Special Government Standard of unit magnetic induction over the 2.0?10-T range

1976 ◽  
Vol 19 (3) ◽  
pp. 401-403 ◽  
Author(s):  
G. K. Yagola ◽  
Yu. I. Kazantsev ◽  
V. M. Simonov ◽  
D. I. Cheburkov
Keyword(s):  
Magnetic Induction ◽  
Government Standard ◽  
Special Government Standard

Special Government Standard of unit alternating current within the 40?1�105-Hz frequency range

1976 ◽  
Vol 19 (3) ◽  
pp. 384-386
Author(s):  
O. P. Galakhova ◽  
E. K. Dranishnikova ◽  
M. S. Belyaeva
Keyword(s):  
Alternating Current ◽  
Frequency Range ◽  
Government Standard ◽  
Special Government Standard

Special Government Standard of unit alternating voltage within the 20?3�107-Hz frequency range

1976 ◽  
Vol 19 (3) ◽  
pp. 387-390 ◽  
Author(s):  
T. B. Rozhdestvenskaya ◽  
R. F. Aknaev ◽  
O. P. Galakhova
Keyword(s):  
Frequency Range ◽  
Government Standard ◽  
Special Government Standard ◽  
Alternating Voltage

Special Government Standard of unit electric field intensity within frequency range 30?1000 MHz

1976 ◽  
Vol 19 (3) ◽  
pp. 391-392
Author(s):  
G. M. Belyakova ◽  
V. S. Buzinov ◽  
M. E. Melekhov ◽  
V. A. Tishchenko ◽  
A. N. Filonov
Keyword(s):  
Electric Field ◽  
Field Intensity ◽  
Electric Field Intensity ◽  
Frequency Range ◽  
Government Standard ◽  
Special Government Standard

Special Government Standard of unit specific heat for solids within the 4.2?90�K temperature range

1976 ◽  
Vol 19 (3) ◽  
pp. 377-380 ◽  
Author(s):  
N. P. Rybkin ◽  
M. N. Orlova ◽  
A. K. Baranyuk ◽  
N. G. Nurullaev ◽  
E. S. Zhuchkov
Keyword(s):  
Specific Heat ◽  
Government Standard ◽  
Temperature Range ◽  
Special Government Standard

Analysis of magneto rheological elastomers for energy harvesting systems

2020 ◽  
Vol 64 (1-4) ◽  
pp. 439-446
Author(s):  
Gildas Diguet ◽  
Gael Sebald ◽  
Masami Nakano ◽  
Mickaël Lallart ◽  
Jean-Yves Cavaillé
Keyword(s):  
Magnetic Field ◽  
Energy Harvesting ◽  
Shear Strain ◽  
Magnetic Induction ◽  
Magnetic Particles ◽  
Homogeneous Magnetic Field ◽  
Electrical Signal ◽  
Harvesting Systems ◽  
Dc Bias ◽  
Magneto Rheological

Magneto Rheological Elastomers (MREs) are composite materials based on an elastomer filled by magnetic particles. Anisotropic MRE can be easily manufactured by curing the material under homogeneous magnetic field which creates column of particles. The magnetic and elastic properties are actually coupled making these MREs suitable for energy conversion. From these remarkable properties, an energy harvesting device is considered through the application of a DC bias magnetic induction on two MREs as a metal piece is applying an AC shear strain on them. Such strain therefore changes the permeabilities of the elastomers, hence generating an AC magnetic induction which can be converted into AC electrical signal with the help of a coil. The device is simulated with a Finite Element Method software to examine the effect of the MRE parameters, the DC bias magnetic induction and applied shear strain (amplitude and frequency) on the resulting electrical signal.

Research on expanding the range of reproduction of magnetic flux density of the DC field of the state primary standard GET12-2011

2020 ◽  
pp. 3-7
Author(s):  
Vladlen Ya. Shifrin ◽  
Denis I. Belyakov ◽  
Alexander E. Shilov ◽  
Denis D. Kosenko
Keyword(s):  
Magnetic Flux ◽  
Magnetic Induction ◽  
Frequency Conversion ◽  
Magnetic Measurements ◽  
Primary Standard ◽  
Uncertainty Budget ◽  
The State ◽  
State Primary Standard ◽  
Magnetic Flux Density ◽  
Constant Field

The results of works aimed at increasing the level of uniformity of measurements of the magnetic induction of a constant field – the basic value in the field of magnetic measurements. A set of equipment for reproducing a unit of magnetic induction of a constant field in the range of 1–25 mT was created and described. The inclusion of this complex in the State primary standard of units of magnetic induction, magnetic flux, magnetic moment and magnetic induction gradient GET 12-2011 will ensure the reproduction and direct transmission of the unit of permanent magnetic induction in the ranges of not only weak (10–3–1 mT), but medium (1–25 mT) and strong (0.025–1 T) magnetic fields. A quantum cesium magnetometer based on the resolved structure of cesium atoms was created to transmit the unit of magnetic induction to the region of medium fields. The procedure for calculating the frequency conversion coefficients to magnetic induction of the created quantum cesium magnetometer is described. The uncertainty budget for reproducing a unit of magnetic induction of a constant field using the created complex is estimated.

Inspection of defects in CFRP by improved magnetic induction tomography

2019 ◽  
Vol 61 (3) ◽  
pp. 255-259
Author(s):  
Lipan Zhang ◽  
Qifeng Meng ◽  
Kai Song ◽  
Ming Gao ◽  
Zhiyuan Cheng
Keyword(s):  
Magnetic Induction ◽  
Magnetic Induction Tomography

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

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