scholarly journals Research on key technologies of edge computing in discrete manufacturing industry in the era of big data

2021 ◽  
Vol 1952 (4) ◽  
pp. 042021
Author(s):  
Guanghua Lu ◽  
Mingbo Liu ◽  
Zenghai Wang ◽  
Lei Gao
2021 ◽  
Vol 687 (1) ◽  
pp. 012123
Author(s):  
Haoyue Wang ◽  
Dunnan Liu ◽  
Hongtao Zhao
Keyword(s):  
Big Data ◽  

Author(s):  
Luiz Angelo Steffenel ◽  
Manuele Kirsch Pinheiro ◽  
Lucas Vaz Peres ◽  
Damaris Kirsch Pinheiro

The exponential dissemination of proximity computing devices (smartphones, tablets, nanocomputers, etc.) raises important questions on how to transmit, store and analyze data in networks integrating those devices. New approaches like edge computing aim at delegating part of the work to devices in the “edge” of the network. In this article, the focus is on the use of pervasive grids to implement edge computing and leverage such challenges, especially the strategies to ensure data proximity and context awareness, two factors that impact the performance of big data analyses in distributed systems. This article discusses the limitations of traditional big data computing platforms and introduces the principles and challenges to implement edge computing over pervasive grids. Finally, using CloudFIT, a distributed computing platform, the authors illustrate the deployment of a real geophysical application on a pervasive network.


2021 ◽  
Vol 2083 (4) ◽  
pp. 042086
Author(s):  
Yuqi Qin

Abstract Machine learning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing algorithms in the discrete manufacturing industry, this paper proposes a new 0-1 coding method to optimize the learning algorithm, and finally proposes a learning algorithm of “IG type learning only from the best”.


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