The Experts below are selected from a list of 18525 Experts worldwide ranked by ideXlab platform
Zhang Zhizheng - One of the best experts on this subject based on the ideXlab platform.
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a class of Computational Formula involving the multiple sum on genocchi number and riemann zeta function
Journal of Mathematical Research and Exposition, 2001Co-Authors: Zhang ZhizhengAbstract:The purpose of this note is to establish the general results of multiple summations on Genocchi numbers and Riemann Zeta-function by calculation technique. These results generalize the results of [5].
Pan Xiangwen - One of the best experts on this subject based on the ideXlab platform.
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anslysis of continous u shaped girder s shear lag effect based on the principle of energy
Structural Engineers, 2012Co-Authors: Pan XiangwenAbstract:In the paper,Resisner E's method was adopted to build the governing equation of shear lag effect in continuous U-shape girder.The results of shear lag ratio of a(40+64+40)m railway continuous U-shaped girder in an actual bridge obtained by using the finite elements method were compared with those by using the analytical method.At the same time,the effective width of the girder was also calculated.The Computational Formula and distribution law of shear lag effect offered in this paper can be referenced for the design and research of the same style bridges in the future.
Kyunghwan Song - One of the best experts on this subject based on the ideXlab platform.
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a reciprocal sum related to the riemann zeta function at s 6
arXiv: Number Theory, 2017Co-Authors: Wontae Hwang, Kyunghwan SongAbstract:We introduce an explicit Formula for a reciprocal sum related to the Riemann zeta function at s=6, and pose one question related to a Computational Formula for larger values of s.
Arigovindan Muthuvel - One of the best experts on this subject based on the ideXlab platform.
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Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation
'Institute of Electrical and Electronics Engineers (IEEE)', 2021Co-Authors: Viswanath Sanjay, De Beco Simon, Dahan Maxime, Arigovindan MuthuvelAbstract:Total Variation (TV) and related extensions have been popular in image restoration due to their robust performance and wide applicability. While the original Formulation is still relevant after two decades of extensive research, its extensions that combine derivatives of first- and second-order are now being explored for better performance, with examples being Combined Order TV (COTV) and Total Generalized Variation (TGV). As an improvement over such multi-order convex Formulations, we propose a novel non-convex regularization functional which adaptively combines Hessian-Schatten (HS) norm and first order TV (TV1) functionals with spatially varying weight. This adaptive weight itself is controlled by another regularization term; the total cost becomes the sum of this adaptively weighted HS-TV1 term, the regularization term for the adaptive weight, and the data-fitting term. The reconstruction is obtained by jointly minimizing w.r.t. the required image and the adaptive weight. We construct a block coordinate descent method for this minimization with proof of convergence, which alternates between minimization w.r.t. the required image and the adaptive weights. We derive exact Computational Formula for minimization w.r.t. the adaptive weight, and construct an ADMM algorithm for minimization w.r.t. to the required image. We compare the proposed method using image recovery examples including MRI reconstruction and microscopy deconvolution.Comment: 17 pages, Journal Draft 19 pages, 9 figures, 2 tables, Updated journal draft 24 pages, Revised submission to IEEE TIP, Copyright added after publicatio
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Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation
2019Co-Authors: Viswanath Sanjay, De Beco Simon, Dahan Maxime, Arigovindan MuthuvelAbstract:Total Variation (TV) and related extensions have been popular in image restoration due to their robust performance and wide applicability. While the original Formulation is still relevant after two decades of extensive research, its extensions that combine derivatives of first- and second-order are now being explored for better performance, with examples being Combined Order TV (COTV) and Total Generalized Variation (TGV). As an improvement over such multi-order convex Formulations, we propose a novel non-convex regularization functional which adaptively combines Hessian-Schatten (HS) norm and first order TV (TV1) functionals with spatially varying weight. This adaptive weight itself is controlled by another regularization term; the total cost becomes the sum of this adaptively weighted HS-TV1 term, the regularization term for the adaptive weight, and the data-fitting term. The reconstruction is obtained by jointly minimizing w.r.t. the required image and the adaptive weight. We construct a block coordinate descent method for this minimization with proof of convergence, which alternates between minimization w.r.t. the required image and the adaptive weights. We derive exact Computational Formula for minimization w.r.t. the adaptive weight, and construct an ADMM algorithm for minimization w.r.t. to the required image. We compare the proposed method using image recovery examples including MRI reconstruction and microscopy deconvolution.Comment: 17 pages, Journal Draft 19 pages, 9 figures, 2 tables, Updated journal draf
Wontae Hwang - One of the best experts on this subject based on the ideXlab platform.
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a reciprocal sum related to the riemann zeta function at s 6
arXiv: Number Theory, 2017Co-Authors: Wontae Hwang, Kyunghwan SongAbstract:We introduce an explicit Formula for a reciprocal sum related to the Riemann zeta function at s=6, and pose one question related to a Computational Formula for larger values of s.