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• W4283379424 abstract "In order to achieve high-precision and continuous measurement of methane (CH4) isotope abundance (δ13CH4), a near-infrared CH4 sensor system was developed based on back propagation (BP) neural network using tunable diode laser absorption spectroscopy (TDLAS). A pair of CH4 isotope absorption lines with similar low-state energy levels near 1658 nm were selected for minimizing temperature dependence. A LabVIEW-based signal processing platform was realized for real-time signal processing. Wavelet denoising (WD) algorithm was employed to pre-process the absorption signal for increasing signal-to-noise ratio (SNR), and the BP neural network algorithm was adopted to invert δ13CH4. The BP neural network algorithm exhibits potential for multi-parameter analysis, including gas temperature and pressure. Related experiments were carried out to prove the high-precision and reliability of the neural network inversion method. Through experimental comparison, the measurement precision of δ13CH4 is enhanced by ∼ 11 times using the neural network inversion method compared to the maximum absorbance extracting method." @default.
• W4283379424 created "2022-06-25" @default.
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• W4283379424 date "2022-09-01" @default.
• W4283379424 modified "2023-09-23" @default.
• W4283379424 title "Multi-parameter methane measurement using near-infrared tunable diode laser absorption spectroscopy based on back propagation neural network" @default.
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• W4283379424 doi "https://doi.org/10.1016/j.infrared.2022.104275" @default.
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