A deep learning method for the prediction of 6-DoF ship motions in real conditions
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A deep learning method for the prediction of 6-DoF ship motions in real conditions
Authors
Keywords
-
Journal
Proceedings of the Institution of Mechanical Engineers Part M-Journal of Engineering for the Maritime Environment
Volume -, Issue -, Pages 147509022311578
Publisher
SAGE Publications
Online
2023-03-11
DOI
10.1177/14750902231157852
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm
- (2022) Ming-Wei Li et al. NONLINEAR DYNAMICS
- Deep learning method for 3-DOF motion prediction of unmanned surface vehicles based on real sea maneuverability test
- (2022) Jiankun Lou et al. OCEAN ENGINEERING
- Convergence analysis of hydrodynamic coefficients estimation using regularization filter functions on free-running ship model tests with noise
- (2022) Haitong Xu et al. OCEAN ENGINEERING
- Data-Driven system identification of 6-DoF ship motion in waves with neural networks
- (2022) Kevin M. Silva et al. APPLIED OCEAN RESEARCH
- A two-way coupled FSI model for the rapid evaluation of accidental loads following ship hard grounding
- (2022) Ghalib Taimuri et al. JOURNAL OF FLUIDS AND STRUCTURES
- Maneuvering modeling of a twin-propeller twin-rudder inland container vessel based on integrated CFD and empirical methods
- (2022) Suli Lu et al. APPLIED OCEAN RESEARCH
- Data driven control based on Deep Q-Network algorithm for heading control and path following of a ship in calm water and waves
- (2022) Sivaraman Sivaraj et al. OCEAN ENGINEERING
- A machine learning method for the evaluation of ship grounding risk in real operational conditions
- (2022) Mingyang Zhang et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery
- (2022) Huanhuan Li et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Spatial-Temporal Aware Inductive Graph Neural Network for C-ITS Data Recovery
- (2022) Wei Liang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Ship Dynamics
- (2021) Spyros Hirdaris et al. Journal of Marine Science and Engineering
- Multiscale attention-based LSTM for ship motion prediction
- (2021) Tao Zhang et al. OCEAN ENGINEERING
- A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions
- (2021) Mingyang Zhang et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Nonparametric modeling of ship maneuvering motion based on Gaussian process regression optimized by genetic algorithm
- (2021) Zi-Lu Ouyang et al. OCEAN ENGINEERING
- A concept of critical safety area applicable for an obstacle-avoidance process for manned and autonomous ships
- (2021) Mateusz Gil RELIABILITY ENGINEERING & SYSTEM SAFETY
- Vessel Trajectory Prediction Using Historical Automatic Identification System Data
- (2020) Danial Alizadeh et al. JOURNAL OF NAVIGATION
- Determination of the dynamic critical maneuvering area in an encounter between two vessels: Operation with negligible environmental disruption
- (2020) Mateusz Gil et al. OCEAN ENGINEERING
- A 6-DoF maneuvering model for the rapid estimation of hydrodynamic actions in deep and shallow waters
- (2020) Ghalib Taimuri et al. OCEAN ENGINEERING
- Data-Driven Trajectory Quality Improvement for Promoting Intelligent Vessel Traffic Services in 6G-Enabled Maritime IoT Systems
- (2020) Ryan Wen Liu et al. IEEE Internet of Things Journal
- Experimental and numerical investigations of advancing speed effects on hydrodynamic derivatives in MMG model, part I: Xvv,Yv,Nv
- (2019) Chenliang Zhang et al. OCEAN ENGINEERING
- Ship trajectory uncertainty prediction based on a Gaussian Process model
- (2019) H. Rong et al. OCEAN ENGINEERING
- Deep reinforcement learning-based controller for path following of an unmanned surface vehicle
- (2019) Joohyun Woo et al. OCEAN ENGINEERING
- Identification of ship manoeuvring motion based on nu-support vector machine
- (2019) Zihao Wang et al. OCEAN ENGINEERING
- Non-parametric dynamic system identification of ships using multi-output Gaussian Processes
- (2018) Wilmer Ariza Ramirez et al. OCEAN ENGINEERING
- A numerical method for manoeuvring simulation in regular waves
- (2018) Guillermo Chillcce et al. OCEAN ENGINEERING
- A 4 DOF simulation model developed for fuel consumption prediction of ships at sea
- (2018) Fabian Tillig et al. Ships and Offshore Structures
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Identification of Abkowitz model for ship manoeuvring motion using ɛ-support vector regression
- (2011) Xin-guang ZHANG et al. Journal of Hydrodynamics
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More