Optimization of Electromagnetic Interference Measurement by Convolutional Neural Network

Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

Abstract:

With ever-increasing use of equipment, device or more generally any electrical or electronic system, the chance of Electromagnetic incompatibility incidents has considerably increased which demands more attention to ensure the possible risks of these technologies. Therefore, complying with certain Electromagnetic compatibility (EMC) rules and not overtaking an acceptable level of radiated emissions are utmost importance for the diffusion of electronic products. In this paper, developed measure tool and a convolutional neural network were used to propose a method to reduce the required time to carry out the final measurement phase of Electromagnetic interference (EMI) measurement according to the norm EN 55032 by predicting the radiated emission and determining the height of the antenna that meets the maximum radiation value.

Keywords: Antenna height, Convolutional Neural Network, Electromagnetic Compatibility, Mean Absolute Error, position error.

References:

[1] T. Sudo, H. Sasaki, N. Masuda, and J. L. Drewniak, ``Electromagnetic interference (EMI) of system-on-package (SOP),'' IEEE Trans. Adv. Packag., vol. 27, no. 2, pp. 304_314, May 2004.
[2] X.-L. Yang, L. Zhang, Y.-S. Li, H. Jin, P. Cheng, Y. Li, and E.-P. Li, A novel package lid using mushroom-type ebg structures for unintentional radiation mitigation,'' IEEE Trans. Electromagn. Compat., vol. 60, no. 6, pp. 1882_1888, Dec. 2018
[3] C. Zhang, J. Jin, W. Na, Q. J. Zhang, and M. Yu, “Multivalued Neural Network Inverse Modeling and Applications to Microwave Filters,” IEEE Trans. Microw. Theory Techn., vol. 66, no. 8, pp. 3781-3797, Aug. 2018.
[4] H. Ma and E. Li, "Prediction of IC Equivalent Magnetic Dipoles Using Deep Convolutional Neural Network," 2018 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS), 2018, pp. 1-3, doi: 10.1109/EDAPS.2018.8680864.
[5] CISPR 32, ‘Electromagnetic compatibility of multimedia equipment Emission requirements, 2015.
[6] CISPR 16-1-4: Specification for radio disturbance and immunity measuring apparatus and methods – Part 1-4: Radio disturbance and immunity measuring apparatus – Antennas and test sites for radiated disturbance measurements, IEC Standard, Edition 3.1, July 2012.
[7] E. Alan, C. C. Keskin, G. Kiraz, U. S. Ceran and U. Dogan, "Experimental Analysis of Radiated Emission Limits Regarding Test Facilities According to EN 55032," 2019 Fifth International.
[8] EN 55032:2015 “Electromagnetic compatibility of multimedia equipment - Emission requirements,” 2012
[9] M. Ishii, H. Yoshida, Y. Danjo, S. Kurokawa and K. Fujii, "A study on the characteristics of semi-anechoic chambers below 30 MHz," 2014 International Symposium on Electromagnetic Compatibility, 2014, pp. 934-939, doi: 10.1109/EMCEurope.2014.6931037.
[10] CISPR 16-4-2 Edition 2.0 “Specification for radio disturbance and immunity measuring apparatus and methods– Part 4-2: Uncertainties, statistics and limit modelling– Measurement instrumentation uncertainty,” 2011.
[11] "American National Standard for Methods of Measurement of Radio-Noise Emissions from Low-Voltage Electrical and Electronic Equipment in the Range of 9 kHz to 40 GHz," in ANSI C63.4-2009, vol., no., pp.1-155, 15 Sept. 2009, doi: 10.1109/IEEESTD.2009.5246989.
[12] N. Dandanov, H. Al-Shatri, A. Klein and V. Poulkov,"Dynamic self-optimization of the antenna tilt for besttrade-off between coverage and capacity in mobile
[13] S. Kiranyaz, T. Ince, O. Abdeljaber, O. Avci and M. Gabbouj, "1-D Convolutional Neural Networks for Signal Processing Applications," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 8360-8364, doi: 10.1109/ICASSP.2019.8682194.
[14] Kline, Douglas M. "Methods for Multi-Step Time Series Forecasting Neural Networks." Neural Networks in Business Forecasting, edited by G. Peter Zhang, IGI Global, 2004, pp. 226-250. http://doi:10.4018/978-1-59140-176-6.ch012.
[15] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classifica- tion with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105.
[16] R. Larson and B. Farber, Elementary Statistics: Picturing the World. London, U.K.: Pearson, 2011.