Zobrazeno 1 - 10
of 18
pro vyhledávání: '"André Eugenio Lazzaretti"'
Autor:
Péricles Valera Rialto Júnior, Eduardo Henrique Dureck, Alessandra Kalinowski, Carlos Ruiz Zamarreño, Abian B. Socorro-Leranoz, Jean Carlos Cardozo da Silva, André Eugenio Lazzaretti, Uilian José Dreyer
Publikováno v:
Journal of Microwaves, Optoelectronics and Electromagnetic Applications, Vol 22, Iss 1, Pp 208-218 (2023)
Abstract This paper describes the automation of a forearm prosthesis using the signal collected by a Fiber Bragg Grating (FBG) sensor. The FBG sensor is applied to one subject's forearm to measure the deformation as a result of the index and middle f
Externí odkaz:
https://doaj.org/article/828e730bcd0d4c06bd0d4426ec489f0c
Publikováno v:
Sensors, Vol 23, Iss 7, p 3567 (2023)
In recent decades, falls have posed multiple critical health issues, especially for the older population, with their emerging growth. Recent research has shown that a wrist-based fall detection system offers an accessory-like comfortable solution for
Externí odkaz:
https://doaj.org/article/6c3404078ebd4660916c9815b0e3f264
Autor:
Stéphani de Pol, Eduardo Borba Neves, André Eugenio Lazzaretti, Suhaila Mahmoud Smaili, Eddy Krueger
Publikováno v:
Bioscience Journal, Vol 37, Pp e37069-e37069 (2021)
Spasticity is a motor condition present in 75 to 88% of children with Cerebral Palsy (CP). One form of treatment is called punctual mechanical oscillation (PO). The current study aimed to study different protocols for the application of PO and the ma
Externí odkaz:
https://doaj.org/article/6d2074d879454a61a9d54179f9556035
Autor:
Everton Luiz de Aguiar, André Eugenio Lazzaretti, Bruna Machado Mulinari, Daniel Rodrigues Pipa
Publikováno v:
Energies, Vol 14, Iss 20, p 6796 (2021)
Nonintrusive Load Monitoring (NILM) uses computational methods to disaggregate and classify electrical appliances signals. The classification is usually based on the power signatures of the appliances obtained by a feature extractor. State-of-the-art
Externí odkaz:
https://doaj.org/article/22b96e712f074e9ea2f673fff73d7a1a
Autor:
Douglas Paulo Bertrand Renaux, Fabiana Pottker, Hellen Cristina Ancelmo, André Eugenio Lazzaretti, Carlos Raiumundo Erig Lima, Robson Ribeiro Linhares, Elder Oroski, Lucas da Silva Nolasco, Lucas Tokarski Lima, Bruna Machado Mulinari, José Reinaldo Lopes da Silva, Júlio Shigeaki Omori, Rodrigo Braun dos Santos
Publikováno v:
Energies, Vol 13, Iss 20, p 5371 (2020)
A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes cha
Externí odkaz:
https://doaj.org/article/908ef4cb9d9d44689ad5b633b55c0588
Autor:
André Eugenio Lazzaretti, Douglas Paulo Bertrand Renaux, Carlos Raimundo Erig Lima, Bruna Machado Mulinari, Hellen Cristina Ancelmo, Elder Oroski, Fabiana Pöttker, Robson Ribeiro Linhares, Lucas da Silva Nolasco, Lucas Tokarski Lima, Júlio Shigeaki Omori, Rodrigo Braun dos Santos
Publikováno v:
Energies, Vol 13, Iss 17, p 4396 (2020)
A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms
Externí odkaz:
https://doaj.org/article/7ed9e61838bf43aa8ad9f9c8b7deeeae
Publikováno v:
Learning and Nonlinear Models. 21:19-35
The Scattering Transform (ST) presents itself as an alternative approach to the classic methods that involve neural networks and deep learning techniques for the feature extraction and classification of electrical signals. Among its main advantages,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::217cbcb5ecfa5815bcb58b2c2418d68d
https://doi.org/10.21528/lnlm-vol21-no1-art2
https://doi.org/10.21528/lnlm-vol21-no1-art2
Autor:
Pedro Von Hohendorff Seger, Cristian Roberto Pastro, André Eugenio Lazzaretti, Flavio Lori Grando, Miguel Moreto, Gustavo Weber Denardin
Publikováno v:
IEEE Transactions on Industry Applications. 58:3153-3163
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::088dd3eced374e4f224da52a6dd5b7ee
https://doi.org/10.1109/tia.2022.3151045
https://doi.org/10.1109/tia.2022.3151045
Publikováno v:
Advances in Computational Collective Intelligence ISBN: 9783031162091
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c474d0020007cc9df22359cad781651
https://doi.org/10.1007/978-3-031-16210-7_41
https://doi.org/10.1007/978-3-031-16210-7_41
Publikováno v:
Electric Power Systems Research. 214:108953
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6a0449891c1ae8880f4d51e1e347b76
https://doi.org/10.1016/j.epsr.2022.108953
https://doi.org/10.1016/j.epsr.2022.108953