针对应急场景中无人机(Unmanned Aerial Vehicle,UAV)辅助物联网节点(Internet of Things Node,IoTN)收集数据过程数据时效性差的问题,提出了一种基于费马点最小化数据收集时间的UAV路径优化方法。费马点的选取能够有效地优化UAV飞行路...针对应急场景中无人机(Unmanned Aerial Vehicle,UAV)辅助物联网节点(Internet of Things Node,IoTN)收集数据过程数据时效性差的问题,提出了一种基于费马点最小化数据收集时间的UAV路径优化方法。费马点的选取能够有效地优化UAV飞行路径,从而使数据收集时间最小,确保收集数据的及时性。该方法通过路径离散化将UAV飞行路径分段,利用连续凸优化(Successive Convex Approximation,SCA)转化复杂混合整数问题的非凸约束,围绕节点的联通性,优化UAV飞行速度与悬停点,求解出最小化数据收集时间的飞行路径。仿真结果表明,所提方法在收集数据时间方面相较于传统方法有6%的提升。展开更多
Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean...Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.展开更多
The South China Sea(SCS)is a marginal sea connecting the Pacific and Indian oceans and has gained much attention in recent decades.The dynamics in the northeast SCS are considerably influenced by topography,monsoons,t...The South China Sea(SCS)is a marginal sea connecting the Pacific and Indian oceans and has gained much attention in recent decades.The dynamics in the northeast SCS are considerably influenced by topography,monsoons,tropical cyclones,the Kuroshio intrusion,and water exchange through the Luzon Strait(LS).展开更多
文摘针对应急场景中无人机(Unmanned Aerial Vehicle,UAV)辅助物联网节点(Internet of Things Node,IoTN)收集数据过程数据时效性差的问题,提出了一种基于费马点最小化数据收集时间的UAV路径优化方法。费马点的选取能够有效地优化UAV飞行路径,从而使数据收集时间最小,确保收集数据的及时性。该方法通过路径离散化将UAV飞行路径分段,利用连续凸优化(Successive Convex Approximation,SCA)转化复杂混合整数问题的非凸约束,围绕节点的联通性,优化UAV飞行速度与悬停点,求解出最小化数据收集时间的飞行路径。仿真结果表明,所提方法在收集数据时间方面相较于传统方法有6%的提升。
基金The National Key R&D Program of China under contract No.2021YFC3101603.
文摘Ocean temperature is an important physical variable in marine ecosystems,and ocean temperature prediction is an important research objective in ocean-related fields.Currently,one of the commonly used methods for ocean temperature prediction is based on data-driven,but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature.Existing graph neural network-based methods usually use predefined graphs or learned static graphs,which cannot capture the dynamic associations among data.In this study,we propose a novel dynamic spatiotemporal graph neural network(DSTGN)to predict threedimensional ocean temperature(3D-OT),which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge.Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions.We also integrated dynamic graph learning,static graph learning,graph convolution,and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data.In this study,we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis,with data covering the vertical variation of temperature from the sea surface to 1000 m below the sea surface.We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.
基金The National Natural Science Foundation of China under contract Nos 41920104006the Scientific Research Fund of Second Institute of Oceanography+3 种基金Ministry of Natural Resources under contract Nos JZ2001,XRJH2410,and QNYC2102the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University under contract No.SL2021MS021the Global Climate Changes and Air-sea Interaction Program under contract No.GASI-02-PAC-ST-Wwinthe Taishan Scholars Program under contract No.tsqn202306282。
文摘The South China Sea(SCS)is a marginal sea connecting the Pacific and Indian oceans and has gained much attention in recent decades.The dynamics in the northeast SCS are considerably influenced by topography,monsoons,tropical cyclones,the Kuroshio intrusion,and water exchange through the Luzon Strait(LS).
