• 동력기계공학회지
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• 제2권3호
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• pp.60-67
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• 1998

Edge detection is the first step and very important step in image analysis. We cast edge detection as a problem in cost minimization. This is achieved by the formulation of a cost function that evaluates the quality of edge configurations. The cost function can be used as a basis for comparing the performances of different detectors. This cost function is made of desirable characteristics of edges such as thickness, continuity, length, region dissimilarity. And we use a simulated annealing algorithm for minimum of cost function. Simulated annealing are a class of adaptive search techniques that have been intensively studied in recent years. We present five strategies for generating candidate states. Experimental results(building image and test image) which verify the usefulness of our simulated annealing approach to edge detection are better than other operator.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 1998년도 The 12th Asia Quality Management Symposium* Total Quality Management for Restoring Competitiveness
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• pp.696-704
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• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• 한국경영과학회지
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• 제20권1호
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• pp.63-81
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• 1995

This paper presents a model and algorithm for solving the multi-factor plant layout problem. The model can incorporate more than two factors that may be either quantitative or qualitative. The algorithm is based on simulated annealing, which has been successfully applied for the solution of combinatorial problems. A set of problems previously used by various authors is solved to demonstrate the effectiveness of the proposed methods. The results indicate that the proposed methods can yield good quality for each of eleven test problems.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2005년도 춘계공동학술대회 발표논문
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• pp.1020-1025
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• 2005

The set covering(SC) problem has many practical application of modeling not only real world problems in civilian but also in military. In this paper we study optimal allocation model for maximizing utility of consolidating old fashioned and new air defense weapon system like Patriot missile and develop the new computational algorithm for the SC problem by using simulated annealing(SA) algorithm. This study examines three different methods: 1) simulated annealing(SA); 2) accelerated simulated annealing(ASA); and 3) selection by effectiveness degree(SED) with SA. The SED is adopted as an enhanced SA algorithm that the neighboring solutions could be generated only in possible optimal feasible region at the PERTURB function. Furthermore, we perform various experiments for both a reduced and an extended scale sized situations depending on the number of customers(protective objective), service(air defense), facilities(air defense artillery), threat, candidate locations, and azimuth angles of Patriot missile. Our experiment shows that the SED obtains the best results than others.

• 산업공학
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• 제12권1호
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• pp.94-101
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• 1999

The set covering(SC) problem is the problem of covering all the rows of an $m{\times}n$ matrix of ones and zeros by a subset of columns with a minimal cost. It has many practical applications of modeling of real world problems. The SC problem has been proven to be NP-complete and many algorithms have been presented to solve the SC problem. In this paper we present hybrid simulated annealing(HSA) algorithm based on the Simulated Annealing(SA) for the SC problem. The HSA is an algorithm which combines SA with a crossover operation in a genetic algorithm and a local search method. Our experimental results show that the HSA obtains better results than SA does.

• 융합신호처리학회논문지
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• 제3권1호
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• pp.87-92
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• 2002

배치(Placement)는 VLSI 회로의 physical design에서 중요한 단계로서 회로의 성능을 최대로 하기 위하여 회로 모듈의 집합을 배치시키는 문제이며, 배치 문제에서 최적의 해를 얻기 위해 클러스터 성장(cluster growth), 시뮬레이티드 어닐링(simulated annealing; SA), ILP(integer linear programming)등의 방식이 이용된다. 본 논문에서는 배치 문제에 대하여 확률 진화 알고리즘(stochastic evolution algorithm; StocE)을 이용한 해 공간 탐색(solution space search) 방식을 제안하였으며, 제안한 방식을 시뮬레이티드 어닐링 방식과 비교, 분석하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부A
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• pp.335-337
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• 1998

This paper presents a efficient algorithm for loss reduction of distribution system by automatic sectionalizing switch operation in distribution systems of radial type. Simulated Annealing can avoid escape from local minima by accepting improvements in cost, but the use of this algorithm is also responsible for an excessive computation time requirement. To overcome this major limitation of Simulated Annealing Algorithm, we may use advanced Simulated Annealing Algorithm. Numerical examples demonstrate the validity and effectiveness of the proposed methodology.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.562-574
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• 2022

As traditional ways of evaluation prove to be ineffective in evaluating the effect of interactive multimedia physical education (PE) teaching, this study develops a new evaluation model based on the simulated annealing algorithm. After the evaluation subjects and the principle of the evaluation system are determined, different subjects are well chosen to constitute the evaluation system and given the weight. The backpropagation neural network has been improved through the simulated annealing algorithm, whose improvement indicates the completion of the evaluation model. Simulation results show that the evaluation model is highly efficient. Compared with traditional evaluation models, the proposed one enhances students' performance in PE classes by 50%.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.1025-1036
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• 2003

Variable selection algorithm for principal component analysis using penalty function is proposed. We use the fact that usual principal component problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Simulated annealing algorithm is used in searching for optimal solutions with penalty functions. Comparisons between several well-known penalty functions through simulation reveals that the HARD penalty function should be suggested as the best one in several aspects. Illustrations with real and simulated examples are provided.

• 대한전자공학회논문지SD
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• 제44권5호
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• pp.42-49
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• 2007

본 논문에서는 열전도 환경하의 MCM과 진공에서 작동하는 우주전자 장비의 신뢰도 최적화를 위한 부품 배치 연구에 관해 기술하고 있다. 최적배치를 위해 초기 부품 배치 후 FDM을 solver로 이용하여 부품의 접합온도를 계산하였으며 접합온도를 이용하여 전자장치의 신뢰도를 예측한 후 시뮬레이티드 어닐링 방법을 통해 신뢰도 최적배치 결과가 기술되었다. 시뮬레이티드 어닐링 적용 시 흔들기는 부품 치환방식을 이용하였으며 온도 감소계수 및 열 평형 계수의 변화에 따른 시뮬레이션 결과를 기술하였으며 특히 장치의 고장률 최소화 목적함수와 평균 접합온도 최소화 목적함수에 대해 각 적용결과에 대한 비교분석을 통하여 새로운 신뢰도 최적화 접근방법을 제안하였다.