• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.1
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• pp.815-824
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• 2019

It is not easy to present a generalized operating model because the operating environment of a sport event is composed of a complex matrix structure. In this study, we propose 'standardization' which provides guidance of concept and process of sport event management to solve this sports event management problem. To purpose complete of this study, the frame was developed by the international standard ISO 9001: 2015 is applied. The standards framework for sports event process management system structure proposed in this study consists of scope of application, citing standard, Terms and definitions, organizational situation, leadership, planning, support, operation, performance evaluation, and improvement. This sport event process management system standard can be applied to any organization that wants to host, prepare, and operate sport events regardless of the form or scale of the sport event. In addition, this standard was focused on process management of life-cycle stages of sporting events, therefor it was possible to manage interrelationships and dependencies between processes and processes.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.1132-1139
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• 2006

Railway risk is evaluated by a method of linking event trees and fault trees as the general PSA(Probabilistic Safety Assessment) model for the risk assessment of complex systems. Accident scenarios causing undesirable events are modeled by event trees comprised of several accident sequences. Each branch located in the accident progression of the event tree is modeled by an fault tree or can be represented by some value too simply. We usually evaluate the frequency of the whole sequence by adding them after calculating the frequency of each sequence at a time. However, since there are quite a number of event trees and fault trees in the railway risk assessment model, the number of sequence to evaluate increases and preparation for the risk assessment costs much time all the more. Also, it may induce errors when analysts perform the work of quantification. Therefore, the systematic maintenance and control of event trees and fault trees will be essential for the railway risk assessment. In this paper we introduce an integrated assessment method using one-top model and develop a risk assessment tool for the maintenance and control of the railway risk model.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.49-55
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• 2019

Organizations design and operate business process models to achieve their goals efficiently and systematically. With the advancement of IT technology, the number of items that computer systems can participate in and the process becomes huge and complicated. This phenomenon created a more complex and subdivide flow of business process.The process instances that contain workcase and events are larger and have more data. This is an essential resource for process mining and is used directly in model discovery, analysis, and improvement of processes. This event log is getting bigger and broader, which leads to problems such as capacity management and I / O load in management of existing row level program or management through a relational database. In this paper, as the event log becomes big data, we have found the problem of management limit based on the existing original file or relational database. Design and apply schemes to archive and analyze large event logs through Hadoop, an open source distributed file system, and HBase, a NoSQL database system.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.1
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• pp.38-47
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• 2014

This paper describes the application of discrete event systems theory to the design of an automated laboratory system. Current automated laboratory systems typically consist of several interacting processes that must be carefully sequenced to avoid any possible process conflicts. Discrete Event Systems (DES) theory and Supervisory Control Theory (SCT) can be applied together as effective methods of modeling the system dynamics and designing supervisory controllers to precisely sequence the many processes that such systems might involve. Classical approaches to supervisory controller design tend to result in complex controller structures that are difficult to implement, maintain, and upgrade. In this paper, a new approach to designing supervisory controllers for automated laboratory systems is introduced. This new approach uses a modular controller structure that is easier to implement, maintain, and upgrade, and deals with "state explosion" issues in a novel and efficient way.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.594-597
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• 2001

As many industrial systems become more complex, they become extremely difficult to diagnose the cause of failures. The subject of this paper is ECCS(Emergency Core Cooling System) part of PWR(Pressurized Water Reactor). This paper presents modeling and diagnoser construction of ECCS based on discrete event system theory. Also, this paper presents that the ECCS system is diagnosible in our approach.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.21
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• pp.43-50
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• 1990

Fault tree analysis(FTA) is available to the engineer for determining reliability of complex industrial safety system. Therefore quantitative aspects of FTA greatly multiply its power this paper proceeds of presenting the methodology of FTA, including an approach to constructing in fault tree. A working guide to the use of FTA for the purpose of cost/benefit determination in industrial safety system is given. Finally, an analytic method for uncertainty analysis of the top event of a complex system is described.

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.314.2-314.2
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• 2002

DNA topoisomerases catalyze changes in DNA topology through cycles of transient DNA strand breakage and religation. During this process. the active site tyrosine in human DNA topoisomerase Ⅰ(Top Ⅰ) becomes covalently linked to the 3'-ends of a single-stranded nick in the DNA duplex, Stabilization of the Top Ⅰ-DNA cleavable complex is the common initial event leading to the cytotoxicity of top 1 inhibitors. (omitted)

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.145-149
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• 1997

This paper describes a methodology for the development of models of discrete event system. The methodology is based on transformation of continuous state space into discrete one to homomorphically represent dynamics of continuous processes in discrete events. This paper proposes a formal structure which can coupled discrete event system models within a framework. The structure employs the discrete event specification formalism for the discrete event system models. The proposed formal structure has been applied to develop a discrete event specification model for the complex spectral density analysis of strip for urin analyzer system. For this, spectral density data of strip is partitioned into a set of Phases based on events identified through urine spectrophotometry. For each phase, a continuous system of the continuous model for the urine spectral density analysis has been simulated by programmed C++. To validate this model, first develop the discrets event specification model, then simulate the model in the DEVSIM++ environment. It has the similar simulation results for the data obtained from the continuous system simulation. The comparison shows that the discrete event specification model represents dynamics of the urine spectral density at each phase.

• 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.

• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.