• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.729-731
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• 2004

This paper deals with the design of a low order $H_{\infty}$ controller by using an iterative linear matrix inequality (LMI) method. The low order $H_{\infty}$ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, a linear penalty function is incorporated into the objective function so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Numerical experiments show the effectiveness of the proposed algorithm.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권11호
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• pp.747-752
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• 2004

This paper presents an iterative linear matrix inequality (LMI) approach to the design of a static output feedback (SOF) stabilization controller. A linear penalty function is incorporated into the objective function for the non-convex rank constraint so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. Hence, the overall procedure results in solving a series of semidefinite programs (SDPs). With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Extensive numerical experiments are Deformed to illustrate the proposed algorithm.

• Journal of the Korean Data and Information Science Society
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• 제27권3호
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• pp.827-833
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• 2016

In this paper, we propose a deep least squares support vector machine (LS-SVM) for regression problems, which consists of the input layer and the hidden layer. In the hidden layer, LS-SVMs are trained with the original input variables and the perturbed responses. For the final output, the main LS-SVM is trained with the outputs from LS-SVMs of the hidden layer as input variables and the original responses. In contrast to the multilayer neural network (MNN), LS-SVMs in the deep LS-SVM are trained to minimize the penalized objective function. Thus, the learning dynamics of the deep LS-SVM are entirely different from MNN in which all weights and biases are trained to minimize one final error function. When compared to MNN approaches, the deep LS-SVM does not make use of any combination weights, but trains all LS-SVMs in the architecture. Experimental results from real datasets illustrate that the deep LS-SVM significantly outperforms state of the art machine learning methods on regression problems.

• 대한전기학회논문지:전력기술부문A
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• 제53권12호
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• pp.655-660
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• 2004

This paper deals with the design of a fixed-structure $H_\infty$ power system stabilizer (PSS) by using an iterative linear matrix inequality (LMI) method. The fixed-structure $H_\infty$ controller is represented in terms of LMIs with a rank condition. To solve the non-convex rank-constrained LMI problem, a linear penalty function is incorporated into the objective function so that minimizing the penalized objective function subject to LMIs amounts to a convex optimization problem. With an increasing sequence of the penalty parameter, the solution of the penalized optimization problem moves towards the feasible region of the original non-convex problem. The proposed algorithm is, therefore, convergent. Numerical experiments show the practical applicability of the proposed algorithm.

• 한국산학기술학회논문지
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• 제20권3호
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• pp.500-506
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• 2019

본 연구에서는 arPLS(asymmetrically reweighted penalized least squares) 방법에서 분광신호의 길이와 차수를 이용한 최적의 평활화 매개변수를 위한 결정함수를 제안한다. 분광신호의 기준선 보정은 분석 시스템의 성능을 좌우하는 매우 중요한 과정으로 많은 경우에 육안 검사로 매개변수를 선택하여 추정한다. 이 과정은 매우 주관적이고 특히 대량의 데이터인 경우 지루한 작업을 동반하므로 좋은 분석 결과를 보장하기 어렵다. 이러한 이유로 기준선 보정에서 최적의 매개변수를 결정하기 위한 객관적인 방법이 필요하다. 제안한 결정함수는 기준선 보정에 사용 가능한 매개변수 범위의 중앙값이 신호의 길이가 길어질수록 증가하고, 신호의 차수가 작아질수록 감소하는 관계를 정리하여 모델링하였다. 모의실험 데이터는 신호의 길이 7가지에 대해 조합한 분석신호 4가지와 선형 기준선과 2차, 3차, 4차 곡선 기준선을 각각 더하여 모두 112개를 생성하였다. 모의실험 데이터와 실제 라만 분광신호를 이용한 실험에서 제안한 결정함수의 평활화 매개변수가 기준선 보정에 효과적으로 적용될 수 있음을 확인하였다.

• Journal of applied mathematics & informatics
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• 제29권5_6호
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• pp.1533-1539
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• 2011

In this paper, a class of nonlinear bilevel programs, i.e. the lower level problem is linear programs, is considered. Aiming at this special structure, we append the duality gap of the lower level problem to the upper level objective with a penalty and obtain a penalized problem. Using the penalty method, we give an existence theorem of solution and propose an algorithm. Then, a numerical example is given to illustrate the algorithm.

• IEIE Transactions on Smart Processing and Computing
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• 제5권5호
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• pp.319-322
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• 2016

We propose a parametric blind deconvolution method for bi-level images with unknown intensity levels that estimates unknown parameters for point spread functions and images by minimizing a penalized nonlinear least squares objective function based on normalized correlation coefficients and two regularization functions. Unlike conventional methods, the proposed method does not require knowledge about true intensity values. Moreover, the objective function of the proposed method can be effectively minimized, since it has the special structure of nonlinear least squares. We demonstrate the effectiveness of the proposed method through simulations and experiments.

• Journal of the Korean Data and Information Science Society
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• 제25권3호
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• pp.677-684
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• 2014

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. To improve the estimation performance of SVQR we propose to use SVQR ensemble with bagging (bootstrap aggregating), in which SVQRs are trained independently using the training data sets sampled randomly via a bootstrap method. Then, they are aggregated to obtain the estimator of the quantile regression function using the penalized objective function composed of check functions. Experimental results are then presented, which illustrate the performance of SVQR ensemble with bagging.

• 전자공학회논문지
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• 제53권3호
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• pp.124-131
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• 2016

분광법을 이용한 많은 응용에서 스펙트럼 데이터의 기준선 보정은 분석 시스템의 성능을 좌우하는 매우 중요한 과정이다. 기준선은 많은 경우에 육안 검사로 매개변수를 선택하여 추정한다. 이 과정은 매우 주관적이고 특히 대량의 데이터인 경우 지루한 작업을 동반하므로 좋은 분석 결과를 보장하기 어렵다. 이러한 이유로 기준선 보정에서 최적의 매개변수를 자동으로 선택하기 위한 객관적인 방법이 필요하다. 이전의 연구에서 PLS(penalized least squares) 방법에 새로운 가중 방식을 도입하여 기준선을 추정하는 arPLS(asymmetrically reweighted PLS) 방법을 제안하였다. 본 연구에서는 arPLS 방법에서 최적의 매개변수를 자동으로 선택하는 방법을 제안한다. 이 방법은 가능한 매개변수의 범위에서 추정한 기준선의 적응도와 평활도를 계산한 다음 정규화한 적응도와 평활도의 합이 최소가 되는 매개변수를 선택한다. 경사 기준선, 곡선 기준선, 이중 곡선 기준선의 모의실험 데이터와 실제 라만 스펙트럼을 이용한 실험에서 제안한 방법이 기준선 보정을 위한 최적 매개변수의 선택에 효과적으로 적용될 수 있음을 확인하였다.

• 품질경영학회지
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• 제18권2호
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• pp.81-92
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• 1990

This paper considers a scheduling of a set of jobs on single and multiple processors, when all jobs have a common due date and earliness and lateness are penalized at different cost rates. The objective is to determine the optimal value of a common due date and an optimal scheduling to minimize a total penalty function. It is also shown that a schedule having minimum weighted completion time variances must be V-shaped. For identical processors, a polynomial scheduling algorithm with the secondary objectives of minimizing makespan and machine occupancy is developed and a numerical example is presented.