• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.405-410
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• 2002

In this paper, fuzzy system modeling using new hierarchical structure is suggested for the complex and uncertain system. The proposed modeling technique Is to decompose the fuzzy rule base structure into the above-rule base and the sub-rule base. By applying hierarchical fuzzy rules, they can be used efficiently and logically. Also, hieratical fuzzy rules can improve the accuracy and the transparency of structure in the fuzzy system. The genetic algorithm is applied for optimization of the parameters and the structure of the fuzzy rules. To show the effectiveness of the proposed method, fuzzy modeling of the complex nonlinear system is provided.

• Journal of Navigation and Port Research
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• v.32 no.7
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• pp.529-535
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• 2008

As a method to reduce collision accidents of ships at sea, this paper suggests an expert system for collision avoidance and navigation (hereafter "ESCAN"). The ESCAN is designed and developed by using the theory and technology of expert system and based on the information provided by AIS and RADAR/ARPA system. In this paper the ESCAN is composed of four(4) components; Facts/Data Base in charge of preserving data from navigational equipment, Knowledge Base storing production rules of the ESCAN, Inference Engine deciding which rules are satisfied by facts or objects, User System Interface for communication between users and ESCAN. The ESCAN has the function of real--time analysis and judgment of various encountering situations between own ship and targets, and is to provide navigators with appropriate plans of collision avoidance and additional advice and recommendation This paper, as a basic study, is to introduce the basic design and function of ESCAN.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1111-1116
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• 1993

In this study, we discuss the design of the expert system for the scheduling of the FMC(Flexible Manufacturing Cell) consisting of the several versatile machines. Due to the NP property, the scheduling problem of several machine FMC is very complex task. Thus we proposed the two heuritstic shceduling algorithms for solving the problem and constituted the algorithm based of solving the problem and constituted the algorithm base of ISS(Intelligent Scheduling System) using them. By the rules in the rule base, the best alternative among various algorithms in algorithm base is selected and applied in controlling the FMC. To show the efficiency of ISS, the scheduling output of ISS and the existent dynamic dispatching rule were tested and compared. The results indicate that the ISS is superior to the existent dynamic dispatching rules in various performance indexes.

• The Journal of Information Systems
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• v.1
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• pp.149-178
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• 1992

The field of Artificial Intelligence (AI) is growing, and many firms are investing in expert system, one of AI's subfields. An expert system is defined as a computer program designed to replicate some aspect of the decision making of one or more experts and to be used by nonexperts. The kernel of an expert system is the knowledge base, which consists of the facts and rules that represent the expert's knowledge. Firms need expert systems for training employees to provide competitive advantage. This paper describes the model of an instructional expert training system which interfaces to external programs, such as an ASCII file, a work-sheet program, and a database program. A model for such an expert training system, and its prototype have been developed to demonstrate its functionality. A modular knowledge base has been developed and implemented in support of this study. The modularized knowledge base offers the user an easy and quick maintenance of facts and rules, which are frequently required to change in future.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.2
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• pp.53-61
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• 1988

Games such as baseball, soccer are scheduled by a given game type such as tournament, league or their mixed form. The objective of this paper is to find an efficient game-scheduling method with respect to traveling distance, break-time and other conditions. In this paper we first present two models which minimize traveling distance. The first model that a match is played once each other is solved by a heuristic method. In the second model that a match is played more than once, teams are paired by a modified 0 - 1 programming, and the pairs are rearranged in order to generate a number of workable schedules. Then Expert Systems is applied to solve breake-time and other conditions. In order to represent expertise's knowledge effectively, we present a new design of knowledge-base and data-base, inference engine including many rules and meta-rules which controls the global system. In knowledge-base, binary relation among various attributes is used to ease not only knowledge acquisition but also system execution.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.761-764
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• 1993

In this paper we consider techniques for reducing the generated number of rules in learning fuzzy controllers of the state-space action-reinforcement type that can be simply implemented and that behave well in the presence of process noise. Fewer rules lead to better performance, less contradiction in controller action estimation, smaller required execution-time and make it easier for a human to comprehend the generated rules and possibly intervene.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.438-447
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• 2006

Fuzzy controller's design depends mainly on the rule base and membership functions over the controller's input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/output space as well as membership functions for each input variable. But it is found that some membership functions projected from different clusters have high degree of similarity. The number of membership functions of each input variable is then reduced using a similarity measure. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules and number of membership functions to 4 and 6 for input variables (error and change in error) maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with fuzzy PI and PD type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error (IAE) and integral-of-time multiplied absolute error (ITAE) and in each case the proposed schemes shows an identical performance.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2007.02a
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• pp.61-71
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• 2007

The Association Rules is the most important method in technology of the data mining. This text further study The Association Rules, has analyzed and commented to Apriori algorithm of The Association Rules. Have realized Apriori algorithm base on Visual Basic 6.0, probe into Apriori algorithm application among the digital library, show with experimental data of application of Association Rules in borrow in the data analysis in readers finally.

• The Korean Journal of Archival Studies
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• no.6
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• pp.73-120
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• 2002

Aural archival description has been rarely discussed, although it is one of the important process in managing and using aural archives. Aural archives not being cataloged are difficult to access; moreover they require tools and a lot of time to search appropriate records. This study concerning characteristics of archival description and aural archives analyzed rules for aural archival description in United Kingdom, Canada and United States of America Comparing and analyzing rules, a rules for aural archival description in Korea was proposed and arranged into eight areas modified from seven areas of ISAD(G). This study focused on developing rules for description covering all aspects of aural archives in Korea. The rules for aural archival description will offer a base of Korean Rules for Archival Description and building aural archival databases.