• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2000년도 봄 학술발표회 논문집
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• pp.125-132
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• 2000

This study presents the application of genetic algorithms(GA) to the back analysis of tunnels. GA based on the theory of natural evolution, and have been evaluated very effective for their robust performances, particularly for optimizing structure problems. In the back analysis method, the selection of initial value and uncertainty of field measurements influence significantly on the analysis result. GA can improve this problems through a probabilistic approach. Besides, this technique have two other advantages over the back analysis. One is that it is not significantly affected by the form of problems. Another one is that it can consider two known parameter simultaneously. The propriety of this study is verified as the comparison in the same condition of the back analysis(Gens et al, 1987). In this study, it was performed to estimated the geotechnical parameters in the case of weak rock mass at the Kyung Bu Express railway tunnel. GA have been shown for effective application to a geotechnical engineering.

• Journal of Welding and Joining
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• 제24권5호
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• pp.67-73
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• 2006

Laser welding is suitable for welding to the aluminum alloy sheet. In order to apply the aluminum laser welding to production line, parameters should be optimized. In this study, the optimal welding condition was searched through the genetic algorithm in laser welding of AA5182 sheet with AA5356 filler wire. Second-order polynomial regression model to estimate the tensile strength model was developed using the laser power, welding speed and wire feed rate. Fitness function for showing the performance index was defined using the tensile strength, wire feed rate and welding speed which represent the weldability, product cost and productivity, respectively. The genetic algorithm searched the optimal welding condition that the wire feed rate was 2.7 m/min, the laser power was 4 kW and the welding speed was 7.95 m/min. At this welding condition, fitness function value was 137.1 and the estimated tensile strength was 282.2 $N/mm^2$.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.2080-2082
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• 2009

본 논문은 유전자 알고리즘을 이용하여 네트워크의 시간지연을 고려한 양방향 원격제어시스템의 동기화를 제시하고 있다. 일반적으로 양방향 원격제어시스템에서는 안정성 및 투명성을 주 목표로 한다. 마스터와 슬레이브 사이에 시간지연이 존재하는 경우 시스템의 안정성을 보장하고, 유전자 알고리즘을 이용하여 동기화 제어법칙의 파라미터를 최적화하고자 한다.

• 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.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2002년도 춘계학술대회논문집
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• pp.261-266
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• 2002

In this Paper, we Propose model-based tuning rules of the PID controller incorporating with genetic algorithms. Three sets of optimal PID parameters for step set-point tracking are obtained based on the first-order time delay model of plants and a genetic algorithm which minimizes performance indices(IAE, ISE and ITAE). Then tuning rules are obtained using the tuned parameter sets, potential rule models and a genetic algorithm. Simulation is carried out to verify the effectiveness of the proposed rules.

• 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 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.252-254
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• 2006

The recent improvement in the performance of digital processor, the application of control technology, which used in the HVDC(High Voltage Direct Current) system with the digital processors, has increased. Having this research development as the basis, this paper presents an achievement of progression by tuning the parameter of PI controller based on Genetic Algorithms(GAs) and by controlling with PI controller with a developed simulator by applying the Matrix operating function, voltage source switching element, modified nodal analysis which can include transformer and the backward Euler which does not create the problem of numerical oscillation. As a result, I expect this development in the simulator HVDC System to bring more application in the field of control technology research with an expanded practicality.

• Asian-Australasian Journal of Animal Sciences
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• 제13권8호
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• pp.1035-1039
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• 2000

This study developed a Poisson generalized linear mixed model and a procedure to estimate genetic parameters for count traits. The method derived from a frequentist perspective was based on hierarchical likelihood, and the maximum adjusted profile hierarchical likelihood was employed to estimate dispersion parameters of genetic random effects. Current approach is a generalization of Henderson's method to non-normal data, and was applied to simulated data. Underestimation was observed in the genetic variance component estimates for the data simulated with large heritability by using the Poisson generalized linear mixed model and the corresponding maximum adjusted profile hierarchical likelihood. However, the current method fitted the data generated with small heritability better than those generated with large heritability.

• 한국국방경영분석학회지
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• 제25권2호
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• pp.106-116
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• 1999

The purpose of this study is to present a method for determining the k most vital arcs in the maximum flow problem using genetic algorithms. Generally, the problem which determine the k most vital arcs in maximum flow problem has known as NP-hard. Therefore, in this study we propose a method for determining all the k most vital arcs in maximum flow problem using genetic algorithms. First, we propose a genetic algorithm to find the k most vital arcs removed at the same time and then present the expression and determination method of individuals compatible with the characteristics of the problem, and specify the genetic parameter values of constitution, population size, crossover rate, mutation rate and etc. of the initial population which makes detecting efficiency better. Finally, using the proposed algorithms, we analyzed the performance of searching optimal solution through computer experiment. The proposed algorithms found all alternatives within shorter time than other heuristic methods. The method presented in this study can determine all the alternatives when there exists other alternative solutions.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.