• 한국통신학회논문지
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• 제39B권11호
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• pp.790-802
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• 2014

인지무선 기술에서 주사용자의 보호를 위해 부사용자들은 주기적인 센싱 수행을 통해 주사용자의 부재를 판단하게 되고, 부사용자들 간의 협력 센싱을 통해서 향상된 센싱 결과를 얻을 수 있다. 하지만 주사용자에 대한 검출 확률과 오경보 확률에 대한 비용의 트레이드 오프가 존재하기 때문에, 적절한 협력 집단의 규모 유지가 필요하다. 또한 부사용자들은 자신이 현재 사용중인 주파수 대역은 물론 인가 사용자가 나타났을 시에 스위칭 해야 할 후보 채널에 대한 주기적인 센싱이 요구된다. 본 논문에서는 진화게임이론을 이용하여 분산상황 에서의 인밴드 센싱과 아웃밴드 센싱을 고려한 효율적인 그룹 협력 센싱 방법을 제안한다. 진화 게임을 통해서 협력센싱의 전략을 택한 부사용자들의 집단이 ESS(Evolutionary Stable State)상태로 수렴함을 관찰하였고, 학습 알고리즘을 통해 서로간의 정보교환 없이 평형상태로 수렴함을 관찰하였다.

• 대한전기학회논문지:전력기술부문A
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• 제49권10호
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• pp.494-501
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• 2000

This paper describes a strategy of a daily optimal operational scheduling in cogeneration system for paper mill. The cogeneration system selected to establish the scheduling consists of three units and several auxiliary devices. One unit generates electrical and thermal energy using the back pressure turbine. The rest two units generate the energy using the extraction condensing turbine. Three auxiliary boilers, two waste boilers and three sludge incinerators operate to supply energy to the loads with three units. The cogeneration system is able to supply enough the thermal energy to the thermal load, however it can not sufficiently supply the electrical power to the electrical load. Therefore the insufficient electric energy is compensated by buying electrical energy from utility. When the operational scheduling is performed considering the environmental problem. This paper shows the simulation results for daily operational scheduling obtained using the evolutionary algorithm. This results reveal that the proposed modeling and strategy can be effectively applied to cogeneration system for paper mill.

• Nuclear Engineering and Technology
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• 제48권5호
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• pp.1126-1139
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• 2016

In this research, for the first time, a new optimization method, i.e., strength Pareto evolutionary algorithm II (SPEA-II), is developed for the burnable poison placement (BPP) optimization of a nuclear reactor core. In the BPP problem, an optimized placement map of fuel assemblies with burnable poison is searched for a given core loading pattern according to defined objectives. In this work, SPEA-II coupled with a nodal expansion code is used for solving the BPP problem of Kraftwerk Union AG (KWU) pressurized water reactor. Our optimization goal for the BPP is to achieve a greater multiplication factor ($K_{eff}$) for gaining possible longer operation cycles along with more flattening of fuel assembly relative power distribution, considering a safety constraint on the radial power peaking factor. For appraising the proposed methodology, the basic approach, i.e., SPEA, is also developed in order to compare obtained results. In general, results reveal the acceptance performance and high strength of SPEA, particularly its new version, i.e., SPEA-II, in achieving a semioptimized loading pattern for the BPP optimization of KWU pressurized water reactor.

• International Journal of Quality Innovation
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• 제1권1호
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• pp.89-96
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• 2000

Evolutionary operation (EVOP) is a continuous improvement system which explores a region of process operating conditions by deliberately creating some systematic changes to the process variable levels without jeopardizing the product. It is aimed at securing a satisfactory operating condition in full-scale manufacturing processes, which is generally different from that obtained in laboratory or pilot plant experiments. Information on how to improve the process is generated from a simple experimental design. Traditional EVOP procedures are established on the assumption that the variance of the response variable should be small and stable in the region of the process operation. However, it is often the case that process noises have an influence on the stability of the process. This process instability is due to many factors such as raw materials, ambient temperature, and equipment wear. Therefore, process variables should be optimized continuously not only to meet the target value but also to keep the variance of the response variables as low as possible. We propose a scheme to achieve robust process improvement. As a process performance measure, we adopted the mean square error (MSE) of the replicate response values on a specific operating condition, and used the Kruskal-Wallis test to identify significant differences between the process operating conditions.

• Journal of Microbiology and Biotechnology
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• 제20권5호
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• pp.950-957
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• 2010

Serratiopeptidase (SRP), a 50 kDa metalloprotease produced from Serratia marcescens species, is a drug with potent anti-inflammatory property. In this study, a powerful statistical design, evolutionary operation (EVOP), was applied to optimize the media composition for SRP production in shake-flask culture of Serratia marcescens NRRL B-23112. Initially, factors such as inoculum size, initial pH, carbon source, and organic nitrogen source were optimized using one factor at a time. The most significant medium components affecting the production of SRP were identified as maltose, soybean meal, and $K_2HPO_4$. The SRP so produced was not found to be dependent on whey protein, but rather was notably induced by most of the organic nitrogen sources used in the study and free from other concomitant protease contaminant, as revealed by protease inhibition study. In addition, experiments were performed using different sets of EVOP design with each factor varied at three levels. The experimental data were analyzed with a standard set of statistical formula. The EVOP-optimized medium, with maltose 4.5%, soybean meal 6.5%, $K_2HPO_4$ 0.8%, and NaCl 0.5% (w/v), gave a SRP production of 7,333 EU/ml, which was 17-fold higher than the unoptimized media. The application of EVOP resulted in significant enhancement of SRP production.

• 한국철도학회논문집
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• 제9권1호
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• pp.76-80
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• 2006

The Optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model have been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme are utilized, respectively. As a result, two meta-models for trip time and energy consumption were constructed. The optimization to search an economical running pattern was achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• 제어로봇시스템학회논문지
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• 제16권2호
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• pp.107-113
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• 2010

In this paper, a prototype of cooperative work model for multi-robots system is introduced and the evolutionary approach is applied to generate the motions for the cooperative works of multi-robots system using genetic algorithm. The cooperative tasks can be performed by a humanoid robot and a mobile robot to deliver objects from shelves. Generation of the humanoid motions such as pick up, rotation, and place operation for the cooperative works are evolved. Modeling and computer simulation for the cooperative robots system are executed in Webots environments. Experimental results show the feasible and reasonable solutions for humanoid cooperative tasks are obtained.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.238-240
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• 2005

The Optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model have been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme are utilized, respectively. As a result, two meta-models for trip time and energy consumption were constructed. The optimization to search an economical running pattern was achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• 한국항공우주학회지
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• 제38권4호
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• pp.352-362
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• 2010

복수 무인기의 동시 운용을 통하여 임무 수행 효율성 제고를 꾀할 수 있으며 이를 위해서는 확장성이 용이한 제어 알고리듬을 필요로 하게 되는데 유연성, 강건성, 분산형 제어 및 자기조직화의 특징을 갖는 행동모델 기반의 무리 지능이 현실적인 대안으로 각광받고 있다. 그러나 논리적으로 행동규칙을 설계하기 어렵다는 단점을 극복하기 위하여 최근 진화로봇공학이 무인기 제어에 적용되기 시작하고 있다. 본 논문에서는 제한된 영역을 복수의 무인기로 탐색하는 임무를 진화로봇공학을 적용하여 진화시킨 신경망제어기로 수행한 결과, 직관에 의지하여 설계된 행동모델 기반의 신경망제어기에 비하여 우수한 성능을 보임을 제시하였다.

• 정보처리학회논문지B
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• 제9B권5호
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• pp.673-680
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• 2002

이 논문은 진화 프로그래밍과 개선된 역전파 알고리즘을 이용한 에지 검출 방법을 제안한다. 진화 프로그래밍은 알고리즘의 성능저하와 계산비용을 고려하여 교차 연산은 수행하지 않고, 선택연산자와 돌연변이 연산자를 사용한다. 개선된 역전파 알고리즘은 학습단계에서 연결강도를 변화시킬 때 이전학습단계의 연결강도를 보조적으로 활용하는 방법이다. 이 개선된 역전파 알고리즘은 학습률 $\alpha$를 작은값으로 설정하기 때문에 각 학습단계에서의 연결강도 변화량이 기존의 방법에 비해 상대적으로 줄어들게 되어 학습이 느려지는 문제점을 해결하였다. 실험결과 학습시간과 검출률에 있어서 GA-BP(GA : Genetic Algorithm BP : Back-Propagation)를 이용한 방법보다 제안한 EP-MBP(EP : Evolutionary Programming, MBP :Momentum Back-Propagation)를 이용하여 학습시킨 방법이 학습시간의 단축과 효율적인 에지 검출 결과를 얻을 수 있었다.