• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.738-745
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• 2001

In this paper, when the moving object move to the three-dimentional space, the tracking system track the moving object using the fuzzy reasoning. The joint angle el of the manipulator rotate from $0^{\circ}\; to\; 360^{\circ}$ , and the joint angle $\theta_2$rotate from$0^{\circ}\; to\; 360^{\circ}$. The fuzzy singleton is used for fuzzification and the control rule is twenty five and the fuzzy inference method is simplified Mamdani's reasoning and the defuzzification is the SCOG(Simplified Center Of Gravity) of the fuzzy controller To measure of the performance of the designed system, the fuzzy controller is compared with the CTM(Computed Torque Method) controller at the same condition. when the disturbance torque is ON, the both of CTM and fuzzy controller tracked object without error, However, the disturbance torque changed 0.4N, the CTM controller is 10 times greater than fuzzy controller at the sum of absolute error difference. The designed system is showed it's robustness against with disturbance.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.3
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• pp.381-386
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• 2015

In recent, many researches have been studying sensitivity and psychology of human being on color and the necessity of psychology therapy by color. Among them, a picture of children can be a tool to represent their emotion. Information of colors and direction on a child's picture often represent his internal psychological states unconsciously and is different from the brightness of a color. In this paper, we propose a method to extract domain colors by color classification and subdivision the classes of brightness using fuzzy inference. In addition, it is shown that our method is used for analysing the psychology status of children through their pictures.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2447-2456
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• 1998

Edge detection in the presence of noise is a well-known problem. this pper atempts to implement edge detection algorithms using fuzzy reasoning of fuzzy membership models. It examines an application-motived approach for solving the problem. Our approach is divided into three stages; fitering, segmentation and tracing. Filtering removes the noise from the original image and segmentation determines the edges and deects them. Finally, tracing assembles the edges into the related structure. Proposed method can be used effectively on these procedures by using fuzzy reasoning based on fuzzy models. In is compared with the previous edge detectio algorithms with fvorable results. Simulation results of the research are presented and discussed.

• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.11-18
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• 2014

In this paper, we propose a self-diagnosis system that is based on the ART2 algorithm in order to extract more detailed disease information by fuzzy reasoning method especially when the boundary of perceived symptoms are not clearly classified into disease categories. With that modification from previous version of the self health pre-diagnosis system, the proposed one is verified as more effective by field experts' evaluation as an intelligent assistant tool for public users before they consult with medical experts.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.3
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• pp.14-22
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• 1994

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1239-1241
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• 1999

In this paper, we propose real time transient stability assessment and energy margin estimation using fuzzy approximate reasoning. The proposed method used rotor angle, kinetic energy and acceleration power of generators at clearing time as fuzzy input. In order to calculate energy margin in transient energy function (TEF), we obtained controlling unstable equilibrium point (UEP) using mode of disturbance procedure (MOD). The proposed algorithm is tested on 4-machine, 6-bus, 7-line power system to prove of effectiveness.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.96-105
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• 1997

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.5
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• pp.793-800
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• 2004

The purpose of this study is to develope a servo system with faster and more accurate response. This paper describes a method of approximate reasoning for fuzzy control of servo system based on the decomposition of $\alpha$-level fuzzy sets. We propose that fuzzy logic algorithm is a body from fuzzy inference to defuzzificaion cases where the output variable u directly is generated PWM The effectiveness for robust and faster response of the fuzzy control scheme are verified for a variable parameter by comparison with a PID control and fuzzy control A position control of DC servo system with a fuzzy logic controller is demonstrated successfully.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.189-192
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• 1996

This paper presents a systematic approach to identify a linguistic fuzzy model for a multi-input and single-output complex system. Such a model is composed of fuzzy rules, and its output is inferred by the simplified reasoning. The structure and membership function parameters for a fuzzy model are automatically and simultaneously identified by GA (Genetic Algorithm). After GA search, optimal parameters for the fuzzy model are finely tuned by a gradient method. A numerical example is provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce the linguistic fuzzy model with higher accuracy and a smaller number of rules than the ones achieved previously in other methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.259-262
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• 1997

In this paper, a fuzzy model based on the polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. The new algorithm uses PNN algorithm based on Group Method of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy anhd feasibility than other works achieved previously.