• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.113-122
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• 2006

In this paper, we propose a fuzzy method to control bit-rate in the DCT Domain. The method consists of a bit-rate allocation with fuzzy measures and a least-distortion bit-rate reduction. Fuzzy measures are calculated by the code length, the discontinuity ambiguity, and the neighborhood momentum in each DCT block. These measures are summed with weights and form a reduction fuzziness to indicate the degree of preferable reduction. Using the reduction fuzziness. each DCT block is filtered by the least-distortion reduction method to adjust the bit-rate for the target bandwidth. In the experiment, we show the results that the transcoded video quality by the method is better and the bandwidth is more regular than those of existing methods in both visually and quantitatively.

• Bulletin of the Korean Mathematical Society
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• v.41 no.1
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• pp.95-107
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• 2004

In this paper, we consider fuzzy number-valued functions and fuzzy number-valued Choquet integrals. And we also discuss positively homogeneous and monotonicity of Choquet integrals of fuzzy number-valued functions(simply, fuzzy number-valued Choquet integrals). Furthermore, we prove convergence theorems for fuzzy number-valued Choquet integrals.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.143-146
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• 2005

In this paper, we define correlation coeeficients of interval-valued fuzzy numbers by utilizing Choquet integrals. Also we discuss some properties of them.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.1
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• pp.66-70
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• 2007

Note that Choquet integral is a generalized concept of Lebesgue integral, because two definitions of Choquet integral and Lebesgue integral are equal if a fuzzy measure is a classical measure. In this paper, we consider interval-valued Choquet integrals with respect to fuzzy measures(see [4,5,6,7]). Using these Choquet integrals, we define a mappings on the classes of Choquet integrable functions and give an example of a mapping defined by interval-valued Choquet integrals. And we will investigate some relations between m-convex mappings ${\phi}$ on the class of Choquet integrable functions and m-convex mappings $T_{\phi}$, defined by the class of closed set-valued Choquet integrals with respect to fuzzy measures.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.57-73
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• 2006

This paper describes a methodology for automating the material handling equipment (MHE) evaluation and selection processes by combining knowledge-based rules and fuzzy multi-criteria decision making approach. The methodology is proposed to solve the MHE selection problems under fuzzy environment. At the primary stage, the most appropriate MHE type among the alternatives for each material flow link is searched. Knowledge-based rules are employed to retrieve the alternatives for each material flow link. To consider and compare the alternatives, multiple design factors are considered. These factors include both quantitative and qualitative measures. The qualitative measures are converted to numerical measures using fuzzy logic. The concept of fuzzy logic is applied to evaluation matrices used for the selection of the most suitable MHE through a fuzzy linguistic approach. Thus, this paper demonstrates the potential applicability of fuzzy theory in the MHE applications and provides a systemic guidance in the decision-making process.

• Communications of the Korean Mathematical Society
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• v.25 no.1
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• pp.37-49
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• 2010

In this paper we introduce the integration of scalar valued functions with respect to a generalized fuzzy number measure which we call the generalized fuzzy number valued Bartle integral. We first establish some properties of the generalized fuzzy number measures and then study the generalized fuzzy number valued Bartle integrals.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.218-223
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• 2010

Discussion and analysis about relative mutual information has been carried out through fuzzy entropy and similarity measure. Fuzzy relative mutual information measure (FRIM) plays an important part as a measure of information shared between two fuzzy pattern vectors. This FRIM is analyzed and explained through similarity measure between two fuzzy sets. Furthermore, comparison between two measures is also carried out.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.9-12
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• 2002

Representation and quantification of fuzziness are required for the uncertain system modelling and controller design. Conventional results show that entropy of fuzzy sets represent the fuzziness of fuzzy sets. In this literature, the relations of fuzzy enropy, distance measure and similarity measure are discussed, and distance measure is proposed. With the help of relations of fuzzy enropy, distance measure and similarity measure, fuzzy entropy is represented by the newly proposed distance measure. With simple fuzzy set, example is illustrated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.394-397
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• 2004

In this paper, we consider interval number-valued random variables and fuzzy number-valued random variables and discuss Choquet integrals of them. Using these properties, we define the Choquet expected value of fuzzy number-valued random variables which is a natural generalization of the Lebesgue expected value of Lebesgue expected value of fuzzy random variables. Furthermore, we discuss some application of them.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.3
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• pp.73-83
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• 2003

Methods of fuzzy C-means have been used to characterize geotechnical information from static cone penetration data. As contrary with traditional classification methods such as Robertson classification chart, the FCM expresses classes not conclusiveness but fuzzy. The results show that the FCM is useful to characterize ground information that can not be easily found by using normal classification chart. But optimal number of classes may not be easily defined. So, the optimal number of classes should be determined considering not only technical measures but engineering aspects.