• Progress in Superconductivity and Cryogenics
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• v.18 no.2
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• pp.30-36
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• 2016

For the magnetic field analysis or design, it is important to know the behavior of the magnetic field in an interesting space. Magnetic field mapping becomes a useful tool for the study of magnetic field. In this paper, a numerical way for mapping the magnetic field within the cylindrical center volume of magnet is presented, based on the solution of the Laplace's equation in the cylindrical coordinate system. The expression of the magnetic field can be obtained by the magnetic flux density, which measured in the mapped volume. According to the form of the expression, the measurement points are arranged with the parallel cylindrical line (PCL) method. As example, the magnetic flux density generated by an electron cyclotron resonance ion source (ECRIS) magnet and a quadrupole magnet were mapped using the PCL method, respectively. The mapping results show the PCL arrangement method is feasible and convenience to map the magnetic field within a cylindrical center volume generated by the general magnet.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1963-1969
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• 2017

In order to overcome the manufacturing difficulty of the transverse-flux permanent magnet linear motor (TFPMLM) and enhance its performance much better, a novel TFPMLM with E-core and 3 dimension (3D) magnetic structures is proposed in this paper. Firstly, its basic structure and operating principle are presented. Then the equivalent 2D configuration of the TFPMLM is transformed, so that the Schwarz-Christoffel (SC) mapping method can be used to analyze the motor. Furthermore, the air gap flux density distribution is solved by SC mapping method, based on which, the EMF waveform, no-load cogging force waveform and load force waveform are obtained. Finally, the prototyped TLPMLM is manufactured and the results are obtained from the experiment and 3D FEM, respectively, which are used to compare with those from SC mapping method.

• 한국초전도학회:학술대회논문집
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• 2007.07a
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• pp.88-88
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• 2007

• Progress in Superconductivity and Cryogenics
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• v.11 no.2
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• pp.15-19
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• 2009

Bulk $YBa_2Cu_3O_{7-{\delta}}$ (YBCO) superconductor was manufactured with the top-seeded melt grown method. The 9, 16, and 25 holes, as small as 0.7mm in diameter, parallel to the c-axis were mechanically drilled. Magnetic flux mapping and levitation force were measured and compared to estimate the influence of multiple holes on the magnetic properties of YBCO superconductor at 77K. According to the measurements, the maximum magnetic flux density obtained from the plain sample was 2.48kG, while the maximum magnetic flux density of the sample with 25 holes was low as around 2.29kG. The levitation force measured on the sample with 9 holes increased from 91N to 105N. The levitation force measured on the samples with 9 holes is relatively higher than the plain sample without any holes. In this case, increase of the levitation force in the perforated samples could be explained by enhancement of the cooling efficiency more effectively. We investigated that the magnetic properties of YBCO superconductor were strongly influenced by the artificial holes.

• 한국초전도학회:학술대회논문집
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• 2008.08a
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• pp.36-36
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• 2008

• Journal of Biomedical Engineering Research
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• v.27 no.4
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• pp.154-163
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• 2006

A 64-channel magnetocardiogram (MCG) system using low-noise superconducting quantum interference device (SQUID) planar gradiometers was developed for the measurements of cardiac magnetic fields generated by the heart electric activity. Owing to high flux-to-voltage transfers of double relaxation oscillation SQUID (DROS) sensors, the flux-locked loop electronics for SQUID operation could be made simpler than that of conventional DC SQUIDs, and the SQUID control was done automatically through a fiber-optic cable. The pickup coils are first-order planar gradiometers with a baseline of 4 em. The insert has 64 planar gradiometers as the sensing channels and were arranged to measure MCG field components tangential to the chest surface. When the 64-channel insert was in operation everyday, the average boil-off rate of the dewar was 3.6 Lid. The noise spectrum of the SQUID planar gradiometer system was about 5 fT$_{rms}$/$\checkmark$Hz at 100 Hz, operated inside a moderately shielded room. The MCG measurements were done at a sampling rate of 500 Hz or 1 kHz, and realtime display of MCG traces and heart rate were displayed. After the acquisition, magnetic field mapping and current mapping could be done. From the magnetic and current information, parameters for the diagnosis of myocardial ischemia were evaluated to be compared with other diagnostic methods.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.18-20
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• 1999

In this paper, the space mapping algorithm is proposed for the design of electric machines. By the algorithm, we can combine the magnetic equivalent circuit and the finite element models mathematically and got the final design parameters with a few iterations while preserving the accuracy offered by the finite element model. The finite element model is generated by parametric techniques. For the validity of this algorithm, a simple permanent magnet device with fringing and leakage flux is dealt as a numerical example.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.