Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics
In this paper, a methodology for the application of neural networks in phase-match calibration of gas–microphone photoacoustics in frequency domain is developed. A two-layer deep neural network is used to determine, in real-time, reliably and accurately, the phase transfer function of the used micro...
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| Veröffentlicht in: | International journal of thermophysics 2020, Vol.41 (6), Article 73 |
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| container_title | International journal of thermophysics |
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| creator | Jordovic-Pavlovic, Miroslava I. Markushev, Dragan D. Kupusinac, Aleksandar D. Djordjevic, Katarina Lj Nesic, Mioljub V. Galovic, Slobodanka P. Popovic, Marica N. |
| description | In this paper, a methodology for the application of neural networks in phase-match calibration of gas–microphone photoacoustics in frequency domain is developed. A two-layer deep neural network is used to determine, in real-time, reliably and accurately, the phase transfer function of the used microphone, applying the photoacoustic response of aluminum as reference material. This transfer function was used to correct the photoacoustic response of laser-sintered polyamide and to compare it with theoretical predictions. The obtained degree of correlation of the corrected and theoretical signal tells us that our method of phase-match calibration in photoacoustics can be generalized to a photoacoustic response coming from a solid sample made of different materials. |
| doi_str_mv | 10.1007/s10765-020-02650-7 |
| format | Article |
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A two-layer deep neural network is used to determine, in real-time, reliably and accurately, the phase transfer function of the used microphone, applying the photoacoustic response of aluminum as reference material. This transfer function was used to correct the photoacoustic response of laser-sintered polyamide and to compare it with theoretical predictions. The obtained degree of correlation of the corrected and theoretical signal tells us that our method of phase-match calibration in photoacoustics can be generalized to a photoacoustic response coming from a solid sample made of different materials.</description><subject>Aluminum</subject><subject>Artificial neural networks</subject><subject>Calibration</subject><subject>Classical Mechanics</subject><subject>Condensed Matter Physics</subject><subject>Geophysics</subject><subject>Icppp 20</subject><subject>ICPPP-20: Selected Papers of the 20th International Conference on Photoacoustic and Photothermal Phenomena</subject><subject>Industrial Chemistry/Chemical Engineering</subject><subject>Laser sintering</subject><subject>Neural networks</subject><subject>Physical Chemistry</subject><subject>Physics</subject><subject>Physics and Astronomy</subject><subject>Polyamide resins</subject><subject>Thermodynamics</subject><subject>Transfer functions</subject><issn>0195-928X</issn><issn>1572-9567</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kLFOwzAQhi0EEqXwAkyRmAPnc-wkY1WgILXAABKb5TgOSSlxsBMhNt6BN-RJcAkSG8PpH-67_-5-Qo4pnFKA9MxTSAWPASGU4BCnO2RCeYpxzkW6SyZAcx7nmD3ukwPv1wCQpzmbEHVuTBfdmMGpTZD-zbrnaNZ1m0arvrFt1LRRX5vorlbexCvV6zqaq01TuLFtq2ih_NfH56rRzna1bbes7a3SdvB9o_0h2avUxpujX52Sh8uL-_lVvLxdXM9ny1gzLvqY5xUkgEkJUDBAVogyA1pmhhpeUFFkOtFFUmWIyNBoNJir8FPJBIgMqpRNycno2zn7Ohjfy7UdXBtWSmQZpsgReaBwpMK13jtTyc41L8q9SwpyG6Uco5QhSvkTpdxas3HIB7h9Mu7P-p-pb3jcdzQ</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Jordovic-Pavlovic, Miroslava I.</creator><creator>Markushev, Dragan D.</creator><creator>Kupusinac, Aleksandar D.</creator><creator>Djordjevic, Katarina Lj</creator><creator>Nesic, Mioljub V.</creator><creator>Galovic, Slobodanka P.</creator><creator>Popovic, Marica N.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>2020</creationdate><title>Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics</title><author>Jordovic-Pavlovic, Miroslava I. ; Markushev, Dragan D. ; Kupusinac, Aleksandar D. ; Djordjevic, Katarina Lj ; Nesic, Mioljub V. ; Galovic, Slobodanka P. ; Popovic, Marica N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-59f04024d00b3023b6d801d8e1e5b16b8c4cb4f822232ec2e29a195d360680f73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aluminum</topic><topic>Artificial neural networks</topic><topic>Calibration</topic><topic>Classical Mechanics</topic><topic>Condensed Matter Physics</topic><topic>Geophysics</topic><topic>Icppp 20</topic><topic>ICPPP-20: Selected Papers of the 20th International Conference on Photoacoustic and Photothermal Phenomena</topic><topic>Industrial Chemistry/Chemical Engineering</topic><topic>Laser sintering</topic><topic>Neural networks</topic><topic>Physical Chemistry</topic><topic>Physics</topic><topic>Physics and Astronomy</topic><topic>Polyamide resins</topic><topic>Thermodynamics</topic><topic>Transfer functions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jordovic-Pavlovic, Miroslava I.</creatorcontrib><creatorcontrib>Markushev, Dragan D.</creatorcontrib><creatorcontrib>Kupusinac, Aleksandar D.</creatorcontrib><creatorcontrib>Djordjevic, Katarina Lj</creatorcontrib><creatorcontrib>Nesic, Mioljub V.</creatorcontrib><creatorcontrib>Galovic, Slobodanka P.</creatorcontrib><creatorcontrib>Popovic, Marica N.</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of thermophysics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jordovic-Pavlovic, Miroslava I.</au><au>Markushev, Dragan D.</au><au>Kupusinac, Aleksandar D.</au><au>Djordjevic, Katarina Lj</au><au>Nesic, Mioljub V.</au><au>Galovic, Slobodanka P.</au><au>Popovic, Marica N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics</atitle><jtitle>International journal of thermophysics</jtitle><stitle>Int J Thermophys</stitle><date>2020</date><risdate>2020</risdate><volume>41</volume><issue>6</issue><artnum>73</artnum><issn>0195-928X</issn><eissn>1572-9567</eissn><abstract>In this paper, a methodology for the application of neural networks in phase-match calibration of gas–microphone photoacoustics in frequency domain is developed. A two-layer deep neural network is used to determine, in real-time, reliably and accurately, the phase transfer function of the used microphone, applying the photoacoustic response of aluminum as reference material. This transfer function was used to correct the photoacoustic response of laser-sintered polyamide and to compare it with theoretical predictions. The obtained degree of correlation of the corrected and theoretical signal tells us that our method of phase-match calibration in photoacoustics can be generalized to a photoacoustic response coming from a solid sample made of different materials.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10765-020-02650-7</doi></addata></record> |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Aluminum Artificial neural networks Calibration Classical Mechanics Condensed Matter Physics Geophysics Icppp 20 ICPPP-20: Selected Papers of the 20th International Conference on Photoacoustic and Photothermal Phenomena Industrial Chemistry/Chemical Engineering Laser sintering Neural networks Physical Chemistry Physics Physics and Astronomy Polyamide resins Thermodynamics Transfer functions |
| title | Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics |
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