• Journal of Gifted/Talented Education
• /
• v.26 no.2
• /
• pp.257-280
• /
• 2016

This study is focused on how the science gifted students can improve their system thinking by applying 3-steps mind map activities about astronomy contents. Through the pre-study two times, 9 students in 6th grade were participated in this study. It was analyzed the degree of improvement of systems thinking through the science gifted students' mind maps, causal maps and their interviews. The results of the study showed that the experimental group's system thinking who learned 3-steps mind map activities was more complex and diverse than the controlled group's. Therefore 3-steps mind map activities that have been developed in this study will be valuable as an alternative to improve their system thinking. Through this study, 3-steps mind map activities were confirmed that by inducing background knowledge activation and thinking systematization can be an alternative to improve the system thinking of science gifted students. So the 3-steps mind map activities can be helpful in cultivate human resources with creative problem solving ability based on their system thinking.

• Transactions on Control, Automation and Systems Engineering
• /
• v.1 no.1
• /
• pp.54-61
• /
• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.8
• /
• pp.1238-1246
• /
• 1989

In this paper, a texture synthesis technique based on the NCAR(non-causal auto-regressive) model and the pyramid structure is proposed. In order to estimate the NCAR model parameters accurately from a noisy texture, the MAP(maximum a posteriori) estimation technique is also employed. In our approach, since the input texture is decomposed into the Laplacian oyramid planes first and then the NCAR model is applied to each plane, we are able to obtain a good synthesized texture even if the texture exhibits some non-random local structure or non-homogenity. The usrfulness of the proposed method is demonstrated with seveal real textures in the Brodatz album. Finally, the 2-dimensional MAP estimation technique can be used to the image restoration for noisy images as well as a texture image synthesis.

• Journal of Korean Society of Water and Wastewater
• /
• v.34 no.2
• /
• pp.105-114
• /
• 2020

In this study, based on the System Dynamics (SD) methodology, the interrelationship between the factors inherent in the operation of the New Technology Certification System (NTCS) in Korea was identified by a causal map containing a feedback loop mechanism in connection with 'new technology development investment', 'commercialization of new technology', and 'sales by new technology'. This conceptualized causal map was applied to the simulation of the operations of the New Excellent Technology and Environmental Technology Verification System (NET&ETV) run by the Ministry of Environment among various NTCSs in Korea. A SD computer simulation model was developed to analyze and predict the operational performance of the NET&ETV in terms of key performance indices such as 'sales by new technology'. Using this model, we predicted the future operational status the NET&ETV and found a policy leverage that greatly influences the operation of the NET&ETV. Also the sensitivity of the key indicators to changes in the external variables in the model was analyzed to find policy leverage.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 1999.12a
• /
• pp.557-575
• /
• 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
• /
• 1999.12a
• /
• pp.537-555
• /
• 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.

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

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.568-571
• /
• 2004

In this study, we propose an efficient expert system (ES) construction mechanism by using dynamic knowledge map (DKM) and database management systems (DBMS). Generally, traditional ES and ES developing tools has some limitations such as, 1) a lot of time to extend the knowledge base (KB), 2) too difficult to change the inference path, 3) inflexible use of inference functions and operators. First, to overcome these limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. Then, elation database (RDB) and its management systems will help to transform the relationships from diagram to relational table. Therefore, our mechanism can help the ES or KBS (Knowledge-Based Systems) developers in several ways efficiently. In the experiment section, we used medical data to show the efficiency of our mechanism. Experimental results with various disease show that the mechanism is superior in terms of extension ability and flexible inference.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.1
• /
• pp.101-107
• /
• 2006

In this research, we propose a hybrid inference architecture for medical diagnosis based on dynamic knowledge map (DKM) and relational database (RDB). Conventional expert systems (ES) and developing tools of ES has some limitations such as, 1) time consumption to extend the knowledge base (KB), 2) difficulty to change the inference path, 3) inflexible use of inference functions and operators. To overcome these Limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. The DKM also can help the knowledge engineers to change the inference path rapidly and easily. Then, RDB and its management systems help us to transform the relationships from diagram to relational table.

• Journal of the Korean Society of Safety
• /
• v.36 no.4
• /
• pp.62-70
• /
• 2021

Representative systematic accident analysis methods proposed so far include AcciMap, STAMP, and FRAM. This study used these three techniques to analyze a fire accident case that occurred during routine manufacturing work in a domestic chemical plant and compared the results. The methods used different approaches to identify the cause of the accident, but they all highlighted similar causal factors. In addition to technical issues, the three accident analysis methods identified factors related to safety education, risk assessment, and the operation of the process safety management system, as well as management philosophy and company culture as problems. The AcciMap and STAMP models play complementary roles because they use hierarchical structures, while FRAM is more effective in analyses centered on human and organizational functions than in technical analyses.