• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.9
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• pp.49-57
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• 1999

The fusion of fuzzy set theory and neural networks technologies have concentrated on applying neural networks to obtain the optimal rule bases of fuzzy logic system. Unfortunately, this is very hard to achieve due to limited learning capabilities of neural networks. To overcome this difficulty, we propose a new approach in which rough set theory and neuro-fuzzy fusion are combined to obtain the optimal rule base from input/output data. Compared with conventional FNN, the proposed algorithm is considerably more realistic because it reduces overlapped data when construction a rule base. This results are applied to the construction of inference rules for controlling the temperature at specified points in a refrigerator.

• Journal of the Korean Society of Safety
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• v.26 no.4
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• pp.69-79
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• 2011

This paper proposes a GA-based optimal fuzzy control technique for the vibration control of earthquakeexcited adjacent structures interconnected with semi-active magneto-rheological(MR) dampers. Rule-based fuzzy logic controllers are designed first by implementing heuristic knowledge and the genetic algorithm(GA) is then introduced to optimally tune the fuzzy controllers for enhancing the seismic performance of semi-active control system. For practical implementation, the fuzzy controller simply uses locally measured responses of the dampers involved and directly returns the input voltage to the magneto-rheological dampers in real time through the fuzzy inference mechanism. The local measurement based fuzzy controller provides optimal damping force in a decentralized manner so that it does not require a primary central controller unlike the conventional semi-active control techniques. As a result, it can avoid the unbridgeable discrepancy between the desired control force and the actual damper force that may occur in the conventional control approaches. The validity and effectiveness of the proposed control method are shown numerically on two 20-story earthquake-excited buildings interconnected with MR dampers.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1849-1850
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• 2008

Type-2 퍼지 집합은 Type-1 퍼지 집합의 확장으로 Type-1 퍼지 집합으로는 다루기 힘든 언어적인 불확실성을 다루기 위해 고안되었다. 대표적인 퍼지 논리 시스템(Fuzzy Logic System; FLS)으론 Mamdani FLS 모델과 TSK FLS모델이 있다. 본 논문에서는 Interval Type-2 TSK FLS를 구성한다. FLS 구성을 위한 전반부는 가우시안 형태의 Type-2 멤버쉽 함수를 사용하며, 전.후반부 파라미터들은 오류역전파 알고리즘을 통한 학습으로 결정한다. 본 논문에서는 Type-1 TSK FLS와 Interval Type-2 TSK FLS를 설계하고 가스로 공정 데이터에 적용하여 성능을 비교 분석한다. 또한 노이즈를 추가한 데이터들을 통하여 노이즈에 대한 성능도 비교 분석한다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.5
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• pp.34-39
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• 2012

The DAS(Distribution Atomization System) determines a faulted section by using a FI(Fault Indicator) when the fault is occurred on the distribution networks. Sometimes FI is malfunction when the ground fault is occurred on a the distribution networks. As a result difficulties to make decision of faulted section. The cause of the FI malfunction is that the determination using the limited information of the installed area. In this study, a method is proposed to determine faulted section using the amount of the fault current instead of using the FI. This method is determinated faulted section using the fuzzy inference for the collected information from the all switches. The usefulness of the proposed algorithm is verified through the simulation test using PSCAD/EMTDC.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.615-621
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• 2014

Power transformer is one of the most important equipments in electrical power system. The detection of certain gases generated in transformer is the first indication of a malfunction that may lead to failure if not detected. Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results falls outside conventional method codes or when more than one fault exist in transformer. To overcome these limitations, fuzzy inference system (FIS) is proposed. 250 different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

• International Journal of Safety
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• v.11 no.2
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• pp.15-21
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• 2012

This paper is aimed to develop an electrical fire detection system (EFDS) which can analyze the possibility of electrical fire for overcurrent, leakage current and arc signals of panel board in traditional market shop. The EFDS adopted fuzzy logic and precursory data for overcurrent, leakage current and arc signals to evaluate the possibility of electrical fire. The signals are obtained directly from panel board in traditional market shops and fuzzy membership function is obtained from experiment, simulation, expert's advice. The overcurrent data is acquired by thermal data of normal and abnormal states (partial disconnection) on the insulated electrical wire, in accordance with the increase of the current signal, The leakage current data is obtained under various environments. The arc signal is acquisited by waveforms of instantaneous value in time domain and frequency band in frequency domain. The Fuzzy algorithm for DB of EFDS consists of fuzzification, inference engine by Mamdani's method and defuzzification by center of gravity method. In order to verify the performance and reliability of EFDS, it was applied to Jeon-Ju traditional market shops (90 shops) in Korea. Results show that EFDS in this paper is useful in alarming the fire case, which will prevent severe damage to human beings and properties, and reduce the electrical fires in a vulnerable area of electrical disaster.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.714-716
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• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.402-408
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• 2016

In this paper, a new approach to the terminal sliding mode control using adaptive fuzzy sliding surfaces is proposed. The idea behind this approach is to utilize an adaptive sliding surface, in which the slope of the surface is updated on line using a SISO fuzzy logic inference system. We expanded the concepts of terminal sliding mode controller and proposed the terminal sliding mode control input with continuous reaching laws. The computer simulation results have shown the improved performance of the proposed control approach in terms of a decrease in the reaching and settling times and chattering free as compared to the conventional terminal sliding mode control with a fixed sliding surface. The proposed controller has also an advantage that has less computational burden to the conventional terminal sliding mode control using one-directional fuzzy rules.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.4
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• pp.388-398
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• 1998

In constructing fuzzy control systems. there are many parameters such as rule base. membership functions. inference m method. defuzzification. and I/O scaling factors. To control the system in properly using fuzzy logic. we have to consider t the correlation with those parameters. This paper deals with self-tuning of fuzzy control systems. The fuzzy controller h has parameters that are input and output scaling factors to effect control output. And we propose the looklongleftarrowup table b based self-tuning fuzy controller. We propose the PMTM(Plus-Minus Tuning Method) for self tuning method, self-tuning the initial look-up table to the appropriate table by adding and subtracting the values.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.27-32
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• 2006

Verbal communication is the most commonly used mean of communication. A spoken word carries a lot of informations about speakers and their emotional states. In this paper we designed a model to recognize emotional states in a speech, a first phase of two phases in developing a toy machine that recognizes emotional states in a speech. We conducted an experiment to extract and analyse the emotional state of a speaker in relation with speech. To analyse the signal output we referred to three characteristics of sound as vector inputs and they are the followings: frequency, intensity, and period of tones. Also we made use of eight basic emotional parameters: surprise, anger, sadness, expectancy, acceptance, joy, hate, and fear which were portrayed by five selected students. In order to facilitate the differentiation of each spectrum features, we used the wavelet transform analysis. We applied ANFIS (Adaptive Neuro Fuzzy Inference System) in designing an emotion recognition model from a speech. In our findings, inference error was about 10%. The result of our experiment reveals that about 85% of the model applied is effective and reliable.