• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Wang, Zhishan (Wang, Zhishan.) | Mei, Qiang (Mei, Qiang.) | Wang, Peng (Wang, Peng.) | Li, Yong (Li, Yong.) | Yang, Yang (Yang, Yang.) | Li, Zhaoxuan (Li, Zhaoxuan.) | Xie, Wenxin (Xie, Wenxin.)

收录:

EI Scopus

摘要:

In the process of monitoring a myriad of vessels, maritime administrators recurrently confront situations where crucial static fields, such as vessel type, are frequently missing from automatic identification system (AIS) data. This omission presents a significant obstacle to effective safety management, prompting the need for a method to rectify incomplete data. In terrestrial traffic, constraints dictated by road widths do not apply to marine traffic, thus allowing vessels of an identical type to opt for routes that diverge by numerous nautical miles. This condition renders trajectory clustering insufficient. To tackle this issue, this paper proposes a division of the sea area into spatiotemporal grids and a transformation of vessel trajectories into a coded sequence of navigational grids. Word2vec embedding is merged with deep neural networks predicated on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms through the utilization of natural language processing techniques. The goal is to facilitate efficient vessel classification via deep learning. Applying the Taiwan Strait as a case study, it is shown that under the conditions of a five-dimensional word embedding, the F1 score for vessel classification using RNN models can attain 0.94. This investigation provides a unique method for processing vessel trajectory data, and it also assists in the effective completion of missing vessel types. © 2023 IEEE.

关键词:

Natural language processing systems Deep neural networks Trajectories Data handling Embeddings Automation Recurrent neural networks Convolutional neural networks Classification (of information) Text processing Semantics

作者机构:

  • [ 1 ] [Wang, Zhishan]Beijing University of Technology, School of Software Engineering, Beijing, China
  • [ 2 ] [Mei, Qiang]Jimei University, Navigation College, Xiamen, China
  • [ 3 ] [Wang, Peng]Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China
  • [ 4 ] [Li, Yong]Beijing University of Technology, School of Software Engineering, Beijing, China
  • [ 5 ] [Yang, Yang]Jimei University, Navigation College, Xiamen, China
  • [ 6 ] [Li, Zhaoxuan]Beijing University of Technology, School of Software Engineering, Beijing, China
  • [ 7 ] [Xie, Wenxin]Beijing University of Technology, School of Software Engineering, Beijing, China

通讯作者信息:

电子邮件地址:

查看成果更多字段

相关关键词:

相关文章:

来源 :

年份: 2023

页码: 165-175

语种: 英文

被引次数:

WoS核心集被引频次:

SCOPUS被引频次:

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 1

归属院系:

在线人数/总访问数:501/4932414
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司