• Computers and Concrete
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• 제7권2호
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• pp.119-143
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• 2010

This paper presents results from a study concerning the capability afforded by the RKPM (reproducing kernel particle method) meshfree analysis formulation to predict responses of concrete and UHPC components resulting from projectile impacts and blasts from nearby charges. In this paper, the basic features offered by the RKPM method are described, especially as they are implemented in the analysis code KC-FEMFRE, which was developed by Karagozian & Case (K&C).

• 한국통신학회논문지
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• 제29권4B호
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• pp.397-402
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• 2004

본 논문에서는 파일 전송 시 발생하는 메모리 복사(memo교 copy)와 문맥 교환(context switch)을 최소화하여 시스템의 성능(performance)을 향상시킬 수 있는 네트워크 시스템 호출에 관한 연구를 수행하였다. 기존 파일 전송 기법에서 사용자가 하나의 패킷을 전송할 때, 사용자와 커널(Kernel) 공간 사이에서의 메모리 복사가 2회에 걸쳐 수행된다. 이러한 사용자와 커널 공간 사이에서 이루어지는 메모리 복사는 데이터 전송에 소요되는 시간을 증가시키고, 시스템의 성능에 좋지 않은 영향을 준다. 본 논문은 이러한 문제점들을 해결하기 위하여 필요한 경우 사용자와 커널 사이에서의 메모리 복사를 수행하지 않고, 데이터가 커널 공간 내에서 송수신될 수 있는 새로운 알고리즘을 제시하였다. 또한 실제의 시스템에서 제안된 알고리즘의 성능을 분석하기 위하여 리눅스 커널 버전 2.6.0의 소스 코드를 수정하였고, 새로운 네트워크 시스템 호출을 구현하였다. 성능 측정 결과, 본 연구에서 제안한 파일 전송 방식이 기존의 파일 전승 방식에 비하여 짧은 파일 전송 시간을 보여주었다.

• Journal of the Korean Data and Information Science Society
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• 제20권4호
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• pp.749-755
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• 2009

Support vector classification (SVC) provides more complete description of the lin-ear and nonlinear relationships between input vectors and classifiers. In this paper. we propose the sparse kernel classifier to solve the optimization problem of classification with a modified hinge loss function and absolute loss function, which provides the efficient computation and the sparsity. We also introduce the generalized cross validation function to select the hyper-parameters which affects the classification performance of the proposed method. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.247-255
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• 2003

Hypothesis testing for the equality of two distributions were considered. Nonparametric kernel density estimates were used for testing equality of distributions. Cross-validatory choice of bandwidth was used in the kernel density estimation. Sampling distribution of considered test statistic were developed by resampling method, called the bootstrap. Small sample Monte Carlo simulation were conducted. Empirical power of considered tests were compared for variety distributions.

• Journal of the Korean Statistical Society
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• 제31권2호
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• pp.261-276
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• 2002

Probability density or its derivatives may have a discontinuity/change point at an unknown location. We propose a method of estimating the location and the jump size of the discontinuity point based on kernel type density or density derivatives estimators with one-sided equivalent kernels. The rates of convergence of the proposed estimators are derived, and the finite-sample performances of the methods are illustrated by simulated examples.

• Journal of the Korean Data and Information Science Society
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• 제21권5호
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• pp.973-981
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• 2010

Quantile regression investigates the quantiles of the conditional distribution of a response variable given a set of covariates. M-quantile regression extends this idea by a "quantile-like" generalization of regression based on influence functions. In this paper we propose a new method of estimating M-quantile regression functions, which uses kernel machine technique. Simulation studies are presented that show the finite sample properties of the proposed M-quantile regression.

• Journal of applied mathematics & informatics
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• 제6권1호
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• pp.163-174
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• 1999

The purpose of this paper is to obtain the solution of Fredholm-Volterra integral equation with singular kernel in the space $L_2(-1, 1)\times C(0,T), 0 \leq t \leq T< \infty$, under certain conditions,. The numerical method is used to solve the Fredholm integral equation of the second kind with weak singular kernel using the Toeplitz matrices. Also the error estimate is computed and some numerical examples are computed using the MathCad package.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.213-220
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• 2006

Since Beran (1977) developed the minimum Hellinger distance estimation, this method has been a popular topic in the field of robust estimation. In the process of defining a distance, a kernel density estimator has been widely used as a density estimator. In this article, however, we show that a combination of a kernel density estimator and an empirical density could result a smaller bias of the minimum Hellinger distance estimator than using just a kernel density estimator for a location parameter.

• International journal of advanced smart convergence
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• 제4권2호
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• pp.177-185
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• 2015

In this paper we introduce Kernel Fisher Discriminant Analysis (KFDA) to transform our database of received signal strength (RSS) measurements into a smaller dimension space to maximize the difference between reference points (RP) as possible. By KFDA, we can efficiently utilize RSS data than other method so that we can achieve a better performance.

• Journal of the Korean Data and Information Science Society
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• 제6권1호
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• pp.31-37
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• 1995

We consider density estimates of the usual type generated by a kernel function. By using the limit theorems for the maximum of normalized deviation of the estimate from its expected value, we propose to use data dependent bandwidth in the tests of goodness of fit based on these statistics. Also a small sample Monte Carlo simulation is conducted and proposed method is compared with Kolmogorov-Smirnov test.