Event Analysis System Research Articles

Collection of IDHEAS-based human error probability data for nuclear power plant commissioning through expert elicitation

Human error probability (HEP) is one of the most important indicators to assess human reliability. In this study, we collected nominal HEP (NHEP) data for nuclear power plant commissioning through a formal expert elicitation process. Cognitive failure modes (CFMs) were adapted from the Integrated Human Event Analysis System (IDHEAS) method, and included three different detail levels. Obtained NHEP values for detection, understanding, action execution, and inter-team coordination seem to be consistent with those in IDHEAS-ECA, while the NHEP for decision-making is significantly lower than that in IDHEAS-ECA. Several challenges have been identified, which are related with the IDHEAS-based CFMs and the expert elicitation process. Because the IDHEAS-based CFMs are technology neutral and reflect the failures of human cognition, the obtained NHEP values could be applicable in other application domains.

Digital Media Hotspot Mining Algorithm Implementation with Complex Systems in the Mobile Internet Environment

With the development of the Internet, the amount of information present on the network has grown rapidly, leading to increased difficulty in obtaining effective information. Especially for individuals, enterprises, and institutions with a large amount of information, it is an almost impossible task to integrate and analyze Internet information with great difficulty just by human resources. Internet hot events mining and analysis technology can effectively solve the above problems by alleviating information overload, integrating redundant information, and refining core information. In this paper, we address the above problems and research hot event topic sentence generation techniques in the field of hot event mining and design a hybrid event candidate set construction algorithm based on topic core word mapping and event triad selection. The algorithm uses the PAT-Tree technique to extract high-frequency core words in topic hotspots and maps the high-frequency words into sentences to generate a part of event core sentences. The other part of event core sentences is extracted from the topic hotspots by making event triples as candidate elements, and sentences containing event elements are extracted from the topic hotspots. The sets of event core sentences generated by the two methods are mixed and filtered and sorted to obtain the candidate set, which can be used to build a word graph-based main service channel (MSC) model. In this paper, we also propose an improved word graph-based MSC model and use it for the extraction of event topic sentences. Based on the above research, a hot event analysis system is implemented. The system analyzes the existing topic data and uses the event topic sentence generation algorithm studied in this paper to generate the titles of hot spots, that is, hot events. At the same time, the topics are displayed from different dimensions, and data visualization is completed. The visualization includes the trend change of event hotness, trend change of event sentiment polarity, and distribution of event article sources.

효율적인 침해대응을 위한 보안이벤트 시스템 설계 구현

효율적인 침해대응을 위한 보안이벤트 시스템 설계 구현

Fast simulation of detector effects in Rivet

We describe the design and implementation of detector-bias emulation in the Rivet MC event analysis system. Implemented using C++ efficiency and kinematic smearing functors, it allows detector effects to be specified within an analysis routine, customised to the exact phase-space and reconstruction working points of the analysis. A set of standard detector functions for the physics objects of Runs 1 and 2 of the ATLAS and CMS experiments is also provided. Finally, as jet substructure is an important class of physics observable usually considered to require an explicit detector simulation, we demonstrate that a smearing approach, tuned to available substructure data and implemented in Rivet, can accurately reproduce jet-structure biases observed by ATLAS.

A Unified Semantic Model for Cross-Media Events Analysis in Online Social Networks

The gap between the raw data from various data sources and the diverse intelligent applications has been an obstacle in the field of events analysis in online social networks. Most existing analysis systems focus on data from a certain single online social network platform and a limited range of analysis applications. To comprehensively understand events, the sources of data usually include multiple online social network platforms and different existing corpora. Thus, it is necessary to build a bridge to handle the online social data from different sources and support various analysis application requirements. In this paper, a unified semantic model for events analysis is proposed. The model contains well-designed classes and properties to tackle the lack of unified representation, and provenance information is also taken into consideration. The reasoning is supported to check the consistency of the data and to discover hidden knowledge such as tacit classification and implicit relationships. The schema mapping and data transformation methods are provided to handle the heterogeneous data from various online social network platforms and datasets. The design of the cross-media event analysis system is also presented. The comparison shows the advantages of this paper. The case study shows the applicability and effectiveness of our model and system.

Real-time Event Recognition and Analysis System for Nanopore Study

To achieve fast and accurate analysis of weak current signal of nanopore-based single molecule detection, an online data process based on adaptive threshold algorithm with data buffering technique and finite impulse response filtering was designed. A software system based on the data process was developed for online recognition and analysis of nanopore events during nanopore experiment. To testify the performance of the algorithm and software system, ideal signals with different noise level (20–100 pA) were generated at bandwidth ranging from 3 kHz to 100 kHz. The result showed that this software system was stable at different bandwidths and sampling rates and could be used in analyzing the signals at high noise. The proposed software system was further applied to aerolysin nanopore experiment for detection of poly(dA)4 molecules. The results showed that the data process system could be applied in real nanopore recording experiment with high accuracy and speed.

Event-based media processing and analysis: A survey of the literature

Research on event-based processing and analysis of media is receiving an increasing attention from the scientific community due to its relevance for an abundance of applications, from consumer video management and video surveillance to lifelogging and social media. Events have the ability to semantically encode relationships of different informational modalities, such as visual–audio–text, time, involved agents and objects, with the spatio-temporal component of events being a key feature for contextual analysis. This unveils an enormous potential for exploiting new information sources and opening new research directions. In this paper, we survey the existing literature in this field. We extensively review the employed conceptualization of the notion of event in multimedia, the techniques for event representation and modeling, the feature representation and event inference approaches for the problems of event detection in audio, visual, and textual content. Furthermore, we review some key event-based multimedia applications, and various benchmarking activities that provide solid frameworks for measuring the performance of different event processing and analysis systems. We provide an in-depth discussion of the insights obtained from reviewing the literature and identify future directions and challenges.

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

This article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. These challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.

Insights from Pilot Testing of the IDHEAS HRA Method

Human reliability analysis (HRA) is used in the context of probabilistic risk assessment (PRA) to provide risk information regarding human performance to support risk-informed decision-making with respect to high-reliability industries. The IntegrateD Human Event Analysis System (IDHEAS) is a new HRA method developed for internal, at-power nuclear power plant (NPP) events. It was motivated by the intention to reduce unnecessary and inappropriate variability in HRA results and improve the reliability of human error probability (HEP) estimates. The method has a strong foundation in human performance and cognitive psychology theories, and employs a cause-based quantification model. This paper documents a study conducted to pilot test IDHEAS to (1) identify issues that needed to be addressed and (2) provide feedback to refine the method before the method was finalized. An introduction on IDHEAS is provided first. Then sample IDHEAS analysis results are presented for illustration purposes. Next, insights from the testing in terms of method strengths and weaknesses are discussed, which is followed by concluding remarks.

Video Object Matching Based on SIFT and Rotation Invariant LBP

Object detection and tracking is an essential preliminary task in event analysis systems (e.g. Visual surveillance).Typically objects are extracted and tagged, forming representative tracks of their activity. Tagging is usually performed by probabilistic data association. However, as data may have been collected at different times or in different locations, it is often impossible to establish such associations in systems capturing disjoint areas. In this case, appearance matching is a valuable aid. This paper proposes a object matching method for multi-camera by combining HOG and block LBP, and computes accuracy rate by SVM. Using independent tracks of 30 different persons, we show that the proposed representation effectively discriminates visual object and that it presents high resilience to incorrect object segmentation and illumination. Experimental results show that the average accuracy DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3418

Hazardous Driving Event Detection and Analysis System in Vehicular Networks

This study proposes a novel highway traffic surveillance system that is capable of detecting hazardous driving maneuvers through the use of an in-vehicle sensor and transmission of the event data detected to a traffic management center through vehicle-to-vehicle and vehicle-to-infrastructure wireless communication; this system is referred to as the “hazardous driving event detection and analysis system in vehicular networks.” Compared with existing surveillance systems, the main novel feature of the proposed system lies in its ability to detect hazardous driving maneuvers that have the potential to lead to crashes. Three major components of the system are introduced in this study: an algorithm for detecting hazardous driving events, a method for deriving a new index incorporating expert judgment for evaluation of the risk level of the traffic stream on the basis of analyses of hazardous events that are detected, and field implementation of the proposed system in a test bed with real-time and historical data. Extensive field tests were conducted in the test bed to fine-tune the prototypical system. The methodology and field implementation presented in this study have potential value to help highway traffic agencies monitor and evaluate traffic streams with a focus on traffic safety. The proposed system is expected to be effective as support for the development of various traffic information control strategies to enhance traffic safety on highways.

SU-E-T-191: PITSTOP: Process Improvement Techniques, Software Tools, and Operating Principles for a Quality Initiative Discovery Framework.

To develop a quality initiative discovery framework using process improvement techniques, software tools and operating principles. Process deviations are entered into a radiotherapy incident reporting database. Supervisors use an in-house Event Analysis System (EASy) to discuss incidents with staff. Major incidents are analyzed with an in-house Fault Tree Analysis (FTA). A meta-Analysis is performed using association, text mining, key word clustering, and differential frequency analysis. A key operating principle encourages the creation of forcing functions via rapid application development. 504 events have been logged this past year. The results for the key word analysis indicate that the root cause for the top ranked key words was miscommunication. This was also the root cause found from association analysis, where 24% of the time that an event involved a physician it also involved a nurse. Differential frequency analysis revealed that sharp peaks at week 27 were followed by 3 major incidents, two of which were dose related. The peak was largely due to the front desk which caused distractions in other areas. The analysis led to many PI projects but there is still a major systematic issue with the use of forms. The solution we identified is to implement Smart Forms to perform error checking and interlocking. Our first initiative replaced our daily QA checklist with a form that uses custom validation routines, preventing therapists from proceeding with treatments until out of tolerance conditions are corrected. PITSTOP has increased the number of quality initiatives in our department, and we have discovered or confirmed common underlying causes of a variety of seemingly unrelated errors. It has motivated the replacement of all forms with smart forms.

KFD 웹 데이터베이스 시스템을 위한 동영상 이벤트 분석 및 검색 시스템

KFD 웹 데이터베이스 시스템은 가족미술치료사들의 요구사항을 반영하여, 방대한 평가 자료의 관리 및 효율적인 사정평가 과정의 지원을 위해 개발된 프로토타입 시스템이다. 그러나 KFD 웹 데이터베이스 시스템은 내담자가 그림을 그리는 동안의 행동패턴, 얼굴 표정, 그리고 음성 정보등과 같은 중요한 관찰요소들에 관한 정보는 얻을 수 없다는 문제점이 있다. 따라서 본 논문에서는 웹 카메라와 그림판 툴을 이용하여 내담자의 KFD 작업 과정을 녹화하고, 녹화된 영상 내의 주요 이벤트들을 인덱싱하여 가족미술치료사가 KFD 동영상을 분석하고 검색할 수 있도록 온톨로지 기반의 이벤트 분석 및 검색 시스템을 제안한다. 즉 새롭게 제안된 시스템에서는 가족 구성원에 관한 내담자의 이벤트 및 행동분석에 관한 요약 보고서와 함께 비디오 검색 서비스가 제공된다. 제안된 KFD 동영상 내의 이벤트 분석 및 검색 시스템은 가족치료사에게 내담자의 작업태도 및 행동, 그리고 KFD 작성과정에 대한 정량적이고, 객관적인 정보를 제공함으로써 보다 강화된 KFD 사정 평가를 지원할 수 있다. The typical Kinetic Family Drawing (KFD) Web database system, a form of prototype system, has been developed, relying on the suggestions from family art therapists, with an aim to handle large amounts of assessment data and to facilitate effective implement of assessment activities. However, unfortunately such a system has an intrinsic problem that it fails to collect clients' behaviors, attitudes, facial expressions, voices, and other critical information observed while they are drawing. Accordingly we propose the ontology based video event analysis and video retrieval system in this paper, in order to enhance the function of a KFD Web database system by using a web camera and drawing tool. More specifically, a newly proposed system is designed to deliver two kinds of services: the client video retrieval service and the sketch video retrieval service, accompanied by a summary report of occurred events and dynamic behaviors relative to each family member object, respectively. The proposed system can support the reinforced KFD assessments by providing quantitative and subjective information on clients' working attitudes and behaviors, and KFD preparation processes.

HUMAN ERRORS DURING THE SIMULATIONS OF AN SGTR SCENARIO: APPLICATION OF THE HERA SYSTEM

Due to the need of data for a Human Reliability Analysis (HRA), a number of data collection efforts have been undertaken in several different organizations. As a part of this effort, a human error analysis that focused on a set of simulator records on a Steam Generator Tube Rupture (SGTR) scenario was performed by using the Human Event Repository and Analysis (HERA) system. This paper summarizes the process and results of the HERA analysis, including discussions about the usability of the HERA system for a human error analysis of simulator data. Five simulated records of an SGTR scenario were analyzed with the HERA analysis process in order to scrutinize the causes and mechanisms of the human related events. From this study, the authors confirmed that the HERA was a serviceable system that can analyze human performance qualitatively from simulator data. It was possible to identify the human related events in the simulator data that affected the system safety not only negatively but also positively. It was also possible to scrutinize the Performance Shaping Factors (PSFs) and the relevant contributory factors with regard to each identified human event.

Video object matching across multiple independent views using local descriptors and adaptive learning

Object detection and tracking is an essential preliminary task in event analysis systems (e.g. visual surveillance). Typically objects are extracted and tagged, forming representative tracks of their activity. Tagging is usually performed by probabilistic data association, however, in systems capturing disjoint areas it is often not possible to establish such associations, as data may have been collected at different times or in different locations. In this case, appearance matching is a valuable aid. We propose using bag-of-visterms, i.e. an histogram of quantized local feature descriptors, to represent and match tracked objects. This method has proven to be effective for object matching and classification in image retrieval applications, where descriptors can be extracted a priori. An important difference in event analysis systems is that relevant information is typically restricted to the foreground. Descriptors can, therefore, be extracted faster, approaching real-time requirements. Also, unlike image retrieval, objects can change over time and therefore their model needs to be updated continuously. Incremental or adaptive learning is used to tackle this problem. Using independent tracks of 30 different persons, we show that the bag-of-visterms representation effectively discriminates visual object tracks and that it presents high resilience to incorrect object segmentation. Additionally, this methodology allows the construction of scalable object models that can be used to match tracks across independent views.

SUERC AMS ion detection

In a short time Be, C, Al, Cl, Ca and I accelerator mass spectrometry (AMS) have been established on the National Electrostatics Corporation (NEC) 5MV pelletron system at the Scottish Universities Environmental Research Centre (SUERC). While summarising the present performance of the system, this report will focus on the details of ion detection, which sample materials are used and the analytical procedures employed for each individual species during routine analysis.All rare isotope detection is with a single flexible detector and ion event analysis system, but switching of analysed species typically requires a detector reconfiguration. Configurations for routine 10Be, 14C, 26Al, 36Cl, 41Ca and 129I detection have been established and will be presented here. Notably, there has proven to be sufficient suppression of the isobaric interferences of 36Cl and 41Ca in the 5+ charge state using an argon gas stripper at a terminal voltage of 5.0MV to allow for routine analysis of these isotopes.

Description logics for an autonomic IDS event analysis system

Internet has grown by several orders of magnitude in recent years, and this growth has escalated the importance of computer security. Intrusion Detection System (IDS) is used to protect computer networks. However, the overwhelming flow of log data generated by IDS hamper security administrators from uncovering the hidden attack scenarios. Therefore, the autonomic IDS event analysis system is essential to make the IDS console smarter and more efficient. In this paper, we propose an IDS autonomic event analysis system represented by description logics, which allows inferring the attack scenarios and enabling the attack knowledge semantic queries. The modified case grammar PCTCG is used to convert raw alerts into frame-structured alert streams, and the alert semantic network 2-AASN is used to generate the attack scenarios, which can then inform the security administrator. Afterwards, based on the alert contexts, attack scenario instances are extracted, and attack semantic query results on attack scenario instances using spreading activation technique are forwarded to the security administrator.

Human reliability analysis—where shouldst thou turn?

Human error probability (HEP) is one of the most important indicators to assess human reliability. In this study, we collected nominal HEP (NHEP) data for nuclear power plant commissioning through a formal expert elicitation process. Cognitive failure modes (CFMs) were adapted from the Integrated Human Event Analysis System (IDHEAS) method, and included three different detail levels. Obtained NHEP values for detection, understanding, action execution, and inter-team coordination seem to be consistent with those in IDHEAS-ECA, while the NHEP for decision-making is significantly lower than that in IDHEAS-ECA. Several challenges have been identified, which are related with the IDHEAS-based CFMs and the expert elicitation process. Because the IDHEAS-based CFMs are technology neutral and reflect the failures of human cognition, the obtained NHEP values could be applicable in other application domains.

An expert system prototype for event diagnosis and real-time operation planning in power system control

A prototype system called EKA, which is an attempt to apply sophisticated knowledge-engineering methods to power systems, is described. EKA is based on an object-oriented model and model-based reasoning, including multiple ways to represent knowledge: rules, facts, methods, daemons, procedures, logic programs, etc. The EKA system consists of three separate but structurally similar systems: an operation planning system, an event analysis system, and a training simulator. As a development environment, hybrid software and a Lisp computer have been used. The tasks and methods involved are described with and without an expert system. The application environment and prototype implementation are outlined. >