• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.369-369
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• 2000

Control system by means of fuzzy theory has demonstrated its robustness in applying to the high-order and nonlinear dynamic system in that it can utilizes the human expert knowledges in system design. In this paper, first, the design methodology of HFC combined PID controller with fuzzy controller by membership function of weighting coefficient is proposed. Second, Second, an auto-tuning algorithms utilizing the simplified reasoning method and genetic algorithms is presented to improve the performance of hybrid fuzzy controller. Especially, in order to obtain the optimal scaling factors and PID parameters of HFC using GA based on advanced initial individual, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The proposed HFC is evaluated and discussed in ITAE, overshoot and rising time to show applicability and superiority with simulation results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.179-182
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• 2000

A Classifier System processes a discrete coded information from the environment. When the system codes the information to discontinuous data, it loses excessively the information of the environment. The Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit Bucket Brigade Algorithms. Also the FCS employs the Genetic Algorithms(GAs) to make new rules and modify rules when performance of the system needs to be improved. We will verify the effectiveness of the proposed FCS by applying it to AMR avoiding the obstacle.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.891-894
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• 1998

This paper introduces an automatic face region extraction method. This method consists of two part: face recognition and extraction of facial organs which are eye, eyebrow, nose and mouth. In first stage, we use genetic algorithms(GAs) to get face region in complex background. In second stage, we use Geometrical Face Model to textract eye, eyebrow, nose and mouth. In both stage, stochastic component is used to deal with the problems caused by had lighting condition. According to this value, blurring number is determined. Average Computation time is less than 1 sec, and using this method we can extract facial feature efficiently from several images which has different lightning condition.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.1
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• pp.162-170
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• 2001

This paper presents a scheme for designing a fuzzy model-bsaed state observer for nonlinear system. For this scheme, a Tagaki-Sugeno type fuzzy model whose consequent part is of the state space form is obtained. In describes the locally linear input/output relationship of a system. The parameters of the fuzzy model are adjusted using a genetic algorithm. Then. fuzzy full-order and reduced-order state observers are designed based on the fuzzy model. A set of simulation works is carried out to demonstrate the effectiveness of the proposed scheme.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.820-823
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• 2003

In this paper, we describe a design procedure for cascade controller for induction motor drives based on Genetic Algorithms(GAs). Most electric drives have two separate controllers for current and speed control, which are in general designed in two consecutive steps(firstly the current controller and then the speed controller). We search simultaneously for the couple of discrete anti-windup controllers achieving the optimal compromise of weighted cost and performance indices related to both current and speed responses.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1998.03a
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• pp.213-219
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• 1998

Genetic Algorithms(GAs ; 유전자 알고리즘)은 자연적 선택(natural selection)의 유전적인 메카니즘에 기초한 탐색 알고리즘(search algo-rithms)이다. GA는 세대(generation)를 거듭함에 따라 어떤 최적화화하는 해에 수렴해가는 탐색 알고리즘으로 전세대의 우수개체로부터 새로운 세대의 개체들이 집합이 형성되는 과정을 이용한 탐색 알고리즘이다. GA에 대한 최근의 활발한 연구와 많은 관심은 주로 기존의 기법이 특정 영역의 지식을 많이 필요로하는데 비해서 GA는 효율적인 영역독립 탐색경험들의 집합을 제공하여 최적해를 얻는 기법으로서 전역함수 최적화와 NP 등의 문제에 유용하다는 연구결과가 제시되고 있기 때문이다. 본 연구에서는 GA에 대한 명확한 이해와 세대의 형성 , 개체를 선택하기 위한 타당한 연산자(operator)에 관한 내용을 고찰하고, GA가 언제, 어떻게 사용되는가에 대해 응용사례를 중심으로 GA의 향후 연구방향에 대해 논의하고 GA가 앞으로 어떤 분야에서 어떻게 발전해 나가야 할 지에 대한 과제에 대해 논의한다.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.566-568
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• 1998

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance index, we use the time series data for gas furnace and the sewage treatment process.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.533-549
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• 1996

This paper is concerned with a sequencing problem in mixed model assembly lines, which is important to efficient utilization of the lines. In the problem, we deal with the two objectives of minimizing the risk of stoppage and leveling part usage, and consider sequence-dependent setup time. In this paper, we present a genetic algorithm(GA) suitable for the multi-objective optimization problem. The aim of multi-objective optimization problems is to find all possible non-dominated solutions. The proposed algorithm is compared with existing multi-objective GAs such as vector evaluated GA, Pareto GA, and niched Pareto GA. The results show that our algorithm outperforms the compared algorithms in finding good solutions and diverse non-dominated solutions.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.517-519
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• 2002

침입탐지 시스템은 정밀성자 적응성, 그리고 확장성을 필요로 한다. 이와 같은 조건을 포함하면서 복잡한 Network 환경에서 중요하고 기밀성이 유지되어야 할 리소스를 보호하기 위해, 우리는 더욱 구조적이며 지능적인 IDS(Intrusion Detection Systems) 개발의 필요성이 요구되고 있다. 본 연구는 데이터 마이닝(Data mining)을 통해 입 패턴, 즉 침입 규칙(Rules)을 생성한다. 데이터 마이닝 기법 중 분류(Classification)에 초점을 맞추어 분석과 실험을 하였으며, 사용된 데이터는 KDD데이터이다. 이 데이터를 중심으로 침입 규칙을 생성하였다. 규칙생성에는 유전자알고리즘(Genetic Algorithm : GAs)을 적용하였다. 즉, 오용탐지(Misuse Detection) 기법을 실험하였으며, 생성된 규칙은 침입데이터를 대표하는 규칙으로 비정상 사용자와 정상 사용자를 분류하게 된다. 규칙은 "Time Based Traffic Model", "Host Based Traffic Model", "Content Model" 이 세 가지 모듈에서 각각 상이한 침입 규칙을 생성하게 된다. 본 시스템에서 도출된 침입 규칙은 430M Test data set에서 테스트한 결과 평균 약94.3%의 성능 평가 결과를 얻어 만족할 만한 성과를 보였다.의 성능 평가 결과를 얻어 만족할 만한 성과를 보였다.

• Smart Structures and Systems
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• v.16 no.3
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• pp.401-414
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• 2015

A developed hybrid method for crack identification of beams is presented. Based on the Euler-Bernouli beam theory and concepts of fracture mechanics, governing equation of the cracked beams is reformulated. Finite element (FE) method as a powerful numerical tool is used to discritize the equation in space domain. After transferring the equations from time domain to frequency domain, frequencies and mode shapes of the beam are obtained. Efficiency of the governed equation for free vibration analysis of the beams is shown by comparing the results with those available in literature and via ANSYS software. The used equation yields to move the influence of cracks from the stiffness matrix to the mass matrix. For crack identification measured data are produced by applying random error to the calculated frequencies and mode shapes. An objective function is prepared as root mean square error between measured and calculated data. To minimize the function, hybrid genetic algorithms (GAs) and particle swarm optimization (PSO) technique is introduced. Efficiency, Robustness, applicability and usefulness of the mixed optimization numerical tool in conjunction with the finite element method for identification of cracks locations and depths are shown via solving different examples.