Computationally intelligent description of a photoacoustic detector

Jordović-Pavlović, M. I.; Kupusinac, Aleksandar; Djordjević, Katarina; Galović, S.; Markushev, D. D.; Nesic, M. V.; Popović, Marica N.

doi:10.1007/s11082-020-02372-y

Cited by 12 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hidden layer consists of 50 neurons, following the criteria that the number of neurons is less than half the size of the input layer. The output layer consists of 3 neurons, each corresponding to the number of predictions we want to make (typical investigated sample thermal ( ) First type of normalization applied on is the normalization to the maximum absolute value (max norm) of the base frequency vectors (Figure 2.b), defined as [23,24]:…”

Section: Neural Network Model Designmentioning

confidence: 99%

Influence of data scaling and normalization on overall neural network performances in photoacoustics

et al. 2022

Self Cite

View full text Add to dashboard Cite

In this paper, the influence of the input and output data scaling and normalization on the neural network overall performances is investigated aimed at inverse problem-solving in photoacoustics of semiconductors. The logarithmic scaling of the photoacoustic signal amplitudes as input data and numerical scaling of the sample thermal parameters as output data are presented as useful tools trying to reach maximal network precision. Max and min-max normalizations to the input data are presented to change their numerical values in the dataset to common scales, without distorting differences. It was demonstrated in theory that the largest network prediction error of all targeted parameters is obtained by a network with non-scaled output data. Also, it was found out that the best network prediction was achieved with min-max normalization of the input data and network predicted output data scale within the range of [1][2][3][4][5][6][7][8][9][10]. Network training and prediction performances analyzed with experimental input data show that the benefits and improvements of input and output scaling and normalization are not guaranteed but are strongly dependent on a specific problem to be solved.

show abstract

Section: Neural Network Model Designmentioning

confidence: 99%

Influence of data scaling and normalization on overall neural network performances in photoacoustics

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Neural Network Model Designmentioning

confidence: 99%

Influence of Data Scaling and Normalization on Overall Neural Network Performances in Photoacoustics

Jordović-Pavlović

Djordjević

Ćojbašić

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper, the influence of the input and output data scaling and normalization on the neural network overall performances is investigated aimed at inverse problem-solving in photoacoustics of semiconductors. The logarithmic scaling of the photoacoustic signal amplitudes as input data and numerical scaling of the sample thermal parameters as output data are presented as useful tools trying to reach maximal network precision. Max and min-max normalizations to the input data are presented to change their numerical values in the dataset to common scales, without distorting differences. It was demonstrated in theory that the largest network prediction error of all targeted parameters is obtained by a network with non-scaled output data. Also, it was found out that the best network prediction was achieved with min-max normalization of the input data and network predicted output data scale within the range of [110]. Network training and prediction performances analyzed with experimental input data show that the benefits and improvements of input and output scaling and normalization are not guaranteed but are strongly dependent on a specific problem to be solved.

show abstract

“…The earlier developed procedure based on neural networks [ 10 , 11 , 29 , 30 , 31 ] for processing of experimentally recorded photoacoustic signals of silicon samples by the open photoacoustic cell [ 32 , 33 , 34 , 35 ] shows effective recognition and removal of instrumental influence [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ], and, consequently, provides a detailed and precise characterization of the sample [ 41 , 42 , 43 , 44 , 45 , 46 ]. On the other hand, a very thin TiO 2 layer (nano-layer) is easily deposited in a silicon substrate.…”

Section: Introductionmentioning

confidence: 99%

Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

et al. 2023

View full text Add to dashboard Cite

In this paper, the possibility of determining the thermal, elastic and geometric characteristics of a thin TiO2 film deposited on a silicon substrate, with a thickness of 30 μm, in the frequency range of 20 to 20 kHz with neural networks were analysed. For this purpose, the geometric (thickness), thermal (thermal diffusivity, coefficient of linear expansion) and electronic parameters of substrates were known and constant in the two-layer model, while the following nano-layer thin-film parameters were changed: thickness, expansion and thermal diffusivity. Predictions of these three parameters of the thin-film were analysed separately with three neural networks. All of them together were joined by a fourth neural network. It was shown that the neural network, which analysed all three parameters at the same time, achieved the highest accuracy, so the use of networks that provide predictions for only one parameter is less reliable. The obtained results showed that the application of neural networks in determining the thermoelastic properties of a thin film on a supporting substrate enables the estimation of its characteristics with great accuracy.

show abstract

Computationally intelligent description of a photoacoustic detector

Cited by 12 publications

References 14 publications

Influence of data scaling and normalization on overall neural network performances in photoacoustics

Influence of data scaling and normalization on overall neural network performances in photoacoustics

Influence of Data Scaling and Normalization on Overall Neural Network Performances in Photoacoustics

Photoacoustic Characterization of TiO2 Thin-Films Deposited on Silicon Substrate Using Neural Networks

Contact Info

Product

Resources

About