• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.1-6
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• 2012

In this paper, we present an obstacle avoidance control algorithm for mobile robots based on SFLC (single-input fuzzy logic controller) with an efficient fuzzy logic look-up table to replace the traditional complicated operation. This method achieves better performance than traditional methods in terms of efficiency. The output of a SFLC leads the robot to the target automatically although many obstacles on the path. Our experiments show that the robot has good performance in the view of path tracking and other efficiency.

• Journal of The Korean Association of Information Education
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• v.4 no.2
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• pp.232-240
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• 2001

The aim of this study was to develop the performance evaluation processing system in elementary schools. First of all, the researcher reviewed theoretical aspects of performance evaluation. Fuzzy was used to devise the system for learning course. The performance evaluation processing system has been applied to Practical Arts Subject in elementary schools, from which good results were obtained.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.31-37
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• 2009

This paper casts an application of an extended fuzzy integral on the selective extraction of color clusters characterized by a particular color hue from color document images. Despite of the negative role of fuzzy integral, the presented approach attains the detection of the seals through the neighborhood information via the center of area method. The conventional fuzzy integral evaluates the negative aspects of the importance about the items by min operator, which result in the discontinuous parts of seals. In an attempt to cope with the drawback our approach considers the integral aspects via the center of area method, which results in the robustness of the images. Finally, the framework is successfully tested on a data set formed by documents from a real application for the detection.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.646-650
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• 2014

In this paper, in order to avoid the deterioration of the pattern classification performance which results from the curse of dimensionality, we propose a new feature selection method. The newly proposed feature selection method is based on Fuzzy C-Means clustering algorithm which analyzes the data points to divide them into several clusters and the concept of a function with fuzzy numbers. When it comes to the concept of a function where independent variables are fuzzy numbers and a dependent variable is a label of class, a fuzzy number should be related to the only one class label. Therefore, a good feature is a independent variable of a function with fuzzy numbers. Under this assumption, we calculate the goodness of each feature to pattern classification problem. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2567-2569
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• 2003

본 논문은 비선형 시스템의 퍼지모델을 위해 정보 Granules 기반 퍼지추론 시스템 모델의 최적화를 제시한다. 퍼지모델은 주로 경험적 방법에 의해 추출되기 때문에 보다 구체적이고 체계적인 방법에 의한 동정 및 최적화 될 필요성이 요구된다. 제안된 규칙베이스 퍼지모델은 HCM 클러스터링 방법, 컴플렉스 알고리즘 및 퍼지추론 방법을 이용하여 시스템 구조와 파라미터 동정을 수행한다. 두 가지 형태의 퍼지모델 추론 방법은 간략추론, 선형추론에 의해 시행된다. 본 논문에서는 퍼지모델의 입력변수와 퍼지 입력 공간 분할 및 입출력 데이타의 중심값을 구해서 후반부 다항식함수에 의한 정보 Granules 기반 구조 동정과 파라미터 동정을 통해 비선형 시스템을 표현한다. 전반부 파라미터의 동정에는 HCM 클러스터링 방법과 컴플렉스 알고리즘을 사용하고, 후반부는 표준 HCM 클러스터링과 표준 최소자승법을 사용하여 동정한다. 그리고 학습 및 테스트 데이타의 성능견과의 상호균형을 얻기 위한 하중값을 가진 성능지수를 제시함으로써 근사화와 예측성능의 향상을 꾀한다. 제안된 비선형 모델의 성능평가를 통해 그 우수성을 보인다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.169-177
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• 2015

This paper described a fuzzy comprehensive evaluation method and implemented assessment system for multi-layer decision making. The proposed method is a assessment before bidding through the key questions using fuzzy comprehensive evaluation method and the entropy weights. The key questions are given by the wider investigation of major sports event organizers. The paper carried out evaluation of single factor and fuzzy comprehensive evaluation from low layer to high layer step by step. In order to verify the effectiveness of proposed method, we built the sports event management service platform (SEMSP) for assessment of applicant city. This method represents a unified one of the quantitative results and the qualitative results based on the judgment of experts.

• Journal of KIISE:Software and Applications
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• v.29 no.8
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• pp.565-572
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• 2002

학습과 추론을 위하여 유용한 방법으로 퍼지연상기억장치가 있다. 본 논문에서는 보다 효과적으로 추론결과를 유도하기 위하여 퍼지연상기억장치를 학습하는 단계에서 오류 역전파를 통하여 노드들 사이의 연결가중치를 재조정하는 방법과 퍼지규칙들을 간결화하는 방법을 제안한다. 제안된 방법은 비디오 데이타의 장면전환을 검출하는 분야에 적용하여 성능평가를 수행한다.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.751-758
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• 2021

Munitions must be analyzed to identify any risks for quality assurance in development and mass production. Risk identification for parts, compositions, and systems is carried out through failure mode effects analysis (FMEA) as one of the most reliable methods. FMEA is a design tool for the failure mode of risk identification and relies on the RPN (risk priority number). FMEA has disadvantages because its severity, occurrence, and detectability are rated at the same level. Fuzzy FMEA applies fuzzy logic to compensate for the shortcomings of FMEA. The fuzzy logic of Fuzzy FMEA is to express uncertainties about the phenomenon and provides quantitative values. In this paper, Fuzzy FMEA is applied to the failure mode of a rotorcraft landing system. The Fuzzy rule and membership functions were conducted in the Fuzzy model to study the RPN in the failure mode of a landing system. This method was selected to demonstrate crisp values of severity, occurrence, and detectability. In addition, the RPN was obtained. The results of Fuzzy FMEA for the landing system were analyzed for the RPN and ranking by fuzzy logic. Finally, Fuzzy FMEA confirmed that it could use the data in quality assurance activities for rotorcraft.