Evaluation of electromagnetic shielding effectiveness for loaded metallic enclosures with apertures based on machine learning

Liu Zhengyang; Yan Liping; Zhao Xiang

doi:10.11884/HPLPB201931.190079

Volume 31 Issue 8

Aug. 2019

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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: 083201. doi: 10.11884/HPLPB201931.190079

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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: 083201. doi: 10.11884/HPLPB201931.190079

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Evaluation of electromagnetic shielding effectiveness for loaded metallic enclosures with apertures based on machine learning

doi: 10.11884/HPLPB201931.190079

School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China

Received Date: 2019-03-22
Rev Recd Date: 2019-05-27
Publish Date: 2019-08-15

Abstract

Abstract

A machine learning based evaluation method for shielding effectiveness (SE) of loaded metallic enclosures with apertures under electromagnetic wave radiation is proposed.The SEs of a variety of metallic enclosures loaded with different printed circuit boards (PCBs) is calculated using full wave analysis simulation in the frequency range of 0-5 GHz, and 5250 samples are obtained.The random forest model which is one of the popular machine learning aggression algorithms is employed to train stochastically the selected 4200 samples.Consequently, the model capable to fast predict the SE for loaded shielding enclosures characterized by 16 parameters is implemented.The rest 1050 samples are used to verify the proposed random forest model.Resultsshow that the proposed model can quickly predict the electromagnetic shielding effectiveness of the enclosure loaded with PCBs.
- loaded metallic enclosures,
- shielding effectiveness,
- random forest,
- machine learning

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References(17)

References

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