Industrial Artificial Intelligence Technologies and Applications
The 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 deci...
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| Autor*in: |
Vermesan, Ovidiu [herausgeberIn] Wotawa, Franz [herausgeberIn] diaz, mario [herausgeberIn] Debaillie, Björn [herausgeberIn] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Erscheinungsort nicht ermittelbar: Taylor & Francis ; 2023 |
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| Rechteinformationen: |
Open Access Namensnennung - Nicht-kommerziell 4.0 International ; CC BY-NC 4.0 |
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| Umfang: |
1 Online-Ressource (242 p.) |
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| Links: | |
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| ISBN: |
978-1-003-37738-2 978-87-7022-791-9 |
| Katalog-ID: |
1904886817 |
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| 520 | |a The 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 | ||
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9781003377382 978-1-003-37738-2 9788770227919 978-87-7022-791-9 (DE-627)1904886817 (DE-599)KEP108465551 (OAPEN)76147 (EBP)108465551 DE-627 ger DE-627 rda eng Industrial Artificial Intelligence Technologies and Applications [Erscheinungsort nicht ermittelbar] Taylor & Francis 2023 1 Online-Ressource (242 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Open Access Unrestricted online access star The 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 Namensnennung - Nicht-kommerziell 4.0 International CC BY-NC 4.0 cc https://creativecommons.org/licenses/by-nc/4.0/ English Vermesan, Ovidiu herausgeberin edt Wotawa, Franz herausgeberin edt diaz, mario herausgeberin edt Debaillie, Björn herausgeberin edt https://library.oapen.org/bitstream/id/a3304a85-bf26-4830-83b5-84f8a1c397ba/9781000852035.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/76147 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 4606181648 OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 4606687436 OLR-OAPEN f z 29-10-24 30 01 0104 4607020168 GBV-OAPEN z 29-10-24 31 01 0027 4606348011 z 29-10-24 34 01 3551 4604625913 OLR-OAPEN zi002 28-10-24 39 01 0547 4607186333 GBV-OAPEN ke 29-10-24 40 01 0007 4606015470 OLR-OAPEN xsn 29-10-24 63 01 3401 4607522074 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607688592 OLR-OAPEN z 29-10-24 70 01 0089 4606521112 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604426317 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4604903298 OLR-OAL Vervielfältigungen (z.B. 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| spelling |
9781003377382 978-1-003-37738-2 9788770227919 978-87-7022-791-9 (DE-627)1904886817 (DE-599)KEP108465551 (OAPEN)76147 (EBP)108465551 DE-627 ger DE-627 rda eng Industrial Artificial Intelligence Technologies and Applications [Erscheinungsort nicht ermittelbar] Taylor & Francis 2023 1 Online-Ressource (242 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Open Access Unrestricted online access star The 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 Namensnennung - Nicht-kommerziell 4.0 International CC BY-NC 4.0 cc https://creativecommons.org/licenses/by-nc/4.0/ English Vermesan, Ovidiu herausgeberin edt Wotawa, Franz herausgeberin edt diaz, mario herausgeberin edt Debaillie, Björn herausgeberin edt https://library.oapen.org/bitstream/id/a3304a85-bf26-4830-83b5-84f8a1c397ba/9781000852035.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/76147 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 4606181648 OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 4606687436 OLR-OAPEN f z 29-10-24 30 01 0104 4607020168 GBV-OAPEN z 29-10-24 31 01 0027 4606348011 z 29-10-24 34 01 3551 4604625913 OLR-OAPEN zi002 28-10-24 39 01 0547 4607186333 GBV-OAPEN ke 29-10-24 40 01 0007 4606015470 OLR-OAPEN xsn 29-10-24 63 01 3401 4607522074 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607688592 OLR-OAPEN z 29-10-24 70 01 0089 4606521112 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604426317 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4604903298 OLR-OAL Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 28-10-24 110 01 3110 4607352586 OLR-GBV-OAPEN Open Access z 29-10-24 136 01 3526 460568302X OLR-OAPEN z 29-10-24 147 01 3528 4607856169 OLR-OAPEN frei verfügbar für Europa-Universität Flensburg, Hochschule Flensburg und Zentrale Hochschulbibliothek Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 29-10-24 161 01 0960 4603874480 OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 187 01 3520 460423115X JSTOR Open Access eBook z 28-10-24 213 01 0551 4605350578 OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 230 01 0552 4605516743 OLR-OAPEN OAPEN Online Library Open Access eBook z 29-10-24 283 01 3283 4605849289 OLR-OAL z 29-10-24 293 01 3293 4604049025 OLR-OAPEN OAPEN Online Library Open Access eBook z 28-10-24 370 01 4370 4605179518 OLR-FREE Kostenloser Zugriff z 29-10-24 603 01 4603 460370724X OLR-OAPEN Bitte beachten Sie die Nutzungsbedingungen und Copyright-Bestimmungen des Verlages/Herausgebers. z 28-10-24 808 01 4808 4606853727 OLR-OAPEN OAPEN Library zg 29-10-24 22 01 0018 Volltextzugang https://library.oapen.org/handle/20.500.12657/76147 23 01 0830 https://library.oapen.org/handle/20.500.12657/76147 30 01 0104 Open Access https://library.oapen.org/handle/20.500.12657/76147 31 01 0027 eBook GBV-OAPEN https://library.oapen.org/handle/20.500.12657/76147 34 01 3551 OpenAccess https://library.oapen.org/handle/20.500.12657/76147 40 01 0007 Volltext, Open Access https://library.oapen.org/handle/20.500.12657/76147 63 01 3401 E-Book https://library.oapen.org/handle/20.500.12657/76147 65 01 0003 Open Access https://library.oapen.org/handle/20.500.12657/76147 65 01 0003 Dieser Titel ist Teil einer Datenbank http://www.bibliothek.uni-regensburg.de/dbinfo/detail.php?titel_id=10728&bib_id=ulb_hal 70 01 0089 https://library.oapen.org/handle/20.500.12657/76147 LF 72 01 0035 https://library.oapen.org/handle/20.500.12657/76147 95 01 3095 https://library.oapen.org/handle/20.500.12657/76147 110 01 3110 Open Access https://library.oapen.org/handle/20.500.12657/76147 136 01 3526 Open Access https://library.oapen.org/handle/20.500.12657/76147 147 01 3528 https://library.oapen.org/handle/20.500.12657/76147 161 01 0960 https://library.oapen.org/handle/20.500.12657/76147 LF 187 01 3520 https://library.oapen.org/handle/20.500.12657/76147 213 01 0551 https://library.oapen.org/handle/20.500.12657/76147 230 01 0552 https://library.oapen.org/handle/20.500.12657/76147 283 01 3283 https://library.oapen.org/handle/20.500.12657/76147 293 01 3293 https://library.oapen.org/handle/20.500.12657/76147 LF 370 01 4370 https://library.oapen.org/handle/20.500.12657/76147 603 01 4603 https://library.oapen.org/handle/20.500.12657/76147 808 01 4808 Volltextzugang https://library.oapen.org/handle/20.500.12657/76147 31 01 0027 00 eBook GBV-OAPEN 22 01 0018 OLR-ZDB-94-OAL 23 01 0830 OLR-OAPEN 30 01 0104 GBV-OAPEN 34 01 3551 OLR-OAPEN 39 01 0547 GBV-OAPEN 39 01 0547 LF 40 01 0007 OLR-OAPEN 63 01 3401 E-Books LF GBV-OAPEN 65 01 0003 OLR-OAPEN 70 01 0089 OLR-OAPEN-OA 72 01 0035 OLR-OAPEN-OA 95 01 3095 OLR-OAL 110 01 3110 OLR-GBV-OAPEN 136 01 3526 OLR-OAPEN 147 01 3528 OLR-OAPEN 161 01 0960 OLR-OAPEN 213 01 0551 OLR-OAPEN 230 01 0552 OLR-OAPEN 283 01 3283 OLR-OAL 293 01 3293 OLR-OAPEN 370 01 4370 OLR-FREE 370 01 4370 olr-ebook GBV-OAPEN 603 01 4603 OLR-OAPEN 808 01 4808 OLR-OAPEN |
| allfields_unstemmed |
9781003377382 978-1-003-37738-2 9788770227919 978-87-7022-791-9 (DE-627)1904886817 (DE-599)KEP108465551 (OAPEN)76147 (EBP)108465551 DE-627 ger DE-627 rda eng Industrial Artificial Intelligence Technologies and Applications [Erscheinungsort nicht ermittelbar] Taylor & Francis 2023 1 Online-Ressource (242 p.) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Open Access Unrestricted online access star The 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 Namensnennung - Nicht-kommerziell 4.0 International CC BY-NC 4.0 cc https://creativecommons.org/licenses/by-nc/4.0/ English Vermesan, Ovidiu herausgeberin edt Wotawa, Franz herausgeberin edt diaz, mario herausgeberin edt Debaillie, Björn herausgeberin edt https://library.oapen.org/bitstream/id/a3304a85-bf26-4830-83b5-84f8a1c397ba/9781000852035.pdf X:OAPEN Verlag kostenfrei https://library.oapen.org/handle/20.500.12657/76147 X:OAPEN Verlag kostenfrei ZDB-94-OAL GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_31 ISIL_DE-27 GBV_ILN_34 ISIL_DE-18-302 GBV_ILN_39 ISIL_DE-547 GBV_ILN_40 ISIL_DE-7 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_72 ISIL_DE-35 GBV_ILN_95 ISIL_DE-542 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_136 ISIL_DE-Wis1 GBV_ILN_147 ISIL_DE-Fl3 GBV_ILN_161 ISIL_DE-960 GBV_ILN_187 ISIL_DE-Ki95 GBV_ILN_213 ISIL_DE-551 GBV_ILN_230 ISIL_DE-552 GBV_ILN_283 ISIL_DE-Ha163 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_603 ISIL_DE-B1556 GBV_ILN_808 BO 22 01 0018 4606181648 OLR-ZDB-94-OAL OAPEN Library zu 29-10-24 23 01 0830 4606687436 OLR-OAPEN f z 29-10-24 30 01 0104 4607020168 GBV-OAPEN z 29-10-24 31 01 0027 4606348011 z 29-10-24 34 01 3551 4604625913 OLR-OAPEN zi002 28-10-24 39 01 0547 4607186333 GBV-OAPEN ke 29-10-24 40 01 0007 4606015470 OLR-OAPEN xsn 29-10-24 63 01 3401 4607522074 E-Books LF GBV-OAPEN z 29-10-24 65 01 0003 4607688592 OLR-OAPEN z 29-10-24 70 01 0089 4606521112 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 29-10-24 72 01 0035 4604426317 OLR-OAPEN-OA OAPEN Online Library Open Access eBook z 28-10-24 95 01 3095 4604903298 OLR-OAL Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 28-10-24 110 01 3110 4607352586 OLR-GBV-OAPEN Open Access z 29-10-24 136 01 3526 460568302X OLR-OAPEN z 29-10-24 147 01 3528 4607856169 OLR-OAPEN frei verfügbar für Europa-Universität Flensburg, Hochschule Flensburg und Zentrale Hochschulbibliothek Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. 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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. 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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. 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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000nam a22002652 4500</leader><controlfield tag="001">1904886817</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241007085259.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241007s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781003377382</subfield><subfield code="9">978-1-003-37738-2</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9788770227919</subfield><subfield code="9">978-87-7022-791-9</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1904886817</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP108465551</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OAPEN)76147</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)108465551</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Industrial Artificial Intelligence Technologies and Applications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Erscheinungsort nicht ermittelbar]</subfield><subfield code="b">Taylor & Francis</subfield><subfield code="c">2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (242 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">Open Access</subfield><subfield code="f">Unrestricted online access</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The 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. 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The 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 |
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The 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 |
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The 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 |
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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. 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