Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm
This paper presents the description of the wavelength modulation spectroscopy (WMS) experiment, the parameters of which were established by use of the Artificial Intelligence (AI) algorithm. As a result, a significant improvement in the signal power to noise power ratio (SNR) was achieved, ranging f...
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/6/1842 |
| _version_ | 1797239358511644672 |
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| author | Filip Musiałek Dariusz Szabra Jacek Wojtas |
| author_facet | Filip Musiałek Dariusz Szabra Jacek Wojtas |
| author_sort | Filip Musiałek |
| collection | DOAJ |
| description | This paper presents the description of the wavelength modulation spectroscopy (WMS) experiment, the parameters of which were established by use of the Artificial Intelligence (AI) algorithm. As a result, a significant improvement in the signal power to noise power ratio (SNR) was achieved, ranging from 1.6 to 6.5 times, depending on the harmonic. Typically, optimizing the operation conditions of WMS-based gas sensors is based on long-term simulations, complex mathematical model analysis, and iterative experimental trials. An innovative approach based on a biological-inspired genetic algorithm (GA) and custom-made electronics for laser control is proposed. The experimental setup was equipped with a 31.23 m Heriott multipass cell, software lock-in, and algorithms to control the modulation process of the quantum cascade laser (QCL) operating in the long-wavelength-infrared (LWIR) spectral range. The research results show that the applied evolutionary approach can efficiently and precisely explore a wide range of WMS parameter combinations, enabling researchers to dramatically reduce the time needed to identify optimal settings. It took only 300 s to test approximately 1.39 × 10<sup>32</sup> combinations of parameters for key system components. Moreover, because the system is able to check all possible component settings, it is possible to unquestionably determine the operating conditions of WMS-based gas sensors for which the limit of detection (LOD) is the most favorable. |
| first_indexed | 2024-04-24T17:50:16Z |
| format | Article |
| id | doaj.art-295bc4df04294e8aae0c3b1ab4cd9fdb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:50:16Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-295bc4df04294e8aae0c3b1ab4cd9fdb2024-03-27T14:03:56ZengMDPI AGSensors1424-82202024-03-01246184210.3390/s24061842Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic AlgorithmFilip Musiałek0Dariusz Szabra1Jacek Wojtas2Institute of Optoelectronics, Military University of Technology, 2 Kaliskiego Str., 00-908 Warsaw, PolandInstitute of Optoelectronics, Military University of Technology, 2 Kaliskiego Str., 00-908 Warsaw, PolandInstitute of Optoelectronics, Military University of Technology, 2 Kaliskiego Str., 00-908 Warsaw, PolandThis paper presents the description of the wavelength modulation spectroscopy (WMS) experiment, the parameters of which were established by use of the Artificial Intelligence (AI) algorithm. As a result, a significant improvement in the signal power to noise power ratio (SNR) was achieved, ranging from 1.6 to 6.5 times, depending on the harmonic. Typically, optimizing the operation conditions of WMS-based gas sensors is based on long-term simulations, complex mathematical model analysis, and iterative experimental trials. An innovative approach based on a biological-inspired genetic algorithm (GA) and custom-made electronics for laser control is proposed. The experimental setup was equipped with a 31.23 m Heriott multipass cell, software lock-in, and algorithms to control the modulation process of the quantum cascade laser (QCL) operating in the long-wavelength-infrared (LWIR) spectral range. The research results show that the applied evolutionary approach can efficiently and precisely explore a wide range of WMS parameter combinations, enabling researchers to dramatically reduce the time needed to identify optimal settings. It took only 300 s to test approximately 1.39 × 10<sup>32</sup> combinations of parameters for key system components. Moreover, because the system is able to check all possible component settings, it is possible to unquestionably determine the operating conditions of WMS-based gas sensors for which the limit of detection (LOD) is the most favorable.https://www.mdpi.com/1424-8220/24/6/1842laser absorption spectroscopySNR optimizationWMSmethane sensorartificial intelligencegenetic algorithm |
| spellingShingle | Filip Musiałek Dariusz Szabra Jacek Wojtas Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm Sensors laser absorption spectroscopy SNR optimization WMS methane sensor artificial intelligence genetic algorithm |
| title | Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm |
| title_full | Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm |
| title_fullStr | Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm |
| title_full_unstemmed | Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm |
| title_short | Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm |
| title_sort | time efficient snr optimization of wms based gas sensor using a genetic algorithm |
| topic | laser absorption spectroscopy SNR optimization WMS methane sensor artificial intelligence genetic algorithm |
| url | https://www.mdpi.com/1424-8220/24/6/1842 |
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