• Journal of Korean Society on Water Environment
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• v.22 no.6
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• pp.1088-1093
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• 2006

In this study, the method of annual sediment estimation for reservoir long-term operation is proposed. Long-term daily precipitation and evaporation are predicted by Markov Chain. Using these values, reservoir inflow is simulated by NWS-PC model. Reservoir sediment load is estimated by sediment rating relation curve which is observed. From the simulation results, it was found that each simulated value by Markov Chain and NWS-PC was well compared to the observed ones and also estimated reservoir sediment was appropriate to the compared values using empirical equations. It is thought that the proposed method for estimation of reservoir sediment can be useful used to operate the reservoir.

• International Journal of Quality Innovation
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• v.2 no.2
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• pp.136-144
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• 2001

The Markov Chain approach is used to develop an economic adjustment model of a process whose quality can be affected by a single special cause, resulting in changes of the process mean by incorrect adjustment of the process when it is operating according to its capability. The $\bar{X}$ control chart is thus used to signal the special cause. It is demonstrated that the expressions for the expected cycle time and the expected cycle cost are easier to obtain by the proposed approach than by adopting that in Collani, Saniga and Weigang (1994). Furthermore, this approach would be easily extended to derive the expected cycle cost and the expected cycle time for the case of multiple special causes or multiple control charts. A numerical example illustrates the proposed method and its application.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2499-2522
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• 2017

Multi-hop networks are a low-setup-cost solution for enlarging an area of network coverage through multi-hop routing. Carrier sense multiple access with collision avoidance (CSMA/CA) is frequently used in multi-hop networks. Multi-hop networks face multiple problems, such as a rise in contention for the medium, and packet loss under heavy-load, saturated conditions, which consumes more bandwidth due to re-transmissions. The number of re-transmissions carried out in a multi-hop network plays a major role in the achievable quality of service (QoS). This paper presents a statistical, analytical model for the end-to-end delay of contention-based medium access control (MAC) strategies. These strategies schedule a packet before performing the back-off contention for both differentiated heterogeneous data and homogeneous data under saturation conditions. The analytical model is an application of Markov chain theory and queuing theory. The M/M/1 model is used to derive access queue waiting times, and an absorbing Markov chain is used to determine the expected number of re-transmissions in a multi-hop scenario. This is then used to calculate the expected end-to-end delay. The prediction by the proposed model is compared to the simulation results, and shows close correlation for the different test cases with different arrival rates.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.93-109
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• 1994

Even though sojourn time distributions are essential information in analyzing queueing networks, there are few methods to compute them accurately in non-product form queueing networks. In this study, we model the location process of a typical customer as a GMPH semi-Markov chain and develop computationally useful formula for the transition function and the first-passage time distribution in the GMPH semi-Markov chain. We use the formula to develop an effcient method for approximating sojourn time distributions in the non-product form queueing networks under quite general situation. We demonstrate its validity through numerical examples.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.115-128
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• 2002

Hierarchical models are widely used for inference on correlated parameters as a compromise between underfitting and overfilling problems. In this paper, we take a Bayesian approach to analyzing hierarchical models and suggest a Markov chain Monte Carlo methods to get around computational difficulties in Bayesian analysis of the hierarchical models. We apply the method to a real data on smoking and lung cancer which are collected from cities in China.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.501-504
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• 2008

A non-homogeneous markov model which is able to simulate hourly rainfall series is developed for estimating reliable hydrological variables. The proposed approach is applied to simulate hourly rainfall series in Korea. The simulated rainfall is used to estimate the design rainfall and compared to observations in terms of reproducing underlying distributions of the data to assure model's validation. The model shows that the simulated rainfall series reproduce a similar statistical attribute with observations, and expecially maximum value is gradually increased as number of simulation increase.

• Journal of Applied Reliability
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• v.17 no.4
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• pp.332-342
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• 2017

Purpose: In this study, the lifetime distribution of a k-out-of-n system with heterogeneous components is suggested as Markov model, and the time-to-failure (TTF) distribution of each component is considered as phase-type distribution (PHD). Furthermore, based on the model, a redundancy allocation problem with a mix of components (RAPMC) is proposed. Methods: The lifetime distribution model for the system is formulated by the structured Markov chain. From the model, the various information on the system lifetime can be ascertained by the matrix-analytic (or-geometric) method. Conclusion: By the generalization of TTF distribution (PHD) and the consideration of heterogeneous components, the lifetime distribution model can delineate many real systems and be exploited for developing system operation policies such as preventive maintenance, warranty. Moreover, the effectiveness of the proposed RAPMC is verified by numerical experiments. That is, under the equivalent design conditions, it presented a system with higher reliability than RAP without component mixing (RAPCM).

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.123-123
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• 2012

일강수량은 수공구조물 설계 및 수자원계획을 수립하기 위한 입력 자료로 이용된다. 일반적으로 수자원계획은 장기적인 목적을 가지고 수행되어지며, 장기간의 일강수량 자료를 필요로 한다. 하지만 장기간의 일강수량 자료의 획득의 어려움으로 단기간의 일강수량자료를 이용하여 모의한 장기간 강수자료를 이용하게 된다. 이처럼 수자원계획의 수립에 있어서 일강수량 모의기법의 성능은 수자원계획의 신뢰성 및 결과에 큰 영향을 준다. 일강수량 모의기법은 국내외적으로 매우 활발하게 이루어지고 있으며, 수자원계획 및 수공구조물 설계 외에도 매우 다양한 목적으로 활용되어 지고 있다. 일강수량을 모의기법 중 강수계열의 단기간의 기억(memory)을 활용한 Markov Chain 모형이 가장 일반적이지만, 기존 Markov Chain 모형을 통한 일강수량 모의는 극치강수량을 재현하기 어렵다는 문제점이 있다. 또한, 일강수량 모의 기법의 목적인 수자원계획 및 수공구조물 설계 등의 입력자료로 활용되어지기 위해서는 모의 결과가 유역내 지점별 공간 상관성을 재현함으로써 모형의 우수성과 자료결과의 신뢰성을 확보할 수 있어야 하겠다. 이러한 점에서 본 연구에서는 내삽에서 우수한 재현능력을 갖는 핵 밀도함수와 극치강수량 재현에 유리한 GPD분포의 특징을 함께 고려할 수 있는 불연속 Kernel-Pareto Distribution 기반에 공간상관성 재현 알고리즘을 결합한 일강수량모의기법을 개발하였다. 한강유역의 18개 강수지점에 대해서 기존 Gamma분포를 사용한 Markov Chain 모형과 본 연구에서 제안한 방법을 적용하여 모형을 평가해 보고자 한다. Gamma 분포기반 Markov Chain 모형의 경우 일강수량 모의 시 1차모멘트인 평균과 2-3차 모멘트 모두 효과적으로 재현하지 못하는 문제점이 나타났다. 그러나 본 연구에서 적용한 다지점 불연속 Kernel-Pareto 분포 모형은 강수계열의 평균적인 특성뿐만 아니라 표준편차 및 왜곡도의 경우에도 관측치의 통계특성을 매우 효과적으로 재현하며, 100년빈도 강수량 모의결과 기존 모의모형의 문제점을 보완할 수 있는 개선된 결과를 보여주었다. 본 연구에서 제시한 방법론은 유역내의 공간상관성을 재현하며, 평균 및 중간값 등 낮은 차수의 모멘트 등 일강수량 분포특성을 더욱 효과적으로 모의할 수 장점을 확인하였다.

• Journal of the Korea Institute of Building Construction
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• v.11 no.1
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• pp.91-99
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• 2011

Bayesian network is a form of probabilistic graphical model. It incorporates human reasoning to deal with sparse data availability and to determine the probabilities of uncertain cases. In this research, bayesian network is adopted to model the problem of construction project cost. General information, time, cost, and material, the four main factors dominating the characteristic of construction costs, are incorporated into the model. This research presents verify a model that were conducted to illustrate the functionality and application of a decision support system for predicting the costs. The Markov Chain Monte Carlo (MCMC) method is applied to estimate parameter distributions. Furthermore, it is shown that not all the parameters are normally distributed. In addition, cost estimates based on the Gibbs output is performed. It can enhance the decision the decision-making process.