• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.704-711
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• 1999

This paper presents a design of fuzzy power system stabilizer (FPSS) using adaptive evolutionary computation (AEC). We have proposed an adaptive evolutionary algorithm which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations. FPSS shows better control performances than conventional power system stabilizer (CPSS) in three-phase fault with heavy load which is used when tuning FPSS. To show the robustness of the proposed FPSS, it is appliedto damp the low frequency oscillations caused by disturbances such as three-phase fault with normal and light load, the angle deviation of generator with normal and light load and the angle deviation of generator with heavy load. Proposed FPSS shows better robustness than CPSS.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1189-1195
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• 2004

This paper investigates construction of gene (interaction) networks from gene expression time-series data based on evolutionary computation. To illustrate the proposed approach in a comprehensive way, we first assume an artificial gene network and then compare it with the reconstructed network from the gene expression time-series data generated by the artificial network. Next, we employ real gene expression time-series data (Spellman's yeast data) to construct a gene network by applying the proposed approach. From these experiments, we find that the proposed approach can be used as a useful tool for discovering the structure of a gene network as well as the corresponding relations among genes. The constructed gene network can further provide biologists with information to generate/test new hypotheses and ultimately to unravel the gene functions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.643-646
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• 2002

This paper proposes an evolutionary computation approach for automated design of mechanical structures especially in its early stage of design phases. Due to the known characteristics of the stage, the approach basically involves a synthetic design method with the composition of building blocks representing the elements of mechanical objects. In order for the building blocks to be more suitable for representation and evolution of mechanical structures, Elementary Cell Blocks (ECBs) are introduced as new building blocks. A new Darwinian evolution process for the new building blocks is also necessarily involved in the approach. We have demonstrated the implementation of the approach with the design of multi-step gear systems.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1124-1129
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• 2003

In mechanical design, design process is mainly composed of design, explanation and evaluation. In this paper, Using Genetic Algorithms (GA), Evolutionary computation is introduced as new design process. This method promote the efficiency and power of design. Due to the known characteristics of the stage, the approach basically involves a synthetic design method with the composition of building blocks representing the elements of mechanical objects. In order for the building blocks to be more suitable for representation and evolution of mechanical structures, Elementary Cell Blocks (ECBs) are introduced as new building blocks. In this paper, we have demonstrated the implementation of the approach with the design of gear systems.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1071-1076
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• 2004

It is a fuzzy controller that it is the most used method in the control of non-linear system. The most important part in the fuzzy controller is a design of fuzzy rules. Many algorithm that design fuzzy rules have proposed. And attention to the evolutionary computation is increasing in the recent days. Among them, the co-evolutionary algorithm is used in the design of optimal fuzzy rule. This paper takes advantage of a schema co-evolutionary algorithm. In order to verify the efficiency of the schema co-evolutionary algorithm, a fuzzy controller for the mobile robot control is designed by the schema co-evolutionary algorithm and it is compared with other parasitic co-evolutionary algorithm such as a virus-evolutionary genetic algorithm and a co-evolutionary method of Handa.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.317-324
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• 2005

Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how In its own. It is very useful to extract knowledge or information from the accumulated existing data by using datamining technique. This paper treats an evolutionary computation method based on genetic programming (GP), which can be one of the components to realize datamining.

• Journal of The Korean Astronomical Society
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• v.48 no.3
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• pp.165-175
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• 2015

We investigate physical properties of the nearby (∼ 7.5 pc) astrometric binary μ Cas in the context of standard evolutionary theory. Based on the spectroscopically determined relative abundances ([α/Fe] ≳ +0.4 dex, [Fe/H] ∼ −0.7 dex), all physical inputs such as opacities and equation of state are consistently generated. By combining recent spectroscopic analyses with the astrometric observations from the HIPPARCOS parallaxes and the CHARA array, the evolutionary model grids have been constructed. Through the statistical evaluation of the χ²-minimization among alternative models, we find a reliable evolutionary solution (M_A, M_B, t_age) = (0.74 M_⊙, 0.19 M_⊙, 11 Gyr) which excellently satisfies observational constraints. In particular, we find that the helium abundance of μ Cas is comparable with the primordial helium contents (Y_p ∼ 0.245). On the basis of the well-defined stellar parameters of the primary star, the internal structure and the p-mode frequencies have been estimated. From our seismic computation, μ Cas is expected to have a first order spacing ∆ν ∼ 169 μHz. The ultimate goal of this study is to describe physical processes inside a low-mass star through a complete modelling from the spectroscopic observation to the evolutionary computation.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.37-47
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• 1998

This paper proposes a controller design method using the evolutionary computation algorithm and the fuzzy logic to control the wafer temperature in rapid thermal processing. First, we design the feedforward static controller to provide the control powers of the lamps for the given steady state temperature. Second, the feedforward dynamic controller is designed for the additional control powers to achieve a given transient response. These feedforward controllers are implemented by using the fuzzy logic to act as a global nonlinear controller over a wide range of operating points. The parameters of these controllers are optimized by using the evolutionary computation algorithm so that it can be used when the mathematical model is not available. In addition, the feedback error controller is introduced to compensate the feedforward controllers when there exist disturbances and modeling errors. The gain of feedback error controller is also obtained by the evolutionary computation algorithm. Through simulations, we verify the proposed control system can give a satisfactory performance.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.490-492
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• 1998

In this paper, fuzzy-PD control system is designed to control angle and position of the inverted pendulum. To optimize parameters of fuzzy-PD controller, we used adaptive evolutionary computation(AEC). AEC uses a Genetic A1gorithm(GA) and an Evolution Strategy(ES) in an adaptive manner in order to take merits of two different evolutionary computations.