• 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 Biosystems Engineering
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• v.20 no.1
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• pp.87-94
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• 1995

When farmer loads and unloads farm products with a trailer, linked to a tractor, the tractor-trailer is backed up to the loading duck. However, travelling backward is not easy and takes a time for even skilled operators. Therefore, unmanned backing up is necessary to save the effort. A backward controller of tractor-trailer was simulated using fuzzy logic and genetic algorithms. Operators drive the tractor-trailer back and forth several times for backing up to the loading duck. As the operators did it, a backward controller was designed using fuzzy logic. And genetic algorithms was applied to improve the performance of the backward controller. With the strings coded with the fuzzy membership functions, genetic operations were carried out. After 30 generations, the best fitted fuzzy membership functions were found. Those membership functions were used in the fuzzy backward controller. The fuzzy controller combined with genetic algorithms showed the better results than the fuzzy controller did alone.

• Genomics & Informatics
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• v.16 no.4
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• pp.39.1-39.3
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• 2018

The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset-namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.123-128
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• 2002

A fuzzy traffic controller with the control rules and the membership functions generated by using genetic algorithm is presented for crossroad management. Conventional fuzzy traffic controllers use control rules and membership functions generated by human operators. However, this approach does not guarantee the optimal solution to design fuzzy control system. Genetic algorithm is a good solution for an optimal problem requiring domain-specific knowledge that is often heuristic. In this paper, we use genetic algorithms to automatically determine the near optimal rules and their membership functions of fuzzy traffic controllers. The effectiveness of our method was shown through simulation of crossroad network.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.119-126
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• 2002

In this paper, we present a new approach based on an elitist genetic algorithm for the task of aligning the position of a robot gripper using CCD cameras. The vision-based control scheme for the task of aligning the gripper with the desired position is implemented by image information. The relationship between the camera space location and the robot joint coordinates is estimated using a camera-space parameter modal that generalizes known manipulator kinematics to accommodate unknown relative camera position and orientation. To find the joint angles of a robot manipulator for reaching the target position in the image space, we apply an elitist genetic algorithm instead of a nonlinear least square error method. Since GA employs parallel search, it has good performance in solving optimization problems. In order to improve convergence speed, the real coding method and geometry constraint conditions are used. Experiments are carried out to exhibit the effectiveness of vision-based control using an elitist genetic algorithm with a real coding method.

• Plant Breeding and Biotechnology
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• v.5 no.4
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• pp.363-370
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• 2017

In this study, Quercus acutissima seedlings were subjected to drought for 30 days then analyzed to determine their response to water deficit. The growth phenotype, chlorophyll fluorescence response, fresh weight, dry weight, photosynthetic pigment levels, soluble sugar content, and malondialdehyde (MDA) were measured to evaluate the effects of drought on plant growth and physiology. The growth phenotype was observed by infrared (IR) digital thermal imaging after 30 days of drought treatment. The maximum, average, and minimum temperatures of drought-treated plant leaves were $1-2^{\circ}C$ higher than those of the control. In contrast, the fresh and dry weights of the dehydrated leaves were generally lower than those of the control. There were no significant differences between treatments in terms of chlorophyll a, chlorophyll b, total chlorophyll, and carotenoid levels. Nevertheless, for the drought treatment, the $F_v/F_m$ and $F_v/F_o$ ratios (chlorophyll fluorescence response) were lower than those for the control. Therefore, photosynthetic activity was lower in the dehydrated plants than the control. The drought-stressed Q. acutissima S0536 had lower soluble sugar (glucose and fructose) and higher MDA levels than the controls. These findings may explain the early growth and physiological responses of Q. acutissima to dehydration and facilitate the selection of drought-resistant tree families.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.212-219
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• 2006

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• The Transactions of the Korea Information Processing Society
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• v.1 no.3
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• pp.319-326
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• 1994

Automatic control has been for the most part applied to linear systems where ti can be approximately formalized. In case that it is not definitely established the mathematical modelling to control objects, it requires manual control strategies which put under the human rule. In this paper, it constructs an FLC (Fuzzy Logic Controller) in order to turn a hand control into an automatic control in the domain of swimming pool that has been almost absolutely dependant on a skilled worker's experience. Genetic algorithms upgrade the knowledge which is acquired from human expert, using by FLC, so as to maintain knowledge in the very optimal way. It also designs an algorithm that modifies the rule base and the membership function at the same time, and ultimately will show that it can get better result than human controllers.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.127-135
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• 1999

Multivarialbe Boiler-Turbine H_\infty Control System Genetic Algorithm Weighting Functions $W_1$(s), $W_2$(s), and design parameter $\gamma$ that are given by Glover-Doyle algorithm, to optimally follow the output of reference model. The first method to do this is that the gains of weighting functions $W_1$(s), $W_2$(s), and design parameter are optimized simultaneously by genetic algorithm with the tournament method that can search more diversely, in the search domain which guarantees the robust stability of system. And the second method is that not only by genetic algorithm with the roulette-wheel method that can search more fast, in that search domain. The boiler-turbine H_\infty control system designed by theabove second method has not only the robust stability to a modeling error but also the the better command tracking preformance than those of the H_\infty control system designed by trial-and-error method and the above first method. Also, this boiler-turbine H_\infty control system has the better performance than that of the LQG/LTR contro lsystem. The effectiveness of this boiler-turbineH_\infty control system is verified by computer simulation.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.101-107
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• 2010

This paper presents motion control of the precision stage composed of the piezoelectric actuator and flexible hinges. The stage shows approximately 27% overshoot when the stage was applied to 30V square wave input voltage. Also, the stage shows nonlinear response characteristics including hysteresis. This paper proposes feedback control technique to suppress the phenomenon of hysteresis and overshoot using the sliding mode control scheme with the integrator. Also, this paper suggests the method that searches important parameters of sliding mode control and observer using Genetic Algorithm. To demonstrate the effectiveness of the proposed control algorithm, experimental validations are performed.