机构

• 力学研究所 [2]
• 长春光学精密机械与物... [1]
• 数学与系统科学研究院 [1]

采集方式

• OAI收割 [4]

内容类型

• 会议论文 [2]
• 期刊论文 [2]

发表日期

• 2024 [1]
• 2020 [1]
• 2014 [1]
• 2008 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Investigation of radial basis function dynamic mesh method with rotation correction based on adaptive background mesh 期刊论文  OAI收割 COMPUTERS & FLUIDS, 2024, 卷号: 276, 页码: 27 作者:  Tang H(唐晗);  Zheng GN(郑冠男);  Zhang YC(张煜宸)   |  收藏  |  浏览/下载：13/0  |  提交时间：2024/06/14 Adaptive background mesh  Radial basis functions (RBF)  Rotation correction  Boundary layer quality  Computational efficiency   Quantum QR decomposition in the computational basis 期刊论文  OAI收割 QUANTUM INFORMATION PROCESSING, 2020, 卷号: 19, 期号: 8, 页码: 16 作者:  Ma, Guangsheng;  Li, Hongbo;  Zhao, Jiman   |  收藏  |  浏览/下载：20/0  |  提交时间：2020/09/23 Quantum algorithms  Computational basis  Quantum QR decomposition   Drag-reduction design on high-speed trains with intelligent optimization algorithm 会议论文  OAI收割 Ajaccio, Corsica, France, 8-11 April 2014 作者:  Yang GW(杨国伟);  Yao SB(姚拴宝);  Guo DL(郭迪龙)   |  收藏  |  浏览/下载：32/0  |  提交时间：2018/11/08 Aerodynamic drag  Aerodynamics  Ant colony optimization  Computational fluid dynamics  Deformation  Design  Drag reduction  Interpolation  Iterative methods  Radial basis function networks  Railroad cars  Railroad transportation  Railroads  Aerodynamic optimization  Grid deformation  High speed train (HST)  Intelligent optimization algorithm  Kriging surrogate model   Affine object recognition and affine parameters estimation based on covariant matrix (EI CONFERENCE) 会议论文  OAI收割 2008 International Symposium on Information Science and Engineering, ISISE 2008, December 20, 2008 - December 22, 2008, Shanghai, China 作者:  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：133/0  |  提交时间：2013/03/25 A new method of affine object recognition and affine parameters estimation is presented. For a real-time image and a group of templates  in addition  firstly  on the basis of correct recognition  we segment the object regions in them and compute their covariant matrices. Secondly  it can estimate affine parameters exactly  normalize the ellipse regions defined by covariant matrices to circle regions to get rotational invariants  and the estimated error is within 3%. 2008 IEEE.  and compute the similarity function value between rotational invariants of real-time image and every template respectively. Then compare the values with threshold set in advance  if more than one value is larger than threshold  take the corresponding templates as candidates  and compute affine matrix between real-time image and every candidate. Finally  transform the realtime image with every affine matrix and match the result with corresponding candidate by classical matching methods. Experimental results show that the presented method is robust to illumination  with low computational complexity  and it can realize recognition of different affine objects