• 대한수학회보
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• 제56권3호
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• pp.675-680
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• 2019

We, in this note, decompose the invariant Laplacian of the unit complex ball of ${\mathbb{C} }^n$ by the radial part and tangential part as ${\tilde{\Delta}}={\tilde{\Delta}}_{rad}+{\tilde{\Delta}}_{tan}$. We give several properties and interpretations involved with this decomposition.

• East Asian mathematical journal
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• 제34권5호
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• pp.577-585
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• 2018

For the purpose of characterizing subharmonic or ${\mathcal{M}}$-subharmonic Hardy classes in the unit ball of ${\mathbb{C}}^n$, we establish fundamental identities between integral means in terms of volume integrals and Green's functions.

• 전자공학회논문지
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• 제52권2호
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• pp.162-170
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• 2015

컴뮤트 타임 임베딩을 구현하려면 그래프 라플라시안 행렬의 고유값과 고유벡터를 구하여야 하는데, $o(n^3)$의 계산량이 요구되어 대용량 데이터에는 적용하기 어려운 문제가 있다. 이를 줄이기 위하여 표본화 과정을 통하여 크기가 줄어든 그래프 라플라시안 행렬에서 구한 다음, 원래의 고유값과 고유벡터를 근사화시키는 Nystr${\ddot{o}}$m 기법을 주로 채택한다. 이 과정에서 많은 오차가 발생하는데, 이를 개선하기 위하여 본 논문에서는 그래프 라플라시안 대신에 가중치 행렬을 표본화하고 이로부터 구한 고유값과 고유벡터를 그래프 라플라시안의 고유값과 고유벡터로 변환하는 기법을 이용하여 대용량 데이터로 구성된 스펙트럴 그래프를 근사적으로 컴뮤트 타임 임베딩하는 기법을 제안한다. 하지만, 이 방식도 스펙트럼 분해를 계산하여야 하므로 데이터의 크기가 증가하면 적용하기 어려운 문제가 발생한다. 이의 대안으로, 스펙트럼 분해를 계산하지 않고도 데이터 집합의 크기에 영향을 받지 않으면서 컴뮤트 타임을 근사적으로 계산하는 방식을 구현하고 이들의 특성을 실험적으로 분석한다.

• 대한수학회논문집
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• 제16권3호
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• pp.487-508
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• 2001

We consider the Cauchy problem for Laplacian. Using the single layer representation, we obtain an equivalent system of boundary integral equations. We show the singular values of the ill-posed Cauchy operator decay exponentially, which means that a small error is exponentially amplified in the solution of the Cauchy problem. We show the decaying rate is dependent on the geometry of he domain, which provides the information on the choice of numerically meaningful modes. We suggest a pseudo-inverse regularization method based on singular value decomposition and present various numerical simulations.

• 충청수학회지
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• 제22권1호
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• pp.117-121
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• 2009

For $1{\leq}p$, $q{\leq}{\infty}$ and $s{\in}\mathbb{R}$, it is proved that there exists a constant C > 0 such that for any $f{\in}B^{s+2}_{p,q}(\mathbb{R}^n)$ $${\parallel}f{\parallel}_{B^{s+2}_{p,q}(\mathbb{R}^n)}{\leq}C{\parallel}f\;-\;{\Delta}f{\parallel}_{B^{s}_{p,q}(\mathbb{R}^n)}$$, which tells us that the operator $I-\Delta$ is $B^{s+2}_{p,q}$-coercive on the Besov space $B^s_{p,q}$.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 춘계 학술발표회 논문집
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• pp.37-42
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• 2003

We study the asymptotic expansion in small time of the mean distance of Brownian motion on Riemannian manifolds. We compute the first four terms of the asymptotic expansion of the mean distance by using the decomposition of Laplacian into homogeneous components. This expansion can he expressed in terms of the scalar valued curvature invariants of order 2, 4, 6.

• 대한수학회보
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• 제44권4호
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• pp.795-806
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• 2007

We investigate F-traceless component of the conformal curvature tensor defined by (3.6) in $K\ddot{a}hler$ manifolds of dimension ${\geq}4$, and show that the F-traceless component is invariant under concircular change. In particular, we determine $K\ddot{a}hler$ manifolds with parallel F-traceless component and improve some theorems, provided in the previous paper([2]), which are concerned with the traceless component of the conformal curvature tensor and the spectrum of the Laplacian acting on $p(0{\leq}p{\leq}2)$-forms on the manifold by using the F-traceless component.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1160-1167
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• 2019

Because of the dynamic range limitation of digital equipment, it is impossible to obtain dark and bright areas at the same time with one shot. In order to solve this problem, an exposure fusion technique for fusing a plurality of images photographed at different exposure amounts into one is being studied. Among them, Laplacian pyramid decomposition based fusion method can generate natural HDR image by fusing images of various scales. But this requires a lot of computation time. Therefore, in this paper, we propose an approximation technique that achieves similar performance and greatly shortens computation time. The concept of vanishing point image for approximation is introduced, and the validity of the proposed approach is verified by comparing the computation time with the resultant image.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 가을 학술발표논문집 Vol.31 No.2 (1)
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• pp.697-699
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• 2004

Despite many successful spectral clustering algorithm (based on the spectral decomposition of Laplacian(1) or stochastic matrix(2) ) there are several unsolved problems. Most spectral clustering Problems are based on the normalized of algorithm(3) . are close to the classical graph paritioning problem which is NP-hard problem. To get good solution in polynomial time. it needs to establish its convex form by using relaxation. In this paper, we apply a novel optimization technique. semidefinite programming(SDP). to the unsupervised clustering Problem. and present a new multiple Partitioning method. Experimental results confirm that the Proposed method improves the clustering performance. especially in the Problem of being mixed with non-compact clusters compared to the previous multiple spectral clustering methods.