• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.575-583
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• 2000

In this work, we solve the Fredholm-Volterra integral equation(FVIE) when the kernel takes a potential function form under given conditions. we represent this kernel in the Weber-sonin integral form.

• Honam Mathematical Journal
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• v.27 no.2
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• pp.195-203
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• 2005

We show that the exact Bergman kernel function associated to a $C^{\infty}$ bounded domain in the plane relates the derivatives of the Ahlfors map in an explicit way. And we find several formulas relating the exact Bergman kernel to classical kernel functions in potential theory.

• Interaction and multiscale mechanics
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• v.3 no.3
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• pp.277-298
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• 2010

The complex variable reproducing kernel particle method (CVRKPM) and the FEM are coupled in this paper to analyze the two-dimensional potential problems. The coupled method not only conveniently imposes the essential boundary conditions, but also exploits the advantages of the individual methods while avoiding their disadvantages, resulting in improved computational efficiency. A hybrid approximation function is applied to combine the CVRKPM with the FEM. Formulations of the coupled method are presented in detail. Three numerical examples of the two-dimensional potential problems are presented to demonstrate the effectiveness of the new method.

• Honam Mathematical Journal
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• v.36 no.3
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• pp.659-668
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• 2014

In this paper, we express the Green function in terms of the classical kernel functions in potential theory. In particular, we obtain a formula relating the Green function and the Szegő kernel function which consists of only the Szegő kernel function in a $C^{\infty}$ smoothly bounded finitely connected domain in the complex plane.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.895-904
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• 2000

The contact problem of two elastic bodies of arbitrary shape with a general kernel form, investigated from Hertz problem, is reduced to an integral equation of the second kind with Cauchy kernel. A numerical method is adapted to determine the unknown potential function between the two surfaces under certain conditions. Many cases are derived and discussed from the work.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1020-1033
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• 2021

Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1157-1169
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• 2021

Modern graphics processor unit (GPU) architectures offer significant hardware resource enhancements for parallel computing. However, without software optimization, GPUs continuously exhibit hardware resource underutilization. In this paper, we indicate the need to alter different warp scheduler schemes during different kernel execution periods to improve resource utilization. Existing warp schedulers cannot be aware of the kernel progress to provide an effective scheduling policy. In addition, we identified the potential for improving resource utilization for multiple-warp-scheduler GPUs by sharing stalling warps with selected warp schedulers. To address the efficiency issue of the present GPU, we coordinated the kernel-aware warp scheduler and warp sharing mechanism (KAWS). The proposed warp scheduler acknowledges the execution progress of the running kernel to adapt to a more effective scheduling policy when the kernel progress attains a point of resource underutilization. Meanwhile, the warp-sharing mechanism distributes stalling warps to different warp schedulers wherein the execution pipeline unit is ready. Our design achieves performance that is on an average higher than that of the traditional warp scheduler by 7.97% and employs marginal additional hardware overhead.

• Communications of the Korean Mathematical Society
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• v.11 no.2
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• pp.367-375
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• 1996

Suppose the kernel function $\kappa$ belongs to $S(R^2)$ and is symmetric such that $ < \otimes x, \kappa >\geq 0$ for all $x \in S'(R)$. Let A be the class of functions f such that the function f is measurable on $S'(R)$ with $\int_{S'(R)}$\mid$f((I + tK)^{\frac{1}{2}}x$\mid$^2d\mu(x) < M$ for some $M > 0$ and for all t > 0, where K is the integral operator with kernel function $\kappa$. We show that the \lambda$-potential $G_Kf$ of f is a weak solution of $(\lambda I - \frac{1}{2} \tilde{\Xi}_{0,2}(\kappa))_u = f$.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.30 no.4
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• pp.460-470
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• 1985

Experiments were carried out to evaluate the standard gravity in determining potential kernel size and to determine the effective sampling way by analyzing intra - and inter - plant variations for some source and sink characters using eleven semi-dwarf indica and three japonica cultivars including four semi-dwarf indica nearisogenic lines. Also, additional experiments were conducted to understand yearly variation and variety x year interaction effects for ten characters related to source and sink and to characterize the varietal difference of pre- and post-heading self-competition employing three parental varieties and their F$\sub$5/ progenies in 1982 and 1983. It is desirable to determine the potential kernel size by average kernel wight of rice grains showing above 1.15 specific gravity. There was significant difference in leaf area per tiller, spikelets and sink capacity per panicle among vigorous, intermediate and inferior tillers classified by differentiated order and vigorousness. Although it was difficult to find out any significant difference in grain-fill ratio, ratio of perfectly ripened grain, potential kernel size and sink/source ratio between vigorous and intermediate tillers, there was big difference between them and inferior one. The coefficients of variation within each tiller-group for some characters related to source and sink were larger with the order of vigorous tillers < intermediate one '||'&'||'lt; inferior one, and the average heritability of all characters, evaluated by the ratio of varietal variance (equation omitted) to total variance (equation omitted), were higher with the order of inferior tillers '||'&'||'lt; intemediate one '||'&'||'lt; superior one. Therefore, it is desirable to sample the vigorous tillers to represent the varietal difference of these traits. '82-'83 year variations of three parental cultivars were significant for all traits except for leaf area/tiller, panicles/hill, leaf area index and rough rice yield. The characters showing highly significant variance of variety x year interaction were growth duration from transplanting to heading, leaf area/tiller, sink/source ratio, sink capacity/panicle and grain yield. Generalized yearly response of three parental varieties (Suweon 264, Raegyeong, IR1317-70-l) and their F$\sub$5/ progenies on the 1st and 2nd principal components extracted from ten source and sink characters generally exhibited reduction in both source and sink. However, there were diverse variety x year interactions such as progenies showing similar reaction with their parents and intermediate or recombinational yearly response with little or considerable yearly movement on the four-dimensional planes of the two upper principal components between 1982 and 1983. Sink characters revealing highly significant border effect were grain-fill ratio, spikelets and sink capacity per panicle. Among them the latter two especially showed significant variety x border effect interaction. Self-competition characterized by relative weakness of inside plant's sink characters compared to the border one was more severe during the reproductive stage before heading than maturing stage. Though the larger sink capacity per panicle generally disclosed the severer self-competition, some lines (like Suweon 264) revealed severe self-competition with small sink capacity while a few others showed tender self-competition in spite of big sink capacity per panicle.

• Journal of Animal Environmental Science
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• v.20 no.3
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• pp.117-124
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• 2014

The objectives were to assess the use of near-infrared reflectance spectroscopy (NIRS) as a tool for estimating nutrient compositions of corn kernel, and to apply an NIRS-based indium gallium arsenide array detector to the system for collecting spectra and analyzing calibration equations using equipments designed for field application. Partial Least Squares Regression (PLSR) was employed to develop calibration equations based on representative data sets. The kennard-stone algorithm was applied to induce a calibration set and a validation set. As a result, the method for structuring a calibration set significantly affected prediction accuracy. The prediction of chemical composition of corn kernel resulted in the following (kennard-stone algorithm: relative) moisture ($R^2=0.82$, RMSEP=0.183), crude protein ($R^2=0.80$, RMSEP=0.142), crude fat ($R^2=0.84$, RMSEP=0.098), crude fiber ($R^2=0.74$, RMSEP=0.098), and crude ash ($R^2=0.81$, RMSEP=0.048). Result of this experiment showed the potential of NIRS to predict the chemical composition of corn kernel.