• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.7
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• pp.863-871
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• 2007

This paper proposes a methodology to estimate the system coefficients for the infinite impulse response(IIR) digital filters using real code GA. In the traditional real coded GA, it adapts the general genetic operations, whereas in this paper the proposed real coded GA applies improved genetic operations in order to search the optimal solution in given problems. Each of unknown IIR digital coefficients collected as forms of a chromosome. Two illustrative examples including the band pass and band stop IIR digital filters are demonstrated to verify the proposed method.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.56-63
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• 2015

The optimization is an important role in wide geographical distribution of electrical power market, finding the optimum solution for the operation and design of power systems has become a necessity with the increasing cost of raw materials, depleting energy resources and the ever growing demand for electrical energy. In this paper, the real coded biogeography based optimization is proposed to minimize the operating cost with optimal setting of equality and inequality constraints of thermal power system. The proposed technique aims to improve the real coded searing ability, unravel the prematurity of solution and enhance the population assortment of the biogeography based optimization algorithm by using adaptive Gaussian mutation. This algorithm is demonstrated on the standard IEEE-30 bus system and the comparative results are made with existing population based methods.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1056-1060
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• 2002

Model-based tuning rules of the PID controller are proposed incorporating with real-coded genetic algorithms. The optimal parameter sets of the PID controller for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controllers, performance indices(ISE, IAE and ITAE) are adopted. Then tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm A set of simulation works is carried out to verify the effectiveness of the proposed rules.

• Journal of Ship and Ocean Technology
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• v.9 no.4
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• pp.35-46
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• 2005

The performance of optimisation methods, based on penalty functions, is highly problem- dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm (R$\mu$GA) is proposed to find the global optimum of continuous and/or discrete nonlinear constrained engineering problems without handling any of penalty functions. R$\mu$GA can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. The proposed R$\mu$GA approach has been demonstrated by solving three different engineering design problems. From the simulation results, it has been concluded that R$\mu$GA is an effective global optimisation tool for solving continuous and/or discrete nonlinear constrained real­world optimisation problems.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.5
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• pp.785-790
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• 2008

A technique for determining the feedback gain of the states feedback controller using a real-coded genetic algorithm(RCGA) is presented. It is concerned with the states error to the performance index of a RCGA. As for assessing the performance of the controller three performance criteria (ISE. IAE and ITAE) are adopted. And designing the controller involves a constrained optimization problem. Therefore a real-coded genetic algorithm incorporating the penalty strategy is used. The performance of the proposed method is demonstrated through a set of simulation about an inverted pendulum system.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.5
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• pp.599-605
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• 2007

This paper presents a method for identifying various systems based on input-output data and a real-coded genetic algorithm(RCGA). The advantages of this technique are, first, it is not dependent on the deterministic or stochastic nature of the systems and, second, the globally optimized models for the original systems can be identified without the need of a differentiable measure function of linearly separable parameters. Under suitable hypotheses, the estimation error is shown to converge in probability to zero. The performance of the proposed algorithm is demonstrated through several simulations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.727-731
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• 2005

We introduce a method of color-coded illumination that is simple and fast. The purpose of this study is the real-time shape measurement of three-dimensional object by using color-coded information. The object is illuminated by a prism color spectrum. A color spectrum of a white-light source is imaged onto the object by illumination from one certain direction. The object is observed by a color CCD camera from a direction of observation, which is different from the direction of illumination. It can be evaluated by the red, blue, green using a inherence colors of hue value are good point.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.184-191
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• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.4
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• pp.648-661
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• 1997

This paper presents a new method for post-processing of coded images based upon the low-pass filtering followed by the projection onto the NQCS (narrow quantization constraint set). We also investigate how the proposed method works on JPEG-coded real images. The starting point of the QCS-based post-processing techniques is the centroid of the QCS, where the original image belongs. The low-pass filtering followed by the projection onto the QCS makes the images lie on the boundary of the QCS. It is likely that, however, the original image is inside the QCS. Hence projection onto the NQCS gives a lower MSE (mean square error) than does the projection onto the QCS. Simulation results show that setting the narrowing coefficients of the NQCS to be 0.2 yields the best performance in most cases. Even though the JPEG-coded image is low-pass filtered and projected onto the NQCS repeatedly, there is no guarantee that the resultant image has a lower MSE and goes closer to the original image. Thus only one iteration is sufficient for the post-processing of the coded images. This is interesting because the main drawback of the iterative post-processing techniques is the heavy computational burden. The single iteration method reduces the computational burden and gives us an easy way to implement the real time VLSI post-processor.