Protection and detection of physical hazards for the intelligent system under industrial Internet of Things by neural network

2021 ◽  
Author(s):  
Rongxia Wang ◽  
Zhifang Liu ◽  
Fen He ◽  
Weihuang Yang
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Intelligent System ◽  
Industrial Internet Of Things ◽  
Physical Hazards ◽  
Industrial Internet

scholarly journals Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6241
Author(s):  
Israel Campero-Jurado ◽  
Sergio Márquez-Sánchez ◽  
Juan Quintanar-Gómez ◽  
Sara Rodríguez ◽  
Juan M. Corchado
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Health And Safety ◽  
Working Environment ◽  
Training Dataset ◽  
Support Vector ◽  
Industrial Internet Of Things ◽  
Industrial Internet

Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers’ environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

scholarly journals A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 55595-55605
Author(s):  
Zil E. Huma ◽  
Shahid Latif ◽  
Jawad Ahmad ◽  
Zeba Idrees ◽  
Anas Ibrar ◽  
...  
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Random Neural Network
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