• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.89-94
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• 2009

This paper proposes the algorithms that design the highly digital logic circuit and assign the code to each node of DCG(Directed Cyclic Graph) of length ${\zeta}$. The conventional algorithm have some problems, so this paper introduce the matrix equation from DCG of length ${\zeta}$ and proposes highly digital logic circuit design algorithms according to the DCG of length ${\zeta}$. Using the proposed circuit design algorithms in this paper, it become realized that was able to design from former algorithm. Also, making a comparison between the circuit using former algorithm and this paper's, we testify that proposed paper's algorithm is able to realize more optimized circuit design. According to proposed circuit design algorithm in this paper, it is possible to design current that DCG have natural number, so it have the following advantages, reduction of the circuit input/output digits, simplification of circuit composition, reduction of computation time and cost. And we show comparability and verification about this paper's algorithm.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.20-29
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• 1998

This paper proposes the algorithms that design the highly parallel multiple-valued logic curcuit and assign the code to each node of DCG(Directed Cyclic Graph) of length 1. The conventional Nakajima's algorithm have some problems, so this paper introduce the matrix equation from DCG of length 1 and proposes circuit design algorithms according to the DCG of length 1. Using the proposed circuit design algorithms in this paper, it become realized that was not able to design from Nakajima's algorithm. Also, making a comparision between the circuit design using Nakajima's algorithm and this paper's, we testify that proposed paper's algorithm is able to realize more optimized circuit design. According to proposed curcuit design algorithm in this paper, it is possible to design curcuit that DCG have natural number, so it have the following advantages; reduction of the curcuit input/output digits, simplification of curcuit composition, reduction of computation time and cost. And we show compatibility and verification about this paper's algorithm.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.234-237
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• 1987

Recently various control algorithms for industrial manipulators have been proposed. However, computation time, modelling error, and torque type controller design have prevented real-time implementation. As the result, most of performance evaluations of control algorithms have been carried out only by computer simulations. In this paper, we explore real-time implementation to show the feasibility and effectiveness of such algorithms. Experimental results indicate that computed torque method and learning control algorithms can be effectively applied to control industrial manipulators.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.2
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• pp.131-137
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• 2001

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on only particular domian. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. This paper describes the application of random signal-based learning to a genetic algorithm in order to get well tuned fuzzy rules. The key of tis approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.169-178
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• 2011

In this study, a forecasting engine from the user perspective is studied and developed. Characteristics of forecasting engine can be divided into a few categories, an algorithms for predicting variety of situations and the depth of algorithms based on the number and the types of data. Then applying a variety of algorithms that most closely match the predicted values for the actual value that deduce criteria for selecting an appropriate forecasting algorithm is to organize. Through the forecast quality assessment, the suggested distribution algorithm compared to the existing demand forecast algorithms is good indicators for its accuracy.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1996.04a
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• pp.125-129
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• 1996

This paper deals with the problem of placement/sizing of distributed piezo actuators to achieve the control objective of vibration suppression. Using the mean square response as a performance index in optimization, we obtain optimal placement and sizing of the actuator. The use of genetic algorithms as a technique for solving optimization problems of placement and sizing is explored. Genetic algorithms are also used for the control strategy. The analysis of the system and response moment equations are carried out by using the Fokker-Planck equation. This paper presents the design and analysis of an active controller and optimal placement/sizing of distributed piezo actuators based on genetic algorithms for a flexible structure under random disturbance, shows numerical example and the result.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.1
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• pp.57-69
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• 2016

There are three kinds of algorithms(2-ARE, mu-synthesis, LMI) for controller design using closed-loop shaping method. This paper provides the summary of background theory of three algorithms and $H_{\infty}$ controller design results for spacecraft attitude control using the three controller design tools of Matlab$^{TM}$ Toolbox for comparison. As a result, it reveals that LMI design method is more reliable as well as easier than others for spacecraft attitude control design. Comparison results are as follow: 2-ARE method and LMI method provide almost same results in robust stability, robust performance and control authority level. But 2-ARE method is more sensitive than LMI method with respect to proper design of weighting functions: 2-ARE method is more difficult than LMI method in weighting function design. The design result of mu-synthesis method shows worse performance and requires bigger control authority than others.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.41
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• pp.15-23
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• 1997

Because of various request of consummer and rapidly chang, a product change and new-production come out variously. To satisfy the condition, companies must develop the product of rapidity and good quality. But, a product design difficults to consider many parameters and increase the level of each parameter. In order to solve this problem, this paper studies out algorithms taken into account more parameters and increased the level of parameters using the Genetic Algorithms. Because this algorithm can search detailed and wide for the level of parameter, in case of new-product development, we can use it for designing parameters of new-product.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.279-281
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• 2006

In this paper, we study an approach to design a self-tuning Fuzzy-PI controller in HVDC(High Voltage Direct Current) system. In the rectifier of conversional HVDC system, turning on, turning off, triggering and protections of thyristors have lots of problems that can make the dynamic instability and cannot damp the dynamic disturbance efficiently. The above problems are solved by adapting Fuzzy-PI controller for the fire angle control of rectifier.[7] The performance of the Fuzzy-PI controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing(Genetic Algorithms, GAs). Then we can obtain the optimal scaling factors of the Fuzzy-PI controller by Genetic Algorithms. In order to improve Fuzzy-PI controller, we adopt FIS to tune the scaling factors of the Fuzzy-PI controller on line. A comparative study has been performed between Fuzzy-PI and self-tuning Fuzzy-PI controller, to prove the superiority of the proposed scheme.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.155-157
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• 2005

We propose a new category of hybrid multi-layer neural networks with hetero nodes such as Fuzzy Set based Polynomial Neurons (FSPNs) and Polynomial Neurons (PNs). These networks are based on a genetically optimized multi-layer perceptron. We develop a comprehensive design methodology involving mechanisms of genetic optimization and genetic algorithms, in particular. The augmented genetically optimized HFPNN (namely gHFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of HFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFPNNs quantified through experimentation where we use a number of modeling benchmarks-synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.