Bakgrunn og aktiviteter
"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-present: DeepTek Pre-project
- Associated with the SFI MOVE project
Vitenskapelig, faglig og kunstnerisk arbeid
Et utvalg av nyere tidsskriftspublikasjoner, kunstneriske produksjoner, bok, inklusiv bokdeler og rapport-del. Se alle publikasjoner i databasen
- (2020) A Neural Network-Based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion. IEEE Journal of Oceanic Engineering.
- (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.
- (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.
Del av bok/rapport
- (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).