39 citations · 41 across the 10 of their papers we have counts for
10 papers
Benchmarking Sensor-Fault Robustness in Forecasting
Alexander Windmann, Philipp Wittenberg, Gianluca Manca +3
Cyber-physical system (CPS) forecasting models depend on sensor streams with noisy, biased, missing, or temporally misaligned readings, yet standard forecasting evaluation often se…
Industrial AI Robustness Card for Time Series Models
Alexander Windmann, Benedikt Stratmann, Mariya Lyashenko +1
Industrial AI practitioners face vague robustness requirements in emerging regulations and standards but lack concrete, implementation-ready protocols. This paper introduces the In…
MAWIFlow Benchmark: Realistic Flow-Based Evaluation for Network Intrusion Detection
Joshua Schraven, Alexander Windmann, Oliver Niggemann
Benchmark datasets for network intrusion detection commonly rely on synthetically generated traffic, which fails to reflect the statistical variability and temporal drift encounter…
Evaluating Large Language Models for Real-World Engineering Tasks
Rene Heesch, Sebastian Eilermann, Alexander Windmann +3
Large Language Models (LLMs) are transformative not only for daily activities but also for engineering tasks. However, current evaluations of LLMs in engineering exhibit two critic…
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems
Alexander Windmann, Henrik Steude, Daniel Boschmann +1
Cyber-Physical Systems (CPS) in domains such as manufacturing and energy distribution generate complex time series data crucial for Prognostics and Health Management (PHM). While D…
Artificial Intelligence in Industry 4.0: A Review of Integration Challenges for Industrial Systems
Alexander Windmann, Philipp Wittenberg, Marvin Schieseck +1
In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and pr…
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…
On a Uniform Causality Model for Industrial Automation
Maria Krantz, Alexander Windmann, Rene Heesch +2
The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perf…
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…
A Research Agenda for AI Planning in the Field of Flexible Production Systems
Aljosha Köcher, Rene Heesch, Niklas Widulle +4
Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsul…