• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.109-120
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• 1998

An image segmentation approach based on the fuzzy worm searching and MIN-MAX clustering algorithm is proposed in this paper. This algorithm deals with fuzzy worm value and min-max node at a gross scene level, which investigates the edge information including fuzzy worm action and spatial relationship of the pixels as the parameters of its objective function. But the conventional segmentation methods for edge extraction generally need the mask information for the algebraic model, and take long run times at mask operation, whereas the proposed algorithm has single operation according to active searching of fuzzy worms. In addition, we also propose both genetic fuzzy worm searching and genetic min-max clustering using genetic algorithm to complete clustering and fuzzy searching on grey-histogram of image for the optimum solution, which can automatically determine the size of ranges and has both strong robust and speedy calculation. The simulation results showed that the proposed algorithm adaptively divided the quantized images in histogram region and performed single searching methods, significantly alleviating the increase of the computational load and the memory requirements.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.218-227
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• 2000

In this paper, we present a new fuzzy information retrieval method based on thesaurus. In the proposed method th thesaurus is represented by a fuzzy linguistic matrix, where the elements in fuzzy linguistic matrix represent a qualitative linguistic values between terms. In the fuzzy linguistic matrix, there are three kinds of fuzzy relationships between terms, i.e., similar relation, hierarchical relation, and associative relation. The implicit fuzzy relationships between terms are inferred by the transitive closure of the fuzzy linguistic matrix based on fuzzy theory. And the proposed method has the capability to deal with a qualitative linguistic weights in a query and in indexing of information items to reflect qualitative measure of human based on vague and uncertain decisions rather than a quantitiative measure. Therefore the proposed method is more flexible than the ones presented in papers[1-3]. Moreover our method is more effectual of time than the ones presented in papers[1-3] because we use a fuzzy linguistic matrix and AON (Associate Ordinary Number) values in query evaluation process. As a result, the proposed method allows the users to perform fuzzy queries in a more flexible and more intelligent manner.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.1-16
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• 2005

When Web-based special retrieval systems for scientific field extremely restrict the expression of user's information request, the process of the information content analysis and that of the information acquisition become inconsistent. Accordingly, this study suggests the re-ranking retrieval model which reflects the content based similarity between user's inquiry terms and index words by grasping the document knowledge structure. In order to accomplish this, the former constructs a thesaurus and similarity relation matrix to provide the subject analysis mechanism and the latter propose the algorithm which establishes a search model such as query expansion in order to analyze the user's demands. Therefore, the algorithm that this study suggests as retrieval utilizing the information structure of a retrieval system can be content-based retrieval mechanism to establish a 2-step search model for the preservation of recall and improvement of accuracy which was a weak point of the previous fuzzy retrieval model.

• Journal of KIISE:Databases
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• v.29 no.1
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• pp.27-33
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• 2002

There are two significant drawbacks in triple image indexing; one is that is cannot support concept-based image retrieval and the other is that it fails to allow disjunctive labeling of images. To remedy the drawbacks, we propose a new technique supporting a concept-based retrieval against images indexed by indefinite fuzzy triples (I-fuzzy triples). The I-fuzzy triples allow not only a disjunctive image labeling, but also a concept-based matching against images labeled disjunctively. The disjunctive labeling is based on the expended closed world assumption and the concept-based image retrieval is based on fuzzy matching. In this paper, we also propose a concept-based query evaluation against the image database to extract desired answers with the degree of certainty $\alpha$$\in$[1,0].

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.605-615
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• 2000

본 논문에서는 정보검색에서 시소러스를 이용한 효율적이고 효과적인 질의 평가 기법을 제안한다. 제안된 방법에서 시소러스 내부 용어들 간의 관계와 관련도가 용어 매트릭스로 표현되며, 용어들 간의 관계는 동의, 계층, 그리고 연관관계의 세 가지 관계가 제공된다. 시소러스 내부 용어들 간의 무시된 관련도가 퍼지 이론에 근거한 용어 매트릭스의 전이폐쇄 알고리즘에 의해 추론된다. 따라서 다양한 관계에 따른 시소러스에 표현된 지식을 이용할 수 있다. 또한 질의 평가시 용어 매트릭스를 이용하기 때문에 논문[3-7]에서 사용되는 방법보다 시간적으로 효율적이다. 그리고 정의된 용어 매트릭스는 논문[8]에서 발생되는 문제점을 제거하여 검색 효과를 높이기 위해 논문[6]에서 제안된 질의 평가함수와 용이하게 통합시킨다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.7-12
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• 2007

오늘날 웹의 사용자와 컨텐츠가 급격히 증가함에 따라 지능적 정보시스템과 웹 서비스(Web Service) 개념이 여러 분야에서 활발히 연구되고 있으며, 사용자에게 의미 정보를 제공하기 위한 시맨틱 웹서비스의 필요성이 대두되고 있다. 본 논문에서는 시맨틱 웹 서비스를 위한 FXDB (FUZZY XML DATABASE) 시스템 설계 기법과 의미정보(Semantic Information)를 자동으로 실시간 웹 서비스 할 수 있는 방법을 제시한다. 본 논문에서 제시한 FXDB 시스템은 XML 기술과 퍼지기술을 이용하여 웹상의 메타데이타를 데이터베이스에서 의미적으로 자동해석하고 XML 기반 웹 서비스할 수 있는 시스템 구조를 제안한다. 따라서 시맨틱 웹 환경을 위한 메타데이타의 해석을 좀 더 자동화, 지능화 할 수 있는 차세대 웹을 구현하는데 기여할 수 있을 것이라 기대한다. 또한 웹 사용자의 입장에서는 의미정보에 따른 빠른 의사결정과 시맨틱 웹 정보검색이 가능하게 될 것이다.

• Journal of the Korean Society for information Management
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• v.21 no.3
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• pp.251-267
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• 2004

The purpose of this study is to generate the local level knowledge structure of a single document, similar to end-of-the-book indexes and table of contents of printed material through the use of term clustering and cluster representative term selection. Furthermore, it aims to analyze the functionalities of the knowledge structure. and to confirm the applicability of these methods in user-friend1y information services. The results of the term clustering experiment showed that the performance of the Ward's method was superior to that of the fuzzy K -means clustering method. In the cluster representative term selection experiment, using the highest passage frequency term as the representative yielded the best performance. Finally, the result of user task-based functionality tests illustrate that the automatically generated knowledge structure in this study functions similarly to the local level knowledge structure presented In printed material.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.17-35
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• 2011

The collective intelligence is a common-based production by the collaboration and competition of many peer individuals. In other words, it is the aggregation of individual intelligence to lead the wisdom of crowd. Recently, the utilization of the collective intelligence has become one of the emerging research areas, since it has been adopted as an important principle of web 2.0 to aim openness, sharing and participation. This paper introduces an approach to seek the collective intelligence by cognition of the relation and interaction among individual participants. It describes a methodology well-suited to evaluate individual intelligence in information retrieval and classification as an application field. The research investigates how to derive and represent such cognitive intelligence from individuals through the application of fuzzy relational theory to personal construct theory and knowledge grid technique. Crucial to this research is to implement formally and process interpretatively the cognitive knowledge of participants who makes the mutual relation and social interaction. What is needed is a technique to analyze cognitive intelligence structure in the form of Hasse diagram, which is an instantiation of this perceptive intelligence of human beings. The search for the collective intelligence requires a theory of similarity to deal with underlying problems; clustering of social subgroups of individuals through identification of individual intelligence and commonality among intelligence and then elicitation of collective intelligence to aggregate the congruence or sharing of all the participants of the entire group. Unlike standard approaches to similarity based on statistical techniques, the method presented employs a theory of fuzzy relational products with the related computational procedures to cover issues of similarity and dissimilarity.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.154-165
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• 2001

A number of attempts to develop methods for measuring software effort have been focused on the area of software engineering and many models have also been suggested to estimate the effort of software projects. Almost all current models use algorithmic or statistical mechanisms, but the existing algorithmic effort estimation models have failed to produce accurate estimates. Furthermore, they are unable to reflect the rapidly changing technical environment of software development such as module reuse, 4GL, CASE tool, etc. In addition, these models do not consider the paradigm shift of software engineering and information systems(i.e., Object Oriented system, Client-Server architecture, Internet/Intranet based system etc.). Thus, a new approach to software effort estimation is needed. After reviewing and analyzing the problems of the current estimation models, we have developed a model and a system architecture that will improve estimation performance. In this paper, we have adopted a neural network model to overcome some drawbacks and to increase estimation performance. We will also address the efficient system architecture and estimation procedure by a similar case-based approach and finally suggest the heuristic search method to find the best estimate of target project through empirical experiments. According to our experiment with the optimally parsimonious neural network model the mean error rate was significantly reduced to 14.3%.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.357-364
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• 2000

기존의 웹 디자인은 웹이라는 매체의 특성 상 디자인적인 요소가 매우 중요함에도 불구하고 디자인은 위한 구체적인 방법론이 미약하다. 특히, 많은 소비자들을 유인하고 구매를 촉발시켜야 하는 인터넷 쇼핑몰의 경우에는 더욱 더 그럼하에도 불구하고 이를 위한 전략적인 방법론이 부족하다. 즉, 기존 연구들은 제품의 다양성, 서비스, 촉진, 항해량, 편리성, 사용자 인터페이스 등이 중요하다고 하였지만 실제 인터넷 쇼핑몰을 디자인하는 입장에서는 활용하기가 상당히 애매하다. 그 이유는 이들 요인들은 서로 영향관계를 가지고 있어서 사용자 인터페이스가 복잡하면 항해량이 늘어나 편리성이 감소하고, 제품이 늘어나더라도 검색엔진을 사용하면 상대적으로 항해량이 감소하게 되어 편리성이 증가한다. 따라서, 이들 요인을 활용하여 인터넷 쇼핑몰을 구축하려면 요인간의 영향관계를 면밀히 파악하고 이 영향요인이 소비자의 구매행동에 어떠한 영향을 주는지가 충분히 검토되어야 한다.이에 본 연구에서는 퍼지인식도를 이용하여 인터넷 쇼핑몰 상에서 소비자의 구매행동에 영향을 주는 요인을 추출하고 이들 요인간의 인과관계를 도출하여 보다 구체적이고 전략적으로 인터넷 쇼핑몰을 디자인할 수 있는 방법으로 web-Cogmulator를 제시한다. Web-Cogmulator는 소비자의 쇼핑몰에 대한 암묵지식 형태의 구매행동을 형태지식화하여 지식베이스 형태로 가지고 있기 때문에 인터넷 쇼핑몰의 다양한 요인의 변화에 따른 소비자의 구매행동을 추론 시뮬레이션하는 것이 가능하다. 이에 본 연구에서는 기본적인 인터넷 쇼핑몰 시나리오를 바탕으로 추론 시뮬레이션을 실시하여 Web-Cogmulator의 유용성을 검증하였다.를, 지지도(support), 신뢰도(confidence), 리프트(lift), 컨빅션(conviction)등의 관계를 통해 다양한 방법으로 모색해본다. 이 연구에서 제안하는 이러한 개념계층상의 흥미로운 부분의 탐색은, 전자 상거래에서의 CRM(Customer Relationship Management)나 틈새시장(niche market) 마케팅 등에 적용가능하리라 여겨진다.선의 효과가 나타났다. 표본기업들을 훈련과 시험용으로 구분하여 분석한 결과는 전체적으로 재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of computati