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361.
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. 相似文献
城市交通出行选择多模式条件下多种出行方式之间的出行需求关系密切,为了探讨城市居民对地铁、公交车、私家车3种出行方式出行需求之间的耦合关系及时变特征,采用时变参数向量自回归(TVP-VAR)模型,使用道路拥堵指数近似代表私家车出行量,对2个小区一个月内工作日时间地铁出行量、公交车出行量和道路拥堵指数三者的平均值进行分析。研究结果表明:地铁出行量、公交车出行量、私家车出行量三者相互作用关系针对不同的用地类型,整体影响趋势不会变化太大,影响大小在时间上存在差异;城市交通小区内居民对地铁、公交车、私家车3种出行方式的出行需求之间全天存在相互作用关系,地铁出行需求的增加会减少公交车出行需求,公交车出行需求的增加会增加地铁出行需求,而私家车出行需求的增加会增加地铁出行需求并减少公交车出行需求。研究结果有助于深入认识现阶段中国城市多模式交通出行方式之间的耦合关系,以更好地应对城市交通拥堵问题。 相似文献
363.
《中国航空学报》2023,36(4):338-353
Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation. 相似文献
364.