Next Article in Journal
Energy per Operation Optimization for Energy-Harvesting Wearable IoT Devices
Next Article in Special Issue
Local Wireless Sensor Networks Positioning Reliability Under Sensor Failure
Previous Article in Journal
Thermal Stability of Type II Modifications by IR Femtosecond Laser in Silica-based Glasses
Previous Article in Special Issue
Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits
Article

Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks

1
Fakultaet fuer Management und Vertrieb, Campus Schwäbisch-Hall, Hochschule Heilbronn, 74523 Schwäbisch-Hall, Germany
2
Department of Artificial Intelligence, Universidad Politécnica de Madrid, Campus de Montegancedo, 28660 Boadilla del Monte, Madrid, Spain
3
Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain
4
Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
5
Lead Developer Quality Inspection, Matthews International GmbH, Gutenbergstraße 1-3, 48691 Vreden, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(3), 763; https://doi.org/10.3390/s20030763
Received: 31 December 2019 / Revised: 21 January 2020 / Accepted: 29 January 2020 / Published: 30 January 2020
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. View Full-Text
Keywords: Industry 4.0; IIoT; geometric deep learning; lean management Industry 4.0; IIoT; geometric deep learning; lean management
Show Figures

Figure 1

MDPI and ACS Style

Villalba-Díez, J.; Molina, M.; Ordieres-Meré, J.; Sun, S.; Schmidt, D.; Wellbrock, W. Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks. Sensors 2020, 20, 763. https://doi.org/10.3390/s20030763

AMA Style

Villalba-Díez J, Molina M, Ordieres-Meré J, Sun S, Schmidt D, Wellbrock W. Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks. Sensors. 2020; 20(3):763. https://doi.org/10.3390/s20030763

Chicago/Turabian Style

Villalba-Díez, Javier, Martin Molina, Joaquín Ordieres-Meré, Shengjing Sun, Daniel Schmidt, and Wanja Wellbrock. 2020. "Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks" Sensors 20, no. 3: 763. https://doi.org/10.3390/s20030763

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop