LSTMを用いた漁船の短期航路予測に関する検討-Reference-Cited by-同舟云学术

LSTMを用いた漁船の短期航路予測に関する検討

Published:2023 Issue:0 Volume:148 Page:51-57
ISSN:0388-7405
Container-title:The Journal of Japan Institute of Navigation
language:en
Short-container-title:The Journal of Japan Institute of Navigation

Author:

MIYASHITA Tomohito¹,TAMARU Hitoi²

Affiliation:

1. 東京海洋大学水産研究・教育機構

2. 東京海洋大学

Publisher

Japan Institute of Navigation

Subject

Applied Mathematics,General Mathematics

Link

https://www.jstage.jst.go.jp/article/jin/148/0/148_51/_pdf

Reference8 articles.

1. (1) 国土交通省運輸安全委員会：船舶事故の統計，https://jtsb.mlit.go.jp/jtsb/ship/ship-accident-toukei.php，2022.7.22.

2. (2) IEC：Maritime navigation and radio communication equipment and systems – Class B shipborne equipment of the automatic identification system (AIS) – Part 1: Carrier-Sense time division multiple access(CSTDMA) techniques, IEC 62287-1.

3. (3) 総務省：海上における情報通信の関する各種施策について, https://www.soumu.go.jp/main_content/000694811.pdf，2022.7.22.

4. (4) Lázaro F, Raulefs R, Wang W, Clazzer F & Plass S ： VHF Data Exchange System(VDES) : an enabling technology for maritime communications, CEAS Space Journal, vol.11, pp.55-63, 2019.

5. (5) 志尾嘉洋・伊藤博子・川村恭己・河島園子：再帰型ニューラルネットワークを用いた船舶の動静予測モデルの開発，日本航海学会論文集，Vol.143，pp.77-82，2020.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Ship trajectory prediction using AIS data with TransFormer-based AI;2024 IEEE Conference on Artificial Intelligence (CAI);2024-06-25

2. Applying Neural Networks Medium- to Long-Term Movement Prediction of Fishing Vessels by AIS and Oceanographic Data;The Journal of Japan Institute of Navigation;2024