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Christian Endisch
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2020 – today
- 2024
- [j5]Andreas Rauscher
, Johannes Kaiser, Manoj Devaraju, Christian Endisch:
Deep learning and data augmentation for partial discharge detection in electrical machines. Eng. Appl. Artif. Intell. 133: 108074 (2024) - [c36]Markus Kohler
Empirical Investigation and Feed-Forward Control of Wire Tension in Needle Winding Processes. ISIE 2024: 1-8 - [c35]Andreas Rauscher
Impact of High Voltage Waveform on Partial Discharge Inception in Electrical Machines. ISIE 2024: 1-6 - 2022
- [c34]Martin Nachtsheim
Observability Analysis for Parameter Identification of Induction Machines in Automotive Environment. ISIE 2022: 206-213 - [c33]Markus Kohler
A Test Bench Concept and Method for Image-Based Modeling of Geometric Wire Bending Behavior in Needle Winding Processes. ISIE 2022: 1113-1120 - 2021
- [j4]Sebastian Mayer
Modular production control using deep reinforcement learning: proximal policy optimization. J. Intell. Manuf. 32(8): 2335-2351 (2021) - [c32]Dennis Gankin, Sebastian Mayer, Jonas Zinn, Birgit Vogel-Heuser, Christian Endisch:
Modular Production Control with Multi-Agent Deep Q-Learning. ETFA 2021: 1-8 - [c31]Dominik Schneider, Bernhard Liebhart, Christian Endisch:
Adaptive Variance Estimation of Sensor Noise within a Sensor Data Fusion Framework. I2MTC 2021: 1-6 - 2020
- [c30]Dominik Schneider
Robustness and Reliability of Model-based Sensor Data Fusion in a Lithium-Ion Battery System. CCTA 2020: 685-691 - [c29]Bernhard Liebhart, Simon Diehl
Sensitivity Analysis of Battery Cell Aging Estimators based on Impedance Spectroscopy regarding Temperature Compensation. CCTA 2020: 801-806 - [c28]Martin Gerngroß, Markus Kohler
Model-Based Control of Nonlinear Wire Tension in Dynamic Needle Winding Processes. ICIT 2020: 381-388 - [c27]Michael Okon, Johannes Opferkuch, Christian Endisch:
Modular Gear Transmission Model for Analyzing Production Tolerances in Electric Drivetrains. ICIT 2020: 709-716 - [c26]Jan Kleiner, Alexander Heider, Christian Hanzl
Thermal Behavior of an Intelligent Li-Ion Cell under Vehicle Conditions. IECON 2020: 2081-2086
2010 – 2019
- 2019
- [c25]Martin Gerngroß, Patrick Herrmann, Christian Endisch
Nonlinear Modelling of Dynamic Wire Tension for Needle Winding Processes of Distributed Windings. ICIT 2019: 151-156 - [c24]Michael Okon, Christian Endisch
On Air-Gap Approximations for Slotted Electrical Machine Models Assuming Radial Magnetic Fields. ICIT 2019: 292-297 - [c23]Sebastian Mayer, Christian Endisch
Adaptive Production Control in a Modular Assembly System Based on Partial Look-Ahead Scheduling. ICM 2019: 293-300 - [c22]Patrick Herrmann, Martin Gerngross, Christian Endisch
Benefits of Numerical Optimization and CAD/CAM in Needle Winding Processes. ICM 2019: 301-307 - [c21]Martin Gerngross, Patrick Herrmann, Christian Endisch, Christian Westermaier:
Optimization of the End Winding Patterns in Needle Wound Traction Stators. ICM 2019: 314-320 - [c20]Sebastian Mayer, Nikolas Höhme, Dennis Gankin, Christian Endisch
Adaptive Production Control in a Modular Assembly System - Towards an Agent-based Approach. INDIN 2019: 45-52 - [c19]Sebastian Mayer, Christian Arnet, Dennis Gankin, Christian Endisch
Standardized Framework for Evaluating Centralized and Decentralized Control Systems in Modular Assembly Systems. SMC 2019: 113-119 - [c18]Sebastian Mayer, Dennis Gankin, Christian Arnet, Christian Endisch
Adaptive Production Control with Negotiating Agents in Modular Assembly Systems. SMC 2019: 120-127 - 2018
- [c17]Lukas Leitner, Christian Endisch
Robust stochastic process models and parameter estimation for industrial end-of-line-testing. ICIT 2018: 1520-1525 - [c16]Johannes Ziegmann, Florian Denk, Ulrich Vogele, Christian Endisch
Stochastic Driver Velocity Prediction with Environmental Features on Naturalistic Driving Data. ITSC 2018: 1807-1814 - 2017
- [c15]Simon Altmannshofer, Christian Endisch
On-line identification and identifiability analysis of electrical and mechanical parameters of induction machines. AIM 2017: 577-583 - [c14]Tino Muller, Christian Endisch
An estimation based iterative learning control approach for cross-coupled vehicle test systems. AIM 2017: 1375-1381 - [c13]Ulrich Vogele, Johannes Ziegmann, Christian Endisch
Driver adaptive predictive velocity control. ITSC 2017: 1-6 - [c12]Johannes Ziegmann, Jieqing Shi, Tobias Schnorer, Christian Endisch
Analysis of individual driver velocity prediction using data-driven driver models with environmental features. Intelligent Vehicles Symposium 2017: 517-522 - 2016
- [c11]Lukas Leitner, Antoine Lagrange, Christian Endisch
End-of-line fault detection for combustion engines using one-class classification. AIM 2016: 207-213 - [c10]Tino Muller, Ulrich Vogele, Christian Endisch
Disturbance compensation for iterative control of suspension durability test rigs. AIM 2016: 1675-1681 - [c9]Simon Altmannshofer, Christian Endisch
Robust vehicle mass and driving resistance estimation. ACC 2016: 6869-6874 - [c8]Tino Muller, Christian Endisch
Compensation techniques for iterative rig control in multi-axial durability testing. ETFA 2016: 1-7 - [c7]Simon Altmannshofer, Christian Endisch, Jan Martin, Martin Gerngross, Reimund Limbacher:
Robust estimation of vehicle longitudinal dynamics parameters. Intelligent Vehicles Symposium 2016: 566-571 - 2015
- [j3]Peter Stolze, Petros P. Karamanakos
Effective variable switching point predictive current control for ac low-voltage drives. Int. J. Control 88(7): 1366-1378 (2015) - [c6]Rafael K. Jardan, Zoltán Varga, Peter Stumpf, István Nagy, Christian Endisch
Development of a dedicated laboratory system for measurement of iron losses in high speed PMSM. ICIT 2015: 708-713 - [c5]Peer Stenzel, Peter Dollinger, Jan Richnow, Tobias Bader, Jörg Franke
Experimental investigations of the needle winding technology regarding the influence of the wire guide geometry on the tensile wire force. ICIT 2015: 2642-2649 - [c4]Jan Martin, Ulrich Vogele, Christian Endisch
Effects of errors in position- and navigation data on predictive vehicle operational strategy optimization. ICVES 2015: 190-195 - [c3]Ulrich Vogele, Christian Endisch:
Potential analysis of predictive optimal velocity control for electric and conventional vehicles. ICVES 2015: 196-201
2000 – 2009
- 2009
- [j2]Christoph M. Hackl
Contributions to non-identifier based adaptive control in mechatronics. Robotics Auton. Syst. 57(10): 996-1005 (2009) - [j1]Christian Endisch, Peter Stolze, Christoph M. Hackl
Comments on "Backpropagation Algorithms for a Broad Class of Dynamic Networks". IEEE Trans. Neural Networks 20(3): 540-541 (2009) - [c2]Christian Endisch, Peter Stolze, Peter Endisch, Christoph M. Hackl
Levenberg-Marquardt-based OBS Algorithm using Adaptive Pruning Interval for System Identification with Dynamic Neural Networks. SMC 2009: 3402-3408 - 2007
- [c1]Christian Endisch, Christoph M. Hackl, Dierk Schröder:
Optimal Brain Surgeon for General Dynamic Neural Networks. EPIA Workshops 2007: 15-28
Coauthor Index
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