loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Natália F. de C. Meira 1 ; Mateus C. Silva 2 ; Ricardo A. R. Oliveira 2 ; Aline Souza 3 ; Thiago D’Angelo 2 and Cláudio B. Vieira 1

Affiliations: 1 Metallurgical Engineering Department, Federal University of Ouro Preto, Ouro Preto, Brazil ; 2 Department of Computer Science, Federal University of Ouro Preto, Ouro Preto, Brazil ; 3 ArcelorMittal, João Monlevade, Brazil

Keyword(s): AIoT, Artificial Intelligence, Edge Computing, Edge Learning, Computer Vision.

Abstract: The mining and metallurgical industry seeks to adapt to Industry 4.0 with the implementation of Artificial Intelligence in the processes. The purpose of this paper is to develop the first steps of an Artificial Intelligence in Deep Learning with Edge Computing to recognize the quasi-particles from the Hybrid Pelletized Sinter (HPS) process and provide its particle size distribution. We trained our model with the aXeleRate tool using the Keras-Tensorflow framework and the MobileNet architecture and tested it with an embedded system using the SiPEED MaiX Dock board. Our model obtained 98.60% accuracy in training validation using real and synthetic images and 100% accuracy in tests with synthetic images and 70% recall. The tests’ results indicate the feasibility of the proposed system, but with probable overfitting in the training stage.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.142.197.212

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Meira, N.; Silva, M.; Oliveira, R.; Souza, A.; D’Angelo, T. and Vieira, C. (2021). Edge Deep Learning Applied to Granulometric Analysis on Quasi-particles from the Hybrid Pelletized Sinter (HPS) Process. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 527-535. DOI: 10.5220/0010458805270535

@conference{iceis21,
author={Natália F. de C. Meira. and Mateus C. Silva. and Ricardo A. R. Oliveira. and Aline Souza. and Thiago D’Angelo. and Cláudio B. Vieira.},
title={Edge Deep Learning Applied to Granulometric Analysis on Quasi-particles from the Hybrid Pelletized Sinter (HPS) Process},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={527-535},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010458805270535},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Edge Deep Learning Applied to Granulometric Analysis on Quasi-particles from the Hybrid Pelletized Sinter (HPS) Process
SN - 978-989-758-509-8
IS - 2184-4992
AU - Meira, N.
AU - Silva, M.
AU - Oliveira, R.
AU - Souza, A.
AU - D’Angelo, T.
AU - Vieira, C.
PY - 2021
SP - 527
EP - 535
DO - 10.5220/0010458805270535
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic