• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권1호
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• pp.45-61
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• 2010

In the communication channel, the received signal is affected by many factors that can cause errors. These effects mean that received signal strength (RSS) based methods incur more errors in measuring distance and consequently result in low precision in the location detection process. As one of the approaches to overcome these problems, we propose using direction calibration to improve the performance of the RSS-based method for distance measurement, and sequentially a weighted least squares (WLS) method using reliability factors in conjunction with a conventional RSS weighting matrix is proposed to solve an over-determined localization process. The proposed scheme focuses on the features of the RSS method to improve the performance, and these effects are proved by the simulation results.

• 응용통계연구
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• 제21권4호
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• pp.621-630
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• 2008

본 논문은 개체의 특성으로 다가의 명목형 반응변수가 반복측정 요인인 시간요인에 의해 주기적으로 반복측정 되었을 때, 자료를 분석하기 위한 모형으로 일반화된 주변 로짓모형을 논의하고 있다. 다가의 반응변수에 영향을 미치는 공변량중 일부가 처치로써 상대적으로 큰 크기의 실험단위에 배정되고 반복측정 요인인 시간요인의 수준들이 또한 처치요인으로 비확률화에 의해 상대적으로 작은 크기의 실험단위에 배정될 때 이를 고려한 모형구축과정과 예상되는 공분산 구조의 가정하에서 모수를 추정하기 위한 방법으로 가중최소제곱 방법을 이용할 수 있음을 제시하고 있다.

• Journal of Electrical Engineering and Technology
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• 제7권4호
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• pp.636-645
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• 2012

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

• Journal of the Korean Data and Information Science Society
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• 제28권5호
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• pp.1191-1204
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• 2017

Weighted least squares (WLS) estimation is often easily used for the data with heteroscedastic errors because it is intuitive and computationally inexpensive. However, WLS estimator is less robust to a few outliers and sometimes it may be inefficient. In order to overcome robustness problems, Box-Cox transformation, Huber's M estimation, bisquare estimation, and Yohai's MM estimation have been proposed. Also, more efficient estimations than WLS have been suggested such as Bayesian methods (Cepeda and Achcar, 2009) and semiparametric methods (Kim and Ma, 2012) in heteroscedastic error models. Recently, Çelik (2015) proposed the weight methods applicable to the heteroscedasticity patterns including butterfly-distributed residuals and megaphone-shaped residuals. In this paper, we review heteroscedastic regression estimators related to robust or efficient estimation and describe their properties. Also, we analyze cost data of U.S. Electricity Producers in 1955 using the methods discussed in the paper.

• 한국시뮬레이션학회논문지
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• 제23권3호
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• pp.11-17
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• 2014

중심합성계획(CCD)은 2차 선형 모형을 추정하기 위해서 자주 활용된다. 본 연구는 CCD를 활용하는 시뮬레이션 실험에서 공통난수(CRN) 상관유도전략을 사용하여 모형의 파라미터를 효율적으로 추정하고자 한다. CCD의 축점을 적절히 선택하면 모든 표본점에 공통난수를 할당하는 전략으로 얻은 파라미터의 가중최소자승(WLS) 추정량은 정규최소자승(OLS) 추정량과 일치한다. 본 연구는 선형모형의 파라미터를 추정하는 공통난수 상관유도전략이 파라미터 추정 효율성 측면에서 독립 난수 할당전략보다 우수함을 계량적으로 분석하였다. 2차 선형모형에서 상수항을 제외한 나머지 파라미터를 추정하는데 있어서 공통난수 상관유도전략이 우수하며 시뮬레이션 결과도 이러한 분석을 지지하고 있다. 제안된 난수 할당전략이 CCD 시뮬레이션 실험에서 유용하게 활용될 수 있을 것으로 기대한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 추계학술대회 논문집 전력기술부문
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• pp.261-263
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• 2008

In power systems operation, state estimation takes an important role in security control. For the state estimation problem, the weighted least squares(WLS) method and the fast decoupled method have been widely used at present. But these algorithms have disadvantage of converging local optimal solution. In these days, a modern heuristic optimization method such as Particle Swarm Optimization(PSO), are introduced to overcome the problems of classical optimization. In this paper, we proposed particle swarm optimization (PSO) to search an optimal solution of state estimation in power systems. To demonstrate the usefulness of the proposed method, PSO algorithm was tested in the IEEE-57 bus systems. From the simulation results, we can find that the PSO algorithm is applicable for power system state estimation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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• pp.214-215
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• 2006

In power systems, the state estimation takes an important role in security control. At present, the weighted least squares(WLS) method has been widely used to the state estimation computation. This paper presents an application of Adaptive Evolutionary Algorithm(AEA) to state estimation in power systems. AEA is a optimization method to overcome the problems of classical optimization. AEA is employed to solve state estimation on the 6 bus system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.291-293
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• 2006

In power systems, the state estimation takes an important role in security control. At present, the weighted least squares(WLS) method has been widely used to the state estimation computation. This paper presents an application of Particle Swarm Optimization(PSO) to state estimation in power systems. PSO is a modern heuristic optimization method to overcome the problems of classical optimization. PSO is employed to solve state estimation on the IEEE-30 bus system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.303-304
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• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.425-426
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• 2007

In power systems operation, state estimation takes an important role in security control. For the state estimation problem, conventional optimization algorithm, such as weighted least squares (WLS) method, has been widely used. But these algorithms have disadvantages of converging local optimal solution. In these days, a modern heuristic optimization methods such as Particle Swarm Optimization (PSO), are introducing to overcome the problems of classical optimization. In this paper, we suggested parallel particle swarm optimization (PPSO) to search an optimal solution of state estimation in power systems. To show the usefulness of the proposed method over the conventional PSO, proposed method is applied on the IEEE-57 bus system.