Cargando…
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 management sys...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038400/ https://www.ncbi.nlm.nih.gov/pubmed/32019148 http://dx.doi.org/10.3390/s20030763 |
_version_ | 1783500631656366080 |
---|---|
author | Villalba-Díez, Javier Molina, Martin Ordieres-Meré, Joaquín Sun, Shengjing Schmidt, Daniel Wellbrock, Wanja |
author_facet | Villalba-Díez, Javier Molina, Martin Ordieres-Meré, Joaquín Sun, Shengjing Schmidt, Daniel Wellbrock, Wanja |
author_sort | Villalba-Díez, Javier |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-7038400 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70384002020-03-09 Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks Villalba-Díez, Javier Molina, Martin Ordieres-Meré, Joaquín Sun, Shengjing Schmidt, Daniel Wellbrock, Wanja Sensors (Basel) Article 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. MDPI 2020-01-30 /pmc/articles/PMC7038400/ /pubmed/32019148 http://dx.doi.org/10.3390/s20030763 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Villalba-Díez, Javier Molina, Martin Ordieres-Meré, Joaquín Sun, Shengjing Schmidt, Daniel Wellbrock, Wanja Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks |
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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038400/ https://www.ncbi.nlm.nih.gov/pubmed/32019148 http://dx.doi.org/10.3390/s20030763 |
work_keys_str_mv | AT villalbadiezjavier geometricdeepleanlearningdeeplearninginindustry40cyberphysicalcomplexnetworks AT molinamartin geometricdeepleanlearningdeeplearninginindustry40cyberphysicalcomplexnetworks AT ordieresmerejoaquin geometricdeepleanlearningdeeplearninginindustry40cyberphysicalcomplexnetworks AT sunshengjing geometricdeepleanlearningdeeplearninginindustry40cyberphysicalcomplexnetworks AT schmidtdaniel geometricdeepleanlearningdeeplearninginindustry40cyberphysicalcomplexnetworks AT wellbrockwanja geometricdeepleanlearningdeeplearninginindustry40cyberphysicalcomplexnetworks |