• Bulletin of the Korean Chemical Society
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• v.20 no.10
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• pp.1209-1212
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• 1999

The entropies and chemical potentials of hard-sphere solutes solvated in hard-sphere solids were calculated by Monte Carlo method using the radial free-space distribution function. This method is based on calculating the entropy by comparing the free volume of a molecule with that of an ideal gas, and is applicable even when the size of solute is very large and the solvent is a solid. When the diameter of hard-sphere solute is small the solute molecule behaves as like as a fluid in solid structures, but when the diameter of solute becomes large, a fluid-to-solid phase transition takes place. The fluid-to-solid phase transition occurs at the region of the smaller size of solute with the more increase of solvent density. The least square fit values of analytical form of the radial free-space distribution functions of solute molecules are presented for future uses.

• Korean Linguistics
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• v.79
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• pp.63-89
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• 2018

This study explores contrastive relation among voiced bilabial fricative [${\ss}$], voiceless bilabial stop [p] and glide [w] in Middle Korean consonant system based on Probabilistic Model. Preceding researches about voiced bilabial fricative [${\ss}$] proposed two influential arguments. One is voiced bilabial fricative [${\ss}$] was an independent phoneme, the other is it was not an independent phoneme but an allophone of voiceless bilabial stop [p] in Middle Korean. This study applies Probabilistic Phonological Relationship Model (PPRM) for solving the problem of dichotomy about contrastive and allophonic relations. The analysis result of the contrastive entropy by PPRM suggests that voiced bilabial fricative [${\ss}$] was just an allophone of voiceless bilabial stop [p] or glide [w] in Middle Korean. Comparing the entropies between [p] and other consonants with the entropies between [${\ss}$] and other consonants, a continuum defined in terms of entropy reveals that [${\ss}$] in Middle Korean was more allophonic than phonemic.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.10a
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• pp.53-56
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• 1987

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.1
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• pp.133-142
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• 2000

In this note we define typical set and differential entropy for continuous random variables. Using Markov chain, we show that the various properties of the mutual information and entropies (theorems 3.2 and 3.4 ) and show the properties of typical set in continuous random variables( lemma 4.2 and theorem 4.3.)

• The Pure and Applied Mathematics
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• v.23 no.4
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• pp.391-391
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• 2016

• Geomechanics and Engineering
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• v.25 no.2
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• pp.135-142
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• 2021

This study provided a new research perspective for processing and analyzing AE data to evaluate rock failure. Cluster method and information entropy theory were introduced to investigate temporal and spatial correlation of acoustic emission (AE) events during the rock failure process. Laboratory experiments of granite subjected to compression were carried out, accompanied by real-time acoustic emission monitoring. The cumulative length and dip angle curves of single links were fitted by different distribution models and distribution functions of link length and directionality were determined. Spatial scale and directionality of AE event distribution, which are characterized by two parameters, i.e., spatial correlation length and spatial correlation directionality, were studied with the normalized applied stress. The entropies of link length and link directionality were also discussed. The results show that the distribution of accumulative link length and directionality obeys Weibull distribution. Spatial correlation length shows an upward trend preceding rock failure, while there are no remarkable upward or downward trends in spatial correlation directionality. There are obvious downward trends in entropies of link length and directionality. This research could enrich mathematical methods for processing AE data and facilitate the early-warning of rock failure-related geological disasters.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.810-813
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• 2002

We present the information theoretic learning based on the Tsallis entropy maximization principle for various q. The Tsallis entropy is one of the generalized entropies and is a canonical entropy in the sense of physics. Further, we consider the dependency of the learning on the parameter $\sigma$, which is a standard deviation of an assumed a priori distribution of samples such as Parzen window.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.787-798
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• 2011

In this paper, we develop some characterization results in terms of survival entropy of the first order statistic. In addition, we generalize the cumulative entropy recently proposed by Di Crescenzo and Logobardi (2009) to a new measure of information (called the failure entropy) and study some properties of it and its dynamic version. Furthermore, power distribution is characterized based on dynamic failure entropy.

• Proceedings of the Korean Magnestics Society Conference
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• 2002.12a
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• pp.121-121
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• 2002

A theoretical consideration for entropy changes in a magnetic solid is given in a general manner and has been taken into account for such La$\sub$0.7/Ca$\sub$0.3-x/Ba$\sub$x/MnO$_3$(x = 0.12, 0.24, 0.3) compounds. The total entropy changes, in which the total entropy is decomposed into the magnetic, lattice, and electron entropies, are discussed in detail. (omitted)

• 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.