• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.622-624
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• 2005

This paper proposes a time series prediction method for the nonllinear system using the fuzzy system and its genetic algorithm, At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction series system may be obtained. We obtain a good result for the time prediction of the electric load.

• Journal of Ecology and Environment
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• 제40권2호
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• pp.84-88
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• 2016

To understand the effects of habitat characteristics on the genetic diversity of Persicaria thunbergii, three sites of different environmental conditions in a water system were surveyed. Site A was the closest to the source of the water system, and there was a dam between sites A and B. Site C is located on the lowest downstream in the water system. Vegetation survey of four quadrats at each site was performed, and soil samples were collected for physicochemical analysis. Random amplification of polymorphic DNA (RAPD) analysis of ten P. thunbergii individuals at each site was conducted to calculate population genetic diversity and genetic distance among populations. Soil was sterile sand at site A, whereas loamy soil at sites B and C. A pure stand of P. thunbergii appeared at site A, while other species occurred together (such as Humulus japonicus and Phragmites australis) at sites B (Shannon-Wiener index; $H_B=0.309$) and C ($H_C=0.299$). Similar to the species diversity, genetic diversity (Nei's gene diversity; h) within population of site A ($h_A=0.2381$) was relatively lower than sites B ($h_B=0.2761$) and C ($h_C=0.2618$). However, site C was separated from sites A and B in genetic distance rather than the geographical distance (Nei's genetic distance; A~B, 0.0338; B~C, 0.0685; A~C, 0.0833).

• Journal of Advanced Marine Engineering and Technology
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• 제31권3호
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• pp.293-300
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• 2007

The aim of this paper is to design a speed controller of a DC motor by selection of a PID parameters using genetic algorithm. The model of a DC motor is considered as a typical non-oscillatory, second-order system, And this paper compares three kinds of tuning methods of parameter for PID controller. One is the controller design by the genetic algorithm. second is the controller design by the model matching method third is the controller design by Ziegler and Nichols method. It was found that the proposed PID parameters adjustment by the genetic algorithm is better than the Ziegler & Nickels' method. And also found that the results of the method by the genetic algorithm is nearly same as the model matching method which is analytical method. The proposed method could be applied to the higher order system which is not easy to use the model matching method.

• International Journal of Control, Automation, and Systems
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• 제5권1호
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• pp.79-85
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• 2007

In this paper, a real-coded genetic algorithm is proposed for identification of time delay systems from step responses. FOPDT(First-Order Plus Dead-Time) and SOPDT(Second-Order Plus Dead-Time) systems, which are the most useful processes in this field, but are difficult for system identification because of a long dead-time problem and a model mismatch problem. Genetic algorithms have been successfully applied to a variety of complex optimization problems where other techniques have often failed. Thus, the modified crossover operator of a real-code genetic algorithm is proposed to effectively search the system parameters. The proposed method, using a real-coding genetic algorithm, shows better performance characteristics when compared to the usual area-based identification method and the directed identification method that uses step responses.

• 대한산업공학회지
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• 제29권1호
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• pp.32-40
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• 2003

In this study, we develop the Genetic Fuzzy System(GFS) to estimate the link traveling speed. Based on the genetic algorithm, we can get the fuzzy rules and membership functions that reflect more accurate correlation between traffic data and speed. From the fact that there exist missing links that lack traffic data, we added a Case Base Reasoning(CBR) to GFS to support estimating the speed of missing links. The case base stores the fuzzy rules and membership functions as its instances. As cases are accumulated, the case base comes to offer appropriate cases to missing links. Experiments show that the proposed GFS provides the more accurate estimation of link traveling speed than existing methods.

• 한국정밀공학회지
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• 제21권9호
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• pp.48-55
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• 2004

This study presents a genetic algorithm-based method fur optimizing control parameters in the pressure control of electro-hydraulic pump with variable displacement. Genetic algorithms are general-purpose optimization methods based on natural evolution and genetics and search the optimal control parameters maximizing a measure that evaluates the performance of a system. Four control gains of the PI-PD cascade controller for an electro-hydraulic pressure control system are optimized using a genetic algorithm in the experiment. Optimized gains are confirmed by inspecting the fitness distribution which represents system performance in gain spaces. It is shown that genetic algorithm is an efficient scheme in optimizing control parameters of the pressure control of electro-hydraulic pump with variable displacement.

• 한국정보통신학회논문지
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• 제9권2호
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• pp.355-360
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• 2005

본 논문에서는 임베디드 리눅스와 유전자 알고리즘을 이용한 교량 진단 시스템의 설계에 대하여 기술한다. 제안된 시스템은 임베디드 리눅스 환경 하에서 동작하며, 교량의 상태는 인터넷을 통해 원격으로 모니터링을 한다. 그리고 다양한 교량 진단용 센서에 대해서 최적의 이득과 오프셋을 찾기 위해 유전자 알고리즘을 내장하고 있어 계측의 측정 범위가 크다. 제안된 방법의 검증은 시스템 기반에서 이루어졌다.

• 품질경영학회지
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• 제32권4호
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• pp.125-139
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• 2004

Generally, parallel redundancy is used to improve reliability in many systems. However, redundancy increases system cost, weight, volume, power, etc. Due to limited availability of these resources, the system designer has to maximize reliability subject to various constraints or minimize resources while satisfying the minimum requirement of system reliability. This paper presents GAs (Genetic Algorithms) to solve redundancy allocation in series-parallel systems. To apply the GAs to this problem, we propose a genetic representation, the method for initial population construction, evaluation and genetic operators. Especially, to improve the performance of GAs, we develop heuristic operators (heuristic crossover, heuristic mutation) using the reliability-resource information of the chromosome. Experiments are carried out to evaluate the performance of the proposed algorithm. The performance comparison between the proposed algorithm and a pervious method shows that our approach is more efficient.

• Journal of international Conference on Electrical Machines and Systems
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• 제1권1호
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• pp.135-142
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• 2012

In this paper, optimal selection of the unified power flow controller (UPFC) damping controller parameters in order to improve the power system dynamic response and its stability based on two modified intelligent algorithms have been proposed. These algorithms are based on a modified intelligent particle swarm optimization (PSO) and continuous genetic algorithm (GA). After extraction of UPFC dynamic model, intelligent PSO and genetic algorithms are used to select the effective feedback signal of the damping controller; then, to compare the performance of the proposed UPFC controller in damping the critical modes of a single-machine infinite-bus (SMIB) power system, the simulation results are presented. The comparison shows the good performance of both presented PSO and genetic algorithms in an optimal selection of UPFC damping controller parameters and damping oscillations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.40.6-40
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• 2001

Fuzzy controls are widely used in industrial fields using experts knowledge base for its high degree of performance. Genetic Algorithm(GA) is one of the numerical method that has an advantage of optimization. In this paper, we present an acquisition method of fuzzy rules using genetic algorithm. Knowledge of the system is the key to generating the control rules. As these rules, a system can be more stable and it reaches the control goal the faster. To get the optimal fuzzy control rules and the membership functions, we use the GA instead of the experts knowledge base. Information of the system is coded the chromosome with suitable phenotype. Then, it is operated by genetic operator, and evaluated by evaluation function. Passing by the decoding process with the fittest chromosome, the genetic algorithm can tune the fuzzy rules and the membership functions automatically ...