Challenges and Frontiers in Implementing Artificial Intelligence in Process Industry
Data Pre-processing for Efficient Design of Machine Learning-Based Models to be Applied in the Steel Sector
Quantifying Uncertainty in Physics-Informed Variational Autoencoders for Anomaly Detection
Mapping of Standardized State Machines to Utilize Machine Learning Models in Process Control Environments
Quality 4.0
Transparent Product Quality Supervision in the Age of Industry 4.0
AI and ML Techniques for Generation and Assessment of Products Properties Data
The Use of Advanced Data Analytics to Monitor Process-Induced Changes to the Microstructure and Mechanical Properties in Flat Steel Strip
Unsupervised Deep Learning for Detection of Non-uniform Surface Defect Distributions in Flat Steel Production
Machine Learning-Based Models for Supporting Optimal Exploitation of Process Off-Gases in Integrated Steelworks
Industrial Cyber Security at the Network Edge: The BRAINE Project Approach
Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery
TSorage: A Modern and Resilient Platform for Time Series Management at Scale.