Edge Deep Learning Applied to Granulometric Analysis on Quasi-particles from the Hybrid Pelletized Sinter (HPS) Process-Reference-Cited by-同舟云学术

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Edge Deep Learning Applied to Granulometric Analysis on Quasi-particles from the Hybrid Pelletized Sinter (HPS) Process

Published:2021 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 23rd International Conference on Enterprise Information Systems
language:
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Author:

Meira Natália¹,Silva Mateus²,Oliveira Ricardo²,Souza Aline³,D’Angelo Thiago²,Vieira Cláudio¹

Affiliation:

1. Metallurgical Engineering Department, Federal University of Ouro Preto, Ouro Preto, Brazil, --- Select a Country ---

2. Department of Computer Science, Federal University of Ouro Preto, Ouro Preto, Brazil, --- Select a Country ---

3. ArcelorMittal, João Monlevade, Brazil, --- Select a Country ---

Publisher

SCITEPRESS - Science and Technology Publications

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Bringing Deep Learning to the Fields and Forests: Leaf Reconstruction and Shape Estimation;SN Computer Science;2022-03-23

2. Edge Deep Learning Towards the Metallurgical Industry: Improving the Hybrid Pelletized Sinter (HPS) Process;Enterprise Information Systems;2022

3. Sensor-Based Ore Sorting—A Review of Current Use of Electromagnetic Spectrum in Sorting;IEEE Access;2022

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