Đorđević, Katarina Lj.

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orcid::0000-0002-1397-8011
  • Đorđević, Katarina Lj. (5)
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Author's Bibliography

Inverse problem solving in semiconductor photoacoustics by neural networks

Đorđević, Katarina Lj.; Markushev, Dragan D.; Ćojbašić, Žarko M.; Galović, Slobodanka

(2021)

TY  - JOUR
AU  - Đorđević, Katarina Lj.
AU  - Markushev, Dragan D.
AU  - Ćojbašić, Žarko M.
AU  - Galović, Slobodanka
PY  - 2021
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/9075
AB  - We developed a method of inverse problem solving in semiconductor photoacoustics based on neural networks application. Simple structured neural networks, trained on a large set of data obtained by the well–known theoretical models in the 20 Hz–20 kHz modulation frequency range, are applied to determine thermal diffusivity, coefficient of linear expansion and thickness of n–type silicon samples, using undistorted experimental photoacoustic signals. The efficiency of the neural networks was tested depending on the type of input data, showing the best performances in the case when signal amplitudes and phases are simultaneously presented to the network. Real–time parameter prediction is achieved together with high accuracy and reliability allowing one to perform the full characterization of a sample in the frequency domain.
T2  - Inverse Problems in Science and Engineering
T1  - Inverse problem solving in semiconductor photoacoustics by neural networks
VL  - 29
IS  - 2
SP  - 248
EP  - 262
DO  - 10.1080/17415977.2020.1787405
ER  - 
@article{
author = "Đorđević, Katarina Lj. and Markushev, Dragan D. and Ćojbašić, Žarko M. and Galović, Slobodanka",
year = "2021",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/9075",
abstract = "We developed a method of inverse problem solving in semiconductor photoacoustics based on neural networks application. Simple structured neural networks, trained on a large set of data obtained by the well–known theoretical models in the 20 Hz–20 kHz modulation frequency range, are applied to determine thermal diffusivity, coefficient of linear expansion and thickness of n–type silicon samples, using undistorted experimental photoacoustic signals. The efficiency of the neural networks was tested depending on the type of input data, showing the best performances in the case when signal amplitudes and phases are simultaneously presented to the network. Real–time parameter prediction is achieved together with high accuracy and reliability allowing one to perform the full characterization of a sample in the frequency domain.",
journal = "Inverse Problems in Science and Engineering",
title = "Inverse problem solving in semiconductor photoacoustics by neural networks",
volume = "29",
number = "2",
pages = "248-262",
doi = "10.1080/17415977.2020.1787405"
}
Đorđević, K. Lj., Markushev, D. D., Ćojbašić, Ž. M.,& Galović, S. (2021). Inverse problem solving in semiconductor photoacoustics by neural networks.
Inverse Problems in Science and Engineering, 29(2), 248-262.
https://doi.org/10.1080/17415977.2020.1787405
Đorđević KL, Markushev DD, Ćojbašić ŽM, Galović S. Inverse problem solving in semiconductor photoacoustics by neural networks. Inverse Problems in Science and Engineering. 2021;29(2):248-262
Đorđević Katarina Lj., Markushev Dragan D., Ćojbašić Žarko M., Galović Slobodanka, "Inverse problem solving in semiconductor photoacoustics by neural networks" Inverse Problems in Science and Engineering, 29, no. 2 (2021):248-262,
https://doi.org/10.1080/17415977.2020.1787405 .
1

Deep Neural Network Application in the Phase-Match Calibration of Gas–Microphone Photoacoustics

Jordović-Pavlović, Miroslava I.; Markushev, Dragan D.; Kupusinac, Aleksandar; Đorđević, Katarina Lj.; Nešić, Mioljub V.; Galović, Slobodanka; Popović, Marica N.

(2020)

TY  - 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",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/8912",
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.
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. International Journal of Thermophysics. 2020;41(6):73
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" International Journal of Thermophysics, 41, no. 6 (2020):73,
https://doi.org/10.1007/s10765-020-02650-7 .
1

Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure

Đorđević, Katarina Lj.; Galović, Slobodanka; Jordović-Pavlović, Miroslava I.; Nešić, Mioljub V.; Popović, Marica N.; Ćojbašić, Žarko М.; Markushev, Dragan D.

(2020)

TY  - JOUR
AU  - Đorđević, Katarina Lj.
AU  - Galović, Slobodanka
AU  - Jordović-Pavlović, Miroslava I.
AU  - Nešić, Mioljub V.
AU  - Popović, Marica N.
AU  - Ćojbašić, Žarko М.
AU  - Markushev, Dragan D.
PY  - 2020
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8972
AB  - This paper introduces the possibility of the determination of optical absorption and reflexivity coefficient of silicon samples using neural networks and reverse-back procedure based on the photoacoustics response in the frequency domain. Differences between neural network predictions and parameters obtained with standard photoacoustic signal correction procedures are used to adjust our experimental set-up due to the instability of the optical excitation source and the state (contamination) of the illuminated surface. It has been shown that the changes of the optical absorption values correspond to the light source wavelength fluctuations, while changes in the reflexivity coefficient, obtained in this way, correspond to the small effect of the ultrathin layer formation of SiO2 due to the natural process of surface oxidation.
T2  - Optical and Quantum Electronics
T1  - Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure
VL  - 52
IS  - 5
SP  - 247
DO  - 10.1007/s11082-020-02373-x
ER  - 
@article{
author = "Đorđević, Katarina Lj. and Galović, Slobodanka and Jordović-Pavlović, Miroslava I. and Nešić, Mioljub V. and Popović, Marica N. and Ćojbašić, Žarko М. and Markushev, Dragan D.",
year = "2020",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/8972",
abstract = "This paper introduces the possibility of the determination of optical absorption and reflexivity coefficient of silicon samples using neural networks and reverse-back procedure based on the photoacoustics response in the frequency domain. Differences between neural network predictions and parameters obtained with standard photoacoustic signal correction procedures are used to adjust our experimental set-up due to the instability of the optical excitation source and the state (contamination) of the illuminated surface. It has been shown that the changes of the optical absorption values correspond to the light source wavelength fluctuations, while changes in the reflexivity coefficient, obtained in this way, correspond to the small effect of the ultrathin layer formation of SiO2 due to the natural process of surface oxidation.",
journal = "Optical and Quantum Electronics",
title = "Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure",
volume = "52",
number = "5",
pages = "247",
doi = "10.1007/s11082-020-02373-x"
}
Đorđević, K. Lj., Galović, S., Jordović-Pavlović, M. I., Nešić, M. V., Popović, M. N., Ćojbašić, Ž. М.,& Markushev, D. D. (2020). Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure.
Optical and Quantum Electronics, 52(5), 247.
https://doi.org/10.1007/s11082-020-02373-x
Đorđević KL, Galović S, Jordović-Pavlović MI, Nešić MV, Popović MN, Ćojbašić ŽМ, Markushev DD. Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure. Optical and Quantum Electronics. 2020;52(5):247
Đorđević Katarina Lj., Galović Slobodanka, Jordović-Pavlović Miroslava I., Nešić Mioljub V., Popović Marica N., Ćojbašić Žarko М., Markushev Dragan D., "Photoacoustic optical semiconductor characterization based on machine learning and reverse-back procedure" Optical and Quantum Electronics, 52, no. 5 (2020):247,
https://doi.org/10.1007/s11082-020-02373-x .
1
1

Computationally intelligent description of a photoacoustic detector

Jordović-Pavlović, Miroslava I.; Kupusinac, Aleksandar; Đorđević, Katarina Lj.; Galović, Slobodanka; Markushev, Dragan D.; Nešić, Mioljub V.; Popović, Marica N.

(2020)

TY  - JOUR
AU  - Jordović-Pavlović, Miroslava I.
AU  - Kupusinac, Aleksandar
AU  - Đorđević, Katarina Lj.
AU  - Galović, Slobodanka
AU  - Markushev, Dragan D.
AU  - Nešić, Mioljub V.
AU  - Popović, Marica N.
PY  - 2020
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8982
AB  - In this article, a method for determination of photoacoustic detector transfer function as an accurate representation of microphone frequency response is presented. The method is based on supervised machine learning techniques, classification and regression, performed by two artificial neural networks. The transfer function is obtained by determining the microphone type and characteristic parameters closely related to its filtering properties. This knowledge is crucial within the signal correction procedure. The method is carefully designed in order to maintain requirements of photoacoustic experiment accuracy, reliability and real-time performance. The networks training is performed using large base of theoretical signals simulating frequency response of three types of commercial electret microphones frequently used in photoacoustic measurements extended with possible flat response of the so-called ideal microphone. The method test is performed with simulated and experimental signals assuming the usage of open-cell photoacoustic set-up. Experimental testing leads to the microphone transfer function determination used to correct the experimental signals, targeting the “true” undistorted photoacoustic response which can be further used in material characterization process.
T2  - Optical and Quantum Electronics
T1  - Computationally intelligent description of a photoacoustic detector
VL  - 52
IS  - 5
SP  - 246
DO  - 10.1007/s11082-020-02372-y
ER  - 
@article{
author = "Jordović-Pavlović, Miroslava I. and Kupusinac, Aleksandar and Đorđević, Katarina Lj. and Galović, Slobodanka and Markushev, Dragan D. and Nešić, Mioljub V. and Popović, Marica N.",
year = "2020",
url = "https://vinar.vin.bg.ac.rs/handle/123456789/8982",
abstract = "In this article, a method for determination of photoacoustic detector transfer function as an accurate representation of microphone frequency response is presented. The method is based on supervised machine learning techniques, classification and regression, performed by two artificial neural networks. The transfer function is obtained by determining the microphone type and characteristic parameters closely related to its filtering properties. This knowledge is crucial within the signal correction procedure. The method is carefully designed in order to maintain requirements of photoacoustic experiment accuracy, reliability and real-time performance. The networks training is performed using large base of theoretical signals simulating frequency response of three types of commercial electret microphones frequently used in photoacoustic measurements extended with possible flat response of the so-called ideal microphone. The method test is performed with simulated and experimental signals assuming the usage of open-cell photoacoustic set-up. Experimental testing leads to the microphone transfer function determination used to correct the experimental signals, targeting the “true” undistorted photoacoustic response which can be further used in material characterization process.",
journal = "Optical and Quantum Electronics",
title = "Computationally intelligent description of a photoacoustic detector",
volume = "52",
number = "5",
pages = "246",
doi = "10.1007/s11082-020-02372-y"
}
Jordović-Pavlović, M. I., Kupusinac, A., Đorđević, K. Lj., Galović, S., Markushev, D. D., Nešić, M. V.,& Popović, M. N. (2020). Computationally intelligent description of a photoacoustic detector.
Optical and Quantum Electronics, 52(5), 246.
https://doi.org/10.1007/s11082-020-02372-y
Jordović-Pavlović MI, Kupusinac A, Đorđević KL, Galović S, Markushev DD, Nešić MV, Popović MN. Computationally intelligent description of a photoacoustic detector. Optical and Quantum Electronics. 2020;52(5):246
Jordović-Pavlović Miroslava I., Kupusinac Aleksandar, Đorđević Katarina Lj., Galović Slobodanka, Markushev Dragan D., Nešić Mioljub V., Popović Marica N., "Computationally intelligent description of a photoacoustic detector" Optical and Quantum Electronics, 52, no. 5 (2020):246,
https://doi.org/10.1007/s11082-020-02372-y .
1
1

Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks

Đorđević, Katarina Lj.; Markushev, Dragan D.; Ćojbašić, Žarko М.

(2020)

TY  - JOUR
AU  - Đorđević, Katarina Lj.
AU  - Markushev, Dragan D.
AU  - Ćojbašić, Žarko М.
PY  - 2020
UR  - http://vinar.vin.bg.ac.rs/handle/123456789/8759
AB  - In this paper, a simple multilayer perceptron neural network with forward signal propagation was designed and used to simultaneously determine the main physical parameters, such as: the thermal diffusivity, thermal expansion coefficient and thickness, from the transmission, frequency-modulated photoacoustic response of the sample. The amplitude and phase responses of the transmission open-cell photoacoustic signals were calculated in n-type silicon plates using a theoretical model and were used to train and test a neural network. The simulation was done in the modulation frequency range from 20 Hz to 20 kHz and using a wide range of expected values of thermal diffusivity and the thermal coefficient of expansion for semiconductor samples as well as their thickness. The advantages and disadvantages of neural networks utilization as an appropriate mathematical tool designated for semiconductor measurement-oriented purposes are analyzed. Network reliability, precision, and the possibility of operation in real time have been verified on an independent set of signals, establishing photoacoustics as a competitive and powerful technique assigned for material characterization. © 2019, Springer Nature B.V.
T2  - Silicon
T1  - Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks
VL  - 12
IS  - 6
SP  - 1289
EP  - 1300
DO  - 10.1007/s12633-019-00213-6
ER  - 
@article{
author = "Đorđević, Katarina Lj. and Markushev, Dragan D. and Ćojbašić, Žarko М.",
year = "2020",
url = "http://vinar.vin.bg.ac.rs/handle/123456789/8759",
abstract = "In this paper, a simple multilayer perceptron neural network with forward signal propagation was designed and used to simultaneously determine the main physical parameters, such as: the thermal diffusivity, thermal expansion coefficient and thickness, from the transmission, frequency-modulated photoacoustic response of the sample. The amplitude and phase responses of the transmission open-cell photoacoustic signals were calculated in n-type silicon plates using a theoretical model and were used to train and test a neural network. The simulation was done in the modulation frequency range from 20 Hz to 20 kHz and using a wide range of expected values of thermal diffusivity and the thermal coefficient of expansion for semiconductor samples as well as their thickness. The advantages and disadvantages of neural networks utilization as an appropriate mathematical tool designated for semiconductor measurement-oriented purposes are analyzed. Network reliability, precision, and the possibility of operation in real time have been verified on an independent set of signals, establishing photoacoustics as a competitive and powerful technique assigned for material characterization. © 2019, Springer Nature B.V.",
journal = "Silicon",
title = "Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks",
volume = "12",
number = "6",
pages = "1289-1300",
doi = "10.1007/s12633-019-00213-6"
}
Đorđević, K. Lj., Markushev, D. D.,& Ćojbašić, Ž. М. (2020). Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks.
Silicon, 12(6), 1289-1300.
https://doi.org/10.1007/s12633-019-00213-6
Đorđević KL, Markushev DD, Ćojbašić ŽМ. Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks. Silicon. 2020;12(6):1289-1300
Đorđević Katarina Lj., Markushev Dragan D., Ćojbašić Žarko М., "Photoacoustic Measurements of the Thermal and Elastic Properties of n-Type Silicon Using Neural Networks" Silicon, 12, no. 6 (2020):1289-1300,
https://doi.org/10.1007/s12633-019-00213-6 .
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