• Journal of Power System Engineering
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• v.22 no.6
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• pp.80-86
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• 2018

A damper is a hydraulic device designed to absorb or eliminate shock impulses which is acting on the sprung mass of car body. It converts the kinetic energy of the shock into another form of energy, typically heat. The main mechanism for providing damping is by shearing the hydraulic fluid as it flows through restrictions. Since the damping mechanism depends on the flow restrictions, these restrictions are very important in damper design. Damper engineers often try several combinations of valve shims, piston orifices and bleed orifices before finding the best combination for a particular setup on a car. Therefore, the ability to tune a damper properly without testing is of great interest in damper design. For this reason, many previous researches have been done on modeling and simulation of the damper. This paper explains a genetic algorithm method to find the optimal parameters for the design objective and the simulation results agree well with the targeted damping characteristics.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.239-245
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• 2012

This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.310-312
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• 1996

In this paper, we propose a design method for nonlinear SISO system using Takagi-Sugeno fuzzy model and Genetic Algorithm. Our method can reduce the number of design parameters and has advantage of small search space of Genetic Algorithm. The proposed nonlinear controller, which can be implemented by fuzzy controller and simple nonlinear controller, cancels the original nonlinear dynamics and gives the optimal nonlinear dynamics. We illustrated the performance of the proposed controller by simple simulation example.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.436-443
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• 2007

In this paper, an intelligent method called BELBIC (Brain Emotional Learning Based Intelligent Controller) is used to control of Locally Linear Neuro-Fuzzy Model (LOLIMOT) of Washing Machine. The Locally Linear Neuro-Fuzzy Model of Washing Machine is obtained based on previously extracted data. One of the important issues in using BELBIC is its parameters setting. On the other hand, the controller design for Washing Machine is a multi objective problem. Indeed, the two objectives, energy consumption and effectiveness of washing process, are main issues in this problem, and these two objectives are in contrast. Due to these challenges, a Multi Objective Genetic Algorithm is used for tuning the BELBIC parameters. The algorithm provides a set of non-dominated set points rather than a single point, so the designer has the advantage of selecting the desired set point. With considering the proper parameters after using additional assumptions, the simulation results show that this controller with optimal parameters has very good performance and considerable saving in energy consumption.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.61-66
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• 2002

A position control algorithm for a flexible manipulator is studied. The proposed algorithm is based on a fuzzy theory with a Steady State Genetic Algorithm(SSGA) and an Adaptive Genetic Algorithms(AGA). The proposed controller for a flexible manipulator have decreased 90.8%, 31.8%, 31.3% in error when compared with a conventional fuzzy controller, fuzzy controller using neural network, fuzzy controller using evolution strategies, respectively when the weight and the velocity of end-point are 0.8k9 and 1m/s, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.180-184
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• 2009

The memory is very sensitive to the soft error because the integration of the memory increases under low power environment. Error correcting codes (ECCs) are commonly used to protect against the soft errors. This paper proposes a new genetic ECC design method which reduces power consumption. Power is minimized using the degrees of freedom in selecting the parity check matrix of the ECCs. Therefore, the genetic algorithm which has the novel genetic operators tailored for this formulation is employed to solve the non-linear power optimization problem. Experiments are performed with Hamming code and Hsiao code to illustrate the performance of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.341-346
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• 2004

Nowadays, versatile robots are developed around the world. Novel algorithms are needed for controlling such robots. A 2-Layer fuzzy controller can deal with many inputs as well as many outputs, and its overall structure is much simpler than that of a general fuzzy controller. The main problem encountered in fuzzy control is the design of the fuzzy controller. In this paper, the fuzzy controller is designed by the schema co-evolutionary algorithm. This algorithm can quickly and easily find a global solution. Therefore, the schema co-evolutionary algorithm is used to design a 2-layer fuzzy controller in this study. We apply it to a mobile robot and verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm through the experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.137-138
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• 2006

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.90-96
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• 1996

This paper presents a method that determines the stern profile dimensions for full, slwo-speed ship using fuzzy modeling, which is applied the genetic algorithm and the Hooke & Jeeves method. The infreed stern profile dimensions have compared with real ships.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.