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CORDIS

Optimizing Manufacturing Processes through Artificial Intelligence and Virtualization

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

OPTIMAI commercialization and exploitation strategy - 1st version (opens in new window)

This deliverable will report on OPTIMAIs exploitation strategy and includes interaction with standardization bodies It will be periodically updated

OPTIMAI commercialization and exploitation strategy - 3rd version (opens in new window)

This deliverable will report on OPTIMAI’s exploitation strategy and includes interaction with standardization bodies. It is the final version.

Forum and information pack for key stakeholders (opens in new window)

This deliverable will report on communication activities with relevant stakeholders

The OPTIMAI architecture specifications - 1st version (opens in new window)

This deliverable will document the technical requirements and architecture formal specification As such it will describe the overall system components with their interfaces This deliverable will be updated based on the developments of WP3 through WP6 in M18

State of the art survey (opens in new window)

This deliverable will report on the state of the art in related scientific fields and will also identify related research initiatives

Report on communication and dissemination activities - 2nd version (opens in new window)

This report will monitor the execution of OPTIMAI’s dissemination strategy. This is the final version.

OPTIMAI commercialization and exploitation strategy - 2nd version (opens in new window)

This deliverable will report on OPTIMAIs exploitation strategy and includes interaction with standardization bodies It will be periodically updated

Training Material - 2nd version (opens in new window)

It provides the training material for OPTIMAI end-users. This is the final version.

Ethics recommendations and regulatory framework (opens in new window)

It defines the ethics and legal framework for pilot deployment

The OPTIMAI architecture specifications - 2nd version (opens in new window)

This deliverable will document the technical requirements and architecture formal specification As such it will describe the overall system components with their interfaces

User and ethics and legal requirements - 1st version (opens in new window)

This deliverable will gather together the results from T21 defining the user requirements for the project resulting from a codesign methodology between endusers technology providers as well as ethics and legal experts

Report on communication and dissemination activities - 1st version (opens in new window)

This report will monitor the execution of OPTIMAIs dissemination strategy

Training Material - 1st version (opens in new window)

This deliverable provides the training material for OPTIMAI endusers This is the initial version

OPTIMAI use cases definition (opens in new window)

This report will deliver a shared vision of the targeted OPTIMAI concepts within the context of the use cases Section 133 in the form of usage scenarios and KPIs

Communication and dissemination strategy (opens in new window)

This report will define projects dissemination plan

User and ethics and legal requirements - 2nd version (opens in new window)

This deliverable will gather together the results from T21 defining the user requirements for the project resulting from a codesign methodology between endusers technology providers as well as ethics and legal experts

Report analysis of the developed virtualized sensor network - 1st version (opens in new window)

It will deliver report and virtual abstraction of installed sensors and actuators. It will be updated in M33.

Report analysis of the developed virtualized sensor network - 2nd version (opens in new window)

It will deliver report and virtual abstraction of installed sensors and actuators. This is the final version.

Data Management Plan - 2nd version (opens in new window)

This deliverable will describe the adopted plan and measures for managing data within the project It will describe the processes and guidelines for managing the research data inside the project including legal and ethical responsibilities ensuring compliance with the applicable legal framework

Data Management Plan - 4th version (opens in new window)

This deliverable will describe the adopted plan and measures for managing data within the project. It will describe the processes and guidelines for managing the research data inside the project including legal and ethical responsibilities, ensuring compliance with the applicable legal framework.

Data Management Plan - 1st version (opens in new window)

This deliverable will describe the adopted plan and measures for managing data within the project It will describe the processes and guidelines for managing the research data inside the project including legal and ethical responsibilities ensuring compliance with the applicable legal framework

Data Management Plan - 3rd version (opens in new window)

This deliverable will describe the adopted plan and measures for managing data within the project. It will describe the processes and guidelines for managing the research data inside the project including legal and ethical responsibilities, ensuring compliance with the applicable legal framework.

Project website and branding (opens in new window)

This deliverable will establish projects profile to external entities

Publications

Short Survey of Artificial Intelligent Technologies for Defect Detection in Manufacturing (opens in new window)

Author(s): Elpiniki I. Papageorgiou, Theodosis Theodosiou, George Margetis, Nikolaos Dimitriou, Paschalis Charalampous, Dimitrios Tzovaras, Ioannis Samakovlis
Published in: International Conference on Information, Intelligence, Systems and Applications (IISA), Issue 1, 2021
Publisher: IEEE
DOI: 10.1109/iisa52424.2021.9555499

Autoencoders for Anomaly Detection in an Industrial Multivariate Time Series Dataset (opens in new window)

Author(s): Theodoros Tziolas, Konstantinos Papageorgiou, Theodosios Theodosiou, Elpiniki Papageorgiou, Theofilos Mastos and Angelos Papadopoulos
Published in: 8th International conference on Time Series and Forecasting (ITISE2022), Issue 18(1), 2022, Page(s) 23
Publisher: 8th International conference on Time Series and Forecasting (ITISE2022)
DOI: 10.3390/2022018023

A Deep Regression Framework Towards Laboratory Accuracy in the Shop Floor of Microelectronics (opens in new window)

Author(s): Apostolos Evangelidis, Nikolaos Dimitriou, Lampros Leontaris, Dimosthenis Ioannidis, Gregory Tinker, Dimitrios Tzovaras
Published in: IEEE Transactions on Industrial Informatics, Issue 1, 2022, Page(s) 1-10, ISSN 1551-3203
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tii.2022.3182343

An Autonomous Illumination System for Vehicle Documentation Based on Deep Reinforcement Learning (opens in new window)

Author(s): Lampros Leontaris; Nikolaos Dimitriou; Dimosthenis Ioannidis; Konstantinos Votis; Dimitrios Tzovaras; Elpiniki I. Papageorgiou
Published in: IEEE Xplore, Issue 1, 2021, Page(s) 75336 - 75348, ISSN 2169-3536
Publisher: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2021.3081736

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