{"2a396f5bb367f22145257cfb82419ccdalexbaier":{"DOI":"10.18419/darus-2905","ISBN":"","ISSN":"","URL":"","abstract":"This dataset contains data of 125 1-hour simulations of ship motion during various sea states performing random maneuvers in 4 degrees of freedom (surge-sway-yaw-roll). The original ship is a patrol ship developed by Perez et al. [1]. We have extended it with a set of two symmetrically placed rudder propellers. Additionally, we simulate wind forces according to Isherwood's wind model [2]. Wind-induced waves are generated with the JONSWAP spectrum [3] and the corresponding wave forces are then computed using wave force response amplitude operators (ROA).Implementations of the ship model, Isherwood's wave model, wave force ROAs and the JONSWAP spectrum can be found in the Marine Systems Simulator toolbox by Fossen and Perez [4].The dataset is split into a routine operation set (96 hours) and into an Out-Of-Distribution (OOD) set (29 hours). The routine operation set is split into train-validation-test with a 60-10-30 split, while the OOD set is used solely for testing.The dataset is used for the evaluation of nonlinear system identification methods for multi-step predictions. The following inputs and outputs are considered for the identification problem. Inputs are the shaft speeds of both propellers, their azimut angles, wind angle of attack, and wind speed. Measured states or outputs are surge velocity, sway velocity and roll rate, as well as yaw angle and roll angle.Please see the README.txt file for details regarding the file structure of this dataset and a description of the variables in the .tab files.This research is funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016. We acknowledge the support by the Stuttgart Center for Simulation Science (SimTech).[1] T. Perez, A. Ross, and T. I. Fossen, “A 4-DOF SIMULINK model of acoastal patrol vessel for manoeuvring in waves,” in IFAC MCMC, 2006.[2] R. M. Isherwood, “Wind resistance of merchant ships,” The RoyalInstitution of Naval Architects, 1972.[3] K. Hasselmann, T. Barnett, E. Bouws, H. Carlson, D. Cartwright, K. Enke,J. Ewing, H. Gienapp, D. Hasselmann, P. Kruseman, A. Meerburg,P. Muller, D. Olbers, K. Richter, W. Sell, and H. Walden, “Measurementsof wind-wave growth and swell decay during the joint north sea waveproject (JONSWAP),” Deut. Hydrogr. Z., vol. 8, pp. 1-95, 01 1973.[4] T. I. Fossen and T. Perez, “Marine Systems Simulator (MSS),” https://github.com/cybergalactic/MSS, 2004, last accessed: 2022-06-14.","annote":"","author":[{"family":"Baier","given":"Alexandra"},{"family":"Staab","given":"Steffen"}],"citation-label":"baier2022simulated","collection-editor":[],"collection-title":"","container-author":[],"container-title":"","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2022"]],"literal":"2022"},"event-place":"","id":"2a396f5bb367f22145257cfb82419ccdalexbaier","interhash":"18ba9683dbb5d493e66e8b43811a22df","intrahash":"2a396f5bb367f22145257cfb82419ccd","issue":"","issued":{"date-parts":[["2022"]],"literal":"2022"},"keyword":"EXC2075 deeplearning exc2075 machinelearning myown pn4 simtech","misc":{"affiliation":"Baier, Alexandra/Universität Stuttgart, Staab, Steffen/Universität Stuttgart","orcid-numbers":"Baier, Alexandra/0000-0001-5609-3400, Staab, Steffen/0000-0002-0780-4154","doi":"10.18419/darus-2905"},"note":"Related to: Baier, A., Boukhers, Z., & Staab, S. (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. arXiv: 2103.06727","number":"","page":"","page-first":"","publisher":"","publisher-place":"","status":"","title":"A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances","type":"dataset","username":"alexbaier","version":"","volume":""},"2a396f5bb367f22145257cfb82419ccdanalyticcomp":{"DOI":"10.18419/darus-2905","ISBN":"","ISSN":"","URL":"","abstract":"This dataset contains data of 125 1-hour simulations of ship motion during various sea states performing random maneuvers in 4 degrees of freedom (surge-sway-yaw-roll). The original ship is a patrol ship developed by Perez et al. [1]. We have extended it with a set of two symmetrically placed rudder propellers. Additionally, we simulate wind forces according to Isherwood's wind model [2]. Wind-induced waves are generated with the JONSWAP spectrum [3] and the corresponding wave forces are then computed using wave force response amplitude operators (ROA).Implementations of the ship model, Isherwood's wave model, wave force ROAs and the JONSWAP spectrum can be found in the Marine Systems Simulator toolbox by Fossen and Perez [4].The dataset is split into a routine operation set (96 hours) and into an Out-Of-Distribution (OOD) set (29 hours). The routine operation set is split into train-validation-test with a 60-10-30 split, while the OOD set is used solely for testing.The dataset is used for the evaluation of nonlinear system identification methods for multi-step predictions. The following inputs and outputs are considered for the identification problem. Inputs are the shaft speeds of both propellers, their azimut angles, wind angle of attack, and wind speed. Measured states or outputs are surge velocity, sway velocity and roll rate, as well as yaw angle and roll angle.Please see the README.txt file for details regarding the file structure of this dataset and a description of the variables in the .tab files.This research is funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016. We acknowledge the support by the Stuttgart Center for Simulation Science (SimTech).[1] T. Perez, A. Ross, and T. I. Fossen, “A 4-DOF SIMULINK model of acoastal patrol vessel for manoeuvring in waves,” in IFAC MCMC, 2006.[2] R. M. Isherwood, “Wind resistance of merchant ships,” The RoyalInstitution of Naval Architects, 1972.[3] K. Hasselmann, T. Barnett, E. Bouws, H. Carlson, D. Cartwright, K. Enke,J. Ewing, H. Gienapp, D. Hasselmann, P. Kruseman, A. Meerburg,P. Muller, D. Olbers, K. Richter, W. Sell, and H. Walden, “Measurementsof wind-wave growth and swell decay during the joint north sea waveproject (JONSWAP),” Deut. Hydrogr. Z., vol. 8, pp. 1-95, 01 1973.[4] T. I. Fossen and T. Perez, “Marine Systems Simulator (MSS),” https://github.com/cybergalactic/MSS, 2004, last accessed: 2022-06-14.","annote":"","author":[{"family":"Baier","given":"Alexandra"},{"family":"Staab","given":"Steffen"}],"citation-label":"baier2022simulated","collection-editor":[],"collection-title":"","container-author":[],"container-title":"","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2022"]],"literal":"2022"},"event-place":"","id":"2a396f5bb367f22145257cfb82419ccdanalyticcomp","interhash":"18ba9683dbb5d493e66e8b43811a22df","intrahash":"2a396f5bb367f22145257cfb82419ccd","issue":"","issued":{"date-parts":[["2022"]],"literal":"2022"},"keyword":"myown simtech deeplearning pn4 from:alexbaier machinelearning EXC2075 exc2075","misc":{"affiliation":"Baier, Alexandra/Universität Stuttgart, Staab, Steffen/Universität Stuttgart","orcid-numbers":"Baier, Alexandra/0000-0001-5609-3400, Staab, Steffen/0000-0002-0780-4154","doi":"10.18419/darus-2905"},"note":"Related to: Baier, A., Boukhers, Z., & Staab, S. (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. arXiv: 2103.06727","number":"","page":"","page-first":"","publisher":"","publisher-place":"","status":"","title":"A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances","type":"dataset","username":"analyticcomp","version":"","volume":""},"4cb785e55420811695b8536b20a6cd38alexbaier":{"DOI":"","ISBN":"","ISSN":"","URL":"http://arxiv.org/abs/2103.06727","abstract":"Physical motion models offer interpretable predictions for the motion of\r\nvehicles. However, some model parameters, such as those related to aero- and\r\nhydrodynamics, are expensive to measure and are often only roughly approximated\r\nreducing prediction accuracy. Recurrent neural networks achieve high prediction\r\naccuracy at low cost, as they can use cheap measurements collected during\r\nroutine operation of the vehicle, but their results are hard to interpret. To\r\nprecisely predict vehicle states without expensive measurements of physical\r\nparameters, we propose a hybrid approach combining deep learning and physical\r\nmotion models including a novel two-phase training procedure. We achieve\r\ninterpretability by restricting the output range of the deep neural network as\r\npart of the hybrid model, which limits the uncertainty introduced by the neural\r\nnetwork to a known quantity. We have evaluated our approach for the use case of\r\nship and quadcopter motion. The results show that our hybrid model can improve\r\nmodel interpretability with no decrease in accuracy compared to existing deep\r\nlearning approaches.","annote":"","author":[{"family":"Baier","given":"Alexandra"},{"family":"Boukhers","given":"Zeyd"},{"family":"Staab","given":"Steffen"}],"citation-label":"baier2021hybrid","collection-editor":[],"collection-title":"","container-author":[],"container-title":"","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2021"]],"literal":"2021"},"event-place":"","id":"4cb785e55420811695b8536b20a6cd38alexbaier","interhash":"a135e9dacfd0b251d72027ff6e0bc0c1","intrahash":"4cb785e55420811695b8536b20a6cd38","issue":"","issued":{"date-parts":[["2021"]],"literal":"2021"},"keyword":"EXC2075 deeplearning machinelearning myown pn4 prePrint simtech","misc":{"language":"English"},"note":"cite arxiv:2103.06727","number":"","page":"","page-first":"","publisher":"","publisher-place":"","status":"","title":"Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction","type":"manuscript","username":"alexbaier","version":"","volume":""}}