• Journal of IKEEE
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• v.17 no.2
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• pp.104-110
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• 2013

In this paper, we introduce a smart care surveillance system which can support various CCTV cameras. In order to monitor an emergency requester in case of emergency, the server performs automatical CCTV Pan, Tilt, and Zoom control based on the location coordinates of the emergency requester. Also, a server finds and tracks the emergency requester using image processing and the updated location information. We implement a smart care surveillance system using the Genetec SDK tool to support various CCTV cameras. The efficiency of the rescue operation with the smart care surveillance system can be improved because rescuer can quickly control and monitor the requester's CCTV images.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.531-533
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• 1998

This paper proposes the method that estimate optimally the parameters of Fuzzy-PID controller using genetic Algorithm. The controller is desined with the proposed method, and then is applied to 3-phase induction motor. Simulation results show that proposed method is more excellent then FPID and PID.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.36-43
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• 1999

This paper proposes a new hybrid genetic algorithm for auto-tuning fuzzy controllers improving the performance. In general, fuzzy controllers use pre-determined moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a hybrid genetic algorithm. The object of the proposed algorithm is to promote search efficiency by the hybrid optimization technique. The proposed hybrid genetic algorithm is based on both the standard genetic algorithm and a modified gradient method. If a maximum point is not be changed around an optimal value at the end of performance during given generation, the hybrid genetic algorithm searches for an optimal value using the the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algoritms. Simulation results verify the validity of the presented method.