• KIEAE Journal
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• 제15권3호
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• pp.29-36
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• 2015

Purpose: This study is as only basic research for the model Development of the New-Hanok Type Service Facilities in Apartment Housing, which is as a decisive factor used as a planning element for developing the model inherited tradition, There aimed at extracting the genetic factor of Korea's traditional architecture. Method: For this purpose, Consider the concept and regulations of the New-Hanok Type Service Facilities in Apartment Housing and examined the Domestic Application Status of the New-Hanok Type Service Facilities in Apartment Housing. It sets direction of the New-Hanok Type models development based on Expert advice and the literature, and was reviewed a primal reason system of Korea as an extraction base of genetic factors. Result: Then Through the framework of the vertical axis (the form), the horizontal axis (space), It extracted the genetic factors of the Korea Traditional Architecture, classified the genetic factors extracted as the structure(layout, construction, space), features, traditional beauty, investigated the content of the form representation and spatial meaning, and were characterized. Based on the result, It were comprehensive the genetic factors extracted as plan Elements for inheriting of the traditions.

• 한국컴퓨터정보학회논문지
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• 제28권12호
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• pp.79-87
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• 2023

본 연구는 조선소 소조립 공정에서 스키드 투입 순서 최적화를 통해 전체 작업시간을 단축시키는 새로운 유전 알고리즘 방법을 제안한다. 하나의 해는 스키드 번호들의 순열로 표현되며 그러한 표현에 적합한 유전 연산자들을 적용하였다. 또한 탐색 성능의 개선을 위해 UniDev라 불리우는 기존의 휴리스틱 알고리즘을 적절하게 변형하여 유전 알고리즘과 결합하였다. 특히 UniDev에서 느린 스키드 탐색 부분을 그리디 알고리즘의 형태로 변경하였다. 매우 큰 규모의 문제에 대해 시뮬레이션을 수행한 결과 Multi-Start 탐색과 UniDev기반 혼합형 유전알고리즘에 비해 본 연구에서 제안하는 방법이 안정적으로 작업시간을 최소화함을 관찰하였다.

• Journal of Electrical Engineering and Technology
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• 제2권3호
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• pp.312-319
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• 2007

The solution of the power flow is one of the most important problems in electrical power systems. These traditional methods such as Gauss-Seidel method and Newton-Raphson (NR) method have had drawbacks up to now such as initial values, abnormal operating solutions and divergences in heavy loads. In order to overcome theses problems, the power flow solution incorporating genetic algorithm (GA) is introduced in this paper. General operator of genetic algorithm, arithmetic crossover, and non-uniform mutation operator of GA are suggested to solve the power flow problem. While abnormal solution cannot be obtained by a NR method, multiple power flow solution can be obtained by a GA method. With a heavy load, both normal solution and abnormal solution can be obtained by a proposed method. In this paper, a floating number representation instead of the binary number representation is introduced for accuracy. Simulation results have been compared with traditional methods.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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• pp.809-812
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• 2008

통신 시스템에 대한 관심은 인터넷의 급격한 발전에 의해 가상공간의 출현과 유비쿼터스 컴퓨팅 환경 구축에 대한 요구가 증대됨에 따라 관련 이론 및 기술의 발전을 주도해 왔다. 이와 관련하여 가장 근간이 되는 문제들 중 하나는 최적 정보 통신 스패닝 트리 (OCST: Optimal Communication Spanning Tree) 설계 문제이다. 본 논문에서는 이러한 OCST 설계 문제를 네트워크 신뢰도를 고려하여 해결하기 위해 유전 알고리즘 (GA)를 이용한다. 본 논문에서는 유전 알고리즘을 이용함에 있어서 n개의 노드들로 구성된 네트워크 문제에서 n-2개의 숫자열로 표현 가능한 유전자 표현법을 이용하고 신뢰성 있는 OCST 설계 문제 해결을 위한 해법으로서 유전 알고리즘을 제안한다. 임의로 생성된 예제에 대한 수치 실험을 통해 통신시스템의 기본 문제 중 하나인 OCST 설계 문제의 해법으로서의 제안 알고리즘의 유용성과 효율성을 확인한다.

• Journal of Advanced Marine Engineering and Technology
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• 제25권2호
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• pp.383-394
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• 2001

The stabilization control of inverted pendulum (IP) system is difficult because of its nonlinearity and structural unstability. In this paper, an Evolving Neural Network Controller (ENNC) without Error Back Propagation (EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC)are compared with the one of conventional optimal controller and the conventional evolving neural network controller (CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• 한국수학사학회지
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• 제26권5_6호
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• pp.389-400
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• 2013

The article makes a discussion to conceptualize a histo-genetic principle in the real historical view point. The classical histo-genetic principle appeared in 19th century was founded by the recapitulation law suggested by biologist Haeckel, but recently it was shown that the theory on it is no longer true. To establish the alternative rationale, several metaphoric characterizations from the history of mathematics are suggested: among them, problem solving, transition of conceptual knowledge to procedural knowledge, generalization, abstraction, circulation from phenomenon to substance, encapsulation to algebraic representation, change of epistemological view, formation of algorithm, conjecture-proof-refutation, swing between theory and application, and so on.

• 한국지능시스템학회논문지
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• 제21권2호
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• pp.206-211
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• 2011

본 논문에서는 연속적인 파라미터 공간에 대한 최적화에 대해 진화적 알고리즘의 특징적인 형상화를 제시한다. 이 방법은유전알고리즘이 연속적인 탐색공간에서의 파라미터 식별에 대해 가장 강점을 지녔다는 점에 착안한 것이다. 유전알고리즘과 제안한 알고리즘과의 주요한 차이점은 개별적 또는 연속적인 묘사의 차이가 있다는 것이다. 잘 알려진 실험함수의 최적화문제를 도입하여 연속 탐색공간 문제에 대해 제안하는 알고리즘에 대해 계산시간 및 사용메모리 등의 성능이 우수하다는 효율성을 보였다.

• 대한안전경영과학회지
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• 제5권1호
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• pp.69-82
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• 2003

The sequence which minimizes overall utility work in car assembly lines reduces the cycle time, the number of utility workers, and the risk of conveyor stopping. This study suggests mathematical formulation of the sequencing problem to minimize overall utility work, and present a genetic algorithm which can provide a near optimal solution in real time. To apply a genetic algorithm to the sequencing problem in car assembly lines, the representation, selection methods, and genetic parameters are studied. Experiments are carried out to compare selection methods such as roullette wheel selection, tournament selection and ranking selection. Experimental results show that ranking selection method outperforms the others in solution quality, whereas tournament selection provides the best performance in computation time.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.10-14
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• 2004

When providing flexible data transmission for future CDMA(Code Division Multiple Access) cellular networks, problems arise in two aspects: transmission rate. This paper has proposed an approach to maximize the cellular network capacity by combining the genetic transmission rate allocation and a rapid power control algorithm. We present a genetic chromosome representation to express call drop numbers and transmission rate to control mobile's transmission power levels while handling their flexible transmission rates. We suggest a rapid power control algorithm, which is based on optimal control theory and Steffenson acceleration technique comparing with the existing algorithms. Computer simulation results showed effectiveness and efficiency of the proposed algorithm Conclusively, our proposed scheme showed high potential for increasing the cellular network capacity and it can be the fundamental basis of future research.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2001년도 춘계학술대회 논문집
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.