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ELO-X

Financiado

Cerrado

Embedded learning and optimization for the next generation of smart industrial c...

Embedded learning and optimization for the next generation of smart industrial control systems Thanks to the increasing capabilities of digital technologies, the next generation of industrial control systems are expected to learn from streams of data and to take optimal decisions in real-time, leading to increased performan... ver más

30/06/2025

ALU-FR

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

ALBERTLUDWIGSUNIVERSITAET FREIBURG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 17 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-08-13

MSCA-ITN-2020: Innovative Training Networks

I+D

Cerrada hace 5 años

0% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

17 Participantes

ALU-FR

758.37K€ | Lider

Cie Mecauto SA

348.38K€ | Participante

GRUPO COMPONENTES VILANOVA

1.27M€ | Participante

INFRANOR SPAIN

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Duración del proyecto: 58 meses Fecha Inicio: 2020-08-13
Fecha Fin: 2025-06-30

Líder del proyecto

ALBERTLUDWIGSUNIVERSITAET FREIBURG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Thanks to the increasing capabilities of digital technologies, the next generation of industrial control systems are expected to learn from streams of data and to take optimal decisions in real-time, leading to increased performance, safety, energy efficiency, and ultimately value creation. Numerical optimization is at the very core of both learning and decision-making, and machine learning algorithms and artificial intelligence raise huge worldwide research interest, often using cloud computing and large data centers for their optimization computations. However, in order to bring learning- and optimization-based automated decision-making into smart industrial control systems (SICS), two important bottlenecks have to be overcome: (1) computational resources on industrial control systems are locally embedded and limited, and (2) industrial control applications require reliable algorithms, with interpretable and verifiable behavior. Both requirements partially stem from safety aspects, which are crucial in applications where a single computation error can cause high economic and environmental cost or even damage to people. Pushing the performance boundary of SICS to leverage advanced digital technologies will therefore involve both fundamental new research questions and technological solutions, calling for a new set of advanced methods for embedded learning- and optimization-based control algorithms. Through its 15 PhD students hosted and seconded at 11 top European research centers (6 academic, 5 industrial) and 4 partner organizations in the US, Japan and China, ELO-X will address the timely and pressing need for highly qualified and competent researchers who will develop embedded learning- and optimization-based control methodologies for SICS, thus enabling new and possibly game-changing digital technologies for important EU industries.

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