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ORCIDDieter Schramm
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2020 – today
- 2024
[j15]Jan Hauke Harmening
, Fabian Pioch, Lennart Fuhrig, Franz-Josef Peitzmann, Dieter Schramm, Ould el Moctar:
Data-assisted training of a physics-informed neural network to predict the separated Reynolds-averaged turbulent flow field around an airfoil under variable angles of attack. Neural Comput. Appl. 36(25): 15353-15371 (2024)- [i1]Peter Baumann, Lars Mikelsons, Oliver Kotte, Dieter Schramm:
Analyzing the coupling process of distributed mixed real-virtual prototypes. CoRR abs/2401.11779 (2024) - 2023
- [j14]Benedikt Jooß, Dieter Schramm:
Modular Test Kit - A Modular Approach for Efficient and Function-Oriented Testing. J. Electron. Test. 39(3): 267-274 (2023) - [j13]Julia Rosenberger, Andreas Selig, Mirjana Ristic, Michael Bühren, Dieter Schramm:
Virtual Commissioning of Distributed Systems in the Industrial Internet of Things. Sensors 23(7): 3545 (2023) - [c33]Christian Hürten, Thomas M. Weber, Felix Mueller, Jason Sutanto, Frédéric Etienne Kracht, Dieter Schramm:
Concept of an Alert Management System for Remote-Controlled Inland Shipping Vessels. ITSC 2023: 5889-5894 - [c32]Maximilian Hohn, Maximilian Jarofka, Frédéric Etienne Kracht, Dieter Schramm:
Smart and Green Ship: A Platform for the Development of Automation, Remote Control and Connectivity. ITSC 2023: 5948-5953 - 2022
- [j12]Frédéric Etienne Kracht, Maximilian Jarofka, Jan Oberhagemann, Rupert Henn, Dieter Schramm:
VeLABi - Research and control center for autonomous inland vessels. Autom. 70(5): 411-419 (2022) - [j11]Philipp Maximilian Sieberg, Dieter Schramm:
Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems. Sensors 22(9): 3513 (2022) - [j10]Julia Rosenberger, Michael Urlaub, Felix Rauterberg, Tina Lutz, Andreas Selig, Michael Bühren, Dieter Schramm:
Deep Reinforcement Learning Multi-Agent System for Resource Allocation in Industrial Internet of Things. Sensors 22(11): 4099 (2022) - [j9]Philipp Maximilian Sieberg, Sebastian Blume, Sebastian Reicherts, Niko Maas, Dieter Schramm:
Hybrid State Estimation-A Contribution Towards Reliability Enhancement of Artificial Neural Network Estimators. IEEE Trans. Intell. Transp. Syst. 23(7): 6337-6346 (2022) - [c31]Robert J. Martin, Magnus Liebherr, Roland Boumann, Stephan Schweig, Frédéric Etienne Kracht, Dieter Schramm:
Digital, Online, Take-Home - University Students' Attitude towards Different Examination Formats. GeCon 2022: 1-5 - [c30]Thomas M. Weber, Christian Hürten, Dieter Schramm:
Concept of a Teleoperation System for Inland Shipping Vessels. ITSC 2022: 349-354 - [c29]Maximilian Jarofka, Philipp Maximilian Sieberg, Christian Hürten, Tim Benedens, Ricarda Peters, Frédéric Etienne Kracht, Dieter Schramm:
Template for Preparation of Papers for IEEE Sponsored Conferences & Symposia. ITSC 2022: 1548-1553 - [c28]Christian Hürten, Philipp Maximilian Sieberg, Dieter Schramm:
Generating a Multi-fidelity Simulation Model Estimating the Models' Applicability with Machine Learning Algorithms. SIMULTECH 2022: 131-141 - 2021
- [j8]Xiaoyi Ma, Xiaowei Hu, Thomas M. Weber, Dieter Schramm:
Effects of Automated Vehicles on Traffic Flow With Different Levels of Automation. IEEE Access 9: 3630-3637 (2021) - [j7]Magnus Liebherr, Silke M. Müller, Stephan Schweig, Niko Maas, Dieter Schramm, Matthias Brand:
Stress and Simulated Environments: Insights From Physiological Marker. Frontiers Virtual Real. 2: 618855 (2021) - [j6]Sebastian Blume, Tim Benedens, Dieter Schramm:
Hyperparameter Optimization Techniques for Designing Software Sensors Based on Artificial Neural Networks. Sensors 21(24): 8435 (2021) - [j5]C. H. Chin, Salwani Abdullah, S. S. K. Singh, Khairul Akram Zainol Ariffin, Dieter Schramm:
Computing low-frequency vibration energy with Hölder singularities as durability predictive criterion of random road excitation. Soft Comput. 25(8): 6469-6487 (2021) - [c27]Torben Gräber, Sebastian Vetter, Sascha Sarajalew, Michael Unterreiner, Dieter Schramm:
AGLVQ - Making Generalized Vector Quantization Algorithms Aware of Context. ESANN 2021 - [c26]Julia Rosenberger, Felix Rauterberg, Dieter Schramm:
Performance study on IOTA Chrysalis and Coordicide in the Industrial Internet of Things. GCAIoT 2021: 88-93 - [c25]Julia Rosenberger, Michael Urlaub, Dieter Schramm:
Multi-agent reinforcement learning for intelligent resource allocation in IIoT networks. GCAIoT 2021: 118-119 - [c24]Julia Rosenberger, Michael Bühren, Dieter Schramm:
Perspective on efficiency enhancements in processing streaming data in industrial IoT networks. GCAIoT 2021: 120-121 - 2020
- [c23]Philipp Maximilian Sieberg, Christian Hürten, Dieter Schramm:
Representation of an Integrated Non-Linear Model-Based Predictive Vehicle Dynamics Control System by a Co-Active Neuro-Fuzzy Inference System. IV 2020: 572-577 - [c22]Maximilian Jarofka, Stephan Schweig, Niko Maas, Frédéric Etienne Kracht, Dieter Schramm:
Application of Photogrammetric Object Reconstruction for Simulation Environments in the Context of Inland Waterways. SIMULTECH (Selected Papers) 2020: 1-17 - [c21]Maximilian Jarofka, Stephan Schweig, Niko Maas, Dieter Schramm:
Toolchain Development for Automated Scene Reconstruction using Artificial Neural Network Object Detection and Photogrammetry for the Application in Driving Simulators. SIMULTECH 2020: 25-34 - [c20]Patrick Driesch, Kai Horwat, Niko Maas, Dieter Schramm:
Evaluation of Alternative Propulsion Concepts for Mobile Machinery: A Modelling Approach using the Example on LNG-powered Port Handling Equipment. SIMULTECH 2020: 225-232
2010 – 2019
- 2019
- [j4]Uwe Moser, Dieter Schramm:
Multivariate dynamic time warping in automotive applications: A review. Intell. Data Anal. 23(3): 535-553 (2019) - [j3]Torben Gräber, Stefan Lupberger, Michael Unterreiner, Dieter Schramm:
A Hybrid Approach to Side-Slip Angle Estimation With Recurrent Neural Networks and Kinematic Vehicle Models. IEEE Trans. Intell. Veh. 4(1): 39-47 (2019) - [j2]Tobias Rehder, Alexander Koenig, Michel Goehl, Lawrence Louis, Dieter Schramm:
Lane Change Intention Awareness for Assisted and Automated Driving on Highways. IEEE Trans. Intell. Veh. 4(2): 265-276 (2019) - [c19]Lukas Herrmann, Tobias Bruckmann, Markus Bröcker, Dieter Schramm:
Development of a Dynamic Electronic Speed Controller for Multicopters. ECC 2019: 4010-4015 - [c18]Peter Baumann, Martin Krammer, Mario Driussi, Lars Mikelsons, Josef Zehetner, Werner Mair, Dieter Schramm:
Using the Distributed Co-Simulation Protocol for a Mixed Real-Virtual Prototype. ICM 2019: 440-445 - [c17]Philipp Maximilian Sieberg, Sebastian Blume, Nele Harnack, Niko Maas, Dieter Schramm:
Hybrid State Estimation Combining Artificial Neural Network and Physical Model. ITSC 2019: 894-899 - [c16]Sebastian Blume, Philipp Maximilian Sieberg, Niko Maas, Dieter Schramm:
Neural Roll Angle Estimation in a Model Predictive Control System. ITSC 2019: 1625-1630 - [c15]Matthias Bahr, Sebastian Reicherts, Philipp Maximilian Sieberg, Luca Morss, Dieter Schramm:
Development and Validation of Active Roll Control based on Actor-critic Neural Network Reinforcement Learning. SIMULTECH 2019: 36-46 - [c14]Thomas M. Weber, Patrick Driesch, Dieter Schramm:
Introducing Road Surface Conditions into a Microscopic Traffic Simulation. SUMO 2019: 172-186 - 2018
- [c13]Stephan Schweig, Magnus Liebherr, Dieter Schramm, Matthias Brand, Niko Maas:
The Impact of Psychological and Demographic Parameters on Simulator Sickness. SIMULTECH 2018: 91-97 - 2017
- [c12]Xiao-kun Dai, Yang Song, Mira Schüller, Dieter Schramm:
Stability Analysis of Wind Turbines Combined with Rechargeable Batteries Based on Markov Jump Linear Systems. LSMS/ICSEE (3) 2017: 126-136 - [c11]Niko Maas, Frédéric Etienne Kracht, Mira Schüller, Weiyan Hou, Dieter Schramm:
Passing Control Between Driver and Highly Automated Driving Functions. LSMS/ICSEE (2) 2017: 629-638 - 2016
- [c10]Tobias Rehder, Wolfgang Muenst, Lawrence Louis, Dieter Schramm:
Learning lane change intentions through lane contentedness estimation from demonstrated driving. ITSC 2016: 893-898 - 2014
- [c9]Christian Heigele, Holger Mielenz, Joerg Heckel, Dieter Schramm:
Accurate and fast localization in unstructured environment based on shape context keypoints. FUSION 2014: 1-7 - [c8]Jan Erik Stellet, Christian Heigele, Florian Kuhnt, J. Marius Zöllner, Dieter Schramm:
Performance evaluation and statistical analysis of algorithms for ego-motion estimation. ITSC 2014: 2125-2131 - [c7]Sebastian Feldmann, Tobias Bruckmann, Dieter Schramm:
Simulation of a cable-driven actuation concept for a humanoid robot prototype. MESA 2014: 1-6 - [c6]Niko Maas, Benjamin Hesse, Martin Koppers, Dieter Schramm:
Simulator setup according to use case scenarios - A human-oriented method for virtual development. MESA 2014: 1-6 - 2013
- [c5]Andreas von Eichhorn, Moritz Werling, Peter Zahn, Dieter Schramm:
Maneuver prediction at intersections using cost-to-go gradients. ITSC 2013: 112-117 - [c4]Christian Heigele, Holger Mielenz, Joerg Heckel, Dieter Schramm:
Multi level modeling and loop closing with GridTiles. MMAR 2013: 724-729 - [c3]Andreas von Eichhorn, Peter Zahn, Dieter Schramm:
Automatic generation of intersection topologies using numerous GPS traces. WiVeC 2013: 1-5 - 2011
- [c2]Hongchao Ji, Oliver Lenord, Dieter Schramm:
A Model Driven Approach for Requirements Engineering of Industrial Automation Systems. EOOLT 2011: 9-18
2000 – 2009
- 2008
- [c1]Lars Mikelsons, Tobias Bruckmann, Manfred Hiller, Dieter Schramm:
A real-time capable force calculation algorithm for redundant tendon-based parallel manipulators. ICRA 2008: 3869-3874
1980 – 1989
- 1989
- [j1]Wolfgang Hirschberg, Dieter Schramm:
Application of NEWEUL in Robot Dynamics. J. Symb. Comput. 7(2): 199-204 (1989)
Coauthor Index
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