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Synthetic data : revolutionizing the industrial metaverse

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dc.contributor.author Nassif, Jimmy en_US
dc.contributor.author Tekli, Joe en_US
dc.contributor.author Kamradt, Marc en_US
dc.date.accessioned 2024-12-05T12:48:10Z
dc.date.available 2024-12-05T12:48:10Z
dc.date.copyright 2024 en_US
dc.date.issued 2024-01-03
dc.identifier.isbn 9783031475603 en_US
dc.identifier.uri http://hdl.handle.net/10725/16336
dc.description.abstract The book concentrates on the impact of digitalization and digital transformation technologies on the Industry 4.0 and smart factories, how the factory of tomorrow can be designed, built, and run virtually as a digital twin likeness of its real-world counterpart, before the physical structure is actually erected. It highlights the main digitalization technologies that have stimulated the Industry 4.0, how these technologies work and integrate with each other, and how they are shaping the industry of the future. It examines how multimedia data and digital images in particular are being leveraged to create fully virtualized worlds in the form of digital twin factories and fully virtualized industrial assets. It uses BMW Groups latest SORDI dataset (Synthetic Object Recognition Dataset for Industry), i.e., the largest industrial images dataset to-date and its applications at BMW Group and Idealworks, as one of the main explanatory scenarios throughout the book. It discusses the need of synthetic data to train advanced deep learning computer vision models, and how such datasets will help create the robot gym of the future: training robots on synthetic images to prepare them to function in the real world. en_US
dc.language.iso en en_US
dc.publisher Springer Nature en_US
dc.subject Artificial intelligence -- Industrial applications en_US
dc.subject Industry 4.0 en_US
dc.title Synthetic data : revolutionizing the industrial metaverse en_US
dc.type Book / Chapter of a Book en_US
dc.author.school SOE en_US
dc.author.idnumber 201306321 en_US
dc.author.department Electrical and Computer Engineering en_US
dc.description.physdesc 1 online resource en_US
dc.publication.place Cham en_US
dc.description.bibliographiccitations Includes bibliographical references. en_US
dc.identifier.doi https://doi.org/ 10.1007/978-3-031-47560-3 en_US
dc.identifier.ctation Nassif, J., Tekli, J., & Kamradt, M. (2024). Synthetic Data: Revolutionizing the Industrial Metaverse. Springer Nature. en_US
dc.author.email joe.tekli@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://link.springer.com/book/10.1007/978-3-031-47560-3 en_US
dc.orcid.id https://orcid.org/0000-0003-3441-7974 en_US
dc.publication.date 2024 en_US
dc.author.affiliation Lebanese American University en_US


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