CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.
Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .
Resultado final
[T6.2-3] Report on the dissemination and standardisation activities carried out in tasks T6.2 and T6.3.
Methodology for explainable, trustworthy and human centric AI based system and function development (se abrirá en una nueva ventana)[T1.1-3] This deliverable will materialize the AITHENA methodology into a reference report to include definition of KPI related to trusted AI features (see GO-8).
Report on data sharing and integration with European data lakes, OpenData and OpenTool (se abrirá en una nueva ventana)[T6.4] Report on the plan and actions taken to integrate created data into data sharing initiatives at European level, including novel OpenData and OpenTool concepts.
Report on initial AI algorithm development (se abrirá en una nueva ventana)[T3.2-5] This report covers the initial AI algorithm developments including their initial feature validation according to the specified requirements and specifications.
Report on initial use case evaluation (se abrirá en una nueva ventana)[T5.2-4] This report covers the initial demonstrator validation and evaluation of the demonstrators against the specified requirements and specifications.
Privacy-preserving methods (se abrirá en una nueva ventana)[T2.3] This deliverable will report the designed privacy-preserving methods for application of GDPR-compliant ML.
Lessons learned, policy recommendations (se abrirá en una nueva ventana)[T6.5] Lessons learned, and derived policy recommendations for the exploitation of AI solutions in CCAM.
Life-cycle management framework for ML models (se abrirá en una nueva ventana)[T3.1] This report covers the designed AI-framework used for the development and life-cycle assessment of individual ML algorithms.
User group needs report and technical use case definition (se abrirá en una nueva ventana)[T1.4] This deliverable reports the action taken to identify user groups and gather from them requirements to detail the AITHENA use cases
Initial communication, dissemination and standardisation plan (se abrirá en una nueva ventana)T613 Initial strategy for AITHENA based on an initial market and stakeholder analysis
Report on final use case evaluation (se abrirá en una nueva ventana)[T5.2-4] This report covers the final demonstrator validation and evaluation of the demonstrators against the specified requirements and specifications. In addition, for each specific AI-driven approach in the use cases a dedicated AI lifecycle assessment is outlined.
Report on physical set-up, digital twin and hybrid testing approaches (se abrirá en una nueva ventana)[T4.2-4] This is the deliverable about activities T4.2-4, including report on physical set-up, digital twin and hybrid testing approaches.
Testing and evaluation methodology for AI-driven CCAM systems (se abrirá en una nueva ventana)[T5.1] This report outlines a joint testing and evaluation methodology for AI-driven CCAM systems to be applied in the corresponding use cases of task 5.2 to task 5.4.
Report on final AI algorithm development (se abrirá en una nueva ventana)[T3.2-5] This report covers the final AI algorithm developments including their final feature validation according to the specified requirements and specifications. In addition, all strength and weaknesses of the specific AI-driven approaches are outlined.
Updated communication, dissemination and standardisation plan (se abrirá en una nueva ventana)Update of the communication, dissemination and standardisation plan
ML DevOps-oriented data life-cycle governance and provenance framework (se abrirá en una nueva ventana)[T2.2] This deliverable will report RTD activities related to ML DevOps tools for data governance and provenance.
Publicaciones
Autores:
A. Forrai (Siemens Industry Software Netherlands B.V.), V. Neelgundmath, K.K. Unni, I. Barosan (Eindhoven University of Technology)
Publicado en:
Proceedings: 2023 7th International Conference on System Reliability and Safety (ICSRS), 2023, ISSN 1272-4017
Editor:
Zenodo
DOI:
10.5281/zenodo.12724017
Autores:
Hassan Hotait (HAN – University of Applied Sciences), Alexandru Forrai (Siemens Industry Software Netherlands B.V.)
Publicado en:
Product solutions paper: 22nd Driving Simulation & Virtual Reality Conference, 2023, ISSN 1272-3883
Editor:
Zenodo
DOI:
10.5281/zenodo.12723882
Autores:
Paraskevas Karachatzis, Jan Ruh, Silviu S. Craciunas (TTTech Computertechnik AG, Vienna, Austria)
Publicado en:
RTNS '23: Proceedings of the 31st International Conference on Real-Time Networks and Systems, 2023, ISBN 9781450399838
Editor:
ACM
DOI:
10.1145/3575757.3593660
Autores:
Beemelmanns, Till; Zahr, Wassim; Eckstein, Lutz
Publicado en:
Machine Learning for Autonomous Driving Workshop 2023 (NeurIPS), 2023, ISSN 2331-8422
Editor:
ML4AD/arXiv
DOI:
10.48550/arxiv.2312.14606
Autores:
Georg Stettinger (Infineon Technologies AG); Patrick Weissensteiner (Virtual Vehicle Research GmbH); Siddartha Khastgir (International Manufacturing Centre, The University of Warwick)
Publicado en:
IEEE Access, Edición Volume: 12, 2024, ISSN 2169-3536
Editor:
IEEE
DOI:
10.1109/ACCESS.2024.3364387
Autores:
Itziar Urbieta, Andoni Mujika, Gonzalo Piérola, Eider Irigoyen, Marcos Nieto, Estibaliz Loyo, Naiara Aginako
Publicado en:
Multimedia Tools and Applications, Edición Volume 83, 2023, ISSN 2213-7793
Editor:
Springer
DOI:
10.1007/s11042-023-16664-4
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