• Korean Journal of Computational Design and Engineering
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• v.4 no.1
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• pp.52-59
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• 1999

The CAD/CAM System for deep drawing transfer die tin mechanical press process has been developed. The developed CAD system can generate the drawing of transfer die in mechanical press. Using thee results from CAD system, it can generate the NC data to machine die's elements on the CAD system. This system can reduce design man-hour an human errors. In order to construct the system, it is used to automated the design process and generate the NC data using concepts of the designing rule and the machining rule. The developed system is based on the knowledge base system which is involved a lot of expert's technology in the practice field. Using AutoLISP language under the AutoCAD system, CTK customer language of SmartCAM is used as the overall CAD/CAM environment. Results of this system will be provide effective aids to the designer and manufacturer in this field.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.5
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• pp.163-170
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• 2005

Urban transit is a complex system that is combined electrically and mechanically, it is necessary to construct maintenance system for securing safety accompanying high-speed driving and maintaining promptly. Expert system is a computer program which uses numerical or non-numerical domain-specific knowledge to solve problems. In this research, we intend to develop the expert system which diagnose failure causes quickly and display measures. For the development of expert system, standardization of failure code classification system and creation of BOM(Bill Of Materials) have been first performed. Through the analysis of failure history and maintenance manuals, knowledge base has been constructed. Also, for retrieving the procedure of failure diagnosis and repair linking with the knowledge base, we have built RBR(Rule Based Reasoning) engine by pattern matching technique and CBR(Case Based Reasoning) engine by similarity search method. This system has been developed based on web to maximize the accessibility.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.852-855
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• 1995

In modern plant lndustry, dignosis system is an essential implement because a human operator cannot check the state of system all the time. The recent facility needs a computer system which is able to replace and extense the function of the human expert. Checking the state of the plant system, the computer system uses signals form sensors attached to the plant systems. But, It is difficult to predict the cause of the failure from the sensing signals. Because the relationship among the signals cannot be easily represented by mathematical models. So expert system based on a fuzzy rule and Neural network method is sugguested. Expert system decide whether aa state of the system is ordinary of failure by the evaluation of the signals. If the state of the system is unstable, expert system preprocess the signals. When fault is occurred in the machine, the expert system dignoses the state of the system and find the cause as a primary tool. If the expert system dose not find the adequate cause successfully, neural network system uses the preprocessed signals as an input and propose a cause of the failure.

• Journal of Ocean Engineering and Technology
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• v.17 no.1
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• pp.67-71
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• 2003

This paper presents the use of an evert system to automate process planning of block assembly, a task that is usually completed manually. In order to determine the sequence of assembly operation, a search method guided by rules, such as merging of related operations, grouping of similar operations, and precedence rules based on know-hows and geometrical reasoning, is used. In this paper, the expert system developed is explained in detail regarding a global database, control strategies, and rule bases. For verification purposes, the evert system has been applied to simple examples. Since the rule bases are isolated from the inference engine in the developed system, it is easy to add more rules in the future.

• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.55-74
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• 2000

A knowledge-based with production rules is a representation of static knowledge of an expert. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a method to reflect the dynamic nature of a system when we make inferences with a knowledge-based system. This paper suggests a strategy of dynamic inference that can be used to take into account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy production rules. A degree of match(DM) between actual input information and a condition of a rule is represented by a value [0,1]. Weights of relative importance of attributes in a rule are obtained by the AHP(Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected in an inference with fuzzy production systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.792-797
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• 2011

This paper discusses the application of a hybrid discretiziation method for the discretization procedure that needs to be included in discrete particle swarm optimization (DPSO) for the problem of allocating PV (photovoltaic) systems onto distribution power systems. For this purpose, this paper proposes a rule-based expert system considering the objective function value and its optimizing speed as the input parameters and applied it to the PV allocation problem including discrete decision variables. For multi-level discretization, this paper adopts a hybrid method combined with a simple rounding and sigmoid funtion based 3-step and 5-step quantization methods, and the application of the rule based expert system proposing the adequate discretization method at each PSO iteration so that the DPSO with the hybrid discretization can provide better performance than the previous DPSO.

• 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 Institute of Telematics and Electronics A
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• v.30A no.9
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• pp.46-58
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• 1993

Owing to the nature of locality in mask layouts, it appears that most mask layout problems can be solved by transforming a part of the given mask layout into a better layout segment continuously toward a global suboptimal solution. This notion of local transformation addresses major weak points of existing mask layout processing systems, which lack both extensibility and unifiability. This paper attempts to elaborate upon developing a new rule-based mask layout transformation system wherein most of the mask layout problems can be solved under the unified framework of local mask layout transformation. The rule-based mask layout transformation system is applicable to various mask layout problems such as net extraction, mask layout compaction, mask layout editing, and design rule checking. The experimental results show that the rule-based expert system approach is an efficient means of solving those mask layout problems, and thus confronting major drawbacks of existing layout processing systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.161-165
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• 1994

The shipbuilding industry is one of the domains which need an effective computer application. Particularly the productivity of process planning of a shipbuilding for crude -oil tanker can be greatly enhanced by introducing CAPP(Computer Aied Process Planning). In this paper we describe a prototype expert system which enables block division process planning in shipbuilding. The system determines block division lines of the midship sections of oiltanker. Case-based reasoning(CBR) approach is applied for this purpose instead of rule-based one.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.841-842
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• 1988

For determination of black-out area and restoration area by an expert system for fault section estimation and power system restoration using information from circuit breakers, it is necessary that the recognition of system isolation state and a method of finding the change of system isolation state by the state transition of breakers in isolated system. This paper presents a method of resolving the above problem by rule-based approach.