• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.590-592
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• 1997

A Discrete Event Dynamic System is a system whose states change in response to the occurrence of events from a predefined event set. A major difficulty in developing analytical results for the systems is the lack of appropriate modeling techniques. This paper proposes the use of Real-time Temporal Logic as a modeling tool for the analysis and control of DEDS. The Real-time Temporal Logic Frameworks is extended with a suitable structure of modeling hard real-time constraints. Modeling rules are developed for several specific situations. It is shown how the graphical model can be translated to a system of linear equations and constraints.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1083-1090
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• 2016

In clinical studies, different types of outcomes (e.g. repeated measures data and time-to-event data) for the same subject tend to be observed, and these data can be correlated. For example, a response variable of interest can be measured repeatedly over time on the same subject and at the same time, an event time representing a terminating event is also obtained. Joint modelling using a shared random effect is useful for analyzing these data. Inferences based on marginal likelihood may involve the evaluation of analytically intractable integrations over the random-effect distributions. In this paper we propose a joint HGLM approach for analyzing such outcomes using the HGLM (hierarchical generalized linear model) method based on h-likelihood (i.e. hierarchical likelihood), which avoids these integration itself. The proposed method has been demonstrated using various numerical studies.

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.215-224
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• 2011

U-Health collects patient information with various kinds of sensor. Stream data can be summarized as an interval event which has aninterval between start-time-point and end-time-point. Most of temporal mining techniques consider only the event occurrence-time-point and ignore stream data change of state. In this paper, we propose the temporal mining technique considering the event change of state in U-Health. Our method overcomes the restrictions of the environment by sending a significant event in U-Health from sensors to a server. We define four event states of stream data and perform the temporal data mining considered the event change of state. Finally, we can remove an ambiguity of discovered rules by describing cause-and-effect relations among events in temporal relation sequences.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1165-1171
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• 2017

The traditional form of parallelism in Monte Carlo particle transport simulations, wherein each individual particle history is considered a unit of work, does not lend itself well to data-level parallelism. Event-based algorithms, which were originally used for simulations on vector processors, may offer a path toward better utilizing data-level parallelism in modern computer architectures. In this study, a simple model is developed for estimating the efficiency of the event-based particle transport algorithm under two sets of assumptions. Data collected from simulations of four reactor problems using OpenMC was then used in conjunction with the models to calculate the speedup due to vectorization as a function of the size of the particle bank and the vector width. When each event type is assumed to have constant execution time, the achievable speedup is directly related to the particle bank size. We observed that the bank size generally needs to be at least 20 times greater than vector size to achieve vector efficiency greater than 90%. When the execution times for events are allowed to vary, the vector speedup is also limited by differences in the execution time for events being carried out in a single event-iteration.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.1
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• pp.17-24
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• 2015

Smart factory requires 4 Zero factors including Zero Waiting-time, Zero Inventory, Zero Defect, Zero Down-time) that needs IT convergence for production resources of 4M1E(Man, Machine, Material, Method, Energy) in real time and event processing in all type of manufacturing enterprises. This paper will be explaining about core emerging production IT convergence technologies including cyber device security, 4M1E integration, real time event driven architecture, common platform of manufacturing standard applications, smart factory to-be model for small and medium manufacturing enterprises.

• Proceedings of the Safety Management and Science Conference
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• 2010.11a
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• pp.349-355
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• 2010

EPCIS(EPC Information Services) system is a core component of EPCglobal Architecture Framework offering information of the freights, the time of awareness and the location of awareness on the EPCglobal Network. The role of EPCIS is to exchange information based on EPC. There are four kinds of event data which are object event data, aggregation event data, quantity event data, and transaction event data. These EPCIS events data are stored and managed in EPCIS repository. This paper leads to separate a in-out data overload of integrated EPCIS event and suggests the method to effectively manage EPCIS repository for exchanging data smoothly. In order to verify a effectiveness, we measure the performance of the system using a simple testing simulation by comparing existing method and suggested method.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.12-15
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• 2003

Korea Institute of Geoscience and Mineral Resources(KIGAM) is operating Wonju Korea Seismic Research Station(KSRS), 29 regional seismic research stations and 5 Korea-China joint seismic stations in China. Also KIGAM is operating Korea Earthquake Monitoring System (KEMS) to archive the real-time data stream and to determine event parameters (epicenter, origin time, and magnitude) by the automatic processing and analyst review. To do this, KEMS used KIGAM's regional seismic network and other institute's network in a near real-time base. From Dec. 1, 2001 to Nov. 30, 2002, 3,827 seismic events were analyzed in a automatic processing procedure and finally 3,437 events were analyzed by analyst and archived. But problem is this event catalog includes not only natural earthquake, but also artificial events produced by the blast. More than 80 % events were concentrated in daytime and many events were concentrated in the confirmed blast sites, Pyeongyang, Pocheon, Yeongjong-do, Donghae city, etc. Because these artificial events are a major potential cause of error when estimating the seismicity of a specific region, discrimination procedure has to be developed in the first place.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.592-596
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• 1988

PCS(Process Computer System) is installed to monitor various kinds of process parameters in Power Plant and networked for synthetic monitor and event analysis in all site. But when an event is occured sequentially or simultaneously among the plants, it is difficult to analyze it because of different Standard-Time in each Plant. Standard-Time Receiving Device is developed to solve this problem and development procedure is descried here.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.385-389
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• 1988

PCS(Process Computer System) is installed to monitor various kinds of process parameters in Power Plant and networked for synthetic monitor and event analysis in all site. But when an event is occured sequentially or simultaneously among the plants, it is difficult to analyze it because of different Stardard-Time in each plant. Standard-Time Receiving Device is developed to solve this problem and development procedure is described here.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.153-158
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• 2016

As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.