Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks
In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber−physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean mana...
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/3/763 |
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| author | Javier Villalba-Díez Martin Molina Joaquín Ordieres-Meré Shengjing Sun Daniel Schmidt Wanja Wellbrock |
| author_facet | Javier Villalba-Díez Martin Molina Joaquín Ordieres-Meré Shengjing Sun Daniel Schmidt Wanja Wellbrock |
| author_sort | Javier Villalba-Díez |
| collection | DOAJ |
| description | In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber−physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyber−physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber−physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyber−physical environment. |
| first_indexed | 2024-04-14T03:29:02Z |
| format | Article |
| id | doaj.art-494f8556a8794c53ba093b4cfc4a2f4d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T03:29:02Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-494f8556a8794c53ba093b4cfc4a2f4d2022-12-22T02:15:01ZengMDPI AGSensors1424-82202020-01-0120376310.3390/s20030763s20030763Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex NetworksJavier Villalba-Díez0Martin Molina1Joaquín Ordieres-Meré2Shengjing Sun3Daniel Schmidt4Wanja Wellbrock5Fakultaet fuer Management und Vertrieb, Campus Schwäbisch-Hall, Hochschule Heilbronn, 74523 Schwäbisch-Hall, GermanyDepartment of Artificial Intelligence, Universidad Politécnica de Madrid, Campus de Montegancedo, 28660 Boadilla del Monte, Madrid, SpainEscuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, SpainEscuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, SpainEscuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, SpainFakultaet fuer Management und Vertrieb, Campus Schwäbisch-Hall, Hochschule Heilbronn, 74523 Schwäbisch-Hall, GermanyIn the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber−physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyber−physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber−physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyber−physical environment.https://www.mdpi.com/1424-8220/20/3/763industry 4.0iiotgeometric deep learninglean management |
| spellingShingle | Javier Villalba-Díez Martin Molina Joaquín Ordieres-Meré Shengjing Sun Daniel Schmidt Wanja Wellbrock Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks Sensors industry 4.0 iiot geometric deep learning lean management |
| title | Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks |
| title_full | Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks |
| title_fullStr | Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks |
| title_full_unstemmed | Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks |
| title_short | Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks |
| title_sort | geometric deep lean learning deep learning in industry 4 0 cyber physical complex networks |
| topic | industry 4.0 iiot geometric deep learning lean management |
| url | https://www.mdpi.com/1424-8220/20/3/763 |
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