一种基于卫星节点的时变拓扑网络模型

卫星网络与地面固定网络有着本质的差别,特别是它的时变特性,使得成熟的固定拓扑网络理论不能很好地应用到卫星网络中。将卫星网络抽象为以时间为变量的周期函数矩阵,并引入图论,提出一种基于卫星节点的时变拓扑网络模型,讨论模型中节点间的关系和模型离散化条件,给出该模型的离散化形式以及离散时间序列选取原则和相应的证明和仿真,最后指出现有的虚拟节点、有限状态自动机、快照系列、动态虚拟拓扑等研究卫星网络的方法都可以纳入所提出的模型框架中。     

基于隐式位姿空间分层量化的空间机械手无碰撞路径规划方法

本文在概述空间机械手无碰撞路径规划问题的特殊性的基础上提出了相应的路径规划策略。依据这些策略，本文描述了一种基于隐式位姿空间分层量化的路径规划划方法，并通过仿真验证了所提出规划策略和规划方法的有效性。     

基于图着色模型飞机智能化排班算法的研究

飞机排班问题关系到航空公司的系统运作,作用十分重要。通过对飞机排班问题的分析,把飞机分配问题转化为图着色,应用图着色理论,建立了飞机分配问题的图着色模型,引入时间片算法确定了航班使用飞机的时间冲突集合,根据“先到先服务”的原则给出了飞机分配的顶点序列着色算法。用一个具体的算例,验证了飞机智能化排班比现有航空公司调度员人工排班更先进、更实用、更加节省资源,使资源配置更加合理。     

潜在通路存在的判定方法研究

为判断给定系统是否存在潜在通路,研究了潜在通路存在的判定方法.用图论模型对电路建模,利用图论的邻接矩阵找到在某一开关组合状态下电源到地的所有路径,通过路径和功能之间的一一对应关系确定电路实现的功能数.提出了一种求短路屏蔽路径数算法,可以求得短路屏蔽的功能数.通过比较在某一开关状态下的设计功能数和实现的功能数,确定是否存在潜在通路以及潜在通路存在数目.仿真实例验证了方法的有效性.     

Dynamic airspace configuration method based on a weighted graph model

This paper proposes a new method for dynamic airspace configuration based on a weighted graph model. The method begins with the construction of an undirected graph for the given airspace, where the vertices represent those key points such as airports, waypoints, and the edges represent those air routes. Those vertices are used as the sites of Voronoi diagram, which divides the airspace into units called as cells. Then, aircraft counts of both each cell and of each air-route are computed. Thus, by assigning both the vertices and the edges with those aircraft counts, a weighted graph model comes into being. Accordingly the airspace configuration problem is described as a weighted graph partitioning problem. Then, the problem is solved by a graph partitioning algorithm, which is a mixture of general weighted graph cuts algorithm, an optimal dynamic load balancing algorithm and a heuristic algorithm. After the cuts algorithm partitions the model into sub-graphs, the load balancing algorithm together with the heuristic algorithm transfers aircraft counts to balance workload among sub-graphs. Lastly, airspace configuration is completed by determining the sector boundaries. The simulation result shows that the designed sectors satisfy not only workload balancing condition, but also the constraints such as convexity, connectivity, as well as minimum distance constraint.     

Pose matters: Pose guided graph attention network for person re-identification

Person re-Identification (reID), aiming at retrieving a person across different cameras, has been playing a more and more important role in the construction of smart city and social security. For deep-learning-based reID methods, it has been proved that using local feature together with global feature could help to give robust representation for person retrieval. Human pose information can provide the locations of human skeleton to effectively guide the network to pay more attention to these key areas, and can also help to reduce the noise distractions from background or occlusions. Based on human pose, a Pose Guided Graph Attention (PGGA) network is proposed in this paper, which is a multi-branch architecture consisting of one branch for global feature and two branches for local key-point features. A graph attention convolution layer is carefully designed to re-assign the contribution weight of each extracted local feature by modeling the similarity relations. The experimental results demonstrate the effectiveness of our approach on discriminative feature learning. Our model achieves the state-of-the-art performance on several mainstream evaluation datasets. A plenty of ablation studies and different kinds of comparison experiments are conducted to prove the effectiveness of this work, including the tests on occluded datasets and cross-domain datasets. Moreover, we further design supplementary tests in practical scenario to indicate the advantage of our work in real-word applications.     

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.