• The KIPS Transactions:PartD
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• v.8D no.6
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• pp.823-834
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• 2001

In the active databases, whenever an event occurs, active rules with the matching event specifications are triggered automatically, its action will be executed. Because these rules may in turn trigger other rules including themselves, the set of rules may be executing each other indefinitely. These problem can be solved by rule termination analysis, and it is efficient for the rule termination to execute in compile time of rule. In this paper we not only design rule compiler with rule termination analyzer, but also propose its execution model and algorithm. The completeness of proposed model is verified by algorithm formalization of rule termination analysis.

• Journal of the Korean Society of Safety
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• v.32 no.3
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• pp.160-171
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• 2017

US national research laboratories developed the first Vital Area Identification (VAI) method for the physical protection of nuclear power plants that is based on Event Tree Analysis (ETA) and Fault Tree Analysis (FTA) techniques in 1970s. Then, Korea Atomic Energy Research Institute proposed advanced VAI method that takes advantage of fire and flooding Probabilistic Safety Assessment (PSA) results. In this study, in order to minimize the burden and difficulty of VAI, (1) a set of streamlined VAI rules were developed, and (2) this set of rules was applied to PSA fault tree and event tree at the initial stage of VAI process. This new rule-based VAI method is explained, and its efficiency and correctness are demonstrated throughout this paper. This new rule-based VAI method drastically reduces problem size by (1) performing PSA event tree simplification by applying VAI rules to the PSA event tree, (2) calculating preliminary prevention sets with event tree headings, (3) converting the shortest preliminary prevention set into a sabotage fault tree, and (4) performing usual VAI procedure. Since this new rule-based VAI method drastically reduces VAI problem size, it provides very quick and economical VAI procedure. In spite of an extremely reduced sabotage fault tree, this method generates identical vital areas to those by traditional VAI method. It is strongly recommended that this new rule-based VAI method be applied to the physical protection of nuclear power plants and other complex safety-critical systems such as chemical and military systems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.59-66
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• 2004

We define a SG(String Graph) and an ASG(Alive String Graph) to the purpose to do static analysis. For a life and death judgment, we apply the rule to the situation which the stone is included and not included. We define the rules that are SR(String Reduction), ER(Empty Reduction), ET(Edge Transform), and CG(Circular Graph), when the stone is not included. We define the rules that are DESR(Dead Enemy Strings Reduction) and SCSR(Same Color String Reduction), when the stone is included. We evaluate a SG that it is an ASG or not by using rules. And we use APC(Articulation Point Check) nile according to number of articulation points lot a life and death judgment. The performance of our method has been tested on the problem set IGS_31_counted form the Computer Go Test Collection. The test set contains 11,191 Points and 1,123 Strings. We obtain 92.5% accuracy of Points and 95.7% accuracy of Strings.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.135-142
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• 2009

Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. The major techniques used in data mining are mining association rules, classification and clustering. Since these techniques are used individually, it is necessary to develop the methodology for rule extraction using a process of integrating these techniques. Rule extraction techniques assist humans in analyzing of large data sets and to turn the meaningful information contained in the data sets into successful decision making. This paper proposes an autonomous method of rule extraction using clustering and rough set theory. The experiments are carried out on data sets of UCI KDD archive and present decision rules from the proposed method. These rules can be successfully used for making decisions.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.703-707
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• 1991

During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for reaserch in the applications of fuzzy set theory. A key component of the fuzzy controller is a rule-based system which provides a linguistic description of control strategy. This strategy has the form of a collection of fuzzy conditional statements which are implemented and manipulated using fuzzy set theory. In this paper, we propose the rule-based controller for DC motor speed control. The result of performance compare with PID controller to verify the validity of proposed algorithm.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.37-43
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• 2011

In pattern analysis, dimensionality reduction and reasonable identification rule generation are very important parts. This paper performed effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, this paper constructed the hierarchically identifiable rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, this paper demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.315-320
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• 1997

Rule minimization technique adapted from rough set theory was applied to remove redundant knowledge which is not necessary to make a knowledge base. New algorithm to diagnose fault using Improved Fuzzy Cognitive Maps(I-FCMs), and Fuzzy Associative Memory(FAM) is proposed. I-FCM[22] is superior to gathering knowledge from many experts and descries dynamic behaviors of systems very well. I-FCM is not only a knowledge base, but also a inference engine. FAM has learning capability like neural network[12]. Rule minimization and composition of I-FCM and FAM make it possible to construct compact knowledge base and breaks the border between inference engine and knowledge base.

• Asia pacific journal of information systems
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• v.14 no.1
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• pp.79-100
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• 2004

As a part of ebXML(Electronic Business using eXtensible Markup Language) framework, BPSS(Business Process Specification Schema) has been provided to support the direct specification of the set of elements required to configure a runtime system in order to execute a set of ebXML business transactions. The BPS,' is available in two stand-alone representations, a UML version and an XML version. Due to the limitations of UML notations and XML syntax, however, current ebXML BPSS specification fails to specify formal semantic constraints completely. In this study, we propose a constraint classification scheme for the BPSS specification and describe how to formally represent those semantic constraints using OCL(Object Constraint Language). As a way to validate p Business Process Specification(BPS) with the formal semantic constraints, we suggest a rule-based approach to represent the formal constraints and demonstrate its detailed mechanism for applying the rule-based constraints to the BPS with a prototype implementation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.5
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• pp.527-537
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• 1990

This paper describes an intelligent control-based job scheduler, named ESIJOBS, for flexible manufacturing system. In order to construct rulebase of this system, traditional rules of job scheduling in FMS are examined and evaluated. This result and the repetitional simulations with graphic monitoring system are used to form the rulebase of ESIJOBS, which is composed of three parts:six part selection rules, four machine center selection rules, and twenty-one metarules. Appropriate scheduling rule sets are selected by this rulebase and manufacturing system status. The performances of all simulations are affected by random breakdowns of major FMS components during each simulation. Six criteria are used to evaluate the performance of each scheduling. The two modes of ESIJOBS are simulated and compared with combinational 24 rule-set simulations. In this comparison ESIJOBS dominated the other rule-set simulations and showed the most excellent performance particularly in three criteria.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.83-91
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• 1997

DELVAUX is an inductive learning environment that learns Bayesian classification rules from a set o examples. In DELVAUX, a genetic a, pp.oach is employed to learn the best rule-set, in which a population consists of rule-sets and rule-sets generate offspring by exchanging some of their rules. We have explored a meta-learning a, pp.oach in the DELVAUX learning environment to improve the classification performance of the DELVAUX system. The meta-learning a, pp.oach learns the bias of a classifier so that it can evaluate the prediction made by the classifier for a given example and thereby improve the overall performance of a classifier system. The paper discusses the meta-learning a, pp.oach in details and presents some empirical results that show the improvement we can achieve with the meta-learning a, pp.oach.