30 citations · 50 across the 16 of their papers we have counts for
6 papers · 1 filter
Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a Set of Time Series
Jan-Philipp Roche, Oliver Niggemann, Jens Friebe
Existing black box modeling approaches in machine learning suffer from a fixed input and output feature combination. In this paper, a new approach to reconstruct missing variables…
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study
Alexander Windmann, Henrik Steude, Oliver Niggemann
Deep learning (DL) models have seen increased attention for time series forecasting, yet the application on cyber-physical systems (CPS) is hindered by the lacking robustness of th…
Learning Physical Concepts in Cyber-Physical Systems: A Case Study
Henrik S. Steude, Alexander Windmann, Oliver Niggemann
Machine Learning (ML) has achieved great successes in recent decades, both in research and in practice. In Cyber-Physical Systems (CPS), ML can for example be used to optimize syst…
LSTM for Model-Based Anomaly Detection in Cyber-Physical Systems
Benedikt Eiteneuer, Oliver Niggemann
Anomaly detection is the task of detecting data which differs from the normal behaviour of a system in a given context. In order to approach this problem, data-driven models can be…
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata
Nemanja Hranisavljevic, Oliver Niggemann, Alexander Maier
Performing anomaly detection in hybrid systems is a challenging task since it requires analysis of timing behavior and mutual dependencies of both discrete and continuous signals.…
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder
Benedikt Eiteneuer, Nemanja Hranisavljevic, Oliver Niggemann
Unsupervised anomaly detection (AD) is a major topic in the field of Cyber-Physical Production Systems (CPPSs). A closely related concern is dimensionality reduction (DR) which is:…