An evaluation of iron ore characteristics through machine learning and 2-D LiDAR technology (2024)
- Authors:
- USP affiliated authors: UEYAMA, JO - ICMC ; MATOS, SAULO NEVES - ICMC ; RANIERI, CAETANO MAZZONI - ICMC
- Unidade: ICMC
- DOI: 10.1109/TIM.2023.3342220
- Subjects: APRENDIZADO COMPUTACIONAL; INDÚSTRIA MINERAL; ESTATÍSTICA; MINERAÇÃO
- Keywords: Conveyor belt; light detection and ranging (LiDAR)
- Language: Inglês
- Imprenta:
- Publisher place: Piscataway
- Date published: 2024
- Source:
- Título: IEEE Transactions on Instrumentation and Measurement
- ISSN: 1557-9662
- Volume/Número/Paginação/Ano: v. 73, p. 1-11, 2024
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MATOS, Saulo Neves et al. An evaluation of iron ore characteristics through machine learning and 2-D LiDAR technology. IEEE Transactions on Instrumentation and Measurement, v. 73, p. 1-11, 2024Tradução . . Disponível em: https://doi.org/10.1109/TIM.2023.3342220. Acesso em: 11 fev. 2026. -
APA
Matos, S. N., Pinto, T. V. B. e, Domingues, J. D., Ranieri, C. M., Albuquerque, K. S., Moreira, V. da S., et al. (2024). An evaluation of iron ore characteristics through machine learning and 2-D LiDAR technology. IEEE Transactions on Instrumentation and Measurement, 73, 1-11. doi:10.1109/TIM.2023.3342220 -
NLM
Matos SN, Pinto TVB e, Domingues JD, Ranieri CM, Albuquerque KS, Moreira V da S, Souza ES, Ueyama J, Euzébio TAM, Pessin G. An evaluation of iron ore characteristics through machine learning and 2-D LiDAR technology [Internet]. IEEE Transactions on Instrumentation and Measurement. 2024 ; 73 1-11.[citado 2026 fev. 11 ] Available from: https://doi.org/10.1109/TIM.2023.3342220 -
Vancouver
Matos SN, Pinto TVB e, Domingues JD, Ranieri CM, Albuquerque KS, Moreira V da S, Souza ES, Ueyama J, Euzébio TAM, Pessin G. An evaluation of iron ore characteristics through machine learning and 2-D LiDAR technology [Internet]. IEEE Transactions on Instrumentation and Measurement. 2024 ; 73 1-11.[citado 2026 fev. 11 ] Available from: https://doi.org/10.1109/TIM.2023.3342220 - Data-driven soft sensor development for ore type estimation in mineral crushing processes
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Informações sobre o DOI: 10.1109/TIM.2023.3342220 (Fonte: oaDOI API)
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