Estimation of Scattering Parameters of U-Slotted Rectangular RFID Patch Antenna with Machine Learning Models
Estimation of Scattering Parameters of U-Slotted Rectangular RFID Patch Antenna with Machine Learning Models
In this study, machine learning-based models have been used to estimate the return loss parameters of the operational resonant frequency of the U-slotted UHF RFID antenna. The data set utilized, consisting of 544 instances, has been collected from the simulation software as a consequence of the parametric evaluation of the antenna design parameters. Distinct machine learning methods have been used on two different types of output data, complex and linear scattering parameters, and the models' prediction performance has been evaluated. In the single-output regression models, a mean-square error value of 0.25% with an R2 value of 95.54% was obtained with the Random Forest regression model, and a mean-square error value of 0.85% has been obtained with an R2 value of 91.32% in the multiple-output regression technique.
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- [1] C. R. M. Silva and S. R. Martins, “An Adaptive Evolutionary Algorithm for UWB Microstrip Antennas Optimization Using a
Machine Learning Technique,” Microw. Opt. Technol. Lett., vol. 55, no. 8, pp. 1864–1868, 2013, doi:
https://doi.org/10.1002/mop.27692.
- [2] Z. Zheng, X. Chen, and K. Huang, “Application of support vector machines to the antenna design,” Int. J. RF Microw. Comput.
Eng., vol. 21, no. 1, pp. 85–90, 2011, doi: https://doi.org/10.1002/mmce.20491.
- [3] J. Á. Muñiz, R. G. Ayestarán, J. Laviada, and F. Las-Heras, “Support vector regression for near_field multifocused antenna arrays
considering mutual coupling,” Int. J. Numer. Model. Networks Devices Fields, vol. 29, pp. 146–156, 2016.
- [4] T. Khan and C. Roy, “Prediction of slot-position and slot-size of a microstrip antenna using support vector regression,” Int. J. RF
Microw. Comput. Eng., vol. 29, no. 3, p. e21623, 2019, doi: https://doi.org/10.1002/mmce.21623.
- [5] S. Fei-Yan, T. Yu-Bo, and R. Zuo-Lin, “Modeling the resonant frequency of compact microstrip antenna by the PSO-based SVM
with the hybrid kernel function,” Int. J. Numer. Model. Electron. Networks, Devices Fields, vol. 29, no. 6, pp. 1129–1139, 2016,
doi: https://doi.org/10.1002/jnm.2171.
- [6] K. He, X. Zhang, S. Ren, and J. Sun, “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet
Classification,” IEEE Int. Conf. Comput. Vis. (ICCV 2015), vol. 1502, 2015, doi: 10.1109/ICCV.2015.123.
- [7] R. Geirhos, D. Janssen, H. Schütt, J. Rauber, M. Bethge, and F. Wichmann, “Comparing deep neural networks against humans:
object recognition when the signal gets weaker,” 2017.
- [8] W. Zhu et al., “AnatomyNet: Deep Learning for Fast and Fully Automated Whole‐volume Segmentation of Head and Neck
Anatomy,” Med. Phys., vol. 46, 2018, doi: 10.1002/mp.13300.
- [9] F. Liu, Z. Zhou, H. Jang, A. Samsonov, G. Zhao, and R. Kijowski, “Deep convolutional neural network and 3D deformable
approach for tissue segmentation in musculoskeletal magnetic resonance imaging: deep learning approach for segmenting MR
image,” Magn. Reson. Med., vol. 79, 2017, doi: 10.1002/mrm.26841.
- [10] B. Zhang, S. Li, Z. Huang, B. H. Rahi, Q. Wang, and M. Li, “Transfer learning-based online multiperson tracking with gaussian
process regression,” Concurr. Comput. Pract. Exp., vol. 30, no. 23, p. e4917, 2018, doi: https://doi.org/10.1002/cpe.4917.
- [11] J. Son, M. Baek, M. Cho, and B. Han, “Multi-object tracking with quadruplet convolutional neural networks,” Proc. - 30th IEEE
Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 3786–3795, 2017, doi: 10.1109/CVPR.2017.403.
- [12] Y. Moazzen, A. Çapar, A. Albayrak, N. Çalık, and B. U. Töreyin, “Metaphase finding with deep convolutional neural networks,”
Biomed. Signal Process. Control, vol. 52, pp. 353–361, 2019, doi: https://doi.org/10.1016/j.bspc.2019.04.017.
- [13] A. Albayrak et al., “A whole slide image grading benchmark and tissue classification for cervical cancer precursor lesions with
inter-observer variability,” 2018.
- [14] P. Phasukkit and T. Wongketsada, “Triple coaxial-half-slot antenna scheme with deep learning-based temperature prediction for
hepatic microwave ablation: finite element analysis and in vitro experiment,” IEEE Access, vol. 9, pp. 79572–79587, 2021, doi:
10.1109/ACCESS.2021.3083088.
- [15] İ. Akdağ, C. Göçen, M. Palandöken, and A. Kaya, “Estimation of the scattering sarameter at the resonance frequency of the UHF
band of the e-shaped RFID antenna using machine learning techniques,” in 2020 4th International Symposium on
Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2020, pp. 1–5, doi: 10.1109/ISMSIT50672.2020.9254362.
- [16] S. Koziel and A. Pietrenko-Dabrowska, “Global EM-driven optimization of multi-band antennas using knowledge-based inverse
response-feature surrogates,” Knowledge-Based Syst., vol. 227, p. 107189, 2021, doi: https://doi.org/10.1016/j.knosys.2021.107189.
- [17] J. Zhou et al., “A trust-region parallel bayesian optimization method for simulation-driven antenna design,” IEEE Trans. Antennas
Propag., vol. 69, no. 7, pp. 3966–3981, 2021, doi: 10.1109/TAP.2020.3044393.
- [18] N. Calik, M. A. Belen, P. Mahouti, and S. Koziel, “Accurate modeling of frequency selective surfaces using fully-connected
regression model with automated architecture determination and parameter selection based on bayesian optimization,” IEEE
Access, vol. 9, pp. 38396–38410, 2021, doi: 10.1109/ACCESS.2021.3063523.
- [19] S. Koziel, P. Mahouti, N. Calik, M. A. Belen, and S. Szczepanski, “Improved modeling of microwave structures using
performance-driven fully-connected regression surrogate,” IEEE Access, vol. 9, pp. 71470–71481, 2021, doi: 10.1109/ACCESS.2021.3078432.