• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.266-266
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• 2019

Marine accidents are increasing year by year, and various accidents occur such as engine failure, collision, stranding, and fire. These marine accidents present a risk of large casualties. It is important to prevent accidents beforehand. In this study, we propose a modeling to predict the occurrence of marine accidents by applying the Markov Chain Process that can predict the future based on past data. Applying the proposed modeling, the probability of future marine accidents was calculated and compared with the actual frequency. Through this, a probabilistic model was proposed to prepare a prediction system for marine accidents, and it is expected to contribute to predicting various marine accidents.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.31-36
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• 2006

대기행렬 모형은 통신시스템이나 통신망 구현에 가장 적합한 수리모형으로 알려져 있고, 이에 대한 연구가 상당히 많이 진행되고 있다. 본 논문에서는 재해가 발생될 수 있는 BMAP/SM/1 대기시스템으로, 재해가 발생했을 경우 시스템 복구가 즉시 이루어지지 않고 임의 시간 후 복구 되는 시스템을 고려대상으로 하고 있다. 시스템의 정보입력흐름은 상호종속 또는 그룹 입력이 허용되는 배치마코프 도착과정으로 가정하였고, 또한 서비스분포는 세미 마코프 프로세스를 따른다고 가정하였다. 아울러 시스템에 재해가 발생하면 모든 고객은 즉시 시스템을 떠나게 되고, 재해복구는 임의 시간 후에 이루어진다. 임베디드 마코프체인의 안전상태 확률분포가 유도를 위한 정상 알고리즘 개발이 이루어졌다.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.627-641
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• 2016

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.3
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• pp.549-555
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• 2000

We propose an approximate algorithm to analyze the queuing system with n bursty and heterogeneous arrival processes. Each input process is modeled by Interrupted Bernoulli Process(IBP). We approximate N arrival processes by a single state variable and subsequently simplify the transition probability matrix of the Markov chain associated with these N arrival processes. Using this single state variable of arrival processes, we describe the state of the queuing system and analyze the system numerically with the reduced transition probability matrix. We compute the queue length distribution, the delay distribution, and the loss probability. Comparisons with simulation data show that the approximation algorithm has a good accuracy.