• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3055-3073
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• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.1-5
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• 2002

We consider the problem of optimal bandwidth choice for nonparametric classification, based on kernel density estimators, where the problem of interest is distinguishing between two univariate distributions. When the densities intersect at a single point, optimal bandwidth choice depends on curvatures of the densities at that point. The problem of empirical bandwidth selection and classifying data in the tails of a distribution are also addressed.

• Journal of the Korean Mathematical Society
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• v.44 no.3
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• pp.525-539
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• 2007

Consider the regression model $Y_i=g(x_i)+e_i\;for\;i=1,\;2,\;{\ldots},\;n$, where: (1) $x_i$ are fixed design points, (2) $e_i$ are independent random errors with mean zero, (3) g($\cdot$) is unknown regression function defined on [0, 1]. Under $Y_i$ are censored randomly, we discuss the asymptotic normality of the weighted kernel estimators of g when the censored distribution function is known or unknown.

• Journal of the Korean Statistical Society
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• v.25 no.2
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• pp.265-276
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• 1996

The nonparametric deconvolution problems are studied to recover an unknown density when the data are contaminated with Gaussian error. We propose the estimator which is a linear combination of kernel type estimates of derivertives of the observed density function. We show that this estimator is consistent and also consider the properties of estimator at small sample by simulation.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.113-132
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• 1998

In this paper, we consider a minimum distance (M.D.) estimation based on kernels for U-statistics. We use Cramer-von Mises type distance function which measures the discrepancy between U-empirical distribution function(d.f.) and modeled d.f. of kernel. In the distance function, we allow various integrating measures, which can be finite, $\sigma$-finite or discrete. Then we derive the asymptotic normality and study the qualitative robustness of M. D. estimates.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.107-117
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• 2004

Change point testing problem is considered. Kernel density estimators are used for constructing proposed change point test statistics. The proposed method can be used to the hypothesis testing of not only parameter change but also distributional change. Bootstrap method is applied to get the sampling distribution of proposed test statistic. Small sample Monte Carlo Simulation were also conducted in order to show the performance of proposed method.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.911-915
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• 1997

The purpose of this paper is to consider the problem of selection of optimal smoothing parameter for kernel-type distribution function estimator, which asymptotically minimizes mean Hellinger distance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.131-137
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• 2006

The secondary system of nuclear power plants consists of sophisticated piping systems operating in very aggressive erosion and corrosion environments, which make a piping system vulnerable to the wear and degradation due to the several chemical components and high flow rate (~10 m/sec) of the coolant. To monitor the wear and degradation on a pipe, the vibration signals are measured from the pipe with an accelerometer For analyzing the vibration signal the time-frequency analysis (TFA) is used, which is known to be effective for the analysis of time-varying or transient signals. To reduce the inteferences (cross-terms) due to the bilinear structure of the time-frequency distribution, an adaptive cone-kernel distribution (ACKD) is proposed. The cone length of ACKD to determine the characteristics of distribution is optimally selected through an adaptive algorithm using the normalized Shannon's entropy And the ACKD's are compared with the results of other analyses based on the Fourier Transform (FT) and other TFA's. The ACKD shows a better signature for the wear/degradation within a pipe and provides the additional information in relation to the time that any analysis based on the conventional FT can not provide.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.272-276
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• 2009

In this paper, the arc region of a gas circuit breaker (GCB) is analyzed using the fast moving least square reproducing kernel method (FMLSRKM) which can simultaneously calculate an approximated solution and its derivatives. For this problem, an axisymmetric and inhomogeneous formulation of the FMLSRKM is used and applied. The field distribution obtained by the FMLSRKM is compared to that of the finite element method. Then, a whole breaking period of a GCB is simulated, including analysis of the arc gas flow by finite volume fluid in the cell, and the electric field of the arc region using the FMLSRKM.

• Communications of the Korean Mathematical Society
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• v.35 no.1
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• pp.125-136
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• 2020

In this article, we discuss classes of generalized functions for certain modified integral operator of Bessel-type involving Fox's H-function kernel. We employ a known differentiation formula of Fox's H-function to obtain the definition and properties of the distributional modified Bessel-type integral. Further, we derive a smoothness theorem for its kernel in a complete countably multi-normed space. On the other hand, using an appropriate class of convolution products, we derive axioms and establish spaces of modified Boehmians which are generalized distributions. On the defined spaces, we introduce addition, convolution, differentiation and scalar multiplication and further properties of the extended integral.