An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes(SNN) and local-Delaunay-trian...An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes(SNN) and local-Delaunay-triangulation(LDT) tech-niques, which were suitable and effective for h-adaptivity analysis on 2-D problems with the regular or irregular distribution of the nodes. The results of multiresolution and h-adaptivity analyses on 2-D linear elastostatics and bending plate problems demonstrate that the improper high-gradient indicator will reduce the convergence property of the h-adaptivity analysis, and that the efficiency of the LDT node refinement strategy is better than SNN, and that the presented h-adaptivity analysis scheme is provided with the validity, stability and good convergence property.展开更多
The meshless method is a new numerical technology presented in recent years.It uses the moving least square(MLS) approximation as its shape function,and it is determined by the basic function and weight function.The w...The meshless method is a new numerical technology presented in recent years.It uses the moving least square(MLS) approximation as its shape function,and it is determined by the basic function and weight function.The weight function is the mainly determining factor,so it greatly affects the accuracy of the computational results.The process of cylinder compression was analyzed by using rigid-plastic meshless variational principle and programming reproducing kernel partial method(RKPM),the influence of node number,weight functions and size factor on the solution was discussed and the suitable range of size factor was obtained.Compared with the finite element method(FEM),the feasibility and validity of the method were verified,which proves a good supplement of FEM in this field and provides a good guidance for the application of meshless in actual engineering.展开更多
虽然软大间隔聚类(Soft large margin clustering,SLMC)相比其他诸如K-Means等算法具有更优的聚类性能与某种程度的可解释性,然而当面对大规模分布存储数据时,均遭遇了同样的可扩展瓶颈,其涉及的核矩阵计算需要高昂的时间代价。消减此...虽然软大间隔聚类(Soft large margin clustering,SLMC)相比其他诸如K-Means等算法具有更优的聚类性能与某种程度的可解释性,然而当面对大规模分布存储数据时,均遭遇了同样的可扩展瓶颈,其涉及的核矩阵计算需要高昂的时间代价。消减此代价的有效策略之一是采用随机Fourier特征变换逼近核函数,而逼近精度所依赖的特征维度常常过高,隐含着可能过拟合的风险。本文将稀疏性嵌入核SLMC,结合交替方向乘子法(Alternating direction method of multipliers,ADMM),给出了一个分布式稀疏软大间隔聚类算法(Distributed sparse SLMC,DS-SLMC)来克服可扩展问题,同时通过稀疏化获得更好的可解释性。展开更多
基金Project supported by the National Natural Science Foundation of China (No.10202018)
文摘An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes(SNN) and local-Delaunay-triangulation(LDT) tech-niques, which were suitable and effective for h-adaptivity analysis on 2-D problems with the regular or irregular distribution of the nodes. The results of multiresolution and h-adaptivity analyses on 2-D linear elastostatics and bending plate problems demonstrate that the improper high-gradient indicator will reduce the convergence property of the h-adaptivity analysis, and that the efficiency of the LDT node refinement strategy is better than SNN, and that the presented h-adaptivity analysis scheme is provided with the validity, stability and good convergence property.
基金Project(02103) supported by the National Education Department of ChinaProject(200509) supported by the Central South University of Forestry and Technology+1 种基金Project(07031B) supported by Scientific Research Fund of Central South University of Forestry and TechnologyProject supported by the Rewarding Project for Excellent PhD Thesis of Hunan Province,China
文摘The meshless method is a new numerical technology presented in recent years.It uses the moving least square(MLS) approximation as its shape function,and it is determined by the basic function and weight function.The weight function is the mainly determining factor,so it greatly affects the accuracy of the computational results.The process of cylinder compression was analyzed by using rigid-plastic meshless variational principle and programming reproducing kernel partial method(RKPM),the influence of node number,weight functions and size factor on the solution was discussed and the suitable range of size factor was obtained.Compared with the finite element method(FEM),the feasibility and validity of the method were verified,which proves a good supplement of FEM in this field and provides a good guidance for the application of meshless in actual engineering.
文摘虽然软大间隔聚类(Soft large margin clustering,SLMC)相比其他诸如K-Means等算法具有更优的聚类性能与某种程度的可解释性,然而当面对大规模分布存储数据时,均遭遇了同样的可扩展瓶颈,其涉及的核矩阵计算需要高昂的时间代价。消减此代价的有效策略之一是采用随机Fourier特征变换逼近核函数,而逼近精度所依赖的特征维度常常过高,隐含着可能过拟合的风险。本文将稀疏性嵌入核SLMC,结合交替方向乘子法(Alternating direction method of multipliers,ADMM),给出了一个分布式稀疏软大间隔聚类算法(Distributed sparse SLMC,DS-SLMC)来克服可扩展问题,同时通过稀疏化获得更好的可解释性。