Andreas Theissler
Andreas Theissler
Aalen University of Applied Sciences, Germany
Verified email at - Homepage
Cited by
Cited by
Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
A Theissler, J Pérez-Velázquez, M Kettelgerdes, G Elger
Reliability engineering & system safety 215, 107864, 2021
Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection
A Theissler
Knowledge-Based Systems 123, 163-173, 2017
Interpretable Machine Learning: A brief survey from the predictive maintenance perspective
S Vollert, M Atzmueller, A Theissler
2021 26th IEEE international conference on emerging technologies and factory …, 2021
Anomaly detection in recordings from in-vehicle networks
A Theissler
Big data and applications 23, 26, 2014
Explainable AI for time series classification: a review, taxonomy and research directions
A Theissler, F Spinnato, U Schlegel, R Guidotti
IEEE Access 10, 100700-100724, 2022
Challenges of machine learning-based RUL prognosis: A review on NASA's C-MAPSS data set
S Vollert, A Theissler
2021 26th IEEE international conference on emerging technologies and factory …, 2021
Autonomously Determining the Parameters for SVDD with RBF Kernel from a One-Class Training Set
A Theissler, I Dear
Proceedings of World Academy of Science, Engineering and Technology, 732, 2013
ConfusionVis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices
A Theissler, M Thomas, M Burch, F Gerschner
Knowledge-Based Systems 247, 108651, 2022
Detecting anomalies in multivariate time series from automotive systems
A Theissler
Brunel University School of Engineering and Design PhD Theses, 2013
XAI4EEG: spectral and spatio-temporal explanation of deep learning-based seizure detection in EEG time series
D Raab, A Theissler, M Spiliopoulou
Neural Computing and Applications 35 (14), 10051-10068, 2022
Multi-class novelty detection in diagnostic trouble codes from repair shops
A Theissler
2017 IEEE 15th International Conference on Industrial Informatics (INDIN …, 2017
The machine learning model as a guide: pointing users to interesting instances for labeling through visual cues
B Grimmeisen, A Theissler
Proceedings of the 13th International Symposium on Visual Information …, 2020
Failure Detection of Electronic Control Units Using Piezoresistive Stress Sensor
A Prisacaru, A Palczynska, A Theissler, P Gromala, B Han, GQ Zhang
IEEE Transactions on Components, Packaging and Manufacturing Technology 8 (5 …, 2018
Fingertip 6-axis force/torque sensing for texture recognition in robotic manipulation
T Markert, S Matich, E Hoerner, A Theissler, M Atzmueller
2021 26th IEEE International Conference on Emerging Technologies and Factory …, 2021
Cluster-Clean-Label: An interactive Machine Learning approach for labeling high-dimensional data
D Beil, A Theissler
Proceedings of the 13th International Symposium on Visual Information …, 2020
VIAL-AD: Visual Interactive Labelling for Anomaly Detection-An Approach and Open Research Questions.
A Theissler, AL Kraft, M Rudeck, F Erlenbusch
IAL@ PKDD/ECML, 84-89, 2020
Detecting anomalies in recordings from test drives based on a training set of normal instances
A Theissler, I Dear
Proceedings of the IADIS International Conference Intelligent Systems and …, 2012
Interactive knowledge discovery in recordings from vehicle tests
A Theissler, D Ulmer, I Dear
Proc. 33rd FISITA World Automotive Congress. Budapest, Hungary, 2010
On why the system makes the corner case: Ai-based holistic anomaly detection for autonomous driving
J Pfeil, J Wieland, T Michalke, A Theissler
2022 IEEE Intelligent Vehicles Symposium (IV), 337-344, 2022
ML-ModelExplorer: An explorative model-agnostic approach to evaluate and compare multi-class classifiers
A Theissler, S Vollert, P Benz, LA Meerhoff, M Fernandes
Machine Learning and Knowledge Extraction: 4th IFIP TC 5, TC 12, WG 8.4, WG …, 2020
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