Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics
Samo za registrovane korisnike
2020
Autori
Jordović-Pavlović, Miroslava I.Markushev, Dragan D.
Kupusinac, Aleksandar
Đorđević, Katarina Lj.
Nešić, Mioljub V.
Galović, Slobodanka
Popović, Marica N.
Članak u časopisu (Objavljena verzija)
,
© 2020, Springer Science+Business Media, LLC, part of Springer Nature
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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.
Izvor:
International Journal of Thermophysics, 2020, 41, 6, 73-Finansiranje / projekti:
- Funkcionalni, funkcionalizovani i usavršeni nano materijali (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-45005)
- Atomski sudarni procesi i fotoakustička spektrometrija molekula i čvrstih tela (RS-MESTD-Basic Research (BR or ON)-171016)
- Reprezentacije logičkih struktura i formalnih jezika i njihove primene u računarstvu (RS-MESTD-Basic Research (BR or ON)-174026)
- Razvoj novih informaciono-komunikacionih tehnologija, korišćenjem naprednih matematičkih metoda, sa primenama u medicini, telekomunikacijama, energetici, zaštititi nacionalne baštine i obrazovanju (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-44006)
DOI: 10.1007/s10765-020-02650-7
ISSN: 0195-928X
WoS: 000522191900001
Scopus: 2-s2.0-85082526367
Kolekcije
Institucija/grupa
VinčaTY - JOUR AU - Jordović-Pavlović, Miroslava I. AU - Markushev, Dragan D. AU - Kupusinac, Aleksandar AU - Đorđević, Katarina Lj. AU - Nešić, Mioljub V. AU - Galović, Slobodanka AU - Popović, Marica N. PY - 2020 UR - https://vinar.vin.bg.ac.rs/handle/123456789/8912 AB - 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. T2 - International Journal of Thermophysics T1 - Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics VL - 41 IS - 6 SP - 73 DO - 10.1007/s10765-020-02650-7 ER -
@article{ author = "Jordović-Pavlović, Miroslava I. and Markushev, Dragan D. and Kupusinac, Aleksandar and Đorđević, Katarina Lj. and Nešić, Mioljub V. and Galović, Slobodanka and Popović, Marica N.", year = "2020", 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.", journal = "International Journal of Thermophysics", title = "Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics", volume = "41", number = "6", pages = "73", doi = "10.1007/s10765-020-02650-7" }
Jordović-Pavlović, M. I., Markushev, D. D., Kupusinac, A., Đorđević, K. Lj., Nešić, M. V., Galović, S.,& Popović, M. N.. (2020). Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics. in International Journal of Thermophysics, 41(6), 73. https://doi.org/10.1007/s10765-020-02650-7
Jordović-Pavlović MI, Markushev DD, Kupusinac A, Đorđević KL, Nešić MV, Galović S, Popović MN. Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics. in International Journal of Thermophysics. 2020;41(6):73. doi:10.1007/s10765-020-02650-7 .
Jordović-Pavlović, Miroslava I., Markushev, Dragan D., Kupusinac, Aleksandar, Đorđević, Katarina Lj., Nešić, Mioljub V., Galović, Slobodanka, Popović, Marica N., "Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics" in International Journal of Thermophysics, 41, no. 6 (2020):73, https://doi.org/10.1007/s10765-020-02650-7 . .