• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.129-137
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• 2016

To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today's systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.

• Journal of KIISE
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• v.43 no.7
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• pp.781-785
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• 2016

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individual's daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users" daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3822-3830
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• 2023

The large-area Compton camera (LACC), featuring significantly high detection sensitivity, was developed for high-speed localization of gamma-ray sources. Due to the high gamma-ray interaction event rate induced by the high sensitivity, however, the multiplexer-based data acquisition system (DAQ) rapidly saturated, leading to deteriorated energy and imaging resolution at event rates higher than 4.7 × 10³ s^-1. In the present study, a new simultaneous multi-channel DAQ was developed to improve the energy and imaging resolution of the LACC even under high event rate conditions (10⁴-10⁶ s^-1). The performance of the DAQ was evaluated with several point sources under different event rate conditions. The results indicated that the new DAQ offers significantly better performance than the existing DAQ over the entire energy and event rate ranges. Especially, the new DAQ showed high energy resolution under very high event rate conditions, i.e., 6.9% and 8.6% (for 662 keV) at 1.3 × 10⁵ and 1.2 × 10⁶ s^-1, respectively. Furthermore, the new DAQ successfully acquired Compton images under those event rates, i.e., imaging resolutions of 13.8° and 19.3° at 8.7 × 10⁴ and 10⁶ s^-1, which correspond to 1.8 and 73 μSv/hr or about 18 and 730 times the background level, respectively.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.50-54
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• 2001

Information systems development entails planning, analysis, design and construction phases. The analysis phase identifying user requirements is the most important of these phases. Since unidentified defects in the early phase causes increased work and costs as development proceeds, the quality of analysis results affects the quality of the resultant system. Major tasks in the analysis phase are data modeling and process modeling. Research on building a knowledge-based system for data modeling have been conducted much, however, not sufficiently for process modeling. As a system environment with high user interaction increases, research on process modeling methods and knowledge- based systems considering such environment are required. In this research, a process modeling framework for information systems with high user interaction is suggested and a knowledge-based system for supporting the suggested framework is implemented. A proposed model consists of the following tasks: event analysis, process analysis, and event/process interaction analysis. Event analysis identifies business events and their responses. Process analysis break down the processes of an enterprise into progressively increasing details. Decomposition begins at the function level and ends when the elementary process level is reached. Event/process interaction analysis verifies the results of process analysis and event analysis. A knowledge-based system for supporting a proposed process modeling framework is implemented in a web-based environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.511-514
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• 2005

The application service to use recently an electronic tag have been activated. Therefore research about a data processing of an electronic tag have been processed widely. Therefore, object information to be collected efficiently from various former tag for requesting a data processing of XML based on used widely in Web. For this EPCIS defined the standard interface to share EPC relation data. Currently, Core Event Type of EPCIS defined the Core Type to need in EPC network currently and Types about a business context do not define according to Apply field. We apply to EPC network to a specific business We need the type about the information which defines with Core Event Type. in this paper. We are defining an reusable object type which is used often in a business area. in this way design the schema based to an object information data model of a marine logistics apply to the object type to define in this way and Core Event Type to define EPCIS Specnt.

• Journal of Broadcast Engineering
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• v.8 no.4
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• pp.399-409
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• 2003

In this paper, we present an Event Reporting which is one of the major items of MPEG-21 and exemplify two digital data broadcasting use case scenarios, and according1y we propose the Event Reporting message structure for MPEG-21 broadcasting contents that is based on these use case scenarios. Every interaction with a Digital Item in the multimedia framework can be called an Event. The need to standardize Event Reporting within the Multimedia Framework arise from the need to monitor and communicate amongst Peers and Users the Events relating to Digital Items and/or the programs and devices that operate on them at any given time. However, there are a number of difficulties in providing an accurate report about an Event. Different observers of the Event may have vastly different perspectives. In this paper, we describe a structure and vision of the Event Reporting. Besides we show the activities on MPEG-21 Event Reporting standardization.

• Journal of Korean Society of Forest Science
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• v.99 no.5
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• pp.755-761
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• 2010

This study analyzed water quality data from a coniferous forest catchment in order to quantify the contributions of runoff components to stormflow, and to understand the effects of antecedent moisture conditions within catchment on the contributions of runoff components. Hydrograph separation by the twocomponent mixing model analysis was used to partition stormflow discharge into pre-event and event components for total 10 events in 2005 and 2008. To simplify the analysis, this study used single geochemical tracer with Na+. The result shows that the average contributions of event water and pre-event water were 34.8% and 65.2% of total stormflow of all 10 events, respectively. The event water contributions for each event varied from 18.8% to 47.9%. As the results of correlation analysis between event water contributions versus some storm event characteristics, 10 day antecedent rainfall and 1 day antecedent streamflow are significantly correlated with event water contributions. These results can provide insight which will contribute to understand the importance of antecedent moisture conditions in the generation of event water, and be used basic information to stormflow generation process in forest catchment.

• Spatial Information Research
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• v.20 no.5
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• pp.99-109
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• 2012

Recently, due to the growth of social media and spread of smart-phone, the amount of data has considerably increased by full use of SNS (Social Network Service). According to it, the Big Data concept is come up and many researchers are seeking solutions to make the best use of big data. To maximize the creative value of the big data held by many companies, it is required to combine them with existing data. The physical and theoretical storage structures of data sources are so different that a system which can integrate and manage them is needed. In order to process big data, MapReduce is developed as a system which has advantages over processing data fast by distributed processing. However, it is difficult to construct and store a system for all key words. Due to the process of storage and search, it is to some extent difficult to do real-time processing. And it makes extra expenses to process complex event without structure of processing different data. In order to solve this problem, the existing Complex Event Processing System is supposed to be used. When it comes to complex event processing system, it gets data from different sources and combines them with each other to make it possible to do complex event processing that is useful for real-time processing specially in stream data. Nevertheless, unstructured data based on text of SNS and internet articles is managed as text type and there is a need to compare strings every time the query processing should be done. And it results in poor performance. Therefore, we try to make it possible to manage unstructured data and do query process fast in complex event processing system. And we extend the data complex function for giving theoretical schema of string. It is completed by changing the string key word into integer type with filtering which uses keyword set. In addition, by using the Complex Event Processing System and processing stream data at real-time of in-memory, we try to reduce the time of reading the query processing after it is stored in the disk.

• Transactions of the Korean hydrogen and new energy society
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• v.31 no.1
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• pp.73-81
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• 2020

For the fault tree analysis (FTA) analysis of the packaged hydrogen filling station, the composition of the charging station was analyzed and the fault tree (FT) diagram was prepared. FT diagrams were created by dividing the causes of events into external factors and internal factors with the hydrogen event as the top event. The external factors include the effects of major disasters caused by natural disasters and external factors as OR gates. Internal factors are divided into tube tailer, compressor & storage tank, and dispenser, which are composed of mistakes in operation process and causes of accidents caused by parts leakage. In this study, the purpose was to improve the hydrogen station. The subjects of this study were domestic packaged hydrogen stations and FTA study was conducted based on the previous studies, failure mode & effect analysis (FMEA) and hazard & operability study (HAZOP). Top event as a hydrogen leaking event and constructed the flow of events based on the previous study. Refer to "Off shore and onshore reliability data 6th edition", "European Industry Reliability Data Bank", technique for human error rate prediction (THERP) for reliability data. We hope that this study will help to improve the safety and activation of the hydrogen station.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.715-724
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• 2010

Recurrent event data occurs when a subject experience the event of interest several times and has been found in biomedical studies, sociology and engineering. Several diverse approaches have been applied to analyze the recurrent events (Cook and Lawless, 2007). In this study, we analyzed the YTOP(Young Traffic Offenders Program) dataset which consists of 192 drivers with conviction dates by speeding violation and traffic rule violation. We consider a subject-specific effect, frailty, to reflect the individual's driving behavior and extend to time-varying frailty effect. Another feature of this study is about the redefinition of risk set. During the study, subject may be under suspension and this period is regarded as non-risk period. Thus the risk variables are reformatted according to suspension and termination time.