Curvelet变换结合(2D)~2PCA的人脸识别算法

作为一种新的多尺度多方向性的信号分析工具,Curvelet变换不但具有小波变换多尺度和多分辨率的特点,还具有很强的方向性,对包含大量面部轮廓和五官曲线信息的人脸图像能实现最优的稀疏表示。本文提出并实现了一种基于Curvelet变换结合双向二维主成分分析((2D)~2PCA)的人脸识别算法,以Yale人脸数据库进行人脸识别实验,结果表明,该算法相对于传统基于小波变换的人脸识别算法,能有效提高识别率,缩短识别时间。     

Improved kernel gradient free-smoothed particle hydrodynamics and its applications to heat transfer problems

Kernel gradient free-smoothed particle hydrodynamics(KGF-SPH) is a modified smoothed particle hydrodynamics(SPH) method which has higher precision than the conventional SPH.However,the Laplacian in KGF-SPH is approximated by the two-pass model which increases computational cost.A new kind of discretization scheme for the Laplacian is proposed in this paper,then a method with higher precision and better stability,called Improved KGF-SPH,is developed by modifying KGF-SPH with this new Laplacian model.One-dimensional(1D) and two-dimensional(2D) heat conduction problems are used to test the precision and stability of the Improved KGF-SPH.The numerical results demonstrate that the Improved KGF-SPH is more accurate than SPH,and stabler than KGF-SPH.Natural convection in a closed square cavity at different Rayleigh numbers are modeled by the Improved KGF-SPH with shifting particle position,and the Improved KGF-SPH results are presented in comparison with those of SPH and finite volume method(FVM).The numerical results demonstrate that the Improved KGF-SPH is a more accurate method to study and model the heat transfer problems.