• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1338-1343
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• 2003

Data is the expression of the language or numerical values that show some characteristics. And information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns and make decisions. Today, knowledge extraction and application of the knowledge are broadly accomplished to improve the comprehension and to elevate the performance of systems in several industrial fields. The knowledge extraction could be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge can be drawn by rules. Clustering (CU, input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for expression the knowledge by rules. In this paper, the various approaches of the knowledge extraction are examined by categories that separate the methods by the applied industrial fields. Also, the several test data and the experimental results are compared and analysed based upon the applied techniques that include CL, ISP, NF, NN, EM, and so on.

• 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 information and communication convergence engineering
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• v.8 no.1
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• pp.89-94
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• 2010

The most important of rule-base system is the knowledge base that determines the power of rule-base system. The important form of this knowledge is how to descript kinds of rules. The Rule-Base System (RBS) has been using in many field that need reflect quickly change of business rules in management system. As far, when develop the Rule-Based System, we must make a rule engine with a general language. There are three disadvantage of in this developed method. First, while there are many data that must be processed in the system, the speed of processing data will become very slow so that we cannot accept it. Second, we cannot change the current system to make it adaptive to changes of business rules as quickly as possible. Third, large data make the rule engine become very complex. Therefore, in this paper, we propose the two important methods of raising efficiency of Rule-Base System. The first method refers to using the Relational database technology to process the rules of the Rule-Base System, the second method refers to a algorithm of according to Quine McCluskey formula compress the rows of rule table. Because the expressive languages of rule are still remaining many problems, we will introduce a new expressive language, which is Rule-Base Data Model short as RBDM in this paper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.194-199
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• 2003

Data are an expression of the language or numerical values that show some features. And the information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns or make a decision. Today, knowledge extraction and application of that are broadly accomplished for the easy comprehension and the performance improvement of systems in the several industrial fields. The knowledge extraction can be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge is drawn by rules with data mining techniques. Clustering (CL), input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for the knowledge expression based upon rules. In this paper, the various approaches of the knowledge extraction are surveyed and categorized by methodologies and applied industrial fields. Also, the trend and examples of each approaches are shown in the tables and graphes using the categories such as CL, ISP, NF, NN, EM, and so on.

• Management Science and Financial Engineering
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• v.15 no.2
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• pp.101-118
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• 2009

In quantitative contexts, data mining is widely applied to the prediction of stock prices from financial time-series. However, few studies have examined the potential of data mining for shedding light on the qualitative problem-solving knowledge of experts who make stock price predictions. This paper presents a GA-based data mining approach to characterizing the qualitative knowledge of such experts, based on their observed predictions. This study is the first of its kind in the GA literature. The results indicate that this approach generates rules with higher accuracy and greater coverage than inductive learning methods or neural networks. They also indicate considerable agreement between the GA method and expert problem-solving approaches. Therefore, the proposed method offers a suitable tool for eliciting and representing expert decision rules, and thus constitutes an effective means of predicting the stock price index.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.735-739
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• 2001

This paper describes a research work of developing computer-aided design of a product with bending and piercing for progressive working. An approach to the system for progressive working os based on the knowledge-based rules. Knowledge for the system is formulated from plasticity theorise, experimental results and the empirical knowledge of field experts. the system has been written in AutoLISP on the AutoCAD with a personal computer and is composed of three main modules, which are input and shape treatment, flat pattern layout and strip layout modules. Strip layout of the system is designed by using fuzzy set theory. Process planning is determinated by fuzzy value according to several rules. Strip layout drawing generated in strip layout module is presented in 3-D graphic forms, including bending sequences and piercing processes with punch profiles divided into for external area. Results obtained using the modules enable the manufacturer for progressive working of electric products to be more efficient in this field.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.229-232
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• 2001

Wireless Internet based in WAP(Wireless Application Protocol) has been a very useful and attractive means picking up information through service irrelavant to time and space. And more the request of user has been diverse. In this paper, we implemented a Diet Menu System using expert knowledge-based rules on the WAP environment. This enables the moving man interested in diet to get information through wireless mobil hand phone. There are only connection-oriented system for this type of service till now. This paper has designed basic expert knowledge rules for result display, verified and implemented for WAP environment.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.2
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• pp.177-186
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• 2002

This paper describes a research work on the development of computer-aided design system far the deep drawing & ironing of high pressure gas cylinder. An approach to the design system is based on the knowledge-based rules. Knowledge for the system is formulated from plasticity theories, handbook, experimental results and the empirical knowledge of field experts. This system has been written in AutoLISP on the AutoCAD Rl4.0 using personal computer. This system is composed of three modules which are input. process design and drawing.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.2
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• pp.38-47
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• 2001

This paper decribes the development of computer-aided design of a very precise progressice die for lead frame of semiconductor chip. The approach to the system is based on knowledgr-based rules. Knowledge of fie이 experts. This system has been written in AutoLISP using AutoCAD ona personal computer and the I-DEAS drafting programming Language on the I-DEAS mater series drafting with on HP9000/715(64) workstation. Data exchange between AutoCAD and I-DEAS master series drafting is accomplished using DXF(drawing exchange format) and IGES(initial graphics exchange specification) files. This system is composed of six main modules, which are input and shape treatment, production feasibility check, strip layout, data conversion, die layout, and post processing modules. Based on Knowledge-based rules, the system considers several factors, such as V-notches, dimple, pad chamfer, spank, cavity punch, camber, coined area, cross bow, material and thickness of product, complexities of blank geometry and punch profiles, specifications of available presses, and the availability of standard parts. As forming processes and the die design system using 2D geometry recognition are integrated with the technology of process planning, die design, and CAE analysis, the standardization of die part for lead frames requiting a high precision process is possible. The die layout drawing generated by the die layout module s displayed in graphic form. The developed system makes it possible to design and manufacture lead frame of a semiconductor more efficiently.