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dc.contributor.editorVermesan, Ovidiu
dc.contributor.editorWotawa, Franz
dc.contributor.editordiaz, mario
dc.contributor.editorDebaillie, Björn
dc.date.accessioned2023-11-17T08:27:04Z
dc.date.available2023-11-17T08:27:04Z
dc.date.issued2023
dc.date.submitted2023-09-06T09:01:27Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/76147
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/121694
dc.description.abstractThe advances in industrial edge artificial intelligence (AI) are transforming the way industrial equipment and machines interact with the real world, with other machines and humans during manufacturing processes. These advances allow Industrial Internet of Things (IIoT) and edge devices to make decisions during the manufacturing processes using sensors and actuators. Digital transformation is reshaping the manufacturing industry, and industrial edge AI aims to combine the potential advantages of edge computing (low latency times, reduced bandwidth, distributed architecture, improved trustworthiness, etc.) with the benefits of AI (intelligent processing, predictive solutions, classification, reasoning, etc.). The industrial environments allow the deployment of highly distributed intelligent industrial applications in remote sites that require reliable connectivity over wireless and cellular connections. Intelligent connectivity combines IIoT, wireless/cellular and AI technologies to support new autonomous industrial applications by enabling AI capabilities at the edge and allowing manufacturing companies to improve operational efficiency and reduce risks and costs for industrial applications. There are several critical issues to consider when introducing AI to industrial IoT applications considering training AI models at the edge, the deployment of the AI-trained inferencing models on the target edge hardware platforms, and the benchmarking of solutions compared to other implementations. Next-generation trustworthy industrial AI systems offer dependability in terms of their design, transparency, explainability, verifiability, and standardised industrial solutions can be implemented in various applications across different industrial sectors. New AI techniques such as embedded machine learning (ML) and deep learning (DL), capture edge data, employ AI models, and deploy these in hardware target edge devices, from ultra-low-power microcontrollers to embedded devices, gateways, and on-premises servers for industrial applications. These techniques reduce latency, increase scalability, reliability, and resilience; and optimise wireless connectivity, greatly expanding the capabilities of the IIoT. This book provides an overview of the latest research results and activities in industrial AI technologies and applications, based on the innovative research, developments and ideas generated by the ECSEL JU AI4DI, ANDANTE and TEMPO projects. The authors describe industrial AI's challenges, the approaches adopted, and the main industrial systems and applications to give the reader extensive insight into the technical nature of this field. The chapters provide insightful material on industrial AI technologies and applications. This book is a valuable resource for researchers, post-graduate students, practitioners, and technoloyg developers interested in gaining insight into industrial edge AI, the IIoT, embedded machine and deep learning, new technologies, and solutions to advance intelligent processing at the edge.
dc.languageEnglish
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence
dc.subject.otherArtificial intelligence
dc.titleIndustrial Artificial Intelligence Technologies and Applications
dc.typebook
oapen.identifier.doi10.1201/9781003377382
oapen.relation.isPublishedByfa69b019-f4ee-4979-8d42-c6b6c476b5f0
oapen.relation.isFundedBy3f0a4da2-418f-411a-ae5f-8d27e0601aec
oapen.relation.isFundedBy178e65b9-dd53-4922-b85c-0aaa74fce079
oapen.relation.isbn9781003377382
oapen.relation.isbn9788770227919
oapen.collectionEuropean Research Council (ERC)
oapen.imprintRiver Publishers
oapen.pages242
oapen.grant.number876925
oapen.grant.programANDANTE
oapen.peerreviewProposal review
peerreview.review.typeProposal
peerreview.anonymitySingle-anonymised
peerreview.reviewer.typeInternal editor
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityPublisher
peerreview.idbc80075c-96cc-4740-a9f3-a234bc2598f1
dc.relationisFundedBy178e65b9-dd53-4922-b85c-0aaa74fce079
peerreview.titleProposal review


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