• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1314-1323
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• 2011

The maintenance of generating units is implicitly related with power system reliability and has a tremendous bearing on the operation of the power system. A technique using a fuzzy search method which is based on fuzzy multi-criteria function has been proposed for GMS (generator maintenance scheduling) in order to consider multi-objective function. In this study, a new technique using combined fuzzy set theory and genetic algorithm(GA) is proposed for generator maintenance scheduling. The genetic algorithm(GA) is expected to make up for that fuzzy search method might search the local solution. The effectiveness of the proposed approach is demonstrated by the simulation results on a practical size test systems.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.412-415
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• 2003

It is important to classily fault types, discriminate fault section and calculate the fault location by any detecting technique for combined transmission lines. This paper proposes the technique to classily the fault types and fault section using neuro-fuzzy systems. Neuro-fuzzy systems are composed of three parts to perform different works. First, neuro-fuzzy system for fault type classification is performed with approximation coefficient of currents obtained by wavelet transform. The second neuro-fuzzy system discriminates the fault section between overhead and underground with detail coefficients of voltage and current. The last neuro-fuzzy system calculates the fault location with impedance in this paper, neuro-furry system shows the excellent results for classification of fault types and discrimination of fault section.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.480-483
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• 2003

This paper deals with speed control of Sensorless DC servo motor using a FTHGO(FuzEy-Tuning High Gain observer). In this paper, we improved the problem from row speed section, the problem of sensor for detecting speed of motor, using FTHGO(Fuzzy-Tuning High-Gain Observer) with fuzzy control technique which is a class of adaptive control technique. In order to verify the performance of the FTHGO(Fuzzy-Tuning High Gain Observer) which is proposed in this paper, it is proved from the experiment to compare the case with a speed sensor to the case with FTHGO(Fuzzy-Tuning High Gain observer) in the speed control of DC servo motor.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1527-1539
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• 2015

In this paper the hardware implementation of the direct torque control based on the fuzzy logic technique of induction motor on the Field-Programmable Gate Array (FPGA) is presented. Due to its complexity, the fuzzy logic technique implemented on a digital system like the DSP (Digital Signal Processor) and microcontroller is characterized by a calculating delay. This delay is due to the processing speed which depends on the system complexity. The limitation of these solutions is inevitable. To solve this problem, an alternative digital solution is used, based on the FPGA, which is characterized by a fast processing speed, to take the advantage of the performances of the fuzzy logic technique in spite of its complex computation. The Conventional Direct Torque Control (CDTC) of the induction machine faces problems, like the high stator flux, electromagnetic torque ripples, and stator current distortions. To overcome the CDTC problems many methods are used such as the space vector modulation which is sensitive to the parameters variations of the machine, the increase in the switches inverter number which increases the cost of the inverter, and the artificial intelligence. In this paper an intelligent technique based on the fuzzy logic is used because it is allows controlling the systems without knowing the mathematical model. Also, we use a new method based on the Xilinx system generator for the hardware implementation of Direct Torque Fuzzy Control (DTFC) on the FPGA. The simulation results of the DTFC are compared to those of the CDTC. The comparison results illustrate the reduction in the torque and stator flux ripples of the DTFC and show the Xilinx Virtex V FPGA performances in terms of execution time.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.1137-1140
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• 1997

This paper presents a fuzzy-neural network controller technique on the load frequency control of two-area power system. Firstly, Fuzzy controller a series of initial selected rules are improved by means of the proposed technique. Secondly, scale factors for error, change rate of error and control input are optimized by the given error back-pagation teaming algorithms. Finally, the related simulation results show that the proposed fuzzy neural network controller technique are more powerful than conventional ones.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.2
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• pp.3-17
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• 1993

The fuzzy pattern matching technique has been developed in the framework of fuzzy set and possibility theory in order to take into account the imprecision and the uncertainty pervading values which have to be compared to requirements (which may be fuzzy) in a pattern matching process. This paper restates the basic principles and extends them to situations where (sub)patterns are only required to be satisfied up to a given tolerance (which may be fuzzy), or where the different subparts of a compound pattern may have various levels of importance. Both cases correspond to a weakening of elementary patterns. which can be expressed by a fuzzy relations modelling an approximate equality or an uncertain strict equality respectively. We also study the more sophisticated case where some elementary patterns have not to be satisfied with the highest priority provided that weaker requirements remain satisfied. The fuzzy pattern matching technique applies in a variety of problems including the evaluation of soft queries with respect to a fuzzy database, the evaluation of the fuzzy condition parts of rules in approximate reasoning, or the evaluation of the belonging of an ill-known object to a flexible class in classification problems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.895-898
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• 1993

Instead of Cartesian product for in combining multiple inputs for fuzzy logic controllers, a method using fuzzy relation in inference is proposed. Moreover, fuzzy control rule described by fuzzy relations is derived from given conventional fuzzy control rule by fitting concept. It will be shown through several examples that the proposed technique gives smoother interpolation than conventional ones.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.158-163
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• 2012

This paper proposes an FHIDS(Fuzzy logic based Hybrid Intrusion Detection System) design that detects anomaly and misuse attacks by using a Naive Bayesian algorithm, Data Mining, and Fuzzy Logic. The NB-AAD(Naive Bayesian based Anomaly Attack Detection) technique using a Naive Bayesian algorithm within the FHIDS detects anomaly attacks. The DM-MAD(Data Mining based Misuse Attack Detection) technique using Data Mining within it analyzes the correlation rules among packets and detects new attacks or transformed attacks by generating the new rule-based patterns or by extracting the transformed rule-based patterns. The FLD(Fuzzy Logic based Decision) technique within it judges the attacks by using the result of the NB-AAD and DM-MAD. Therefore, the FHIDS is the hybrid attack detection system that improves a transformed attack detection ratio, and reduces False Positive ratio by making it possible to detect anomaly and misuse attacks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.298-301
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• 1996

This paper describes the structure Identification of nonlinear function using Adaptive Neuro-Fuzzy Inference Technique(ANFIS). Nonlinear mapping relationship between inputs and outputs were modeled by Sugeno-Takaki's Fuzzy Inference Method. Specially, the consequent parts were identified using a series of 1st order equations and the antecedent parts using triangular type membership function or bell type ones. According to learning Rules of ANFIS, adjustable parameters were converged rapidly and accurately.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.183-186
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• 2000

In this paper, a new method for colour measuring is presented using fuzzy modeling technique. The fuzzy and polynomial inferences are used for obtaining RGB characteristic curve. The eight RGB real data from expert dye-stuff manufacturer, are simulated. The results show that the proposed method will is more excellent than other methods, In the colour measuring process of textile field.