Evaluating conveyor belt health with signal processing applied to inertial sensing (2023)
- Authors:
- USP affiliated authors: UEYAMA, JO - ICMC ; COLETTI, OTAVIO FERRACIOLI - ICMC ; UGUCIONI FILHO, FERNANDO - EESC ; BARROS, LUIZ GUILHERME DIAS DE - EESC ; MATOS, SAULO NEVES - ICMC ; RANIERI, CAETANO MAZZONI - ICMC
- Unidades: ICMC; EESC
- DOI: 10.1109/SIOT60039.2023.10390088
- Subjects: INDÚSTRIA MINERAL; FALHA; ANÁLISE ESTATÍSTICA DE DADOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2023
- Source:
- Título: Proceedings
- Conference titles: Symposium on Internet of Things - SIoT
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
MATOS, Saulo Neves et al. Evaluating conveyor belt health with signal processing applied to inertial sensing. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/SIoT60039.2023.10390088. Acesso em: 20 fev. 2026. -
APA
Matos, S. N., Coletti, O. F., Ugucioni Filho, F., Carvalho, R. C. C. L., Pinto, T. V. B. e, Barros, L. G. D. de, et al. (2023). Evaluating conveyor belt health with signal processing applied to inertial sensing. In Proceedings. Piscataway: IEEE. doi:10.1109/SIOT60039.2023.10390088 -
NLM
Matos SN, Coletti OF, Ugucioni Filho F, Carvalho RCCL, Pinto TVB e, Barros LGD de, Ranieri CM, Lopes BE, Ueyama J, Pessin G. Evaluating conveyor belt health with signal processing applied to inertial sensing [Internet]. Proceedings. 2023 ;[citado 2026 fev. 20 ] Available from: https://doi.org/10.1109/SIoT60039.2023.10390088 -
Vancouver
Matos SN, Coletti OF, Ugucioni Filho F, Carvalho RCCL, Pinto TVB e, Barros LGD de, Ranieri CM, Lopes BE, Ueyama J, Pessin G. Evaluating conveyor belt health with signal processing applied to inertial sensing [Internet]. Proceedings. 2023 ;[citado 2026 fev. 20 ] Available from: https://doi.org/10.1109/SIoT60039.2023.10390088 - An evaluation of iron ore characteristics through machine learning and 2-D LiDAR technology
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- Incremental learning approaches for flood detection in dynamic river environments
- Data-driven soft sensor development for ore type estimation in mineral crushing processes
- Development of a bench system with capacitive sensor, sample compression, and TinyML for iron ore moisture measurement
- Improving soft sensor reliability in the mining industry using incremental learning
- Enhancing operational safety with conformal prediction in soft sensors
- Water level identification with laser sensors, inertial units, and machine learning
- Memory-based pruning of deep neural networks for IoT devices applied to flood detection
- Artificial neural networks applied to time series for flood prediction
Informações sobre o DOI: 10.1109/SIOT60039.2023.10390088 (Fonte: oaDOI API)
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