• Structural Engineering and Mechanics
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• 제38권1호
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• pp.125-140
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• 2011

This study presents an innovative method to estimate the reliability sensitivity based on the low-discrepancy sampling which is a new technique for structural reliability analysis. Two advantages are contributed to the method: one is that, by developing a general importance sampling procedure for reliability sensitivity analysis, the partial derivative of the failure probability with respect to the distribution parameter can be directly obtained with typically insignificant additional computations on the basis of structural reliability analysis; and the other is that, by combining various low-discrepancy sequences with the above importance sampling procedure, the proposed method is far more efficient than that based on the classical Monte Carlo method in estimating reliability sensitivity, especially for problems of small failure probability or problems that require a large number of costly finite element analyses. Examples involving both numerical and structural problems illustrate the application and effectiveness of the method developed, which indicate that the proposed method can provide accurate and computationally efficient estimates of reliability sensitivity.

• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.63-75
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• 2002

Most attempts at Bayesian analysis of neural networks involve hierarchical modeling. We believe that similar results can be obtained with simpler models that require less computational effort, as long as appropriate restrictions are placed on parameters in order to ensure propriety of posterior distributions. In particular, we adopt a model first introduced by Lee (1999) that utilizes an improper prior for all parameters. Straightforward Gibbs sampling is possible, with the exception of the bias parameters, which are embedded in nonlinear sigmoidal functions. In addition to the problems posed by nonlinearity, direct sampling from the posterior distributions of the bias parameters is compounded due to the duplication of hidden nodes, which is a source of multimodality. In this regard, we focus on sampling from the marginal posterior distribution of the bias parameters with Markov chain Monte Carlo methods that combine traditional Metropolis sampling with a slice sampler described by Neal (1997, 2001). The methods are illustrated with data examples that are largely confined to the analysis of nonparametric regression models.

• 한국시뮬레이션학회논문지
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• 제13권3호
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• pp.1-10
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• 2004

The cell loss probability required in the ATM network is in the range of 10$^{-9}$ ∼10$^{-12}$ . If Monte Carlo simulation is used to analyze the performance of the ATM node, an enormous amount of computer time is required. To obtain large speed-up factors, importance sampling may be used. Since the Markov-modulated processes have been used to model various high-speed network traffic sources, we consider discrete time single server queueing systems with Markov-modulated arrival processes which can be used to model an ATM node. We apply importance sampling based on the Large Deviation Theory for the performance evaluation of, MMBP/D/1/K, ∑MMBP/D/1/K, and two stage tandem queueing networks with Markov-modulated arrival processes and deterministic service times. The simulation results show that the buffer overflow probabilities obtained by the importance sampling are very close to those obtained by the Monte Carlo simulation and the computer time can be reduced drastically.

• 한국안전학회지
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• 제18권1호
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• pp.116-122
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• 2003

몬테칼로법은 복잡한 특히 비선형문제를 푸는데 강력한 공학도구중의 하나이다. 이 방법은 컴퓨터에서의 통계적인 표본추출방법을 이용하여 각종 공학적인 문제에 근사적인 해를 준다. 교량 하나의 요소나 전체교량의 시간 의존적 파괴확률을 예측하는 방법중의 하나로 생애함수가 있다. 이 논문에서는 교량의 요소나 전체 교량의 파괴확률을 예측하기 위하여, 시스템 신뢰성과 생애함수를 이용하여 포트란 프로그램을 개발하였다. 몬테칼로법은 생애함수의 매개변수를 생성하는데 이용되었다. 적용례로서, 개발된 프로그램은 콘크리트-강 합성 교량에 적용되어, 파괴확률을 예측하는데 이용되었다.

• Nuclear Engineering and Technology
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• 제55권12호
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• pp.4685-4694
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• 2023

The ability to calculate the material density sensitivity coefficients of power with respect to the material density has broad application prospects for accelerating Monte Carlo-Thermal Hydraulics iterations. The second-order material density sensitivity coefficients for the general Monte Carlo score have been derived based on the differential operator sampling method in this paper, and the calculation of the sensitivity coefficients of cell power scores with respect to the material density has been realized in continuous-energy Monte Carlo code RMC. Based on the power-density sensitivity coefficients, the sensitivity coefficients of power scores to some other physical quantities, such as power-boron concentration coefficients and power-temperature coefficients considering only the thermal expansion, were subsequently calculated. The effectiveness of the proposed method is demonstrated in the power-density coefficients problems of the pressurized water reactor (PWR) moderator and the heat pipe reactor (HPR) reflectors. The calculations were carried out using RMC and the ENDF/B-VII.1 neutron nuclear data. It is shown that the calculated sensitivity coefficients can be used to predict the power scores accurately over a wide range of boron concentration of the PWR moderator and a wide range of temperature of HPR reflectors.

• 한국지리정보학회지
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• 제23권4호
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• pp.16-41
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• 2020

실제 공간의 분포 또는 양적 속성을 대변하는 지리정보 입력은 지구시스템 모의 내에서 주요 관심사가 되고 있다. 많은 연구에서 다양한 격자 해상도에서의 지표면 특성에 대한 부정확한 추정이 모델링 결과를 크게 바꾸는 것으로 나타났다. 따라서, 이 논문은 도시지역 건물들의 분포와 면적·체적 속성을 반영하기 위해서, 항공라이다로 수집된 3DPC(three-dimensional point cloud) 샘플링 체계에 Monte Carlo Integration(MCI) 기법 기반 공간확률(spatial probability)을 적용을 제안하였다. 건물 식별과 관련해 공간확률(SP) 임계치, 격자 크기, 3차원점군 밀도 세 인자의 결정규칙 적용 결과가 비교되었다. 연구 결과, 건물의 격자가 커짐에 따라 식별되는 건물의 면적 속성이 증가하였다. 공간 모델링 및 분석의 신뢰성을 높이기 위해서는 샘플링 체계에서의 결정규칙을 사용하여 건물의 면적 속성을 조정하는 것이 권장된다. 제안된 방법은 모델링 분야가 요구하는 크고 작은 격자의 변화에서도 일정하게 건물 면적 속성이 유지되도록 지원할 것이다.

• ETRI Journal
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• 제14권4호
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• pp.19-27
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• 1992

Since. as is well known, direct computation of the reliability for a large-scaled and complex net work generally requires exponential time, a variety of alternative methods to estimate the network reliability using simulation have been proposed. Monte Carlo sampling is the major approach to estimate the network reliability using simulation. In the paper, a dynamic Monte Carlo sampling method, called conditional minimal cut set (CMCS) algorithm, is suggested. The CMCS algorithm simulates a minimal cut set composed of arcs originated from the (conditional) source node until s-t connectedness is confirmed, then reduces the network on the basis of the states of simulated arcs. We develop the importance sampling estimator and the total hazard estimator and compare the performance of these simulation estimators. It is found that the CMCS algorithm is useful in reducing variance of network reliability estimator.

• Structural Engineering and Mechanics
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• 제82권3호
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• pp.271-282
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• 2022

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

• Journal of the Korean Statistical Society
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• 제23권2호
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• pp.463-482
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• 1994

In Bayesian analysis of randomized response models, the likelihood function does not combine tractably with typical priors for the parameters of interest, causing computational difficulties in posterior analysis of the parameters of interest. In this article, the difficulties are solved by introducing appropriate latent variables to the model and using the Gibbs sampling algorithm.

• 충청수학회지
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• 제21권4호
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• pp.573-583
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• 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.