• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.239-250
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• 2003

In this research, we propose a hybrid group decision support mechanism (H-GDSM) based on Fuzzy AHP (Analytic Hierarchy Process) and FCM (Fuzzy Cognitive Map). The AHP elicits a corresponding priority vector interpreting the preferred information among the decision makers. Corresponding vector was composed of the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale. However, AHP couldn't represent the causal relationship among information, which were used by decision makers. In contrast to AHP, FCM could represent the causal relationship among variables or information. Therefore, FCMs were successfully developed and used in several ill-structured domains, such as strategic decision-making, policy making, and simulations. Nonetheless, many researchers used subjective and voluntary inputs to simulate the FCM. As a result of subjective inputs, it couldn't avoid the rebukes of businessman. To overcome these limitations, we incorporated the Fuzzy membership functions, AHP and FCM into a H-GDSM. In contrast to current AHP methods and FCMs, the H-GDSM method developed herein could concurrently tackle the pairwise comparison involving causal relationships under a group decision-making environment. The strengths and contributions of our mechanism were 1) handling of qualitative knowledge and causal relationships, 2) extraction of objective input value to simulate the FCM, 3) multi-phase group decision support based on H-GDSM. To validate our proposed mechanism we developed a simple prototype system to support negotiation-based decisions in electronic commerce (EC).

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.600-605
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• 2003

PCA(Principle Component Analysis) has emerged as a useful tool for process monitoring and fault diagnosis. The general approach requires the user to identify the root cause by interpreting the residual or principle components. This could be tedious and often impossible for a large process. In this paper, PCA scheme is combined with the FCM-based fault diagnostic algorithm to enhance the diagnostic results. The implementation of the FCM-based fault diagnostic system by using PCA is done and its application is illustrated on the two-tank system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.73-76
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• 2003

신경생리학적으로 밝혀진 바에 의하면, 대뇌의 시상에 분포한 일차 감각영역에서 감각 정보를 수집한다. 수집된 감각 정보는 과거 기억과의 비교를 통해 인식되고 인식된 정보는 일차 운동영역으로 전달되어 행동으로 나타난다. 수집된 감각 정보를 판단하는 기관은 감각 연합 영역으로 알려져 있으며, 과거 정보를 통해 비교하여 판단하는 방식이다. 하지만, 과거 기억 정보로 존재하지 않는 새로운 감각 입력에 대해서는 대뇌피질 내의 파페츠 회로를 통해 새로이 기억하게 된다. 이 과정에는 변연계의 편도체(Amygdala)의 감정 반응을 이용하여 강한 감정 반응을 유도하는 감각 입력에 대해서는 강한 기억을 하게 되고, 반대의 경우에는 약한 기억을 하게 되는 특징이 고려된다. 본 논문에서는 기억되지 않은 새로운 감각 자극에 대해 감정 반응 정도에 따라 기억되는 정도의 변화를 관찰할 수 있는 모델을 제시하고자 한다. 이 모델은 대뇌피질의 정보 처리 및 감각 학습 과정을 인공적으로 구현하는 과정에 바탕이 될 것이다.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.233-238
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• 1993

In this paper, two diagnosis algorithms using the simple fuzzy, cognitive map (FCM) that is an useful qualitative model are proposed. The first basic algorithm is considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. And the second one is an extended version of the basic algorithm. In the extension, three important concepts, modified temporal associative memory (TAM) recall, temporal pattern matching algorithm and hierarchical decomposition are adopted. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large scale and complex processes that conventional diagnosis methods cannot be applied. The diagnosis system can be trained by the basic algorithm and generates FCM model for every experienced process fault. In on-line application, the self-generated fault model FCM generates predicted pattern sequences, which are compared with observed pattern sequences to declare the origin of fault. In practical case, observed pattern sequences depend on transport time. So if predicted pattern sequences are different from observed ones, the time weighted FCM with transport delay can be used to generate predicted ones. The fault diagnosis procedure can be completed during the actual propagation since pattern sequences of tvo different faults do not coincide in general.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.11-17
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• 2018

IDS/IPS and networked computer systems are playing an increasingly important role in our society. They have been the targets of a malicious attacks that actually turn into intrusions. That is why computer security has become an important concern for network administrators. Recently, various Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems is useful for existing intrusion patterns on standard-only systems. Therefore, probe detection of private clouds using BlockChain has become a major security protection technology to detection potential attacks. In addition, BlockChain and Probe detection need to take into account the relationship between the various factors. We should develop a new probe detection technology that uses BlockChain to fine new pattern detection probes in cloud service security in the end. In this paper, we propose a probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) based on service security using BlockChain technology.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.1
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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.

• 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.

• Proceedings of the Korea Association of Information Systems Conference
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• 2001.12a
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• pp.340-347
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• 2001

전자상거래를 위한 인터넷의 웹사이트 구축 문제는 기업이나 조직의 중요한 새로운 마케팅 창구로서의 역할 때문에 그 중요성을 인식하고 효과적 디자인을 기반한 사이트구축이 이슈화 되었다. 그리하여 성공적인 인터넷 비즈니스를 위한 웹사이트의 구축 방법론이나 가이드라인의 연구가 필요하게 되며, 최근 소비자의 행동을 분석하여 이를 소비자 구매욕구를 증진시키는 방안으로 활용하여 전략적인 웹사이트를 구축하도록 제시하는 많은 연구들이 제시되고 있다. 그러나 전략적인 관점에서 웹사이트를 구축하거나 이미 구축된 웹사이트를 전략적 관점에서 개편하려고 할 때 사용될 수 있는 뚜렷한 방법론이 존재하지 않기 때문에 이런 관점의 분석모형이 절대적으로 필요한 실정이다. 이에 본 연구에서는 전략 형성과정에서 유용하게 사용될 수 있는 FCM(Fuzzy Cognitive Map)을 소개하고 이를 바탕으로 보다 구체적인 웹사이트 디자인 요소를 분석, 평가 할 수있는 방안을 제시하고자 한다. 본 논문에서 제시하는 FCM기반의 분석은 웹사이트의 성공요인들로 꼽히는 요인들간의 인과관계를 고려하여 웹사이트 구축의 요인간의 영향력의 민감도 분석을 할 수 있는 접근법으로서의 활용도가 기대된다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.19-25
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• 2017

Nowadays, networked computer systems play an increasingly important role in our society and its economy. They have become the targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason computer security has become an essential concern for network administrators. Recently, a number of Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors ant the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an hybrid probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) in dynamic environment such as Cloud and IoT. Also, in order to verify the proposed method, experiments about measuring detection rate in dynamic environments and possibility of countermeasure against intrusion were performed. From experimental results, decrease of false detection and the possibilities of countermeasures against intrusions were confirmed.