176 citations · 192 across the 6 of their papers we have counts for
5 papers · 1 filter
Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation
Christian Internò, Andrea Castellani, Sebastian Schmitt +2
Industrial Non-Intrusive Load Monitoring (NILM) is limited by the scarcity of high-quality datasets and the complex variability of industrial energy consumption patterns. To addres…
Stream-based Active Learning with Verification Latency in Non-stationary Environments
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
Data stream classification is an important problem in the field of machine learning. Due to the non-stationary nature of the data where the underlying distribution changes over tim…
Task-Sensitive Concept Drift Detector with Constraint Embedding
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
Detecting drifts in data is essential for machine learning applications, as changes in the statistics of processed data typically has a profound influence on the performance of tra…
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise
Andrea Castellani, Sebastian Schmitt, Barbara Hammer
In complex industrial settings, it is common practice to monitor the operation of machines in order to detect undesired states, adjust maintenance schedules, optimize system perfor…
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning
Andrea Castellani, Sebastian Schmitt, Stefano Squartini
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologie…