2023
Autores
Schlemmer, E; Frank Kersch, D;
Publicação
CADERNOS DE PESQUISA: PENSAMENTO EDUCACIONAL
Abstract
2023
Autores
Christian Cooke; Ben Mestel;
Publicação
Energy Systems
Abstract
2023
Autores
Martins, ML; Pedroso, M; Libânio, D; Dinis Ribeiro, M; Coimbra, M; Renna, F;
Publicação
2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC
Abstract
Gastric Intestinal Metaplasia (GIM) is one of the precancerous conditions in the gastric carcinogenesis cascade and its optical diagnosis during endoscopic screening is challenging even for seasoned endoscopists. Several solutions leveraging pre-trained deep neural networks (DNNs) have been recently proposed in order to assist human diagnosis. In this paper, we present a comparative study of these architectures in a new dataset containing GIM and non-GIM Narrow-band imaging still frames. We find that the surveyed DNNs perform remarkably well on average, but still measure sizeable interfold variability during cross-validation. An additional ad-hoc analysis suggests that these baseline architectures may not perform equally well at all scales when diagnosing GIM.
2023
Autores
Santo, LE; Pereira, M; Araújo, RE;
Publicação
2023 IEEE VEHICLE POWER AND PROPULSION CONFERENCE, VPPC
Abstract
Switched reluctance machines are gaining importance due to their low cost, simple construction, and non-use of rare earth magnets. However, for the development of advanced torque controllers, accurate torque estimation is crucial, especially under varying load conditions. There are different torque estimation methods, which fall into different well-established classes, however, the characterization of their performance and operating conditions are not well known. This paper provides a comparative study of the most significant estimation algorithms: average torque, analytical and area approximation estimators. To assess the performance of these algorithms, a set of numerical simulations is presented and their results are compared based on signal similarity criteria. Results show a better performance when using the area approximation algorithm in comparison with the other two.
2023
Autores
Gregório, N; Bispo, J; Fernandes, JP; de Medeiros, SQ;
Publicação
J. Comput. Lang.
Abstract
2023
Autores
Melo, R; Pinto, P; Pinto, A;
Publicação
BLOCKCHAIN
Abstract
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