基于机器学习的开孔加载金属腔电磁屏蔽效能评估
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摘要
利用全波分析方法计算了不同电路板加载、不同孔缝和尺寸的开孔金属腔在0!5GHz范围内的屏蔽效能(SE),获得共计5250个样本。进而利用机器学习中的随机森林回归算法,对其中4200个样本数据进行训练,获得了可以根据开孔腔物理尺寸、加载物材料及电磁特性和位置、频率等共计16个输入参数快速评估开孔加载金属腔屏蔽效能的机器学习模型。利用其余的1050个样本进行模型验证,结果表明该模型可以快速准确地计算加载腔的电磁屏蔽效能。该模型具有随时根据样本量增加不断训练提高其普适性的特点,可为实际工程中加载开孔腔的屏蔽设计及SE评估提供高效途径。
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.Results show that the proposed model can quickly predict the electromagnetic shielding effectiveness of the enclosure loaded with PCBs.
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.Results show that the proposed model can quickly predict the electromagnetic shielding effectiveness of the enclosure loaded with PCBs.
引文
[1]王楠,陈贵齐,赵延安,等.复杂城市环境电磁态势的场数值分析与测量对比[J].电波科学学报,2018,33(6):671-676.(Wang Nan,Chen Guiqi,Zhao Yan’an,et al.Numerical analysis and measured comparison on electromagnetic problems in complex city environments.The Chinese Journal of Radio Science,2018,33(6):671-676)
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[9]尚宇炜,马钊,彭晨阳,等.内嵌专业知识和经验的机器学习方法探索(二):引导学习的应用与实践[J].中国电机工程学报,2017,37(20):5852-5861.(Shang Yuwei,Ma Zhao,Peng Chenyang,et al.Study of a novel machine learning method embedding expertise(partⅡ):Applications and practices of guiding learning.Proceedings of the Chinese Society for Electrical Engineering.2017,37(20):5852-5861)
[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.
[15]李航.统计学习方法[M].北京:清华大学出版社,2012:55-73.(Li Hang.Statistical learning method.Beijing:Tsinghua University Press,2012:55-73)
[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.
[2]王天乐,闫丽萍,赵翔,等.包含非线性组件的系统级电磁效应分析方法[J].强激光与粒子束,2014,26:073204.(Wang Tianle,Yan Liping,Zhao Xiang,et al.System-level analysis method of electromagnetic effects on an electronic system containing nonlinear components[J].High Power Laser and Particle Beams,2014,26:073204)
[3] IEEE Standard 299,Method for measuring the shielding effectiveness of enclosures and boxes having all dimensions between 0.1mand 2m[S].
[4]孙勇,刘强,闫丽萍,等.开孔金属腔屏蔽效能快速算法适用性评估[J].强激光与粒子束,2017,29:083203.(Sun Yong,Liu Qiang,Yan Liping,et al.Applicability evaluation of fast algorithm for shielding effectiveness prediction of metallic enclosures with apertures.High Power Laser and Particle Beams,2017,29:083203)
[5]安静,王泽锴,韩承江,等.孔缝对内置带MSL电路板壳体耦合特性研究[J].微波学报,2016,32(1):36-40.(An Jing,Wang Zekai,Han Chen jiang,et al.Study of slot on the coupling characteristics for the shelled of loaded PCB with MSL.Journal of Microwaves,2016,32(1):36-40)
[6]路宏敏,刘国强,余志勇,等.加装印刷电路板的圆孔阵矩形机壳屏蔽效能[J].强激光与粒子束,2009,21(1):108-112(Lu Hongmin,Liu Guoqiang,Yu Zhiyong,et al.Shielding effectiveness of PCB loaded rectangular enclosure with circular-aperture array.High Power Laser and Particle Beams,2009,21(1):108-112)
[7]汪柳平,高攸纲,沈远茂,等.装有PCB有孔矩形腔屏蔽效能的传输线法分析[J].电波科学学报,2008,23(4):740-744.(Wang Liuping,Gao Yougang,Shen Yuanmao,et al.Analysis of shielding effectiveness of rectangular cavity of loaded PCB with aperture by transmission line method[J].The Chinese Journal of Radio Science,2008,23(4):740-744)
[8]郝建红,公延飞,范杰清,等.一种内置条状金属板的双层金属腔体屏蔽效能的理论模型[J].物理学报,2016,65:044101.(Hao Jianhong,Gong Yanfei,Fan Jieqing,et al.An analytical model for shielding effectiveness of double layer rectangular enclosure with inner stripshaped metallic plate.Acta Physica Sinica,2016,65:044101)
[9]尚宇炜,马钊,彭晨阳,等.内嵌专业知识和经验的机器学习方法探索(二):引导学习的应用与实践[J].中国电机工程学报,2017,37(20):5852-5861.(Shang Yuwei,Ma Zhao,Peng Chenyang,et al.Study of a novel machine learning method embedding expertise(partⅡ):Applications and practices of guiding learning.Proceedings of the Chinese Society for Electrical Engineering.2017,37(20):5852-5861)
[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.
[15]李航.统计学习方法[M].北京:清华大学出版社,2012:55-73.(Li Hang.Statistical learning method.Beijing:Tsinghua University Press,2012:55-73)
[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.