Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/20973
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dc.contributor.authorJordovic Pavlovic, Miroslava-
dc.contributor.authorMarkushev, Dragan-
dc.contributor.authorKupusinac, Aleksandar-
dc.contributor.authorDjordjevic, Katarina-
dc.contributor.authorNesic, Mioljub-
dc.contributor.authorGalovic, Slobodanka-
dc.contributor.authorPopović, Marica-
dc.date.accessioned2024-07-16T06:41:42Z-
dc.date.available2024-07-16T06:41:42Z-
dc.date.issued2019-
dc.identifier.isbn978-5-6041187-1-9en_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/20973-
dc.descriptionAbstracten_US
dc.description.abstractCalibration in gas-microphone photoacoustics is not easily achieved, mainly due to the impossibility of finding two identical microphones needed for the differential set-up (each of them introducing non-linear influence on the recorded PA response). In this work, the methodology is developed which determines the influence of the used microphone, manifested through five characteristic frequencies which are functionally bonded to electronic and geometrical properties of the device. This is accomplished with the implementation of two-layer deep neural networks (fig. 1), enabling the filtration of the measured signal and thus removing the influence of the measurement chain on the photoacoustic response. Case study is done on PA measurements of laser-sintered polyamide (PA12), calibrated onto PA response of aluminum. Analysis of the obtained regression model for the prediction of the microphone parameters of PA response of aluminum is given in table 1. It is proven that this methodology successfully calibrates the measurement of examined samples onto a reference sample response, also filtrated from the measurement chain influence. It is also demonstrated that this procedure expands the frequency range for inverse solving of the PA problem, aiming at the estimation of thermal and optical sample properties, as well as improving their accuracy.en_US
dc.description.sponsorshipMinistry of Education and Science of the Republic of Serbia - Projects Nos. III 45005 and OI 171016.en_US
dc.language.isoenen_US
dc.subjectdeep learningen_US
dc.subjectphotoacousticen_US
dc.subjectcalibrationen_US
dc.subjectmicrophoneen_US
dc.subjectPA12en_US
dc.titleDeep neural network applied in calibration of transmission frequency gas-microphone photoacousticen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisheden_US
dc.type.versionPublishedVersionen_US
Appears in Collections:Faculty of Mechanical and Civil Engineering, Kraljevo

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