Trace gases analysis in pulsed photoacoustics based on swarm intelligence optimization

Lukić, Mladena; Ćojbašić, Žarko; Markushev, D. D.

doi:10.1007/s11082-022-04059-y

Cited by 5 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The choice of starting point has no effect on solution accuracy. It was confirmed that the SA algorithm with carefully selected parameters, provides accurate prediction of parameters, despite a broadly defined parameters range and starting point [30]. Table 1 shows the influence of algorithms parameters for several GAs and SA runs.…”

Section: Metaheuristic Algorithms For Simultaneous Determination Of P...mentioning

confidence: 62%

“…To support realtime operation and overcome problems with simultaneous determination of V−T and spatial laser beam profile, we applied artificial neural networks (ANNs), and two metaheuristic algorithms: genetic algorithms (GA) and simulated annealing (SA). Metaheuristic algorithms are useful tools for parameter estimation, particularly in a dynamic and changeable environment, and under limited knowledge of problems to be solved [13,14,30].…”

Section: Artificial Intelligence Application In Photoacoustics Of Gasesmentioning

confidence: 99%

“…Recently, a number of successful alternatives to capacitive microphones have been developed and put into use: optical sensors (uncharged and highly sensitive), resonant tuning forks (QTF) (with small size, environmental noise immunity, and corrosion resistance) [9,10], miniature silicon or micromechanical cantilever (extended dynamic range) [11,12], etc. Unlike the mentioned methods, and based on our previous research, we will review artificial intelligence (AI) applications in PAS that proved as effective tools to achieve better precision and real-time operation with no changes in experimental setup [13,14,15,16,17]. Recently, the growing interest in deep learning neural network applications is noticeable in many techniques based on the PA method and inverse problem solving (mainly in photoacoustic imaging -PAI) [18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence Application in Photoacoustic of Gases

Lukić,

Ćojbašić,

Markushev

2023

FU Work Liv Env Prot

View full text Add to dashboard Cite

Photoacoustic spectroscopy is a powerful, non-destructive, ultrasensitive technique that covers a wide range of applications including atmospheric monitoring, industrial, environmental, and biomedical practice. In this paper, our attention is focused on the area of artificial intelligence implementation in photoacoustic spectroscopy of gases. Artificial intelligence has been proven as a very successful, effective, and promising method for the accurate and real-time determination of photoacoustic signal parameters, related to relaxation, thermal and other physical properties of various media (i.e., for solving the inverse photoacoustic problem). To improve the sensitivity and selectivity of the photoacoustic method feedforward multilayer perceptron network is applied for real-time simultaneous determination of photoacoustic signal parameters: vibrational-to-translational relaxation time, and radius of the laser beam. Also, to solve the problem of finding optimal values of these photoacoustic parameters, metaheuristic algorithms, genetic algorithms and simulated annealing are used. The performance of artificial intelligence methods has been tested on a set of experimental signals generated in the (SF6+Ar) gas. The potential advantages and disadvantages of those methods are discussed.

show abstract

Section: Metaheuristic Algorithms For Simultaneous Determination Of P...mentioning

confidence: 62%

Section: Artificial Intelligence Application In Photoacoustics Of Gasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence Application in Photoacoustic of Gases

Lukić,

Ćojbašić,

Markushev

2023

FU Work Liv Env Prot

View full text Add to dashboard Cite

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