• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -1
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• pp.302-305
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• 2000

This paper proposed an optimal register resource allocation algorithm using graph coloring for minimal register at high level synthesis. The proposed algorithm constructed interference graph consist of the intermediated representation CFG to description VHDL. and at interference graph fur the minimal select color selected a position node at stack, the next inserted spill code and the graph coloring process executes for optimal register allocation. The proposed algorithm proves to effect that result compare another allocation techniques through experiments of bench mark.

• 한국컴퓨터정보학회논문지
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• 제20권10호
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• pp.77-83
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• 2015

This paper suggests heuristic algorithm to obtain optimal solution of minimum number of delay aircraft in airport arrivals/departures problem. This problem can be solved only mathematical optimization method. The proposed algorithm selects the minimum delays capacity in various airport capacities for number of arrivals/departures aircraft in $i^{th}$ time interval (15 minutes). In details, we apply median selection method and left-right selection method. This algorithm can be get the optimal solution of minimum number of delay aircraft for sixes actual experimental data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 추계학술대회 논문집 전력기술부문
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• pp.472-474
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• 2003

This paper presents a contains contingency constrained Optimal Power Flow(CCOPF) algorithm. The proposed algorithm maintains the nodal voltage levels within the specified limits after contingency. A case study demonstrate the proposed algorithm with the IEEE-14RTS under N-1 contingency criterion.

• 해양환경안전학회지
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• 제30권1호
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• pp.118-126
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• 2024

본 논문에서는 컨테이너선의 선형 최적 설계 자동화와 관련하여 연구한 내용과 결과를 정리하였다. 컨테이너선은 일반적으로 프루우드 수 0.26 근처에서 운항하는 선박으로 이 속도에서 운항하는 선박 전용 선형 최적 설계 자동화를 구현하기 위하여 최적화 알고리즘, 선형 변경 알고리즘, 선박 성능 예측 알고리즘, 자동화 알고리즘 그리고 반복적 계산 기법을 적용하여 컨테이너선의 선형 최적 설계 자동화가 가능한 수치해석 컴퓨터 프로그램을 개발하였으며, HOTCONTAINER라고 명명하였다. 본 연구에서는 선형 최적 설계를 위한 설계 변수의 적절한 선정을 위하여 민감도 분석 알고리즘을 개발하여 적용하였다. 개발된 선형 최적 설계 자동화 알고리즘의 신뢰성과 실선 적용성을 파악하기 위하여 세계적으로 다양한 연구가 진행된 컨테이너 선박인 KCS 선박을 대상 선박으로 하여 선형 최적 설계 자동화 수치해석을 수행하여 그 결과물로써 최적 선박을 도출하고, 대상 선박과 최적 선박의 조파저항과 파계 그리고 파고를 비교하였다. 결론적으로 최적 선박이 대상 선박과 비교하여 조파저항이 47.63% 감소한 것을 볼 수 있었으며, 배수량과 접수 표면적은 각각 0.50%, 0.39% 감소한 것을 볼 수 있었다.

• 한국통신학회논문지
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• 제28권5C호
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• pp.468-475
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• 2003

본 논문에서는 패턴생성을 위한 최적의 이진 위상 컴퓨터형성 홀로그램을 설계하기 위해 합성된 SA알고리듬 및 유전 알고리듬을 이용하였다. 제안된 방법의 탐색과정에서 sGA를 사용하여 BPCGH를 생성하고. 결과 홀로그램 패턴을 SA 알고리듬의 초기 랜덤 투과함수로 이용하여 최적의 BPCGH를 설계하였다. 컴퓨터 시뮬레이션에서 독립적으로 사용된 SA 알고리듬과 유전 알고리듬을 비교한 결과 제안한 알고리듬이 회절 효율이 향상된 것을 확인할 수 있었다.

• Structural Engineering and Mechanics
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• 제54권2호
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• pp.305-317
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• 2015

Optimal sensor placement (OSP) is an integral component in the design of an effective structural health monitoring (SHM) system. This paper describes the implementation of a novel collaborative-climb monkey algorithm (CMA), which combines the artificial fish swarm algorithm (AFSA) with the monkey algorithm (MA), as a strategy for the optimal placement of a predefined number of sensors. Different from the original MA, the dual-structure coding method is adopted for the representation of design variables. The collaborative-climb process that can make the full use of the monkeys' experiences to guide the movement is proposed and incorporated in the CMA to speed up the search efficiency of the algorithm. The effectiveness of the proposed algorithm is demonstrated by a numerical example with a high-rise structure. The results show that the proposed CMA algorithm can provide a robust design for sensor networks, which exhibits superior convergence characteristics when compared to the original MA using the dual-structure coding method.

• 대한전기학회논문지:전력기술부문A
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• 제50권2호
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• pp.76-82
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• 2001

This paper presents a approach for a service restoration and optimal reconfiguration of distribution network using Genetic algorithm(GA) and Tabu search(TS) method. Restoration and reconfiguration problems in distribution network are difficult to solve in short times, because distribution network supplies power for customers combined with many tie-line switches and sectionalizing switches. Furthermore, the solutions of these problems have to satisfy radial operation conditions and reliability indices. To overcome these time consuming and sub-optimal problem characteristics, this paper applied Genetic-Tabu algorithm. The Genetic-Tabu algorithm is a Tabu search combined with Genetic algorithm to complement the weak points of each algorithm. The case studies with 7 bus distribution network showed that not the loss reduction but also the reliability cost should be considered to achieve the economic service restoration and reconfiguration in the distribution network. The results of suggested Genetic-Tabu algorithm and simple Genetic algorithm are compared in the case study also.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.79-84
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• 1992

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the optimal collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy and a traveling salesman problem strategy (TSP). The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Hopfield Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is used to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm.

• 대한전기학회논문지
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• 제45권1호
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.

• 한국정밀공학회지
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• 제14권2호
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• pp.66-73
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• 1997

In many optimal methods for the structural design, the structural analysis is performed with the given design parameters. Then the design sensitivity is calculated based on its structural anaysis results. There-after, the design parameters are changed iteratively. But genetic algorithm is a optimal searching technique which is not depend on design sensitivity. This method uses for many design para- meter groups which are generated by a designer. The generated design parameter groups are become initial population, and then the fitness of the all design parameters are calculated. According to the fitness of each parameter, the design parameters are optimized through the calculation of reproduction process, degradation and interchange, and mutation. Those are the basic operation of the genetic algorithm. The changing process of population is called a generation. The basic calculation process of genetic algorithm is repeatly accepted to every generation. Then the fitness value of the element of a generation becomes maximum. Therefore, the design parameters converge to the optimal. In this study, the optimal design pro- cess of a machine tool structure for static loading is presented to determine the optimal base supporting points and structure thickness using a genetic algorithm.