• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.271-275
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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)

• 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).

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.308-317
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• 2016

Multiple evidences based decision making is an important functionality for computers and robots. To combine multiple evidences, mathematical theory of evidence has been developed, and it involves the most vital part called Dempster's rule of combination. The rule is used for combining multiple evidences. However, the combined result gives a counterintuitive conclusion when highly conflicting evidences exist. In particular, when we obtain two different sources of evidence for a single hypothesis, only one of the sources may contain evidence. In this paper, we introduce a modified combination rule based on the partial conflict measurement by using an absolute difference between two evidences' basic probability numbers. The basic probability number is described in details in Section 2 "Mathematical Theory of Evidence". As a result, the proposed combination rule outperforms Dempster's rule of combination. More precisely, the modified combination rule provides a reasonable conclusion when combining highly conflicting evidences and shows similar results with Dempster's rule of combination in the case of the both sources of evidence are not conflicting. In addition, when obtained evidences contain multiple hypotheses, our proposed combination rule shows more logically acceptable results in compared with the results of Dempster's rule.

• 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.

• 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.

• Honam Mathematical Journal
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• v.36 no.2
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• pp.291-303
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• 2014

In this paper, through a direct computation with subintervals partitioning [0, 1], we compute better a posteriori bounds for the average case error of the difference between the true value of $I(f)=\int_{0}^{1}f(x)dx$ with $f{\in}C^r$[0, 1] minus the composite trapezoidal rule and the composite trapezoidal rule minus the basic trapezoidal rule for $r{\geq}3$ by using zero mean-Gaussian.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.4
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• pp.81-95
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• 2000

A knowledge-based system with fuzzy production rules is a representation of static knowledge of an expert. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a strategy to reflect the dynamic nature of real system when we make inferences with a knowledge-based system. This paper proposes a strategy of dynamic inferencing for a knowledge-based system with fuzzy production rules. The strategy suggested in this paper applies weights of attributes of conditions of a rule in the knowledge-base. A degree of match(DM) between actual input information and a condition of a rule is represented by a value [0,1]. Weights of relative importance of attributes in a rule are obtained by AHP(Analytic Hierarcy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with MIN operator, into a single DM for the rule. In this way, overall DM for a rule changes depending on the importance of attributes of the rule. As a result, the dynamic nature of a real system can be incorporated in an inference with fuzzy production rules.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.12
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• pp.9-18
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• 1991

In this Paper, a rule-based method for the phenomenon of Korean vocal sound change is proposed. This method could be used to solve a problem between symbolic(Hangul)and phonetic language(Korean) for the study of Korean speech processing. A rule on the phenomenon of vocal sound rearranged for the rule base with a end-consonents on the authority of standard pronunciation rule. The proposed rule base system is simplified by the implementation for the vocal sound change. Also, it is useful to create the data base with phonetic value for the Korean voice processing by syllable unit.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.643-649
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• 1993

The problem of locating a periodically inserted frame synchronization pattern in random data for a binary pulse amplitude modulated (PAM) digital communication system over a additive white Gaussian noise(AWGN) channel with co-channel interference is considered. The performance degradation of frame synchronization for the correlation rule due to the presence of co-channel interference is shown. The maximum likelihood(ML) decision rule for the frame synchronization over an AWGN channel with co-channel interference is derived. For the entire range of SNR considered, the ML frame synchronization rule obtains about 1dB signal energy gain over the correlation rule. Specially, the ML rule obtains as much as 2dB gain over the correlation rule when the SNR is greater than 0dB.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4014-4021
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• 2021

If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails. Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation is necessary to prevent this progression. In this study, the corresponding time was defined as the golden time. To achieve the objective of accurately predicting the golden time, the prediction was performed using the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has an architecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in which the fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affect inference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regression model. By using the prediction result through the proposed DFNN with rule-dropout, it is expected to prevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery, which failed in the LOCA situation.