Challenges and Frontiers in Implementing Artificial Intelligence in Process Industry

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Impact and opportunities of artificial intelligence techniques in the steel industry
1. Verfasser: Neuer, Marcus J. (VerfasserIn)
Weitere Verfasser: Wolff, Andreas (VerfasserIn), Holzknecht, Norbert (VerfasserIn)
Format: UnknownFormat
Sprache:eng
Veröffentlicht: 2021
Schlagworte:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Titel Jahr Verfasser
Data Pre-processing for Efficient Design of Machine Learning-Based Models to be Applied in the Steel Sector 2021 Cateni, Silvia
Quantifying Uncertainty in Physics-Informed Variational Autoencoders for Anomaly Detection 2021 Neuer, Marcus J.
AI and ML Techniques for Generation and Assessment of Products Properties Data 2021 Vannucci, Marco
Machine Learning-Based Models for Supporting Optimal Exploitation of Process Off-Gases in Integrated Steelworks 2021 Matino, Ismael
Unsupervised Deep Learning for Detection of Non-uniform Surface Defect Distributions in Flat Steel Production 2021 Camargo-Torres, Nicolas
Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery 2021 Albayrak, Özlem
Quality 4.0 - Transparent Product Quality Supervision in the Age of Industry 4.0 2021 Brandenburger, Jens
The Use of Advanced Data Analytics to Monitor Process-Induced Changes to the Microstructure and Mechanical Properties in Flat Steel Strip 2021 Berg, Frenk Van Den
Industrial Cyber Security at the Network Edge: The BRAINE Project Approach 2021 Paolucci, Francesco
TSorage: A Modern and Resilient Platform for Time Series Management at Scale 2021 Goeminne, Mathieu
Challenges and Frontiers in Implementing Artificial Intelligence in Process Industry 2021 Neuer, Marcus J.
Mapping of Standardized State Machines to Utilize Machine Learning Models in Process Control Environments 2021 Grothoff, Julian
Alle Artikel auflisten