• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.247-250
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• 2003

Due to the increasing use of very large databases, mining useful information and implicit knowledge from databases is evolving. However, most conventional data mining algorithms identify the relationship among features using binary values (TRUE/FALSE or 0/1) and find simple If-THEN rules at a single concept level. Therefore, implicit knowledge and causal relationships among features are commonly seen in real-world database and applications. In this paper, we thus introduce the mechanism of mining fuzzy association rules and constructing causal knowledge base form database. Acausal knowledge base construction algorithm based on Fuzzy Cognitive Map(FCM) and Srikant and Agrawal's association rule extraction method were proposed for extracting implicit causal knowledge from database. Fuzzy association rules are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. It integrates fuzzy set concept and causal knowledge-based data mining technologies to achieve this purpose. The proposed mechanism consists of three phases: First, adaptation of the fuzzy membership function to the database. Second, extraction of the fuzzy association rules using fuzzy input values. Third, building the causal knowledge base. A credit example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstration the effectiveness of the proposed algorithm.

• 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.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.1
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• pp.13-23
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• 1996

Weld defects degrade the strength and safety of astructure and are resulted from the various cases. The complexity of causal relation of weld defects requires an expert for the analysis of weld defects and the measures counter to them. An expert system has the intelligent functions such as the representation of knowledge and the inference. On this research, weld defect are systematically analysed and their causal model is developed. This information is saved to the knowledge base. The suitable inference algorithm for the diagnosis of weld defects is developed and realized with C++ programming.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.5-8
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• 2008

This study proposed the Knowledge Base Building method reflecting the user's preferences based on the fuzzy set theory to develop information contents which support pedestrian's navigation. This research evaluated subject's preferences on the commercial spaces set to the hypothetical destination. Also it surveyed the causal relationship between the visual characteristics and the emotional characteristics to propose the methods of Navigation Knowledge Base (NKB). The NKB was composed by three elements; 1.the correlation model between emotional characteristics, 2.the causal relationship between visual characteristics and emotional characteristics, 3.the transformation model between visual characteristics and the physical characteristics.

• Korean Journal Plant Pathology
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• v.10 no.2
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• pp.99-104
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• 1994

A computer-based diagnosing system for diseases of grasses, ornamental plant and fruit trees was developed using a 16 bit personal computer (Model Acer 900) and BASIC was used as a programing language. the developed advisory system was named as Korean Plant Disease Advisory System (KOPDAS). The diagraming system files were composed of a system operation file and several database files. The knowledge-base files are composed of text files, code files and implement program files. The knowledge-base of text files are composed of 79 files of grasses diseases, 122 files of ornamental plant diseases and 67 files of fruit tree diseases. The information of each text file include disease names, causal agents, diseased parts, symptoms, morphological characteristics of causal organisms and control methods for the diagnosing of crop diseases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.568-571
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• 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 Korea Society for Simulation
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• v.23 no.4
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• pp.85-95
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• 2014

Simulation for product design requires a lot of causal knowledge. Hence, knowledge integration is required for obtaining a new knowledge from existing knowledge. For example, a user requests knowledge for the heating cup. However, the knowledge base only has knowledge for heating and cup, not heating cup. At his situation, knowledge integration can generate a new heating cup knowledge from existing heating and cup knowledge. Therefore, the user can obtain the knowledge for heating cup. Hence, this study aims to propose a novel knowledge integration method for product design.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.101-107
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• 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 for Precision Engineering
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• v.12 no.10
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• pp.32-39
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• 1995

The design of injection molded polymeric parts has been done empirically, since it requires profound knowledge about the moldability and causal effects on the properties of the part, which are not available to designers through current CAD systems. An interactive computer-based design system is developed in order to realize the concept of rational design for the productivity and quality of mold making. The knowledge-based CAD system is constructed by adding the knowledge -base module for mold feature synthesis and appropriate CAE programs for mold design analysis in order to provide designers, at the initial design stage, with comprehensive process knowledge for feature synthesis, performance analysis and feature-based geometric modeling. A knowledge-based CAD system is a new tool which enables the concurrent design with integrated and balanced design decisions at the initial design stage of injection molding.