• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.175-178
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• 2005

In the present paper, we observe a relation between fuzzy norms and induced crisp norms on a linear space. We first prove that if $\rho_1,\;\rho_2$ are equivalent fuzzy norms on a linear space, then for every $\varepsilon\in(0.1)$, the induced crisp norms $P_\varepsilon^1,\;and\;P_\varepsilon^2$, respectively are equivalent. Since the converse does not hold, we prove it under some strict conditions. And consider the following theorem proved in [8]: Let $\rho$ be a lower semicontinuous fuzzy norm on a normed linear space X, and have the bounded support. Then $\rho$ is equivalent to the fuzzy norm $\chi_B$ where B is the closed unit ball of X. The lower semi-continuity of $\rho$ is an essential condition which guarantees the continuity of $P_\varepsilon$, where 0 < e < 1. As the last result, we prove that : if $\rho$ is a fuzzy norm on a finite dimensional vector space, then $\rho$ is equivalent to $\chi_B$ if and only if the support of $\rho$ is bounded.

• Journal of Power Electronics
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• v.5 no.1
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• pp.20-28
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• 2005

This paper presents a fuzzy predictive sliding mode control for high performance induction motor position drives. A new simplified inner-loop sliding-mode current control scheme based on a nonlinear mathematical model of an induction motor is introduced. Novel predictive fuzzy logic PI and PID controllers are used in speed and position loops, respectively. Sliding-mode current controllers and fuzzy predictive logic controllers are designed based on indirect vector control. The overall system performance is examined under different dynamic operating conditions. The performance of the drive system is robust and stable, and insensitive to parameters and operating condition variations even though non-exact system parameters are used in the implementation of the proposed controllers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.299-301
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• 2005

본 연구는 생체정보를 이용하여 개인을 인증하고 확인하기 위한 방법으로 기존 단일 생체인식 기법의 단점을 보완하기 위해 홍채와 얼굴을 이용한 다중생체인식(Multi-Modal Biometrics Recognition)기법을 연구하였다. 중국 홍채 데이터베이스 CASIA(Chinese Academy of Science)에 Gabor Wavelet과 FLDA(Fuzzy Linear Discriminant Analysis)를 사용하여 특징벡터를 획득하였으며, FERET(FERET(Face Recognition Technology) 얼굴영상데이터를 사용하여 FERET 연구에서 매우 우수한 성능을 보인 EBGM알고리듬으로 특징벡터를 획득하였다. 이로부터 얻어진 두 score 값에 대하여 다양한 균등화 과정을 시도해 보았으며, 등록자와 침입자를 구분하기 위한 Fusion Algorithm으로 Bayesian Classifier, Support vector machine, Fisher's linear discriminant를 사용하였다. 또한, 널리 사용되는 방법 중 Weighted Summation을 이용하여 다중생체인식의 성능을 비교해 보았다.

• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2742-2744
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• 1999

In this paper, an auto-tuning method for fuzzy logic controller based on the genetic algorithm is presented. In the proposed method, normalization parameters and membership function parameters of fuzzy controller are translated into binary bit-strings, which are processed by the genetic algorithm in order to be optimized for the well-chosen objective function (i.e. fitness function). To examine the validity of the proposed method. a genetic algorithm based fuzzy controller for an indirect vector control of induction motors is simulated and experiment is carried out. The simulation and experimental results show a significant enhancement in shortening development time and improving system performance over a traditional manually tuned fuzzy logic controller.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.222-224
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• 2002

This paper investigates the adaptive control of a fuzzy logic based speed and flux controller for 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(mAC) 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.

• Journal of Power Electronics
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• v.12 no.2
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• pp.305-316
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• 2012

The paper presents an accurate loss model based controller of an induction motor to calculate the optimal air gap flux. The model includes copper losses, iron losses, harmonic losses, friction and windage losses, and stray losses. These losses are represented as a function of the air gap flux. By using the calculated optimal air gap flux compared with rated flux for speed sensorless indirect vector controlled induction motor, an improvement in motor efficiency is achieved. The motor speed performance is improved using a fuzzy logic speed controller instead of a PI controller. The fuzzy logic speed controller was simulated using the fuzzy control interface block of MATLAB/SIMULINK program. The control algorithm is experimentally tested within a PC under RTAI-Linux. The simulation and experimental results show the improvement in motor efficiency and speed performance.

• Proceedings of the IEEK Conference
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• summer
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• pp.447-450
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• 2004

This paper proposes remotely sensed image classification method by fuzzy c-means clustering algorithm using average intra-cluster distance. The average intra-cluster distance acquires an average of the vector set belong to each cluster and proportionates to its size and density. We perform classification according to pixel's membership grade by cluster center of fuzzy c-means clustering using the mean-values of training data about each class. Fuzzy c-means algorithm considered membership degree for inter-cluster of each class. And then, we validate degree of overlap between clusters. A pixel which has a high degree of overlap applies to the maximum likelihood classification method. Finally, we decide category by comparing with fuzzy membership degree and likelihood rate. The proposed method is applied to IKONOS remote sensing satellite image for the verifying test.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1684-1690
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• 2003

In this paper, we proposed a fuzzy controller for the evaluation of the quantization Parameters in the H.263 coder to optimize the subjective quality of each coded frame, keeping the transmission rate constant. We adopted the Mamdani method for fuzzification and the centroid method for defuzzification. The energy and entropy are correlated to features of the HVS in spatial domain, while motion vectors are used to estimate the temporal characteristics of the signal. And then, the fuzzy inputs adapted the variance and the entropy in spatial domain, and the motion vector in temporal domain. We induced the fuzzy membership function and decided the fuzzy relevance to be compatible in visual characteristics. And then, we designed FAM banks. The fuzzy technology has been applied to a practical video compression. This results is obtained an effective rate control technique, an optimum bit allocation and a high subjective quality using fuzzy quantization.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.5
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• pp.22-33
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• 2011

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

• Proceedings of the KIEE Conference
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• 2008.04c
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• pp.119-121
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• 2008

This paper is proposed a high Performance speed control of the synchronous reluctance motor through the SV-PWM(Space Vector Pulse Width Modulation) of FAM-PI(Fuzzy Adaptive Mechanism-PI). SV-PWM is controlled using FAM-PI control. SV-PWM can be maximum used maximum do link voltage and is excellent control method due to characteristic to reducing harmonic more than others. Fuzzy control has a advantage which can be robustly controlled. FAM-PI controller is changed fixed gain of PI controller using fuzzy adaptive mechanism(FAM) to match operating condition. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.