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
【目的】柔性互联配电网遭遇故障停运后,通过软开关(soft open point,SOP)等设备可以快速、合理地进行功率转供或孤岛运行。然而现有研究暂未考虑柔性设备的不同控制状态对恢复结果的影响,常见孤岛预划分方法也难以确定柔性互联设备支...【目的】柔性互联配电网遭遇故障停运后,通过软开关(soft open point,SOP)等设备可以快速、合理地进行功率转供或孤岛运行。然而现有研究暂未考虑柔性设备的不同控制状态对恢复结果的影响,常见孤岛预划分方法也难以确定柔性互联设备支撑的孤岛半径和恢复优先级。针对互联设备的可行控制方式,提出了基于SOP等效模型的故障恢复策略。【方法】首先设计含多种控制方式的SOP潮流交替迭代算法,以计算恢复后的功率与电压分布。其次以SOP控制方式选择以及非预设重构为优化手段,以最小化加权运行损失为目标,得到综合考虑潮流约束与多端口SOP模式约束的恢复模型。最后针对寻优范围增加,采用协同图拉普拉斯算子的遗传算法进行求解。基于互联的双IEEE 33系统算例进行了故障后恢复效果验证。【结果】结果表明:针对不同线路停运后的拓扑变动及分布式电源出力情况,所提方法能够形成相应的非预设重构方案,并灵活调整不同位置的SOP控制方式进行协同,负荷恢复比例较重构方式提升14%。【结论】非预设网络重构带来了更高的故障后负荷恢复比例,结合优化SOP的控制状态可取得更优的恢复后电压分布,从而支撑柔性互联配电网的高供电韧性。展开更多
Inverse Sum Indeg指数(ISI指数)是预测辛烷异构体总表面积的重要拓扑指数。针对ISI指数在图变换下缺乏系统表达式推导的问题,本文给出了具有n个顶点和m条边的简单连通图的细分图、线图、全图、半全点图、半全线图和广义变换图的ISI指...Inverse Sum Indeg指数(ISI指数)是预测辛烷异构体总表面积的重要拓扑指数。针对ISI指数在图变换下缺乏系统表达式推导的问题,本文给出了具有n个顶点和m条边的简单连通图的细分图、线图、全图、半全点图、半全线图和广义变换图的ISI指数的表达式,完善了ISI指数的图变换理论体系,为后续开展多重图变换下的拓扑指数研究奠定了基础。证明过程中,首先根据所研究图的定义确定其顶点和边的度,再对所研究图的边集进行分类并结合ISI指数的定义,建立了所研究图与原图之间的ISI指数关系,最后通过分类讨论,得到了各类图变换下的ISI指数的表达式。本文结果可应用于化学图论与复杂网络科学领域,既能为分子性质预测、分子结构筛选提供量化工具,也能刻画通信、交通等网络的结构演化过程,并为网络拓扑分析与优化设计提供理论依据。展开更多
The authors provided a simple method for calculating Wiener numbers of molecular graphs with symmetry in 1997.This paper intends to further improve on it and simplifies the calculation of the Wiener numbers of the mol...The authors provided a simple method for calculating Wiener numbers of molecular graphs with symmetry in 1997.This paper intends to further improve on it and simplifies the calculation of the Wiener numbers of the molecular graphs.展开更多
The non-wandering set Ω(f) for a graph map f is investigated. It is showed that Ω(f) is contained in the closure of the set ER(f) of eventually recurrent points of f and ω-limit set ω(Ω(f)) of Ω(f) is containe...The non-wandering set Ω(f) for a graph map f is investigated. It is showed that Ω(f) is contained in the closure of the set ER(f) of eventually recurrent points of f and ω-limit set ω(Ω(f)) of Ω(f) is contained in the closure of the set R(f) of recurrent points of f.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【目的】柔性互联配电网遭遇故障停运后,通过软开关(soft open point,SOP)等设备可以快速、合理地进行功率转供或孤岛运行。然而现有研究暂未考虑柔性设备的不同控制状态对恢复结果的影响,常见孤岛预划分方法也难以确定柔性互联设备支撑的孤岛半径和恢复优先级。针对互联设备的可行控制方式,提出了基于SOP等效模型的故障恢复策略。【方法】首先设计含多种控制方式的SOP潮流交替迭代算法,以计算恢复后的功率与电压分布。其次以SOP控制方式选择以及非预设重构为优化手段,以最小化加权运行损失为目标,得到综合考虑潮流约束与多端口SOP模式约束的恢复模型。最后针对寻优范围增加,采用协同图拉普拉斯算子的遗传算法进行求解。基于互联的双IEEE 33系统算例进行了故障后恢复效果验证。【结果】结果表明:针对不同线路停运后的拓扑变动及分布式电源出力情况,所提方法能够形成相应的非预设重构方案,并灵活调整不同位置的SOP控制方式进行协同,负荷恢复比例较重构方式提升14%。【结论】非预设网络重构带来了更高的故障后负荷恢复比例,结合优化SOP的控制状态可取得更优的恢复后电压分布,从而支撑柔性互联配电网的高供电韧性。
文摘Inverse Sum Indeg指数(ISI指数)是预测辛烷异构体总表面积的重要拓扑指数。针对ISI指数在图变换下缺乏系统表达式推导的问题,本文给出了具有n个顶点和m条边的简单连通图的细分图、线图、全图、半全点图、半全线图和广义变换图的ISI指数的表达式,完善了ISI指数的图变换理论体系,为后续开展多重图变换下的拓扑指数研究奠定了基础。证明过程中,首先根据所研究图的定义确定其顶点和边的度,再对所研究图的边集进行分类并结合ISI指数的定义,建立了所研究图与原图之间的ISI指数关系,最后通过分类讨论,得到了各类图变换下的ISI指数的表达式。本文结果可应用于化学图论与复杂网络科学领域,既能为分子性质预测、分子结构筛选提供量化工具,也能刻画通信、交通等网络的结构演化过程,并为网络拓扑分析与优化设计提供理论依据。
文摘The authors provided a simple method for calculating Wiener numbers of molecular graphs with symmetry in 1997.This paper intends to further improve on it and simplifies the calculation of the Wiener numbers of the molecular graphs.
基金The first author is supported by the Natural Science Foundation of the Committee of Education ofJiangsu Province ( 0 2 KJB1 1 0 0 0 8)
文摘The non-wandering set Ω(f) for a graph map f is investigated. It is showed that Ω(f) is contained in the closure of the set ER(f) of eventually recurrent points of f and ω-limit set ω(Ω(f)) of Ω(f) is contained in the closure of the set R(f) of recurrent points of f.
