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230 results on '"Cherif R"'

12. Anisotropic Models of Nonlinear Magnetic Behavior Laws for Finite Element Modeling of Iron Losses in a Toroidal Core

Author

Drappier, J., Guyomarch, F., Cherif, R., Le Menach, Y., Messal, O., Chevallier, L., and Benabou, A.

Abstract

This article explores the implications of nonlinear anisotropic behavior models on the simulation of magnetic losses in a toroidal core made of a conventional grain-oriented electrical steel (GOES) within a finite element method (FEM) simulation environment. GO laminations are commonly used to improve the efficiency of electrical machines, but their complex behavior requires accurate modeling. Nonlinear resolutions for different anisotropy models were implemented and compared with a measurement-based model. Then, an anisotropic iron loss model from Appino et al. has been applied to evaluate the iron losses. This method provides a decisive way to qualitatively assess various models directly, relying on measurable quantities of interest.

Published
2024
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40. Comparative Study of Approximate Strategies for Playing Sum Games Based on Subgame Types

Author

Andraos, Cherif R. S., Zaky, Manal M., Ghoneim, Salma A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, van den Herik, H. Jaap, editor, Ciancarini, Paolo, editor, and Donkers, H. H. L. M. (Jeroen), editor

Published
2007
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50. COVID-19 detection from Xray and CT scans using transfer learning

Author

Berrimi, M. (Mohamed), Hamdi, S. (Skander), Yahia Cherif, R. (Raoudha), Moussaoui, A. (Abdelouahab), Oussalah, M. (Mourad), Chabane, M. (Mafaza), Berrimi, M. (Mohamed), Hamdi, S. (Skander), Yahia Cherif, R. (Raoudha), Moussaoui, A. (Abdelouahab), Oussalah, M. (Mourad), and Chabane, M. (Mafaza)

Abstract

Since the novel coronavirus SARS-CoV-2 outbreak, intensive research has been conducted to find suitable tools for diagnosis and identifying infected people in order to take appropriate action. Chest imaging plays a significant role in this phase where CT and Xrays scans have proven to be effective in detecting COVID-19 within the lungs. In this research, we propose deep learning models using Transfer learning to detect COVID-19. Both X-ray and CT scans were considered to evaluate the proposed methods.

Published
2021

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