The composition, microstructure, mechanical and frictional properties of PTFE and its fillers were represented and analyzed by XRD, SEM, DSC, XPS and large-scale polarizing microscope. The results show that PTFE has a...The composition, microstructure, mechanical and frictional properties of PTFE and its fillers were represented and analyzed by XRD, SEM, DSC, XPS and large-scale polarizing microscope. The results show that PTFE has a flocculent structure with high melt temperature and decomposition temperature, big contact angle and crystallinity, and low surface hardness, compression strength, friction coefficient, wearing capacity and surface energy. Cooling rate influenced the friction coefficient and wear resistance. Graphite and molybdenum disulfide have a flake structure, and molybdenum disulfide has a big contact angle and low surface energy. Copper powder has a globular structure and its chief component is Cu-Pb alloy, and there is a loose layer on the surface. Carbon fiber has a rod structure and there are C=O and C-O-C polar groups on the skeleton surface. The decreasing order of water absorption capacity is graphite, carbon fiber, molybdenum disulfide, PTFE and copper powder.展开更多
Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational anatomy.The existing registration methods based on the dense deformation fields involve too many ...Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational anatomy.The existing registration methods based on the dense deformation fields involve too many parameters,which is not conducive to the exploration of correct spatial correspondence between the float and reference images.Meanwhile,the unidirectional registration may involve the deformation folding,which will result in the change of topology during registration.To address these issues,this work has presented an unsupervised image registration method using the free form deformation(FFD)and the symmetry constraint‐based generative adversarial networks(FSGAN).The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters,thereby producing two deformation fields.Meanwhile,the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously.Besides,the symmetry constraint is utilised to construct the loss function,thereby avoiding the deformation folding.Experiments on BrainWeb,high grade gliomas,IXI and LPBA40 show that compared with state‐of‐the‐art methods,the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value,target registration error and computational efficiency.展开更多
Scene graphs of point clouds help to understand object-level relationships in the 3D space.Most graph generation methods work on 2D structured data,which cannot be used for the 3D unstructured point cloud data.Existin...Scene graphs of point clouds help to understand object-level relationships in the 3D space.Most graph generation methods work on 2D structured data,which cannot be used for the 3D unstructured point cloud data.Existing point-cloud-based methods generate the scene graph with an additional graph structure that needs labor-intensive manual annotation.To address these problems,we explore a method to convert the point clouds into structured data and generate graphs without given structures.Specifically,we cluster points with similar augmented features into groups and establish their relationships,resulting in an initial structural representation of the point cloud.Besides,we propose a Dynamic Graph Generation Network(DGGN)to judge the semantic labels of targets of different granularity.It dynamically splits and merges point groups,resulting in a scene graph with high precision.Experiments show that our methods outperform other baseline methods.They output reliable graphs describing the object-level relationships without additional manual labeled data.展开更多
In this paper,an improved graphic representation for Structured Program Design——N-S-Z (Nassi-Shneiderman-Zhou Diagram)is proposed.It not only preserves the advantages of the conventional graphic and non-graphic repr...In this paper,an improved graphic representation for Structured Program Design——N-S-Z (Nassi-Shneiderman-Zhou Diagram)is proposed.It not only preserves the advantages of the conventional graphic and non-graphic representations,but also adds some new features which will enhance the representa- tive power of the original diagram.展开更多
The concept WALKING on structures is proposed, and the partial ordering between a structure and a query structure (substructure) is also created by means of WALKING. Based upon the above concepts, authors create the H...The concept WALKING on structures is proposed, and the partial ordering between a structure and a query structure (substructure) is also created by means of WALKING. Based upon the above concepts, authors create the Heuristic-Backtracking Algorithm (HBA) of structural match with high performance. In the last part of the paper, the applications of HBA in molecular graphics, synthetic planning, spectrum simulation , the representation and recognition of general structures are discussed.展开更多
Novel viewpoint image synthesis is very challenging,especially from sparse views,due to large changes in viewpoint and occlusion.Existing image-based methods fail to generate reasonable results for invisible regions,w...Novel viewpoint image synthesis is very challenging,especially from sparse views,due to large changes in viewpoint and occlusion.Existing image-based methods fail to generate reasonable results for invisible regions,while geometry-based methods have difficulties in synthesizing detailed textures.In this paper,we propose STATE,an end-to-end deep neural network,for sparse view synthesis by learning structure and texture representations.Structure is encoded as a hybrid feature field to predict reasonable structures for invisible regions while maintaining original structures for visible regions,and texture is encoded as a deformed feature map to preserve detailed textures.We propose a hierarchical fusion scheme with intra-branch and inter-branch aggregation,in which spatio-view attention allows multi-view fusion at the feature level to adaptively select important information by regressing pixel-wise or voxel-wise confidence maps.By decoding the aggregated features,STATE is able to generate realistic images with reasonable structures and detailed textures.Experimental results demonstrate that our method achieves qualitatively and quantitatively better results than state-of-the-art methods.Our method also enables texture and structure editing applications benefiting from implicit disentanglement of structure and texture.Our code is available at http://cic.tju.edu.cn/faculty/likun/projects/STATE.展开更多
Non-dominated sorting genetic algorithm II(NSGA-II)with multiple constraints handling is employed for multi-objective optimization of the topological structure of telescope skin,in which a bit-matrix is used as the ...Non-dominated sorting genetic algorithm II(NSGA-II)with multiple constraints handling is employed for multi-objective optimization of the topological structure of telescope skin,in which a bit-matrix is used as the representation of a chromosome,and genetic algorithm(GA)operators are introduced based on the matrix.Objectives including mass,in-plane performance,and out-of-plane load-bearing ability of the individuals are obtained by fnite element analysis(FEA)using ANSYS,and the matrix-based optimization algorithm is realized in MATLAB by handling multiple constraints such as structural connectivity and in-plane strain requirements.Feasible confgurations of the support structure are achieved.The results confrm that the matrix-based NSGA-II with multiple constraints handling provides an effective method for two-dimensional multi-objective topology optimization.展开更多
This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology,inspired by the needs of problem solving for biological,agricultural,a...This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology,inspired by the needs of problem solving for biological,agricultural,aquacultural and environmental systems.The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based,unified models that can also be applied for the effective coupling amongst the various(sophisticated field-specific,sensor data processing-based,upper level optimization-driven,etc.)models.The scientific problem addressed in this innovation is to develop Programmable Process Structures(PPS)by combining functional basis of systems theory,structural approach of net theory and computational principles of agent based modeling.PPS offers a novel framework for the automatic generation of easily extensible and connectible,unified models for the underlying complex systems.PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure.The models consist of unified state and transition elements.The local program containing prototype elements,derived also from the meta-prototypes,are responsible for the case-specific calculations.The integrity and consistency of PPS architecture are based on the meta-prototypes,prepared to distinguish between the conservation-laws-based measures and the signals.The simulation is based on data flows amongst the state and transition elements,as well as on the unification based data transfer between these elements and their calculating prototypes.This architecture and its AI language-based(Prolog)implementation support the integration of various field-and task-specific models,conveniently.The better understanding is helped by a simple example.The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications,focusing on the recently studied agricultural and aquacultural cases.展开更多
文摘The composition, microstructure, mechanical and frictional properties of PTFE and its fillers were represented and analyzed by XRD, SEM, DSC, XPS and large-scale polarizing microscope. The results show that PTFE has a flocculent structure with high melt temperature and decomposition temperature, big contact angle and crystallinity, and low surface hardness, compression strength, friction coefficient, wearing capacity and surface energy. Cooling rate influenced the friction coefficient and wear resistance. Graphite and molybdenum disulfide have a flake structure, and molybdenum disulfide has a big contact angle and low surface energy. Copper powder has a globular structure and its chief component is Cu-Pb alloy, and there is a loose layer on the surface. Carbon fiber has a rod structure and there are C=O and C-O-C polar groups on the skeleton surface. The decreasing order of water absorption capacity is graphite, carbon fiber, molybdenum disulfide, PTFE and copper powder.
基金supported in part by the National Key Research and Development Program of China under Grant 2018Y FE0206900in part by the National Natural Science Foundation of China under Grant 61871440in part by the CAAIHuawei MindSpore Open Fund.We gratefully acknowledge the support of MindSpore for this research.
文摘Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational anatomy.The existing registration methods based on the dense deformation fields involve too many parameters,which is not conducive to the exploration of correct spatial correspondence between the float and reference images.Meanwhile,the unidirectional registration may involve the deformation folding,which will result in the change of topology during registration.To address these issues,this work has presented an unsupervised image registration method using the free form deformation(FFD)and the symmetry constraint‐based generative adversarial networks(FSGAN).The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters,thereby producing two deformation fields.Meanwhile,the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously.Besides,the symmetry constraint is utilised to construct the loss function,thereby avoiding the deformation folding.Experiments on BrainWeb,high grade gliomas,IXI and LPBA40 show that compared with state‐of‐the‐art methods,the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value,target registration error and computational efficiency.
基金This work was supported by the National Natural Science Foundation of China(Nos.62173045 and 61673192)the Fundamental Research Funds for the Central Universities(No.2020XD-A04-2)the BUPT Excellent PhD Students Foundation(No.CX2021222).
文摘Scene graphs of point clouds help to understand object-level relationships in the 3D space.Most graph generation methods work on 2D structured data,which cannot be used for the 3D unstructured point cloud data.Existing point-cloud-based methods generate the scene graph with an additional graph structure that needs labor-intensive manual annotation.To address these problems,we explore a method to convert the point clouds into structured data and generate graphs without given structures.Specifically,we cluster points with similar augmented features into groups and establish their relationships,resulting in an initial structural representation of the point cloud.Besides,we propose a Dynamic Graph Generation Network(DGGN)to judge the semantic labels of targets of different granularity.It dynamically splits and merges point groups,resulting in a scene graph with high precision.Experiments show that our methods outperform other baseline methods.They output reliable graphs describing the object-level relationships without additional manual labeled data.
文摘In this paper,an improved graphic representation for Structured Program Design——N-S-Z (Nassi-Shneiderman-Zhou Diagram)is proposed.It not only preserves the advantages of the conventional graphic and non-graphic representations,but also adds some new features which will enhance the representa- tive power of the original diagram.
文摘The concept WALKING on structures is proposed, and the partial ordering between a structure and a query structure (substructure) is also created by means of WALKING. Based upon the above concepts, authors create the Heuristic-Backtracking Algorithm (HBA) of structural match with high performance. In the last part of the paper, the applications of HBA in molecular graphics, synthetic planning, spectrum simulation , the representation and recognition of general structures are discussed.
基金This work was supported in part by the National Natural Science Foundation of China(62171317 and 62122058).
文摘Novel viewpoint image synthesis is very challenging,especially from sparse views,due to large changes in viewpoint and occlusion.Existing image-based methods fail to generate reasonable results for invisible regions,while geometry-based methods have difficulties in synthesizing detailed textures.In this paper,we propose STATE,an end-to-end deep neural network,for sparse view synthesis by learning structure and texture representations.Structure is encoded as a hybrid feature field to predict reasonable structures for invisible regions while maintaining original structures for visible regions,and texture is encoded as a deformed feature map to preserve detailed textures.We propose a hierarchical fusion scheme with intra-branch and inter-branch aggregation,in which spatio-view attention allows multi-view fusion at the feature level to adaptively select important information by regressing pixel-wise or voxel-wise confidence maps.By decoding the aggregated features,STATE is able to generate realistic images with reasonable structures and detailed textures.Experimental results demonstrate that our method achieves qualitatively and quantitatively better results than state-of-the-art methods.Our method also enables texture and structure editing applications benefiting from implicit disentanglement of structure and texture.Our code is available at http://cic.tju.edu.cn/faculty/likun/projects/STATE.
基金supported by the National Natural Science Foundation of China(Nos.50905085 and 91116020)the National Science Foundation for Post-doctoral Scientists of China(No.2012M511263)
文摘Non-dominated sorting genetic algorithm II(NSGA-II)with multiple constraints handling is employed for multi-objective optimization of the topological structure of telescope skin,in which a bit-matrix is used as the representation of a chromosome,and genetic algorithm(GA)operators are introduced based on the matrix.Objectives including mass,in-plane performance,and out-of-plane load-bearing ability of the individuals are obtained by fnite element analysis(FEA)using ANSYS,and the matrix-based optimization algorithm is realized in MATLAB by handling multiple constraints such as structural connectivity and in-plane strain requirements.Feasible confgurations of the support structure are achieved.The results confrm that the matrix-based NSGA-II with multiple constraints handling provides an effective method for two-dimensional multi-objective topology optimization.
文摘This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology,inspired by the needs of problem solving for biological,agricultural,aquacultural and environmental systems.The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based,unified models that can also be applied for the effective coupling amongst the various(sophisticated field-specific,sensor data processing-based,upper level optimization-driven,etc.)models.The scientific problem addressed in this innovation is to develop Programmable Process Structures(PPS)by combining functional basis of systems theory,structural approach of net theory and computational principles of agent based modeling.PPS offers a novel framework for the automatic generation of easily extensible and connectible,unified models for the underlying complex systems.PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure.The models consist of unified state and transition elements.The local program containing prototype elements,derived also from the meta-prototypes,are responsible for the case-specific calculations.The integrity and consistency of PPS architecture are based on the meta-prototypes,prepared to distinguish between the conservation-laws-based measures and the signals.The simulation is based on data flows amongst the state and transition elements,as well as on the unification based data transfer between these elements and their calculating prototypes.This architecture and its AI language-based(Prolog)implementation support the integration of various field-and task-specific models,conveniently.The better understanding is helped by a simple example.The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications,focusing on the recently studied agricultural and aquacultural cases.