• 한국수자원학회논문집
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• 제36권6호
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• pp.1025-1033
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• 2003

수공구조물의 설계를 비롯하여, 수자원 분야의 기술적 설계의 기초는 수문자료의 처리와 분석에 중심을 두고 있다고 할 수 있다. 수문 자료의 분석방법 중 가장 보편적이면서도 중요한 방법은 자료들의 관계를 도식적으로 규명하는 회귀분석이다. 수위-유량 관계곡선과 같은 수문 자료에 대한 기존의 매개변수적 회귀모형이 갖는 단점은 자료의 특성에 따라, 복수의 회귀식이 산정되거나 동일자료에 대해서도 서로 다른 회귀식이 산정됨으로써 신뢰할 수 있는 회귀곡선을 만들기가 어렵다는 것이다. 이에 비해 주어진 자료에 의해 도출되는 kernel 회귀모형은 자료의 특성과 경향성을 적절히 표현해 줄 수 있는 방법이다. 본 논문에서는 비매개변수적 방법인 kernel 회귀모형을 분석하고, kernel 회귀모형의 중요 인자인 bandwidth의 선택 방법에 따른 kernel 회귀모형의 특성에 대해 비교 분석하였다.

• 한국산학기술학회논문지
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• 제12권7호
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• pp.3251-3257
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• 2011

본 논문에서는 논리합성을 위한 공통식 추출 방법을 새롭게 제안한다. 제안하는 방법은 주어진 각 논리식들에서 커널/커널 쌍들과 코커널/커널 쌍을 추출한다. 커널/커널 쌍은 주어진 논리식을 부울 나눗셈에 의해 제수, 몫, 나머지로 논리식을 다시 표현하게 된다. 다음, 여러 논리식에서 산출된 제수, 몫들에서 공통식을 추출하는 커널 교집합에 의해 공통식을 구하는 방법을 제안한다. 실험 결과 기존의 공통식 산출 결과들과 비교했을 때 제안한 방법은 리터럴 개수를 줄일 수 있었다.

• Communications for Statistical Applications and Methods
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• 제31권3호
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• pp.337-347
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• 2024

Kernel density estimation is a prevalent technique employed for nonparametric density estimation, enabling direct estimation from the data itself. This estimation involves two crucial elements: selection of the kernel function and the determination of the appropriate bandwidth. The selection of the bandwidth plays an important role in kernel density estimation, which has been developed over the past decade. A range of methods is available for selecting the bandwidth, including the plug-in bandwidth. In this article, the proposed plug-in bandwidth is introduced, which leverages shifted Chebyshev series-based approximation to determine the optimal bandwidth. Through a simulation study, the performance of the suggested bandwidth is analyzed to reveal its favorable performance across a wide range of distributions and sample sizes compared to alternative bandwidths. The proposed bandwidth is also applied for kernel density estimation on real dataset. The outcomes obtained from the proposed bandwidth indicate a favorable selection. Hence, this article serves as motivation to explore additional plug-in bandwidths that rely on function approximations utilizing alternative series expansions.

• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.137-141
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• 2009

We study on selecting the kernel weighting function in smoothing moment conditions for dependent processes. For hypothesis testing in Generalized Method of Moments or Generalized Empirical Likelihood context, we find that smoothing moment conditions by Bartlett kernel delivers smallest size distortions based on empirical Edgeworth expansions of the long-run variance estimator.

• 한국작물학회지
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• 제44권1호
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• pp.78-85
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• 1999

To investigate kernel hardness, a compression test which is widely used to measure the hardness of individual kernels as a physical testing method was made simultaneously with the measurement of friabilin (15KDa) which is strongly associated with kernel hardness and was recently developed as a biochemical marker for evaluating kernel hardness in 79 Korean wheat varieties and experimental lines. With the scattered diagram based on the principal component analysis from the parameters of the compression test, 79 Korean wheat varieties were classified into three groups based on the principal component analysis. Since conventional methods required large amount of flour samples for analysis of friabilin due to the relatively small amount of friabilin in wheat kernels, those methods had limitations for quality prediction in wheat breeding programs. An extraction of friabilin from the starch of a single kernel through cesium chloride gradient centrifugation was successful in this experiment. Among 79 Korean wheat varieties and experimental lines 50 lines (63.3%) exhibited a friabilin band and 29 lines (36.7%) did not show a friabilin band. In this study, lines that contained high maximum force and the lower ratio of minimum force to maximum force showed the absence of the friabilin band. Identification of friabilin, which is the product of a major gene, could be applied in the screening procedures of kernel hardness. The single kernel analysis system for friabilin was found to be an easy, simple and effective screening method for early generation materials in a wheat breeding program for quality improvement.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제13권3호
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• pp.189-202
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• 2009

In this paper we propose new primal-dual interior point algorithms for $P_*(\kappa)$ linear complementarity problems based on a new class of kernel functions which contains the kernel function in [8] as a special case. We show that the iteration bounds are $O((1+2\kappa)n^{\frac{9}{14}}\;log\;\frac{n{\mu}^0}{\epsilon}$) for large-update and $O((1+2\kappa)\sqrt{n}log\frac{n{\mu}^0}{\epsilon}$) for small-update methods, respectively. This iteration complexity for large-update methods improves the iteration complexity with a factor $n^{\frac{5}{14}}$ when compared with the method based on the classical logarithmic kernel function. For small-update, the iteration complexity is the best known bound for such methods.

• 한국기계가공학회지
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• 제8권3호
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• pp.21-26
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• 2009

The finite element analysis of metal forming processes often fails because of severe mesh distortion at large deformation. As the concept of meshless methods, only nodal point data are used for modeling and solving. As the main feature of these methods, the domain of the problem is represented by a set of nodes, and a finite element mesh is unnecessary. This computational methods reduces time-consuming model generation and refinement effort. It provides a higher rate of convergence than the conventional finite element methods. The displacement shape functions are constructed by the reproducing kernel approximation that satisfies consistency conditions. In this research, A meshless method approach based on the reproducing kernel particle method (RKPM) is applied with metal forming analysis. Numerical examples are analyzed to verify the performance of meshless method for metal forming analysis.

• 한국군사과학기술학회지
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• 제17권6호
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• pp.773-785
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• 2014

For decades, modeling of pilots has been intensively studied due to its advantages in reducing costs for training and enhancing safety of pilots. In particular, research for modeling of pilots' non-kinetic behaviors which refer to the decisions made by pilots is beneficial as the expertise of pilots can be inherent in the models. With the recent growth in the amount of combat logs accumulated, employing statistical learning methods for the modeling becomes possible. However, the combat logs consist of heterogeneous data that are not only continuous or discrete but also sequence independent or dependent, making it difficult to directly applying the learning methods without modifications. Therefore, in this paper, we present a kernel function named hybrid sequence kernel which addresses the problem by using multiple kernel learning methods. Based on the empirical experiments by using combat logs obtained from a simulator, the proposed kernel showed satisfactory results.

• Communications for Statistical Applications and Methods
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• 제19권4호
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• pp.517-526
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• 2012

In this paper a support vector method is proposed for use when the sample observations are contaminated by a normally distributed measurement error. The performance of deconvolution density estimators based on the support vector method is explored and compared with kernel density estimators by means of a simulation study. An interesting result was that for the estimation of kurtotic density, the support vector deconvolution estimator with a Gaussian kernel showed a better performance than the classical deconvolution kernel estimator.

• Communications for Statistical Applications and Methods
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• 제20권3호
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• pp.175-184
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• 2013

Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.