• Journal of Animal Science and Technology
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• v.53 no.6
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• pp.505-509
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• 2011

This experiment was conducted to estimate the genetic parameters and breeding values of the economic traits measured from the cows (aged 15 months or older) using ultrasound and to use them as the information for the selection of stock animals at the farm level. The means and standard deviations of longissimus muscle area, backfat thickness and marbling score were $54.11\;cm^2{\pm}9.06$, $3.57\;mm{\pm}2.45$ and $2.65{\pm}2.88$, respectively. While the linear regression coefficients of longissimus muscle area, backfat thickness and marbling score for age (in months) were all positive (0.3532, 0.0868 and 0.0833), the quadratic regression coefficients of them for age (in months) were all negative (-0.0023, -0.0005 and -0.0006), and as the body condition score increased longissimus muscle area, backfat thickness and marbling score increased collectively. The heritability estimates for the longissimus muscle area, backfat thickness and marbling score were 0.39, 0.48 and 0.13, respectively and the estimated annual genetic gains for the longissimus muscle area, backfat thickness and marbling score were 0.00334 $cm^2$, -0.0073 mm and 0.0043 score, respectively, which were not significantly different from zero.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.474-478
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• 1998

This paper proposes a method which can resolve the problem of exisiting fuzzy PI controller using optimal scaling gains obtained by genetic algorithm. The new method adapt a fuzzy logic controller as a high level controller to perform scaling gain algorithm between two pre-determined sets.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.257-266
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• 1999

The aim of this paper is to suggest a design method of the optimal multivariable $H_{\infty}$ control system using genetic algorithm (GA). This $H_{\infty}$ control system is designed by applying GA to the optimal determination of weighting functions and design parameter $\gamma$ that are given by Glover-Doyle algorithm which can design $H_{\infty}$ controller in the state space. The first method to do this is that the gains of weighting functions and $\gamma$ are optimized simultaneously by GA with tournament method. And the second method is that not only the gains and $\gamma$ but also the dynamics of weighting functions are optimized at the same time by eA with roulette-wheel method. The effectiveness of this $H_{\infty}$ control system is verified by computer simulation.

• Nuclear Engineering and Technology
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• v.52 no.3
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• pp.544-551
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• 2020

Reactor control is a standout amongst the most vital issues in the nuclear power plant. In this paper, the optimal design of variable structure controller (VSC) based on the lightning search algorithm (LSA) is proposed for a nuclear reactor power system. The LSA is a new optimization algorithm. It is used to find the optimal parameters of the VSC instead of the trial and error method or experts of the designer. The proposed algorithm is used for the tuning of the feedback gains and the sliding equation gains of the VSC to prove a good performance. Furthermore, the parameters of the VSC are tuned by the genetic algorithm (GA). Simulation tests are carried out to verify the performance and robustness of the proposed LSA-based VSC compared with GA-based VSC. The results prove the high performance and the superiority of VSC based on LSA compared with VSC based on GA.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.924-926
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• 1999

To reduce chattering in sliding mode control, a boundary layer around the sliding surface is used, and a continuous control is applied within the boundary. In this paper, a method of determining the sliding mode controller switching gains and the width of boundary layer is presented. Contrary to the trial and error selection of the switching gains and the width of boundary layer, the selection in the presented work is done using genetic algorithms. Simulation results show that the system performance has been improved.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1327-1329
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• 2005

This paper presents a gam optimization method using genetic algorithm(GA) for a magnetic levitation(Maglev) controller. GA uses the integral of square error(ISE) as performance index. The plant dynamics are described and modelled by mathematical equations. Also, the system apparatus for the Maglev system are described. Using the derived model, to optimize the feedback gains of conventional state feedback controller(SFC), GA is simulated with SIMULINK model. finally, using the optimized feedback gains, SFC is applied to the Maglev system. From the results, we can see that GA can give a solution for the better control performance for the Maglev system.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1041-1062
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• 2015

In this paper a method of finding optimal positions for piezoelectric actuators and sensors on different structures is presented. The genetic algorithm and multi-objective genetic algorithm are selected for optimization and $H_{\infty}$ norm is defined as a cost function for the optimization process. To optimize the placement concerning the selected modes simultaneously, the multi-objective genetic algorithm is used. The optimization is investigated for two different structures: a cantilever beam and a simply supported plate. Vibrating structures are controlled in a closed loop with feedback gains, which are obtained using optimal LQ control strategy. Finally, output of a structure with optimized placement is compared with the output of the structure with an arbitrary, non-optimal placement of piezoelectric patches.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.11
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• pp.1013-1021
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• 2000

This paper suggests fuzzy-PI controllers for a heavy-duty gas turbine. The fuzzy-PI controllers are designed to regulate rotor speed and exhaust temperature of the gas turbine. The controller gains are tuned by genetic algorithm(GA). This paper also proposes a new fitness function of GA using a desired output response. The suggested controller is compared with previous controllers via simulations and it is shown that the rotor speed variation of our controller is smaller than those of previous ones.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2268-2271
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• 1998

This paper proposes a method which can resolve the problem of existing fuzzy Pl controller using optimal scaling gains obtained by genetic algorithm. The new method adapt a fuzzy logic controller as a high level controller to perform scaling gain algorithm between two pre-determined sets.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.686-689
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• 2004

Genetic algorithm is well known for stochastic searching method in imitating natural phenomena. In recent times, studies have been conducted in improving conventional evolutionary computation speed and promoting precision. This paper presents an approach to optimize PID controller gains with the application of modified Genetic Algorithm using Support Vector Machine (SVMGA). That is, we aim to explore optimum parameters of PID controller using SVMGA. Simulation results are given to compare to those of tuning methods, based on Simple Genetic Algorithm and Ziegler-Nicholas tuning method.