Deliverables
D72 will report the life cycle sustainability assessment carried out in task 73 with the data collected in task 72
Specification of the new sensor systems: Steel caseReport of the detailed specifications of the steel usecase digitalization
Life cycle sustainability assessment: Final reportD7.3 will report the conclusions of the analysis of the assessment results obtained in task 7.3. Environmental/economic/social hotspots for the entire life cycle will be analysed and potential corrective measures are expected to be identified.
Competency gap analysisAs a result of task 6.2, D6.4 will report insightful analysis and information on involved cognitive functions and will determine model related jobs and qualifications, and as a result will report a concluding competency gap analysis.
FInal learning packAfter having carried out the definition of learning modules (D6.5), learning paths (D6.6), learning strategies (D6.7) and after having defined an initial version of learning pack in D6.8, D6.9 will take advantage of the pilot training and training material finalization carried out in task 6.6, and as a result, D6.9 will report the final learning pack.
Specification of the new sensor systems: Cement & chemical caseReport of the detailed specifications of the cement use case digitalization and of the chemical use case digitalization
Life cycle sustainability assessment: Goal and scope definitionAs a result of task 7.1, D7.1 will report the basis for the life cycle sustainability assessment.
Beta version of learning packAs a result of task 6.5, D6.8 will report an initial version of the whole learning pack of contents and modules for digitalising process industries.
Learning modulesAs a first result of task 6.3, D6.5 will report a set of learning modules.
List of Values & Recommendations reportAs a result of task 3.6, D3.6 will report a list of values of indicators (errors, statistical features, etc...) and list of specifications, recommendations and boundary values feed backed to WP1, WP2, WP5 and WP6.
Learning pathsAs a second result of task 6.3, D6.6 will report learning paths adapted to the profiles of the future trainees (once linking the job profiles to the learning modules defined in D6.5).
Communications PlanAs a result of task 8.1, D8.1 will report the whole detailed and complete communications plan defined for the HyperCOG project.
Dynamic life cycle assessment methodologyAs a result of task 7.4, D7.4 will report a novel dynamic LCA methodology to structure LCA in a dynamic manner so that LCA can be utilized in a more integrated way with the digital solutions for continuously monitoring environmental performance.
Impact evaluation for the use casesD5.6 will report the impact evaluation analysis (based in the KPIs pre-identified) carried out in the 3 use-cases.
Typology/profiles of workerAs the second result of task 61 D62 will report a deep analysis of the typology segmentation and profiles of the workforce involved considering different aspects like gender age previous skills and experience digital abilities and all the interrelations among the different aspects analysed
Set of learning strategies and mixesAs a result of task 6.4, D6.7 will report for each learning module, a set of learning strategies and mixes defined.
Recommendations for the HyperCOG solutions from the operators point of viewAs a result of task 14 D14 will report the analysis of user activity at the three industrial partners SIDENOR CIMSA and SOLVAY and the needs of operators in the digital field through several interviews
Transferability assessment reportAs a result of task 5.7, D5.7 will report the results of the transferability and replicability analysis of the digital solutions developed and validated into other potential applications and sectors.
Qualitative information report with a swot analysis based on workers feedbackAs the third result of task 61 D63 will report a the qualitative information about the operators profiles based in a SWOT analysis with deep involvement of workers feedback
Map of operators jobs and competencesAs the first result of task 61 D61 will report a deep analysis and relation between operators involved and competences detected in different advanced manufacturing technologies considered and particularly analysed in the 3 usecases
As a result of task 4.5, D4.6 will show a clearly defined privacy and cyber-security by design methodology.
Contextualized visualisationAs a result of task 4.4, D4.5 will demonstrate Augmented Reality visualization and a monitor-based user interface to display object state and user state monitoring.
New cognitive sensing systems validation: Cement & chemical caseDemonstrators installed and preliminary validated in the cement plant (image analysis for estimation of free lime in cement clinker & soft sensors for particle-size distribution measurement) and in the chemical plant (liquid-solvent interface quality detection & quality of the solvent measurement & acquisition system behaviour).
New cognitive sensing systems validation: Steel caseDemonstrator installed and preliminary validated in the steel plant (intelligent ladle monitoring & ladle furnace slag characterization).
As one of the key outputs of task 8.2, D8.2 will be the HyperCOG website creation.
Publications
Author(s):
Dylan Molinie, Lurosh Madani
Published in:
Proceedings of the 2nd International COnference on Innovative Intelligent industrial production and logistincs IN4PL, Issue 1, 2021, Page(s) 13-24, ISBN 978-989-758-535-7
Publisher:
Science and Technology Publications, Lda
DOI:
10.5220/0010657500003062
Author(s):
M. Selim, S. Krauß, T. A. Habtegebrial, A. Pagani and D. Stricker
Published in:
2022 12th International Conference on Pattern Recognition Systems (ICPRS), 2022, Page(s) 1-6, ISBN 978-1-6654-6694-3
Publisher:
IEEE
DOI:
10.1109/icprs54038.2022.9854066
Author(s):
Molinié, D.; Madani, K. and Amarger, V. (
Published in:
Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA, Issue Volume 1, 2023, Page(s) 510-517, ISBN 978-989-758-664-4
Publisher:
SciTePress
DOI:
10.5220/0012134500003541
Author(s):
Dylan Molinié and Kurosh Madani
Published in:
Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: ETCIIM, 2022, Page(s) 279-290, ISBN 978-989-758-612-5
Publisher:
SciTePress
DOI:
10.5220/0011562500003329
Author(s):
Francisco Javier Huertos; Manuel Masenlle; Beatriz Chicote; Mikel Ayuso
Published in:
Procedia CIRP, 2021
Publisher:
Elsevier
DOI:
10.1016/j.procir.2021.11.285
Author(s):
Dylan Molinie, Kurosh Madani, Corentin Amarger
Published in:
2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2022, ISBN 978-1-6654-2605-3
Publisher:
IEEE
DOI:
10.1109/idaacs53288.2021.9661018
Author(s):
Nesrine Boussaada, Alvaro Llaria, Guillaume Terrasson, Octavian Curea
Published in:
INSA, Rennes (France), 2021
Publisher:
INSA
Author(s):
A. Adama, L. Laguna Salvado, E. Villeneuve, C. Merlo
Published in:
Proceedings on Conférence Internationale Génie Industriel QUALITA, 2021
Publisher:
INP
Author(s):
Molinié, D., Madani, K., Amarger, V.
Published in:
Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, Issue vol 14135, 2023, Page(s) 435-450, ISBN 978-3-031-43078-7
Publisher:
Springer, Cham
DOI:
10.1007/978-3-031-43078-7_36
Author(s):
A. Arama, E. Villeneuve, C. Merlo and L. L. Salvado
Published in:
2022 IEEE International Systems Conference (SysCon), 2022, Page(s) 1-7, ISBN 978-1-6654-3992-3
Publisher:
IEEE
DOI:
10.1109/syscon53536.2022.9773914
Author(s):
Francisco J. Huertos, Beatriz Chicote, Manuel Masenlle, Mikel Ayuso
Published in:
2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 2021
Publisher:
IEEE
DOI:
10.1109/case49439.2021.9551464
Author(s):
Dylan Molinié; Kurosh Madani
Published in:
2022 International Conference on Control, Automation and Diagnosis (ICCAD) proceedings, 2022, ISBN 978-1-6654-9794-7
Publisher:
IEEE
DOI:
10.1109/iccad55197.2022.9853931
Author(s):
Dylan Milinie, Kurosh Madani, Véronique Amarger
Published in:
Sensors, Issue 22(8), 2022, Page(s) 2939, ISSN 1424-8220
Publisher:
Multidisciplinary Digital Publishing Institute (MDPI)
DOI:
10.3390/s22082939
Author(s):
Dylan Molinié, Kurosh Madani, Véronique Amarger and Abdennasser Chebira
Published in:
Mach. Learn. Knowl. Extr., Issue 5 (3), 2023, Page(s) 979-1009, ISSN 2504-4990
Publisher:
MDPI
DOI:
10.3390/make5030051
Author(s):
Molinié, D., Madani, K., Chebira, A.
Published in:
Innovative Intelligent Industrial Production and Logistics. IN4PL 2020-2021. Communications in Computer and Information Science, Issue 1855, 2023, Page(s) 50-69, ISBN 978-3-031-37228-5
Publisher:
Springer
DOI:
10.1007/978-3-031-37228-5_4
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