• 대한인간공학회지
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• 제17권1호
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• pp.37-46
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• 1998

A human sensibility ergonomics design supporting system was applied to the product development for the customer's satisfaction based on ergonomics technology. The system is composed of three major subsystems such as customer's sensibility analysis, inference mechanism, and presentation technology. The main approaches of the system are to analyze customer's sensibilities and to translate them into product design elements. The purpose of this paper is to develop a design supporting system in which the relationship between customer's sensibility and product design elements is reasoned by a MADM(Multi-Attribute Decision Making) fuzzy model. In this model, three variables such as multiple correlation coefficients, partial correlation coefficients, and category scores were used in reasoning process. The weighted value of the words were also considered in fuzzy decision process. As a case study, the design supporting system with the MADM fuzzy model was applied to the personnel computer design.

• 한국지능시스템학회논문지
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• 제16권5호
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• pp.642-646
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• 2006

퍼지 TAM 네트워크는 입력층, 카테고리층, 출력층으로 구성되어 있는 패턴분석의 감독학습방법이다. 그러나 퍼지 TAM 네트워크 모델에서 패턴분석 하고자 하는 대상의 출력층의 목표값을 모르는 경우에는 무감독 학습방법이 된다. 이러한 경우에는 무감독 학습방법인 SOM을 이용하여 출력층의 목표값을 구하여 대체할 수 있다. 본 논문에서는 SOM의 결과를 퍼지 TAM 네트워크에 적용하고 사례연구를 통하여 그 유용성을 보인다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.29-32
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• 2003

We introduce the subcategories IRe $l_{PR}$ (H), IRe $l_{PO}$ (H) and IRe $l_{E}$(H) of IRe $l_{R}$(H) and study their structures in a viewpoint of the topological universe introduced by L.D.Nel. In particular, the category IRe $l_{R}$(H)(resp. IRe $l_{P}$(H) and IRe $l_{E}$(H)) is a topological universe eve, Set. Moreover, we show that IRe $l_{E}$(H) has exponential objects.ial objects.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권2호
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• pp.132-137
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• 2007

Experimental software data capturing the essence of software projects (expressed e.g., in terms of their complexity and development time) have been a subject of intensive modeling. In this study, we introduce a new category of Hybrid Fuzzy Neural Networks (gHFNN) and discuss their comprehensive design methodology. The gHFNN architecture results from highly synergistic linkages between Fuzzy Neural Networks (FNN) and Polynomial Neural Networks (PNN). We develop a rule-based model consisting of a number of "if-then" statements whose antecedents are formed in the input space and linked with the consequents (conclusion pats) formed in the output space. In this framework, FNNs contribute to the formation of the premise part of the overall network structure of the gHFNN. The consequences of the rules are designed with the aid of genetically endowed PNNs. The experiments reported in this study deal with well-known software data such as the NASA dataset. In comparison with the previously discussed approaches, the proposed self-organizing networks are more accurate and yield significant generalization abilities.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2002년도 추계정기학술대회
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• pp.491-501
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• 2002

Case-based reasoning is emerging as a leading methodology for the application of artificial intelligence. CBR is a reasoning methodology that exploits similar experienced solutions, in the form of past cases, to solve new problems. Hybrid model achieves some convergence of the wide proliferation of credit evaluation modeling. As a result, Hybrid model showed that proposed methodology classify more accurately than any of techniques individually do. It is confirmed that proposed methodology predicts significantly better than individual techniques and the other combining methodologies. The objective of the proposed approach is to determines a set of weighting values that can best formalize the match between the input case and the previously stored cases and integrates fuzzy sit concepts into the case indexing and retrieval process. The GA is used to search for the best set of weighting values that are able to promote the association consistency among the cases. The fitness value in this study is defined as the number of old cases whose solutions match the input cases solution. In order to obtain the fitness value, many procedures have to be executed beforehand. Also this study tries to transform financial values into category ones using fuzzy logic approach fur performance of credit evaluation. Fuzzy set theory allows numerical features to be converted into fuzzy terms to simplify the matching process, and allows greater flexibility in the retrieval of candidate cases. Our proposed model is to apply an intelligent system for bankruptcy prediction.

• 한국산학기술학회논문지
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• 제12권7호
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• pp.3245-3250
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• 2011

이 연구는 지휘관의 위협평가시 의사결정을 도와주기 위한 모델을 제시하였다. 이를 위해 해양조건, 적 함정 제원, 전략환경 3가지 상위평가항목으로 위협요소를 설정하였다. 퍼지추론을 이용하여 각 상위항목별 위협수준을 산정한 후, 퍼지척도를 이용하여 상위평가항목별 중요도를 산정하였다. 마지막으로 상위평가항목의 위협수준을 종합하기 위해 choquet의 퍼지적분을 사용하였다.

• 경영정보학연구
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• 제12권2호
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• pp.189-203
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• 2010

민간 및 정부 전 분야에 걸쳐서 정보화사업에 대해 많은 투자가 이루어지고 있으며, 이에 대한 투자대비 성과를 측정하고자 많은 노력들이 수행되고 있다. 정보화사업에 대한 평가는 크게 수준평가와 성과평가로 구분되고 있으며, 수준평가는 정부차원으로 매년 측정하고 성과평가는 자체 평가로 수행되고 있다. 공공부문에 있어서는 범정부 성과참조모델(Performance Reference Model: PRM) ver. 2.0이 개발되어 공통 참조모델로써 정보화 성과평가의 기준을 제시하고 있다. 범정부 PRM은 가장 근간이 되는 평가분류체계와 표준 가시경로 및 성과관리 표준 양식으로 구성되어 있으며, 이를 통해 성과요소들을 분류하고 인과관계를 정의하고 있다. 효율적인 정보화사업을 관리하기 위해서는 성과에 대한 평가를 객관적으로 할 수 있는 정량적인 수치화가 필요하다. 범정부 PRM은 평가분류체계에서 측정범주는 제공하고 있지만, 측정범주별 적용기준에 대한 상대적 중요도는 제시하지 못하고 있다. 이에 본 연구에서는 공공부문 정보화사업의 성과평가의 공통기준으로 적용되고 있는 범정부 PRM의 측정범주에 대한 중요도 평가 및 우선순위를 도출하고자 한다. 연구모형은 Fuzzy QFD (Quality Function Deployment)를 이용하였으며, 측정범주의 중요도 도출시 범정부 PRM의 개발목적을 잘 반영할 수 있도록 설계하였다. 전문가의 의견을 수렴함에 있어 불확실성과 모호성을 최소화시키기 위하여 퍼지이론을 접목한 Fuzzy AHP(Analytic Hierarchy Process)와 FPP(Fuzzy Preference Programming) 방법을 적용하였다. 범정부 PRM의 개발목적에서는 성과관리 참조모델로써의 가장 기본적인 요구사항이라 할 수 있는 "정보화 성과관리를 위한 표준모형 제공"이 가장 중요한 요소로 도출되고 있다. 측정범주에 서는 고객영역에서 "서비스 품질"이 가장 높은 우선순위를 보이고 있다. 정보시스템의 서비스에 대한 품질 관리 및 향상방안에 보다 많은 투자와 노력이 필요함을 엿볼 수 있다. 범정부 PRM의 측정범주에 대한 중요도는 정부 및 공공기관에 공통의 평가기준을 제공할 수 있으며, 이를 통해 자체 평가결과를 상호 비교하여 보완/발전시킬 수 있는 기회를 제공한다. 향후 연구시 성과분류체계의 구조모형에 대한 정량적인 인과관계를 규명한다면, 범정부 PRM은 보다 객관적이고 효율적인 참조모델로 발전할 수 있을 것이다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.295-299
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• 2002

This paper presents an image contrast enhancement technique for improving low contrast images. We applied fuzzy logic to develop an image contrast enhancement technique in the viewpoint of considering that the low pictorial information of a low contrast image is due to the vaguness or fuzziness of the multivalued levels of brightness rather than randomness. The fuzzy image contrast enhancement technique consists of three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. In the stage of image fuzzification, we need to select a crossover point. To select the crossover point automatically the K-means algorithm is used. The problem of crossover point selection can be considered as the two-category, object and background, classification problem. The proposed method is applied to an experimental image with 256 gray levels and the result of the proposed method is compared with that of the histogram equalization technique. We used the index of fuzziness as a measure of image quality. The result shows that the proposed method is better than the histogram equalization technique.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.33-36
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• 2003

We introduce the subcategory IRel$\_$R/ (H) of IRel (H) consisting of intuitionistic H-fuzzy reflexive relational spaces on sets and we study structures of IRel$\_$R/ (H) in a viewpoint of the topological universe introduce by L.D.Nel. We show that IRel$\_$R/ (H) is a topological universe over Set. Moreover, we show that exponential objects in IRel$\_$R/ (H) are quite different from those in IRel (H) constructed in [7].