摘要
针对使用中央处理器(Central Processing Unit, CPU)硬件实现密度聚类、相似性度量等算法提取船舶习惯航迹的过程中存在复杂度高、计算时间长等方面的不足,提出使用图形处理器(Graphics Processing Unit, GPU)高性能计算及GPU优化算法以提升船舶轨迹相似性度量与聚类的速度性能,大幅缩短船舶轨迹特征提取过程中的时间开销。利用长江南槽交汇水域船舶自动识别系统(Automatic Identification System, AIS)动态船舶轨迹信息进行方法验证,通过对比传统基于CPU的方法验证了所提出的基于GPU的船舶轨迹相似性度量及聚类算法存在较优的速度性能,为快速提取研究水域中的船舶特征提供新的理论依据。
GPU(Graphics Processing Unit),instead of CPU(Central Processing Unit),is used to accomplish ship trajectory similarity measurement and clustering.For CPU,to carry out similarity measurement and clustering needs complex programming and long computing time.On contrary,GPU,featuring high computing capacity,with special optimization algorithm can do the job much faster.The design is verified through processing the AIS(Automatic Identification System)data from the converging area of the Yangtze River South Channel.The improvement in processing speed is proved.
作者
刘奕
李湘
李之琛
周备
许鹏
刘敬贤
LIU Yi;LI Xiang;LI Zhichen;ZHOU Bei;XU Peng;LIU Jingxian(State Key Laboratory of Martime Technology and Safety,Wuhan University of Technology,Wuhan 430063,China;School of Navigation,Wuhan University of Technology,Wuhan 430063,China;Hubei Key Laboratory of Inland Shipping Technology,Wuhan University of Technology,Wuhan 430063,China;National Engineering Research Center for Water Transport Safety,Wuhan University of Technology,Wuhan 430063,China;College of Transportation Engineering,Chang'an University,Xi'an 710064,China;Shipping Development Research Center of Yangtze River,Wuhan 430014,China)
出处
《中国航海》
CSCD
北大核心
2023年第2期33-39,45,共8页
Navigation of China
基金
国家自然科学基金资助(51709219)。