A procedure for reanalysis of various structures subjected tovarious topologic modifi- cations is presented. The procedure isbased on the results of a single exact analysis and the factoriza-tion of the stiffness matr...A procedure for reanalysis of various structures subjected tovarious topologic modifi- cations is presented. The procedure isbased on the results of a single exact analysis and the factoriza-tion of the stiffness matrix of initial structures. It is suitablefor the addition of joints, where the number of the degrees offreedom is increased. The method deals with the stiffness matrix ofstruc- tures directly, so it can be used with a general finiteelement system. It is shown that the proposed ap- proximation methodis most effective in terms of accuracy, efficiency, and ease ofimplementation.展开更多
Using Moore-Penrose inverse theory, a set of formulations for calculating the static responses of a changed finite element structure are given in this paper. Using these formulations by structural analysis may elimina...Using Moore-Penrose inverse theory, a set of formulations for calculating the static responses of a changed finite element structure are given in this paper. Using these formulations by structural analysis may eliminate the need of assembling the stiffness matrix and solving a set of simultaneous equations.展开更多
3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective.When existing methods reconstruct the mesh surface of complex objects,the surface details are difficult ...3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective.When existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too long.In this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology modification.We use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the topology.Meanwhile,a feature fusion method is proposed to make full use of the features of each stage of the image hierarchically.We use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training process.Extensive experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.展开更多
文摘A procedure for reanalysis of various structures subjected tovarious topologic modifi- cations is presented. The procedure isbased on the results of a single exact analysis and the factoriza-tion of the stiffness matrix of initial structures. It is suitablefor the addition of joints, where the number of the degrees offreedom is increased. The method deals with the stiffness matrix ofstruc- tures directly, so it can be used with a general finiteelement system. It is shown that the proposed ap- proximation methodis most effective in terms of accuracy, efficiency, and ease ofimplementation.
文摘Using Moore-Penrose inverse theory, a set of formulations for calculating the static responses of a changed finite element structure are given in this paper. Using these formulations by structural analysis may eliminate the need of assembling the stiffness matrix and solving a set of simultaneous equations.
基金This work was supported,in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401in part,by the National Nature Science Foundation of China under Grant Numbers 61502240,61502096,61304205,61773219in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective.When existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too long.In this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology modification.We use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the topology.Meanwhile,a feature fusion method is proposed to make full use of the features of each stage of the image hierarchically.We use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training process.Extensive experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.
