little engineering and operating experience, which makes it much more difficult to identify the initial events of ADS. Based on the current design project of ADS, the system's safety characteristics and special issues were analyzed in this article. After a series of deductions with Master Logic Diagram (MLD) and considering the relating experience of other advanced research reactors, a preliminary initial events was listed finally, which provided the foundation for the next safety assessment. (authors)

  19. 29 CFR 1910.164 - Fire detection systems.

    Science.gov (United States)

    2010-07-01

    ... 29 Labor 5 2010-07-01 2010-07-01 false Fire detection systems. 1910.164 Section 1910.164 Labor... detection systems. (a) Scope and application. This section applies to all automatic fire detection systems... detection systems and components to normal operating condition as promptly as possible after each test or...

  20. Semblance for microseismic event detection

    Czech Academy of Sciences Publication Activity Database

    Staněk, František; Anikiev, D.; Valenta, Jan; Eisner, Leo

    2015-01-01

    Roč. 201, č. 3 (2015), s. 1362-1369 ISSN 0956-540X R&D Projects: GA ČR GAP210/12/2451 Institutional support: RVO:67985891 Keywords : microseismic event * microseismic monitoring * source mechanisms Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 2.484, year: 2015

  1. Characterisation of the Photon Detection System for the LHCb RICH Detector Upgrade

    CERN Document Server

    AUTHOR|(CDS)2097582; D'Ambrosio, Carmelo; Easo, Sajan

    The LHCb Experiment will be upgraded during Long Shutdown II of the Large Hadron Collider (LHC) in 2019 and 2020. The goal of the upgrade is to efficiently use the increased instantaneous luminosity in LHC Run 3 and to collect data at the proton collision rate of 40 MHz. The Ring Imaging Cherenkov (RICH) particle identification detectors will be upgraded to perform in the new operating conditions with continuing reliability. The photon detection system will be replaced using multi-anode photomultiplier tubes (MaPMTs) and associated read-out electronics. The photon detection chain was studied at CERN using a pulsed laser to test the system under high event rates and high photon intensities. The behaviour of two types of MaPMTs which are foreseen for the upgrade is presented for varying rates and intensities, and different applied bias voltages. A simulation was created to model the photon detection chain using the Geant4 simulation toolkit. The RICH Upgrade test beam using 180 GeV positive hadrons from CERN SP...

  2. An Event-Based Approach to Distributed Diagnosis of Continuous Systems

    Science.gov (United States)

    Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon

    2010-01-01

    Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

  3. Trajectory-probed instability and statistics of desynchronization events in coupled chaotic systems

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Gilson F. de, E-mail: gilson@otica.ufpb.br; Chevrollier, Martine; Oriá, Marcos [Departamento de Física, Universidade Federal da Paraíba, Caixa Postal 5008, 58051-900 João Pessoa-PB (Brazil); Passerat de Silans, Thierry [Departamento de Física, Universidade Federal da Paraíba, Caixa Postal 5008, 58051-900 João Pessoa-PB (Brazil); UAF, Universidade Federal de Campina Grande, 58429-900 Campina Grande, PB (Brazil); Souza Cavalcante, Hugo L. D. de [Departamento de Informática, Centro de Informática, Universidade Federal da Paraíba, Av. dos Escoteiros s/n, Mangabeira VII, 58055-000 João Pessoa, PB (Brazil)

    2015-11-15

    Complex systems, such as financial markets, earthquakes, and neurological networks, exhibit extreme events whose mechanisms of formation are not still completely understood. These mechanisms may be identified and better studied in simpler systems with dynamical features similar to the ones encountered in the complex system of interest. For instance, sudden and brief departures from the synchronized state observed in coupled chaotic systems were shown to display non-normal statistical distributions similar to events observed in the complex systems cited above. The current hypothesis accepted is that these desynchronization events are influenced by the presence of unstable object(s) in the phase space of the system. Here, we present further evidence that the occurrence of large events is triggered by the visitation of the system's phase-space trajectory to the vicinity of these unstable objects. In the system studied here, this visitation is controlled by a single parameter, and we exploit this feature to observe the effect of the visitation rate in the overall instability of the synchronized state. We find that the probability of escapes from the synchronized state and the size of those desynchronization events are enhanced in attractors whose shapes permit the chaotic trajectories to approach the region of strong instability. This result shows that the occurrence of large events requires not only a large local instability to amplify noise, or to amplify the effect of parameter mismatch between the coupled subsystems, but also that the trajectories of the system wander close to this local instability.

  4. Trajectory-probed instability and statistics of desynchronization events in coupled chaotic systems

    International Nuclear Information System (INIS)

    Oliveira, Gilson F. de; Chevrollier, Martine; Oriá, Marcos; Passerat de Silans, Thierry; Souza Cavalcante, Hugo L. D. de

    2015-01-01

    Complex systems, such as financial markets, earthquakes, and neurological networks, exhibit extreme events whose mechanisms of formation are not still completely understood. These mechanisms may be identified and better studied in simpler systems with dynamical features similar to the ones encountered in the complex system of interest. For instance, sudden and brief departures from the synchronized state observed in coupled chaotic systems were shown to display non-normal statistical distributions similar to events observed in the complex systems cited above. The current hypothesis accepted is that these desynchronization events are influenced by the presence of unstable object(s) in the phase space of the system. Here, we present further evidence that the occurrence of large events is triggered by the visitation of the system's phase-space trajectory to the vicinity of these unstable objects. In the system studied here, this visitation is controlled by a single parameter, and we exploit this feature to observe the effect of the visitation rate in the overall instability of the synchronized state. We find that the probability of escapes from the synchronized state and the size of those desynchronization events are enhanced in attractors whose shapes permit the chaotic trajectories to approach the region of strong instability. This result shows that the occurrence of large events requires not only a large local instability to amplify noise, or to amplify the effect of parameter mismatch between the coupled subsystems, but also that the trajectories of the system wander close to this local instability

  5. Trajectory-probed instability and statistics of desynchronization events in coupled chaotic systems

    Science.gov (United States)

    de Oliveira, Gilson F.; Chevrollier, Martine; Passerat de Silans, Thierry; Oriá, Marcos; de Souza Cavalcante, Hugo L. D.

    2015-11-01

    Complex systems, such as financial markets, earthquakes, and neurological networks, exhibit extreme events whose mechanisms of formation are not still completely understood. These mechanisms may be identified and better studied in simpler systems with dynamical features similar to the ones encountered in the complex system of interest. For instance, sudden and brief departures from the synchronized state observed in coupled chaotic systems were shown to display non-normal statistical distributions similar to events observed in the complex systems cited above. The current hypothesis accepted is that these desynchronization events are influenced by the presence of unstable object(s) in the phase space of the system. Here, we present further evidence that the occurrence of large events is triggered by the visitation of the system's phase-space trajectory to the vicinity of these unstable objects. In the system studied here, this visitation is controlled by a single parameter, and we exploit this feature to observe the effect of the visitation rate in the overall instability of the synchronized state. We find that the probability of escapes from the synchronized state and the size of those desynchronization events are enhanced in attractors whose shapes permit the chaotic trajectories to approach the region of strong instability. This result shows that the occurrence of large events requires not only a large local instability to amplify noise, or to amplify the effect of parameter mismatch between the coupled subsystems, but also that the trajectories of the system wander close to this local instability.

  6. A reliable sewage quality abnormal event monitoring system.

    Science.gov (United States)

    Li, Tianling; Winnel, Melissa; Lin, Hao; Panther, Jared; Liu, Chang; O'Halloran, Roger; Wang, Kewen; An, Taicheng; Wong, Po Keung; Zhang, Shanqing; Zhao, Huijun

    2017-09-15

    With closing water loop through purified recycled water, wastewater becomes a part of source water, requiring reliable wastewater quality monitoring system (WQMS) to manage wastewater source and mitigate potential health risks. However, the development of reliable WQMS is fatally constrained by severe contamination and biofouling of sensors due to the hostile analytical environment of wastewaters, especially raw sewages, that challenges the limit of existing sensing technologies. In this work, we report a technological solution to enable the development of WQMS for real-time abnormal event detection with high reliability and practicality. A vectored high flow hydrodynamic self-cleaning approach and a dual-sensor self-diagnostic concept are adopted for WQMS to effectively encounter vital sensor failing issues caused by contamination and biofouling and ensure the integrity of sensing data. The performance of the WQMS has been evaluated over a 3-year trial period at different sewage catchment sites across three Australian states. It has demonstrated that the developed WQMS is capable of continuously operating in raw sewage for a prolonged period up to 24 months without maintenance and failure, signifying the high reliability and practicality. The demonstrated WQMS capability to reliably acquire real-time wastewater quality information leaps forward the development of effective wastewater source management system. The reported self-cleaning and self-diagnostic concepts should be applicable to other online water quality monitoring systems, opening a new way to encounter the common reliability and stability issues caused by sensor contamination and biofouling. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Sources Of Incidental Events In Collective Water Supply System

    Directory of Open Access Journals (Sweden)

    Szpak Dawid

    2015-11-01

    Full Text Available The publication presents the main types of incidental events in collective water supply system. The special attention was addressed to the incidental events associated with a decrease in water quality, posing a threat to the health and life of inhabitants. The security method against incidental contamination in the water source was described.

  8. [Performance and optimisation of a trigger tool for the detection of adverse events in hospitalised adult patients].

    Science.gov (United States)

    Guzmán Ruiz, Óscar; Pérez Lázaro, Juan José; Ruiz López, Pedro

    To characterise the performance of the triggers used in the detection of adverse events (AE) of hospitalised adult patients and to define a simplified panel of triggers to facilitate the detection of AE. Cross-sectional study of charts of patients from a service of internal medicine to detect EA through systematic review of the charts and identification of triggers (clinical event often related to AE), determining if there was AE as the context in which it appeared the trigger. Once the EA was detected, we proceeded to the characterization of the triggers that detected it. Logistic regression was applied to select the triggers with greater AE detection capability. A total of 291 charts were reviewed, with a total of 562 triggers in 103 patients, of which 163 were involved in detecting an AE. The triggers that detected the most AE were "A.1. Pressure ulcer" (9.82%), "B.5. Laxative or enema" (8.59%), "A.8. Agitation" (8.59%), "A.9. Over-sedation" (7.98%), "A.7. Haemorrhage" (6.75%) and "B.4. Antipsychotic" (6.75%). A simplified model was obtained using logistic regression, and included the variable "Number of drugs" and the triggers "Over-sedation", "Urinary catheterisation", "Readmission in 30 days", "Laxative or enema" and "Abrupt medication stop". This model showed a probability of 81% to correctly classify charts with EA or without EA (p <0.001; 95% confidence interval: 0.763-0.871). A high number of triggers were associated with AE. The summary model is capable of detecting a large amount of AE, with a minimum of elements. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  9. Episodic inflation events at Akutan Volcano, Alaska, during 2005-2017

    Science.gov (United States)

    Ji, Kang Hyeun; Yun, Sang-Ho; Rim, Hyoungrea

    2017-08-01

    Detection of weak volcano deformation helps constrain characteristics of eruption cycles. We have developed a signal detection technique, called the Targeted Projection Operator (TPO), to monitor surface deformation with Global Positioning System (GPS) data. We have applied the TPO to GPS data collected at Akutan Volcano from June 2005 to March 2017 and detected four inflation events that occurred in 2008, 2011, 2014, and 2016 with inflation rates of about 8-22 mm/yr above the background trend at a near-source site AV13. Numerical modeling suggests that the events should be driven by closely located sources or a single source in a shallow magma chamber at a depth of about 4 km. The inflation events suggest that magma has episodically accumulated in a shallow magma chamber.

  10. Fluorescence detection system for microfluidic droplets

    Science.gov (United States)

    Chen, Binyu; Han, Xiaoming; Su, Zhen; Liu, Quanjun

    2018-05-01

    In microfluidic detection technology, because of the universality of optical methods in laboratory, optical detection is an attractive solution for microfluidic chip laboratory equipment. In addition, the equipment with high stability and low cost can be realized by integrating appropriate optical detection technology on the chip. This paper reports a detection system for microfluidic droplets. Photomultiplier tubes (PMT) is used as a detection device to improve the sensitivity of detection. This system improves the signal to noise ratio by software filtering and spatial filter. The fluorescence intensity is proportional to the concentration of the fluorescence and intensity of the laser. The fluorescence micro droplets of different concentrations can be distinguished by this system.

  11. Idaho Explosives Detection System

    International Nuclear Information System (INIS)

    Reber, Edward L.; Blackwood, Larry G.; Edwards, Andrew J.; Jewell, J. Keith; Rohde, Kenneth W.; Seabury, Edward H.; Klinger, Jeffery B.

    2005-01-01

    The Idaho Explosives Detection System was developed at the Idaho National Laboratory (INL) to respond to threats imposed by delivery trucks potentially carrying explosives into military bases. A full-scale prototype system has been built and is currently undergoing testing. The system consists of two racks, one on each side of a subject vehicle. Each rack includes a neutron generator and an array of NaI detectors. The two neutron generators are pulsed and synchronized. A laptop computer controls the entire system. The control software is easily operable by minimally trained staff. The system was developed to detect explosives in a medium size truck within a 5-min measurement time. System performance was successfully demonstrated with explosives at the INL in June 2004 and at Andrews Air Force Base in July 2004

  12. Idaho Explosives Detection System

    Energy Technology Data Exchange (ETDEWEB)

    Reber, Edward L. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States)]. E-mail: reber@inel.gov; Blackwood, Larry G. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Edwards, Andrew J. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Jewell, J. Keith [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Rohde, Kenneth W. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Seabury, Edward H. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Klinger, Jeffery B. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States)

    2005-12-15

    The Idaho Explosives Detection System was developed at the Idaho National Laboratory (INL) to respond to threats imposed by delivery trucks potentially carrying explosives into military bases. A full-scale prototype system has been built and is currently undergoing testing. The system consists of two racks, one on each side of a subject vehicle. Each rack includes a neutron generator and an array of NaI detectors. The two neutron generators are pulsed and synchronized. A laptop computer controls the entire system. The control software is easily operable by minimally trained staff. The system was developed to detect explosives in a medium size truck within a 5-min measurement time. System performance was successfully demonstrated with explosives at the INL in June 2004 and at Andrews Air Force Base in July 2004.

  13. Method for the depth corrected detection of ionizing events from a co-planar grids sensor

    Science.gov (United States)

    De Geronimo, Gianluigi [Syosset, NY; Bolotnikov, Aleksey E [South Setauket, NY; Carini, Gabriella [Port Jefferson, NY

    2009-05-12

    A method for the detection of ionizing events utilizing a co-planar grids sensor comprising a semiconductor substrate, cathode electrode, collecting grid and non-collecting grid. The semiconductor substrate is sensitive to ionizing radiation. A voltage less than 0 Volts is applied to the cathode electrode. A voltage greater than the voltage applied to the cathode is applied to the non-collecting grid. A voltage greater than the voltage applied to the non-collecting grid is applied to the collecting grid. The collecting grid and the non-collecting grid are summed and subtracted creating a sum and difference respectively. The difference and sum are divided creating a ratio. A gain coefficient factor for each depth (distance between the ionizing event and the collecting grid) is determined, whereby the difference between the collecting electrode and the non-collecting electrode multiplied by the corresponding gain coefficient is the depth corrected energy of an ionizing event. Therefore, the energy of each ionizing event is the difference between the collecting grid and the non-collecting grid multiplied by the corresponding gain coefficient. The depth of the ionizing event can also be determined from the ratio.

  14. Alarm systems detect volcanic tremor and earthquake swarms during Redoubt eruption, 2009

    Science.gov (United States)

    Thompson, G.; West, M. E.

    2009-12-01

    We ran two alarm algorithms on real-time data from Redoubt volcano during the 2009 crisis. The first algorithm was designed to detect escalations in continuous seismicity (tremor). This is implemented within an application called IceWeb which computes reduced displacement, and produces plots of reduced displacement and spectrograms linked to the Alaska Volcano Observatory internal webpage every 10 minutes. Reduced displacement is a measure of the amplitude of volcanic tremor, and is computed by applying a geometrical spreading correction to a displacement seismogram. When the reduced displacement at multiple stations exceeds pre-defined thresholds and there has been a factor of 3 increase in reduced displacement over the previous hour, a tremor alarm is declared. The second algorithm was to designed to detect earthquake swarms. The mean and median event rates are computed every 5 minutes based on the last hour of data from a real-time event catalog. By comparing these with thresholds, three swarm alarm conditions can be declared: a new swarm, an escalation in a swarm, and the end of a swarm. The end of swarm alarm is important as it may mark a transition from swarm to continuous tremor. Alarms from both systems were dispatched using a generic alarm management system which implements a call-down list, allowing observatory scientists to be called in sequence until someone acknowledged the alarm via a confirmation web page. The results of this simple approach are encouraging. The tremor alarm algorithm detected 26 of the 27 explosive eruptions that occurred from 23 March - 4 April. The swarm alarm algorithm detected all five of the main volcanic earthquake swarm episodes which occurred during the Redoubt crisis on 26-27 February, 21-23 March, 26 March, 2-4 April and 3-7 May. The end-of-swarm alarms on 23 March and 4 April were particularly helpful as they were caused by transitions from swarm to tremor shortly preceding explosive eruptions; transitions which were

  15. Damage Detection and Deteriorating Structural Systems

    DEFF Research Database (Denmark)

    Long, Lijia; Thöns, Sebastian; Döhler, Michael

    2017-01-01

    This paper addresses the quantification of the value of damage detection system and algorithm information on the basis of Value of Information (VoI) analysis to enhance the benefit of damage detection information by providing the basis for its optimization before it is performed and implemented....... The approach of the quantification the value of damage detection information builds upon the Bayesian decision theory facilitating the utilization of damage detection performance models, which describe the information and its precision on structural system level, facilitating actions to ensure the structural...... detection information is determined utilizing Bayesian updating. The damage detection performance is described with the probability of indication for different component and system damage states taking into account type 1 and type 2 errors. The value of damage detection information is then calculated...

  16. Synchronous Parallel Emulation and Discrete Event Simulation System with Self-Contained Simulation Objects and Active Event Objects

    Science.gov (United States)

    Steinman, Jeffrey S. (Inventor)

    1998-01-01

    The present invention is embodied in a method of performing object-oriented simulation and a system having inter-connected processor nodes operating in parallel to simulate mutual interactions of a set of discrete simulation objects distributed among the nodes as a sequence of discrete events changing state variables of respective simulation objects so as to generate new event-defining messages addressed to respective ones of the nodes. The object-oriented simulation is performed at each one of the nodes by assigning passive self-contained simulation objects to each one of the nodes, responding to messages received at one node by generating corresponding active event objects having user-defined inherent capabilities and individual time stamps and corresponding to respective events affecting one of the passive self-contained simulation objects of the one node, restricting the respective passive self-contained simulation objects to only providing and receiving information from die respective active event objects, requesting information and changing variables within a passive self-contained simulation object by the active event object, and producing corresponding messages specifying events resulting therefrom by the active event objects.

  17. Designing and evaluating an automated system for real-time medication administration error detection in a neonatal intensive care unit.

    Science.gov (United States)

    Ni, Yizhao; Lingren, Todd; Hall, Eric S; Leonard, Matthew; Melton, Kristin; Kirkendall, Eric S

    2018-05-01

    Timely identification of medication administration errors (MAEs) promises great benefits for mitigating medication errors and associated harm. Despite previous efforts utilizing computerized methods to monitor medication errors, sustaining effective and accurate detection of MAEs remains challenging. In this study, we developed a real-time MAE detection system and evaluated its performance prior to system integration into institutional workflows. Our prospective observational study included automated MAE detection of 10 high-risk medications and fluids for patients admitted to the neonatal intensive care unit at Cincinnati Children's Hospital Medical Center during a 4-month period. The automated system extracted real-time medication use information from the institutional electronic health records and identified MAEs using logic-based rules and natural language processing techniques. The MAE summary was delivered via a real-time messaging platform to promote reduction of patient exposure to potential harm. System performance was validated using a physician-generated gold standard of MAE events, and results were compared with those of current practice (incident reporting and trigger tools). Physicians identified 116 MAEs from 10 104 medication administrations during the study period. Compared to current practice, the sensitivity with automated MAE detection was improved significantly from 4.3% to 85.3% (P = .009), with a positive predictive value of 78.0%. Furthermore, the system showed potential to reduce patient exposure to harm, from 256 min to 35 min (P patient exposure to potential harm following MAE events.

  18. Gas detection system

    International Nuclear Information System (INIS)

    Allan, C.J.; Bayly, J.G.

    1975-01-01

    The gas detection system provides for the effective detection of gas leaks over a large area. It includes a laser which has a laser line corresponding to an absorption line of the gas to be detected. A He-Xe laser scans a number of retroreflectors which are strategically located around a D 2 O plant to detect H 2 S leaks. The reflected beam is focused by a telescope, filtered, and passed into an infrared detector. The laser may be made to emit two frequencies, one of which corresponds with an H 2 S absorption line; or it may be modulated on and off the H 2 S absorption line. The relative amplitude of the absorbed light will be a measure of the H 2 S present

  19. Licensee Event Report system: description of system and guidelines for reporting

    International Nuclear Information System (INIS)

    Hebdon, F.J.

    1983-09-01

    On July 26, 1983, the Commission published in the Federal Register a final rule (10 CFR 50.73) that modifies and codifies the Licensee Event Report (LER) system. The rule becomes effective on January 1, 1984. This NUREG provides supporting information and guidance that will be of interest to persons responsible for the preparation and review of LERs. The information contained in this NUREG includes: (1) a brief description of how LERs are analyzed by the NRC; (2) a restatement of the guidance contained in the Statement of Consideration that accompanied the publication of the LER rule; (3) a set of examples of potentially reportable events with staff comments on the actual reportability of each event; (4) guidance on how to prepare an LER, including the LER forms; and (5) guidance on submittal of LERs

  20. Amaro-autonomous real-time detection of moving maritime objects: introducing a flight experiment for an on-board ship detection system

    Science.gov (United States)

    Schwenk, Kurt; Willburger, Katharina; Pless, Sebastian

    2017-10-01

    send product data, like position, size and a small image of the ship directly to the user's smart-phone by email. The time between the acquisition of the image data and the delivery of the product data to the end-user is aimed to be less than three minutes. For communication, the SMS-like Iridium Short Burst Data (SBD) Service was chosen, providing a message size of around 300 Bytes. Under optimal sending/receiving conditions, messages can be transmitted bidirectional every 20 seconds. Due to the very small data bandwidth, not all product data may be transmittable at once, for instance, when flying over busy ships traffic zones. Therefore the system offers two services: a query and a push service. With the query service the end user can explicitly request data of a defined location and fixed time period by posting queries in an SQL-like language. With the push service, events can be predefined and messages are received automatically, if and when the event occurs. Finally, the hardware set-up, details of the ship detection algorithms and the current status of the experiment is presented.

  1. Semiconductor radiation detection systems

    CERN Document Server

    2010-01-01

    Covers research in semiconductor detector and integrated circuit design in the context of medical imaging using ionizing radiation. This book explores other applications of semiconductor radiation detection systems in security applications such as luggage scanning, dirty bomb detection and border control.

  2. FREQUENCY OF SOLAR-LIKE SYSTEMS AND OF ICE AND GAS GIANTS BEYOND THE SNOW LINE FROM HIGH-MAGNIFICATION MICROLENSING EVENTS IN 2005-2008

    International Nuclear Information System (INIS)

    Gould, A.; Dong, Subo; Gaudi, B. S.; Han, C.

    2010-01-01

    We present the first measurement of the planet frequency beyond the 'snow line', for the planet-to-star mass-ratio interval -4.5 2 N pl )/(d log q d log s) = (0.36±0.15) dex -2 at the mean mass ratio q = 5 x 10 -4 with no discernible deviation from a flat (Oepik's law) distribution in log-projected separation s. The determination is based on a sample of six planets detected from intensive follow-up observations of high-magnification (A>200) microlensing events during 2005-2008. The sampled host stars have a typical mass M host ∼ 0.5 M sun , and detection is sensitive to planets over a range of planet-star-projected separations (s -1 max R E , s max R E ), where R E ∼ 3.5 AU(M host /M sun ) 1/2 is the Einstein radius and s max ∼ (q/10 -4.3 ) 1/3 . This corresponds to deprojected separations roughly three times the 'snow line'. We show that the observations of these events have the properties of a 'controlled experiment', which is what permits measurement of absolute planet frequency. High-magnification events are rare, but the survey-plus-follow-up high-magnification channel is very efficient: half of all high-mag events were successfully monitored and half of these yielded planet detections. The extremely high sensitivity of high-mag events leads to a policy of monitoring them as intensively as possible, independent of whether they show evidence of planets. This is what allows us to construct an unbiased sample. The planet frequency derived from microlensing is a factor 8 larger than the one derived from Doppler studies at factor ∼25 smaller star-planet separations (i.e., periods 2-2000 days). However, this difference is basically consistent with the gradient derived from Doppler studies (when extrapolated well beyond the separations from which it is measured). This suggests a universal separation distribution across 2 dex in planet-star separation, 2 dex in mass ratio, and 0.3 dex in host mass. Finally, if all planetary systems were 'analogs' of the solar

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

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Yan [Northwesten University

    2013-12-05

    Identifying traffic anomalies and attacks rapidly and accurately is critical for large network operators. With the rapid growth of network bandwidth, such as the next generation DOE UltraScience Network, and fast emergence of new attacks/virus/worms, existing network intrusion detection systems (IDS) are insufficient because they: • Are mostly host-based and not scalable to high-performance networks; • Are mostly signature-based and unable to adaptively recognize flow-level unknown attacks; • Cannot differentiate malicious events from the unintentional anomalies. To address these challenges, we proposed and developed a new paradigm called high-performance network anomaly/intrustion detection and mitigation (HPNAIDM) system. The new paradigm is significantly different from existing IDSes with the following features (research thrusts). • Online traffic recording and analysis on high-speed networks; • Online adaptive flow-level anomaly/intrusion detection and mitigation; • Integrated approach for false positive reduction. Our research prototype and evaluation demonstrate that the HPNAIDM system is highly effective and economically feasible. Beyond satisfying the pre-set goals, we even exceed that significantly (see more details in the next section). Overall, our project harvested 23 publications (2 book chapters, 6 journal papers and 15 peer-reviewed conference/workshop papers). Besides, we built a website for technique dissemination, which hosts two system prototype release to the research community. We also filed a patent application and developed strong international and domestic collaborations which span both academia and industry.

  4. Real-time petroleum spill detection system

    International Nuclear Information System (INIS)

    Dakin, D.T.

    2001-01-01

    A real-time autonomous oil and fuel spill detection system has been developed to rapidly detect of a wide range of petroleum products floating on, or suspended in water. The system consists of an array of spill detection buoys distributed within the area to be monitored. The buoys are composed of a float and a multispectral fluorometer, which looks up through the top 5 cm of water to detect floating and suspended petroleum products. The buoys communicate to a base station computer that controls the sampling of the buoys and analyses the data from each buoy to determine if a spill has occurred. If statistically significant background petroleum levels are detected, the system raises an oil spill alarm. The system is useful because early detection of a marine oil spill allows for faster containment, thereby minimizing the contaminated area and reducing cleanup costs. This paper also provided test results for biofouling, various petroleum product detection, water turbidity and wave tolerance. The technology has been successfully demonstrated. The UV light source keeps the optic window free from biofouling, and the electronics are fully submerged so there is no risk that the unit could ignite the vapours of a potential oil spill. The system can also tolerate moderately turbid waters and can therefore be used in many rivers, harbours, water intakes and sumps. The system can detect petroleum products with an average thickness of less than 3 micrometers floating on the water surface. 3 refs., 15 figs

  5. 46 CFR 108.411 - Smoke detection system.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Smoke detection system. 108.411 Section 108.411 Shipping... EQUIPMENT Fire Extinguishing Systems § 108.411 Smoke detection system. Each smoke accumulator in a smoke detection system must be located on the overhead of the compartment protected by the system in a location...

  6. Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

    Directory of Open Access Journals (Sweden)

    Andrew Cron

    Full Text Available Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less. Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a

  7. Financial system loss as an example of high consequence, high frequency events

    Energy Technology Data Exchange (ETDEWEB)

    McGovern, D.E.

    1996-07-01

    Much work has been devoted to high consequence events with low frequency of occurrence. Characteristic of these events are bridge failure (such as that of the Tacoma Narrows), building failure (such as the collapse of a walkway at a Kansas City hotel), or compromise of a major chemical containment system (such as at Bhopal, India). Such events, although rare, have an extreme personal, societal, and financial impact. An interesting variation is demonstrated by financial losses due to fraud and abuse in the money management system. The impact can be huge, entailing very high aggregate costs, but these are a result of the contribution of many small attacks and not the result of a single (or few) massive events. Public awareness is raised through publicized events such as the junk bond fraud perpetrated by Milikin or gross mismanagement in the failure of the Barings Bank through unsupervised trading activities by Leeson in Singapore. These event,s although seemingly large (financial losses may be on the order of several billion dollars), are but small contributors to the estimated $114 billion loss to all types of financial fraud in 1993. This paper explores the magnitude of financial system losses and identifies new areas for analysis of high consequence events including the potential effect of malevolent intent.

  8. INTEGRAL Detection of the First Prompt Gamma-Ray Signal Coincident with the Gravitational-wave Event GW170817

    DEFF Research Database (Denmark)

    Savchenko, V.; Ferrigno, C.; Kuulkers, E.

    2017-01-01

    We report the INTernational Gamma-ray Astrophysics Laboratory (INTEGRAL) detection of the short gamma-ray burst GRB 170817A (discovered by Fermi-GBM) with a signal-to-noise ratio of 4.6, and, for the first time, its association with the gravitational waves (GWs) from binary neutron star (BNS......) merging event GW170817 detected by the LIGO and Virgo observatories. The significance of association between the gamma-ray burst observed by INTEGRAL and GW170817 is 3.2σ, while the association between the Fermi-GBM and INTEGRAL detections is 4.2σ. GRB 170817A was detected by the SPI-ACS instrument about...

  9. Vibrotactile Detection, Identification and Directional Perception of signal-Processed Sounds from Environmental Events: A Pilot Field Evaluation in Five Cases

    Directory of Open Access Journals (Sweden)

    Parivash Ranjbar

    2008-09-01

    Full Text Available Objectives: Conducting field tests of a vibrotactile aid for deaf/deafblind persons for detection, identification and directional perception of environmental sounds. Methods: Five deaf (3F/2M, 22–36 years individuals tested the aid separately in a home environment (kitchen and in a traffic environment. Their eyes were blindfolded and they wore a headband and holding a vibrator for sound identification. In the headband, three microphones were mounted and two vibrators for signalling direction of the sound source. The sounds originated from events typical for the home environment and traffic. The subjects were inexperienced (events unknown and experienced (events known. They identified the events in a home and traffic environment, but perceived sound source direction only in traffic. Results: The detection scores were higher than 98% both in the home and in the traffic environment. In the home environment, identification scores varied between 25%-58% when the subjects were inexperienced and between 33%-83% when they were experienced. In traffic, identification scores varied between 20%-40% when the subjects were inexperienced and between 22%-56% when they were experienced. The directional perception scores varied between 30%-60% when inexperienced and between 61%-83% when experienced. Discussion: The vibratory aid consistently improved all participants’ detection, identification and directional perception ability.

  10. Event-related stresses in energy systems and their effects on energy security

    International Nuclear Information System (INIS)

    Hughes, Larry; Ranjan, Ashish

    2013-01-01

    Energy systems change over time as events, such as grid failures, new energy sources, and extreme weather conditions, occur, often affecting the system's energy security. Understanding events, their causes, and how they are handled, can help a jurisdiction and its energy stakeholders develop better, evidence-based energy policy. This paper employs a definition of stress in combination with systems analysis to specify methods for explaining the states through which an energy process, chain, or system passes in response to an event and how this response results in energy security improving, deteriorating, or being maintained. The definition uses three dimensions-availability, affordability, and acceptability-derived from the International Energy Agency's definition of energy security to show when and how a system's energy security will change. Examples are used to illustrate the application of the methods. - Highlights: • A generic set of methods and a common terminology to formalize the ongoing energy security discourse is proposed. • The methods define, measure and explain how energy security can change when events cause stresses in an energy system. • Events are classified in terms of three dimensions derived from the IEA's definition of energy security. • The application of the method is illustrated with detailed examples

  11. Application of a Hybrid Detection and Location Scheme to Volcanic Systems

    Science.gov (United States)

    Thurber, C. H.; Lanza, F.; Roecker, S. W.

    2017-12-01

    We are using a hybrid method for automated detection and onset estimation, called REST, that combines a modified version of the nearest-neighbor similarity scheme of Rawles and Thurber (2015; RT15) with the regression approach of Kushnir et al. (1990; K90). This approach incorporates some of the windowing ideas proposed by RT15 into the regression techniques described in K90. The K90 and RT15 algorithms both define an onset as that sample where a segment of noise at earlier times is most "unlike" a segment of data at later times; the main difference between the approaches is how one defines "likeness." Hence, it is fairly straightforward to adapt the RT15 ideas to a K90 approach. We also incorporated the running mean normalization scheme of Bensen et al. (2007), used in ambient noise pre-processing, to reduce the effects of coherent signals (such as earthquakes) in defining noise segments. This is especially useful for aftershock sequences, when the persistent high amplitudes due to many earthquakes biases the true noise level. We use the fall-off of the K90 estimation function to assign uncertainties and the asymmetry of the function as a causality constraint. The detection and onset estimation stage is followed by iterative pick association and event location using a grid-search method. Some fine-tuning of some parameters is generally required for optimal results. We present 2 applications of this scheme to data from volcanic systems: Makushin volcano, Alaska, and Laguna del Maule (LdM), Chile. In both cases, there are permanent seismic networks, operated by the Alaska Volcano Observatory (AVO) and Observatorio Volcanológico de Los Andes del Sur (OVDAS), respectively, and temporary seismic arrays were deployed for a year or more. For Makushin, we have analyzed a year of data, from summer 2015 to summer 2016. The AVO catalog has 691 events in our study volume; REST processing yields 1784 more events. After quality control, the event numbers are 151 AVO events and

  12. Nonstochastic Analysis of Manufacturing Systems Using Timed-Event Graphs

    DEFF Research Database (Denmark)

    Hulgaard, Henrik; Amon, Tod

    1996-01-01

    Using automated methods to analyze the temporal behavior ofmanufacturing systems has proven to be essential and quite beneficial.Popular methodologies include Queueing networks, Markov chains,simulation techniques, and discrete event systems (such as Petrinets). These methodologies are primarily...

  13. Features, Events, and Processes: System Level

    Energy Technology Data Exchange (ETDEWEB)

    D. McGregor

    2004-04-19

    The primary purpose of this analysis is to evaluate System Level features, events, and processes (FEPs). The System Level FEPs typically are overarching in nature, rather than being focused on a particular process or subsystem. As a result, they are best dealt with at the system level rather than addressed within supporting process-level or subsystem level analyses and models reports. The System Level FEPs also tend to be directly addressed by regulations, guidance documents, or assumptions listed in the regulations; or are addressed in background information used in development of the regulations. This evaluation determines which of the System Level FEPs are excluded from modeling used to support the total system performance assessment for license application (TSPA-LA). The evaluation is based on the information presented in analysis reports, model reports, direct input, or corroborative documents that are cited in the individual FEP discussions in Section 6.2 of this analysis report.

  14. Integrated multisensor perimeter detection systems

    Science.gov (United States)

    Kent, P. J.; Fretwell, P.; Barrett, D. J.; Faulkner, D. A.

    2007-10-01

    The report describes the results of a multi-year programme of research aimed at the development of an integrated multi-sensor perimeter detection system capable of being deployed at an operational site. The research was driven by end user requirements in protective security, particularly in threat detection and assessment, where effective capability was either not available or prohibitively expensive. Novel video analytics have been designed to provide robust detection of pedestrians in clutter while new radar detection and tracking algorithms provide wide area day/night surveillance. A modular integrated architecture based on commercially available components has been developed. A graphical user interface allows intuitive interaction and visualisation with the sensors. The fusion of video, radar and other sensor data provides the basis of a threat detection capability for real life conditions. The system was designed to be modular and extendable in order to accommodate future and legacy surveillance sensors. The current sensor mix includes stereoscopic video cameras, mmWave ground movement radar, CCTV and a commercially available perimeter detection cable. The paper outlines the development of the system and describes the lessons learnt after deployment in a pilot trial.

  15. The effectiveness of pretreatment physics plan review for detecting errors in radiation therapy

    International Nuclear Information System (INIS)

    Gopan, Olga; Zeng, Jing; Novak, Avrey; Nyflot, Matthew; Ford, Eric

    2016-01-01

    Purpose: The pretreatment physics plan review is a standard tool for ensuring treatment quality. Studies have shown that the majority of errors in radiation oncology originate in treatment planning, which underscores the importance of the pretreatment physics plan review. This quality assurance measure is fundamentally important and central to the safety of patients and the quality of care that they receive. However, little is known about its effectiveness. The purpose of this study was to analyze reported incidents to quantify the effectiveness of the pretreatment physics plan review with the goal of improving it. Methods: This study analyzed 522 potentially severe or critical near-miss events within an institutional incident learning system collected over a three-year period. Of these 522 events, 356 originated at a workflow point that was prior to the pretreatment physics plan review. The remaining 166 events originated after the pretreatment physics plan review and were not considered in the study. The applicable 356 events were classified into one of the three categories: (1) events detected by the pretreatment physics plan review, (2) events not detected but “potentially detectable” by the physics review, and (3) events “not detectable” by the physics review. Potentially detectable events were further classified by which specific checks performed during the pretreatment physics plan review detected or could have detected the event. For these events, the associated specific check was also evaluated as to the possibility of automating that check given current data structures. For comparison, a similar analysis was carried out on 81 events from the international SAFRON radiation oncology incident learning system. Results: Of the 356 applicable events from the institutional database, 180/356 (51%) were detected or could have been detected by the pretreatment physics plan review. Of these events, 125 actually passed through the physics review; however

  16. The effectiveness of pretreatment physics plan review for detecting errors in radiation therapy

    Energy Technology Data Exchange (ETDEWEB)

    Gopan, Olga; Zeng, Jing; Novak, Avrey; Nyflot, Matthew; Ford, Eric, E-mail: eford@uw.edu [Department of Radiation Oncology, University of Washington Medical Center, 1959 NE Pacific Street, Box 356043, Seattle, Washington 98195 (United States)

    2016-09-15

    Purpose: The pretreatment physics plan review is a standard tool for ensuring treatment quality. Studies have shown that the majority of errors in radiation oncology originate in treatment planning, which underscores the importance of the pretreatment physics plan review. This quality assurance measure is fundamentally important and central to the safety of patients and the quality of care that they receive. However, little is known about its effectiveness. The purpose of this study was to analyze reported incidents to quantify the effectiveness of the pretreatment physics plan review with the goal of improving it. Methods: This study analyzed 522 potentially severe or critical near-miss events within an institutional incident learning system collected over a three-year period. Of these 522 events, 356 originated at a workflow point that was prior to the pretreatment physics plan review. The remaining 166 events originated after the pretreatment physics plan review and were not considered in the study. The applicable 356 events were classified into one of the three categories: (1) events detected by the pretreatment physics plan review, (2) events not detected but “potentially detectable” by the physics review, and (3) events “not detectable” by the physics review. Potentially detectable events were further classified by which specific checks performed during the pretreatment physics plan review detected or could have detected the event. For these events, the associated specific check was also evaluated as to the possibility of automating that check given current data structures. For comparison, a similar analysis was carried out on 81 events from the international SAFRON radiation oncology incident learning system. Results: Of the 356 applicable events from the institutional database, 180/356 (51%) were detected or could have been detected by the pretreatment physics plan review. Of these events, 125 actually passed through the physics review; however

  17. Statistical fault detection in photovoltaic systems

    KAUST Repository

    Garoudja, Elyes

    2017-05-08

    Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array\\'s maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.

  18. Apriori-based network intrusion detection system

    International Nuclear Information System (INIS)

    Wang Wenjin; Liu Junrong; Liu Baoxu

    2012-01-01

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

  19. Homodyne detection of holographic memory systems

    Science.gov (United States)

    Urness, Adam C.; Wilson, William L.; Ayres, Mark R.

    2014-09-01

    We present a homodyne detection system implemented for a page-wise holographic memory architecture. Homodyne detection by holographic memory systems enables phase quadrature multiplexing (doubling address space), and lower exposure times (increasing read transfer rates). It also enables phase modulation, which improves signal-to-noise ratio (SNR) to further increase data capacity. We believe this is the first experimental demonstration of homodyne detection for a page-wise holographic memory system suitable for a commercial design.

  20. Detection of invisible and crucial events: from seismic fluctuations to the war against terrorism

    Energy Technology Data Exchange (ETDEWEB)

    Allegrini, Paolo; Fronzoni, Leone; Grigolini, Paolo; Latora, Vito; Mega, Mirko S.; Palatella, Luigi E-mail: luigi.palatella@df.unipi.it; Rapisarda, Andrea; Vinciguerra, Sergio

    2004-04-01

    We argue that the recent discovery of the non-Poissonian statistics of the seismic main-shocks is a special case of a more general approach to the detection of the distribution of the time increments between one crucial but invisible event and the next. We make the conjecture that the proposed approach can be applied to the analysis of terrorist network with significant benefits for the Intelligence Community.

  1. Gas Flow Detection System

    Science.gov (United States)

    Moss, Thomas; Ihlefeld, Curtis; Slack, Barry

    2010-01-01

    This system provides a portable means to detect gas flow through a thin-walled tube without breaking into the tubing system. The flow detection system was specifically designed to detect flow through two parallel branches of a manifold with only one inlet and outlet, and is a means for verifying a space shuttle program requirement that saves time and reduces the risk of flight hardware damage compared to the current means of requirement verification. The prototype Purge Vent and Drain Window Cavity Conditioning System (PVD WCCS) Flow Detection System consists of a heater and a temperature-sensing thermistor attached to a piece of Velcro to be attached to each branch of a WCCS manifold for the duration of the requirement verification test. The heaters and thermistors are connected to a shielded cable and then to an electronics enclosure, which contains the power supplies, relays, and circuit board to provide power, signal conditioning, and control. The electronics enclosure is then connected to a commercial data acquisition box to provide analog to digital conversion as well as digital control. This data acquisition box is then connected to a commercial laptop running a custom application created using National Instruments LabVIEW. The operation of the PVD WCCS Flow Detection System consists of first attaching a heater/thermistor assembly to each of the two branches of one manifold while there is no flow through the manifold. Next, the software application running on the laptop is used to turn on the heaters and to monitor the manifold branch temperatures. When the system has reached thermal equilibrium, the software application s graphical user interface (GUI) will indicate that the branch temperatures are stable. The operator can then physically open the flow control valve to initiate the test flow of gaseous nitrogen (GN2) through the manifold. Next, the software user interface will be monitored for stable temperature indications when the system is again at

  2. Multi Agent System Based Wide Area Protection against Cascading Events

    DEFF Research Database (Denmark)

    Liu, Zhou; Chen, Zhe; Liu, Leo

    2012-01-01

    In this paper, a multi-agent system based wide area protection scheme is proposed in order to prevent long term voltage instability induced cascading events. The distributed relays and controllers work as a device agent which not only executes the normal function automatically but also can...... the effectiveness of proposed protection strategy. The simulation results indicate that the proposed multi agent control system can effectively coordinate the distributed relays and controllers to prevent the long term voltage instability induced cascading events....

  3. Study of Rare Events at the Split Field Magnet

    CERN Multimedia

    2002-01-01

    The aim of the experiment is the study of full events associated with special, rare triggers using the almost 4 @p geometry of the improved Split Field Magnet (SFM) detector. Two triggers are used: a) a trigger on an electron produced around 90|0 with a transverse momentum p^t @$>$ 0.5 GeV/c; b) a trigger on very high p^t hadrons (p^t $>$ 5 GeV/c) at @Q @C 45|0, using the SFM chambers to define roads pointing to the interaction region. In the first case, the associated event will be studied to determine the origin of direct electron production in charmed particle production. In the second case, the production of jets or jet-like objects when a high p^t particle is produced in pp collisions will be investigated. To carry out this program, the detection system consists of : \\item a) An improved SFM detector chamber system and layout to detect more of the produced particles and to simplify the pattern recognition problem in multiparticle events. \\item b) An improved program chain for the analysis of SFM events, ...

  4. Methodological Considerations for Comparison of Brand Versus Generic Versus Authorized Generic Adverse Event Reports in the US Food and Drug Administration Adverse Event Reporting System (FAERS).

    Science.gov (United States)

    Rahman, Md Motiur; Alatawi, Yasser; Cheng, Ning; Qian, Jingjing; Peissig, Peggy L; Berg, Richard L; Page, David C; Hansen, Richard A

    2017-12-01

    The US Food and Drug Administration Adverse Event Reporting System (FAERS), a post-marketing safety database, can be used to differentiate brand versus generic safety signals. To explore the methods for identifying and analyzing brand versus generic adverse event (AE) reports. Public release FAERS data from January 2004 to March 2015 were analyzed using alendronate and carbamazepine as examples. Reports were classified as brand, generic, and authorized generic (AG). Disproportionality analyses compared reporting odds ratios (RORs) of selected known labeled serious adverse events stratifying by brand, generic, and AG. The homogeneity of these RORs was compared using the Breslow-Day test. The AG versus generic was the primary focus since the AG is identical to brand but marketed as a generic, therefore minimizing generic perception bias. Sensitivity analyses explored how methodological approach influenced results. Based on 17,521 US event reports involving alendronate and 3733 US event reports involving carbamazepine (immediate and extended release), no consistently significant differences were observed across RORs for the AGs versus generics. Similar results were obtained when comparing reporting patterns over all time and just after generic entry. The most restrictive approach for classifying AE reports yielded smaller report counts but similar results. Differentiation of FAERS reports as brand versus generic requires careful attention to risk of product misclassification, but the relative stability of findings across varying assumptions supports the utility of these approaches for potential signal detection.

  5. Event-triggered output feedback control for distributed networked systems.

    Science.gov (United States)

    Mahmoud, Magdi S; Sabih, Muhammad; Elshafei, Moustafa

    2016-01-01

    This paper addresses the problem of output-feedback communication and control with event-triggered framework in the context of distributed networked control systems. The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem. The scheme is developed for the distributed system where only partial states are available. In this scheme, a subsystem uses local observers and share its information to its neighbors only when the subsystem's local error exceeds a specified threshold. The developed method is illustrated by using a coupled cart example from the literature. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  6. A Hospital Nursing Adverse Events Reporting System Project: An Approach Based on the Systems Development Life Cycle.

    Science.gov (United States)

    Cao, Yingjuan; Ball, Marion

    2017-01-01

    Based on the System Development Life Cycle, a hospital based nursing adverse event reporting system was developed and implemented which integrated with the current Hospital Information System (HIS). Besides the potitive outcomes in terms of timeliness and efficiency, this approach has brought an enormous change in how the nurses report, analyze and respond to the adverse events.

  7. Embedded Systems - Missile Detection/Interception

    Directory of Open Access Journals (Sweden)

    Luis Cintron

    2010-01-01

    Full Text Available Missile defense systems are often related to major military resources aimed at shielding a specific region from incoming attacks. They are intended to detect, track, intercept, and destruct incoming enemy missiles. These systems vary in cost, efficiency, dependability, and technology. In present times, the possession of these types of systems is associated with large capacity military countries. Demonstrated here are the mathematical techniques behind missile systems which calculate trajectories of incoming missiles and potential intercept positions after initial missile detection. This procedure involved the use of vector-valued functions, systems of equations, and knowledge of projectile motion concepts.

  8. Radiation detection system

    Science.gov (United States)

    Whited, R.C.

    A system for obtaining improved resolution in relatively thick semiconductor radiation detectors, such as HgI/sub 2/, which exhibit significant hole trapping. Two amplifiers are used: the first measures the charge collected and the second the contribution of the electrons to the charge collected. The outputs of the two amplifiers are utilized to unfold the total charge generated within the detector in response to a radiation event.

  9. Hybrid Intrusion Detection System for DDoS Attacks

    Directory of Open Access Journals (Sweden)

    Özge Cepheli

    2016-01-01

    Full Text Available Distributed denial-of-service (DDoS attacks are one of the major threats and possibly the hardest security problem for today’s Internet. In this paper we propose a hybrid detection system, referred to as hybrid intrusion detection system (H-IDS, for detection of DDoS attacks. Our proposed detection system makes use of both anomaly-based and signature-based detection methods separately but in an integrated fashion and combines the outcomes of both detectors to enhance the overall detection accuracy. We apply two distinct datasets to our proposed system in order to test the detection performance of H-IDS and conclude that the proposed hybrid system gives better results than the systems based on nonhybrid detection.

  10. A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    ZiQi Hao

    2015-01-01

    Full Text Available As limited energy is one of the tough challenges in wireless sensor networks (WSN, energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

  11. Flat Surface Damage Detection System (FSDDS)

    Science.gov (United States)

    Williams, Martha; Lewis, Mark; Gibson, Tracy; Lane, John; Medelius, Pedro; Snyder, Sarah; Ciarlariello, Dan; Parks, Steve; Carrejo, Danny; Rojdev, Kristina

    2013-01-01

    The Flat Surface Damage Detection system (FSDDS} is a sensory system that is capable of detecting impact damages to surfaces utilizing a novel sensor system. This system will provide the ability to monitor the integrity of an inflatable habitat during in situ system health monitoring. The system consists of three main custom designed subsystems: the multi-layer sensing panel, the embedded monitoring system, and the graphical user interface (GUI). The GUI LABVIEW software uses a custom developed damage detection algorithm to determine the damage location based on the sequence of broken sensing lines. It estimates the damage size, the maximum depth, and plots the damage location on a graph. Successfully demonstrated as a stand alone technology during 2011 D-RATS. Software modification also allowed for communication with HDU avionics crew display which was demonstrated remotely (KSC to JSC} during 2012 integration testing. Integrated FSDDS system and stand alone multi-panel systems were demonstrated remotely and at JSC, Mission Operations Test using Space Network Research Federation (SNRF} network in 2012. FY13, FSDDS multi-panel integration with JSC and SNRF network Technology can allow for integration with other complementary damage detection systems.

  12. Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

    Energy Technology Data Exchange (ETDEWEB)

    Silva, Rodrigo Tavares; Martinelli Filho, Martino, E-mail: martino@cardiol.br; Peixoto, Giselle de Lima; Lima, José Jayme Galvão de; Siqueira, Sérgio Freitas de; Costa, Roberto; Gowdak, Luís Henrique Wolff [Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP (Brazil); Paula, Flávio Jota de [Unidade de Transplante Renal - Divisão de Urologia do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP (Brazil); Kalil Filho, Roberto; Ramires, José Antônio Franchini [Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP (Brazil)

    2015-11-15

    The recording of arrhythmic events (AE) in renal transplant candidates (RTCs) undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used. We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR). A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE. During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT) occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01–26.35; p = 0.002), and the duration of the PR interval (OR = 1.05; 95% CI, 1.02–1.08; p < 0.001) were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD) was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01–7.96; p = 0.041). In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT.

  13. Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

    Directory of Open Access Journals (Sweden)

    Rodrigo Tavares Silva

    2015-11-01

    Full Text Available AbstractBackground:The recording of arrhythmic events (AE in renal transplant candidates (RTCs undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used.Objective:We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR.Methods:A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE.Results:During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01–26.35; p = 0.002, and the duration of the PR interval (OR = 1.05; 95% CI, 1.02–1.08; p < 0.001 were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01–7.96; p = 0.041.Conclusions:In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT.

  14. Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

    Science.gov (United States)

    Silva, Rodrigo Tavares; Martinelli Filho, Martino; Peixoto, Giselle de Lima; de Lima, José Jayme Galvão; de Siqueira, Sérgio Freitas; Costa, Roberto; Gowdak, Luís Henrique Wolff; de Paula, Flávio Jota; Kalil Filho, Roberto; Ramires, José Antônio Franchini

    2015-01-01

    Background The recording of arrhythmic events (AE) in renal transplant candidates (RTCs) undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used. Objective We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR). Methods A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE. Results During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT) occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01–26.35; p = 0.002), and the duration of the PR interval (OR = 1.05; 95% CI, 1.02–1.08; p < 0.001) were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD) was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01–7.96; p = 0.041). Conclusions In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT. PMID:26351983

  15. Developing an early laekage detection system for thermal power plant boiler tubes by using acoustic emission technology

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sang Bum [RECTUSON, Co., LTD, Masan (Korea, Republic of); Roh, Seon Man [Samcheonpo Division, Korea South-East Power Co., Samcheonpo (Korea, Republic of)

    2016-06-15

    A thermal power plant has a heat exchanger tube to collect and convert the heat generated from the high temperature and pressure steam to energy, but the tubes are arranged in a complex manner. In the event that a leakage occurs in any of these tubes, the high-pressure steam leaks out and may cause the neighboring tubes to rupture. This leakage can finally stop power generation, and hence there is a dire need to establish a suitable technology capable of detecting tube leaks at an early stage even before it occurs. As shown in this paper, by applying acoustic emission (AE) technology in existing boiler tube leak detection equipment (BTLD), we developed a system that detects these leakages early enough and generates an alarm at an early stage to necessitate action; the developed system works better that the existing system used to detect fine leakages. We verified the usability of the system in a 560 MW-class thermal power plant boiler by conducting leak tests by simulating leakages from a variety of hole sizes (⌀2, ⌀5, ⌀10 mm). Results show that while the existing fine leakage detection system does not detect fine leakages of ⌀2 mm and ⌀5 mm, the newly developed system could detect leakages early enough and generate an alarm at an early stage, and it is possible to increase the signal to more than 18 dB.

  16. Developing an early laekage detection system for thermal power plant boiler tubes by using acoustic emission technology

    International Nuclear Information System (INIS)

    Lee, Sang Bum; Roh, Seon Man

    2016-01-01

    A thermal power plant has a heat exchanger tube to collect and convert the heat generated from the high temperature and pressure steam to energy, but the tubes are arranged in a complex manner. In the event that a leakage occurs in any of these tubes, the high-pressure steam leaks out and may cause the neighboring tubes to rupture. This leakage can finally stop power generation, and hence there is a dire need to establish a suitable technology capable of detecting tube leaks at an early stage even before it occurs. As shown in this paper, by applying acoustic emission (AE) technology in existing boiler tube leak detection equipment (BTLD), we developed a system that detects these leakages early enough and generates an alarm at an early stage to necessitate action; the developed system works better that the existing system used to detect fine leakages. We verified the usability of the system in a 560 MW-class thermal power plant boiler by conducting leak tests by simulating leakages from a variety of hole sizes (⌀2, ⌀5, ⌀10 mm). Results show that while the existing fine leakage detection system does not detect fine leakages of ⌀2 mm and ⌀5 mm, the newly developed system could detect leakages early enough and generate an alarm at an early stage, and it is possible to increase the signal to more than 18 dB

  17. A novel technique to extract events from access control system and locate persons

    International Nuclear Information System (INIS)

    Vincent, M.; Vaidyanathan, Mythili; Patidar, Suresh Chandra; Prabhakara Rao, G.

    2011-01-01

    Indira Gandhi Centre for Atomic Research houses many laboratories which handle radioactive materials and classified materials. Protection and accounting of men and material and critical facilities are important aspect of nuclear security. Access Control System (ACS) is used to enhance the protective measures against elevated threat environment. Access control system hardware consists of hand geometry readers, RFID readers, Controllers, Electromagnetic door locks, Turnstiles, fiber cable laying and termination etc. Access Control System controls and monitors the people accessing the secured facilities. Access Control System generates events on: 1. Showing of RFID card, 2. Rotation of turnstile, 3. Download of valid card numbers, 4. Generation of alarms etc. Access control system turnstiles are located in main entrance of a facility, entrance of inside laboratory and door locks are fixed on secured facilities. Events are stored in SQL server database. From the events stored in database a novel technique is developed to extract events and list the persons in a particular facility, list all entry/exit events on one day, list the first in and last out entries. This paper discusses the complex multi level group by queries and software developed to extract events from database, locate persons and generate reports. Software is developed as a web application in ASP.Net and query is written in SQL. User can select the doors, type of events and generate reports. Reports are generated using the master data stored about employees RFID cards and events data stored in tables. Four types of reports are generated 1. Plant Emergency Report, 2. Locate User Report, 3. Entry - Exit Report, 4. First in Last out Report. To generate plant emergency report for whole plant only events generated in outer gates have to be considered. To generate plant emergency report for inside laboratory, events generated in entrance gates have to be ignored. (author)

  18. Inter-Event Time Definition Setting Procedure for Urban Drainage Systems

    Directory of Open Access Journals (Sweden)

    Jingul Joo

    2013-12-01

    Full Text Available Traditional inter-event time definition (IETD estimate methodologies generally take into account only rainfall characteristics and not drainage basin characteristics. Therefore, they may not succeed in providing an appropriate value of IETD for any sort of application to the design of urban drainage system devices. To overcome this limitation, this study presents a method of IETD determination that considers basin characteristics. The suggested definition of IETD is the time period from the end of a rainfall event to the end of a direct runoff. The suggested method can identify the independent events that are suitable for the statistical analysis of the recorded rainfall. Using the suggested IETD, the IETD of the Joong-Rang drainage system was determined and the area-IETD relation curve was drawn. The resulting regression curve can be used to determinate the IETD of ungauged urban drainage systems, with areas ranging between 40 and 4400 ha. Using the regression curve, the IETDs and time distribution of the design rainfall for four drainage systems in Korea were determined and rainfall-runoff simulations were performed with the Storm Water Management Model (SWMM. The results were compared with those from Huff's method which assumed a six-hour IETD. The peak flow rates obtained by the suggested method were 11%~15% greater than those obtained by Huff’s method. The suggested IETD determination method can identify independent events that are suitable for the statistical analysis of the recorded rainfall aimed at the design of urban drainage system devices.

  19. Fault detection and fault tolerant control of a smart base isolation system with magneto-rheological damper

    International Nuclear Information System (INIS)

    Wang, Han; Song, Gangbing

    2011-01-01

    Fault detection and isolation (FDI) in real-time systems can provide early warnings for faulty sensors and actuator signals to prevent events that lead to catastrophic failures. The main objective of this paper is to develop FDI and fault tolerant control techniques for base isolation systems with magneto-rheological (MR) dampers. Thus, this paper presents a fixed-order FDI filter design procedure based on linear matrix inequalities (LMI). The necessary and sufficient conditions for the existence of a solution for detecting and isolating faults using the H ∞ formulation is provided in the proposed filter design. Furthermore, an FDI-filter-based fuzzy fault tolerant controller (FFTC) for a base isolation structure model was designed to preserve the pre-specified performance of the system in the presence of various unknown faults. Simulation and experimental results demonstrated that the designed filter can successfully detect and isolate faults from displacement sensors and accelerometers while maintaining excellent performance of the base isolation technology under faulty conditions

  20. First Satellite-detected Perturbations of Outgoing Longwave Radiation Associated with Blowing Snow Events over Antarctica

    Science.gov (United States)

    Yang, Yuekui; Palm, Stephen P.; Marshak, Alexander; Wu, Dong L.; Yu, Hongbin; Fu, Qiang

    2014-01-01

    We present the first satellite-detected perturbations of the outgoing longwave radiation (OLR) associated with blowing snow events over the Antarctic ice sheet using data from Cloud-Aerosol Lidar with Orthogonal Polarization and Clouds and the Earth's Radiant Energy System. Significant cloud-free OLR differences are observed between the clear and blowing snow sky, with the sign andmagnitude depending on season and time of the day. During nighttime, OLRs are usually larger when blowing snow is present; the average difference in OLRs between without and with blowing snow over the East Antarctic Ice Sheet is about 5.2 W/m2 for the winter months of 2009. During daytime, in contrast, the OLR perturbation is usually smaller or even has the opposite sign. The observed seasonal variations and day-night differences in the OLR perturbation are consistent with theoretical calculations of the influence of blowing snow on OLR. Detailed atmospheric profiles are needed to quantify the radiative effect of blowing snow from the satellite observations.

  1. CoreFlow: Enriching Bro security events using network traffic monitoring data

    NARCIS (Netherlands)

    Koning, R.; Buraglio, N.; de Laat, C.; Grosso, P.

    Attacks against network infrastructures can be detected by Intrusion Detection Systems (IDS). Still reaction to these events are often limited by the lack of larger contextual information in which they occurred. In this paper we present CoreFlow, a framework for the correlation and enrichment of IDS

  2. Detection technique of targets for missile defense system

    Science.gov (United States)

    Guo, Hua-ling; Deng, Jia-hao; Cai, Ke-rong

    2009-11-01

    Ballistic missile defense system (BMDS) is a weapon system for intercepting enemy ballistic missiles. It includes ballistic-missile warning system, target discrimination system, anti-ballistic-missile guidance systems, and command-control communication system. Infrared imaging detection and laser imaging detection are widely used in BMDS for surveillance, target detection, target tracking, and target discrimination. Based on a comprehensive review of the application of target-detection techniques in the missile defense system, including infrared focal plane arrays (IRFPA), ground-based radar detection technology, 3-dimensional imaging laser radar with a photon counting avalanche photodiode (APD) arrays and microchip laser, this paper focuses on the infrared and laser imaging detection techniques in missile defense system, as well as the trends for their future development.

  3. Detection of Water Contamination Events Using Fluorescence Spectroscopy and Alternating Trilinear Decomposition Algorithm

    Directory of Open Access Journals (Sweden)

    Jie Yu

    2017-01-01

    Full Text Available The method based on conventional index and UV-vision has been widely applied in the field of water quality abnormality detection. This paper presents a qualitative analysis approach to detect the water contamination events with unknown pollutants. Fluorescence spectra were used as water quality monitoring tools, and the detection method of unknown contaminants in water based on alternating trilinear decomposition (ATLD is proposed to analyze the excitation and emission spectra of the samples. The Delaunay triangulation interpolation method was used to make the pretreatment of three-dimensional fluorescence spectra data, in order to estimate the effect of Rayleigh and Raman scattering; ATLD model was applied to establish the model of normal water sample, and the residual matrix was obtained by subtracting the measured matrix from the model matrix; the residual sum of squares obtained from the residual matrix and threshold was used to make qualitative discrimination of test samples and distinguish drinking water samples and organic pollutant samples. The results of the study indicate that ATLD modeling with three-dimensional fluorescence spectra can provide a tool for detecting unknown organic pollutants in water qualitatively. The method based on fluorescence spectra can be complementary to the method based on conventional index and UV-vision.

  4. Semantic Complex Event Processing over End-to-End Data Flows

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Qunzhi [University of Southern California; Simmhan, Yogesh; Prasanna, Viktor K.

    2012-04-01

    Emerging Complex Event Processing (CEP) applications in cyber physical systems like SmartPower Grids present novel challenges for end-to-end analysis over events, flowing from heterogeneous information sources to persistent knowledge repositories. CEP for these applications must support two distinctive features - easy specification patterns over diverse information streams, and integrated pattern detection over realtime and historical events. Existing work on CEP has been limited to relational query patterns, and engines that match events arriving after the query has been registered. We propose SCEPter, a semantic complex event processing framework which uniformly processes queries over continuous and archived events. SCEPteris built around an existing CEP engine with innovative support for semantic event pattern specification and allows their seamless detection over past, present and future events. Specifically, we describe a unified semantic query model that can operate over data flowing through event streams to event repositories. Compile-time and runtime semantic patterns are distinguished and addressed separately for efficiency. Query rewriting is examined and analyzed in the context of temporal boundaries that exist between event streams and their repository to avoid duplicate or missing results. The design and prototype implementation of SCEPterare analyzed using latency and throughput metrics for scenarios from the Smart Grid domain.

  5. Review of events at large pool-type irradiators

    International Nuclear Information System (INIS)

    Trager, E.A. Jr.

    1989-03-01

    Large pool-type gamma irradiators are used in applications such as the ''cold'' sterilization of medical and pharmaceutical supplies, and recent changes in federal regulations make it possible they will be used extensively in the preservation of foodstuffs. Because of this possible large increase in the use of irradiators, the Office of Nuclear Materials Safety and Safeguards was interested in knowing what events had occurred at irradiators. The event data would be used as background in developing new regulations on irradiators. Therefore, AEOD began a study of the operating experience at large, wet source storage gamma irradiators. The scope of the study was to assess all available operating information on large (≥ 250,000 curie), pool-type irradiators licensed by both the NRC and the Agreement States, and events at foreign facilities. The study found that about 0.12 events have been reported per irradiator-year. Most of these events were precursor events, in that there was no evidence of damage to the radioactive sources or degradation in the level of safety of the facility. Events with more significant impacts had a reported frequency of about 0.01 event per irradiator-year. However, the actual rate of occurrence of events of concern to the staff may be higher because there are few specific reporting requirements for events at irradiators. It is suggested that during development of a regulation for large pool-type irradiators consideration be given to specifying requirements for: reporting breakdowns in access control systems; periodic inspection of the source movement and suspension system; systems to detect source leakage and product contamination; allowable pool leakage; and feedback of information on operational events involving safety-important systems

  6. High transverse momentum events in Kr+Au, Th central collisions at 43 MeV/u

    International Nuclear Information System (INIS)

    Bougault, R.; Colin, J.; Delaunay, F.; Genoux-Lubain, A.; Le Brun, C.; Lecolley, J.F.; Lefebvres, F.; Louvel, M.; Steckmeyer, J.C.

    1992-05-01

    The multi-fragment production of the systems Kr+(Ag,Au,Th) is studied at 43 A.MeV. Heavy fragments (Z ≥ 6) were identified in coincidence by 4 π arrangement of low threshold detectors. Up to six fragments have been detected per event. An analysis based on relative velocities between the detected fragments allowed us to separate peripheral reactions from central collisions. For the central events large transverse energy values, clue for dynamical effects, are observed for Au- and Th-system

  7. Real-time prediction of the occurrence of GLE events

    Science.gov (United States)

    Núñez, Marlon; Reyes-Santiago, Pedro J.; Malandraki, Olga E.

    2017-07-01

    A tool for predicting the occurrence of Ground Level Enhancement (GLE) events using the UMASEP scheme is presented. This real-time tool, called HESPERIA UMASEP-500, is based on the detection of the magnetic connection, along which protons arrive in the near-Earth environment, by estimating the lag correlation between the time derivatives of 1 min soft X-ray flux (SXR) and 1 min near-Earth proton fluxes observed by the GOES satellites. Unlike current GLE warning systems, this tool can predict GLE events before the detection by any neutron monitor (NM) station. The prediction performance measured for the period from 1986 to 2016 is presented for two consecutive periods, because of their notable difference in performance. For the 2000-2016 period, this prediction tool obtained a probability of detection (POD) of 53.8% (7 of 13 GLE events), a false alarm ratio (FAR) of 30.0%, and average warning times (AWT) of 8 min with respect to the first NM station's alert and 15 min to the GLE Alert Plus's warning. We have tested the model by replacing the GOES proton data with SOHO/EPHIN proton data, and the results are similar in terms of POD, FAR, and AWT for the same period. The paper also presents a comparison with a GLE warning system.

  8. Microlensing events by Proxima Centauri in 2014 and 2016: Opportunities for mass determination and possible planet detection

    Energy Technology Data Exchange (ETDEWEB)

    Sahu, Kailash C.; Bond, Howard E.; Anderson, Jay [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Dominik, Martin, E-mail: ksahu@stsci.edu, E-mail: jayander@stsci.edu, E-mail: heb11@psu.edu, E-mail: md35@st-andrews.ac.uk [SUPA, School of Physics and Astronomy, University of St. Andrews, North Haugh, St. Andrews KY16 9SS (United Kingdom)

    2014-02-20

    We have found that Proxima Centauri, the star closest to our Sun, will pass close to a pair of faint background stars in the next few years. Using Hubble Space Telescope (HST) images obtained in 2012 October, we determine that the passage close to a mag 20 star will occur in 2014 October (impact parameter 1.''6), and to a mag 19.5 star in 2016 February (impact parameter 0.''5). As Proxima passes in front of these stars, the relativistic deflection of light will cause shifts in the positions of the background stars of ∼0.5 and 1.5 mas, respectively, readily detectable by HST imaging, and possibly by Gaia and ground-based facilities such as the Very Large Telescope. Measurement of these astrometric shifts offers a unique and direct method to measure the mass of Proxima. Moreover, if Proxima has a planetary system, the planets may be detectable through their additional microlensing signals, although the probability of such detections is small. With astrometric accuracies of 0.03 mas (achievable with HST spatial scanning), centroid shifts caused by Jovian planets are detectable at separations of up to 2.''0 (corresponding to 2.6 AU at the distance of Proxima), and centroid shifts by Earth-mass planets are detectable within a small band of 8 mas (corresponding to 0.01 AU) around the source trajectories. Jovian planets within a band of about 28 mas (corresponding to 0.036 AU) around the source trajectories would produce a brightening of the source by >0.01 mag and could hence be detectable. Estimated timescales of the astrometric and photometric microlensing events due to a planet range from a few hours to a few days, and both methods would provide direct measurements of the planetary mass.

  9. Nitrated nucleosome levels and neuropsychiatric events in systemic lupus erythematosus;

    DEFF Research Database (Denmark)

    Ferreira, Isabel; Croca, Sara; Raimondo, Maria Gabriella

    2017-01-01

    BACKGROUND: In patients with systemic lupus erythematosus (SLE) there is no serological test that will reliably distinguish neuropsychiatric (NP) events due to active SLE from those due to other causes. Previously we showed that serum levels of nitrated nucleosomes (NN) were elevated in a small...... number of patients with NPSLE. Here we measured serum NN in samples from a larger population of patients with SLE and NP events to see whether elevated serum NN could be a marker for NPSLE. METHODS: We obtained serum samples from patients in the Systemic Lupus International Collaborative Clinics (SLICC...

  10. A new scanning system for alpha decay events as calibration sources for range-energy relation in nuclear emulsion

    Science.gov (United States)

    Yoshida, J.; Kinbara, S.; Mishina, A.; Nakazawa, K.; Soe, M. K.; Theint, A. M. M.; Tint, K. T.

    2017-03-01

    A new scanning system named "Vertex picker" has been developed to rapid collect alpha decay events, which are calibration sources for the range-energy relation in nuclear emulsion. A computer-controlled optical microscope scans emulsion layers exhaustively, and a high-speed and high-resolution camera takes their micrographs. A dedicated image processing picks out vertex-like shapes. Practical operations of alpha decay search were demonstrated by emulsion sheets of the KEK-PS E373 experiment. Alpha decays of nearly 28 events were detected in eye-check work on a PC monitor per hour. This yield is nearly 20 times more effective than that by the conventional eye-scan method. The speed and quality is acceptable for the coming new experiment, J-PARC E07.

  11. A new scanning system for alpha decay events as calibration sources for range-energy relation in nuclear emulsion

    International Nuclear Information System (INIS)

    Yoshida, J.; Kinbara, S.; Mishina, A.; Nakazawa, K.; Soe, M.K.; Theint, A.M.M.; Tint, K.T.

    2017-01-01

    A new scanning system named “Vertex picker” has been developed to rapid collect alpha decay events, which are calibration sources for the range-energy relation in nuclear emulsion. A computer-controlled optical microscope scans emulsion layers exhaustively, and a high-speed and high-resolution camera takes their micrographs. A dedicated image processing picks out vertex-like shapes. Practical operations of alpha decay search were demonstrated by emulsion sheets of the KEK-PS E373 experiment. Alpha decays of nearly 28 events were detected in eye-check work on a PC monitor per hour. This yield is nearly 20 times more effective than that by the conventional eye-scan method. The speed and quality is acceptable for the coming new experiment, J-PARC E07.

  12. Spatiotemporal detection of unusual human population behavior using mobile phone data.

    Directory of Open Access Journals (Sweden)

    Adrian Dobra

    Full Text Available 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.

  13. Westinghouse Small Modular Reactor passive safety system response to postulated events

    International Nuclear Information System (INIS)

    Smith, M. C.; Wright, R. F.

    2012-01-01

    The Westinghouse Small Modular Reactor (SMR) is an 800 MWt (>225 MWe) integral pressurized water reactor. This paper is part of a series of four describing the design and safety features of the Westinghouse SMR. This paper focuses in particular upon the passive safety features and the safety system response of the Westinghouse SMR. The Westinghouse SMR design incorporates many features to minimize the effects of, and in some cases eliminates the possibility of postulated accidents. The small size of the reactor and the low power density limits the potential consequences of an accident relative to a large plant. The integral design eliminates large loop piping, which significantly reduces the flow area of postulated loss of coolant accidents (LOCAs). The Westinghouse SMR containment is a high-pressure, compact design that normally operates at a partial vacuum. This facilitates heat removal from the containment during LOCA events. The containment is submerged in water which also aides the heat removal and provides an additional radionuclide filter. The Westinghouse SMR safety system design is passive, is based largely