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"https://puma.ub.uni-stuttgart.de/bibtex/281c34cc99047f704291efc46ac243f05/pegenlauf", "tags" : [ "MachineLearning","PatrickEgenlauf","QuantumMechanics","myown","patrickegenlauf","physics","publist" ], "intraHash" : "81c34cc99047f704291efc46ac243f05", "interHash" : "bfaa0ca6928b69acfaaebff9f6a029f8", "label" : "Gaussian-process-regression-based method for the localization of exceptional points in complex resonance spectra", "user" : "pegenlauf", "description" : "", "date" : "2024-10-21 15:34:45", "changeDate" : "2025-09-12 11:13:58", "count" : 2, "pub-type": "article", "journal": "Machine Learning: Science and Technology", "year": "2024", "url": "https://doi.org/10.1088/2632-2153/ad2e16", "author": [ "Patrick Egenlauf","Patric Rommel","Jörg Main" ], "authors": [ {"first" : "Patrick", "last" : "Egenlauf"}, {"first" : "Patric", "last" : "Rommel"}, {"first" : "Jörg", "last" : "Main"} ], "doi" : "10.1088/2632-2153/ad2e16", "bibtexKey": "egenlauf2024gaussianprocessregressionbased" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/22a396f5bb367f22145257cfb82419ccd/alexbaier", "tags" : [ "EXC2075","deeplearning","exc2075","machinelearning","myown","pn4","simtech" ], "intraHash" : "2a396f5bb367f22145257cfb82419ccd", "interHash" : "18ba9683dbb5d493e66e8b43811a22df", "label" : "A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances", "user" : "alexbaier", "description" : "", "date" : "2022-10-10 09:56:21", "changeDate" : "2023-06-14 16:59:59", "count" : 9, "pub-type": "dataset", "year": "2022", "url": "", "author": [ "Alexandra Baier","Steffen Staab" ], "authors": [ {"first" : "Alexandra", "last" : "Baier"}, {"first" : "Steffen", "last" : "Staab"} ], "note": "Related to: Baier, A., Boukhers, Z., & Staab, S. (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. arXiv: 2103.06727","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, \u201CA 4-DOF SIMULINK model of acoastal patrol vessel for manoeuvring in waves,\u201D in IFAC MCMC, 2006.[2] R. M. Isherwood, \u201CWind resistance of merchant ships,\u201D 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, \u201CMeasurementsof wind-wave growth and swell decay during the joint north sea waveproject (JONSWAP),\u201D Deut. Hydrogr. Z., vol. 8, pp. 1-95, 01 1973.[4] T. I. Fossen and T. Perez, \u201CMarine Systems Simulator (MSS),\u201D https://github.com/cybergalactic/MSS, 2004, last accessed: 2022-06-14.", "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", "bibtexKey": "baier2022simulated" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/22a396f5bb367f22145257cfb82419ccd/analyticcomp", "tags" : [ "myown","simtech","deeplearning","pn4","from:alexbaier","machinelearning","EXC2075","exc2075" ], "intraHash" : "2a396f5bb367f22145257cfb82419ccd", "interHash" : "18ba9683dbb5d493e66e8b43811a22df", "label" : "A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances", "user" : "analyticcomp", "description" : "", "date" : "2022-10-10 09:56:21", "changeDate" : "2023-06-14 16:59:59", "count" : 9, "pub-type": "dataset", "year": "2022", "url": "", "author": [ "Alexandra Baier","Steffen Staab" ], "authors": [ {"first" : "Alexandra", "last" : "Baier"}, {"first" : "Steffen", "last" : "Staab"} ], "note": "Related to: Baier, A., Boukhers, Z., & Staab, S. (2021). Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction. arXiv: 2103.06727","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, \u201CA 4-DOF SIMULINK model of acoastal patrol vessel for manoeuvring in waves,\u201D in IFAC MCMC, 2006.[2] R. M. Isherwood, \u201CWind resistance of merchant ships,\u201D 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, \u201CMeasurementsof wind-wave growth and swell decay during the joint north sea waveproject (JONSWAP),\u201D Deut. Hydrogr. Z., vol. 8, pp. 1-95, 01 1973.[4] T. I. Fossen and T. Perez, \u201CMarine Systems Simulator (MSS),\u201D https://github.com/cybergalactic/MSS, 2004, last accessed: 2022-06-14.", "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", "bibtexKey": "baier2022simulated" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/24cb785e55420811695b8536b20a6cd38/alexbaier", "tags" : [ "EXC2075","deeplearning","machinelearning","myown","pn4","prePrint","simtech" ], "intraHash" : "4cb785e55420811695b8536b20a6cd38", "interHash" : "a135e9dacfd0b251d72027ff6e0bc0c1", "label" : "Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction", "user" : "alexbaier", "description" : "", "date" : "2021-11-12 09:05:06", "changeDate" : "2023-06-14 17:00:24", "count" : 7, "pub-type": "preprint", "year": "2021", "url": "http://arxiv.org/abs/2103.06727", "author": [ "Alexandra Baier","Zeyd Boukhers","Steffen Staab" ], "authors": [ {"first" : "Alexandra", "last" : "Baier"}, {"first" : "Zeyd", "last" : "Boukhers"}, {"first" : "Steffen", "last" : "Staab"} ], "note": "cite arxiv: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.", "language" : "English", "bibtexKey": "baier2021hybrid" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/22206e2581e8df62b93eb7b14a5bf487d/dr.romanklinger", "tags" : [ "machinelearning","metalearning","myown","ner","nlp" ], "intraHash" : "2206e2581e8df62b93eb7b14a5bf487d", "interHash" : "df55d142345bbf368a43dd600ec9b704", "label" : "Dissecting Span Identification Tasks with Performance Prediction", "user" : "dr.romanklinger", "description" : "", "date" : "2020-10-08 17:45:01", "changeDate" : "2020-10-08 15:45:01", "count" : 2, "pub-type": "inproceedings", "booktitle": "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing","publisher":"Association for Computational Linguistics", "year": "2020", "url": "http://www.romanklinger.de/publications/PapayKlingerPado2020.pdf", "author": [ "Sean Papay","Roman Klinger","Sebastian Padó" ], "authors": [ {"first" : "Sean", "last" : "Papay"}, {"first" : "Roman", "last" : "Klinger"}, {"first" : "Sebastian", "last" : "Padó"} ], "bibtexKey": "Papay2020" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/22a6688b02d28ad57f32e658489309379/isw-bibliothek", "tags" : [ "MachineLearning","grk2198","isw","myown" ], "intraHash" : "2a6688b02d28ad57f32e658489309379", "interHash" : "4fdefd5d5c45d2646ee9e64564f524b9", "label" : "Reinforcement Learning of a Robot Cell Control Logic using a Software-in-the-Loop Simulation as Environment", "user" : "isw-bibliothek", "description" : "", "date" : "2020-06-09 11:14:14", "changeDate" : "2020-06-18 12:23:01", "count" : 2, "pub-type": "inproceedings", "booktitle": "2019 Second International Conference on Artificial Intelligence for Industries (AI4I)", "year": "2019", "url": "https://ieeexplore.ieee.org/document/9027783/", "author": [ "Florian Jaensch","Akos Csiszar","Janik Sarbandi","Alexander Verl" ], "authors": [ {"first" : "Florian", "last" : "Jaensch"}, {"first" : "Akos", "last" : "Csiszar"}, {"first" : "Janik", "last" : "Sarbandi"}, {"first" : "Alexander", "last" : "Verl"} ], "pages": "79-84","abstract": "This paper introduces a method for automatic robot programming of industrial robots using reinforcement learning on a Software-in-the-loop simulation. The focus of the the paper is on the higher levels of a hierarchical robot programming problem. While the lower levels the skills are stored as domain specific program code, the combination of the skills into a robot control program to solve a specific task is automated. The reinforcement learning learning approach allows the shopfloor workers and technicians just to define the end result of the manufacturing process through a reward function. The programming and process optimization is done within the learning procedure. The Software-in-the-loop simulation with the robot control software makes it possible to to interpret the real program code and generate the exact motion. The exact motion of the robot is needed in order to find not just an optimal but also a collision-free policy.", "doi" : "10.1109/AI4I46381.2019.00027", "bibtexKey": "9027783" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c9324e84efe8aab59d8f55f1b95a8d99/isw-bibliothek", "tags" : [ "DigitalTwin","MachineLearning","grk2198","isw","myown" ], "intraHash" : "c9324e84efe8aab59d8f55f1b95a8d99", "interHash" : "359b8b3bc1b319e37551a5aff6196ff4", "label" : "Reinforcement Learning of Material Flow Control Logic Using Hardware-in-the-Loop Simulation", "user" : "isw-bibliothek", "description" : "", "date" : "2019-05-15 01:21:27", "changeDate" : "2020-01-20 14:24:16", "count" : 4, "pub-type": "inproceedings", "booktitle": "2018 First International Conference on Artificial Intelligence for Industries (AI4I)", "year": "2018", "url": "https://ieeexplore.ieee.org/document/8665712/", "author": [ "Florian Jaensch","Akos Csiszar","Annika Kienzlen","Alexander Verl" ], "authors": [ {"first" : "Florian", "last" : "Jaensch"}, {"first" : "Akos", "last" : "Csiszar"}, {"first" : "Annika", "last" : "Kienzlen"}, {"first" : "Alexander", "last" : "Verl"} ], "pages": "77-80","abstract": "In this paper the concept of reinforcement learning agent is presented, which can deduce the correct control policy of a plant by acting in its digital twin (the HiL simulation). This way the agent substitutes a real control system. By using reinforcement learning methods, a proof of concept application is presented for a simplistic material flow system, with the same type of access to the digital twin which a PLC controller-hardware would have. With the presented approach the agent is able to find the correct control policy.", "doi" : "10.1109/AI4I.2018.8665712", "bibtexKey": "8665712" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/208e7136aaad4731582ec69d690483ae6/isw-bibliothek", "tags" : [ "DigitalTwin","DigitaleFabrik","MachineLearning","grk2198","isw","myown" ], "intraHash" : "08e7136aaad4731582ec69d690483ae6", "interHash" : "5b083907fa1783d06c168d70a4adf590", "label" : "Digital Twins of Manufacturing Systems as a Base for Machine Learning", "user" : "isw-bibliothek", "description" : "", "date" : "2019-01-17 22:17:56", "changeDate" : "2020-01-20 14:27:38", "count" : 4, "pub-type": "inproceedings", "booktitle": "2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)", "year": "2018", "url": "https://ieeexplore.ieee.org/document/8600844/", "author": [ "Florian Jaensch","Akos Csiszar","Christian Scheifele","Alexander Verl" ], "authors": [ {"first" : "Florian", "last" : "Jaensch"}, {"first" : "Akos", "last" : "Csiszar"}, {"first" : "Christian", "last" : "Scheifele"}, {"first" : "Alexander", "last" : "Verl"} ], "pages": "1-6","abstract": "In the engineering phase of modern manufacturing systems, simulation-based methods and tools have been established to face the increasing demands on time-efficiency and profitability. In the application of these simulation solutions, model-based digital twins are created, as multi-domain simulation models to describe the behavior of the manufacturing system. During the production process, a data-driven digital twin arises in the context of industry 4.0 based on an increasing networking and new cloud technologies. Recent developments in machine learning of fer new possibilities in conjunction with the digital twin. These range from data-based learning of models to learning control logic of complex systems. This paper proposes a combined model-based and data-driven concept of a digital twin. It shows how to use machine learning in connection with these models, in order to archive faster development times of manufacturing systems.", "doi" : "10.1109/M2VIP.2018.8600844", "bibtexKey": "8600844" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/27d8831a0dd078600113eb94fed0a4886/dr.romanklinger", "tags" : [ "domain-adaptation","machinelearning","models","myown","neural","nlp","sentiment" ], "intraHash" : "7d8831a0dd078600113eb94fed0a4886", "interHash" : "25b9bc436bcf31db2ce04f15185559c6", "label" : "Projecting Embeddings for Domain Adaptation:\r\nJoint Modeling of Sentiment Analysis in Diverse Domains", "user" : "dr.romanklinger", "description" : "", "date" : "2018-05-16 23:54:15", "changeDate" : "2018-06-20 21:18:47", "count" : 2, "pub-type": "inproceedings", "booktitle": "Proceedings of COLING 2018, the 27th International Conference on Computational Linguistics","address":"Santa Fe, USA", "year": "2018", "url": "http://www.romanklinger.de/publications/BarnesKlingerSIW2018blsedomain.pdf", "author": [ "Jeremy Barnes","Roman Klinger","Sabine Schulte im Walde" ], "authors": [ {"first" : "Jeremy", "last" : "Barnes"}, {"first" : "Roman", "last" : "Klinger"}, {"first" : "Sabine", "last" : "Schulte im Walde"} ], "bibtexKey": "Barnes2018a" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2d16cd432b8cd5b0ab84b92b1fac7239a/thomaskuestner", "tags" : [ "iqa","machinelearning","myown" ], "intraHash" : "d16cd432b8cd5b0ab84b92b1fac7239a", "interHash" : "1ef98ee573ba42788e7ac443bf068641", "label" : "Impact of Labeling Process on Automated Motion Artifact Detection in Whole-Body MR Images with a Deep Learning Approach: A Comparative Study", "user" : "thomaskuestner", "description" : "", "date" : "2018-01-30 10:49:03", "changeDate" : "2018-01-30 09:49:03", "count" : 1, "pub-type": "inproceedings", "booktitle": "Proceedings of the ISMRM Workshop on Machine Learning", "year": "2018", "url": "", "author": [ "Annika Liebgott","Sebastian Milde","Marvin Jandt","Lukas Mauch","Petros Martirosian","Fabian Bamberg","Fritz Schick","Konstantin Nikolaou","Bin Yang","Sergios Gatidis","Thomas Küstner" ], "authors": [ {"first" : "Annika", "last" : "Liebgott"}, {"first" : "Sebastian", "last" : "Milde"}, {"first" : "Marvin", "last" : "Jandt"}, {"first" : "Lukas", "last" : "Mauch"}, {"first" : "Petros", "last" : "Martirosian"}, {"first" : "Fabian", "last" : "Bamberg"}, {"first" : "Fritz", "last" : "Schick"}, {"first" : "Konstantin", "last" : "Nikolaou"}, {"first" : "Bin", "last" : "Yang"}, {"first" : "Sergios", "last" : "Gatidis"}, {"first" : "Thomas", "last" : "Küstner"} ], "bibtexKey": "Liebgott2018" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/29f8ed4691222cbfb979e969f1493aef7/thomaskuestner", "tags" : [ "MachineLearning","kSpaceAstronauts","myown" ], "intraHash" : "9f8ed4691222cbfb979e969f1493aef7", "interHash" : "d89fa116d4bface2d9d44ff75ee866b1", "label" : "Combined Unsupervised-Supervised Classification of Multiparametric PET/MRI Imaging Data of the Prostate", "user" : "thomaskuestner", "description" : "", "date" : "2017-01-10 12:15:30", "changeDate" : "2017-01-30 21:20:09", "count" : 1, "pub-type": "inproceedings", "booktitle": "Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM)","address":"Milano, Italy", "year": "2014", "url": "", "author": [ "Sergios Gatidis","Petros Martirosian","Thomas Küstner","Ilja Bezrukov","M. Scharpf","Christina Schraml","Nina F. Schwenzer","Holger Schmidt" ], "authors": [ {"first" : "Sergios", "last" : "Gatidis"}, {"first" : "Petros", "last" : "Martirosian"}, {"first" : "Thomas", "last" : "Küstner"}, {"first" : "Ilja", "last" : "Bezrukov"}, {"first" : "M.", "last" : "Scharpf"}, {"first" : "Christina", "last" : "Schraml"}, {"first" : "Nina F.", "last" : "Schwenzer"}, {"first" : "Holger", "last" : "Schmidt"} ], "bibtexKey": "Gatidis2014" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2424c63a38bbda9b1d3ea5c94eeadb81e/thomaskuestner", "tags" : [ "MachineLearning","kSpaceAstronauts","myown" ], "intraHash" : "424c63a38bbda9b1d3ea5c94eeadb81e", "interHash" : "79d6ab41b74cba9427745b10975d2406", "label" : "Combined unsupervised-supervised classification of multiparametric PET/MRI data: Application to prostate cancer", "user" : "thomaskuestner", "description" : "", "date" : "2017-01-10 12:12:38", "changeDate" : "2017-01-22 15:54:02", "count" : 1, "pub-type": "article", "journal": "NMR in biomedicine", "year": "2015", "url": "https://dx.doi.org/10.1002/nbm.3329", "author": [ "Sergios Gatidis","M. Scharpf","Petros Martirosian","Ilja Bezrukov","Thomas Küstner","J. Hennenlotter","S. Kruck","Sascha Kaufmann","Christina Schraml","Christian La Fougère","Nina F. Schwenzer","Holger Schmidt" ], "authors": [ {"first" : "Sergios", "last" : "Gatidis"}, {"first" : "M.", "last" : "Scharpf"}, {"first" : "Petros", "last" : "Martirosian"}, {"first" : "Ilja", "last" : "Bezrukov"}, {"first" : "Thomas", "last" : "Küstner"}, {"first" : "J.", "last" : "Hennenlotter"}, {"first" : "S.", "last" : "Kruck"}, {"first" : "Sascha", "last" : "Kaufmann"}, {"first" : "Christina", "last" : "Schraml"}, {"first" : "Christian", "last" : "La Fougère"}, {"first" : "Nina F.", "last" : "Schwenzer"}, {"first" : "Holger", "last" : "Schmidt"} ], "volume": "28","number": "7","pages": "914-22", "bibtexKey": "Gatidis2015" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/289f6b5742d084e2889a6468b90fed696/thomaskuestner", "tags" : [ "IQA","MachineLearning","kSpaceAstronauts","myown" ], "intraHash" : "89f6b5742d084e2889a6468b90fed696", "interHash" : "072eba2e0ab5803c7296535bd03fb093", "label" : "Automated image quality assessment in whole-body MRI", "user" : "thomaskuestner", "description" : "", "date" : "2017-01-10 10:53:31", "changeDate" : "2017-02-28 13:13:57", "count" : 1, "pub-type": "inproceedings", "booktitle": "Proceedings of the Radiological Society of North America (RSNA)","address":"Chicago, USA", "year": "2016", "url": "", "author": [ "Sergios Gatidis","Annika Liebgott","Holger Schmidt","Nina F. Schwenzer","Petros Martirosian","Konstantin Nikolaou","Fritz Schick","Bin Yang","Thomas Küstner" ], "authors": [ {"first" : "Sergios", "last" : "Gatidis"}, {"first" : "Annika", "last" : "Liebgott"}, {"first" : "Holger", "last" : "Schmidt"}, {"first" : "Nina F.", "last" : "Schwenzer"}, {"first" : "Petros", "last" : "Martirosian"}, {"first" : "Konstantin", "last" : "Nikolaou"}, {"first" : "Fritz", "last" : "Schick"}, {"first" : "Bin", "last" : "Yang"}, {"first" : "Thomas", "last" : "Küstner"} ], "bibtexKey": "Gatidis2016RSNA" } ] }