• High Power Laser and Particle Beams
  • Vol. 31, Issue 8, 83201 (2019)
Liu Zhengyang*, Yan Liping, and Zhao Xiang
Author Affiliations
  • [in Chinese]
  • show less
    DOI: 10.11884/hplpb201931.190079 Cite this Article
    Liu Zhengyang, Yan Liping, Zhao Xiang. Evaluation of electromagnetic shielding effectiveness for loaded metallic enclosures with apertures based on machine learning[J]. High Power Laser and Particle Beams, 2019, 31(8): 83201 Copy Citation Text show less
    References

    [3] IEEE Standard 299, Method for measuring the shielding effectiveness of enclosures and boxes having all dimensions between 0.1 m and 2 m[S].

    [10] Medico R, Lambrecht N, Pues H, et al. Machine learning based error detection in transient susceptibility tests[J]. IEEE Trans Electromagnetic Compatibility, 2019, 61(2): 352-360.

    [11] Trinchero R, Manfredi P, Stievano I S, et al. Machine learning for the performance assessment of high-speed links[J]. IEEE Trans Electromagnetic Compatibility, 2018, 60(6): 1627-1634.

    [12] Br′eard A, Moulla R, Vollaire C. Metamodel of power electronic converters using learning SVR method coupling with wavelet compression[J]. IEEE Trans Electromagnetic Compatibility, 2016, 58(2): 588-598.

    [13] Lozano A, Robinson M P, Diaz A, et al. Evaluation and optimization of an equivalent model for printed circuit boards inside metallic enclosures[C]//XXIX General Assembly Int. Union Radio Science. 2008: EBp6.

    [14] Marvin A C, Dawson J F, Ward S, et al. A proposed new definition and measurement of the shielding effect of equipment enclosures[J]. IEEE Trans Electromagnetic Compatibility, 2004, 46(3): 459-468.

    [16] Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.

    [17] Horvath L, Kokoszka P. A bootstrap approximation to a unit root test statistic for heavy-tailed observations[J]. Statistics and Probability Letters, 2003, 63(2): 163-173.

    Liu Zhengyang, Yan Liping, Zhao Xiang. Evaluation of electromagnetic shielding effectiveness for loaded metallic enclosures with apertures based on machine learning[J]. High Power Laser and Particle Beams, 2019, 31(8): 83201
    Download Citation