• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.226-228
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• 2005

This paper deals with power system stabilization problem using a network control system in which the control is applied through a communication channel in feedback form. Analysis and synthesis issues are investigated by modeling the packet delivery characteristics of the network as a Bernoulli random variable, which is described by a two state Markov chain. This model assumption yields an overall system which is described by a discrete-time Markov jump linear system. These employ the norm to measure the performance of the system, and they compute the norm via a necessary and sufficient matrix inequality condition.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.515-521
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• 2011

In this paper we consider failure detection equipment that which find failures in repairable systems and enable repair operations. In practical situations, failure detection equipment may come across troubles that can cause the omissions in detecting system failures and have a serious effect on system reliability. We analyze this effect through the appropriate modeling of Markov processes.

• Nuclear Engineering and Technology
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• v.52 no.3
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• pp.560-567
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• 2020

The complexity of safety critical systems of Nuclear Power Plant continues to increase rapidly due its transition from analog to digital systems. It has thus become progressively more imperative to model these systems prior to their implementation in order to meet the high performance, safety and reliability requirements. Timed Petri Nets (TPNs) have been widely used to model such systems for non-functional analysis. The paper presents a novel methodology for the analysis of the performance metrics using PN modeling. The paper uses the isomorphism property of the TPNs and the Markov chains for the performance analysis of the safety critical systems. The presented methodology has been validated on a Shutdown System of a Nuclear Power Plant.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.147-149
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• 2006

We designed an intelligent intrusion detection scheme that works based on target system's operational states and doesn't depend on humans' analysis. As a prior work, we presents a scheme to describe computer system's operational states. For this, Hidden Markov Model is used. As input to modeling, huge amount of system audit trail including data on events occurred in target system connected to network and target system's resource usage monitoring data is used. We can predict system's future state based on current events' sequence using developed model and determine whether it would be in daniel or not.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.37-47
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• 2020

A significant proportion of information subjects experience information security breaches, and the number of reports and counseling cases of personal information infringements is increasing. Increased awareness of the importance of information security has raised interest in the personnel in charge of such tasks. However, hiring excellent new workers and preventing turnovers in information security remain unresolved. In this paper, by modeling the job career path of information security workforce as a Markov chain, we analyze the workforce turnover process and long-term turnover trends by information security jobs, and further analyze the number and duration of turnovers required to engage in specific jobs. The results of this study are expected to be a reference to balancing the supply and demand of information security workers for the government and to ensuring efficient management of the workforce for businesses.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10c
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• pp.306-308
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• 1999

사회분야 전반이 전산화되면서 전산시스템에 대한 효과적인 침입방지와 탐지가 중요한 문제로 대두되었다. 침입행위도 정상사용행위와 마찬가지로 전산시스템 서비스를 사용하므로 호출된 서비스의 순서로 나타난다. 본 논문에서는 정상사용행위에 대한 서비스 호출순서를 모델링 한 후 사용자의 사용패턴을 정상행위와 비교해서 비정상행위(anomaly)를 탐지하는 접근방식을 사용한다. 정상 행위 모델링에는 순서정보를 통계적으로 모델링하고 펴가하는데 널리 쓰이고 있는 HMM(Hidden Markov Model)을 사용하였다. Sun사의 BSM 모듈로 얻어진 3명 사용자의 사용로그에 대하여 본 시스템을 적용한 결과, 학습되지 않은 u2r 침입에 대해 2.95%의 false-positive 오류에서 100%의 탐지율을 보여주었다.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.63-75
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• 2002

Most attempts at Bayesian analysis of neural networks involve hierarchical modeling. We believe that similar results can be obtained with simpler models that require less computational effort, as long as appropriate restrictions are placed on parameters in order to ensure propriety of posterior distributions. In particular, we adopt a model first introduced by Lee (1999) that utilizes an improper prior for all parameters. Straightforward Gibbs sampling is possible, with the exception of the bias parameters, which are embedded in nonlinear sigmoidal functions. In addition to the problems posed by nonlinearity, direct sampling from the posterior distributions of the bias parameters is compounded due to the duplication of hidden nodes, which is a source of multimodality. In this regard, we focus on sampling from the marginal posterior distribution of the bias parameters with Markov chain Monte Carlo methods that combine traditional Metropolis sampling with a slice sampler described by Neal (1997, 2001). The methods are illustrated with data examples that are largely confined to the analysis of nonparametric regression models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.764-784
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• 2017

Energy efficiency is an essential requirement in designing a MAC protocol for wireless sensor networks (WSNs) using battery-operated sensor nodes. We proposed a new receiver-initiated MAC protocol, RIX-MAC, based on the X-MAX protocol with asynchronous duty cycles. In this paper, we analyzed the performance of RIX-MAC protocol in terms of throughput, delay, and energy consumption using the model. For modeling the protocol, we used the Markov chain model, derived the transmission and state probabilities, and obtained the equations to solve the performance of throughput, delay, and energy consumption. Our proposed model and analysis are validated by comparing numerical results obtained from the model, with simulation results using NS-2.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.628-646
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• 2016

This paper proposes a novel channel assignment scheme that is based on finite projective plane (FPP) theory. The proposed scheme involves using a Markov chain model to allocate N channels to N users through intermixed channel group arrangements, particularly when channel resources are idle because of inefficient use. The intermixed FPP-based channel group arrangements successfully related Markov chain modeling to punch through ratio formulations proposed in this study, ensuring fair resource use among users. The simulation results for the proposed FPP scheme clearly revealed that the defined throughput increased, particularly under light traffic load conditions. Nevertheless, if the proposed scheme is combined with successive interference cancellation techniques, considerably higher throughput is predicted, even under heavy traffic load conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.231-240
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• 1999

A major concern in Concurrent Engineering is the control and management of workload in a period of process. As a general rule, leveling the peak of workload in certain period is difficult because concurrent processing is comprised of various processes, including overlapping, paralleling looping and so on. Therefore, the workload management with resource constraints is so beneficial that effective methods to analyze design process are momentous. This study presents the Timed Petri Nets approach of precedence logic networks, and provides an alternative for users to analyze constraint processes to resolve conflicts of resources. Another approach to Continuous Time Markov Chain using Stochastic Petri Nets is also proposed. These approaches are expected to facilitate resolving resource constrained scheduling problems more systematically in Concurrent Engineering environment.