• Journal of applied mathematics & informatics
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• 제7권2호
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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.

• 호남수학학술지
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• 제27권2호
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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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• 제3권3호
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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.

• 호남수학학술지
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• 제36권3호
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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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• 제7권3호
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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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• 제17권5호
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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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• 제17권6호
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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.

• 대한수학회논문집
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• 제11권2호
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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 $(\lambda I - \frac{1}{2} \tilde{\Xi}_{0,2}(\kappa))_u = f$.

• 한국작물학회지
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• 제30권4호
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• pp.460-470
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• 1985

수도의 source 및 sink관련형질의 품종간 차이를 평가함에 있어서 semi-dwarf indica 및 japonica 조.중만생 10품종 및 semi-dwarf indica 4개 조.만 near-isogenic계통을 각각 네 작기 및 두 작기로 재배하여 수확된 벼 종자를 비중 1.0에서 1.21까지 각 비중별 입수와 입중을 조사, potential kernel size의 결정기준을 설정하고자 했으며, 이 중 semi-dwarf indica 6개 품종을 대상으로 수개 source 및 sink 관련형질의 개체내 및 개체간변이를 조사, 분석하여 품종간차이평가를 위한 효율적인 표본채취방법에 대해서 검토해 보았고, 그 중 3개 신품종과 그들 F$_{5}$계통들에 대한 연차변이 및 품종$\times$환경간 교호작용을 파악함과 동시에 주연개체와 내부개체간의 sink 특성을 비교하여 자체경쟁력의 계통간차이를 평가하고자 했다. 그 결과를 요약하면 다음과 같다. 1. Potential kernel size는 semi-dwarf indica 및japonica 품종 모두 비중 1.15이상인 벼 알의 평균입중으로 결정하는 것이 바람직하다. 2. 분얼의 분화순차와 세력(이삭크기)에 따라 나누어 본 3개경군(강.중.약)간에는 경당엽면적, 수당영화수 및 수당 sink 용량이 현저한 차이를 보였고, 종실축적율, 등숙충실도, potential kernel size 및 sink/source ratio는 강세경과 중세경간에는 유의한 차이를 보이지 않았으나 약세경과는 현저한 차이를 보였는데 품종에 따라서 형질별로 약간씩 그 경향을 달리했다. 3. Source 및 sink 관련형질에 있어서 경군내변이 계수는 강세경<중세경<약세경의 순으로 컸고 전형질에 대한 평균유전율은 약세경 <중세경 <강세경의 순으로 커서 강세경을 개체의 대표치로 품종간변이를 평가하는 것이 바람직할 것으로 생각되었다. 4. 세 신품종의 '82-'83년간 연차변이는 경당엽면적, 주당수수, 엽면적지수 및 정조수량을 제외한 모든 형질에서 유의하게 인정되었으며 년차$\times$품종간 교호작용이 현저히 유의했던 형질은 출수까지 생육일수, 경당엽면적, sink/source ratio, 경당 sink용량 및 수량이었다. 5. 수원 264호/IR1317-70-1 및 내경/IR 1317-70-1 조합에서 선발된 F$_{5}$계통 및 신품종들의 10개 source 및 sink관련형질을 중심으로 주성분 분석을 실시하여 년차간 총합적인 특성의 변이양상을 살펴 본 결과 대체로 '82년에 비해 '83년에 source 및 sink의 양면적인 감소경향을 나타냈는데 제1 및 제 2주성분좌표상에서 년차간 변이정도에 상당한 차이를 보이면서 신품종과 유사한 방향 또는중간형 반응을 보이는 다양한 계통간 차이를 나타냈다. 6. 주연효과가 현저했던 것은 주당영화수, 수당 sink용량 및 종실축적률이었고 potential kernel size는 거의 차이가 없었는데, 특히 수당영화수 및 sink용량에 대한 주연효과정도는 계통에 따라 상당한 차이를 보였다. 계통의 자체경쟁정도는 출수후보다는 출수전 생식생장기간에 더욱 심하고 계통간차이도 큰 것 같았다. 일반적으로 수당sink용량이 클수록 출수전 자체경쟁정도가 심한 경향이었으나 계통에 따라서는 수당 sink용량이 작으면서 자체경쟁도가 심한 것(수원264호 등)이 있는 반면에 수당sink용량이 크면서 자체경쟁도가 심하지 않은 계통도 있었다.

• 한국축산시설환경학회지
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• 제20권3호
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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.