• Smart Structures and Systems
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• 제15권3호
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.239-239
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• 2015

This study presents a bivariate extension of the goodness-of-fit measure for regional frequency distributions developed by Hosking and Wallis [1993] for use with the method of L-moments. Utilising the approximate joint normal distribution of the regional L-skewness and L-kurtosis, a graphical representation of the confidence region on the L-moment diagram can be constructed as an ellipsoid. Candidate distributions can then be accepted where the corresponding the oretical relationship between the L-skewness and L-kurtosis intersects the confidence region, and the chosen distribution would be the one that minimises the Mahalanobis distance measure. Based on a set of Monte Carlo simulations it is demonstrated that the new bivariate measure generally selects the true population distribution more frequently than the original method. An R-code implementation of the method is available for download free-of-charge from the GitHub code depository and will be demonstrated on a case study of annual maximum series of peak flow data from a homogeneous region in Italy.

• 한국환경보건학회지
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• 제45권5호
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• pp.557-557
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• 2019

• EDISON SW 활용 경진대회 논문집
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• 제6회(2017년)
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• pp.677-687
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• 2017

Caffeine has a long history of human consumption but the consumption of caffeine due to caffeinated energy drinks(CEDs) is rapidly growing. Marketing targets of CED sales are children, adolescents and young adults, possibly caffeine-sensitive groups and its effect for them can be significantly different from healthy adults. Caffeine-related toxicities among these groups are growing in number and a number of countries are recognizing severity of caffeine toxicities. Previous research showed prediction of maximal plasma caffeine concentration profiles after the single CED ingestion and the primary aim of this study is to visually predict plasma caffeine concentration after the single and multiple ingestion of standard servings of CED. Based on the population pharmacokinetic model using Monte Carlo simulation, prediction of caffeine concentration leading to caffeine intoxication in the sensitive groups is quantitatively presented and visualized. This research also broadens the perspective by creating and utilizing diverse open science tools including R package, Edison Science App and Shiny apps.

• Nuclear Engineering and Technology
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• 제16권3호
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• pp.136-140
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• 1984

원자력발전소 사고 후 그 위험도를 평가하는 새로운 방법으로 몬테칼로 방법을 제시한다. 본 연구에서는 발전소 주위의 주민에게 주는 방사선의 영향을 평가하기 위하여 공기중의 확산계산에 부지에서 측정한 기상조건을 직접 사용하고 있다. 사고가 일어나는 순간에서의 화산조건은 주어진 기상자료로부터 분석된 pdf에 의하여 결정되고 그이후의 조건(풍향, 풍속, 안정도)은 마르코프 조건을 만족시킨다고 가정하였다. 예제로써 KNU-1의 냉각재 상실사고를 분석한 절과 50마일내의 주민이 받는 선량은 50퍼센트 신뢰도를 갖고 200 man-Sv이다.

• 한국환경과학회지
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• 제28권1호
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• pp.19-35
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• 2019

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

• Structural Engineering and Mechanics
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• 제66권6호
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• pp.677-691
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• 2018

Reliability assessment of complex structures using simulation methods is time-consuming. Thus, surrogate models are usually employed to reduce computational cost. AK-MCS is a surrogate-based Active learning method combining Kriging and Monte-Carlo Simulation for structural reliability analysis. This paper proposes three modifications of the AK-MCS method to reduce the number of calls to the performance function. The first modification is related to the definition of an initial Design of Experiments (DoE). In the original AK-MCS method, an initial DoE is created by a random selection of samples among the Monte Carlo population. Therefore, samples in the failure region have fewer chances to be selected, because a small number of samples are usually located in the failure region compared to the safe region. The proposed method in this paper is based on a uniform selection of samples in the predefined domain, so more samples may be selected from the failure region. Another important parameter in the AK-MCS method is the size of the initial DoE. The algorithm may not predict the exact limit state surface with an insufficient number of initial samples. Thus, the second modification of the AK-MCS method is proposed to overcome this problem. The third modification is relevant to the type of regression trend in the AK-MCS method. The original AK-MCS method uses an ordinary Kriging model, so the regression part of Kriging model is an unknown constant value. In this paper, the effect of regression trend in the AK-MCS method is investigated for a benchmark problem, and it is shown that the appropriate choice of regression type could reduce the number of calls to the performance function. A stepwise approach is also presented to select a suitable trend of the Kriging model. The numerical results show the effectiveness of the proposed modifications.

• 응용통계연구
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• 제18권1호
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• pp.1-13
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• 2005

최근 많은 연구자와 실무자들이 모집단에 내재해 있는 여러 다른 그룹(class, segment)간의 이질성을 밝혀내고 객체들을 그룹별로 세분화하는 방법 중 하나로 잠재그룹 모델(Latent class model)을 고려하고 있다. 이 논문에서는 2000년도에 국립 암 센터에 접수된 한국 내 연령별 전립선암 사망자수 자료를 기반으로, 잠재그룹 포아송 모형을 이용하여 전립선암 환자의 연령에 따른 그룹화를 시도한다. 최우추정법 등 고전적 추론방법의 한계를 극복하기 위하여 Markov Chain Monte Carlo (MCMC) 방법을 도구로 한 베이지안 추정 방법을 제안한다. 제안된 베이지안 방법의 장점은 용이한 모수추정과 추정오차의 제공, 그리고 각 객체의 소속그룹의 판정과 이에 따르는 오차, 즉, 객체의 각 군집에 속할 확률, 도 구할 수 있다는 것이다. 또한 주어진 자료들에 대해 가장 적합한 그룹의 수를 결정하는 방법을 제시하여 그룹의 수나 세분화의 근거를 사전에 제공하지 않아도 자료가 주는 정보로부터 이들을 자동으로 결정하는 방법을 제시한다.

• Nuclear Engineering and Technology
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• 제49권4호
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• pp.710-720
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• 2017

In Korea, many nuclear power plants operate at a single site based on geographical characteristics, but the population density near the sites is higher than that in other countries. Thus, multiunit accidents are a more important consideration than in other countries and should be addressed appropriately. Currently, there are many issues related to a multiunit probabilistic safety assessment (PSA). One of them is the quantification of a multiunit PSA model. A traditional PSA uses a Boolean manipulation of the fault tree in terms of the minimal cut set. However, such methods have some limitations when rare event approximations cannot be used effectively or a very small truncation limit should be applied to identify accident sequence combinations for a multiunit site. In particular, it is well known that seismic risk in terms of core damage frequency can be overestimated because there are many events that have a high failure probability. In this study, we propose a quantification method based on a Monte Carlo approach for a multiunit PSA model. This method can consider all possible accident sequence combinations in a multiunit site and calculate a more exact value for events that have a high failure probability. An example model for six identical units at a site was also developed and quantified to confirm the applicability of the proposed method.

• 대한토목학회논문집
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• 제14권6호
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• pp.1357-1363
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• 1994

난류전단 흐름에서의 기포응집에 따른 기포의 크기분포를 예측하기 위한 Monte-Carlo 모의모형을 개발하였다. 임의로 선택된 각 초기위치에 일련의 기포들을 매시각 발생시키고, 각 기포들의 움직임과 충돌을 모의함으로써 각각의 위치와 크기를 추적하도록 하였다. 기포의 횡방향 변위는 이송확산 방정식의 수치해를 이용하여 부여하였으며, 종방향 변위는 흐름의 대수유속분포 및 기포 상승속도로부터 주어지도록 하였다. 각 기포들간의 초기 상대위치와 상대변위를 이용한 기하학적 해석에 의하여 매시간단계에서의 기포응집을 탐지하여, 시간단계 말기에서의 기포 총수, 각 기포의 위치 및 크기를 결정하였다. 기포들의 크기 및 위치를 나타내기 위하여 소요되는 기억용량을 최소화하도록 전산모형을 구성하였다.