| A hybrid approach to motion prediction for ship docking—Integration of a neural network model into the ship dynamic model R Skulstad, G Li, TI Fossen, B Vik, H Zhang IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2020 | 67 | 2020 |
| An efficient neural-network based approach to automatic ship docking Y Shuai, G Li, X Cheng, R Skulstad, J Xu, H Liu, H Zhang Ocean Engineering 191, 106514, 2019 | 48 | 2019 |
| Data-driven uncertainty and sensitivity analysis for ship motion modeling in offshore operations X Cheng, G Li, R Skulstad, P Major, S Chen, HP Hildre, H Zhang Ocean Engineering 179, 261-272, 2019 | 48 | 2019 |
| Dead reckoning of dynamically positioned ships: Using an efficient recurrent neural network R Skulstad, G Li, TI Fossen, B Vik, H Zhang IEEE Robotics & Automation Magazine 26 (3), 39-51, 2019 | 43 | 2019 |
| Autonomous net recovery of fixed-wing UAV with single-frequency carrier-phase differential GNSS R Skulstad, C Syversen, M Merz, N Sokolova, T Fossen, T Johansen IEEE Aerospace and Electronic Systems Magazine 30 (5), 18-27, 2015 | 43 | 2015 |
| Incorporating approximate dynamics into data-driven calibrator: A representative model for ship maneuvering prediction T Wang, G Li, LI Hatledal, R Skulstad, V Æsøy, H Zhang IEEE Transactions on Industrial Informatics 18 (3), 1781-1789, 2021 | 35 | 2021 |
| Modeling and analysis of motion data from dynamically positioned vessels for sea state estimation X Cheng, G Li, R Skulstad, S Chen, HP Hildre, H Zhang 2019 International Conference on Robotics and Automation (ICRA), 6644-6650, 2019 | 33 | 2019 |
| A neural-network-based sensitivity analysis approach for data-driven modeling of ship motion X Cheng, G Li, R Skulstad, S Chen, HP Hildre, H Zhang IEEE Journal of Oceanic Engineering 45 (2), 451-461, 2019 | 29 | 2019 |
| Net recovery of UAV with single-frequency RTK GPS R Skulstad, CL Syversen, M Merz, N Sokolova, TI Fossen, TA Johansen 2015 IEEE Aerospace Conference, 1-10, 2015 | 28 | 2015 |
| A neural network approach to control allocation of ships for dynamic positioning R Skulstad, G Li, H Zhang, TI Fossen IFAC-PapersOnLine 51 (29), 128-133, 2018 | 27 | 2018 |
| A deep learning approach to detect and isolate thruster failures for dynamically positioned vessels using motion data P Han, G Li, R Skulstad, S Skjong, H Zhang IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2020 | 26 | 2020 |
| Co-simulation as a fundamental technology for twin ships LI Hatledal, R Skulstad, G Li, A Styve, H Zhang Institutt for teknisk kybernetikk, NTNU, 2020 | 24 | 2020 |
| A digital twin of the research vessel gunnerus for lifecycle services: Outlining key technologies H Zhang, G Li, LI Hatledal, Y Chu, A Ellefsen, P Han, P Major, R Skulstad, ... IEEE Robotics & Automation Magazine 30 (3), 6-19, 2022 | 20 | 2022 |
| Spectralseanet: Spectrogram and convolutional network-based sea state estimation X Cheng, G Li, R Skulstad, H Zhang, S Chen IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics …, 2020 | 16 | 2020 |
| Data-driven modeling for transferable sea state estimation between marine systems X Cheng, G Li, P Han, R Skulstad, S Chen, H Zhang IEEE Transactions on Intelligent Transportation Systems 23 (3), 2561-2571, 2021 | 14 | 2021 |
| A multiple-output hybrid ship trajectory predictor with consideration for future command assumption M Kanazawa, R Skulstad, G Li, LI Hatledal, H Zhang IEEE sensors journal 21 (23), 27124-27135, 2021 | 9 | 2021 |
| Low-cost instrumentation system for recovery of fixed-wing UAV in a net R Skulstad, CL Syversen Master's thesis, Norwegian University of Science and Technology, 2014 | 9 | 2014 |
| A co-operative hybrid model for ship motion prediction R Skulstad, G Li, TI Fossen, T Wang, H Zhang | 8 | 2021 |
| Knowledge and data in cooperative modeling: Case studies on ship trajectory prediction M Kanazawa, T Wang, R Skulstad, G Li, H Zhang Ocean Engineering 266, 112998, 2022 | 5 | 2022 |
| Probability-based ship encounter classification using ais data M Zhu, W Tian, R Skulstad, H Zhang, G Li 2023 3rd International Conference on Computer, Control and Robotics (ICCCR …, 2023 | 4 | 2023 |
