• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.983-987
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• 2001

This paper deals with an automated computer-aided process planning system by which designer can determine operation sequences even if they have little experience in process planning of wheel bolt products by a multi-stage former. The approach to the system is based on knowledge-based rules and a process knowledge base consisting of design rules is built. Knowledge for the system is formulated from plasticity theories, empirical results and the empirical knowledge of field experts. Programs for the system have been written in AutoLISP for the AutoCAD using a personal computer and are composed of two main modules. An attempt is made to link programs incorporationg a number of expert design rules to form a useful package. Results obtained using the modules enable the designer and manufacturer of wheel bolt product to be more efficient in this field.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.1-16
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• 2007

According to the expansion of the Internet use and the utilization of e-business, there are an increasing number of studies of intelligent-based systems for the preparation of ubiquitous environment. In addition, expert systems have been developed from Stand Alone types to web-based Client-Server types, which are now used in various Internet environments. In this paper, we investigated the environment of development for web-based expert systems, we classified and analyzed them according to type, and suggested general typical models of web-based expert systems and their architectures. We classified the web-based expert systems with two perspectives. First, we classified them into the Server Oriented model and Client Oriented model based on the Load Balancing aspect between client and server. Second, based on the degree of knowledge and inference-sharing, we classified them into the No Sharing model, Server Sharing model, Client Sharing model and Client-Server Sharing model. By combining them we derived eight types of web-based expert systems. We also analyzed the location problems of Knowledge Bases, Fact Bases, and Inference Engines on the Internet, and analyzed the pros & cons, the technologies, the considerations, and the service types for each model. With the framework proposed from this study, we can develop more efficient expert systems in future environments.

• Journal of the Korean Society of Safety
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• v.7 no.4
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• pp.33-43
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• 1992

A methodology to develop an expert system for chemical plant fault diagnosis based on functional-structure of chemical plant is proposed. A methodology to generalize and utilize the heuristic know-edge of plant operators is also developed. A plant can be seen as a Hierarchical set of subsystems. Each subsystem is called a SCOPE. The state of the plant and the behavior of each subsystem is managed by the SCOPES. An expert system based on this functional structure and knowledge base has been developed and ar plied to the subprocess of etylene plant to evaluate the effectiveness of the methodology.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.375-381
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• 2007

Cognitive map (CM) has long been used as an effective way of constructing the human thinking process. In literature regarding CM, a number of successful researches were reported, where CM based what-if analysis could enhance firm's performance. However, there exit very few researches investigating the CM generation method. Therefore this study proposes a GENCOM (Genetic Algorithm based Cognitive Map Generator). In this model combined with CM and GA, GA will find the optimal weight and input vector so that the CM generation. To empirically prove the effectiveness of GENCOM, we collected valid questionnaires from expert in S/W sales cases. Empirical results showed that GENCOM could contribute to effective CM simulation and very useful method to extracting the tacit knowledge of sales experts.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.4
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• pp.153-180
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• 1994

The assemblability is determined by the structure of product and the relationship between composing parts and machining parts. In this paper, the bolt was divided into bolt-head, -shaft, -thread and -end. For higher assemblability in bolting process, it was analyzed the geometric and technological characteristics of bolts were analysed regarding pre- and in-assembly process. And this paper presents the knowledge-based expert system to assist for designer in the processor of designing bolt for easier assembly. The developed expert system for supporting bolt design assemblability which is named as BDFA SYSTEM consists of two system such as "BOLT DESIGN SYSTEM" which provide feasible assembly bolt design to designer and "EVALUATION SYSTEM" which provide assembly evaluation to alternative of bolt design.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.11
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• pp.596-603
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• 2004

This paper proposes an expert system based on the pattern recognition method which can enhance the accuracy and effectiveness of real-time bus reconfiguration strategy for the transfer of faulted load when a main transformer fault occurs in the automated substation. The minimum distance classification method is adopted as the pattern recognition method of expert system. The training pattern set is designed MTr by MTr to minimize the searching time for target load pattern which is similar to the real-time load pattern. But the control pattern set, which is required to determine the corresponding bus reconfiguration strategy to these trained load pattern set is designed as one table by considering the efficiency of knowledge base design because its size is small. The training load pattern generator based on load level and the training load pattern generator based on load profile are designed, which are can reduce the size of each training pattern set from max L/sup (m+f)/ to the size of effective level. Here, L is the number of load level, m and f are the number of main transformers and the number of feeders. The one reduces the number of trained load pattern by setting the sawmiller patterns to a same pattern, the other reduces by considering only load pattern while the given period. And control pattern generator based on exhaustive search method with breadth-limit is designed, which generates the corresponding bus reconfiguration strategy to these trained load pattern set. The inference engine of the expert system and the substation database and knowledge base is implemented in MFC function of Visual C++ Finally, the performance and effectiveness of the proposed expert system is verified by comparing the best-first search solution and pattern recognition solution based on diversity event simulations for typical distribution substation.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.781-788
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• 1999

In this paper, an expert system is proposed, which can be effectively applied to the large-scale systems with the diversity time constraints, the objectives and the unfixed system structure. The inference scheme of the expert system have the integrated structure composed of the intuitive inference module and logical inference module in order to support effectively the operating constraints of system. The intuitive inference module is designed using the pattern matching or pattern recognition method in order to search a same or similar pattern under the fixed system structure. On the other hand, the logical inference module is designed as the structure with the multiple inference mode based on the heuristic search method in order to determine the optimal or near optimal control strategies satisfing the time constraints for system events under the unfixed system structure, and in order to use as knowledge generator. Here, inference mode consists of the best-first, the local-minimum tree, the breadth-iterative, the limited search width/time method. Finally, the application results for large-scale distribution SCADA system proves that the inference scheme of the expert system is very effective for the large-scale system. The expert system is implemented in C language for the dynamic mamory allocation method, database interface, compatability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.213-222
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• 1998

The objective of this study is to develop a knowledge-based scheduler applying simulation and knowledge base. This study utilizes a machine induction to build knowledge base which enables knowledge acquisition without domain expert. In this study, the best job dispatching rule for each order is selected according to the specifications of the order information. And these results are built to the fact base and knowledge base using the attribute-oriented induction method and simulation. When a new order enters in the developed system, the scheduler retrieves the knowledge base in order to find a matching record. If there is a matching record, the scheduling will be carried out by using the job dispatching rule saved in the knowledge base. Otherwise the best rule will be added to the knowledge base as a new record after scheduling to all the rules. When all these above steps finished the system will furnish a learning function.

• Journal of Electrical Engineering and information Science
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• v.3 no.3
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• pp.355-365
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• 1998

The purposes of this study are to propose a network for the characterizing of the input data and to show how to design predictive neural net재가 expert system which doesn't need previous knowledge base. We derived this network from the radial basis function networks(RBFN), and named it as a statistical EBFN. The proposed network can replace the statistical methods for analyzing dynamic relations between target disease and other parameters in medical studies. We compared statistical RBFN with the probabilistic neural network(PNN) and fuzzy logic(FL). And we testified our method in the diabetes prediction and compared our method with the well-known multilayer perceptron(MLP) neural network one, and showed good performance of our network. At last, we developed the diabetes prediction expert system based on the proposed statistical RBFN without previous knowledge base. Not only the applicability of the characterizing of parameters related to diabetes and construction of the diabetes prediction expert system but also wide applicabilities has the proposed statistical RBFN to other similar problems.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.20-30
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• 2005

In the anaerobe identification, when we develop the clinical decision support system for department of laboratory medicine, we must consider expression of an incomplete knowledge structure and addition of an evolving knowledge based on an expert's informal and heuristic knowledge is very complicated work flow. In the present study, we developed the system for anaerobe identification to advise on identification of unknown bacillus using knowledge base and inference engine. In the future, we are planning to develop the clinical decision support system for the whole bacteria not only an anaerobe but also aerobe to offer an expert's static and dynamic knowledge.