Predicting the residual fatigue life of a cargo hull tank using a deep-learning technique-Reference-Cited by-同舟云学术

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Predicting the residual fatigue life of a cargo hull tank using a deep-learning technique

Published:2020-07-02 Issue:3 Volume:4 Page:84-92
ISSN:2572-5084
Container-title:Journal of International Maritime Safety, Environmental Affairs, and Shipping
language:en
Short-container-title:Journal of International Maritime Safety, Environmental Affairs, and Shipping

Author:

Lee Seung Geon¹,Yang Young-Jun²,Chang Won-Du³^ORCID,Sohn Jung Min¹^ORCID

Affiliation:

1. Naval Architecture and Marine Systems Engineering, Pukyong National University, Busan, Republic of Korea

2. School of Naval Architecture and Ocean Engineering, Tongmyong University, Busan, Republic of Korea

3. Department of Computer Engineering, Pukyong National University, Busan, Republic of Korea

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/25725084.2020.1795463

Reference15 articles.

1. Andrew, N. 2020. “Machine Learning Yearning.” Accessed 25 June, 2020. https://www.deeplearning.ai/machine-learning-yearning

2. Fatigue life improvement of weathervaning ship-shaped offshore units by a smart heading control system

3. Fatigue analysis of ship deck structure accounting for imperfections

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