• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.47-54
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• 2014

Based on the indiscernible relation of rough set, the inevitability of superposition and inconsistency of data makes the reduction of attributes very important in information system. Rough set has difficulty in the difference of attribute reduction between consistent and inconsistent information system. In this paper, we propose the new uncertainty measure and attribute reduction algorithm by Bayesian posterior probability for correlation analysis between condition and decision attributes. We compare the proposed method and the conditional information entropy to address the uncertainty of inconsistent information system. As the result, our method has more accuracy than conditional information entropy in dealing with uncertainty via mutual information of condition and decision attributes of information system.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.1
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• pp.39-47
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• 2004

The objective of this study is to analyse the spray characteristics according to the injection duration under ambient pressure condition and to investigate the relationship between vorticity and entropy for controlling diffusion process that is the most important thing during the intake stroke injection process. Therefore, the spray velocity was obtained by using the PIV method that has been an useful optical diagnostics technology, and vorticity calculated from spray velocity component with vorticity algorithm. In addition, the homogeneous diffusion rate of spray was quantified by using the entropy analysis based on the Boltzmann's statistical thermodynamics. From these method, we found that as injection duration increases, spray velocity increases and the location of vortex is moved to the downstream of spray. In the same condition, as the entropy decrease, mean vorticity increases. This means that the concentration of spray droplets caused by the increase of injection duration is more effective than the increase of momentum dissipation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.61-71
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• 2018

In the Internet of Things, in which plenty of devices have connection to each other, cryptographically secure Random Number Generators (RNGs) are essential. Particularly, entropy source, which is the only one non-deterministic part in generating random numbers, has to equip with an unpredictable noise source(or more) for the required security strength. This might cause an requirement of additional hardware extracting noise source. Although additional hardware resources has better performance, it is needed to make the best use of existing resources in order to avoid extra costs, such as area, power consumption. In this paper, we suggest an entropy source which uses a multi-threaded program without any additional hardware. As a result, it reduces the difficulty when implementing on lightweight, low-power devices. Additionally, according to NIST's entropy estimation test suite, the suggested entropy source is tested to be secure enough for source of entropy input.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.31-36
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• 2009

Bayesian networks are known as the best tools to express and predict the domain knowledge with uncertain environments. However, bayesian learning could be too difficult to do effective and reliable searching. To solve the problems of overtime demand, the nodes should be arranged orderly, so that effective structural learning can be possible. This paper suggests the classification learning model to reduce the errors in the independent condition, in which a lot of variables exist and data can increase the reliability by calculating the each entropy of probabilities depending on each circumstances. Also efficient learning models are suggested to decide the order of nodes, that has lowest entropy by calculating the numerical values of entropy of each node in K2 algorithm. Consequently the model of the most suitably settled Bayesian networks could be constructed as quickly as possible.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.83-88
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• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.10
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• pp.947-954
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• 2009

Starting from the Eu's GH(Generalized Hydrodynamic) theory, the multi-species GH numerical solver is developed in this research and its computatyional behaviors are examined for the hypersonic rarefied flow over an axisymmetric body. To improve the accuracy of the developed multi-species GH solver, various slip-wall boundary conditions are tested and the computed results are compared. Additionally, in order to validate the entropy characteristics of the GH equation, the entropy production and entropy generation rates of the GH equation are investigated in the 1-dimensional normal shock structure test at a high Knudsen number.

• Bulletin of the Korean Mathematical Society
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• v.51 no.2
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• pp.317-328
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• 2014

In Bae et al. [2], we have considered the uniform CLT for the martingale difference arrays under the uniformly integrable entropy. In this paper, we prove the same problem under the bracketing entropy condition. The proofs are based on Freedman inequality combined with a chaining argument that utilizes majorizing measures. The results of present paper generalize those for a sequence of stationary martingale differences. The results also generalize independent problems.

• Bulletin of the Korean Mathematical Society
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• v.47 no.1
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• pp.39-51
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• 2010

In this paper we consider the uniform central limit theorem for a martingale-difference array of a function-indexed stochastic process under the uniformly integrable entropy condition. We prove a maximal inequality for martingale-difference arrays of process indexed by a class of measurable functions by a method as Ziegler [19] did for triangular arrays of row wise independent process. The main tools are the Freedman inequality for the martingale-difference and a sub-Gaussian inequality based on the restricted chaining. The results of present paper generalizes those of Ziegler [19] and other results of independent problems. The results also generalizes those of Bae and Choi [3] to martingale-difference array of a function-indexed stochastic process. Finally, an application to classes of functions changing with n is given.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.126-131
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• 1998

In computer vision system of modern industry precise measuring has lots of dfficulties because of measurement error due to distortion phenomenon. Among the difficulties, the distortion of edge is regraded as a dominent problem. which is caused by the vlurred image. The blurred image apperar when camera can not discriminate its precise focus. So. it is very important to decide focus of lens and to develop algorithm in order to correct distortion phenomenon. Thus. discrimination criteria obtained by image information of precise focus must be fixed in advance. The gray level histogram of image acquired from blurred edge tends to show a uniform distribution. Bimodal intensity histogram is related with condition of focus, and it is possible to find good condition of focus by using bimodal histogram of entropy.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.223-229
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• 2014

In an attempt to retrieve useful information for an efficient decision in the large knowledge system, it is generally necessary and important for a refined feature selection. Rough set has difficulty in generating optimal reducts and classifying boundary objects. In this paper, we propose quick reduction algorithm generating optimal features by rough entropy analysis for condition and decision attributes to improve these restrictions. We define a new conditional information entropy for efficient feature extraction and describe procedure of feature selection to classify the significance of features. Through the simulation of 5 datasets from UCI storage, we compare our feature selection approach based on rough set theory with the other selection theories. As the result, our modeling method is more efficient than the previous theories in classification accuracy for feature selection.