Results 261 to 270 of about 7,856,512 (343)

Transfer Learning Methods for Magnetic Core Loss Modeling

Compel, 2021
Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point to initiate the model for another.
Evan Dogariu, Haoran Li, Diego Serrano López, Shukai Wang, Min Luo, Minjie Chen   +5 more
semanticscholar   +1 more source

Losses in magnetic bearings

Materials Science and Engineering: B, 1998
The application of HTSC bulk materials in magnetic bearings requires an understanding and minimization of the friction in such systems. Since the rotors in these bearings are not completely axially symmetric, their rotation generates an ac magnetic field acting on the superconductors and the metallic components of the arrangement.
M. Zeisberger, W. Gawalek
openaire   +1 more source

Losses in magnetic switches

Conference Record of the Twenty-Fifth International Power Modulator Symposium, 2002 and 2002 High-Voltage Workshop., 2003
Thyratrons are limited in lifetime and maximum repetition rate. For some applications it is necessary to replace the thyratron by a semiconductor switch. Therefore it is necessary to compress the pulses by nonlinear inductances. In a magnetic pulse compression network three main losses exist.
C. Strowitzki, A. Gortler, M. Baumann
openaire   +1 more source

Strategy‐Induced Strong Exchange Interaction for Enhancing High‐Temperature Magnetic Loss in High‐Entropy Alloy Powders

Advanced Functional Materials
Magnetic loss in high‐temperature microwave absorbers typically decreases sharply with rising temperature, limited by the Curie temperature (TC). Conventional alloys rely on high‐proportion additions of magnetic metals to enhance TC; however, this ...
Zerui Li, Y. Duan, Xiaoji Liu, Huifang Pang, Chenxu Dou, Yupeng Shi, Wei Chen   +6 more
semanticscholar   +1 more source

Magnetic MoS2: a promising microwave absorption material with both dielectric loss and magnetic loss properties

Nanotechnology, 2019
In order to obtain magnetic MoS2 and investigate the influence of magnetic moment on the microwave absorption properties of MoS2, transition metal element Ni-doped MoS2 (0–30 at%) was obtained by a hydrothermal synthesis.
Jing Wang, Xiaoyu Lin, Zengyong Chu, Zhenyu Huang, Taotao Guo, Lingni Yang, Shun Li   +6 more
semanticscholar   +1 more source

Identifying Hysteresis Losses in Magnetic Media

IEEE Transactions on Magnetics, 2010
A magnetic medium can have different hysteresis losses when subject to a rotating field. We discuss the pertinent parameters that determine these losses and a method of identifying them. We discuss the effect of particle interaction, and isotropic and anisotropic media.
E. Della Torre, CARDELLI, Ermanno, L. Bennett   +2 more
openaire   +2 more sources

Confined Diffusion Strategy for Customizing Magnetic Coupling Spaces to Enhance Low‐frequency Electromagnetic Wave Absorption

Advanced Functional Materials, 2023
The rational design of magnetic composites has great potential for electromagnetic (EM) absorption, particularly in the low‐frequency range of 2–8 GHz.
L. Rao, Lei Wang, Chendi Yang, Ruixuan Zhang, Jincang Zhang, C. Liang, R. Che   +6 more
semanticscholar   +1 more source

Loss of magnetism in CePd2−xNixAl3

Physica B: Condensed Matter, 2002
Hexagonal CePd2-xNixAl3 exhibits a crossover from a Kondo lattice showing magnetic order at TN ? 2.8 K for x = 0.2 to intermediate valence (IV) for x ? 2. Though TN(x) is weakly affected upon Pd/Ni substitution before vanishing at finite temperature, the Kondo temperature TK progressively increases. Above x ? 0.3 the phase boundary no longer exists and
Bauer, Ernst, Galatanu, Andrei N., Rogl, Peter Franz, Gomez-Berisso, M, Pedrazzini, P, Sereni, Julián Gustavo Renzo   +5 more
openaire   +2 more sources

Cobalt doping of bismuth ferrite for matched dielectric and magnetic loss

, 2019
We get insight into the atomic-molecular evolution of crystal engineering on BiFeO3 (BFO). Doping a small amount of Co ions into BFO enables us to introduce more oxygen vacancies that transform the dipole configuration.
Min Zhang, Huijing Yang, Yong Li, W. Cao, X. Fang, Jie Yuan, M. Cao   +6 more
semanticscholar   +1 more source

MagNet: A Machine Learning Framework for Magnetic Core Loss Modeling

Compel, 2020
This paper presents a two-stage machine learning framework – MagNet – for magnetic core loss modeling. The first stage of MagNet is a waveform transformation network, which generates 2-D images (tensors) and extracts both the frequency and time domain ...
Haoran Li, S. Lee, Min Luo, C. Sullivan, Yuxin Chen, Minjie Chen   +5 more
semanticscholar   +1 more source