• 한국세라믹학회지
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• 제40권12호
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• pp.1170-1176
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• 2003

진화최적방법을 이용하여 alkali earth borosilicate 계열(Eu, Mg, Ca, Sr, Ba)$_{x}$ $B_{y}$S $i_{z}$ $O_{d}$에 E $u^{3+}$ 를 도핑 하여 고효율 적색 형광체를 합성하였다. 본 연구는 삼원색 백색 LED로의 적용을 목적으로 한다. 진화최적방법은 유전알고리즘과 조합화학을 연계하여, LED형광체 개발을 위해 개발하였다. 유전알고리즘을 조합화학에 접목함으로써 시간과 자원의 낭비 없이 매우 효율적인 형광체 탐색을 꾀할 수 있었다. 실질적인 실험에 앞서 다양한 목적함수를 이용하여 시뮬레이션을 실시하여 본 연구의 타당성을 증명하고 실제 합성한 결과 삼원색 백색 LED용 적색형광체(E $u_{0.14}$M $g_{0.18}$C $a_{0.07}$B $a_{0.12}$ $B_{0.17}$S $i_{0.32}$ $O_{{\delta}}$)를 얻었다.얻었다.다.얻었다.얻었다.다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.838-840
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• 1997

This presents a design method for fuzzy logic controllers of TCSC using genetic algorithm. Fuzzy logic controllers are applied to damp the dynamic disturbances sum as sudden changes of AC system loads. The dynamic performances of fuzzy logic controllers are compared with those of PI controllers. The simulation results show that dynamic performances of fuzzy controllers have better response than those of PI controllers when the AC system load changes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 B
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• pp.971-973
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• 1993

Deflection Yoke(the following, DY) is the important electric device of CRT which deflects R, G, B beans influencing magnetic field produced by yoke coils. Recently, DY is designed to the saddle/saddle type of coils, being proposed for high-definite and high-efficient CRT. This paper presents the optimization of pin-sectioned saddle coil's shape for minimizing gap between desired and practical deflections of electron beams by using Genetic Algorithm. Evolution Startegy is utilized in this paper, since evolution strategy is a kind of genetic algorithms finding the optimized values by choicing the better generation with comparing the parents and their children. Here, the children are generated by only mutations from the normal random variables. Evolution strategy has shown better powerful converge rate than the other genetic algorithms becuase of using only the mutation-operator.

• Journal of Electrical Engineering and Technology
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• 제5권4호
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• pp.597-605
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• 2010

This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2015년도 춘계 학술논문 발표대회
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• pp.132-133
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• 2015

Efficient scheduling and resource management are the key factor to reduce construction project budget (e.g., labor cost, equipment cost, material cost, etc.). Resource-based line of balance (LOB) technique has been used to complement the limitations of time-driven scheduling techniques (e.g., critical-path method). Optimizing construction alternatives contributes cost savings while honoring the project deadline. However, existing LOB scheduling is lack of identifying optimal resource combination. This study presents a method which identifies the optimal construction alternatives, hence achieving resource minimization in a repetitive construction by using genetic algorithm (GA). The method provides efficient planning tool that enhances the usability of the system.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.158-160
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• 2015

Efficient scheduling and resource management are the key factor to reduce construction project budget (e.g., labor cost, equipment cost, material cost, etc.). Resource-based line of balance (LOB) technique has been used to complement the limitations of existing time-driven scheduling techniques (e.g., critical-path method). Optimizing construction alternatives contributes to cost savings while honoring the project deadline. However, existing LOB scheduling is lack of identifying optimal resource combination. This study presents a method which identifies the optimal construction alternatives, hence achieving resource minimization in a repetitive construction by using genetic algorithm (GA). The method provides efficient planning tool that enhances the usability of the system.

• Communications for Statistical Applications and Methods
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• 제31권3호
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• pp.279-289
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• 2024

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying meta-classifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.

• 한국지능시스템학회논문지
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• 제14권5호
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• pp.660-665
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• 2004

In this study, a new architecture and comprehensive design methodology of genetically optimized Multi-layer Fuzzy Neural Networks (gMFNN) are introduced and a series of numeric experiments are carried out. The gMFNN architecture results from a synergistic usage of the hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gMFNN. The consequence part of the gMFNN is designed using PNN. The optimization of the FNN is realized with the aid of a standard back-propagation learning algorithm and genetic optimization. The development of the PNN dwells on the extended Group Method of Data Handling (GMDH) method and Genetic Algorithms (GAs). To evaluate the performance of the gMFNN, the models are experimented with the use of a numerical example.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.174-176
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• 1997

This paper proposes a unit commitment scheduling method based on hybrid genetic algorithm(GA). When the systems are scaled up, conventional genetic algorithms suffer from computational time limitations because of the growth of the search space. So greatly reduce the search space of the GA and to efficiently deal with the constraints of the problem, priority list unit ordering scheme are incorporated as the initial solution and the minimum up and down time constraints of the units are included. The violations of other constraints are handled by integrating penalty factors. To show the effectiveness of the proposed method. test results for system of 10 units is compared with results obtained using other methods.

• 정보처리학회논문지B
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• 제15B권2호
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• pp.131-136
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• 2008

그래프 G = (V, E) 의 L(2,1)-labeling 이란 함수 f: V(G) $\rightarrow$ {0, 1, 2, ...} 를 정의하는 것으로서 함수 f 는 만일 G 내의 두 개 정점 u, $\upsilon$ 사이의 최단거리가 1 인 경우 $|f(u)\;-\;f(\upsilon)|\;{\geq}\;2$ 라는 조건 및 최단거리가 2 인 경우 $|f(u)\;-\;f(\upsilon)|\;{\geq}\;1$ 라는 조건을 만족시켜야 한다. ${\lambda}(G)$ 로 표기되는 G 의 L(2,1)-labeling 수는 모든 가능한 f 들 사이에서 사용된 가장 큰 정수가 가장 작은 값을 나타낸다. 상기한 문제는 NP-complete 계열의 문제이기 때문에 본 논문에서는 L(2,1)-labeling 에 적용 가능한 유전자 알고리즘을 개발한 후 개발된 알고리즘을 최적값이 알려진 그래프들에 적용하여 그 효율성을 보이고자 한다.