Using Big Data-Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning

In high-paced and efficient life and work, fatigue is one of the important factors that cause accidents such as traffic and medical accidents. This study designs a feature map-based pruning strategy (PFM), which effectively reduces redundant parameters and reduces the time and space complexity of pa...

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Vydané v:Computational intelligence and neuroscience Ročník 2021; číslo 1; s. 2747940
Hlavní autori: Sun, Yudong, He, Yahui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Hindawi 2021
John Wiley & Sons, Inc
Predmet:
Accidents
Algorithms
Artificial neural networks
Big Data
Blinking
Classification
Computer Simulation
Conjugate gradient method
Conjugates
Convergence
Data processing
Design
Efficiency
Fatigue
Fatigue tests
Feature maps
Feature selection
Humans
Mathematical optimization
Neural networks
Neural Networks, Computer
Optimization
Parallel processing
Physiology
Traffic
ISSN:1687-5265, 1687-5273, 1687-5273
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Shrnutí:In high-paced and efficient life and work, fatigue is one of the important factors that cause accidents such as traffic and medical accidents. This study designs a feature map-based pruning strategy (PFM), which effectively reduces redundant parameters and reduces the time and space complexity of parallelized deep convolutional neural network (DCNN) training; a correction is proposed in the Map stage. The secant conjugate gradient method (CGMSE) realizes the fast convergence of the conjugate gradient method and improves the convergence speed of the network; in the Reduce stage, a load balancing strategy to control the load rate (LBRLA) is proposed to achieve fast and uniform data grouping to ensure the parallelization performance of the parallel system. Finally, the related fatigue algorithm’s research and simulation based on the human eye are carried out on the PC. The human face and eye area are detected from the video image collected using the USB camera, and the frame difference method and the position information of the human eye on the face are used. To track the human eye area, extract the relevant human eye fatigue characteristics, combine the blink frequency, closed eye duration, PERCLOS, and other human eye fatigue determination mechanisms to determine the fatigue state, and test and verify the designed platform and algorithm through experiments. This system is designed to enable people who doze off, such as drivers, to discover their state in time through the system and reduce the possibility of accidents due to fatigue.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editor: Syed Hassan Ahmed
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2021/2747940