• Journal of Korean Society of Steel Construction
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• v.12 no.5 s.48
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• pp.503-513
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• 2000

This study treats the criteria, considering the fuzziness occurred by optimization design. And we applied two weighting methods to show the relative importance of criteria. This study develops multi-objective optimization programs implementing plain stress analysis by FEM and discrete optimization design uniformaly. The developed program performs a sample design of 10-member steel truss. This study can carry over the multi-objective optimization based on total system fuzzy-genetic algorithms while performing the stress analysis and optimization design. Especially, when general optimization with unreliable constraints is cannot be solve this study can make optimization design closed to realistic with fuzzy theory.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.419-426
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• 2018

An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.171-179
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• 2004

The purpose of this study is to generate cycling motion for FES (functional electrical stimulation) using knee muscles only. We investigated the possibility by simulation. The musculoskeletal model used in this simulation was simplified as 5-rigid links and 2 muscles (knee extensor and flexor). For the improvement of the present feedforward control in FES, we included feedback path in the control system. The control system was developed based on the biological neuronal system and was represented by three sub-systems. The first is a higher neuronal system that generates the motion command for each joint. The second is the lower neuronal system that divides the motion command to each muscle. And the third is a sensory feedback system corresponding to the somatic sensory system. Control system parameters were adjusted by a genetic algorithm (GA) based on the natural selection theory. GA searched the better parameters in terms of the cost function where the energy consumption, muscle force smoothness, and the cycling speed of each parameter set (individual) are evaluated. As a result, cycling was implemented using knee muscles only. The proposed control system based on the nervous system model worked well even with disturbances.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.6
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• pp.133-149
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• 2017

As cities face increasing problems such as aging, environmental pollution and growth limits, we have been trying to incorporate sustainability into urban planning and related policies. However, it is very difficult to generate a 'sustainable spatial plans' because there are trade-offs among environmental, society, and economic values. This is a kind of non-linear problem, and has limitations to be solved by existing qualitative expert knowledge. Many researches from abroad have used the meta heuristic optimization algorithms such as Genetic Algorithms(GAs), Simulated Annealing(SA), Ant Colony Optimization(ACO) and so on to synthesize competing values in spaces. GAs is the most frequently applied theory and have been known to produce 'good-enough plans' in a reasonable time. Therefore we collected the research on 'spatial optimization model based GAs' and analyzed in terms of 'study area', 'optimization objective', 'fitness function', and 'effectiveness/efficiency'. We expect the results of this study can suggest that 'what problems the spatial optimization model can be applied to' and 'linkage possibility with existing planning methodology'.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.5
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• pp.913-921
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• 2009

This paper suggests a deign method of the model-following $H{\infty}$ control system having robust performance. This $H{\infty}$ control system is designed by applying genetic algorithm(GA) with reference model to the optimal determination of weighting functions and design parameter ${\gamma}$ that are given by $H{\infty}$ control theory. These weighting functions and design parameter ${\gamma}$ are optimized simultaneously in the search domain guaranteeing the robust performance of closed-loop system. The effectiveness of this $H_{\infty}$ control system is verified by computer simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.12-18
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• 2010

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.2
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• pp.77-84
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• 1998

Speed control systems of the governor in power station used in this study is organized by the regulator (PID controller), actuator and turbine. Considering parameter uncertainties and disturbances in this system, the performance may not be achieved by the PID control. Therefore, a design technique is necessary that accomplish the desired system performance tolerance in despite of plant uncertainty i\I1d disturbances. In this study, we used QFT(Quantitative Feedback Theory) to provide stable operation in power plant and presented the genetic algorithm for loop shaping approximation technique of QFT. And we designed speed control systems for the governor using the above approach.proach.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.4
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• pp.106-113
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• 1998

The steam generator waste level control system in a nuclear power station has difficulty in its mathematical modeling and theoretical application in both a transient and steady state operation. Therefore, the stability problem of the conventional control methods brings many researches interests to the various methods of a system design in recent years. In this study, an improved loop shaping approach is proposed by applying the genetic algorithm to QFT (Quantitative Feedback Theory) in designing a control system in order to the performance of the system. And the effects of the proposed methods are shown by the simulation results.

• Journal of Power Electronics
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• v.15 no.4
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• pp.1119-1130
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• 2015

This study investigates the multi-objective fuzzy optimization of crowbar resistance for the doubly fed induction generator (DFIG) low-voltage ride-through (LVRT). By integrating the crowbar resistance of the crowbar circuit as a decision variable, a multi-objective model for crowbar resistance value optimization has been established to minimize rotor overcurrent and to simultaneously reduce the DFIG reactive power absorbed from the grid during the process of LVRT. A multi-objective genetic algorithm (MOGA) is applied to solve this optimization problem. In the proposed GA, the value of the crowbar resistance is represented by floating-point numbers in the GA population. The MOGA emphasizes the non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy-set-theory-based is employed to obtain the best solution. The proposed approach has been evaluated on a 3 MW DFIG LVRT. Simulation results show the effectiveness of the proposed approach for solving the crowbar resistance multi-objective optimization problem in the DFIG LVRT.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.4
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• pp.286-289
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• 2019

In this paper, we proposed a method to control input powers and receiver loads for maximum efficiency in multiple-input multiple-output(MIMO) wireless power transfer(WPT) systems. The input voltage ratio between transmitters and receiver loads for maximum transfer efficiency is derived in terms of figure of merits. The theoretically derived input voltages for the transmitters and optimum loads for the receivers were found to be similar to those obtained by a genetic algorithm. We demonstrate the effectiveness of the theory using a few design examples. Using the results obtained from this study, effective and simplified designs of MIMO WPT systems will be possible.