Contrast reconstruction of overfilled cavities by incorporating multi-frequency scattering fields and attention mechanism into two-step learning method. (English) Zbl 07892944
Keywords:
non-body-fitted grids; multiple frequency; channel attention mechanism; two-step learning method; contrast reconstruction; overfilled cavitySoftware:
SegNetReferences:
| [1] | Huang, H.; Pan, M.; Lu, Z., Hardware-in-the-loop simulation technology of wide-band radar targets based on scattering center model, Chin J Aeronaut, 28, 1476-1484, 2015 |
| [2] | Hong, L.; Dai, F.; Wang, X., Knowledge-based wideband radar target detection in the heterogeneous environment, Signal Process, 144, 169-179, 2018 |
| [3] | Liu, Q.; Zhang, X.; Liu, Y., SCNet: Scattering center neural network for radar target recognition with incomplete target-aspects, Signal Process, 219, Article 109409 pp., 2024 |
| [4] | Zdunek, R., On image reconstruction algorithms for binary electromagnetic geotomography, Theoret Comput Sci, 406, 160-170, 2008 · Zbl 1167.68461 |
| [5] | Lin, J.; Chen, J.; Zhang, Y., Rapid and high-resolution detection of urban underground space using transient electromagnetic method, IEEE Trans Ind Inf, 18, 2622-2631, 2022 |
| [6] | Liu, Y.; Na, X.; Yin, C.; Su, Y.; Sun, S.; Zhang, B., 3-D joint inversion of airborne electromagnetic and magnetic data based on local pearson correlation constraints, IEEE Trans Geosci Remote Sens, 60, 1-13, 2022 |
| [7] | Wood, A., Analysis of electromagnetic scattering from an overfilled cavity in the ground plane, J Comput Phys, 215, 630-641, 2006 · Zbl 1100.78014 |
| [8] | Du, K., Two transparent boundary conditions for the electromagnetic scattering from two-dimensional overfilled cavities, J Comput Phys, 230, 5822-5835, 2011 · Zbl 1220.78044 |
| [9] | Bürgel, F.; Kazimierski, K.; Lechleiter, A., A sparsity regularization and total variation based computational framework for the inverse medium problem in scattering, J Comput Phys, 339, 1-30, 2017 · Zbl 1375.78025 |
| [10] | Nair, S.; Walsh, T.; Pickrell, G.; Semperlotti, F., GRIDS-Net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning, Comput Methods Appl Mech Engrg, 414, Article 116167 pp., 2023 · Zbl 1539.74556 |
| [11] | Zhou, W.; Liu, A.; Wu, L.; Chen, X., A \(L_1\) normalization enhanced dynamic window method for SSVEP-based BCIs, J Neurosci Methods, 380, Article 109688 pp., 2022 |
| [12] | Ren J, Lu X, Guan J, Yin Z, Chen W. Fast high-resolution 3D radar imaging based on 3D FISTA. In: 2017 IEEE Radar conference. 2017, p. 815-8. |
| [13] | Su, H.; Xu, F.; Lu, S.; Jin, Y., Iterative ADMM for inverse FE-BI problem: A potential solution to radio tomography of asteroids, IEEE Trans Geosci Remote Sens, 54, 5226-5238, 2016 |
| [14] | Brignone, M.; Coyle, J.; Piana, M., The use of the linear sampling method for obtaining super-resolution effects in Born approximation, J Comput Appl Math, 203, 145-158, 2007 · Zbl 1121.78007 |
| [15] | Köhn, A.; Guidi, L.; Holzhauer, E.; Maj, O.; Poli, E.; Snicker, A., Microwave beam broadening due to turbulent plasma density fluctuations within the limit of the Born approximation and beyond, Plasma Phys Control Fusion, 60, Article 075006 pp., 2018 |
| [16] | Vouldis, A.; Nikita, K.; Uzunoglu, N., An inverse scattering algorithm using the Rytov approximation for a transient three-dimensional problem, Nucl Instrum Methods Phys Res A, 569, 587-590, 2006 |
| [17] | Wang, W.; Wu, Z.; Shang, Q.; Lu, B., Propagation of Bessel Gaussian beams through non-Kolmogorov turbulence based on Rytov theory, Optics Express, 26, 21712-21724, 2018 |
| [18] | Lu, C.; Lin, J.; Chew, W.; Otto, G., Image reconstruction with acoustic measurement using distorted Born iteration method, Ultrason Imaging, 18, 140-156, 1996 |
| [19] | Guo, L.; Nie, Z.; Liu, G., A detection method for cable local defects based on Born iteration, Electr Power Syst Res, 226, Article 109956 pp., 2024 |
| [20] | He, Q.; Han, B.; Chen, Y.; Li, Y., Application of the finite-difference contrast source inversion method to multiparameter reconstruction using seismic full-waveform data, J Appl Geophys, 124, 4-16, 2016 |
| [21] | Lee, K., Shape reconstructions from phaseless data, Eng Anal Bound Elem, 71, 174-178, 2016 · Zbl 1403.76158 |
| [22] | Han, X.; Yang, Y.; Liu, Y., Determining the defect locations and sizes in elastic plates by using the artificial neural network and boundary element method, Eng Anal Bound Elem, 139, 232-245, 2022 · Zbl 1521.74133 |
| [23] | Kong, F.; Zhou, X.; Guo, C.; Lu, D.; Du, X., Elastic analytical method with machine learning for predicting the stratum displacement field induced by shallow tunneling, Eng Anal Bound Elem, 159, 201-212, 2024 · Zbl 07855266 |
| [24] | Zhang, Z.; Wang, H.; Xu, F.; Jin, Y., Complex-valued convolutional neural network and its application in polarimetric sar image classification, IEEE Trans Geosci Remote Sens, 55, 7177-7188, 2017 |
| [25] | Sun, S.; He, Z.; Ding, D.; Ding, F., Convolutional neural network-based perturbation shooting and bouncing rays solution for recognition of targets with uncertain geometrical shapes, Eng Anal Bound Elem, 143, 613-631, 2022 · Zbl 1521.74355 |
| [26] | Gao, Y.; Liu, H.; Wang, X.; Zhang, K., On an artificial neural network for inverse scattering problems, J Comput Phys, 448, Article 110771 pp., 2022 · Zbl 1537.78008 |
| [27] | Lin, S.; Dong, M.; Liang, Z.; Guo, H.; Zheng, H., Image-based 3D reconstruction and permeability modelling of rock using enhanced interpretable deep residual learning, Eng Anal Bound Elem, 160, 187-200, 2024 · Zbl 07855299 |
| [28] | Zhang, H.; He, M.; Li, J.; Ng, M., Solving electromagnetic inverse scattering problems in inhomogeneous media by deep convolutional encoder-decoder structure, IEEE Trans Antennas and Propagation, 71, 2867-2872, 2023 |
| [29] | Badrinarayanan, V.; Kendall, A.; Cipolla, R., Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans Pattern Anal Mach Intell, 39, 2481-2495, 2017 |
| [30] | Yin, W.; Yang, W.; Liu, H., A neural network scheme for recovering scattering obstacles with limited phaseless far-field data, J Comput Phys, 417, Article 109594 pp., 2020 · Zbl 1437.68157 |
| [31] | Li, L.; Wang, L.; Teixeira, F.; Liu, C.; Nehorai, A.; Cui, T., Deepnis: Deep neural network for nonlinear electromagnetic inverse scattering, IEEE Trans Antennas and Propagation, 67, 1819-1825, 2019 |
| [32] | He, M.; Wei, S.; Li, J., Two-step enhanced deep learning approach for electromagnetic inverse scattering problems, IEEE Antennas Wirel Propag Lett, 18, 2254-2258, 2019 |
| [33] | He, M.; Guo, R.; Li, J.; Li, M.; Li, J.; Ng, M., Enhanced supervised descent learning technique for electromagnetic inverse scattering problems by the deep convolutional neural networks, IEEE Trans Antennas and Propagation, 70, 6195-6206, 2022 |
| [34] | Zhang, H.; He, M.; Li, J.; Ng, M., Enhanced two-step deep-learning approach for electromagnetic-inverse-scattering problems: Frequency extrapolation and scatterer reconstruction, IEEE Trans Antennas and Propagation, 71, 1662-1672, 2023 |
| [35] | Ng, M.; He, M., Deep learning based source reconstruction method using asymmetric encoder-decoder structure and physics-induced loss, J Comput Appl Math, 438, Article 115503 pp., 2024 · Zbl 1533.68312 |
| [36] | Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q. ECA-Net: Efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF conference on computer vision and pattern recognition. 2020, p. 11531-9. |
| [37] | Zhu, M.; Jiao, L.; Liu, F.; Yang, S.; Wang, J., Residual spectral-spatial attention network for hyperspectral image classification, IEEE Trans Geosci Remote Sens, 59, 449-462, 2021 |
| [38] | Zhao, M.; Zhu, N.; Wang, L., The electromagnetic scattering from multiple arbitrarily shaped cavities with inhomogeneous anisotropic media, J Comput Phys, 489, Article 112274 pp., 2023 · Zbl 07705915 |
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