• Journal of the Korean Institute of Gas
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• v.2 no.1
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• pp.53-58
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• 1998

By knowledge-based approach, the SDG(Signed Directed Graph) is automatically synthesized, which is commonly used to represent the causal effects between process variables. Automatic synthesis of SDG is progressed by two steps : (1)inference step uses knowledge base and (2)verification step uses Loop-Verifier. First, Topology and Knowledge Base are constructed by using the information on equipment. And then, Primary-SDG is synthesized by Character Pattern Matching between Variable-Relation-Representation generated by using Topology and Variable-Tendency-Data contained in Knowledge Base. Finally, a modified SDG is made after the Primary-SDG is verified by Loop-Verifier.

• Korean Journal of Mathematics
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• v.21 no.1
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• pp.55-62
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• 2013

The base $l(S)$ of a signed digraph S is the maximum number $k$ such that for any vertices $u$, $v$ of S, there is a pair of walks of length $k$ from $u$ to $v$ with different signs. A graph can be regarded as a digraph if we consider its edges as two-sided arcs. A signed cyclic graph $\tilde{C_n}$ is a signed digraph obtained from the cycle $C_n$ by giving signs to all arcs. In this paper, we compute the base of a signed cyclic graph $\tilde{C_n}$ when $\tilde{C_n}$ is neither symmetric nor antisymmetric. Combining with previous results, the base of all signed cyclic graphs are obtained.

• East Asian mathematical journal
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• v.28 no.5
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• pp.579-585
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• 2012

Let D be a finite digraph with the vertex set V(D) and arc set A(D). A two-valued function $f:V(D){\rightarrow}\{-1,\;1\}$ defined on the vertices of a digraph D is called a signed 2-independence function if $f(N^-[v]){\leq}1$ for every $v$ in D. The weight of a signed 2-independence function is $f(V(D))=\sum\limits_{v{\in}V(D)}\;f(v)$. The maximum weight of a signed 2-independence function of D is the signed 2-independence number ${\alpha}_s{^2}(D)$ of D. Recently, Volkmann [3] began to investigate this parameter in digraphs and presented some upper bounds on ${\alpha}_{s}^{2}(D)$ for general digraph D. In this paper, we improve upper bounds on ${\alpha}_s{^2}(D)$ given by Volkmann [3].

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.860-863
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• 1997

FCM(Fuzzy Cognitive Map) is a fuzzy signed directed graph for representing causal reasoning which has fuzziness between causal concepts. Authors have already proposed FCM-based fault diagnostic scheme. However, the previously proposed scheme has the problem of lower diagnostic resolution. In order to improve the diagnostic resolution, a new diagnostic scheme based on extended FCM which incorporates the concept of fuzzy number into FCM is developed in this paper. Furthermore, an enhanced TAM(Temporal Associative Memory) recall procedure and pattern matching scheme are also proposed.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.389-394
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• 1989

본 연구에서는 화학공정의 자동화와 관련된 실시간 나프타 분해로 이상진단 시스템의 한 부분을 구성하고 있는 지식기반의 구현을 위하여 공정변수 상호간의 관계를 나타낸 signed directed graph(SDG)를 기반으로 이상에서 파급되는 증상들을 순서대로 나타내는 fault-consequence digraph(FCD)를 지식모델로 사용했고, 이를 사고사례를 이용하여 검토해본 결과 초기에 정확한 이상원인 후보를 찾아내므로써 이상진단 전문가 시스템의 지식모델로 적합함을 알 수 있었다.

• Journal of the Korean Institute of Gas
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• v.2 no.3
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• pp.12-17
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• 1998

Fault tree construction for hazard assessment requires so much time and labor, so it is very difficult to be applied to the large scale chemical plant. For the synthesis of fault tree in chemical processes, this study represents the cause-effect relations between process variables by using the Signed Directed Graph(SDG), and has synthesized Fault Tree(FT) by searching the causes of fault events using the general operator and loop operators defined corresponding to path characteristics on the SDG.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.247-251
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• 1997

FCM(Fuzzy Cognitive Map) is a fuzzy signed directed graph for representing causal reasoning which has fuzziness between causal concepts. Authors have already proposed FCM-based fault diagnostic scheme and verified its usefulness. However, the previously proposed scheme has the problem of lower diagnostic resolution as in the case of other qualitative approaches. In order to improve the diagnostic resolution, a concept of fuzzy number is introduced into the basic FCM-based fault diagnostic algorithm. By incorporation the fuzzy number into fault FCM models, quantitative information such as the transfer gain between the state variables can be effectively utilized for better diagnostic resolution. Furthermore, an enhanced TAM(Temporal Associative Memory) recall procedure and modified and modified pattern matching scheme are also proposed.

• 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.