Ship trajectory anomaly detection method based on encoder-decoder architecture composed of Transformer_LSTM modules

ObjectiveIn order to improve the accuracy and efficiency of ship trajectory anomaly detection, and solve the problems of traditional anomaly detection methods such as limited feature characterization ability, insufficient compensation accuracy, gradient disappearance and overfitting, an unsupervised...

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書誌詳細

出版年:	Zhongguo Jianchuan Yanjiu
主要な著者:	Kexin LI, Jian GUO, Ranchong LI, Yujun WANG, Zongming LI, Kun MIU
フォーマット:	論文
言語:	英語
出版事項:	Editorial Office of Chinese Journal of Ship Research 2024-02-01
主題:	anomaly detection deep learning encoder-decoder transformer longshort-term memory (lstm) trajectory reconstruction
オンライン･アクセス:	http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03291