• The Journal of the Korea Contents Association
• /
• v.13 no.4
• /
• pp.53-63
• /
• 2013

This paper presents an importance sampling(IS) embedded experimental frame(EF) design for efficient Monte Carlo (MC) simulation. To achieve IS principles, the proposed EF contains two embedded sub-models, which are classified into Importance Sampler(IS) and Bias Compensator(BC) models. The IS and BC models stand between the existing system model and EF, which leads to enhancement of model reusability. Furthermore, the proposed EF enables to achieve fast stochastic simulation as compared with the crude MC technique. From the abstract two case studies with the utilization of the proposed EF, we can gain interesting experimental results regarding remarkable enhancement of simulation performance. Finally, we expect that this work will serve various content areas for enhancing simulation performance, and besides, it will be utilized as a tool to understand and analyze social phenomena.

• Structural Engineering and Mechanics
• /
• v.32 no.1
• /
• pp.21-36
• /
• 2009

An importance sampling method is presented for computing the first passage probability of elasto-plastic structures under stochastic excitations. The importance sampling distribution corresponds to shifting the mean of the excitation to an 'adapted' stochastic process whose future is determined based on information only up to the present. A stochastic control approach is adopted for designing the adapted process. The optimal control law is determined by a control potential, which satisfies the Bellman's equation, a nonlinear partial differential equation on the response state-space. Numerical results for a single-degree-of freedom elasto-plastic structure shows that the proposed method leads to significant improvement in variance reduction over importance sampling using design points reported recently.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1999.10a
• /
• pp.351-358
• /
• 1999

The structural responses of underground structures are examined in probability by using the elasto-plastic stochastic finite element method in which the spatial distributions of material properties are assumed to be stochastic fields. In addition, the adaptive importance sampling method using the response surface technique is used to improve simulation efficiency. The method is found to provide appropriate information although the nonlinear Limit State involves a large number of basic random variables and the failure probability is small. The probability of plastic local failures around an excavated area is effectively evaluated and the reliability for the limit displacement of the ground is investigated. It is demonstrated that the adaptive importance sampling method can be very efficiently used to evaluate the reliability of a large scale stochastic finite element model, such as the underground structures located in the multi-layered ground.

• Journal of the Chungcheong Mathematical Society
• /
• v.21 no.4
• /
• pp.573-583
• /
• 2008

In this paper, we introduce the techniques of estimating the probability of failure in reliability analysis. The basic idea of each technique is explained and drawbacks of these techniques are examined.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.1
• /
• pp.227-237
• /
• 2004

In this paper we presented a Bayesian inference approach for estimating the location and scale parameters of the lognormal distribution using iterative Gibbs sampling algorithm. We also presented estimation of location parameter by two non iterative methods, importance sampling and weighted bootstrap assuming scale parameter as known. The estimates by non iterative techniques do not depend on the specification of hyper parameters which is optimal from the Bayesian point of view. The estimates obtained by more sophisticated Gibbs sampler vary slightly with the choices of hyper parameters. The objective of this paper is to illustrate these tools in a simpler setup which may be essential in more complicated situations.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.5A
• /
• pp.668-673
• /
• 2000

In this paper, not only the problems which could not be solved with Conventional Importance Sampling and Improved Importance Sampling of the early simulation method, and but also the improvements obtained in terms of computer run-time were studied, by applying the central moment algorithm to the digital communication channels. That is, the channel performance evaluation is done for obtaining the cumulative probability function of the statistical characteristics of received signal with estimating the central moment of the received signal mixed the noise in the digital communication receiver. We confirm the simulation run-time after we implemented the quaternary phase shift keying(QPSK) satellite communication channels using the Signal Processing Worksystem(SPW) of the Cadence incorporation to verify the suggested algorithm.

• Journal of Korean Society of Transportation
• /
• v.9 no.2
• /
• pp.127-134
• /
• 1991

본 논문의 목적은 교통망 분석에 있어서 중요한 그러나 흔희 발생하지 않는 사건의 발생확률을 추정하는 방법론 개발에 있다. 예를 들어, 안정적(stable) 교통망에서 일시적인 혼잡현상이 발생할 확률을 씨뮬레이숀을 통해 추정하는 방법에 관한 것이다. 이 분야에서 활발한 연구([3], [12]) 가 있어 왔으나 개괄적(Heuristic) 방법에 제한되어 있었다. 본 논문은 위 문제에 대하여 포괄적(unified)이며 이론적인 방법론을 제시하였다. 이를 위해 대 분산이론(Large Deviation Theory)과 중요표본추출(Importance Sampling)기법이 이용되었으며 예로서 사용된 망은 두개의 구간이 이어진 교통망이다. 부수적으로 혼잡현상의 가장 대표적 형태를 구하는 방법이 제시되었다.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.4
• /
• pp.1045-1056
• /
• 2007

We consider M/M/1 feedback queues with multi-class customers. We assume that different classes of customers have different arrival rates, service rates and feedback probabilities. Using the h-transforms of McDonald(999) we derive an importance sampling estimator for an overflow probability that the total number of customers in the system reaches a high level before emptying.

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