A novel continuum-based fast projection scheme is proposed for cloth simulation.Cloth geometry is described by NURBS,and the dynamic response is modeled by a displacement-only Kirchhoff-Love shell element formulated d...A novel continuum-based fast projection scheme is proposed for cloth simulation.Cloth geometry is described by NURBS,and the dynamic response is modeled by a displacement-only Kirchhoff-Love shell element formulated directly on NURBS geometry.The fast projection method,which solves strain limiting as a constrained Lagrange problem,is extended to the continuum version.Numerical examples are studied to demonstrate the performance of the current scheme.The proposed approach can be applied to grids of arbitrary topology and can eliminate unrealistic over-stretching efficiently if compared to spring-based methodologies.展开更多
Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been ...Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been researched.The simulations were made for the performance of these algorithms.The extraction of fetal electrocardiogram(FECG) is applied to compare the application effect of the above algorithms.The proposed FAP algorithm has obvious advantages in computational complexity,convergence speed and steadystate error.展开更多
The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classificati...The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classification machines (SVM). The manuscript describes the ALFPGM algorithm, provides numerical results for training SVM on large data sets, and compares the training times of ALFPGM and Sequential Minimal Minimization algorithms (SMO) from Scikit-learn library. The numerical results demonstrate that ALFPGM with the improved working selection scheme is capable of training SVM with tens of thousands of training examples in a fraction of the training time of some widely adopted SVM tools.展开更多
基金Chao Zheng thanks the support from Sichuan Science and Technology Program[Grant No.2021JDRC0007].
文摘A novel continuum-based fast projection scheme is proposed for cloth simulation.Cloth geometry is described by NURBS,and the dynamic response is modeled by a displacement-only Kirchhoff-Love shell element formulated directly on NURBS geometry.The fast projection method,which solves strain limiting as a constrained Lagrange problem,is extended to the continuum version.Numerical examples are studied to demonstrate the performance of the current scheme.The proposed approach can be applied to grids of arbitrary topology and can eliminate unrealistic over-stretching efficiently if compared to spring-based methodologies.
基金the National Key Technologies R&D Program (No. 2006BAI22B01)
文摘Aimed at the problem of adaptive noise canceling(ANC),three implementary algorithms which are least mean square(LMS) algorithm,recursive least square(RLS) algorithm and fast affine projection(FAP) algorithm,have been researched.The simulations were made for the performance of these algorithms.The extraction of fetal electrocardiogram(FECG) is applied to compare the application effect of the above algorithms.The proposed FAP algorithm has obvious advantages in computational complexity,convergence speed and steadystate error.
文摘The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classification machines (SVM). The manuscript describes the ALFPGM algorithm, provides numerical results for training SVM on large data sets, and compares the training times of ALFPGM and Sequential Minimal Minimization algorithms (SMO) from Scikit-learn library. The numerical results demonstrate that ALFPGM with the improved working selection scheme is capable of training SVM with tens of thousands of training examples in a fraction of the training time of some widely adopted SVM tools.
