This paper presents a high-order coupled compact integrated RBF(CC IRBF)approximation based domain decomposition(DD)algorithm for the discretisation of second-order differential problems.Several Schwarz DD algorithms,...This paper presents a high-order coupled compact integrated RBF(CC IRBF)approximation based domain decomposition(DD)algorithm for the discretisation of second-order differential problems.Several Schwarz DD algorithms,including one-level additive/multiplicative and two-level additive/multiplicative/hybrid,are employed.The CCIRBF based DD algorithms are analysed with different mesh sizes,numbers of subdomains and overlap sizes for Poisson problems.Our convergence analysis shows that the CCIRBF two-level multiplicative version is the most effective algorithm among various schemes employed here.Especially,the present CCIRBF two-level method converges quite rapidly even when the domain is divided into many subdomains,which shows great promise for either serial or parallel computing.For practical tests,we then incorporate the CCIRBF into serial and parallel two-level multiplicative Schwarz.Several numerical examples,including those governed by Poisson and Navier-Stokes equations are analysed to demonstrate the accuracy and efficiency of the serial and parallel algorithms implemented with the CCIRBF.Numerical results show:(i)the CCIRBF-Serial and-Parallel algorithms have the capability to reach almost the same solution accuracy level of the CCIRBF-Single domain,which is ideal in terms of computational calculations;(ii)the CCIRBF-Serial and-Parallel algorithms are highly accurate in comparison with standard finite difference,compact finite difference and some other schemes;(iii)the proposed CCIRBF-Serial and-Parallel algorithms may be used as alternatives to solve large-size problems which the CCIRBF-Single domain may not be able to deal with.The ability of producing stable and highly accurate results of the proposed serial and parallel schemes is believed to be the contribution of the coarse mesh of the two-level domain decomposition and the CCIRBF approximation.It is noted that the focus of this paper is on the derivation of highly accurate serial and parallel algorithms for second-order differential problems.The scope展开更多
Improvement in two aspects is done of the one-level mesoscale numerical model of Mass et al.(1985)and the re- vised model is used to make a simulation of a severe convective weather process in North China,with the res...Improvement in two aspects is done of the one-level mesoscale numerical model of Mass et al.(1985)and the re- vised model is used to make a simulation of a severe convective weather process in North China,with the result showing the pronounced effects of the topography upon the mesoscale systems.展开更多
受到移动设备计算能力和存储资源受限的局限,设计高效、高精度的人脸检测器是一个开放性的挑战.因此,文中提出融合多尺度特征的轻量级人脸检测算法(Lightweight Face Detection Algorithm with Multi-scale Feature Fusion,LFDMF),摒弃...受到移动设备计算能力和存储资源受限的局限,设计高效、高精度的人脸检测器是一个开放性的挑战.因此,文中提出融合多尺度特征的轻量级人脸检测算法(Lightweight Face Detection Algorithm with Multi-scale Feature Fusion,LFDMF),摒弃被视为人脸检测核心组件的多级检测结构.首先,利用现有的轻量级主干特征提取网络编码输入图像.然后,利用提出的颈部网络扩张特征图感受野,并将含有不同感受野的多尺度信息融至单级特征图中.最后,利用提出的多任务敏感检测头对该单级特征图进行人脸分类、回归和关键点检测.相比分而治之的人脸检测器,LFDMF精度更高、计算量更少.LFDMF按模型计算量高低可构建3个不同大小的网络,大模型LFDMF-L在Wider Face数据集上性能较优,中等模型LFDMF-M和小模型LFDMF-S以极低的模型参数量和计算量实现可观性能.展开更多
文摘This paper presents a high-order coupled compact integrated RBF(CC IRBF)approximation based domain decomposition(DD)algorithm for the discretisation of second-order differential problems.Several Schwarz DD algorithms,including one-level additive/multiplicative and two-level additive/multiplicative/hybrid,are employed.The CCIRBF based DD algorithms are analysed with different mesh sizes,numbers of subdomains and overlap sizes for Poisson problems.Our convergence analysis shows that the CCIRBF two-level multiplicative version is the most effective algorithm among various schemes employed here.Especially,the present CCIRBF two-level method converges quite rapidly even when the domain is divided into many subdomains,which shows great promise for either serial or parallel computing.For practical tests,we then incorporate the CCIRBF into serial and parallel two-level multiplicative Schwarz.Several numerical examples,including those governed by Poisson and Navier-Stokes equations are analysed to demonstrate the accuracy and efficiency of the serial and parallel algorithms implemented with the CCIRBF.Numerical results show:(i)the CCIRBF-Serial and-Parallel algorithms have the capability to reach almost the same solution accuracy level of the CCIRBF-Single domain,which is ideal in terms of computational calculations;(ii)the CCIRBF-Serial and-Parallel algorithms are highly accurate in comparison with standard finite difference,compact finite difference and some other schemes;(iii)the proposed CCIRBF-Serial and-Parallel algorithms may be used as alternatives to solve large-size problems which the CCIRBF-Single domain may not be able to deal with.The ability of producing stable and highly accurate results of the proposed serial and parallel schemes is believed to be the contribution of the coarse mesh of the two-level domain decomposition and the CCIRBF approximation.It is noted that the focus of this paper is on the derivation of highly accurate serial and parallel algorithms for second-order differential problems.The scope
基金supported by the National Natural Science Foundation of China
文摘Improvement in two aspects is done of the one-level mesoscale numerical model of Mass et al.(1985)and the re- vised model is used to make a simulation of a severe convective weather process in North China,with the result showing the pronounced effects of the topography upon the mesoscale systems.
文摘受到移动设备计算能力和存储资源受限的局限,设计高效、高精度的人脸检测器是一个开放性的挑战.因此,文中提出融合多尺度特征的轻量级人脸检测算法(Lightweight Face Detection Algorithm with Multi-scale Feature Fusion,LFDMF),摒弃被视为人脸检测核心组件的多级检测结构.首先,利用现有的轻量级主干特征提取网络编码输入图像.然后,利用提出的颈部网络扩张特征图感受野,并将含有不同感受野的多尺度信息融至单级特征图中.最后,利用提出的多任务敏感检测头对该单级特征图进行人脸分类、回归和关键点检测.相比分而治之的人脸检测器,LFDMF精度更高、计算量更少.LFDMF按模型计算量高低可构建3个不同大小的网络,大模型LFDMF-L在Wider Face数据集上性能较优,中等模型LFDMF-M和小模型LFDMF-S以极低的模型参数量和计算量实现可观性能.
