Araştırma Makalesi
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Yıl 2021, Cilt: 63 Sayı: 2, 127 - 141, 30.12.2021

Sultan Can Gökhan Soysal

https://doi.org/10.33769/aupse.992350

Öz

Kaynakça

  • Can, S., Yilmaz, A.E., Reduction of specific absorption rate with artificial magnetic conductors, Int. J. RF Microw. Comput. Aided Eng., 26 (4) (2016), 349-354. https://doi.org/10.1002/mmce.20974
  • Munk, B.A., Frequency Selective Surfaces: Theory and Design, John Wiley and Sons Inc., New York, 2000.
  • Vapnik, V.N., The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
  • Tsai, H.H., Chang, Y.C., Facial expression recognition using a combination of multiple facial features and support vector machine. Soft Comput., 22 (2018), 4389-4405. https://doi.org/10.1007/s00500-017-2634-3
  • Aly, S., Mohamed, A., Unknown-length handwritten numeral string recognition using cascade of PCA-SVMNet classifiers, IEEE Access, 7 (2019), 52024-52034. https://doi.org/10.1109/ACCESS.2019.2911851
  • Yaman, S., Pelecanos, J., Using polynomial kernel support vector machines for speaker verification, IEEE Signal Process. Lett., 20 (9) (2013), 901-904. https://doi.org/10.1109/LSP.2013.2273127
  • Gunes, F., Tokan, N.T., Gurgen, F., Support vector design of the microstrip lines, Int. J. RF Microw. Comput. Aided Eng., 18 (2008), 326-336. https://doi.org/10.1002/mmce.20290
  • Zheng, Z., Chen, X., Huang, K., Application of support vector machines to the antenna design, Int. J. RF Microw. Comput. Aided Eng., 21 (1) (2011), 85–90. https://doi.org/10.1002/mmce.20491
  • El Misilmani, H.M., Naous, T., Al Khatib, S.K., A review on the design and optimization of antennas using machine learning algorithms and techniques, Int. J. RF Microw. Comput. Eng., 30 (2020), 22356. https://doi.org/10.1002/mmce.22356
  • Gunn, S.R., Support vector machines for classification and regression, SISI Tech. Reports, (1998) 6459.
  • Cortes, C., Vapnik, V.N., Support vector networks, Mach. L., 20 (1997), 73-297.
  • Yilmaz, A.E., Kuzuoglu, M., Design of the square loop frequency selective surfaces with particle swarm optimization via the equivalent circuit model, Radioengineering, 18 (2) (2009), 95-102.
  • Li, J., Li, Y., Cen,Y., Zhang, C., Luo, T., Yang, D., Applications of neural networks for spectrum prediction and inverse design in the terahertz band, IEEE Photonics J., 12 (5) (2020),1-9. https://doi.org/10.1109/JPHOT.2020.3022053
  • Qiu, T., Deep learning: a rapid and efficient route to automatic metasurface design, Adv. Sci. Lett., 6 (12) (2019), 1900128. https://doi.org/ 10.1002/advs.201900128
  • Alcantara Neto, M.C.A, Oeiras Ferreira, H.R., Leite de Araujo, J.P., Brito Barros F.J., Gomes Neto, A., Oliveira Alencar, M., dos Santos Cavalcante, G.P., Compact ultra-wideband FSS optimized through fast and accurate hybrid bio-inspired multi objective technique, IET Microwaves, Antennas & Propag., 14 (2020), 884-890. https://doi.org/10.1049/iet-map.2019.0821
  • Chaudhary, V., Panwar, R., FSS derived using a new equivalent circuit model backed deep neural network., IEEE Antennas Wirel. Propag. Lett., 20 (10) (2021), 1963-1967. https://doi.org/ 10.1109/LAWP.2021.3101225
  • Bozzi, M., Manara, G., Monorchio, A., Perregrini, L., Automatic design of inductive FSSs using the genetic algorithm and the MoM/BI-RME analysis, IEEE Antennas Wirel. Propag. Lett., 1 (2002), 91-93. https://doi.org/10.1109/LAWP.2002.805129
  • Chakravarty, S., Mittra, R., Design of a frequency selective surface (FSS) with very low cross-polarization discrimination via the parallel micro-genetic algorithm (PMGA), IEEE Trans. Antennas Propag., 51 (7) (2003), 1664-1668. https://doi.org/10.1109/TAP.2003.813637
  • Zhu, D.Z., Werner, P.L., Werner, D.H., Design and optimization of 3-D frequency selective surfaces based on a multi objective lazy ant colony optimization algorithm, IEEE Trans. Antennas Propag., 65 (12) (2017), 7137-7149. https://doi.org/10.1109/TAP.2017.2766660
  • Ohira, M., Deguchi, H., Tsuji, M., Shigesawa, H. Multiband single-layer frequency selective surface designed by combination of genetic algorithm and geometry-refinement technique, IEEE Trans. Antennas Propag., 52 (11) (2004), 2925-2931. https://doi.org/10.1109/TAP.2004.835289
  • Monorchio, A., Manara, G., Serra, U., Marola, G., Pagana, E., Design of waveguide filters by using genetically optimized frequency selective surfaces., IEEE Microw. Wirel. Compon. Lett., 15 (6) (2005), 407-409. https://doi.org/10.1109/LMWC.2005.850482
  • Cui, S., Weile, D.S., Volakis, J.L., Novel planar electromagnetic absorber designs using genetic algorithms., IEEE Trans. Antennas Propag., 54 (6) (2006), 1811-1817. https://doi.org/10.1109/TAP.2006.87546

Estimating the frequency and bandwidth of square-split ring resonator (S-SRR) designs via support vector machines (SVM)

Yıl 2021, Cilt: 63 Sayı: 2, 127 - 141, 30.12.2021

Sultan Can Gökhan Soysal

https://doi.org/10.33769/aupse.992350

Öz

In this study Support Vector Machine (SVM) based estimation technique is proposed for estimating the bandstop frequency and bandwidth of square-split ring resonators. Artificially engineered surfaces especially the planar frequency selective surfaces like the SRRs have narrowband properties so that estimating the filtering frequencies and the bandwidth is essential in a cost and design-effective way. The proposed method, which is superior to optimization methods and 3D electromagnetic solvers in terms of cost and computational burden, achieved accurate results via SVMs generalization capability. This study represents two SVM regression models one for predicting frequency and the other for predicting bandwidth having fast response and accuracy. Results of the proposed model reveal that resonance frequency estimation error, in terms of percentage, is bounded in the interval [0.0542, 3.5938], with an overall error of 0.89 % for the test data. The mean and standard deviation of the percentage error is obtained as 0.9861 and 0.9376, respectively. In addition to that -10dB bandwidth is estimated with the bounded error where estimation error in terms of percentage would be lie in the interval [0.068925, 6.876800] with an overall error of 3.68% for the test data.

Anahtar Kelimeler

Bandwidth estimation filtering frequency estimation machine learning support vector machine

Kaynakça

  • Can, S., Yilmaz, A.E., Reduction of specific absorption rate with artificial magnetic conductors, Int. J. RF Microw. Comput. Aided Eng., 26 (4) (2016), 349-354. https://doi.org/10.1002/mmce.20974
  • Munk, B.A., Frequency Selective Surfaces: Theory and Design, John Wiley and Sons Inc., New York, 2000.
  • Vapnik, V.N., The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
  • Tsai, H.H., Chang, Y.C., Facial expression recognition using a combination of multiple facial features and support vector machine. Soft Comput., 22 (2018), 4389-4405. https://doi.org/10.1007/s00500-017-2634-3
  • Aly, S., Mohamed, A., Unknown-length handwritten numeral string recognition using cascade of PCA-SVMNet classifiers, IEEE Access, 7 (2019), 52024-52034. https://doi.org/10.1109/ACCESS.2019.2911851
  • Yaman, S., Pelecanos, J., Using polynomial kernel support vector machines for speaker verification, IEEE Signal Process. Lett., 20 (9) (2013), 901-904. https://doi.org/10.1109/LSP.2013.2273127
  • Gunes, F., Tokan, N.T., Gurgen, F., Support vector design of the microstrip lines, Int. J. RF Microw. Comput. Aided Eng., 18 (2008), 326-336. https://doi.org/10.1002/mmce.20290
  • Zheng, Z., Chen, X., Huang, K., Application of support vector machines to the antenna design, Int. J. RF Microw. Comput. Aided Eng., 21 (1) (2011), 85–90. https://doi.org/10.1002/mmce.20491
  • El Misilmani, H.M., Naous, T., Al Khatib, S.K., A review on the design and optimization of antennas using machine learning algorithms and techniques, Int. J. RF Microw. Comput. Eng., 30 (2020), 22356. https://doi.org/10.1002/mmce.22356
  • Gunn, S.R., Support vector machines for classification and regression, SISI Tech. Reports, (1998) 6459.
  • Cortes, C., Vapnik, V.N., Support vector networks, Mach. L., 20 (1997), 73-297.
  • Yilmaz, A.E., Kuzuoglu, M., Design of the square loop frequency selective surfaces with particle swarm optimization via the equivalent circuit model, Radioengineering, 18 (2) (2009), 95-102.
  • Li, J., Li, Y., Cen,Y., Zhang, C., Luo, T., Yang, D., Applications of neural networks for spectrum prediction and inverse design in the terahertz band, IEEE Photonics J., 12 (5) (2020),1-9. https://doi.org/10.1109/JPHOT.2020.3022053
  • Qiu, T., Deep learning: a rapid and efficient route to automatic metasurface design, Adv. Sci. Lett., 6 (12) (2019), 1900128. https://doi.org/ 10.1002/advs.201900128
  • Alcantara Neto, M.C.A, Oeiras Ferreira, H.R., Leite de Araujo, J.P., Brito Barros F.J., Gomes Neto, A., Oliveira Alencar, M., dos Santos Cavalcante, G.P., Compact ultra-wideband FSS optimized through fast and accurate hybrid bio-inspired multi objective technique, IET Microwaves, Antennas & Propag., 14 (2020), 884-890. https://doi.org/10.1049/iet-map.2019.0821
  • Chaudhary, V., Panwar, R., FSS derived using a new equivalent circuit model backed deep neural network., IEEE Antennas Wirel. Propag. Lett., 20 (10) (2021), 1963-1967. https://doi.org/ 10.1109/LAWP.2021.3101225
  • Bozzi, M., Manara, G., Monorchio, A., Perregrini, L., Automatic design of inductive FSSs using the genetic algorithm and the MoM/BI-RME analysis, IEEE Antennas Wirel. Propag. Lett., 1 (2002), 91-93. https://doi.org/10.1109/LAWP.2002.805129
  • Chakravarty, S., Mittra, R., Design of a frequency selective surface (FSS) with very low cross-polarization discrimination via the parallel micro-genetic algorithm (PMGA), IEEE Trans. Antennas Propag., 51 (7) (2003), 1664-1668. https://doi.org/10.1109/TAP.2003.813637
  • Zhu, D.Z., Werner, P.L., Werner, D.H., Design and optimization of 3-D frequency selective surfaces based on a multi objective lazy ant colony optimization algorithm, IEEE Trans. Antennas Propag., 65 (12) (2017), 7137-7149. https://doi.org/10.1109/TAP.2017.2766660
  • Ohira, M., Deguchi, H., Tsuji, M., Shigesawa, H. Multiband single-layer frequency selective surface designed by combination of genetic algorithm and geometry-refinement technique, IEEE Trans. Antennas Propag., 52 (11) (2004), 2925-2931. https://doi.org/10.1109/TAP.2004.835289
  • Monorchio, A., Manara, G., Serra, U., Marola, G., Pagana, E., Design of waveguide filters by using genetically optimized frequency selective surfaces., IEEE Microw. Wirel. Compon. Lett., 15 (6) (2005), 407-409. https://doi.org/10.1109/LMWC.2005.850482
  • Cui, S., Weile, D.S., Volakis, J.L., Novel planar electromagnetic absorber designs using genetic algorithms., IEEE Trans. Antennas Propag., 54 (6) (2006), 1811-1817. https://doi.org/10.1109/TAP.2006.87546
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Article
Yazarlar

Sultan Can 0000-0002-9001-0506

Gökhan Soysal 0000-0002-1397-8564

Yayımlanma Tarihi 30 Aralık 2021
Gönderilme Tarihi 7 Eylül 2021
Kabul Tarihi 18 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 63 Sayı: 2

Kaynak Göster

APA Can, S., & Soysal, G. (2021). Estimating the frequency and bandwidth of square-split ring resonator (S-SRR) designs via support vector machines (SVM). Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 63(2), 127-141. https://doi.org/10.33769/aupse.992350
AMA Can S, Soysal G. Estimating the frequency and bandwidth of square-split ring resonator (S-SRR) designs via support vector machines (SVM). Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. Aralık 2021;63(2):127-141. doi:10.33769/aupse.992350
Chicago Can, Sultan, ve Gökhan Soysal. “Estimating the Frequency and Bandwidth of Square-Split Ring Resonator (S-SRR) Designs via Support Vector Machines (SVM)”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63, sy. 2 (Aralık 2021): 127-41. https://doi.org/10.33769/aupse.992350.
EndNote Can S, Soysal G (01 Aralık 2021) Estimating the frequency and bandwidth of square-split ring resonator (S-SRR) designs via support vector machines (SVM). Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63 2 127–141.
IEEE S. Can ve G. Soysal, “Estimating the frequency and bandwidth of square-split ring resonator (S-SRR) designs via support vector machines (SVM)”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., c. 63, sy. 2, ss. 127–141, 2021, doi: 10.33769/aupse.992350.
ISNAD Can, Sultan - Soysal, Gökhan. “Estimating the Frequency and Bandwidth of Square-Split Ring Resonator (S-SRR) Designs via Support Vector Machines (SVM)”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 63/2 (Aralık 2021), 127-141. https://doi.org/10.33769/aupse.992350.
JAMA Can S, Soysal G. Estimating the frequency and bandwidth of square-split ring resonator (S-SRR) designs via support vector machines (SVM). Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2021;63:127–141.
MLA Can, Sultan ve Gökhan Soysal. “Estimating the Frequency and Bandwidth of Square-Split Ring Resonator (S-SRR) Designs via Support Vector Machines (SVM)”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, c. 63, sy. 2, 2021, ss. 127-41, doi:10.33769/aupse.992350.
Vancouver Can S, Soysal G. Estimating the frequency and bandwidth of square-split ring resonator (S-SRR) designs via support vector machines (SVM). Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2021;63(2):127-41.

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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