Skip to main content
SpringerLink
Log in

An attention mechanism model based on positional encoding for the prediction of ship maneuvering motion in real sea state

  • Original article
  • Published:
Journal of Marine Science and Technology Aims and scope Submit manuscript

Abstract

This paper proposes an positional encoding-based attention mechanism model which can quantify the temporal correlation of ship maneuvering motion to predict the future ship motion in real sea state. To represent the temporal information of the sequential motion status, the positional encoding consisted by sine and cosine functions of different frequencies is chosen as the input of the model. First, the reasonableness of the improved architecture of the model is validated on the standard turning test datasets of an unmanned surface vehicle. Then, the absolute positional encoding based-scaled-dot product attention mechanism model is compared with other two attention mechanism models with different positional encoding and attention calculation methods and its superiority is verified. As demonstrated by exhaustive experiments, the model has the highest prediction accuracy when the input sequence length equals the output sequence length and the accuracy defined in this paper of the model will drop to less than 90% when the predicted length exceeds 45. Finally, the attention mechanism model is compared with the LSTM model with different lengths of input sequences to demonstrate that the attention mechanism model has a faster training speed when dealing with long sequences.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

Data availability

The authors regret that the data underlying this study can not be made publicly available. Due to confidentiality agreements, the data are not accessible for external researchers. However, interested researchers may contact State Key Laboratory of Ocean Engineering for further information or potential collaboration.

References

  1. Abkowitz MA (1964) Lectures on ship hydrodynamics-Steering and maneuverability. Hydro and Aerodynamics Lab, Lyngby

  2. Ogawa A, Kasai H (1978) On the mathematical model of manoeuvring motion of ships. Int Shipbuild Prog 25:306–319

    Article  Google Scholar 

  3. Carrica PM, Ismail F, Hyman M, Bhushan S, Stern F (2014) RANS-based simulated ship maneuvering accounting for hull-propulsor-engine interaction. Ship Technol Res 61:142–161

    Article  Google Scholar 

  4. Gong JY, Li YB, Jiang F (2020) Numerical simulation about the manoeuvre of trimaran and asymmetric twin hull with hull attitude taken into account by OpenFOAM. J Mar Sci Technol 25:769–786

    Article  Google Scholar 

  5. Kim D, Song S, Jeong B, Tezdogan T, incecik A (2021) Unsteady RANS CFD simulations of ship maneuverability and course keeping control under various wave height conditions. Appl Ocean Res 117:102940

  6. Guo HP, Zou ZJ (2022) CFD and system-based investigation on the turning maneuver of a twin-screw ship considering hull-engine-propeller interaction. Ocean Eng 251:110893

    Article  Google Scholar 

  7. Liu XJ, Fan SM, Wang JH, Wan DC (2015) Hydrodynamic simulation of pure sway tests with ship speed and water depth effects. In: Conference proceedings, International Ocean and Polar Engineering (in America), 25

  8. Guo HP, Zou ZJ (2017) System-based investigation on 4-DOF ship maneuvering with hydrodynamic coefficients determined by RANS simulation of captive model tests. Appl Ocean Res 68:11–25

    Article  Google Scholar 

  9. Araki M, Sadat-Hosseini H, Sanada Y, Tanimoto K, Umeda N, Stern F (2012) Estimating coefficients using system identification methods with experimental, system-based, and CFD free-running trial data. Ocean Eng 51:63–84

    Article  Google Scholar 

  10. kang CG, Suh SH, Kim JS (1984) Maneuverability analysis of a ship by system identification technique. B Soc Nav Archit Korea 21:10-20

  11. Åström KJ (1980) Maximum likelihood and prediction error methods. Automatica 16:551–574

    Article  Google Scholar 

  12. Abkowitz MA (1980) Measurement of hydrodynamic characteristics from ship trials by system identification. Soc Nav Archit Mar Eng 88:283–318

    Google Scholar 

  13. Luo WL, Zou ZJ (2009) Parametric Identification of Ship Models by Using Support Vector Machine. J Ship Research 53:19–30

    Article  Google Scholar 

  14. Zhang XG, Zou ZJ (2011) Identification of Abkowitz model for ship manoeuvring motion using ɛ-support vector regression. J Hydrodyn 23:353–360

    Article  Google Scholar 

  15. Luo WL, Moreira L, Guedes SC (2014) Manoeuvring simulation of catamaran by using implicit models based on support vector machines. Ocean Eng 82:150–159

    Article  Google Scholar 

  16. Tiano A, Blanke M (1997) Multivariable identification of ship steering and roll motions. T I Meas Control 19:63–77

    Article  Google Scholar 

  17. Luo WL, Guedes Soares C, Zou ZJ (2016) Parameter Identification of Ship Model Based on Support Vector Machines and Particle Swarm Optimization. J Offshore Mech Arct Eng 138:031101

    Article  Google Scholar 

  18. Xu HT, Guedes Soares C (2019) Hydrodynamic coefficient estimation for ship maneuvering in shallow water using an optimal truncated LS-SVM. Ocean Eng 191:106488

    Article  Google Scholar 

  19. Xu HT, Guedes Soares C (2020) Maneuvering modeling of a containership in shallow water based on optimal truncated nonlinear kernel-based least square support vector machine and quantum-inspired evolutionary algorithm. Ocean Eng 195:106676

    Article  Google Scholar 

  20. Wang ZH, Zou ZJ, Guedes Soares C (2019) Identification of ship maneuvering motion based on nu-support vector machine. Ocean Eng 183:270–281

    Article  Google Scholar 

  21. Sutulo S, Guedes SC (2014) An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests. Ocean Eng 79:10–25

    Article  Google Scholar 

  22. Rajesh G, Bhattacharyya SK (2008) System identification for nonlinear maneuvering of large tankers using artificial neural network. Appl Ocean Res 30:256–263

    Article  Google Scholar 

  23. Wakita K, Maki A, Umeda N, Miyauchi Y, Shimoji T, Rachman DM, Akimoto Y (2022) On neural network identification for low-speed ship maneuvering model. J Mar Sci Technol 27:772–785

    Article  Google Scholar 

  24. He HW, Wang ZH, Zou ZJ, Yi L (2022) Nonparametric modeling of ship motion based on self-designed fully connected neural network. Ocean Eng 251:111113

    Article  Google Scholar 

  25. Mei B, Sun LC, Shi GY (2019) White-Black-Box Hybrid Model Identification Based on RM-RF for Ship. IEEE Access 7:57691–57705

    Article  Google Scholar 

  26. Moreira L, Guedes Soares C (2003) Dynamic model of maneuverability using recursive neural networks. Ocean Eng 30:1669–1697

    Article  Google Scholar 

  27. Hess DE, Faller WE, Minnick L, Fu T (2007) simulation of Sea Fighter using a fast nonlinear time domain technique. In: 9th International Conference on Numerical Ship Hydrodynamics, Ann Arbor, Michigan, USA

  28. Hao LZ, Han Y, Shi C, Pan ZY (2022) Recurrent neural networks for nonparametric modeling of ship motion. Int J Nav Arch Ocean 14:100436

    Article  Google Scholar 

  29. Guo XX, Zhang XT, Tian XL, Li X, Lu WY (2021) Predicting heave and surge motions of a semi-submersible with neural networks. Appl Ocean Res 112:102708

    Article  Google Scholar 

  30. Liu YC, Duan WY, Huang LM, Duan SL, Ma XW (2020) The input vector space optimization for LSTM deep learning model in real-time prediction of ship motions. Ocean Eng 213:107681

    Article  Google Scholar 

  31. Mnih V, Heess N, Graves A, Kavukcuoglu K (2014) Recurrent Models of Visual Attention. arXiv:1406.6247.

  32. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukin L (2017) Attention is all you need. arXiv:1706.03762.

  33. Zhang S, Wang L, Zhu MD, Chen SW, Zhang HS (2021) A Bi-directional LSTM ship trajectory prediction method based on attention mechanism. In: 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference, Chongqing, China

  34. Xu GQ, Ma JW, Zhu MD, Wu CH (2021) A method of ship navigation prediction based on Attention-LSTM neural network. Ship Sci Tec 41:177–180

    Google Scholar 

  35. Zhang T, Zheng XQ, Liu MX (2021) Multiscale attention-based LSTM for ship motion prediction. Ocean Eng 230:109066

    Article  Google Scholar 

  36. Zeng A, Chen M, Zhang L, Xu Q (2022) Are transformers effective for time series forecasting? arXiv:2205.13504.

  37. Lou JK, Wang HD, Wang JY, Cai Q, Yi H (2022) Deep learning method for 3DOF motion prediction of unmanned surface vehicle based on real sea maneuverability test. Ocean Eng 250:111015

    Article  Google Scholar 

  38. Xu PF, Cheng C, Cheng HX, Shen YL, Ding YX (2014) 38. Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2020. Identification-based 3 DOF model of unmanned surface vehicle using support vector machines enhanced by cuckoo search algorithm. Ocean Eng 197:106898

  39. Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

    MathSciNet  Google Scholar 

Download references

Acknowledgements

This paper is sponsored by the National Natural Science Foundation of China (52271348). In addition, great appreciation is given to the Jiangsu Automation Research Institute for providing the test data of JARI-USV.

Author information

Authors and Affiliations

  1. MOE Key Laboratory of Marine Intelligent Equipment and System, Shanghai Jiao Tong University, Shanghai, 200240, China

    Lei Dong, Hongdong Wang & Jiankun Lou

Authors
  1. Lei Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongdong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiankun Lou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongdong Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, L., Wang, H. & Lou, J. An attention mechanism model based on positional encoding for the prediction of ship maneuvering motion in real sea state. J Mar Sci Technol 29, 136–152 (2024). https://doi.org/10.1007/s00773-023-00978-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00773-023-00978-x

Keywords

Search

Navigation