CORDIS provides links to public deliverables and publications of HORIZON projects.
Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .
Deliverables
Final version to be provided at month 30 Design about the Autonomic managers in charge of coordinate different self-X AI components of use cases to integrate appropriately the flow of “New RAW data” from industrial data sources and the human support according to self-X abilities available (configuration, optimization and healing). Initial version to be provided at month 18
Education and Training Program and Results (opens in new window)Document explaining the impact of the training program ensuring the toolkit’s evolution after project’s end, learn resources, will cover: lectures, seminars, workshops, conferences, in house training and on-line materials
Autonomic managers for Data in Motion and Humans support in AI solutions-initial version (opens in new window)Initial version to be provided at month 18 Design about the Autonomic managers in charge of coordinate different self-X AI components of use cases to integrate appropriately the flow of “New RAW data” from industrial data sources and the human support according to self-X abilities available (configuration, optimization and healing). Initial version to be provided at month 18
s-X-AIPI open-source toolset-v2 (opens in new window)Second version of open source algorithms, data sources and example of applications usage to create new self-X AI applications.
s-X-AIPI open-source toolset-v1 (opens in new window)First version of open source algorithms, data sources and example of applications usage to create new self-X AI applications.
Preliminary design of the Reference Architecture based on existing standards (FIWARE, RAMI4.0, IDSA, Apache) and requirements from use cases to implement self-X AI applications in those use cases.
Validation report for Data in Motion and human support in IT/OT use case's infrastructures (opens in new window)The s-X-AIPI “New RAW Data” integration will be validated in all scenarios and feedbacks will be collected for improving performance, tuning and bug fixing of the different self-X AI appications in the use cases.
Report on the standardization landscape and applicable standards (opens in new window)Report on the standards affected and related with project's environment and technologies.
General s-X-AIPI technology requirements validation report (opens in new window)Report of the methodology which will allow to validate (assumptions, designs, implementations and deployments) the different components developed in WP4
Guidelines for trustworthy AI in process industry (opens in new window)Update EC guidelines for a trustworthy AI based on experiences from project and use cases in particular, adaptation of guideline to process industries.
Scenarios and Requirements for self-X AI adoption in Process Industry (opens in new window)Analysis of the industrial Scenarios and the preliminar design of the requirements from use cases of the project for adoption in Process Industry of self-X or autonomic AI.
Report on the contribution to standardization-final version (opens in new window)Report about how the selected results of the project will contribute to standardization activities and how the partners will be involved in such activities. Final version to be provided at month 36
AI Maturity Model for Process Industry (opens in new window)AI Maturity Model customized for the process industry to carry out an initial barrier analysis, based on the experiences of use cases.
Report on the contribution to standardization-intermediate version (opens in new window)Report about how the selected results of the project will contribute to standardization activities and how the partners will be involved in such activities. Intermediate version to be provided at month 18
s-X-AIPI solutions impact evaluation on use cases (opens in new window)Evalutaion of the impact in use cases of their performance improvements across different KPIs as a consecuence of use of self-X AI applications.
Report on the contribution to standardization-initial version (opens in new window)Report about how the selected results of the project will contribute to standardization activities and how the partners will be involved in such activities. Initial version to be provided at month 9
Assessment of the models according to each specific of use case requirements and foreseen self-X abilities and enable the learning of the models that reflect usual behaviour of the system with the information available in this stage of the project.
Initial Data Management Plan for the S-X-AIPI project. This plan will ensure that all research data generated during is findable, accessible, interoperable and reusable (FAIR). It will include a set of guidelines and templates for an apprpriate description of the generated data sets.
Data management plan-final version (opens in new window)Final Data Management Plan for the S-X-AIPI project. This plan will ensure that all research data generated during is findable, accessible, interoperable and reusable (FAIR). It will include a set of guidelines and templates for an apprpriate description of the generated data sets.
Publications
Author(s):
M Galende; A Corral; A Reñones
Published in:
Open Research Europe, 2024
Publisher:
Open Research Europe
DOI:
10.12688/OPENRESEUROPE.18070.1
Author(s):
Nölle, Christoph; Kannisto, Petri
Published in:
2023
Publisher:
arXiv
DOI:
10.48550/arxiv.2310.03761
Author(s):
Angosto Artigues, Ramon; Coto, Andrea Gregores; Torrez Herrera, Jonathan Josue; Tomás, Fernando Lou; Verardi, Sabrina; Marzano, Mattia Giuseppe; Martínez, Andrea Fernández
Published in:
Proceedings of the 12th International Conference on Human Interaction & Emerging Technologies (IHIET 2024), Issue AHFE (2024) International Conference, vol -1, 2024
Publisher:
AHFE International
DOI:
10.54941/AHFE1005478
Author(s):
Petri Kannisto; Zeinab Kargar; Gorka Alvarez; Bernd Kleimt; Asier Arteaga
Published in:
Processes, 2024, ISSN 2227-9717
Publisher:
MDPI
DOI:
10.3390/PR12122877
Author(s):
Walter Quadrini;Francesco Alessandro Cuzzola; Luca Fumagal; Marco Taisch; Gabriele De Luca; Marta Calderaro;Mattia Giuseppe Marzano; Angelo Marguglio
Published in:
Procedia Computer Science, 2024, ISSN 1877-0509
Publisher:
Elsevier
DOI:
10.1016/j.procs.2024.01.044
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