• Proceedings of the KIEE Conference
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• summer
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• pp.944-946
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• 2004

In this paper, a new technique for electromagnetic topology optimization is proposed. The proposed technique is based on the large-step Markov chain (LSMC) method with novel local optimization algorithm. Because the proposed algorithm keeps a good convergence characteristic of LSMC, fast convergence is assured. The proposed LSMC is verified by an application to an inverse reconstruction problem.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.1
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• pp.41-49
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• 2014

In this paper, we consider a method to access the number of aircraft requirement which is a strategic variable in national security. This problem becomes more important considering the F-X and KF-X project in ROKAF. Traditionally, ATO(Air Tasking Order) and fighting power index have been used to evaluate the number of aircrafts required in ROKAF. However, those methods considers static aspect of aircraft requirement. This paper deals with a model to accommodate dynamic feature of aircraft requirement using absorbing Markov chain. In conclusion, we suggest a dynamic model to evaluate the number of aircrafts required with key decision variables such as destroying rate, failure rate and repair rate.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.423-433
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• 1998

In agricultural field experiments, the completely randomized block design is often used for the analysis of variety trials. An important assumption is that every experimental unit in each block has the some fertility. But, in most agricultural field experiments there often exists a systematic heterogeneity in fertility among the experimental units. To account for the heterogeneity, we propose to use the hierarchical generalized linear models. We compare our analysis of the data from Scottish Agricultural colleges list with that using Markov chain Monte Carlo method.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.428-432
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• 2012

SWAT모형을 이용하여 악근천, 호근천, 동흥천을 대표 3개 유역으로 구분하여 1990년 토지이용상태에 1980년부터 1999년까지의 기상자료를 사용하였고 2000년 토지이용상태에 2000년부터 2010년까지의 기상자료를 적용하여 분석한 결과, 적용 기간 평균 지하수 함양률은 30%로 분석되었다. 이러한 연구결과는 기존의 제주지역 지하수 함양량 분석연구에서 나타난 40-50%보다 훨씬 적은 값으로 나타나고 있음을 알 수 있었다. 이 결과는 투수성 다공질 지층이 제주지역의 지질특성을 고려하더라도 개발형태가 비투수성 포장형태이고, 개발된다면 육지지역과 같은 지하수함양특성과 비슷할 수 밖에 없음을 알 수 있었다. 또한 Markov-chain을 이용하여 장래토지이용상태를 분석한 결과 장래의 토지이용은 2000년 현재에 비해 산림 3.4%감소, 농경지 0.2% 증가, 수계 1.0% 증가, 도심지 0.5%증가 하는 것으로 나타났다. 토지이용상태에 따른 SWAT모형 분석을 비교한 결과 현재에 비해 유출량은 평균 22mm 증가하였으며, 증발산량은 평균 5mm감소, 함양량은 평균15mm감소하여 0.7% 정도 감소하는 것으로 토지이용상태가 함양량에 영향을 미치는 것으로 분석되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4034-4053
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• 2015

In order to meet various requirements for transmission quality of both primary users (PUs) and secondary users (SUs) in cognitive radio networks, we introduce a channel bonding mechanism for PUs and a channel reservation mechanism for SUs, then we propose a novel spectrum allocation strategy. Taking into account the mistake detection and false alarm due to imperfect channel sensing, we establish a three-dimensional Markov chain to model the stochastic process of the proposed strategy. Using the method of matrix geometric solution, we derive the performance measures in terms of interference rate of PU packets, average delay and throughput of SU packets. Moreover, we investigate the influence of the number of the reserved (resp. licensed) channels on the system performance with numerical experiments. Finally, to optimize the proposed strategy socially, we provide a charging policy for SU packets.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.660-667
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• 2007

본 논문은 확률특성을 갖는 네트워크 기반 제어시스템(NCS; Networked Control Systems)을 위하여 동적 베이시안 네트워크(DBN; Dynamic Bayesian Networks)와 신경회로망 기법을 이용한 지능제어기법을 제안한다. 신경회로망은 시변 시간지연을 갖는 비선형 시스템의 실시간 오차를 보상하기 위한 제어기의 최적화에 적용된다. 모듈화 신경회로망이 구성되며 이것은 제어기의 파라미터를 출력한다 가장 간단한 DBN 구조인 마코브 체인(MC; Markov Chain)이 구성되며 NCS의 랜덤 관측값을 모델링에 적용되며 예측 제어기의 구성에 또한 사용된다. 제안한 제어기법은 위성시스템의 자세제어에 적용하여 컴퓨터 시뮬레이션을 통해 성능을 검증하였다.

• Computers and Concrete
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• v.13 no.5
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• pp.659-677
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• 2014

A Bayesian response surface updating procedure is applied in order to update the parameters of the covariance function of a random field for concrete properties based on a limited number of available measurements. Formulas as well as a numerical algorithm are presented in order to update the parameters of response surfaces using Markov Chain Monte Carlo simulations. The parameters of the covariance function are often based on some kind of expert judgment due the lack of sufficient measurement data. However, a Bayesian updating technique enables to estimate the parameters of the covariance function more rigorously and with less ambiguity. Prior information can be incorporated in the form of vague or informative priors. The proposed estimation procedure is evaluated through numerical simulations and compared to the commonly used least square method.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.292-302
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• 2018

This study proposes a Bayesian stochastic frontier model that is well-suited to productivity/efficiency analysis particularly using panel data. A unique feature of our proposal is that both production frontier and efficiency are estimable for each individual firm and their linkage to various firm characteristics enriches our understanding of the source of productivity/efficiency. Empirical application of the proposed analysis to Human Capital Corporate Panel data enables identification and quantification of the effects of Human Resource factors on firm efficiency in tandem with those of firm types on production frontier. A comprehensive description of the Markov Chain Monte Carlo estimation procedure is forwarded to facilitate the use of our proposed stochastic frontier analysis.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.131-148
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• 2019

Constant stress partially accelerated life tests are studied according to exponentiated Weibull distribution. Grounded on multiple censoring, the maximum likelihood estimators are determined in connection with unknown distribution parameters and accelerated factor. The confidence intervals of the unknown parameters and acceleration factor are constructed for large sample size. However, it is not possible to obtain the Bayes estimates in plain form, so we apply a Markov chain Monte Carlo method to deal with this issue, which permits us to create a credible interval of the associated parameters. Finally, based on constant stress partially accelerated life tests scheme with exponentiated Weibull distribution under multiple censoring, the illustrative example and the simulation results are used to investigate the maximum likelihood, and Bayesian estimates of the unknown parameters.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.513-532
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• 2022

In this paper, the use of Bayes factors and the deviance information criterion for model selection are compared in a Bayesian accelerated life testing setup. In Bayesian accelerated life testing, the most used tool for model comparison is the deviance information criterion. An alternative and more formal approach is to use Bayes factors to compare models. However, Bayesian accelerated life testing models with more than one stressor often have mathematically intractable posterior distributions and Markov chain Monte Carlo methods are employed to obtain posterior samples to base inference on. The computation of the marginal likelihood is challenging when working with such complex models. In this paper, methods for approximating the marginal likelihood and the application thereof in the accelerated life testing paradigm are explored for dual-stress models. A simulation study is also included, where Bayes factors using the different approximation methods and the deviance information are compared.