Artificial Intelligence in Automation

ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen

01.08.2018 bis 31.07.2021

 

The primary objective of the research project is to bring machine learning (ML) for intelligent technical systems (ITS) to the whole value chain. This requires the development and transfer of the latest ML innovations to the key areas of application in ITS in order to bring ML technologies into products and production chains, and conversely, to raise the awareness of regional companies how and when ML can be integrated into agile business models and production chains. Coupled with the focus on the current application fields: learning assistance systems, cognitive plug and work, cognitive optimization and quality management, predictive maintenance, as well as ML and 5G, the concept of 'ML as a service' for ITS is advanced. The project can build on technical digitization strategies excellently initiated by regional small and medium-sized enterprises (SMEs) and the proven excellence of the participating partners in the ML field to realize the step towards the use of digital data by ML technologies.

This project is promoted by:
Bundesministerium für Bildung und Forschung (BMBF)
Sponsors: Das Deutsche Zentrum für Luft- und Raumfahrt e.V. (DLR)
Funding Code: 01IS18041D
Funding Lines: KT 2020 - Softwareintensive eingebettete Systeme
Stakeholders / Contacts: Anton Pfeifer, M. Sc., Dr.-Ing. Christoph-Alexander Holst
Employees: Anton Pfeifer, M. Sc., Malte Schmidt, M. Sc.
Distributed Self-Organisation of Information Fusion Systems
In: 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2017), Sep 2017
Conflict-based Feature Selection for Information Fusion Systems
In: 27. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA), Nov 2017
A Conflict-Based Drift Detection And Adaptation Approach for Multisensor Information Fusion
In: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2018
Benedikt Eiteneuer, M. Sc., Nemanja Hranisavljevic, M. Sc., Prof. Dr. rer. nat. Oliver Niggemann
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder
In: 20th IEEE International Conference on Industrial Technology Melbourne, Australien, Feb 2019, Feb 2019
A Redundancy Metric based on the Framework of Possibility Theory for Technical Systems
In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2020
Anton Pfeifer, M. Sc., Prof. Dr.-Ing. Volker Lohweg
Classification of Faults in Cyber-Physical Systems with Complex-Valued Neural Networks
In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Sep 2021
Malte Schmidt, M. Sc., Prof. Dr.-Ing. Volker Lohweg
Interval-based Interpretable Decision Tree for Time Series Classification
In: Proceedings - 31. Workshop Computational Intelligence, Nov 2021
Barbara Hammer, Prof. Dr. Eyke Hüllermeier, Prof. Dr.-Ing. Volker Lohweg, Axel Schneider, Wolfram Schenck, Ulrike Kuhl, Marco Braun, Anton Pfeifer, M. Sc., Dr.-Ing. Christoph-Alexander Holst, Malte Schmidt, M. Sc., Gunnar Schomaker, Tanja Tornede
Schlussbericht ITS.ML: Intelligente Technische Systeme der nächsten Generation durch Maschinelles Lernen. Forschungsvorhaben zur automatisierten Analyse von Daten mittels Maschinellen Lernens
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