Time-Efficient SNR Optimization of WMS-Based Gas Sensor Using a Genetic Algorithm

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 × 1032 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.

Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors

“…The higher the accuracy requirement for the morphing of the variable wing leadingedge morphological reconstruction, the greater the number of sensors needed, leading to a significant increase in the complexity of the monitoring system. To address this, researchers utilize optimization algorithms to obtain the optimal sensor layout, such as genetic algorithms [21], simulated annealing [22], and particle swarm optimization [23]. Genetic algorithms require determining suitable crossover and mutation rate parameters based on the size of the established sample library, while simulated annealing exhibits slow convergence and is influenced by multiple parameters to be optimized.…”