• Journal of Information Management
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• v.24 no.3
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• pp.73-100
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• 1993

In attempting to respond to boolean retrieval system's limitations, this paper presents the design of a retrieval system using fuzzy logic. The fuzzy retrieval system introduces the weights of terms in the documents and in the query and makes use of them to determine how much relevant a document is to the given query. After comparing and analyzing the previous researches, an effective model of the fuzzy retrieval system is suggested and the performance of the system is evaluated through actual examples.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.507-512
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• 1998

It is an interesting area in the field of artificial intelligence to and an analytic model of cooperative structure for multi-agent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent co-operative behavior: A modified genetic algorithm was applied to automating the discovery of a fuzzy logic controller jot multi-agents playing a pursuit game. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to and the fuzzy logic controller seems to be promising.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.326-328
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• 1998

This paper presents an optimal fuzzy power system stabilizer to damp out low frequency oscillation. The fuzzy logic controllers has been applied to a power system stabilizing controllers. But the design of a fuzzy logic power system stabilizer relies on empirical and heuristic knowledge of human experts as well as many trial-and-errors in general. This paper presents the optimal design method of the fuzzy logic stabilizer using the genetic algorithm, which is the optimization method based on the mechanics of natural selection and natural genetics. The proposed method tunes the parameters of the fuzzy logic stabilizer in order to minimize the consuming time during the design process. In this paper, the proposed method tunes the shape of membership function of the fuzzy variables. The proposed system is applied to the one-machine infinite-bus model of a power system. Through the case study, the efficiency of the fuzzy stabilizing controller tuned by genetic algorithm is verified.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.28A no.12
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• pp.65-72
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• 1991

A cost model of test generation is presented in this paper. The cost of flat gate-level and hierarchical modular level test generation for combinational logic circuits are modeled. The model shows that the cost of hierarchical test generation grows as GlogGunder some assuptions, while the cost of gate-level test generation grows $G^2<$/TEX>, where G is the number of gates in a circuit under test. The cost model derived in this paper is used to explain why some test generation techniques are faster and why hierarchical test generators should be faster than flat test generators on large circuits.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1288-1295
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• 1990

In this paper, we propose a parallel processing model for the efficient selection of Prime Implicants of Logic Functions. This model consists of simple parallel processing nodes, connections between them, Max Net (a part of Hamming Net) and quasi essential Prime Implicant selection standard in simplified cost form. Through these, this model selects essential Prime Implicants in a certain period of time regardless of the number of given Prime Implicants and minterms and also selects quasi essential Prime Implicants in short time.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.3
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• pp.215-223
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• 2002

This paper investigates the adaptive control of a fuzzy logic based speed and flux controller fur a vector controlled induction motor drive. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the model reference adaptive control(MRAC) fuzzy controller is evaluated by simulation for various operating conditions. The validity of the Proposed MRAC fuzzy controller is confirmed by performance results for induction motor drive system.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.1
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• pp.11-17
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• 2001

In drilling of composite laminates, it is important to minimize of reduce occurrences of delaminations. In particular, a peel -up delamination at entrance and push-up delamination at exit are common. Deleaminations may by avoided by regulating the drill thrust force can be controlled by adjusting the feedrate of the drill. Dynamics involved in drilling of composite laminates is time varying and nonlinear. In this paper, a fuzzy logic model and control strategy are proposed. Simulation results show that the fuzzy model can describe the nonlinear time-varying process well. The fuzzy controller realizes a fast rise time and a little overshoot of drilling force.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2007.06a
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• pp.244-249
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• 2007

In this paper, in order to get the characteristics of the lithium secondary cell, such as cycle life, charge and discharge characteristic, temperature characteristic, self-discharge characteristic and the capacity recovery rate etc, we build a mathematical model of battery. In this one-dimensional model, Seven governing equations are made to solve seven variables $c,\;c_s,\;{\Phi}_1,\;{\Phi}_2,\;i_2,\;j\;and\;T$. The mathematical model parameters used in this model have been adjusted according to the experimental data measured in our lab. The connecting research of this study is to get an accurate estimate of the capacity of battery through comparison of results from simulation and fuzzy logic system. So the result data from this study is reorganized to fit the fuzzy logic algorithm.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.130-132
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• 1997

This paper proposes a reference model following control system using fuzzy logic controller and genetic algorithm. A fuzzy logic controller is designed such that plant output follows the output generated by a reference model. In this paper, First-order and second-order reference model with no overshoot and fast rise time is designed. Experiment results show the effectiveness of the proposed controller in tracking property and robustness.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.16-21
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• 2003

The accuracy in maneuvering target tracking using multiple models is resulted in by the suitability of each target motion model to be used. The interacting multiple model (IMM) method and the adaptive IMM (AIMM) method require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers in order to construct multiple models. In this paper, to solve these problems, a genetic algorithm(GA) based-IMM method using fuzzy logic is proposed. In the proposed method, the acceleration input is regarded as an additive noise and a sub-model is represented as a set of fuzzy rules to calculate the time-varying variances of the process noises of a new piecewise constant white acceleration model. The proposed method is compared with the AIMM algorithm in simulation.