• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.73-82
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• 2004

신호 교차로에서 포화차두시간에 영향을 미치는 영향인자는 도로조건, 교통조건, 환경조건으로 분류된다. 이러한 요인들의 복합적인 관계가 포화차두시간에 영향을 미친다. 현재 포화교통류율은 이상적인 조건일 때의 포화차두시간을 산출하고, 이를 이용해서 기본 포화교통류율을 구하고, 여기에 좌 우회전, 차로폭, 경사, 중차량 보정계수을 고려함으로써 특정 차로군의 포화교통류율을 산정하고 있다. 포화차두시간에 영향을 미치는 인자들 중에서 정량적으로 나타내기 어려운 인자 즉, 퍼지적 성격을 가진 인자들은 고려하지 않고 있다. 따라서 본 연구에서는 퍼지 근사추론 방법을 이용하여 정성적 인자의 영향을 고려한 모형을 구축하였다. 모형의 입력자료는 강우조건과 주변밝기의 정도, 중차량 구성비의 언어적 표현를 사용하였다. 이러한 변수들에 대하여 설문조사를 통해서 퍼지집합의 멤버쉽함수를 설정하였으며. 이에 기초하여 교차로에서 각 조건별로 포화차두시간을 관측하였다. 이러한 현장 관측치를 바탕으로 퍼지 제어규칙을 설정하고 모형을 구축하였다. 모형의 평가는 추론치와 실측치를 비교함으로써 이루어 졌으며, 결정계수인 $R^2$와 평균절대오차(MAE)와 평균제곱오차(MSE)를 사용하여 분석한 결과 본 모형의 설명력이 높은 것으로 평가되었다. 본 연구의 과정에서 강우에 의한 교통용량 감소는 중차량 구성비가 클수록 주변밝기의 정도가 나쁠수록 더욱 큰 것으로 나타났으며 그 감소율은 5.3%에서 21.8%에 이르는 넓은 범위의 값을 보였고. 주변밝기 정도에 따른 교통용량 감소는 4.7$\sim$7.5% 수준으로 나타났다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.814-820
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• 2008

Recently, researches on ontology-based pattern classification have been tried out in many fields. However, in most of the researches, the ontology which represents the knowledge about pattern classification is just referred during the processes of the pattern classification. In this paper, we propose ontology-based fuzzy classifier for pattern classification which is extended from the fuzzy rule-based classifier In order to realize the proposed classifier, we construct an ontology by conceptualizing the method of fuzzy rule-based pattern classification and generate ontology inference rules for pattern classification. Lastly, we show the validity o) the proposed classifier through the experiment of pattern classification on the Fisher's IRIS dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10B
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• pp.854-861
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• 2003

In this paper, we proposed the fuzzy control algorithm for efficient buffer control about traffic that is connected in ATM networks. The proposed Fuzzy control algorithm has total traffic arrival ratio, buffer occupancy ratio and Fuzzy set to search for dynamic service rates in server. That is, is based on Fuzzy logic according to the arrival ratio of total traffic and buffer occupancy ratio that is happened and reasoning. Then, made reasoning result control service rates in server about traffic that is connected with defuzzification value. Performance analysis result: it was confirmed that with the proposed scheme, performance improves at cell loss rate, when compared with the existing PBS scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.252-259
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection of a image. The FCS is based on the fuzzy logic system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. There are two different approaches, Michigan and Pittsburgh approaches, to acquire appropriate fuzzy rules by evolutionary computation. In this paper, we use the Michigan style in which a single fuzzy if-then rule is coded as an individual. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. The proposed method is evaluated by applying it to the edge detection of a gray-level image that is a pre-processing step of the computer vision. the differences of average gray-level of the each vertical/horizontal arrays of neighborhood pixels are represented into fuzzy sets, and then the center pixel is decided whether it is edge pixel or not using fuzzy if-then rules. We compare the resulting image with a conventional edge image obtained by the other edge detection method such as Sobel edge detection.

• Management & Information Systems Review
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• v.35 no.3
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• pp.173-193
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• 2016

The aim of this paper is to extend the TOPSIS(Technique for Order Preference by Similarity to Ideal Solution) to the fuzzy environment for solving the disaster recovery priority decision problem in credit bureau business information system. In this paper, the rating of each information systems and the weight of each criterion are described by linguistic terms which can be expressed in trapezoidal fuzzy numbers. Then, a vertex method is proposed to calculate the distance between two trapezoidal fuzzy numbers. According to the concept of the TOPSIS, a closeness coefficient is defined to determine the ranking order of all information systems. The combination between the fuzzy set and TOPSIS brings several benefits when compared with other approaches, such that the fuzzy TOPSIS require few fuzzy judgements to parameterization, which contributes to the agility of the decision process, it does not limit the number of alternatives simultaneously evaluated, and it does not cause the ranking reversal problem when a new alternative is included in the evaluation process. This paper is demonstrated with a real case study of a credit rating agency involving 9 evaluation criteria and 9 credit bureau business information systems assessed by 6 evaluators, and provide the systematic disaster recovery framework for BCP(Business Continuity Planning) to practitioner. Finally, this paper show that the procedure of the proposed fuzzy TOPSIS method is well suited as a decision-making tool for the disaster recovery priority decision problem in credit bureau business information system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.1-10
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verify the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

• Journal of the Korea Concrete Institute
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• v.14 no.6
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• pp.1022-1031
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• 2002

This paper presents efficient models for bridge structures using CART-ANFIS (classification and regression tree-adaptive neuro fuzzy inference system). A fuzzy decision tree partitions the input space of a data set into mutually exclusive regions, each region is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it continuous and smooth everywhere. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.506-512
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modifY rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verifY the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.212-217
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• 2009

There are rule-based learning method and statistic based learning method and so on which represent learning method for hierarchy relation between domain term. In this paper, we propose to leveling and similarity measure using the extended AHP of fuzzy term in Information system. In the proposed method, we extract fuzzy term in document and categorize ontology structure about it and level priority of fuzzy term using the extended AHP for specificity of fuzzy term. the extended AHP integrates multiple decision-maker for weighted value and relative importance of fuzzy term. and compute semantic similarity of fuzzy term using min operation of fuzzy set, dice's coefficient and Min+dice's coefficient method. and determine final alternative fuzzy term. after that compare with three similarity measure. we can see the fact that the proposed method is more definite than classification performance of the conventional methods and will apply in Natural language processing field.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.53-56
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• 2000

In this paper, we define a concept of some set-theoretical operations of L-R type fuzzy numbers and discuss some properties of these concepts. Using these results, we discuss a concept of cardinality of type-two fuzzy sets.