• 산업공학
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• 제11권3호
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• pp.115-128
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• 1998

This paper presents a development of operation scheduling and reactive operation scheduling system for hull fabrication. The methodology for implementing operation scheduling system is HHGA(Hierarchical Hybrid Genetic Algorithm) which exploits both the global perspective of the genetic algorithm and the rapid convergence of the heuristic search for operation scheduling. The methodology for the reactive operation scheduling is the revised HHGA which consists of manual schedule editor for occurrence of exceptional events and the revised scheduling method used in operation scheduling. As the results of experiment, it has been confirmed that HHGA is able to search good operation scheduling within reasonable time, and the revised HHGA is able to search load-balanced reactive operation scheduling with minimum changes of initial operation schedule within short period of time.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.952-955
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• 1996

It is difficult to determine the feeding rate of coagulant in water treatment process, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the fusion of genetic algorithms and fuzzy inference system was used in determining of feeding rate of coagulant. The genetic algorithms are excellently robust in complex operation problems, since it uses randomized operators and searches for the best chromosome without auxiliary information from a population consists of codings of parameter set. To apply this algorithms, we made the look up table and membership function from the actual operation data of water treatment process. We determined optimum dosages of coagulant (PAC, LAS etc.) by the fuzzy operation, and compared it with the feeding rate of the actual operation data.

• 한국군사과학기술학회지
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• 제12권1호
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• pp.18-23
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• 2009

It is hard to find an operation case using anti-submarine searching pattern(ASSP) developed by Korean navy since Korean navy has begun submarine searching operation. This paper proposes the method to develop hull mount sonar(HMS) based optimal submarine searching pattern by using genetic algorithm. Developing the efficient ASSP based on theory in near sea environment has been demanded for a long time. Submarine searching operation can be executed by using ma ulti-step and multi-layed method. however, In this paper, we propose only HMS based ASSP generation method considering the ocean environment and submarine searching tactics as a step of first research. The genetic algorithm, known as a global opination method, optimizes the parameters affecting efficiency of submarine searching operation. Finally, we confirm the performance of the proposed ASSP by simulation.

• 제어로봇시스템학회논문지
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• 제5권7호
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• pp.876-882
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• 1999

It is difficult to determine the feeding rate of coagulant in the water treatment process, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the genetic-fuzzy system was used in determining the feeding rate of the coagulant. The genetic algorithms are excellently robust in complex optimization problems. Since it uses randomized operators and searches for the best chromosome without auxiliary informations from a population consists of codings of parameter set. To apply this algorithms, we made the lookup table and membership function from the actual operation data of the water treatment process. We determined optimum dosages of coagulant(LAS) by the fuzzy operation, and compared it with the feeding rate of the actual operation data.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.116-119
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• 1998

Abstract-In paper, an operation support software for combustion control of refuse incineration plant is developed using fuzzy model and genetic algorithm. It has two major modules which are simulation module and optimal operation module. In simulation module modelling is performed to obtain fuzzy model of the refuse incineration plant and obtained fuzzy model predicts outputs of the plant when inputs are given. This module can be used to obtain control strategy, and train and enhance operators' skill by simulating the plant. And in optimal operation module, genetic algorithm searches and finds out optimal control inputs over all possible solutions in respect to desired outputs. In order to testify proposed operation support software, computer simulation was carried out.

• 한국전자통신학회논문지
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• 제19권2호
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• pp.417-426
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• 2024

일반적으로 머신러닝을 수행하기 위해서는 딥러닝 모델에 대한 사전 지식과 경험이 필요하고, 데이터를 연산하기 위해 고성능 하드웨어와 많은 시간이 필요하게 된다. 이러한 이유로 머신러닝은 임베디드 프로세서에서 실행하기에는 많은 제약이 있다.본 논문에서는 이러한 문제를 해결하기 위해 머신러닝의 과정 중 콘볼루션 연산(Convolution operation)에 유전 알고리즘을 적용하여 선택적 콘볼루션 연산(Selective convolution operation)과 학습 방법을 제안한다. 선택적 콘볼루션 연산에서는 유전 알고리즘에 의해 추출된 픽셀에 대해서만 콘볼루션을 수행하는 방식이다. 이 방식은 유전 알고리즘에서 지정한 비율만큼 픽셀을 선택하여 연산하는 방식으로 연산량을 지정된 비율만큼 줄일 수 있다. 본 논문에서는 유전 알고리즘을 적용한 머신러닝 연산의 심화학습을 진행하여 해당 세대의 적합도가 목표치에 도달하는지 확인하고 기존 방식의 연산량과 비교한다. 적합도가 충분히 수렴할 수 있도록 세대를 반복하여 학습하고, 적합도가 높은 모델을 유전 알고리즘의 교배와 돌연변이를 통해 다음 세대의 연산에 활용한다.

• 한국소음진동공학회논문집
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• 제19권5호
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• pp.475-481
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• 2009

Real-coded genetic algorithm(RCGA) has better performances than conventional genetic algorithm about dealing with a large domain, the precision and the constrain problem. Also the RCGA has advantage of operation time because it doesn't have to following about decoding operation. In this paper the ranges of PID gains are limited based on Ziegler-Nichols method to consider a long operation time problem that is the main problem of genetic algorithm. Result shows proposed method represents better performance without ignored about result of ZN tuning method and reduces the calculation time.

• 상하수도학회지
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• 제18권3호
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• pp.312-319
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• 2004

The optimum dosage control is presumably the goal of every water treatment plant. However it is difficult to determine the dosage rate of coagulant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the real number genetic-fuzzy system was used in determining the dosage rate of the coagulant. The genetic algorithms are excellently robust in complex optimization problems. Since it uses randomized operators and searches for the best chromosome without auxiliary informations from a population which consists of codings of parameter set. To apply this algorithms, we made the real number rule table and membership function from the actual operation data of the water treatment plant. We determined optimum dosages of coagulant(LAS) using the fuzzy operation and compared them with the dosage rate of the actual operation data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.125-127
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• 1995

This paper describes the optimal operation scheduling technique using genetic algorithms on cogeneration systems with variable efficiency in case of bottoming cycle. Variable efficiency included nonlinear behavior is obtained by least square method based on the real data of industrial cogeneration systems. Genetic algorithms is coded as a vector of floating point numbers. The results of simulation are evaluated that the genetic algorithms can be applied to perform the operation scheduling.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.81-84
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• 1998

Genetic Algorithms (GAs) are stochastic algorithms whose search methods model some natural phenomena. The procedure of GAs may be divided into two sub-procedures : Operation and Selection. Chromosomes can produce new offspring by means of operation, and the fitter chromosomes can produce more offspring than the less fit ones by means of selection. However, operation which is executed randomly and has some limits to its execution can not guarantee to produce fitter chromosomes. Thus, we propose a method which gives a directional information to the genetic operator by reinforcement learning. It can be achived by using neural networks to apply reinforcement learning to the genetic operator. We use the amount of fitness change which can be considered as reinforcement signal to calcualte the error terms for the output units. Then the weights are updated using backpropagtion algorithm. The performance improvement of GAs using reinforcement learning can be measured by applying the pr posed method to GA-hard problem.