• Korean Journal of Food Science and Technology
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• v.11 no.2
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• pp.122-125
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• 1979

Degermed brown rice of Akibare (short grain) and Milyang 23 (medium grain) was abraded fiveconsecutive times to remove outer $5{\sim}6%$ of the kernel per milling. Samples were analyzed for calcium, phosphorus, iron, thiamine and riboflavin. Milled fraction I (about $5{\sim}6%$ of the kernel) contained 8 times as much calcium and phosphorus as did the original kernel; iron, $4{\sim}5$; thiamine, 3; and riboflavin, 4. Contents of fraction I were much greater than those in the residual kernel; 18 times as great for calcium; $32{\sim}36$ times for phosphorus; $5{\sim}10$ times for iron 5 times for thiamine; and $19{\sim}30$ times for riboflavin. Milyang 23 showed a steeper concentration gradient of calcium and riboflavin, but more even distribution of iron than did Akibare. There were no significant differences in phosphorus and thiamine gradients between the two rices.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.709-717
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• 2008

The difference of two one-sided kernel estimators is usually used to detect the location of the discontinuity points of regression function. The large absolute value of the statistic imply discontinuity of regression function, so we may use the difference of two one-sided kernel estimators as the test statistic for testing null hypothesis of a smooth regression function. The problem is, however, we only know the asymptotic distribution of the test statistic under $H_0$ and we hardly expect the good performance of test if we rely solely on the asymptotic distribution for determining the critical points. In this paper, we show that if we adjust the bias of test statistic properly, the asymptotic rules hold for even small sample size situation.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.531-536
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• 2022

This paper compared the performance of the parametric method and the nonparametric method when estimating the distribution for the tail of the distribution with heavy tails. For the parametric method, the generalized extreme value distribution and the generalized Pareto distribution were used, and for the nonparametric method, the kernel density estimation method was applied. For comparison of the two approaches, the results of function estimation by applying the block maximum value model and the threshold excess model using daily fine dust public data for each observatory in Seoul from 2014 to 2018 are shown together. In addition, the area where high concentrations of fine dust will occur was predicted through the return level.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.77-91
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• 2019

Recent Linux operating systems having been increasingly used, ranging from automotive consoles, CCTV, IoT devices, and mobile devices to various versions of the kernel. Because these devices can be used as strong evidence in criminal investigations, there is a risk of destroying evidence through file deletion. Ext filesystem forensics has been studied in depth because it can recovery deleted files without depending on the kind of device. However, studies have been carried out without consideration of characteristics of file system which may vary depending on the kernel. This problem can lead to serious situations, such as those that can impair investigative ability and cause doubt of evidence ability, when an actual investigation attempts to analyze a different version of the kernel. Because investigations can be performed on various distribution and kernel versions of Linux file systems at the actual investigation site, analysis of the metadata changes that occur when files are deleted by Linux distribution and kernel versions is required. Therefore, in this paper, we analyze the difference of metadata according to the Linux kernel as a solution to this and recovery deleted file. After that, the investigating agency needs to consider the metadata change caused by the difference of Linux kernel version when performing Ext filesystem forensics.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• Nuclear Engineering and Technology
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• v.10 no.3
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• pp.145-151
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• 1978

Based on the Butler scattering kernel for D$_2$O, a computer code DEUKEB has been developed to compute the scattering laws, differential scattering cross sections and total scattering cross sections. Interference scattering between ally two atoms of a D$_2$O molecule is important in resolving the distribution of scattered neutrons in thermal energy region. Energy-transfer scattering cross sections are, therefore, studied in the various incident neutron energies. This study may be put in practice to utilize the kernel in determining the neutron spectrum in a reactor system. The study also shows that the scattering process in D$_2$O is somewhat different from that in $H_2O$.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.859-870
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• 2009

We obtain a skewed double Weibull distribution by a double Weibull distribution, and evaluate its coefficient of skewness. And we obtain the approximate maximum likelihood estimator(AML) and moment estimator of skew parameter in the skewed double Weibull distribution, and hence compare simulated mean squared errors(MSE) of those estimators. We compare simulated MSE of two proposed reliability estimators in two independent skewed double Weibull distributions each with different skew parameters. Finally we introduce a skewed double Weibull distribution generated by a uniform kernel.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1093-1101
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• 2009

Quantile regression has become a more widely used technique to describe the distribution of a response variable given a set of explanatory variables. This paper proposes a novel modelfor quantile regression using doubly penalized kernel machine with support vector machine iteratively reweighted least squares (SVM-IRWLS). To make inference about the shape of a population distribution, the widely popularregression, would be inadequate, if the distribution is not approximately Gaussian. We present a likelihood-based approach to the estimation of the regression quantiles that uses the asymmetric Laplace density.

• Environmental and Resource Economics Review
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• v.18 no.1
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• pp.105-138
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• 2009

This study investigates the spatial distribution of the degree of water pollutants in Han River using data obtained by the water pollution observation stations. This study estimates a non -parametric kernel density function for each water pollutants, and tests a significant difference between two estimated distribution functions. Next, Generalized Entropy inequality indices are evaluated and this research tests difference of inequality indices between two years using bootstrapping method. Lastly in a dynamic of view, it is analyzed that there are significant changes in the ranking of water pollution level. Estimation results show that the degree of inequality in spatial distribution of water pollution tends to be stable or decreasing for last 15 years in a great part of water pollutants, and ranking of water pollution level hardly changes in Han River.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.461-470
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• 2014

This paper presents a probabilistic power flow (PPF) analysis method for distribution network incorporating the randomness and correlation of photovoltaic (PV) generation. Based on the multivariate kernel density estimation theory, the probabilistic model of PV generation is proposed without any assumption of theoretical parametric distribution, which can accurately capture not only the randomness but also the correlation of PV resources at adjacent locations. The PPF method is developed by combining the proposed PV model and Monte Carlo technique to evaluate the influence of the randomness and correlation of PV generation on the performance of distribution networks. The historical power output data of three neighboring PV generators in Oregon, USA, and 34-bus/69-bus radial distribution networks are used to demonstrate the correctness, effectiveness, and application of the proposed PV model and PPF method.