Background and activities
"Data-based Ship Motion Prediction in Offshore Operations"
Supervisors: Houxiang Zhang, Thor Inge Fossen, Bjørnar Vik
My research interests mainly lie within ship maneuvering and ship decision support systems. Specifically how machine learning can be used to aid the aforementioned applications.
- 2017-2019: DeepTek Pre-project
- Associated with the SFI MOVE project
Detailed information could be found from Intelligent Systems Lab.
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2021) A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion Data. IEEE Transactions on Instrumentation and Measurement. vol. 70.
- (2021) A Co-operative Hybrid Model For Ship Motion Prediction. Modeling, Identification and Control. vol. 42 (1).
- (2021) A sensitivity quantification approach to significance analysis of thrusters in dynamic positioning operations. Ocean Engineering. vol. 223.
- (2021) Incorporating Approximate Dynamics Into Data-Driven Calibrator: A Representative Model for Ship Maneuvering Prediction. IEEE Transactions on Industrial Informatics.
- (2020) A Neural Network-Based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion. IEEE Journal of Oceanic Engineering. vol. 45 (2).
- (2020) Co-simulation as a Fundamental Technology for Twin Ships. Modeling, Identification and Control. vol. 41 (4).
- (2020) A Hybrid Approach to Motion Prediction for Ship Docking— Integration of a Neural Network Model into the Ship Dynamic Model. IEEE Transactions on Instrumentation and Measurement. vol. 70.
- (2019) Data-driven uncertainty and sensitivity analysis for ship motion modeling in offshore operations. Ocean Engineering. vol. 179.
- (2019) Virtual prototyping: a case study of positioning systems for drilling operations in the Barents Sea. Ships and Offshore Structures. vol. 14 (S1).
- (2019) An efficient neural-network based approach to automatic ship docking. Ocean Engineering. vol. 191.
- (2019) Dead reckoning of dynamically positioned ships: Using an efficient recurrent neural network. IEEE robotics & automation magazine. vol. 26 (3).
- (2018) A Neural Network Approach to Control Allocation of Ships for Dynamic Positioning. IFAC-PapersOnLine. vol. 51 (29).
- (2015) Autonomous net recovery of fixed-wing UAV with single-frequency carrier-phase differential GNSS. IEEE Aerospace and Electronic Systems Magazine. vol. 30 (5).
- (2015) Net Recovery of UAV with Single-Frequency RTK GPS. IEEE Aerospace Conference. Proceedings. vol. 2015-June.
Part of book/report
- (2020) SpectralSeaNet: Spectrogram and Convolutional Network-based Sea State Estimation. The 46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020).
- (2020) An effective model-based thruster failure detection method for dynamically positioned ships. 2020 IEEE International Conference on Mechatronics and Automation.
- (2019) Modeling and Analysis of Motion Data from Dynamically Positioned Vessels for Sea State Estimation. 2019 International Conference on Robotics and Automation (ICRA 2019).
- (2018) A data-driven sensitivity analysis approach for dynamically positioned vessels. Proceedings of The 59th Conference on Simulation and Modelling (SIMS 59).