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Digitale Medien

Magnetic hysteresis of p+ and 3He++ irradiated melt-textured YBa2Cu3O7−δ (1992)

Song, S. N. ; Liu, J. ; Chen, I. G. ; [weitere]

[S.l.] : American Institute of Physics (AIP)

Journal of Applied Physics 72 (1992), S. 4240-4246

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Details

ISSN: 1089-7550

Quelle: AIP Digital Archive

Thema: Physik

Notizen: We have measured the magnetic hysteresis loops and temperature dependent trapped fields in melt-textured YBa2Cu3O7−δ samples before and after p+ and 3He++ irradiation using a Hall effect magnetometer (HEM) as well as a commercial vibrating sample magnetometer (VSM). For proper 3He++ fluence, the critical current density may be enhanced by a factor of 10. Calculations based on various critical state models show that before the irradiation, the hysteresis loops can be well accounted for by a critical current density of a modified power law field dependence Jc(T,B)=J0(T)/(1+B/B0)n with n=1/2; after the irradiation, the best fit has been achieved by using an exponential form such as Jc(T,B)=J0(T)exp(−B/B0), where B0 is a model dependent parameter. Jc and its field dependence deduced from HEM hysteresis loops are in good agreement with those deduced from the VSM loops, suggesting that the Hall effect magnetometer can be conveniently used to characterize bulk high Tc oxide superconductors.

Materialart: Digitale Medien

URL: http://dx.doi.org/10.1063/1.352236

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Digitale Medien

Ultrasonic flaw classification in weldments using probabilistic neural networks (1992)

Song, S. J. ; Schmerr, L. W.

Springer

Journal of nondestructive evaluation 11 (1992), S. 69-77

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Details

ISSN: 1573-4862

Schlagwort(e): Welds ; flaw classification ; ultrasonics ; neural networks

Quelle: Springer Online Journal Archives 1860-2000

Thema: Elektrotechnik, Elektronik, Nachrichtentechnik , Mathematik

Notizen: Abstract A probabilistic neural network is used here to classify flaws in weldments from their ultrasonic scattering signatures. It is shown that such a network is both simple to construct and fast to train. Probabilistic nets are also shown to be able to exhibit the high performance of other neural networks, such as feed forward nets trained via back-propagation, while possessing important advantages of speed, explicitness of their architecture, and physical meaning of their outputs. Probabilistic nets are also demonstrated to have performance equal to common statistical approaches, such as theK-nearest neighbor method, while retaining their unique advantages.