• 한국지능시스템학회논문지
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• 제15권6호
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• pp.754-758
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• 2005

There have been several tipical methods being used tomeasure the fuzziness (entropy) of fuzzy sets. Pedrycz is the original motivation of this paper. Recently, Wang and Chiu [FSS103(1999) 443-455] and Pedrycz [FSS 64(1994) 21-30] showed the relationship(addition, subtraction, multiplication) between the entropies of the resultant fuzzy number and the original fuzzy numbers of same type. In this paper, using Lebesgue-Stieltjes integral, we generalize results of Wang and Chiu [FSS 103(1999) 443-455] concerning entropy arithmetic operations without the condition of same types of fuzzy numbers. And using this results and trade-off relationship between information energy and entropy, we study more properties of information energy of fuzzy numbers.

• IEIE Transactions on Smart Processing and Computing
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• 제5권2호
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• pp.85-93
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• 2016

The existence of correlation characteristics brings significant potential advantages to the development of efficient routing protocols in wireless sensor networks. This research proposes a new simple method of clustering sensor nodes into correlation groups in multiple-correlation areas. At first, the evaluation of joint entropy for multiple-sensed data is considered. Based on the evaluation, the definition of correlation region, based on entropy theory, is proposed. Following that, a correlation clustering scheme with less computation is developed. The results are validated with a real data set.

• 대한수학회지
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• 제49권1호
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• pp.165-185
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• 2012

In this paper, the topological entropy and measure-theoretic entropy for nonautonomous dynamical systems are studied. Some properties of these entropies are given and the relation between them is discussed. Moreover, the bounds of them for several particular nonautonomous systems, such as affine transformations on metrizable groups (especially on the torus) and smooth maps on Riemannian manifolds, are obtained.

• 제어로봇시스템학회논문지
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• 제8권4호
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• pp.333-337
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• 2002

A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n$\times$n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권4호
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• pp.259-262
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• 2010

Study about fuzzy entropy and similarity measure on intuitionistic fuzzy sets (IFSs) were proposed, and analyzed. Unlike fuzzy set, IFSs contains uncertainty named hesistancy, which is contained in fuzzy membership function itself. Hence, designing fuzzy entropy is not easy because of ununified entropy definition. By considering different fuzzy entropy definitions, fuzzy entropy is designed and discussed their relation. Similarity measure was also presented and verified its usefulness to evaluate degree of similarity.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.657-667
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• 2006

The concept of entropy, introduced in communication theory by Shannon (1948) as a measure of uncertainty, is of prime interest in information-theoretic statistics. This paper considers the minimum variance unbiased estimation for the maximum entropy of the transformed inverse Gaussian random variable by $Y=X^{-1/2}$. The properties of the derived UMVU estimator is investigated.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제2권2호
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• pp.125-135
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• 1998

In this paper we define the entropy rate and stationary Markov chain and we show the monotonicity of entropy per element and prove that the random tree $T_n$ grows linearly with n.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.218-227
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• 2018

Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권4호
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• pp.275-280
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• 2010

Verification of efficiency in data management fuzzy entropy and similarity measure were discussed and verified by applying reliable data selection problem and numerical data similarity evaluation. In order to calculate the certainty or uncertainty fuzzy entropy and similarity measure are designed and proved. Designed fuzzy entropy and similarity are considered as dissimilarity measure and similarity measure, and the relation between two measures are explained through graphical illustration. Obtained measures are useful to the application of decision theory and mutual information analysis problem. Extension of data quantification results based on the proposed measures are applicable to the decision making and fuzzy game theory.

• Journal of Computing Science and Engineering
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• 제8권3호
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• pp.173-179
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• 2014

In an opportunistic network, one of the most challenging issues is the equilibrium of the network traffic and transmission delay for forwarding messages. To resolve this problem, we propose a new forwarding scheme, called the direction entropy-based forwarding scheme (DEFS), using the main direction and direction entropy based on the information collected about the directions of the nodes in the network. Since each node sends a message to another node with a different location and less direction entropy, DEFS utilizes those nodes that are more likely to travel to various locations to forward the messages to the destination nodes. Experiments were performed on the network simulator NS-2. The results show that DEFS provides better balance than the typical forwarding schemes, such as Epidemic, PRoPHET, and WAIT.