Bikram Kawan
Faculty of Engineering and Natural Sciences, Norwegian University of Science and Technology, Norway
Hao Wang
Faculty of Engineering and Natural Sciences, Norwegian University of Science and Technology, Norway
Guoyuan Li
Faculty of Maritime Technology and Operations, Norwegian University of Science and Technology, Norway
Khim Chhantyal
Faculty of Technology, Natural Sciences, and Maritime Sciences, University College of Southeast Norway, Norway
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http://dx.doi.org/10.3384/ecp17138350Published in: Proceedings of the 58th Conference on Simulation and Modelling (SIMS 58) Reykjavik, Iceland, September 25th – 27th, 2017
Linköping Electronic Conference Proceedings 138:46, p. 350-354
Published: 2017-09-27
ISBN: 978-91-7685-417-4
ISSN: 1650-3686 (print), 1650-3740 (online)
This paper presents a flexible system structure to analyze
and model for the potential use of huge ship sensor data to
generate efficient ship motion prediction model. The noisy
raw data is cleaned using noise reduction, resampling
and data continuity techniques. For modeling, a flexible
Support Vector Regression (SVR) is proposed to solve
regression problem. In the data set, sensitivity analysis
is performed to find the strength of input attributes for
prediction target. The highly significant attributes are
considered for input feature which are mapped into higher
dimensional feature using non-linear function, thus SVR
model for ship motion prediction is achieved. The
prediction results for trajectory and pitch show that the
proposed system structure is efficient for the prediction of
different ship motion attributes.
