• 한국철도학회:학술대회논문집
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• 한국철도학회 2006년도 춘계학술대회 논문집
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• pp.446-446
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• 2006

• 대한수학회지
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• 제57권5호
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• pp.1267-1286
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• 2020

We pay attention to a coupled problem by a penalty term which is induced from non-overlapping domain decomposition methods based on augmented Lagrangian methodology. The coupled problem is composed by two parts mainly; one is a problem associated with local problems in non-overlapping subdomains and the other is a coupled part over all subdomains due to the penalty term. For the speedup of iterative solvers for the coupled problem, we propose two different types of preconditioners: a block-diagonal preconditioner and an additive Schwarz preconditioner as overlapping domain decomposition methods. We analyze the coupled problem and the preconditioned problems in terms of their condition numbers. Finally we present numerical results which show the performance of the proposed methods.

• Communications for Statistical Applications and Methods
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• 제24권5호
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• pp.493-505
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• 2017

Maximum likelihood estimation (MLE) of the generalized extreme value distribution (GEVD) is known to sometimes over-estimate the positive value of the shape parameter for the small sample size. The maximum penalized likelihood estimation (MPLE) with Beta penalty function was proposed by some researchers to overcome this problem. But the determination of the hyperparameters (HP) in Beta penalty function is still an issue. This paper presents some data adaptive methods to select the HP of Beta penalty function in the MPLE framework. The idea is to let the data tell us what HP to use. For given data, the optimal HP is obtained from the minimum distance between the MLE and MPLE. A bootstrap-based method is also proposed. These methods are compared with existing approaches. The performance evaluation experiments for GEVD by Monte Carlo simulation show that the proposed methods work well for bias and mean squared error. The methods are applied to Blackstone river data and Korean heavy rainfall data to show better performance over MLE, the method of L-moments estimator, and existing MPLEs.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제21권3호
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• pp.125-142
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• 2017

In this paper, development of the three-dimensional structural analysis is performed by applying FETI-local method. In the FETI-local method, the penalty term is added as a preconditioner. The OPT-DKT shell element is used in the present structural analysis. Newmark-${\beta}$ method is employed to conduct the dynamic analysis. The three-dimensional FETI-local static structural analysis is conducted. The contour and the displacement of the results are compared following the different number of sub-domains. The computational time and memory usage are compared with respect to the number of CPUs used. The three-dimensional dynamic structural analysis is conducted while applying FETI-local method. The present results show appropriate scalability in terms of the computational time and memory usage. It is expected to improve the computational efficiency by combining the advantages of the original FETI method, i.e., FETI-mixed using the mixed local-global Lagrange multiplier.

• 대한건축학회논문집:구조계
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• 제33권12호
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• pp.53-62
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• 2017

Many gradient-based mathematical methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. The main objective of this paper is to propose an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm that is derived using penalty function. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. In this paper, a discrete search strategy using the HS algorithm with a static penalty function is presented in detail and its applicability using several standard truss examples is discussed. The numerical results reveal that the HS algorithm with the static penalty function proposed in this study is a powerful search and design optimization technique for structures with discrete-sized members.

• 소성∙가공
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• 제8권1호
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• pp.47-56
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• 1999

The governing functional in plastic deformation has to satisfy the incompressibility constraint. This incompressibility constraint imposed on velocity fields can be removed by introducing either Lagrange multiplier or the penalty constant into the functional. In this study, two-dimensional rigid plastic FEM programs using these schemes were developed. These two programs and DEFORM were applied in a cylinder upsetting and a closed die forging to compare the values of load, local mean stress and volume loss. As the results, the program using Lagrange multiplier obtained a more exact and stable solution, but it took more computational time than the program using the penalty constant. Therefore, according to user's need, one of these two programs can be chosen to simulate a metal forming processes.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.165-173
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• 1996

It is not desirable to have too large control input in control systems, because there are usually a limitation for the input magnitude and cost for the input energy. Previous papers in the iterative learning control did not considered on these points. In this paper, an iterative learning control method is proposed for a class of nonlinear systems with consideration on input magnitude by adopting a concept of cost function consisting of the output error and the input magnitude in quadratic form. We proposed a new input update law with an input penalty function. If we choose a reasonable input penalty function, the two control objectives, good command following and small input energy, can be achieved. The characteristics of the proposed method are shown in the simulation examples.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 춘계학술대회논문집A
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• pp.725-730
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• 2000

Genetic algorithms based on the theory of natural selection, have been applied to many different fields, and have proven to be relatively robust means to search for global optimum and handle discontinuous or even discrete data. Genetic algorithms are widely used for unconstrained optimization problems. However, their application to constrained optimization problems remains unsettled. The most prevalent technique for coping with infeasible solutions is to penalize a population member for constraint violation. But, the weighting of a penalty for a particular problem constraint is usually determined in the heuristic way. Therefore this paper proposes, the effective technique for handling constraints, the ranking penalty method and hybrid genetic algorithms. And this paper proposes dynamic mutation tate to maintain the diversity in population. The effectiveness of the proposed algorithm is tested on several test problems and results are discussed.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.499-503
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• 1997

In this paper, we present a fuzzy ICC using a self-tuning method. To provide robustness and adaptiveness over the vehicle nonlinearities and changes of the driving environments, an on-line self-tuning scheme based on 'Interior Penalty Function' was developed. Road test and computer simulation results verify the feasible performance of the suggested ICC algorithm.