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knowlEdge Project –Concept, Methodology and Innovations for Artificial Intelligence in Industry 4.0

Authors :
Claudio Pastrone
Sergio Alvarez-Napagao
Alexander Grass
Dimosthenis Ioannidis
Alexandros Nizamis
Fabian Berns
Nicola Ciulli
Natalia Jakubiak
Ilaria Bosi
Pedro Megias
Marta Garcia-Gasulla
Marta Barroso
Boki Ashmore
Ville Lamsa
Christian Beecks
Sisay Adugna Chala
Miquel Sànchez-Marrè
Cristian Barrue
Luca Ziliotti
Rosaria Rossini
Karl Kopke
Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
Barcelona Supercomputing Center
Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
Source :
UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC), INDIN, Alvarez-Napagao, S, Ashmore, B, Barroso, M, Barrué, C, Beecks, C, Berns, F, Bosi, I, Chala, S A, Ciulli, N, Garcia-Gasulla, M, Grass, A, Ioannidis, D, Jakubiak, N, Köpke, K, Lämsä, V, Megias, P, Nizamis, A, Pastrone, C, Rossini, R, Sànchez-Marrè, M & Ziliotti, L 2021, knowlEdge Project –Concept, Methodology and Innovations for Artificial Intelligence in Industry 4.0 . in Proceedings-2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021 ., 9557410, Wiley-IEEE Press, 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021, 21/07/21 . https://doi.org/10.1109/INDIN45523.2021.9557410
Publication Year :
2021
Publisher :
Wiley-IEEE Press, 2021.

Abstract

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations. The research leading to these results has received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 957331 for EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop]. Peer Reviewed Treball signat per 21 autors/autores: Sergio Alvarez-Napagao, Barcelona Supercomputing Center, Spain; Boki Ashmore, ICE, United Kingdom; Marta Barroso, Barcelona Supercomputing Center, Spain; Cristian Barrué, Barcelona Supercomputing Center, Spain; Christian Beecks, University of Münster, Germany; Fabian Berns, University of Münster, Germany; Ilaria Bosi, LINKS Foundation, Italy; Sisay Adugna Chala, Fraunhofer FIT, Germany; Nicola Ciulli, Nextworks, Italy; Marta Garcia-Gasulla, Barcelona Supercomputing Center, Spain; Alexander Grass, Fraunhofer FIT, Germany; Dimosthenis Ioannidis, CERTH/ITI, Greece; Natalia Jakubiak, Universitat Politècnica de Catalunya, Spain; Karl Köpke, Kautex Textron, Germany; Ville Lämsä, VTT Technical Research Centre, Finland; Pedro Megias, Barcelona Supercomputing Center, Spain; Alexandros Nizamis, CERTH/ITI, Greece; Claudio Pastrone, LINKS Foundation, Italy; Rosaria Rossini, LINKS Foundation, Italy; Miquel Sànchez-Marrè, Universitat Politècnica de Catalunya, Spain; Luca Ziliotti, Parmalat, Italy

Details

Language :
English
Database :
OpenAIRE
Journal :
UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC), INDIN, Alvarez-Napagao, S, Ashmore, B, Barroso, M, Barrué, C, Beecks, C, Berns, F, Bosi, I, Chala, S A, Ciulli, N, Garcia-Gasulla, M, Grass, A, Ioannidis, D, Jakubiak, N, Köpke, K, Lämsä, V, Megias, P, Nizamis, A, Pastrone, C, Rossini, R, Sànchez-Marrè, M & Ziliotti, L 2021, knowlEdge Project –Concept, Methodology and Innovations for Artificial Intelligence in Industry 4.0 . in Proceedings-2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021 ., 9557410, Wiley-IEEE Press, 2021 IEEE 19th International Conference on Industrial Informatics, INDIN 2021, 21/07/21 . https://doi.org/10.1109/INDIN45523.2021.9557410
Accession number :
edsair.doi.dedup.....167d9f54b3a8621054127ea823887863
Full Text :
https://doi.org/10.1109/INDIN45523.2021.9557410