ArticleSensors (Basel, Switzerland)2024
Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A Residual Amplitude Modulation Noise Suppression Method Based on Multi-Harmonic Component Decoupling.Sensors (Basel, Switzerland) · 2026Article
- An Optimized SVR Algorithm for Pulse Pile-Up Correction in Pulse Shape Discrimination.Sensors (Basel, Switzerland) · 2024Article
- Accelerating the Diagnosis of Pandemic Infection Based on Rapid Sampling Algorithm for Fast-Response Breath Gas Analyzers.Sensors (Basel, Switzerland) · 2024Article
- Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors.Biomimetics (Basel, Switzerland) · 2024Article
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3 authors.
Funding
Abstract
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
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