An Evaluation of Iron Ore Characteristics Through Machine Learning and 2-D LiDAR Technology-Reference-Cited by-同舟云学术

An Evaluation of Iron Ore Characteristics Through Machine Learning and 2-D LiDAR Technology

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Matos Saulo N.¹^ORCID,Pinto Thomas V. B.²^ORCID,Domingues Jacó D.²^ORCID,Ranieri Caetano M.¹^ORCID,Albuquerque Kaike S.³^ORCID,Moreira Vinicius S.³^ORCID,Souza Ernandes S.³^ORCID,Ueyama Jó¹^ORCID,Euzébio Thiago A. M.⁴^ORCID,Pessin Gustavo²^ORCID

Affiliation:

1. Institute of Mathematical and Computer Sciences (ICMC-USP), University of São Paulo, São Carlos, Brazil

2. Robotics, Instrumentation and Control Laboratory, Instituto Tecnológico Vale, Ouro Preto, Robotics, Brazil

3. Vale S.A., Rio de Janeiro, Brazil

4. Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10356046.pdf?arnumber=10356046

Reference55 articles.

1. Improving fine particle flotation using the StackCell™ (raising the tail of the elephant curve)

2. Grindability soft-sensors based on lithological composition and on-line measurements

3. Image-based quality monitoring system of limestone ore grades

4. A machine vision approach to on-line estimation of run-of-mine ore composition on conveyor belts

5. Rock lithological classification by hyperspectral, range 3D and color images

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