• Management Science and Financial Engineering
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• v.20 no.1
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• pp.27-32
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• 2014

This paper introduces three different simulation algorithms of the shifted Poisson distribution. The first algorithm is the inverse transform method, the second is the rejection sampling, and the third is gamma-Poisson hierarchy sampling. Three algorithms have different regions of parameters at which they are efficient. We numerically compare those algorithms with different sets of parameters. As an application, we give a simulation method of the constant elasticity of variance model.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.95-126
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• 2009

The present contribution addresses the parallelization of advanced simulation methods for structural reliability analysis, which have recently been developed for large-scale structures with a high number of uncertain parameters. In particular, the Line Sampling method and the Subset Simulation method are considered. The proposed parallel algorithms exploit the parallelism associated with the possibility to simultaneously perform independent FE analyses. For the Line Sampling method a parallelization scheme is proposed both for the actual sampling process, and for the statistical gradient estimation method used to identify the so-called important direction of the Line Sampling scheme. Two parallelization strategies are investigated for the Subset Simulation method: the first one consists in the embarrassingly parallel advancement of distinct Markov chains; in this case the speedup is bounded by the number of chains advanced simultaneously. The second parallel Subset Simulation algorithm utilizes the concept of speculative computing. Speedup measurements in context with the FE model of a multistory building (24,000 DOFs) show the reduction of the wall-clock time to a very viable amount (<10 minutes for Line Sampling and ${\approx}$ 1 hour for Subset Simulation). The measurements, conducted on clusters of multi-core nodes, also indicate a strong sensitivity of the parallel performance to the load level of the nodes, in terms of the number of simultaneously used cores. This performance degradation is related to memory bottlenecks during the modal analysis required during each FE analysis.

• Proceedings of the Korea Society for Simulation Conference
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• 1997.04a
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• pp.8-8
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• 1997

최근 시뮬레이션은 높은 신뢰도를 요구하는 통신망시스템이나, 높음 품질수준을 요 구하는 제조시스템의 분석 및 설계에 적용되어지고 있다. 이러한 신뢰도가 높은 시스템에 대한 시뮬레이션 적용의 난제는 실제 시스템과 시뮬레이션 모형이 얼마나 정확히 모델링을 하는가 하는 문제와 실제 모델링을 하여 시뮬레이션을 수행하여 얼마나 빠른 시간내에 정확 히 결과를 산출해 낼 수 있는가를 하는 것이다. 이러한 문제점을 극복하기 위해서 속산시뮬 레이션(fast simulation) 기법들이 연구되고 있다. 그러한 기법들로 Importance Sampling (IS), Regenerative Method (RM), Parallel Simulation 등이 연구되고 있다. IS는 잘 알려진 분산축소 기법으로 속산시뮬레이션을 위하여 많이 사용되고 있으나 실제로 복잡한 모델에 적용하기에는 많은 어려움이 따른다. 그 이유는 최적 표본분포 (Optimal Sampling Distribution)를 찾기 위한 방법이 정형화되어 있지 않아 모델마다 최적표본분포를 유사하게 추정해야 하는 어려움이 따르기 때문이다. 이러한 단점을 극복하기 위하여 Nonparametric Improtance Sampling을 제안하고 실제로 M/M/1 대기행렬 모형에 적용하여 보았다.

• International Journal of Reliability and Applications
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• v.14 no.2
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• pp.71-78
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• 2013

The problem of designing an experiment to estimate the reliability of a system that has N subsystems connected in series where each subsystem n has n $T_n$ components connected in parallel is investigated both theoretically and by simulation. An accelerated sampling sheme is introduced. It is shown that the accelerated sampling scheme is asymptotically optimal as the total number of units goes to infinity. Numerical comparisons for a system that has two subsystems connected in series where each subsystem has two components connected in parallel are also given. They indicate that the accelerated sampling scheme performs better than the batch sequential sampling scheme and is nearly optimal.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.133-141
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• 2010

This research suggests a unified scheme for using stratified sampling and control variates method to improve the efficiency of estimation for parameters in simulation experiments. We utilize standardized concomitant variables defined during the course of simulation runs. We first use these concomitant variables to counteract the unknown error of response by the method of control variates, then use a concomitant variable not used in the controlled response and stratify the response into appropriate strata to reduce the variation of controlled response additionally. In case that the covariance between the response and a set of control variates is known, we identify the simulation efficiency of suggested method using control variates and stratified sampling. We conjecture the simulation efficiency of this method is better than that achieved by separated application of either control variates or stratified sampling in a simulation experiments. We investigate such an efficiency gain through simulation on a selected model.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.135-150
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• 1999

Latin hypercube sampling(LHS) introduced by McKay et al. (1979) is a widely used method for Monte Carlo integration. Stratified Latin hypercube sampling(SLHS) proposed by Choi and Lee(1993) improves LHS by combining it with stratified sampling. In this article it is shown that SLHS yields an asymptotically more accurate than both stratified sampling and LHS.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.1-16
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• 1997

The cell loss probability recommended in the B-ISDN is in the range $10^{-8}~10^{-12}$. When a simulation technique is used to analyze the performance of the ATM node, an enormous amount of computer processing time is required. In this study, we derive an importance sampling simulation technique that can be used to evaluate the performance of the ATM node very quickly, that is, the probability that the queue size at the ATM node reaches some large value N. The simulation results show that the backlog probability obtianed by the importance saimpling simulation is very close to that obtained by the ordinary simulation and the computer time can be reduced drastically by the importance sampling simulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.45-53
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• 2011

This paper presents a novel random sampling method for generation adequacy assessment including wind-power. Although a time sequential sampling has advantages than a random sampling in its assessment results, it takes long assessment time. Therefore, an effective random sampling method for generation adequacy assessment is highly recommended to get specific reliability indices quickly. The proposed method is based on the Monte-Carlo simulation with state sampling and it can be applied to generation adequacy assessment with other intermittent power sources.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1832-1842
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• 2014

A novel probabilistic small signal stability analysis (PSSSA) method considering load correlation is proposed in this paper. The superiority Latin hypercube sampling (LHS) technique combined with Monte Carlo simulation (MCS) is utilized to investigate the probabilistic small signal stability of power system in presence of load correlation. LHS helps to reduce the sampling size, meanwhile guarantees the accuracy and robustness of the solutions. The correlation coefficient matrix is adopted to represent the correlations between loads. Simulation results of the two-area, four-machine system prove that the proposed method is an efficient and robust sampling method. Simulation results of the 16-machine, 68-bus test system indicate that load correlation has a significant impact on the probabilistic analysis result of the critical oscillation mode under a certain degree of load uncertainty.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.257-269
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• 2001

The simulation is used to estimate an overflow probability in a stable parallel network with coupled inputs. Since the general simulation needs extremely many trials to obtain such a small probability, the fast simulation is proposed to reduce trials instead. By using the Cramer’s theorem, we first obtain an optimally changed measure under which the variance of the estimator is minimized. Then, we use it to derive an importance sampling estimator of the overflow probability which enables us to perform the fast simulation.