• Title, Summary, Keyword: sequential Monte Carlo

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A Sequential Monte Carlo inference for longitudinal data with luespotted mud hopper data (짱뚱어 자료로 살펴본 장기 시계열 자료의 순차적 몬테 칼로 추론)

  • Choi, Il-Su
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.9 no.6
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    • pp.1341-1345
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    • 2005
  • Sequential Monte Carlo techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. We can use Monte Carlo particle filters adaptively, i.e. so that they simultaneously estimate the parameters and the signal. However, Sequential Monte Carlo techniques require the use of special panicle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and Sequential Hybrid Monte Carlo. We give some examples of applications in fisheries(luespotted mud hopper data).

Development of an Evaluation Technique for Incentive Level of Direct Load Control using Sequential Monte Carlo Simulation (몬테카를로 시뮬레이션을 이용한 직접부하제어의 적정 제어지원금 산정기법 개발)

  • Jeong, Yun-Won;Kim, Min-Soo;Park, Jong-Bae;Shin, Joong-Rin;Kim, Byung-Seop
    • Proceedings of the KIEE Conference
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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.

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Development of an Incentive Level Evaluation Technique of Direct Load Control using Sequential Monte Carlo Simulation (몬테카를로 시뮬레이션을 이용한 직접부하제어의 적정 제어지원금 산정기법 재발)

  • 정윤원;박종배;신중린
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.53 no.2
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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.

Vehicle Tracking using Sequential Monte Carlo Filter (순차적인 몬테카를로 필터를 사용한 차량 추적)

  • Lee, Won-Ju;Yun, Chang-Yong;Kim, Eun-Tae;Park, Min-Yong
    • Proceedings of the KIEE Conference
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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.

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A comparison of group sequential methods in clinical trials (임상실험에서 그룹축차방법들의 비교)

  • 서의훈;안성진;임동훈
    • The Korean Journal of Applied Statistics
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    • v.10 no.2
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    • pp.353-366
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    • 1997
  • In this paper, we derive an approximate optimal Bayes group sequential design for a given loss function. We use the Monte-Carlo method to compare the ASN(average sample size) function and Bayes risk of approximate optimal Bayes group sequential design, Pocock design and O'Brien and Fleming design. Also introduced is the concept of Bayes efficiency and percentage loss of information due to grouping for the group sequential design and use it to measure the loss of information for different group sizes.

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A note on the sample size determination of sequential and multistage procedures

  • Choi, Kiheon
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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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.

Reliability Evaluation of Power Distribution Systems Considering the Momentary Interruptions-Application of Monte Carlo Method (순간정전을 고려한 배전계통에서의 신뢰도 평가-몬테카를로 방식의 적용)

  • Sang-Yun Yun;Jae-Chul Kim
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.52 no.1
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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.

The Sequential Testing of Multiple Outliers in Linear Regression

  • Park, Jinpyo;Park, Heechang
    • Communications for Statistical Applications and Methods
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    • v.8 no.2
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    • pp.337-346
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    • 2001
  • In this paper we consider the problem of identifying and testing the outliers in linear regression. first we consider the problem for testing the null hypothesis of no outliers. The test based on the ratio of two scale estimates is proposed. We show the asymptotic distribution of the test statistic by Monte Carlo simulation and investigate its properties. Next we consider the problem of identifying the outliers. A forward sequential procedure based on the suggested test is proposed and shown to perform fairly well. The forward sequential procedure is unaffected by masking and swamping effects because the test statistic is based on robust estimate.

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Reliability Evaluation of a Distribution System with wind Turbine Generators Based on the Switch-section Partitioning Method

  • Wu, Hongbin;Guo, Jinjin;Ding, Ming
    • Journal of Electrical Engineering and Technology
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    • v.11 no.3
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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.

Study on the Sequential Generation of Monthly Rainfall Amounts (월강우량의 모의발생에 관한 연구)

  • 이근후;류한열
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.18 no.4
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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.

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