• Journal of Information Processing Systems
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• v.16 no.1
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• pp.184-200
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• 2020

Similarity searching is a basic issue in information processing because of the large size of formal contexts and their complicated derivation operators. Recently, some researchers have focused on knowledge reduction methods by using granular computing. In this process, suitable information granules are vital to characterizing the quantities of attributes and objects. To address this problem, a novel approach to obtain an entropy-weighted concept lattice with inclusion degree and similarity distance (ECLisd) has been proposed. The approach aims to compute the combined weights by merging the inclusion degree and entropy degree between two concepts. In addition, another method is utilized to measure the hierarchical distance by considering the different degrees of importance of each attribute. Finally, the rationality of the ECLisd is validated via a comparative analysis.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2876-2878
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• 1999

There have been several tipical methods being used to measure the fuzziness (entropy) of fuzzy sets. Pedrycz is the original motivation of this paper. This paper studies the entropy variation on the fuzzy numbers with arithmetic operations(addition, subtraction, multiplication). It is shown that through the arithmetic operations, the entropy of the resultant fuzzy number has the arithmetic relation with the entropy of each original fuzzy number. This paper generalize earlier results of Pedrycz [FSS 64(1994) 21-30] and Wang and Chiu [FSS 103(1999) 443-455].

• Journal of Environmental Science International
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• v.22 no.11
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• pp.1473-1479
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• 2013

Entropy is a measure of disorder or uncertainty. This terminology is qualitatively used in the understanding of its correlation to pollution in the environmental area. In this research, three different entropies were defined and characterized in order to quantify the qualitative entropy previously used in the environmental science. We are dealing with newly defined distinct entropies $E_1$, $E_2$, and $E_3$ originated from Shannon entropy in the information theory, reflecting concentration of three major green house gases $CO_2$, $N_2O$ and $CH_4$ represented as the probability variables. First, $E_1$ is to evaluate the total amount of entropy from concentration difference of each green house gas with respect to three periods, due to industrial revolution, post-industrial revolution, and information revolution, respectively. Next, $E_2$ is to evaluate the entropy reflecting the increasing of the logarithm base along with the accumulated time unit. Lastly, $E_3$ is to evaluate the entropy with a fixed logarithm base by 2 depending on the time. Analytical results are as follows. $E_1$ shows the degree of prediction reliability with respect to variation of green house gases. As $E_1$ increased, the concentration variation becomes stabilized, so that it follows from linear correlation. $E_2$ is a valid indicator for the mutual comparison of those green house gases. Although $E_3$ locally varies within specific periods, it eventually follows a logarithmic curve like a similar pattern observed in thermodynamic entropy.

• Journal of the Korean Chemical Society
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• v.53 no.1
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• pp.62-72
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• 2009

In this study, entropy definition and explanation in twelve general chemistry textbooks published in USA including what Korean universities select are analyzed. Analysis consists of two parts. One is diachronic analysis which focuses on the change between editions of same authors and the other is contents analysis composed of three sections‐ disorder in entropy definition and explanation, microstates number in entropy definition and explanation, possibility of alternative conception in learning statistical entropy and thermal entropy. As a result, the definition that entropy is the measure of disorder is denied and explanation using microstates number is introduced. Also, caution for the possibility of alternative conception that there are two different entropies is found. Reflecting the change in entropy explanation on new chemistry curriculum and reeducating teachers are meaningful on improvement of Korean chemistry education.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.276-283
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• 2019

The purpose of this study is to evaluate the usefulness of automatic exposure control (AEC) by analyzing entrance surface dose (ESD) and entropy on using automatic exposure and manual exposure. The experimental method was to measure the dose by placing a semiconductor dosimeter on the Rando Phantom for the Pelvis, Abdomen, Skull, and Chest regions. The DICOM file was simultaneously acquired and then entropy was analyzed by using Matlab. As a result, when using the automatic exposure control, dose of all sites was lower than manual exposure's dose and entropy was high. In addition, paired t-test was performed for each item and p<0.05 was found in each item. In conclusion, the use of automatic exposure control can be a useful method to contribute to the optimization of the exposure dose and the image quality by reducing the amount of unnecessary radiation amount and information loss that can occur in X-ray examination.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2865-2872
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• 2018

In the field of data mining technique, there are various methods such as association rules, cluster analysis, decision tree, neural network. Among them, association rules are defined by using various association evaluation criteria such as support, confidence, and lift. Agrawal et al. (1993) first proposed this association rule, and since then research has been conducted by many scholars. Recently, studies related to crossover entropy have been published (Park, 2016b). In this paper, we proposed a purely symmetric J measure considering directionality and purity in the previously published J measure, and examined its usefulness by using examples. As a result, it is found that the pure symmetric J measure changes more clearly than the conventional J measure, the symmetric J measure, and the pure crossover entropy measure as the frequency of coincidence increases. The variation of the pure symmetric J measure was also larger depending on the magnitude of the inconsistency, and the presence or absence of the association was more clearly understood.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1112-1118
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• 2014

In this paper, we propose a local descriptor and a similarity measure that is robust to radiometic variations. The proposed local descriptor is made up Haar wavelet filter and it can contain frequency informations about the feature point and its surrounding pixels in fixed region, and it is able to describe feature point clearly under ununiform illumination condition. And a proposed similarity measure is combined with conventional entropy-based similarity and another similarities that is generated by local descriptor. It can reflect similarities between image regions accurately under radiometic illumination variations. We validate with experimental results on some images and we confirm that the proposed algorithm is more superior than conventional algorithms.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.293-301
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• 2014

In this paper, we propose a new measure based on the confidence of Bayesian posterior probability so as to reduce unimportant information in the clustering process. Because the performance of clustering is up to selecting the important degree of attributes within the databases, the concept of information entropy is added to posterior probability for attributes discernibility. Hence, The same value of attributes in the confidence of the proposed measure is considerably much less due to the natural logarithm. Therefore posterior probability-based clustering algorithm selects the minimum of attribute reducts and improves the efficiency of clustering. Analysis of the validation of the proposed algorithms compared with others shows their discernibility as well as ability of clustering to handle uncertainty with ACME categorical data.

• Bulletin of the Korean Chemical Society
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• v.20 no.1
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• pp.35-41
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• 1999

Stability characteristics of hyperbolic reaction-diffusion equations with a reversible Brusselator model are investigated as an extension of the previous work. Intensive stability analysis is performed for three important parameters, Nrd, β and Dx, where Nrd is the reaction-diffusion number which is a measure of hyperbolicity, β is a measure of reversibility of autocatalytic reaction and Dx is a diffusion coefficient of intermediate X. Especially, the dependence on Nrd of stability exhibits some interesting features, such as hyperbolicity in the small Nrd region and parabolicity in the large Nrd region. The hyperbolic reaction-diffusion equations are solved numerically by a spectral method which is modified and adjusted to hyperbolic partial differential equations. The numerical method gives good accuracy and efficiency even in a stiff region in the case of small Nrd, and it can be extended to a two-dimensional system. Four types of solution, spatially homogeneous, spatially oscillatory, spatio-temporally oscillatory and chaotic can be obtained. Entropy productions for reaction are also calculated to get some crucial information related to the bifurcation of the system. At the bifurcation point, entropy production changes discontinuously and it shows that different structures of the system have different modes in the dissipative process required to maintain the structure of the system. But it appears that magnitude of entropy production in each structure give no important information related for states of system itself.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.