- Titel
- Workshop Proceedings AI in Production
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:l189-qucosa2-941594
- Bandnummer
- 1
- Konferenz
- KI 2024 - 47th German Conference on Artificial Intelligence. Würzburg, 25.-27.09.2024
- Auflage
- 1
- DOI
- https://doi.org/10.33968/2024.74
- Abstract (EN)
- Our workshop aims to bring together researchers and practitioners from the fields of AI and/or production investigating, developing, or exploring AI techniques in production. We aim to provide a platform for the exchange of ideas and experiences under the general topic of ‘AI in Production’, not specializing in certain fields of production nor AI but explicitly including production planning, control and optimization. Ideally, our workshop will enable us to standardize approaches for supporting production applying AI or to transfer these approaches from one area of application in production to another. Thus, the Workshop is not only intended for experts in artificial intelligence (in production), but explicitly also for professionals from production.
- Freie Schlagwörter (EN)
- AI, Artificial Intelligence, Production, Scheduling, Semantics, Machine Learning, Decision Support
- HerausgeberIn
- Dr. Ing. Martin Krockert
- Prof. Dr. Torsten Munkelt
- Herausgeber (Institution)
- Hochschule für Technik, Wirtschaft und Kultur Leipzig, Leipzig
- Förder- / Projektangaben
- Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
Kopa35c
Realistischer Planen mittels Künstlicher Intelligenz.
(ReplaKI)
ID: 741012322 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:l189-qucosa2-941594
- Veröffentlichungsdatum Qucosa
- 21.10.2024
- Dokumenttyp
- Konferenzband
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0- Inhaltsverzeichnis
This Proceedings on 'AI in Production' consists of 5 Proceedings: - Charging Strategies for Automated Guided Vehicles Using Supervised Learning - Optical Neural Networks for Low-latency and Energy-efficient Applications in Production - Flexible Data Architecture for Enabling AI Applications in Production Environments - Perception of Biases in Machine Learning in Production Research - A Structured Literature Review Dissecting Bias Categories - Supporting machine operators in paper production using machine learning based state estimation and user assistance system