• Korean Journal of Computational Design and Engineering
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• v.6 no.2
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• pp.133-139
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• 2001

Since a great number of 2D engineering drawings are being used in industry and at the same time 3D CAD becomes popular in recent years, we need to reconstruct 3D CAD models from 2D legacy drawings. In this thesis, a combination of a feature recognition method and an expert system is suggested for the 3D solid model reconstruction. Modeling primitives of 3D CAD systems are recognized and constructed by using the pattern matching technique of the features modeling. Additional information for the 3D model reconstruction can be generated by extracting symbols or text entities which are related to form entities. For complex and indefinite cases which cannot be solved by the process of feature recognition, an expert system with a rule base has been used for decision-making. A 3D reconstruction system which recognizes 2D DXF drawing files has been implemented where models composed with protrusions, holes, and cutouts can be handled.

• Journal of Korean Ophthalmic Optics Society
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• v.11 no.3
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• pp.207-215
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• 2006

This study surveys the brand and design recognition by an expert vs. non-expert group in the eye glasses industry. This survey is to search for a way for domestic brands to compete and win foreign ones. The non-expert group chose design as the most noteworthy difference between domestic and foreign brands; likewise, the expert group appeared to first consider design on their choice. Brand recognition by the non-expert group appeared very low; on the other hand, expert group's recognition was higher with domestic brands than with foreign ones. In conclusion, the bounce back of domestic eye glasses business does not seem to be possible in a snap by a special recipe; rather, it is necessary to invest and make efforts to develop new techniques for better quality on the one hand, and to increase brand and design recognition on the other.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.455-456
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• 2002

Wear is one of the main factors causing breakdown and fault of machine, so ferrography technique analyzing wear particles can be an effective way for condition monitoring and fault diagnosis. On the base of the forward multilayer neural network, a nodes self-deleting neural network model is provided in this paper. This network can itself deletes the nodes to optimize its construction. On the basis of the nodes self-deleting neural network, an intelligent ferrography diagnosis expert system (IFDES) for wear particles recognition and wear diagnosis is described. This intelligent expert system can automatically slim lip knowledge by learning from samples and realize basically the entirely automatic processing from wear particles recognition to wear diagnosis.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1005-1010
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• 1989

A refined version of automatic micro-organism recognition and identification method, 'O.I.S.M.2' is proposed in this paper, using image processing based on an expert system. This proposed method is based on the segmentation of the organism image, characterizing segment features, which are independent of individual size and length. Complicated shapes of organisms are divided into basic shape segments defined in this paper such as lines, circles, ovals etc. Organisms can then be expressed simply in a set of segments, regardless their individual differences.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.12
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• pp.637-644
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• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.2
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• pp.119-132
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• 1993

This paper deals with the development of an expert process design system interfaced with CAD for porismatic parts in injection mold manufacture. The developed CAD/CAPP system consists of two modules such as CAD interface module and process design module. Parts are represented using AutoCAD system on the IBM PC/AT. CAD interface module recognizes form features and manufacturing features of the part using form feature recognition algorithm and manufacturing feature recognition rule base. Process design module selects operations and determines machine tools, cutting tools and operation sequencing by using knowledge base which is acquired from expert process planners. A case study is implemented to evaluate feasibilities of the function of the proposed system. The CAD/CAPP system can improve the efficiency of process design activities and reduce the time required for process design.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.237-240
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• 1989

This paper deals with the expert system for power system recognition of energized and deenergized system using circuit breaker information. The basic idea is isolating the system with the system matrix representing the system configuration and the states of the circuit breakers. The knowledge base is composed of these isolated systems and decision rules. The isolated system with the sources is recognized as the energized system and the system without the source as the deenergized system. The rules use the system matrix and the the inference scheme is simplified in a great deal. Above all, the overall searching labor of the rules is independent on the system size and it is possible to expand into the real system and the real time restoration can be carried out easily. The expert system is written in PROLOG.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.376-381
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• 1992

This paper presents an expert system for the segmentation of a 2.5-D image. The results of two segmentation approaches, edge-based and region-based, are combined to produce a consistent and reliable segmentation. Rich information embedded in the 2.5-D image is utilized to obtain a view independent surface patch description of the image, which can facilitate object recognition considerably.