• 한국정보통신학회논문지
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• 제9권6호
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• pp.1341-1345
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• 2005

비선형이고 정규분포에 따르지 않는 state-space모형분석에서 순차적 몬테 칼로(SMC)는 유용한 도구 중의 하나이다. 모수와 시그럴을 동시에 추정하기 위해 Monte Carlo particle filters를 사용할 수가 있다. 그러나 SMC는 여러단계의 반복을 요구하는 특별한 particle filtering 기법을 필요로 하게 된다. 본 논문은 particle filtering과 순차적 hybrid Monte Carlo(SHMC)을 결합하는 방법을 제시하고자 한다. 실험을 위해 짱뚱어 자료를 사용하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 A
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• pp.636-638
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• 2003

This paper presents a new approach which is able to determine the reasonable incentive levels of direct load control using sequential Monte Carlo simulation techniques. The economic analysis needs to determine the reasonable incentive level. However, the conventional methods have been based on the scenario methods because they had not considered all cases of the direct load control situations. To overcome there problems, this paper proposes a new technique using sequential Monte Carlo simulation. The Monte Carlo method is a simple and flexible tool to consider large scale systems and complex models for the components of the system. To show its effectiveness, numerical studies were performed to indicate the possible applications of the proposed technique.

• 대한전기학회논문지:전력기술부문A
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• 제53권2호
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• pp.121-128
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• 2004

This paper presents a new approach for determining an accurate incentive levels of Direct Load Control (DLC) program using sequential Monte Carlo Simulation (MCS) techniques. The economic analysis of DLC resources needs to identify the hourly-by-hourly expected energy-not-served resulting from the random outage characteristics of generators as well as to reflect the availability and duration of DLC resources, which results the computational explosion. Therefore, the conventional methods are based on the scenario approaches to reduce the computation time as well as to avoid the complexity of economic studies. In this paper, we have developed a new technique based on the sequential MCS to evaluate the required expected load control amount in each hour and to decide the incentive level satisfying the economic constraints. In addition, the mathematical formulation for DLC programs' economic evaluations are developed. To show the efficiency and effectiveness of the suggested method, the numerical studies have been performed for the modified IEEE reliability test system.

• Structural Engineering and Mechanics
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• 제81권1호
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• pp.103-115
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• 2022

The prediction error variances for frequencies are usually considered as unknown in the Bayesian system identification process. However, the error variances for mode shapes are taken as known to reduce the dimension of an identification problem. The present study attempts to explore the effectiveness of Bayesian approach of model parameters updating using Markov Chain Monte Carlo (MCMC) technique considering the prediction error variances for both the frequencies and mode shapes. To remove the ergodicity of Markov Chain, the posterior distribution is obtained by Gaussian Random walk over the proposal distribution. The prior distributions of prediction error variances of modal evidences are implemented through inverse gamma distribution to assess the effectiveness of estimation of posterior values of model parameters. The issue of incomplete data that makes the problem ill-conditioned and the associated singularity problem is prudently dealt in by adopting a regularization technique. The proposed approach is demonstrated numerically by considering an eight-storey frame model with both complete and incomplete modal data sets. Further, to study the effectiveness of the proposed approach, a comparative study with regard to accuracy and computational efficacy of the proposed approach is made with the Sequential Monte Carlo approach of model parameter updating.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.434-436
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• 2006

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be "distracted" causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• 응용통계연구
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• 제10권2호
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• pp.353-366
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• 1997

본 논문에서는 주어진 손실함수에 대한 근사최적베이즈축차실험방법을 유도한다. 근사최적베이즈축차실험방법을 Pocock 실험방법, O'Brien and Fleming 실험방법과 ASN(average sample size) 함수와 베이즈 위험면에서 비교하기 위하여 몬테칼로(Monte-Carlo) 방법을 사용한다. 그룹축차실험방법에서 그룹 때문에 빚어지는 정보의 손실비율과 베이즈효율의 개념을 소개하고 여러 가지 다른 그룹 크기에 따른 정보의 손실을 측정한다.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1279-1287
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• 2012

We particularly emphasized how to determine the number of replications with sequential and multistage procedures. So, the t-test is used to achieve some predetermined level of accuracy efficiently with loss function in the case of normal, chi-squared, an exponential distributions. We provided that the relevance of procedures are sequential procedure, two-stage procedure, modified two-stage procedure, three-stage procedure and accelerated sequential procedure. Monte Carlo simulation is carried out to obtain the stopping sample size that minimizes the risk.

• 대한전기학회논문지:전력기술부문A
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• 제52권1호
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• pp.9-16
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• 2003

In this paper, we propose a reliability evaluation method considering the momentary interruptions of power distribution systems. The results of research are concentrated on two parts. One is the analytic and probabilistic reliability evaluation of power distribution system considering the momentary interruptions and the other is the reliability cost evaluation that unifies the cost of sustained and momentary interruptions. This proposed reliability cost evaluation methodology is also divided into the analytic and probabilistic approach and the time sequential Monte Carlo method is used for the probabilistic method. The proposed methods are tested using the modified RBTS (Roy Billinton Test System) form and historical reliability data of KEPCO (Korea Electric Power Corporation) system. Through the case studies, it is verified that the proposed reliability evaluation and its cost/worth assessment methodologies can be applied to the actual reliability studies.

• Journal of Electrical Engineering and Technology
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• 제11권3호
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• pp.575-584
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• 2016

Considering the randomness and uncertainty of wind power, a reliability model of WTGs is established based on the combination of the Weibull distribution and the Markov chain. To analyze the failure mode quickly, we use the switch-section partitioning method. After defining the first-level load zone node, we can obtain the supply power sets of the first-level load zone nodes with each WTG. Based on the supply sets, we propose the dynamic division strategy of island operation. By adopting the fault analysis method with the attributes defined in the switch-section, we evaluate the reliability of the distribution network with WTGs using a sequential Monte Carlo simulation method. Finally, using the IEEE RBTS Bus6 test system, we demonstrate the efficacy of the proposed model and method by comparing different schemes to access the WTGs.

• 한국농공학회지
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• 제18권4호
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• pp.4232-4241
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• 1976

This study was carried out to clarify the stochastic characteristics of monthly rainfalls and to select a proper model for generating the sequential monthly rainfall amounts. The results abtained are as follows: 1. Log-Normal distribution function is the best fit theoretical distribution function to the empirical distribution of monthly rainfall amounts. 2. Seasonal and random components are found to exist in the time series of monthly rainfall amounts and non-stationarity is shown from the correlograms. 3. The Monte Carlo model shows a tendency to underestimate the mean values and standard deviations of monthly rainfall amounts. 4. The 1st order Markov model reproduces means, standard deviations, and coefficient of skewness with an error of ten percent or less. 5. A correlogram derived from the data generated by 1st order Markov model shows the charaterstics of historical data exactly. 6. It is concluded that the 1st order Markov model is superior to the Monte Carlo model in their reproducing ability of stochastic properties of monthly rainfall amounts.