The classification of point cloud data is the key technology of point cloud data information acquisition and 3D reconstruction, which has a wide range of applications. However, the existing point cloud classification ...The classification of point cloud data is the key technology of point cloud data information acquisition and 3D reconstruction, which has a wide range of applications. However, the existing point cloud classification methods have some shortcomings when extracting point cloud features, such as insufficient extraction of local information and overlooking the information in other neighborhood features in the point cloud, and not focusing on the point cloud channel information and spatial information. To solve the above problems, a point cloud classification network based on graph convolution and fusion attention mechanism is proposed to achieve more accurate classification results. Firstly, the point cloud is regarded as a node on the graph, the k-nearest neighbor algorithm is used to compose the graph and the information between points is dynamically captured by stacking multiple graph convolution layers;then, with the assistance of 2D experience of attention mechanism, an attention mechanism which has the capability to integrate more attention to point cloud spatial and channel information is introduced to increase the feature information of point cloud, aggregate local useful features and suppress useless features. Through the classification experiments on ModelNet40 dataset, the experimental results show that compared with PointNet network without considering the local feature information of the point cloud, the average classification accuracy of the proposed model has a 4.4% improvement and the overall classification accuracy has a 4.4% improvement. Compared with other networks, the classification accuracy of the proposed model has also been improved.展开更多
In this paper, we propose a new perspective to discuss the N-order fixed point theory of set-valued and single-valued mappings. There are two aspects in our work: we first define a product metric space with a graph fo...In this paper, we propose a new perspective to discuss the N-order fixed point theory of set-valued and single-valued mappings. There are two aspects in our work: we first define a product metric space with a graph for the single-valued mapping whose conversion makes the results and proofs concise and straightforward, and then we propose an <em>SG</em>-contraction definition for set-valued mapping which is more general than some recent contraction’s definition. The results obtained in this paper extend and unify some recent results of other authors. Our method to discuss the N-order fixed point unifies <em>N</em>-order fixed point theory of set-valued and single-valued mappings.展开更多
传统兴趣点(point of interest,POI)推荐方法对用户和POI的关联关系挖掘不充分,无法全面捕捉用户偏好;基于图增强的推荐方法虽能挖掘关联关系,却易引入噪声,降低推荐性能。针对这些问题,本文提出了结合通用轨迹图和多偏好的POI推荐方法...传统兴趣点(point of interest,POI)推荐方法对用户和POI的关联关系挖掘不充分,无法全面捕捉用户偏好;基于图增强的推荐方法虽能挖掘关联关系,却易引入噪声,降低推荐性能。针对这些问题,本文提出了结合通用轨迹图和多偏好的POI推荐方法。首先构建了用户与POI的带权二部图,利用图卷积网络捕捉用户和POI的交互关系,学习用户兴趣偏好;利用兴趣偏好完成用户聚类,进而构建同类型用户通用轨迹图,减少噪声信息影响;利用图卷积网络捕捉同类型用户的群体特征,丰富特征表示。其次,将群体特征与用户当前轨迹中时间类别感知信息、时空上下文信息相结合,利用Transformer挖掘用户的深层行为偏好。再次,构造非线性加性函数并将兴趣偏好和行为偏好动态组合,全面捕捉用户偏好,完成POI推荐。最后,在真实数据集上验证了本文方法的有效性。展开更多
文摘The classification of point cloud data is the key technology of point cloud data information acquisition and 3D reconstruction, which has a wide range of applications. However, the existing point cloud classification methods have some shortcomings when extracting point cloud features, such as insufficient extraction of local information and overlooking the information in other neighborhood features in the point cloud, and not focusing on the point cloud channel information and spatial information. To solve the above problems, a point cloud classification network based on graph convolution and fusion attention mechanism is proposed to achieve more accurate classification results. Firstly, the point cloud is regarded as a node on the graph, the k-nearest neighbor algorithm is used to compose the graph and the information between points is dynamically captured by stacking multiple graph convolution layers;then, with the assistance of 2D experience of attention mechanism, an attention mechanism which has the capability to integrate more attention to point cloud spatial and channel information is introduced to increase the feature information of point cloud, aggregate local useful features and suppress useless features. Through the classification experiments on ModelNet40 dataset, the experimental results show that compared with PointNet network without considering the local feature information of the point cloud, the average classification accuracy of the proposed model has a 4.4% improvement and the overall classification accuracy has a 4.4% improvement. Compared with other networks, the classification accuracy of the proposed model has also been improved.
文摘In this paper, we propose a new perspective to discuss the N-order fixed point theory of set-valued and single-valued mappings. There are two aspects in our work: we first define a product metric space with a graph for the single-valued mapping whose conversion makes the results and proofs concise and straightforward, and then we propose an <em>SG</em>-contraction definition for set-valued mapping which is more general than some recent contraction’s definition. The results obtained in this paper extend and unify some recent results of other authors. Our method to discuss the N-order fixed point unifies <em>N</em>-order fixed point theory of set-valued and single-valued mappings.
文摘传统兴趣点(point of interest,POI)推荐方法对用户和POI的关联关系挖掘不充分,无法全面捕捉用户偏好;基于图增强的推荐方法虽能挖掘关联关系,却易引入噪声,降低推荐性能。针对这些问题,本文提出了结合通用轨迹图和多偏好的POI推荐方法。首先构建了用户与POI的带权二部图,利用图卷积网络捕捉用户和POI的交互关系,学习用户兴趣偏好;利用兴趣偏好完成用户聚类,进而构建同类型用户通用轨迹图,减少噪声信息影响;利用图卷积网络捕捉同类型用户的群体特征,丰富特征表示。其次,将群体特征与用户当前轨迹中时间类别感知信息、时空上下文信息相结合,利用Transformer挖掘用户的深层行为偏好。再次,构造非线性加性函数并将兴趣偏好和行为偏好动态组合,全面捕捉用户偏好,完成POI推荐。最后,在真实数据集上验证了本文方法的有效性。
