Journal of Ocean Engineering and Technology (한국해양공학회지)

Korean Society of Ocean Engineers (한국해양공학회)
권호 표지
DOI QR코드

DOI QR Code

Estimating Hydrodynamic Coefficients of Real Ships Using AIS Data and Support Vector Regression

  • Hoang Thien Vu (Graduate Student, Department of Smart Environmental Energy Engineering, Changwon National University) ;
  • Jongyeol Park (Department of Naval Architecture and Marine Engineering, Changwon National University) ;
  • Hyeon Kyu Yoon (Department of Naval Architecture and Marine Engineering, Changwon National University)
  • Received : 2023.06.23
  • Accepted : 2023.09.17
  • Published : 2023.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

In response to the complexity and time demands of conventional methods for estimating the hydrodynamic coefficients, this study aims to revolutionize ship maneuvering analysis by utilizing automatic identification system (AIS) data and the Support Vector Regression (SVR) algorithm. The AIS data were collected and processed to remove outliers and impute missing values. The rate of turn (ROT), speed over ground (SOG), course over ground (COG) and heading (HDG) in AIS data were used to calculate the rudder angle and ship velocity components, which were then used as training data for a regression model. The accuracy and efficiency of the algorithm were validated by comparing SVR-based estimated hydrodynamic coefficients and the original hydrodynamic coefficients of the Mariner class vessel. The validated SVR algorithm was then applied to estimate the hydrodynamic coefficients for real ships using AIS data. The turning circle test wassimulated from calculated hydrodynamic coefficients and compared with the AIS data. The research results demonstrate the effectiveness of the SVR model in accurately estimating the hydrodynamic coefficients from the AIS data. In conclusion, this study proposes the viability of employing SVR model and AIS data for accurately estimating the hydrodynamic coefficients. It offers a practical approach to ship maneuvering prediction and control in the maritime industry.

Keywords

Acknowledgement

This research was supported by the "Development of Autonomous Ship Technology (PJT201313, Development of Autonomous Navigation System with Intelligent Route Planning Function)" funded by the Ministry of Oceans and Fisheries(MOF, Korea).

References

  1. Fossen, T. I. (1994). Guidance and Control of Ocean Vehicles. A John Wiley and Sons, Ltd., Publication. 
  2. Ho, Y., Song, L., Liu, Z., & Yao, J. (2021). Identification of ship hydrodynamic derivatives based on LS-SVM with wavelet threshold denoising. Journal of Marine Science and Engineering (JMSE), 9(12), 1356. https://doi.org/10.3390/jmse9121356 
  3. Inazu, D., Ikeya, T., Iseki, T., & Waseda, T. (2020). Extracting clearer tsunami currents from shipborne automatic identification system data using ship yaw and equation of ship response. Earth, Planets and Space Journal, 72, 41. https://doi.org/10.1186/s40623-020-01165-7 
  4. Liu, B., Jin, Y., Magee, A. R., Yiew, L. J., & Zhang, S. (2019). System identification of abkowitz model for ship maneuvering motion based on ε-support vector regression. The ASME 2019 38th International Conference on Ocean, Offshore & Arctic Engineering (OMAE2019), OMAE2019-96699. https://doi.org/10.1115/OMAE2019-96699 
  5. Luo, W. L., & Zou, Z. J. (2009). Parametric identification of ship maneuvering models by using support vector machines. Journal of Ship Research, 53(1), 19-30, https://doi.org/10.5957/jsr.2009.53.1.19 
  6. Luo, W. L. (2016). Parameter identifiability of ship maneuvering modeling using system identification. Mathematical Problems in Engineering Journal, 2016, 8909170. https://doi.org/10.1155/2016/8909170 
  7. Mou, J. M., Tang, G. H., Rong, H., & Yue, X. (2013). Predict manoeuvring indices using AIS data by ridge regression. Scientific Journals of Maritime University of Szczecin, 36(108), 137-142. https://bibliotekanauki.pl/articles/359863.pdf 
  8. Vu, H. T., Park, J., & Yoon, H. K. (2023) An Application of Support Vector Machine to Estimate Hydrodynamic Coefficient using AIS Data. In 20th Asian Conference on Maritime System and Safety Research. ACMSSR.