Zobrazeno 1 - 10
of 1 752
pro vyhledávání: '"YU, Haijun"'
Autor:
Yu, Haijun, Zhang, Shuo
Deep neural network approaches show promise in solving partial differential equations. However, unlike traditional numerical methods, they face challenges in enforcing essential boundary conditions. The widely adopted penalty-type methods, for exampl
Externí odkaz:
http://arxiv.org/abs/2411.09898
Autor:
Liu, Shiqin, Yu, Haijun
We propose two efficient energetic spectral-element methods in time for marching nonlinear gradient systems with the phase-field Allen--Cahn equation as an example: one fully implicit nonlinear method and one semi-implicit linear method. Different fr
Externí odkaz:
http://arxiv.org/abs/2406.16287
Autor:
Thies, Mareike, Wagner, Fabian, Maul, Noah, Mei, Siyuan, Gu, Mingxuan, Pfaff, Laura, Vysotskaya, Nastassia, Yu, Haijun, Maier, Andreas
Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensation methods aim to correct such artifacts post-acquisition, often inco
Externí odkaz:
http://arxiv.org/abs/2405.19079
Autor:
Thies, Mareike, Wagner, Fabian, Maul, Noah, Yu, Haijun, Goldmann, Manuela, Schneider, Linda-Sophie, Gu, Mingxuan, Mei, Siyuan, Folle, Lukas, Preuhs, Alexander, Manhart, Michael, Maier, Andreas
Publikováno v:
in IEEE Transactions on Medical Imaging (2024)
Cone-beam computed tomography (CBCT) systems, with their flexibility, present a promising avenue for direct point-of-care medical imaging, particularly in critical scenarios such as acute stroke assessment. However, the integration of CBCT into clini
Externí odkaz:
http://arxiv.org/abs/2401.09283
Lyapunov exponents and Lagrangian chaos suppression in compressible homogeneous isotropic turbulence
Autor:
Yu, Haijun, Fouxon, Itzhak, Wang, Jianchun, Li, Xiangru, Yuan, Li, Mao, Shipeng, Mond, Michael
Publikováno v:
Physics of Fluids, 35(12), 2023
We study Lyapunov exponents of tracers in compressible homogeneous isotropic turbulence at different turbulent Mach number $M_t$ and Taylor-scale Reynolds number $Re_\lambda$. We demonstrate that statistics of finite-time Lyapunov exponents have the
Externí odkaz:
http://arxiv.org/abs/2310.09717
Recently, linear computed tomography (LCT) systems have actively attracted attention. To weaken projection truncation and image the region of interest (ROI) for LCT, the backprojection filtration (BPF) algorithm is an effective solution. However, in
Externí odkaz:
http://arxiv.org/abs/2309.11858
Autor:
Chen, Xiaoli, Soh, Beatrice W., Ooi, Zi-En, Vissol-Gaudin, Eleonore, Yu, Haijun, Novoselov, Kostya S., Hippalgaonkar, Kedar, Li, Qianxiao
One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Suc
Externí odkaz:
http://arxiv.org/abs/2308.04119
Autor:
Wang, Zhisheng, Yu, Haijun, Huang, Yixing, Wang, Shunli, Ni, Song, Li, Zongfeng, Liu, Fenglin, Cui, Junning
Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields. However, its small field-of-view (FOV) cannot meet the pressing demand for imaging relative
Externí odkaz:
http://arxiv.org/abs/2305.18878
Autor:
Huang, Shenghe, Yu, Haijun
Laguerre polynomials are orthogonal polynomials defined on positive half line with respect to weight $e^{-x}$. They have wide applications in scientific and engineering computations. However, the exponential growth of Laguerre polynomials of high deg
Externí odkaz:
http://arxiv.org/abs/2212.13255
Autor:
He, Yuanwei, Zeng, Li, Xu, Qiong, Wang, Zhe, Yu, Haijun, Shen, Zhaoqiang, Yang, Zhaojun, Zhou, Rifeng
With the development of computed tomography (CT) imaging technology, it is possible to acquire multi-energy data by spectral CT. Being different from conventional CT, the X-ray energy spectrum of spectral CT is cutting into several narrow bins which
Externí odkaz:
http://arxiv.org/abs/2212.06934