Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural Networks
In this paper, a neural network approach is applied for solving an electromagnetic inverse problem involving solid dielectric materials subjected to shock impacts and interrogated by a millimeter-wave interferometer. Under mechanical impact, a shock wave is generated in the material and modifies the...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4835 |
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| author | Jérémi Mapas Alexandre Lefrançois Hervé Aubert Sacha Comte Yohan Barbarin Maylis Lavayssière Benoit Rougier Alexandre Dore |
| author_facet | Jérémi Mapas Alexandre Lefrançois Hervé Aubert Sacha Comte Yohan Barbarin Maylis Lavayssière Benoit Rougier Alexandre Dore |
| author_sort | Jérémi Mapas |
| collection | DOAJ |
| description | In this paper, a neural network approach is applied for solving an electromagnetic inverse problem involving solid dielectric materials subjected to shock impacts and interrogated by a millimeter-wave interferometer. Under mechanical impact, a shock wave is generated in the material and modifies the refractive index. It was recently demonstrated that the shock wavefront velocity and the particle velocity as well as the modified index in a shocked material can be remotely derived from measuring two characteristic Doppler frequencies in the waveform delivered by a millimeter-wave interferometer. We show here that a more accurate estimation of the shock wavefront and particle velocities can be obtained from training an appropriate convolutional neural network, especially in the important case of short-duration waveforms of few microseconds. |
| first_indexed | 2024-03-11T03:20:18Z |
| format | Article |
| id | doaj.art-57ce6f895588449e9902cd8ea3eeebff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:18Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-57ce6f895588449e9902cd8ea3eeebff2023-11-18T03:13:21ZengMDPI AGSensors1424-82202023-05-012310483510.3390/s23104835Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural NetworksJérémi Mapas0Alexandre Lefrançois1Hervé Aubert2Sacha Comte3Yohan Barbarin4Maylis Lavayssière5Benoit Rougier6Alexandre Dore7CEA-DAM, GRAMAT, BP80200, F-46500 Gramat, FranceCEA-DAM, GRAMAT, BP80200, F-46500 Gramat, FranceCNRS-LAAS, Toulouse University, 7 Avenue du Colonel Roche, BP54200, F-31031 Toulouse, FranceCEA-DAM, GRAMAT, BP80200, F-46500 Gramat, FranceCEA-DAM, GRAMAT, BP80200, F-46500 Gramat, FranceCEA-DAM, GRAMAT, BP80200, F-46500 Gramat, FranceCEA-DAM, GRAMAT, BP80200, F-46500 Gramat, FranceCNRS-LAAS, Toulouse University, 7 Avenue du Colonel Roche, BP54200, F-31031 Toulouse, FranceIn this paper, a neural network approach is applied for solving an electromagnetic inverse problem involving solid dielectric materials subjected to shock impacts and interrogated by a millimeter-wave interferometer. Under mechanical impact, a shock wave is generated in the material and modifies the refractive index. It was recently demonstrated that the shock wavefront velocity and the particle velocity as well as the modified index in a shocked material can be remotely derived from measuring two characteristic Doppler frequencies in the waveform delivered by a millimeter-wave interferometer. We show here that a more accurate estimation of the shock wavefront and particle velocities can be obtained from training an appropriate convolutional neural network, especially in the important case of short-duration waveforms of few microseconds.https://www.mdpi.com/1424-8220/23/10/4835convolutional neural networkshock propertiesmm-wave interferometrymetrologyshock velocityparticle velocity |
| spellingShingle | Jérémi Mapas Alexandre Lefrançois Hervé Aubert Sacha Comte Yohan Barbarin Maylis Lavayssière Benoit Rougier Alexandre Dore Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural Networks Sensors convolutional neural network shock properties mm-wave interferometry metrology shock velocity particle velocity |
| title | Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural Networks |
| title_full | Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural Networks |
| title_fullStr | Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural Networks |
| title_full_unstemmed | Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural Networks |
| title_short | Shock Properties Characterization of Dielectric Materials Using Millimeter-Wave Interferometry and Convolutional Neural Networks |
| title_sort | shock properties characterization of dielectric materials using millimeter wave interferometry and convolutional neural networks |
| topic | convolutional neural network shock properties mm-wave interferometry metrology shock velocity particle velocity |
| url | https://www.mdpi.com/1424-8220/23/10/4835 |
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