OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
Optimum Identification of Iron Loss Models in NGO Electrical Steel for Power Electronics
S. Quondam Antonio
2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI) (2019), pp. 182-187
Closed Access | Times Cited: 6
S. Quondam Antonio
2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI) (2019), pp. 182-187
Closed Access | Times Cited: 6
Showing 6 citing articles:
An effective neural network approach to reproduce magnetic hysteresis in electrical steel under arbitrary excitation waveforms
S. Quondam Antonio, Francesco Riganti Fulginei, Antonino Laudani, et al.
Journal of Magnetism and Magnetic Materials (2021) Vol. 528, pp. 167735-167735
Closed Access | Times Cited: 48
S. Quondam Antonio, Francesco Riganti Fulginei, Antonino Laudani, et al.
Journal of Magnetism and Magnetic Materials (2021) Vol. 528, pp. 167735-167735
Closed Access | Times Cited: 48
Hysteresis Modelling in Additively Manufactured FeSi Magnetic Components for Electrical Machines and Drives
A. Faba, Francesco Riganti Fulginei, S. Quondam Antonio, et al.
IEEE Transactions on Industrial Electronics (2023) Vol. 71, Iss. 3, pp. 2188-2197
Closed Access | Times Cited: 8
A. Faba, Francesco Riganti Fulginei, S. Quondam Antonio, et al.
IEEE Transactions on Industrial Electronics (2023) Vol. 71, Iss. 3, pp. 2188-2197
Closed Access | Times Cited: 8
Neural Network Modeling of Arbitrary Hysteresis Processes: Application to GO Ferromagnetic Steel
S. Quondam Antonio, V. Bonaiuto, F. Sargeni, et al.
Magnetochemistry (2022) Vol. 8, Iss. 2, pp. 18-18
Open Access | Times Cited: 7
S. Quondam Antonio, V. Bonaiuto, F. Sargeni, et al.
Magnetochemistry (2022) Vol. 8, Iss. 2, pp. 18-18
Open Access | Times Cited: 7
Numerical simulations of vector hysteresis processes via the Preisach model and the Energy Based Model: An application to Fe-Si laminated alloys
S. Quondam Antonio, AbdelRahman M. Ghanim, A. Faba, et al.
Journal of Magnetism and Magnetic Materials (2021) Vol. 539, pp. 168372-168372
Closed Access | Times Cited: 10
S. Quondam Antonio, AbdelRahman M. Ghanim, A. Faba, et al.
Journal of Magnetism and Magnetic Materials (2021) Vol. 539, pp. 168372-168372
Closed Access | Times Cited: 10
On the Use of Feedforward Neural Networks to Simulate Magnetic Hysteresis in Electrical Steels
S. Quondam Antonio, Francesco Riganti Fulginei, Hari Prasad Rimal, et al.
(2020), pp. 119-124
Closed Access | Times Cited: 3
S. Quondam Antonio, Francesco Riganti Fulginei, Hari Prasad Rimal, et al.
(2020), pp. 119-124
Closed Access | Times Cited: 3
Magnetic Hysteresis Simulation by Using a Deep Neural Network for Non-sinusoidal Excitations
E. Cardelli, Antonino Laudani, Francesco Riganti Fulginei
(2023), pp. 292-295
Closed Access
E. Cardelli, Antonino Laudani, Francesco Riganti Fulginei
(2023), pp. 292-295
Closed Access
