TY - JOUR

T1 - Wavelet-Based Machine Learning Algorithms for Photoacoustic Gas Sensing

AU - Kozmin, Artem

AU - Erushin, Evgenii

AU - Miroshnichenko, Ilya

AU - Kostyukova, Nadezhda

AU - Boyko, Andrey

AU - Redyuk, Alexey

N1 - This research was funded by the Ministry of Education and Science of the Russian Federation (FSUS-2021-0015). The PAGS development and PAD data measurement were funded by the Ministry of Education and Science of the Russian Federation (Project No. FSUS-2020-0036).

PY - 2024/6

Y1 - 2024/6

N2 - The significance of intelligent sensor systems has grown across diverse sectors, including healthcare, environmental surveillance, industrial automation, and security. Photoacoustic gas sensors are a promising type of optical gas sensor due to their high sensitivity, enhanced frequency selectivity, and fast response time. However, they have limitations such as dependence on a high-power light source, a requirement for a high-quality acoustic signal detector, and sensitivity to environmental factors, affecting their accuracy and reliability. Machine learning has great potential in the analysis and interpretation of sensor data as it can identify complex patterns and make accurate predictions based on the available data. We propose a novel approach that utilizes wavelet analysis and neural networks with enhanced architectures to improve the accuracy and sensitivity of photoacoustic gas sensors. Our proposed approach was experimentally tested for methane concentration measurements, showcasing its potential to significantly advance the field of gas detection and analysis, providing more accurate and reliable results.

AB - The significance of intelligent sensor systems has grown across diverse sectors, including healthcare, environmental surveillance, industrial automation, and security. Photoacoustic gas sensors are a promising type of optical gas sensor due to their high sensitivity, enhanced frequency selectivity, and fast response time. However, they have limitations such as dependence on a high-power light source, a requirement for a high-quality acoustic signal detector, and sensitivity to environmental factors, affecting their accuracy and reliability. Machine learning has great potential in the analysis and interpretation of sensor data as it can identify complex patterns and make accurate predictions based on the available data. We propose a novel approach that utilizes wavelet analysis and neural networks with enhanced architectures to improve the accuracy and sensitivity of photoacoustic gas sensors. Our proposed approach was experimentally tested for methane concentration measurements, showcasing its potential to significantly advance the field of gas detection and analysis, providing more accurate and reliable results.

KW - accuracy

KW - gas sensing

KW - machine learning

KW - methane

KW - mid-IR range

KW - neural networks

KW - optical parametric oscillator

KW - optical sensing

KW - photoacoustic gas sensors

KW - photoacoustic spectroscopy

KW - sensitivity enhancement

KW - wavelet analysis

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85196841878&origin=inward&txGid=f4873e2ffb0b7d34b6e2e7ff6cd239da

UR - https://www.mendeley.com/catalogue/99cbe0e4-0e46-3b12-98d7-2296fddd316d/

U2 - 10.3390/opt5020015

DO - 10.3390/opt5020015

M3 - Article

VL - 5

SP - 207

EP - 222

JO - Optics

JF - Optics

SN - 2673-3269

IS - 2

ER -