• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.65-81
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• 2001

This paper presets the process of knowledge aquisition by partitioning the phase of analysis & design for knowledge-base module construction of Expert System into five steps to derive rule systematically. Also, this paper presents the process and the task that knowledge engineer must do work each step. The knowledge-base module of STAFS expert system was constructed by considering the destruction rate and the commander\`s intention using the proposed rule derivation methodology.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.53-66
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• 1994

In this paper, a fuzzy expert system is developed for fault diagnoisis of a drum-type boiler system in fossil power plants. The develped fuzzy espert system is composed of knowledge base, fuzzification module, knowledge base process module, knowledge base management module, inference module, and linguistic approximation module. The main objective of the fuzzy expert system is to check the states of the system including the drum level and detect faults such as the feedwater flow sensor fault, feedwater flow control valve fault, and water wall bube rupture. The fuzzy expert system diagnoses faults using process values, manipulated values, and knowledge base which is built via interviews and questionaries with the experts on the plant operations. Finally, the validity of the developed fuzzy expert system is shown via experiments using the digital simulator for boiler system is Seoul Power Plant Unit 4.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.2
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• pp.27-42
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• 1989

REA (Restricted Entity Aspect) model is a knowledge representation model to classify the aspect type, the EA model component, into five aspects (IS-A-aspect, A-PART-OF aspect, attribute aspect, role aspect, and operation aspect). EATPS, the knowledge representation system, consists of user interface module, knowledge creation module, instance management module, schema management module, and integrity checking module. EATPS creates and manages interactively REA model based knowledge base. This paper shows the structure and functions of EATPS, the design and interactive construction of the knowledge definition language EAKDL, the functions and algorithm of class creation module, and the functions and algorithm of instance creation module to include inheritance inference mechanism.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.5
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• pp.1008-1013
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• 2009

In this paper, we proposed the biological signal (body heat, pulse, breathe rate, and blood pressure) analysis system using wireless sensor. In order to analyze, we designed a back-propagation neural network system using expert group system. The proposed system is consist of hardware patt such as UStar-2400 ISP and Wireless sensor and software part such as Knowledge Base module, Inference Engine module and User Interface module which is inserted in Host PC. To improve the accuracy of the system, we implement a FEC (Forward Error Correction) block. For conducting simulation, we chose 100 data sets from Knowledge Base module to train the neural network. As a result, we obtained about 95% accuracy using 128 data sets from Knowledge Base module and acquired about 85% accuracy which experiments 13 students using wireless sensor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.633-639
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• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1018-1023
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• 1993

In mechanical structure design area, many FEM (Finite Element Method) packages are used. But the design using FEM packages depends on an iterative trial and error manner and general CAD systems cannot cope with the change of parameters. This paper presents a methodology for building a designing system of a mechanical structure. This system can generate the drawing for a designed structure automatically. It consists of three steps: generation of a structure by selection of the parameters, stress analysis, and generation of a drawing using CAD system. FEM module and parametric CAD module are developed for this system. Inference engine module generates the parameters with a rule base and a model base, and also evaluates the current structure. The parametric design module generates geometric shapes automatically with given dimension. Parametric design is implemented with the artificial intelligent technique. In older to the demonstrate the effectiveness of the developed system, a frame set of bicycle was designed. The system was implemented on an SUN workstation using C language under OpenWindows environment.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Journal of the Korean Society for Precision Engineering
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• v.8 no.4
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• pp.55-63
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• 1991

This paper deals with development of a Computer Aided Process Planning System based on knowledge base in addition to database for injection mold as a part of Computer Integrated Manufacturing System for injection mold manufacture. The prposed system consists of four modules such as manufacturing feature code generation module machine tool selection and sequencing module, operation and cutting tool selection mudule and standard time estimation module. The system is programmed by using Turbo Pascal on the IBM-PC/AT. The performance of the system is evaluated by using real problems and the test results indicate that the proposed system is a practical and efficient system.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.1
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• pp.9-17
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• 1992

This paper represents the expert system for selecting the superstructure types of bridges in the part of a preliminary bridge design. The system is implemented with the expert system tool called K-CLIPS which uses the production system for knowledge representation and provides the mechanism of forward chaining. This expert system is composed of a knowledge base, data base and a knowledge module built by the tool which consists of the knowledges on design procedures. During symbolic processing the data base supports the sub system in knowledge base.