• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.99-105
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• 2016

The active rule system is a key element of the rule-based mobile agent middleware system for activeness and autonomy of the sensor network. The rule manager, which is the main components of active rule based mobile agent framework and active rule system, performs the control and management of the rule-related processes. In this paper, we design and implement the roles and functions of the rule manager in detail. The proposed rule manager plays an important role in the sensor network environment. The sensor data server loads the active rule on the mobile agent by the rule manager according to the situations, and the mobile agent migrates to the destination node and performs the designated action. This active rule-based mobile agent middleware system presents the usefulness for the various sensor network applications. Through the rule execution experiment using the rule-based mobile agent, we show the adaptability and applicability of rule-based mobile agent middleware system to the dynamic environmental changes in sensor networks.

• Nonlinear Functional Analysis and Applications
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• v.26 no.5
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• pp.935-947
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• 2021

An efficient adaptive scheme based on a triple mixed quadrature rule of precision nine for approximate evaluation of line integral of analytic functions has been constructed. At first, a mixed quadrature rule SM₁(f) has been formed using Gauss-Legendre three point transformed rule and five point Booles transformed rule. A suitable linear combination of the resulting rule and Clenshaw-Curtis seven point rule gives a new mixed quadrature rule SM₁₀(f). This mixed rule is termed as triple mixed quadrature rule. An adaptive quadrature scheme is designed. Some test integrals having analytic function integrands have been evaluated using the triple mixed rule and its constituent rules in non-adaptive mode. The same set of test integrals have been evaluated using those rules as base rules in the adaptive scheme. The triple mixed rule based adaptive scheme is found to be the most effective.

• 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.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.151-173
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• 2005

We developed a methodology of rule acquisition from texts such as Web pages which utilizes ontology in identification of rule components. We expect that the proposed methodology can reduce the bottleneck of rule acquisition and contribute to the utilization of rule based systems. As parts of our research, we designed an ontology for rule acquisition named OntoRule and proposed a rule acquisition methodology through OntoXRML which is an acquisition tool using OntoRule. Also, we evaluated our approach by calculating missed recommendations and wrong recommendations of rule components in rule acquisition experiments over three online bookstores.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.210-214
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• 2002

Association rule mining is an exploratory learning task to discover some hidden dependency relationships among items in transaction data. Quantitative association rules denote association rules with both categorical and quantitative attributes. There have been several works on quantitative association rule mining such as the application of fuzzy techniques to quantitative association rule mining, the generalized association rule mining for quantitative association rules, and importance weight incorporation into association rule mining fer taking into account the users interest. This paper introduces a new method for generalized fuzzy quantitative association rule mining with importance weights. The method uses fuzzy concept hierarchies fer categorical attributes and generalization hierarchies of fuzzy linguistic terms fur quantitative attributes. It enables the users to flexibly perform the association rule mining by controlling the generalization levels for attributes and the importance weights f3r attributes.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.379-385
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• 2006

To improve reusability and adaptation of variable components, rule-based component development has been used. Rule engines usually need additional script languages for rule expression and have difficulty in expressing complex business rules. In this paper, we propose a compiler-based rule engine for rich rule expression and improving performance. This rule engine uses Java programming language to express conditions and action parts of rules and that it can easily express complex business rules. It creates and executes condition and action objects at run time. In view of Performance, the rule engine is better than a script based rule engine. According to our experiments, our compiler-based nile engine shows 2.5 times better performance that script-based JSR 94 rule engine.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• Korean Journal of Logic
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• v.14 no.2
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• pp.105-121
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• 2011

The late professor Young-Jung Kim advanced a view that Existential Specification Rule (ES Rule) can be understood as a kind of polylemma. In arguing for this view, he also claimed that all propositions containing free variables are universal propositions. In this paper, I argue that his view on free variables incur numerous problems. Moreover, I introduce a new rule of inference called 'Universalized Conditionalization Rule' (UC Rule), so that I can show that his insight about ES Rule can be substantialized without an appeal to his problematic view on free variables. Finally, I show that ES Rule can be directly derived from UC Rule.

• Journal of applied mathematics & informatics
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• v.32 no.5_6
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• pp.635-647
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• 2014

In this paper, a generalized perturbed midpoint rule is established. Various error bounds for this generalization are also obtained.