• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.73.5-73
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• 2001

In this paper, we present a procedure to design the robust optimal supervisor which has the minimal cost in the sense of average for a given multiple-modelled discrete event system DES. In order to design the robust optimal supervisor, we extend the optimal supervisor design algorithm for a deterministic DES to the case of multiple-modelled DESs. In addition, using the proposed algorithm with modified costs of events and penalities of states, we can show whether a robust supervisor for a given multiple-modelled DES exists and design the minimally restricted robust supervisor.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.647-650
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• 1999

In this Paper, we propose an optimal feature extraction for multiclass problems through proper choice of initial feature vectors. Although numerous feature extraction algorithms have been proposed, those algorithms are not optimal for multiclass problems. Recently, an optimal feature extraction algorithm for multiclass problems has been proposed, which provides a better performance than the conventional feature extraction algorithms. In this paper, we improve the algorithm by choosing good initial feature vectors. As a result, the searching time is significantly reduced. The chance to be stuck in a local minimum is also reduced.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.77-89
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• 2004

A computer model is developed to assess optimal ground water pumping rates and optimal locations of wells in a coastal region. A sharp interface model is used to simulate the freshwater and salt water flows. Drawdown, upconing, saltwater intrusion and the contamination of well are considered in this model. A genetic algorithm with parallel processing is used to identify the optimal solution.

• Earthquakes and Structures
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• 제14권1호
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• pp.1-10
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• 2018

Seismic retrofitting of existing buildings and design of earth-quake resistant buildings are important issues associated with earthquake-prone zones. Use of metallic-yielding dampers as an energy dissipation system is an acceptable method for controlling damages in structures and improving their seismic performance. In this study, the optimal distribution of dampers for reducing the seismic response of steel frames with multi-degrees freedom is presented utilizing the uniform distribution of deformations. This has been done in a way that, the final configuration of dampers in the frames lead to minimum weight while satisfying the performance criteria. It is shown that such a structure has an optimum seismic performance, in which the maximum structure capacity is used. Then the genetic algorithm which is an evolutionary optimization method is used for optimal arrangement of the steel dampers in the structure. In continuation for specifying the optimal accurate response, the local search algorithm based on the gradient concept has been selected. In this research the introduced optimization methods are used for optimal retrofitting in the moment-resisting frame with inelastic behavior and initial weakness in design. Ultimately the optimal configuration of dampers over the height of building specified and by comparing the results of the uniform deformation method with those of the genetic algorithm, the validity of the uniform deformation method in terms of accuracy, Time Speed Optimization and the simplicity of the theory have been proven.

• 한국정보과학회논문지:시스템및이론
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• 제29권7호
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• pp.377-383
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• 2002

외판원 문제는 잘 알려진 NP-완전 문제로, 최적해(optimal value)를 구하는 다양한 알고리즘들이 개발되었다. 그러나 최악의 경우 지수 시간이 걸리므로 수행시간을 줄이는 다양한 방법들이 제안되고 있다. 최근접 휴리스틱 알고리즘은 최적해를 구하는 다른 알고리즘들에 비해 구조가 비교적 간단하다. 따라서 본 논문에서는 외판원 문제(Traveling Salesman Problem, TSP)의 최적해를 구할 수 있는 분기 함수(bounding function)를 적용한 분산 최근접 휴리스틱(nearest neighbor heuristic) 알고리즘을 PVM(Parallel Virtual Machine)에서 제공하는 마스터/슬래이브(master/slave) 모델을 사용하여 설계하고 구현하였다. 먼저 최적해를 찾는 수행 시간을 줄이기 위해 최적화 문제에서 좋은 성능을 보이는 분산 유전 알고리즘(distributed genetic algorithm)을 수행해 얻은 근사해(near optimal)를 초기 분기 함수로 사용한다. 특히 더욱 좋은 근사해를 구하고자 유전 연산자인 돌연변이를 새롭게 변형하여 적용하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.515-518
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• 1998

This paper proposes a new hybrid algorithm for auto-tuning fuzzy controllers improving the performance. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a genetic-MGM algorithm. The object of the proposed algorithm is to promote search efficiency by a genetic and modified gradient optimization techniques. The proposed genetic and MGM algorithm is based on both the standard genetic algorithm and a gradient method. If a maximum point don't be changed around an optimal value at the end of performance during given generation, the genetic-MGM algorithm searches for an optimal value using the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algorithms. Simulation results verify the validity of the presented method.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2008년도 동계학술발표대회 논문집
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• pp.387-392
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• 2008

In order to improve the efficiency of the main transformer in a tilting train, the optimal operation of a cooling system is necessary. For the development of the optimal control algorithm of a cooling system, the mathematical model of a main transformer cooling system was developed. This includes the dynamic model of a main transformer, an oil pump, an oil cooler and a blower. The system algorithm of a cooling system, which consists of the temperature setpoint algorithm and the temperature control algorithm, was developed. Optimal oil temperatures of the inlet and the outlet of the main transformer were obtained by considering the total electric power consumption of the system. The oil inlet temperature was controlled by the blower and the oil outlet temperature was controlled by the oil pump. A simulation program was developed by using the mathematical model and the system algorithm. Simulation results showed that the system algorithm developed from this study may be effectively used to control the main transformer cooling system in a tilting train.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
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• pp.176-179
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• 2007

In this paper, we develop the path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1].

• 전자공학회논문지B
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• 제33B권3호
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• pp.1-8
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• 1996

This paper proposes a novel transversal filter and an optimal LMS algorithm, and show how these can be realized as an adaptive equalizer. The transversal filter consists of a walsh and block pulse functions. in the LMS algorithm with equalizers, the convergence factor is an improtant design parameter because it governs stability and convergence speed. The conventional adaptation techniques use a fixed time constant convergence factor by the trial and error method. In this paper, an optimal method in the choice of the convergence factor is proposed. The proposed algorithm is obtrained that is tailored for each filter tap and is updated at each iteration. The performance of the proposed algorithm is compared iwth those of the conventional TDL and DFT equalizers by computer simulations.

• 한국정밀공학회지
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• 제16권1호통권94호
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• pp.116-124
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• 1999

In most conventional design optimization of dynamic system, design sensitivities are utilized. However, design sensitivities based optimization method has numbers of drawback. First, computing design sensitivities for dynamic system is mathematically difficult, and almost impossible for many complex problems as well. Second, local optimum is obtained. On the other hand, Genetic Algorithm is the search technique based on the performance of system, not on the design sensitivities. It is the search algorithm based on the mechanics of natural selection and natural genetics. GA search, differing from conventional search techniques, starts with an initial set of random solutions called a population. Each individual in the population is called a chromosome, representing a solution to the problem at hand. The chromosomes evolve through successive iterations, called generations. As the generation is repeated, the fitness values of chromosomes were maximized, and design parameters converge to the optimal. In this study, Genetic Algorithm is applied to the actual dynamic optimization problems, to determine the optimal design parameters of the dynamic system.