• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.890-892
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• 1996

This paper presents a method for optimal placement of series condenser in order to improve the power system transient stability using genetic algorithms(GAs). In applying GAs, this approach utilizes two kinds of strings, one is coded by a binary finite-length for the selection of lines to install series condenser, the other is coded by a real value for the determination of injected condenser capacitance. For the formulation. this paper considers multi-objective function which is the critical energy as decelerating energy in power systems and the total injected condenser capacitance. The proposed method is applied to 9-bus, 18-line, 3-machine model system to show its effectiveness in determining the locations to install series condenser and the series condenser capacitance to be injected, simultaneously.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.166-168
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• 1996

This paper presents an power system optimization method which solves real and reactive power dispatch problems using evolutionary computation such as genetic algorithms(GAs), evolutionary programming(EP), and evolution strategy(ES). Many conventional methods to this problem have been proposed in the past, but most these approaches have the common defect of being caught to a local minimum solution. Recently, global search methods such as GAs, EP, and ES are introduced. The proposed methods, applied to the IEEE 30-bus system, were run for 12 other exogenous parameters. Each simulation result, by which evolutionary computations are compared and analyzed, shows the possibility of applications of evolutionary computation to large scale power systems.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.101-107
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• 2008

We introduce an optimization of fuzzy set-fuzzy systems based on IG (Information Granules). The proposed fuzzy model implements system structure and parameter identification by means of IG and GAs. The concept of information granulation was coped with to enhance the abilities of structural optimization of the fuzzy model. Granulation of information realized with C-Means clustering helps determine the initial parameters of the fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. The initial parameters are adjusted effectively with the help of the GAs and the standard least square method. To optimally identify the structure and the parameters of the fuzzy model we exploit GAs with successive tuning method to simultaneously search the structure and the parameters within one individual. We also consider the variant generation-based evolution to adjust the rate of identification of the structure and the parameters in successive tuning method. The proposed model is evaluated with the performance of the conventional fuzzy model.

• 한국정보통신학회논문지
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• 제9권8호
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• pp.1840-1846
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• 2005

본 논문에서는 유전 알고리즘을 사용하여 PID의 각 계수를 자동적으로 조정함으로써 DC 서보모터의 속도제어에 적용하였다. DC 서보모터는 산업현장 및 로봇분야에 널리 적용되고 있으며 적절한 제어성능을 얻기 위하여 많은 시행착오에 의한 다양한 제어방법이 사용되고 있다. 그러나 산업현장, 플랜트의 변화 및 외란에 강인한 제어알고리즘을 선택하기가 매우 어려우며 많은 시행착오를 통하여 원하는 계수값을 얻어 낼 수 있다. 따라서 본 논문에서는 이러한 문제점을 해결하고 DC 서보모터의 제어성능을 향상시키기 위하여 유전 알고리즘을 적용함으로써 우수한 응답특성을 얻을 수 있었다.

• Steel and Composite Structures
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• 제30권3호
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• pp.293-304
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• 2019

The optimum cost of a reinforced concrete plane and space frames have been found by using the Genetic Algorithm (GA) method. The design procedure is subjected to many constraints controlling the designed sections (beams and columns) based on the standard specifications of the American Concrete Institute ACI Code 2011. The design variables have contained the dimensions of designed sections, reinforced steel and topology through the section. It is obtained from a predetermined database containing all the single reinforced design sections for beam and columns subjected to axial load, uniaxial or biaxial moments. The designed optimum beam sections by using GAs have been unified through MATLAB to satisfy axial, flexural, shear and torsion requirements based on the designed code. The frames' functional cost has contained the cost of concrete and reinforcement of steel in addition to the cost of the frames' formwork. The results have found that limiting the dimensions of the frame's beams with the frame's columns have increased the optimum cost of the structure by 2%, declining the re-analysis of the optimum designed structures through GA.

• 수산해양기술연구
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• 제58권4호
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• pp.359-366
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• 2022

In this study, the load fluctuation of the main engine is considered to be a disturbance for the jacket coolant temperature control system of the low-speed two-stroke main diesel engine on the ships. A nonlinear PID temperature control system with satisfactory disturbance rejection performance was designed by rapidly transmitting the load change value to the controller for following the reference set value. The feed-forwarded load fluctuation is considered the set points of the dual loop control system to be changed. Real-coded genetic algorithms were used as an optimization tool to tune the gains for the nonlinear PID controller. ITAE was used as an evaluation function for optimization. For the evaluation function, the engine jacket coolant outlet temperature was considered. As a result of simulating the proposed cascade nonlinear PID control system, it was confirmed that the disturbance caused by the load fluctuation was eliminated with satisfactory performance and that the changed set value was followed.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2005년도 제25회 추계학술대회논문집
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• pp.149-153
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• 2005

가스터빈 엔진의 성능을 예측하기 위해서는 자신의 성능 특성을 포함한 구성품 성능도가 요구된다. 본 연구에서는 유전 알고리즘을 이용하여 압축기 성능도를 제작사에서 제공한 성능덱으로부터 역으로 식별하는 방법을 제안하였다. 알고리즘은 틸트 로터 방식 스마트 UAV를 위한 PW206C 터보축 엔진에 적용하였다. 제안된 방법을 검증하기 위하여 새롭게 만들어진 압축기 성능도를 이용한 해석 결과와 제작사에서 제공한 EEPP(Estimated Engine Performance Program) 덱을 이용한 해석 결과를 비교하였다. 또한 기존의 스케일링 방법을 이용하여 얻어진 구성품 성능도를 이용한 해석결과와도 비교하였다. 본 연구에서 새롭게 제안된 성능도 생성 방법이 기존의 스케일링 방법보다 더 효과적임을 확인하였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제29권6호
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• pp.396-406
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• 2002

한 개의 모집단으로 구성되는 단순 유전자 알고리즘은 일반적으로 하나의 최적해를 찾는 경우에만 효과적이다. 그러나, 많은 문제들은 여러 개의 최적해를 가질 수 있으며, 그것들 모두를 찾는 것이 중요한 경우가 많다. 이 논문에서는 모집단 내 개체들에 지리적인 이웃의 개념을 부여하여, 각 객체들이 지역적으로 경쟁하면서도 전역적으로 유전자를 교환할 수 있도록 하여, 하나의 모집단이 여러 개의 최적해를 포함하도록 하는 유전자 알고리즘을 제안한다. 또한, 30비트, 6차 바이폴라-디셉티브 함수(bipolar-deceptive function)를 비롯한 여러 개의 최적해를 갖는 다양한 문제들에 적용하여 성능을 평가한다. 마지막으로 제안한 알고리즘에 대한 몇 가지 개선 방향을 제시하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 공동추계학술대회
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.