• Journal of Electrical Engineering and Technology
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• 제9권4호
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• pp.1154-1162
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• 2014

In the restructured electricity market, Performance-Based Regulation (PBR) regime has been introduced to the distribution network. To ensure the network stability, this regime is used along with quality regulations. Quality regulation impose new financial risks on distribution system operators (DSOs). The poor quality of the network will result in reduced revenues for DSOs. The mentioned financial risks depend on the quality indices of the system. Based on annual variation of these indices, the cost of quality regulation will also vary. In this paper with regard to reclosing fault in distribution network, we develop a risk-based method to assess the financial risks caused by quality regulation for DSOs. Furthermore, in order to take the stochastic behavior of the distribution network and quality indices variations into account, time-sequential Monte Carlo simulation method is used. Using the proposed risk method, the effect of taking reclosing time into account will be examined on system quality indicators and the cost of quality regulation in Swedish rural reliability test system (SRRTS). The results show that taking reclosing fault into consideration, affects the system quality indicators, particularly annual average interruption frequency index of the system (SAIFI). Moreover taking reclosing fault into consideration also affects the quality regulations cost. Therefore, considering reclosing time provides a more realistic viewpoint about the financial risks arising from quality regulation for DSOs.

• Journal of information and communication convergence engineering
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• 제20권2호
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• pp.79-89
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• 2022

To compute the mean and variance of component-based reliability software, we focused on path-based reliability analysis. System reliability depends on the transition probabilities of components within a system and reliability of the individual components as basic input parameters. The uncertainty in these parameters is estimated from the test data of the corresponding components and arises from the software architecture, failure behaviors, software growth models etc. Typically, researchers perform Monte Carlo simulations to study uncertainty. Thus, we considered a Markov chain Monte Carlo (MCMC) simulation to calculate uncertainty, as it generates random samples through sequential methods. The MCMC approach determines the input parameters from the probability distribution, and then calculates the average approximate expectations for a reliability estimation. The comparison of different techniques for uncertainty analysis helps in selecting the most suitable technique based on data requirements and reliability measures related to the number of components.

• 조명전기설비학회논문지
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• 제21권10호
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• pp.52-58
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• 2007

본 논문에서는 발전량 적정성 평가를 위한 풍력발전 모형의 제안하였다. 풍력 발전량과 계통 부하량은 일 년을 주기로 하는 주기함수 형태이므로, 둘 중 하나의 물리량이 주어지면 다른 물리량의 발생 확률을 계산할 수 있다. 본 논문에서는 가상의 데이터를 바탕으로 두 물리량을 k-means 클러스터링 알고리즘으로 단계화하였고, 각 단계간의 확률적인 관계를 계산하였다. 제안하는 풍력발전 모형은 상태샘플링(state sampling)에 기반을 둔 몬테카를로 모의로써 발전량 적정성을 평가하는데 적합하다.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.673-685
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• 2003

In this paper we consider the problem of identifying and testing outliers in linear regression. First we consider the problem for testing the null hypothesis of no outliers. A test based on the ratio of two residual scale estimates is proposed. We show the asymptotic distribution of the test statistics by Monte Carlo simulation and investigate its properties. Next we consider the problem of identifying the outliers. A forward sequential procedure using the suggested test is proposed and shown to perform fairly well. Unlike other forward procedures, the present one is unaffected by masking and swamping effects because the test statistic is based on robust scale estimate.

• Smart Structures and Systems
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• 제17권4호
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• pp.611-629
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• 2016

This study presents a new approach of surrogate modeling for time-consuming finite element analysis. A surrogate model is widely used to reduce the computational cost under an iterative computational analysis. Although a variety of the methods have been widely investigated, there are still difficulties in surrogate modeling from a practical point of view: (1) How to derive optimal design of experiments (i.e., the number of training samples and their locations); and (2) diagnostics of the surrogate model. To overcome these difficulties, we propose a sequential surrogate modeling based on Gaussian process model (GPM) with self-adaptive sampling. The proposed approach not only enables further sampling to make GPM more accurate, but also evaluates the model adequacy within a sequential framework. The applicability of the proposed approach is first demonstrated by using mathematical test functions. Then, it is applied as a substitute of the iterative finite element analysis to Monte Carlo simulation for a response uncertainty analysis under correlated input uncertainties. In all numerical studies, it is successful to build GPM automatically with the minimal user intervention. The proposed approach can be customized for the various response surfaces and help a less experienced user save his/her efforts.

• 제어로봇시스템학회논문지
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• 제18권3호
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• pp.285-294
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• 2012

Using bearing measurement only, target motion state is not directly obtained so that TMA (Target Motion Analysis) is needed for this situation. TMA is a nonlinear estimation technique used in passive SONAR systems. Also it is the one of important techniques for underwater combat management systems. TMA can be divided to two parts: batch estimation and sequential estimation. It is preferable to use sequential estimation for reducing computational load as well as adaptively to target maneuvers, batch estimation is still required to attain target initial state vector for convergence of sequential estimation. Selection of batch time interval which depends on observability is critical in TMA performance. Batch estimation in general utilizes predetermined batch time interval. In this paper, we propose a new method called the BTIS (Batch Time Interval and Initial State Estimation). The proposed BTIS estimates target initial status and determines the batch time interval sequentially by using a bank of GMM-TS (Gaussian Mixture Measurement-Track Splitting) filters. The performance of the proposal method is verified by a Monte Carlo simulation study.

• 에너지공학
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• 제14권1호
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• pp.46-53
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• 2005

본 논문은 확률적 기법을 적용하여 직접부하제어의 적정한 지원금을 산정하는 새로운 방법론을 제안한다. 직접부하제어의 경제성 분석은 발전기의 고장정지 특성, 직접부하제어 자원의 차단용량 및 차단시간 등을 모두 고려해야 하기 때문에 현실적으로 불가능한 것으로 인식되었다. 따라서 기존의 연구에서는 시나리오 접근법을 사용하여 직접부하제어의 경제성 평가를 수행하였다. 본 논문에서는 몬테카를로 시뮬레이션을 적용하여 직접부하제어의 제어전력량을 확률적으로 추정하고 이를 기반으로 직접부하제어의 지원금을 산정하는 새로운 접근법을 개발하였다. 또한 시뮬레이션의 효율을 향상시키기 위하여 분산감소 기법을 적용하였다. 본 논문에서 제안한 방법론의 유용성을 보이기 위해 IEEE 24-모선 신뢰도 계통에 적용하여 사례연구를 수행하였다.

• Communications for Statistical Applications and Methods
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• 제23권2호
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• pp.131-146
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• 2016

In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.

• 응용통계연구
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• 제22권5호
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• pp.1033-1046
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• 2009

신약 개발 연구 또는 임상시험에서 개발된 약이 0용량 대조군과 비교해 유의한 효과 차이가 있어 신체에 반응할 수 있는 최소 복용량을 결정하는데, 이 최소 복용량을 최소 효과 용량(Minimum Effective Dose; MED)이라 한다. 이 논문에서는 최소 효과 용량을 확인하기 위하여 업데이티드 대조군을 이용한 모수적 축차 검정법을 제안하였다. 또한 모의 실험을 통하여 기존의 검정법과 제안한 검정법의 실험유의수준(experimental significance level)과 검정력(power)을 비교하였다.

• 응용통계연구
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• 제26권6호
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• pp.1009-1018
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• 2013

회귀분석은 변수들간의 관계를 파악하는데 유용하게 사용된다. 여러개의 모집단을 비교할 때, 여러 모집단이 갖는 각각의 회귀직선의 기울기가 같은지 검정하는 것이 필요할 때가 있다. 본 논문에서는 순차기울기를 추정한 후 ANOVA의 F-검정법과 Kruskal-Wallis (1952)검정법을 이용한 방법을 각각 제안하였다. 또한, 몬테카를로 모의시험 연구를 통해 본 논문에서 제안한 두 가지 방법과 Park과 Kim (2009)이 제안한 기존 방법의 검정력을 비교하였다.