• 한국지능시스템학회논문지
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• 제20권1호
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• pp.123-128
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• 2010

어떤 한 프로젝트를 성공할 가능성을 높이기 위해서는 그 성과에 영향을 줄 수 있는 요소들에 대해 정의할 필요가 있다. 인지도는 관련있는 요소들의 인과관계를 조사함으로써 분석하는데 널리 사용되고 있다. 퍼지인지도는 인관관계의 연결강도를 통해 피드백의 개념을 추가한 인지도의 확장 개념을 가지고 있다. 프로젝트 성과는 입력 자원, 수행가능성, 타인의 관심 정도 등에 의해 영향을 받는다. 본 논문에서는 프로젝트의 성과와 이런 요소들간의 인과관계의 정도를 조사 분석하기 위해 퍼지인지도를 활용하여 기업프로젝트의 성공을 위한 모델을 개발하고자 한다.

• 지능정보연구
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• 제1권2호
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• pp.33-55
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• 1995

포지인식도(Fuzzy Cognitive Map : FCM)는 추상적이고 비구조적이며 동적인 응용영역에서 전문가의 인과관계 지식(causal knowledge)을 표현하는데 매우 유용한 도구이다. FCM이 기존의 다른 네트워크 형태의 지식표현방법과 다른 차이점은 대상 문제의 개념변수들을 퍼지집합으로 묘사하고, 개념 변수간의 관계를 퍼지 인과관계로 다룬다는 것이다. 그런데 FCM의 특성이 아직 충분히 논의되지 않은 상태에서는 FCM의 적용에 있어 오류가 일어날 수 있다. 본 논문의 목적은 첫째, FCM의 특성과 의미를 보다 명확히 하여 이론적인 측면을 보강하고자 한다. 이를 위해 논리적관계(implication)와는 다른 인과관계의 정의를 다시 확인하고, 이정의에 기초한 퍼지 인과관계의 특성을 파악하고, 퍼지 인과관계와 대비되는 퍼지 부분인과관계 및 단방향 개념변수를 새로이 정의함으로써 FCM구축에 있어 잘못된 이해가 없게 하며, 둘째, FCM에서는 추론 방식이 갖추어야 할 원칙을 명시하고 이에 따라 이러한 원칙을 준수하는 새로운 추론 방식을 제시한다.

• 한국시스템다이내믹스연구
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• 제17권3호
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• pp.5-29
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• 2016

The Korean government has constructed the national competency standards (NCS) rapidly since the year 2013. Also, it has pushed 2-year colleges to adopt NCS-based educational programs and public institutions to hire new employees by using NCS. It expects that NCS will deplete the mismatch between the requirements of employers and the abilities of employees. The purpose of this paper is to find correct accelerating policies for universities to adopt NCS-based educational programs. In order to achieve the purpose, primarily the government policies for NCS were analyzed by using causal map. And then, three accelerating policy recommendations for universities to adopt NCS were drawn by using the results of causal map analysis. The three recommendations are as follows. First, the quality of NCS should be kept highly. Secondly, the NCS-based hiring system of public institutions should also be standardized. Finally, the authorities of universities should be guaranted whenever they make educational programs.

• 한국경영과학회지
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• 제19권1호
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• pp.17-40
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• 1994

The objectives of this paper are to apply fuzzy cognitive map (FCM)- related techniques to (1) extract causal knowledge from a specific problem-domain and (2) perform a series of causal analysis in complicated decision making area. We propose a set operation-based augmentation (SOBA) algorithm to combine multiple FCMs developed by multiple experts. Based on the SOBA knowledge acquisition algorithm, we can obtain a causal knowledge base fairly representing multiple experts' knowledge about a problem domain. The causal knowledge base built by SOBA algorithm can be described as a matrix form, guaranteeing mathematically compact operation compared with a production (if-then) knowledge base. We applied out method to stock market analysis problem whichis a typical of highly unstructured problems in OR/MS fields.

• 지식경영연구
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• 제10권2호
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• pp.49-63
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• 2009

In this study, sub domain of flow was investigated on literature survey, and suggested of the measuring method of web-site flow and its simulation analysis. Constructing of measuring method of flow, and using this method what-if analysis was simulated when several condition changed. Using causal map approach to extract knowledge from web-site domain experts and to derives a causal relationship of knowledge. Specially, in our study, describes method of developing and building causal map, and suggests guide line of this method on practical application. This research results show that web-site flow starts "direct searching" or "interesting of special issue(domain)", and when challenges of web-site were accorded with user's skills web-site flow grows. Further, in the web-site, information searching intention results in increase of user's duration time and experience flow to discovery new interesting issues in this process. If user's web-site of interaction is increased, awareness of environment conditions decreased, finally, user's telepresense results in increased web-site flow. This paper contained thai this method make used of measuring flow in the web-site and developing of practical strategy.

• Transactions on Control, Automation and Systems Engineering
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• 제1권1호
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• pp.62-68
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. Authors have already proposed a diagnostic system based on FCM to utilized to identify the true origin of fault by on-line pattern diagnosis. In FCM based fault diagnosis, Temporal Associative Memories (TAM) recall of FCM is utilized to identify the true origin of fault by on-line pattern match where predicted pattern sequences obtained from TAM recall of fault FCM models are compared with actually observed ones. In engineering processes, the propagation delays are induced by the dynamics of processes and may vary with variables involved. However, disregarding such propagation delays in FCM-based fault diagnosis may lead to erroneous diagnostic results. To solve the problem, a concept of FTCM(Fuzzy Time Cognitive Map) is introduced into FCM-based fault diagnosis in this work. Expecially, translation method of FTCM makes it possible to diagnose the fault for some discrete time. Simulation studies through two-tank system is carried out to verify the effectiveness of the proposed diagnostic scheme.

• 한국지능시스템학회논문지
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• 제8권6호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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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.

• 한국시스템다이내믹스연구
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• 제2권1호
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• pp.51-68
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• 2001

Politicians are competing to persuade their policies and to oppose other's. Policy persuasion is based on two independent reasons; moral causes vs. economic outcomes. There have been few studies on investigating and comparing their structural differences. This paper studies how policy makers use moral causes and economic outcomes in persuading their policies. Causal and cognitive structures of persuasions for and against sunshine policy are compared by cognitive map analysis. Finally, this study discusses how to use causal map analysis to understand moral causes and economic outcomes as tools for policy persuasion.

• 한국시스템다이내믹스연구
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• 제1권2호
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• pp.91-111
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• 2000

Causal maps or cognitive maps have been widely used to get insights for complex systems or decision makers. When insights come from the system behavior rather than its structure, we need simulation of causal maps and cognitive maps. In this paper, a method for directly converting causal maps and cognitive maps into stock-flow diagrams that can be simulated in computers in proposed. This method is called as NUMBER. NUMBER is an abbreviation for 'Normal Unit Modeling By Elementary Relationship'. In this paper, NUMBER is applied to a cognitive map of policy maker to show its usefulness.