Industrial Automation Using Mobile Cyber Physical Systems

2022 ◽  
pp. 132-159
Author(s):  
Thangavel M. ◽  
Abhijith V. S. ◽  
Sudersan S.
Keyword(s):  
Deep Learning ◽  
Automobile Industry ◽  
Cyber Physical Systems ◽  
Manufacturing Industries ◽  
Control Mechanisms ◽  
Industrial Automation ◽  
Physical Systems

In recent years, the rise in the demand for quality products and services along with systems that could integrate the control mechanisms with high computational capabilities led to the evolution of cyber-physical systems (CPS). Due to the ongoing COVID-19 pandemic, several industries have remained closed, causing several monetary losses. Automation can help in such scenarios to keep the industries up and running in a way that the system could be monitored and controlled remotely using voice. The chapter deals with the integration of both industrial automation and cyber-physical systems in various industries like the automobile industry, manufacturing industries, construction industries, and so on. A proposed approach for machine handling using CPS, deep learning, and industrial automation with the help of voice. The proposed approach provides greater insights into the application of CPS in the area and the combination of CPS and deep learning to a greater extent.

scholarly journals Embedded Deep Learning Prototyping Approach for Cyber-Physical Systems: Smart LIDAR Case Study

2021 ◽  
Vol 10 (1) ◽  
pp. 18
Author(s):  
Quentin Cabanes ◽  
Benaoumeur Senouci ◽  
Amar Ramdane-Cherif
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Computation Time ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Research Technology ◽  
Standard Design ◽  
Hardware Architectures ◽  
Voxel Grid

Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, which need powerful algorithms combined with robust hardware architectures. On one hand, Deep Learning (DL) is proposed as the main solution algorithm. On the other hand, the standard design and prototyping methodologies for ES are not adapted to modern DL-based CPS. In this paper, we investigate AI design for CPS around embedded DL. The main contribution of this work is threefold: (1) We define an embedded DL methodology based on a Multi-CPU/FPGA platform. (2) We propose a new hardware design architecture of a Neural Network Processor (NNP) for DL algorithms. The computation time of a feed forward sequence is estimated to 23 ns for each parameter. (3) We validate the proposed methodology and the DL-based NNP using a smart LIDAR application use-case. The input of our NNP is a voxel grid hardware computed from 3D point cloud. Finally, the results show that our NNP is able to process Dense Neural Network (DNN) architecture without bias.

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