• International Journal of Aeronautical and Space Sciences
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• 제8권1호
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• pp.122-128
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• 2007

In this paper, a method of station keeping strategy using relative orbital motion and numerical optimization technique is presented for geostationary satellite. Relative position vector with respect to an ideal geostationary orbit is generated using high precision orbit propagation, and compressed in terms of polynomial and trigonometric function. Then, this relative orbit model is combined with optimization scheme to propose a very efficient and flexible method of station keeping planning. Proper selection of objective and constraint functions for optimization can yield a variety of station keeping methods improved over the classical ones. Nonlinear simulation results have been shown to support such concept.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2001년도 전력전자학술대회 논문집
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• pp.159-162
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• 2001

This paper is proposed an efficiency optimization operation algorithm for synchronous reluctance motor (SynRM) using current phase angle control technique. The SynRM has to controlled with the optimal current phase angles with load and operation speed variation, to obtain high efficiency over the wide speed ranges. An efficiency optimization condition in SynRM which minimizes the copper and iron losses is derived based on the equivalent circuit model of the machine. The objective of the efficiency optimization control algorithm compensating the optimum current angle, is to seek a combination of d and q-axis current components which provides minimum losses at a certain operating point in steady state. The usefulness of the proposed efficiency optimization control is verified through vector-controlled inverter system with the SynRM.

• Industrial Engineering and Management Systems
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• 제14권3호
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.100-104
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• 2003

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.816-839
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• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.235-246
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• 2024

In high-dimensional data analysis, sufficient dimension reduction (SDR) has been considered as an attractive tool for reducing the dimensionality of predictors while preserving regression information. The principal support vector machine (PSVM) (Li et al., 2011) offers a unified approach for both linear and nonlinear SDR. This article comprehensively explores a variety of SDR methods based on the PSVM, which we call principal machines (PM) for SDR. The PM achieves SDR by solving a sequence of convex optimizations akin to popular supervised learning methods, such as the support vector machine, logistic regression, and quantile regression, to name a few. This makes the PM straightforward to handle and extend in both theoretical and computational aspects, as we will see throughout this article.

• 대한토목학회논문집
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• 제33권5호
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• pp.2033-2040
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• 2013

선형 설계 시 고려해야 할 수많은 정량적 또는 정성적인 인자들에 대하여 최신 IT기술을 접목하고 컴퓨터의 빠른 연산기능을 이용하여 단시간에 좀 더 많은 선형을 검토할 수 있는 선형 최적화 기술은 그동안 주목할 만 한 발전을 이루어 왔다. 특히 선형 최적화시 정량적인 설계요소의 정확한 반영과 수준 높은 성과품 출력에 대한 사용자의 요구는 설계기술자의 수준을 요구하는 단계에 이르렀다. 이런 요구사항에 부응하여 본 연구는 종단선형 최적화시 기존의 방식을 탈피한 보다 정밀한 Vertical Control Point(VCP) 적용구간을 탐색하고 설계에 적용할 수 있는 방안을 마련하였다. 이는 벡터방식을 이용한 VCP처리 기법으로 빠른 탐색 과 정확한 적용구간을 계산해 냄으로서 종전 래스터방식 결과와 비교해 보았을 때 보다 신뢰할 수 있는 종단선형을 도출할 수 있게 되었다. 특히 래스터 방식에 비해 벡터방식을 적용 할 경우 정밀도가 향상 되리라는 것은 주지의 사실이나, 선형최적화 기술의 선결조건인 처리속도를 저하시키지 않고 벡터방식을 적용 하였다는 것이 본 연구의 성과라고 할 수 있다.

• 한국지능시스템학회논문지
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• 제22권6호
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• pp.774-779
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• 2012

본 논문은 brain-computer interface (BCI)를 통해 움직임 상상 시 측정된 뇌-활동전위신호(EEG)에 내포된 행동의도의 패턴을 보다 정확하게 분류하기 위한 최적 EEG 채널 선택 기법을 제안한다. 기존의 EEG 측정실험에서는 실험 설계자에 의해 대뇌 기능적 피질 분류를 이용하여 인위적으로 선별된 채널을 활용하거나 측정기기가 수용 가능한 전체 채널을 사용해왔으며, 일정 수준의 패턴분류 정확도를 얻을 수 있었지만 다수의 채널로 인해 Common Spatial Pattern (CSP) 등의 패턴특징 추출 시 overfit 및 계산 복잡도 증가의 문제가 발생되었다. 이를 극복하기 위하여 방안으로 본 논문에서는 binary particle swarm optimization (BPSO)을 기반으로 다수의 채널 중 최적 채널을 자동으로 선택하고, 각각의 채널에 대한 impact factor를 부여함으로써 중요 채널 부근의 채널들에 가중치를 부여하는 선택방법을 제안하였으며, Support Vector Machine (SVM)을 이용하여 다수의 채널을 사용 하였을 때의 정확도와 channel impact factor를 고려한 BPSO를 적용시켰을 때의 정확도를 비교, 분석하였다.

• Journal of the Korean Data and Information Science Society
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• 제20권4호
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• pp.749-755
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• 2009

Support vector classification (SVC) provides more complete description of the lin-ear and nonlinear relationships between input vectors and classifiers. In this paper. we propose the sparse kernel classifier to solve the optimization problem of classification with a modified hinge loss function and absolute loss function, which provides the efficient computation and the sparsity. We also introduce the generalized cross validation function to select the hyper-parameters which affects the classification performance of the proposed method. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.765-776
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• 2006

In this paper we propose an estimation method on the regression model with randomly censored observations of the training data set. The weighted least squares support vector machine regression is applied for the regression function estimation by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed estimation method.