• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.353-359
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• 2005

In this research, we propose a Unified Fuzzy rule-based knowledge Inference Systems (UFIS) to help the expert in cosmetic brand detection. Users' preferred cosmetic product detection is very important in the level of CRM. To this purpose, many corporations trying to develop an efficient data mining tool. In this study, we develop a prototype fuzzy rule detection and inference system. The framework used in this development is mainly based on two different mechanisms such as fuzzy rule extraction and RDB (Relational DB)-based fuzzy rule inference. First, fuzzy clustering and fuzzy rule extraction deal with the presence of the knowledge in data base and its value is presented with a value between 0 -1. Second, RDB and SQL (Structured Query Language)-based fuzzy rule inference mechanism provide more flexibility in knowledge management than conventional non-fuzzy value-based KMS (Knowledge Management Systems).

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.852-855
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• 2010

Tree type of Rule Case will be processed by the method that provide practical Rule Case to Rule Engine that is made with procedural language beforehand, then the Rule Engine according to the condition of the special Rule Case to return result in current Rule-Based System. There are two disadvantages in the method; the first is according to specific business rule after construct the Rule Engine when the business rule changing the Rule Engine also must be changed. The second is when Rule have many conditions the Rule Engine will become very complex and the speed of processing Rule Case will become very slow. In this paper, we will propose a simplified algorithm that according to the theory of ID Tree to produce Rules which be used in Rule-Based System. The algorithm can not only produce Rules but also make sure of satisfying change of business rule by execute the algorithm. Because it is not necessary to make a Rule Engine, we will anticipate effect of increasing speed and reducing cost from Rule-Based System of applying the algorithm.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.121-129
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• 2006

Rule-based Expert system using Fuzzy technique inferences various rules by user's input condition and the most proper control signal. This system can lose objectivity by input condition which depends on user's decision. This paper can solve those problems by adding case-based system's technique. The traffic signal control expert system is proposed to store the cases based on the statistics, days, seasons and various circumstances and use them.

• International Journal of Contents
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• v.5 no.3
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• pp.79-85
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• 2009

Emergent requests or urgent information among enterprises require their intimate collaboration in B2B EC (electronic commerce). This paper analyzes the needs of intimate cooperation of distributed EC systems in terms of business contracts and presents an active rule-based methodology of close cooperation among EC systems and an active rule component to support it. Since the rule component provides high level rule patterns and event-based immediate processing, system administrators and programmers can easily program and maintain intimate cooperation of distributed EC systems independently to the application logic. The proposed active rule component facilitates HTTP protocol. Its prototype is implemented in B2B EC environment and evaluated using basic trigger facility of a commercial DBMS.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.1
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• pp.98-103
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• 1989

A new system for logic optimization at gate-level is proposed in this paper. Ths system is rule-based, i which the rules represent the local trnsformation replacing a portion of circuits with the simplified equivalent circuits. In this system, 'rule generalization' and 'local optimization' are proposed for effective pattern matching. Rule generalization is used to reduce the circuit-search for pattern matching, and local optimization, to exclude unnecessary circuit-search. In additionk, in order to reduce unnecessary trial of pattern matching, the matching order of circuit patern is included in the rule descriptions. The effectiveness of this system is shown by its application ot the circuits which are generated by a hardware compiler.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1149-1157
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• 2017

The satisfiability problem is always a core problem in artificial intelligence (AI). And how to improve the efficiency of algorithms solving the satisfiability problem is widely concerned. Algorithm IER (Improved Extension Rule) is based on extension rule. The number of atoms and the number of clauses affect the efficiency of the algorithm IER. DPLL rules are helpful to reduce these numbers. Then a complete algorithm CIER based on splitting rule and extension rule is proposed in this paper in order to improve the efficiency. At first, the algorithm CIER (Complete Improved Extension Rule) reduces the scale of a clause set with DPLL rules. Then, the clause set is split into a group of small clause sets. In the end, the satisfiability of the clause set is got from these small clause sets'. A strategy MOAMD (maximum occurrences and maximum difference) for the algorithm CIER is given. With this strategy, a better arrangement of atoms could be got. This arrangement could make the number of small clause sets fewer and the scale of these sets smaller. So, the algorithm CIER will be more efficient.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.302-304
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• 2011

Rule based machine learning techniques are very important in our real world now. We can list out some important application which we can apply rule based machine learning algorithm such as medical data mining, business transaction mining. The different between rules based machine learning and model based machine learning is that model based machine learning out put some models, which often are very difficult to understand by expert or human. But rule based techniques output are the rule sets which is in IF THEN format. For example IF blood pressure=90 and kidney problem=yes then take this drug. By this way, medical doctor can easy modify and update some usable rule. This is the scenario in medical decision support system. Currently, Rough set is one of the most famous theory which can be used for produce the rule. LEM2 is the algorithm use this theory and can produce the small set of rule on the database. In this paper, we present an improvement of LEM2 algorithm which incorporates the variable precision techniques.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.2
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• pp.185-199
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• 2005

With the growth of internet shopping malls, there is increasing interest in comparison shopping mall. However most comparison sites compare only book prices by collecting simple XML data and do not provide .the exact comparison Including precise shipping costs. Shipping costs vary depending on each customer's address, the delivery method, and the category of selected goods, so rule based system is required in order to calculate exact shipping costs. Therefore, we designed and implemented comparison shopping mall which compares not only book prices but also shipping costs using rule based inference. By adopting the extensible Rule Markup language (XRML) approach, we proposed the methodology of extracting delivery rules from Web pages of each shopping mall. The XRML approach can facilitate nearly automatic rule extraction from Web pages and consistency maintenance between Web pages and rule base. We developed a ConsiderD system which applies our rule acquisition methodology based on XRML. The objective of the ConsiderD system is to compare the exact total cost of books including the delivery cost over Amazon.com, BarnesandNoble.com, and Powells.com. With this prototype, we conducted an experiment to show the potential of automatic rule acquisition from Web pages and illustrate the effect of delivery cost.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.319-326
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• 2000

Expert systems using uncertain and ambiguous knowledge are not of the recent interests about uncertainty problem for performing inference similar to the decision making of a human expert. Human factors on rule-based systems often involve uncertain information. Expert systems had been used the methods of conflict resolution in a rule conflict situation, but this methods not properly solved the rule conflict. If a human expert appends a new rule to an original rule base, the rule base rightly causes a rule conflict. In this paper, the problem of rule conflict is regarded as one in which uncertainty of information is fundamentally involved. In the reduction of problem with uncertainty, we propose an enhanced rule ordering method, which improve the rule ordering method using Dempster-Shafer theory. We also propose a combinative index, which involve human factors of experts decision making.

• The Journal of Information Technology
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• v.4 no.3
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• pp.139-154
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• 2001

In current role-based systems, security officers handle assignments of users to roles. This may increase management efforts in a distributed environment because of the continuous involvement from security officers. The technology of role-based delegation provides a means for implementing RBAC in a distributed environment with empowerment of individual users. The basic idea behind a role-based delegation is that users themselves may delegate role authorities to other users to carry out some functions on behalf of the former. This paper presents a rule-based framework for user-level delegation model in which a user can delegate role authority by creating new delegation roles. Also, a rule-based language for specifying and enforcing the policies is introduced.