• Bulletin of the Korean Chemical Society
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• 제31권5호
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• pp.1247-1251
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• 2010

We propose an improved solvent contact model to estimate the solvation free energies of amino acids from individual atomic contributions. The modification of the solvation model involves the optimization of three kinds of parameters in the solvation free energy function: atomic fragmental volume, maximum atomic occupancy, and atomic solvation parameters. All of these atomic parameters for 17 atom types are developed by the operation of a standard genetic algorithm in such a way to minimize the difference between experimental and calculated solvation free energies. The present solvation model is able to predict the experimental solvation free energies of amino acids with the squared correlation coefficients of 0.94 and 0.93 for the parameterization with Gaussian and screened Coulomb potential as the envelope functions, respectively. This result indicates that the improved solvent contact model with the newly developed atomic parameters would be a useful tool for the estimation of the molecular solvation free energy of a protein in aqueous solution.

• 시스템엔지니어링학술지
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• 제12권2호
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• pp.115-125
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• 2016

An improved RRS (Reactor Regulating System) logic is implemented in this work using systems engineering approach along with GA (Genetic Algorithm) deemed as providing an optimal solution to a given system. The current system works desirably and has been contributed to the safe and stable NPP operation. However, during the ascent and decent section of the reactor power, the RRS output reveals a relatively high steady state error and the output also carries a considerable level of overshoot. In an attempt to consolidate conservatism and minimize the error, this research proposes applying genetic algorithm to RRS and suggests reconfiguring the system. Prior to the use of GA, reverse-engineering is implemented to build a Simulink-based RRS model and re-engineering is followed to apply the GA and to produce a newly-configured RRS generating an output that has a reduced steady state error and diminished overshoot level.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1034-1038
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• 2005

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and develops an improved genetic algorithm (IGA) to solve it. Each individual in the IGA represents a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experimental results show that the proposed method leads to a speedup of 83 on $320{\times}240$ images compared to the traditional exhaustive search method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.180.6-180
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• 2001

Welding/soldering automation is one of the most important manufacturing issues in order to lower the cost, increase the quality, and avoid labor problems. An off-line programming, OLP, is one of the powerful methods to solve this kind of diver sity problem, Unless an OLP system is ready for the path optimization in welding/soldering, a waste of time and cost is unavoidable due to an inefficient path in welding/soldering processes. Therefore, this study attempts to obtain path optimization using a genetic algorithm based on artificial intelligences. The problem of the welding path optimization is defined as conventional TSP (traveling salesman problem), but still paths have to go through welding lines. An improved genetic algorithm was suggested and the problem was formulated as a TSP problem considering ...

• 전기학회논문지
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• 제57권5호
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• pp.783-788
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• 2008

This paper presents the mechanism of the machine and the numerical result of attractive force in the Electro-Permanent Magnet Lifter (EPML) and an improved niching Genetic Algorithm (GA) applying the concept of auto-tuning and detecting traces. Population size and both (right and left) niche radii of each peak in an asymmetrical objective function can be determined automatically. The validity of the proposed method is verified by simulation results.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권7호
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• pp.531-535
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• 2004

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. The GA-GRNN was applied to classify 4 different Promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. Compared to conventional GRNN, GA-GRNN significantly improved the total classification sensitivity as well as the total prediction accuracy. As a result, the proposed GA-GRNN demonstrated improved classification sensitivity and prediction accuracy over the convention GRNN.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2451-2453
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• 2002

In this paper, we present an infrared sensors aided map building method for mobile robot using genetic algorithm and fuzzy logic. Existing Bayesian update model using ultrasonic sensors only has a problem of the quality of map being degraded in the wall with irregularity which is caused by the wide beam width of sonar waves and Gaussian probability distribution. In order to solve this problem we propose an improved method of map building using supplementary infrared sensors. In the method, wide beam width of sonar waves is divided by infrared sensors and probability is distributed according to infrared sensors' information using fuzzy logic and genetic algorithm.

• Journal of international Conference on Electrical Machines and Systems
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• 제1권1호
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• pp.135-142
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• 2012

In this paper, optimal selection of the unified power flow controller (UPFC) damping controller parameters in order to improve the power system dynamic response and its stability based on two modified intelligent algorithms have been proposed. These algorithms are based on a modified intelligent particle swarm optimization (PSO) and continuous genetic algorithm (GA). After extraction of UPFC dynamic model, intelligent PSO and genetic algorithms are used to select the effective feedback signal of the damping controller; then, to compare the performance of the proposed UPFC controller in damping the critical modes of a single-machine infinite-bus (SMIB) power system, the simulation results are presented. The comparison shows the good performance of both presented PSO and genetic algorithms in an optimal selection of UPFC damping controller parameters and damping oscillations.

• 한국정보통신학회논문지
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• 제16권5호
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• pp.953-959
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• 2012

센서네트워크에서는 다수의 유휴노드가 존재하며 네트워크의 이상행위 탐지는 이러한 유휴노드를 이용하여 구현될 수 있다. 최적화 문제로 정의된 탐지노드선정 문제에 대하여, 기존의 방법에서는 중앙처리방식의 유전자 알고리즘을 이용하였다. 본 논문에서는 최적 값으로의 수렴 성을 개선함과 동시에 에너지 효율성을 향상시키는 방법으로써 네트워크의 토폴로지 특성을 고려한 병렬유전자알고리즘을 이용한 방법을 제안하였다. 시뮬레이션을 통하여 제안한 방법이 기존의 방법에 비하여 최적 값으로의 수렴이 개선되었음과 에너지 효율적임을 확인하였다.

• Structural Engineering and Mechanics
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• 제68권5호
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• pp.549-561
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• 2018

Sobol method is applied as a powerful variance decomposition technique in the field of global sensitivity analysis (GSA). The paper is devoted to increase convergence speed of the extracted Sobol indices using a new proposed sampling technique called genetic based Latine hypercube sampling (GBLHS). This technique is indeed an improved version of restricted Latine hypercube sampling (LHS) and the optimization algorithm is inspired from genetic algorithm in a new approach. The new approach is based on the optimization of minimax value of LHS arrays using manipulation of array indices as chromosomes in genetic algorithm. The improved Sobol method is implemented to perform factor prioritization and fixing of an uncertain comprehensive high speed rotor-bearing system. The finite element method is employed for rotor-bearing modeling by considering Eshleman-Eubanks assumption and interaction of axial force on the rotor whirling behavior. The performance of the GBLHS technique are compared with the Monte Carlo Simulation (MCS), LHS and Optimized LHS (Minimax. criteria). Comparison of the GBLHS with other techniques demonstrates its capability for increasing convergence speed of the sensitivity indices and improving computational time of the GSA.