• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.291-292
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• 2009

모바일 장치에서 얻을 수 있는 정보는 의미 있는 다양한 개인 정보를 가지고 있다. 본 논문에서는 모바일 장치에서 얻을 수 있는 정보를 분석하여 특이성을 추론하는 방법을 제안한다. 특이성 추론 방법으로 인과관계의 지식을 모델링하고 표현하며 추론하는 주요 형식화 방법의 하나인 FCM(Fuzzy Cognitive Map)을 사용하였다. 제안된 방법은 모바일 장치에서 얻은 정보와 추론된 특이성을 개념노드로 이용하여 새로운 특이성을 추론하며, 개념노드간의 인과관계를 효율적으로 표현한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.217-220
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• 2007

This paper studies method of controlling dynamic process with Fuzzy Cognitive Map (FCM) built in NI LabVIEW software. FCM is the hybrid methodology that combines fuzzy logic and neural networks. A FCM will be developed using NI LabVIEW software to model and control a process of dynamic system. Nowadays more autonomous and intelligent systems are very useful in many areas of people lives especially related with Complex Systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.461-467
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• 1998

Although many methods of knowledge acquisition has been developed in the expert systems field, such a need for causal knowledge acquisition has not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approcach, we prototyped a causal knowledge-driven inference engine named CAKES and then experime ted with some illustrative examples.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.12a
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• pp.557-575
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• 1999

As the Internet has been used widely in modern firms for gaining competitive advantage in the market, EC (Electronic Commerce) emerged as one of strong alternatives for this purpose. Many researchers and practitioners have proposed a wide variety of EC frameworks that can consider only the structured conditions, but there exists no EC mechanism in which engaged entities can take into account the various unstructured conditions. With the conventional EC framework, the structured EC conditions such as price, quantity, delivery date, etc. can be fully negotiated during the EC process. However, no studies have been conducted on the issue of incorporating those unstructured conditions which are difficult to represent in an explicit form and therefore hard to consider explicitly during the EC process. They are characterized by causal properties. This means that we should have a new EC mechanism which is capable of dealing with causal knowledge. In this sense, we propose a FCM (Fuzzy Cognitive Map)-based simulation framework for EC to resolve the problem of considering the unstructured conditions during the EC process. We experimented our prototype with several illustrative examples and proved that our approach is robust and meaningful.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.12a
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• pp.537-555
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• 1999

As the Internet has been used widely in modern firms for gaining competitive advantage in the market, EC (Electronic Commerce) emerged as one of strong alternatives for this purpose. Many researchers and practitioners have proposed a wide variety of EC frameworks that can consider only the structured conditions, but there exists no EC mechanism in which engaged entities can take into account the various unstructured conditions. With the conventional EC framework, the structured EC conditions such as price, quantity, delivery date, etc. can be fully negotiated during the EC process. However, no studies have been conducted on the issue of incorporating those unstructured conditions which are difficult to represent in an explicit form and therefore hard to consider explicitly during the EC Process. They are characterized by causal properties. This means that we should have a new EC mechanism which is capable of dealing with causal knowledge. In this sense, we propose a FCM (Fuzzy Cognitive Map)-based simulation framework for EC to resolve the problem of considering the unstructured conditions during the EC process. We experimented our prototype with several illustrative examples and proved that our approach is robust and meaningful.

• Korean Management Science Review
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• v.24 no.2
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• pp.157-176
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• 2007

Fuzzy cognitive map(FCM) has long been used as an effective way of constructing the human's decision making process explicitly. By taking advantage of this feature, FCM has been extensively used in providing what-if solutions to a wide variety of business decision making problems. In contrast, the goal-seeking analysis mechanism by using the FCM is rarely observed in literature, which remains a research void in the fields of FCM. In this sense, this study proposes a new type of the FCM-based goal-seeking analysis which is based on utilizing the genetic algorithm. Its main recipe lies in the fact that the what-if analysis as well as goal-seeking analysis are enabled very effectively by incorporating the genetic algorithm into the FCM-driven inference process. To prove the empirical validity of the proposed approach, valid questionnaires were gathered from a number of experts on software sales, and analyzed statistically. Results showed that the proposed approach is robust and significant.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.1-22
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• 1995

본 연구에서 퍼지인식도(Fuzzy Cognitive Map) 개념을 기초로 하여 (1) 특정 문제영역에 대한 전문가의 인과관계 지식(causal knowledge)을 추출하는 알고리즘을 제시하고, (2) 이 알고리즘에 기초하여 작성된 해당 문제영역에 대한 여러 전문가들의 인과관계 지식을 계층별로 분해하여, (3) 해당 계층간의 양방향 추론이 가능한 추론메카니즘을 제시하고자 한다. 특정 문제영역에 있어서의 인과관계 지식이란 해당 문제를 구성하는 여러 개념간에 존재하는 인과관계를 표현한 지식을 의미한다. 이러한 인과관계 지식은 기존의 IF-THEN 형태의 규칙과는 달리 행렬형태로 표현되기 때문에 수학적인 연산이 가능하다. 특정 문제영역에 대한 전문가의 인과관계 지식을 추출하는 알고리즘은 집합연산에 의거하여 개발되었으며, 특히 상반된 의견을 보이는 전문가들의 의견을 통합하여 하나의 통합된 인과관계 지식베이스를 구축하는데 유용하다. 그러나, 주어진 문제가 복잡하여 다양한 개념들이 수반되면, 자연히 인과관계 지식베이스의 규모도 커지게 되므로 이를 다루는데 비효율성이 개재되기 마련이다. 따라서 이러한 비효율성을 해소하기 위하여 주어진 문제를 여러계측(Hierarchy)으로 분해하여, 해당 계층별로 인과관계 지식베이스를 구축하고 각 계층별 인과관계 지식베이스를 연결하여 추론하는 메카니즘을 개발하면 효과적인 추론이 가능하다. 이러한 계층별 분해는 행렬의 분해와 같은 개념으로도 이해될 수 있다는 특징이 있어 그 연산이 간단명료하다는 장점이 있다. 이와같이 분해된 인과관계 지식베이스는 계층간의 추론메카니즘을 통하여 서로 연결된다. 이를 위하여 본 연구에서는 상향 또는 하향방식이 추론이 가능한 양방향 추론방식을 제시하여 주식시장에서의 투자분석 문제에 적용하여 그 효율성을 검증하였다.

• Journal of Digital Convergence
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• v.4 no.2
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• pp.143-151
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• 2006

The purpose of this paper is to make a design and implementation of a diagnosis system of learning misconception of students who learn 'be' verb in the English language by using fuzzy theory. In this system, a fuzzy cognitive map exposes the fact that students' perception and misunderstanding about 'the English' language have an intertwined relationship, and diagnoses causes of misconceptions of students by using fuzzy memory associative memory. It suggests that since most existing systems of rule based expert system have had several limitations, this system will be applied to diagnose learners' misconception of learning in varieties of education areas.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.70-77
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• 1998

Although many methods of knowledge acquisition has been developed in the exper systems field, such a need form causal knowledge acquisition hs not been stressed relatively. In this respect, this paper is aimed at suggesting a causal knowledge acquisition process, and then investigate the causal knowledge-based inference process. A vehicle for causal knowledge acquisition is FCM (Fuzzy Cognitive Map), a fuzzy signed digraph with causal relationships between concept variables found in a specific application domain. Although FCM has a plenty of generic properties for causal knowledge acquisition, it needs some theoretical improvement for acquiring a more refined causal knowledge. In this sense, we refine fuzzy implications of FCM by proposing fuzzy causal relationship and fuzzy partially causal relationship. To test the validity of our proposed approach, we prototyped a causal knowledge-driven inference engine named CAKES and then experimented with some illustrative examples.

• 한국디지털정책학회:학술대회논문집
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• 2006.06a
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• pp.451-459
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• 2006

The purpose of this paper is to make a design and implementation of a diagnosis system of learning misconception of students who learn 'be' verb in the English language by using fuzzy theory. In this system, a fuzzy cognitive map exposes the fact that students' perception and misunderstanding about 'the English' language have an intertwined relationship, and diagnoses causes of misconceptions of students by using fuzzy memory associative memory. It suggests that since most existing systems of rule based expert system have had several limitations, this system will be applied to diagnose learners' misconception of learning in varieties of education areas.