Hyperconnected Architecture for High Cognitive Production Plants

Risultati finali

Life cycle sustainability assessment: Business-as-usual scenarios

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 case

Report of the detailed specifications of the steel usecase digitalization

Life cycle sustainability assessment: Final report

D7.3 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 analysis

As 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 pack

After 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 case

Report of the detailed specifications of the cement use case digitalization and of the chemical use case digitalization

Life cycle sustainability assessment: Goal and scope definition

As a result of task 7.1, D7.1 will report the basis for the life cycle sustainability assessment.

Beta version of learning pack

As 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 modules

As a first result of task 6.3, D6.5 will report a set of learning modules.

List of Values & Recommendations report

As 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 paths

As 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 Plan

As 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 methodology

As 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 cases

D5.6 will report the impact evaluation analysis (based in the KPIs pre-identified) carried out in the 3 use-cases.

Typology/profiles of worker

As 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 mixes

As 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 view

As 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 report

As 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 feedback

As 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 competences

As 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

Cybersecurity for sensor integration

As a result of task 4.5, D4.6 will show a clearly defined privacy and cyber-security by design methodology.

Contextualized visualisation

As 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 case

Demonstrators 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 case

Demonstrator installed and preliminary validated in the steel plant (intelligent ladle monitoring & ladle furnace slag characterization).

Project website launched and maintained

As one of the key outputs of task 8.2, D8.2 will be the HyperCOG website creation.

Pubblicazioni

Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation

Autori: Dylan Molinie, Lurosh Madani
Pubblicato in: Proceedings of the 2nd International COnference on Innovative Intelligent industrial production and logistincs IN4PL, Numero 1, 2021, Pagina/e 13-24, ISBN 978-989-758-535-7
Editore: Science and Technology Publications, Lda
DOI: 10.5220/0010657500003062

Deep Orientation-Guided Gender Recognition from Face Images

Autori: M. Selim, S. Krauß, T. A. Habtegebrial, A. Pagani and D. Stricker
Pubblicato in: 2022 12th International Conference on Pattern Recognition Systems (ICPRS), 2022, Pagina/e 1-6, ISBN 978-1-6654-6694-3
Editore: IEEE
DOI: 10.1109/icprs54038.2022.9854066

Behavioral Modeling of Real Dynamic Processes in an Industry 4.0-Oriented Context

Autori: Molinié, D.; Madani, K. and Amarger, V. (
Pubblicato in: Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA, Numero Volume 1, 2023, Pagina/e 510-517, ISBN 978-989-758-664-4
Editore: SciTePress
DOI: 10.5220/0012134500003541

ECD Test: An Empirical Way based on the Cumulative Distributions to Evaluate the Number of Clusters for Unsupervised Clustering

Autori: Dylan Molinié and Kurosh Madani
Pubblicato in: Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: ETCIIM, 2022, Pagina/e 279-290, ISBN 978-989-758-612-5
Editore: SciTePress
DOI: 10.5220/0011562500003329

HYPERCOG_Hyperconnected Architecture for High Cognitive Production Plants

Autori: Francisco Javier Huertos; Manuel Masenlle; Beatriz Chicote; Mikel Ayuso
Pubblicato in: Procedia CIRP, 2021
Editore: Elsevier
DOI: 10.1016/j.procir.2021.11.285

Identifying the Behaviors of an Industrial Plant: Application to Industry 4.0

Autori: Dylan Molinie, Kurosh Madani, Corentin Amarger
Pubblicato 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
Editore: IEEE
DOI: 10.1109/idaacs53288.2021.9661018

Image compression for WSN applied to the process supervision in Industry 4.0

Autori: Nesrine Boussaada, Alvaro Llaria, Guillaume Terrasson, Octavian Curea
Pubblicato in: INSA, Rennes (France), 2021
Editore: INSA

Modélisation de CPPS : proposition d’un modèle générique

Autori: A. Adama, L. Laguna Salvado, E. Villeneuve, C. Merlo
Pubblicato in: Proceedings on Conférence Internationale Génie Industriel QUALITA, 2021
Editore: INP

Unsupervised Clustering at the Service of Automatic Anomaly Detection in Industry 4.0

Autori: Molinié, D., Madani, K., Amarger, V.
Pubblicato in: Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, Numero vol 14135, 2023, Pagina/e 435-450, ISBN 978-3-031-43078-7
Editore: Springer, Cham
DOI: 10.1007/978-3-031-43078-7_36

An approach of decision support system for drift diagnosis in cyber-physical production systems

Autori: A. Arama, E. Villeneuve, C. Merlo and L. L. Salvado
Pubblicato in: 2022 IEEE International Systems Conference (SysCon), 2022, Pagina/e 1-7, ISBN 978-1-6654-3992-3
Editore: IEEE
DOI: 10.1109/syscon53536.2022.9773914

A Novel Architecture for Cyber-Physical Production Systems in Industry 4.0

Autori: Francisco J. Huertos, Beatriz Chicote, Manuel Masenlle, Mikel Ayuso
Pubblicato in: 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 2021
Editore: IEEE
DOI: 10.1109/case49439.2021.9551464

BSOM: A Two-Level Clustering Method Based on the Efficient Self-Organizing Maps

Autori: Dylan Molinié; Kurosh Madani
Pubblicato in: 2022 International Conference on Control, Automation and Diagnosis (ICCAD) proceedings, 2022, ISBN 978-1-6654-9794-7
Editore: IEEE
DOI: 10.1109/iccad55197.2022.9853931

Clustering at the Disposal of Industry 4.0: Automatic Extraction of Plant Behaviors

Autori: Dylan Milinie, Kurosh Madani, Véronique Amarger
Pubblicato in: Sensors, Numero 22(8), 2022, Pagina/e 2939, ISSN 1424-8220
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/s22082939

Identifying the Regions of a Space with the Self-Parameterized Recursively Assessed Decomposition Algorithm (SPRADA)

Autori: Dylan Molinié, Kurosh Madani, Véronique Amarger and Abdennasser Chebira
Pubblicato in: Mach. Learn. Knowl. Extr., Numero 5 (3), 2023, Pagina/e 979-1009, ISSN 2504-4990
Editore: MDPI
DOI: 10.3390/make5030051

HyDensity: A Hyper-Volume-Based Density Metric for Automatic Cluster Evaluation

Autori: Molinié, D., Madani, K., Chebira, A.
Pubblicato in: Innovative Intelligent Industrial Production and Logistics. IN4PL 2020-2021. Communications in Computer and Information Science, Numero 1855, 2023, Pagina/e 50-69, ISBN 978-3-031-37228-5
Editore: Springer
DOI: 10.1007/978-3-031-37228-5_4

Risultati finali

Pubblicazioni

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