Water vapor temperatures in a heating device were measured by ultra-low sampled and high-precision tunable diode laser spectroscopy (TDLAS) Bichromatic distributed feedback (DFB) lasers were used as light sources. Sampled data at an ultra-low rate was collected after laser passing through a low-pass filter and served as the inputs of an artificial neural network. Classical direct absorption spectroscopy using the line-shape fitting method provided the training dataset, i.e., the integrated absorbances. The proposed method required an ultra-low sampling rate, i.e., only 1/50 of the classical method, but its calculation speed was nearly 14,000 times faster. Also, the proposed method yielded satisfying estimates at temperatures uncovered by the training dataset and was insensitive to random noises.

中文翻译：

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通过人工神经网络进行超低采样和高精度 TDLAS 测温

通过超低采样和高精度可调谐二极管激光光谱 (TDLAS) 双色分布反馈 (DFB) 激光器作为光源测量加热装置中的水蒸气温度。激光通过低通滤波器后以超低速率收集采样数据，并作为人工神经网络的输入。使用线形拟合方法的经典直接吸收光谱提供了训练数据集，即积分吸光度。该方法需要超低采样率，仅为经典方法的1/50，但其计算速度快了近14,000倍。此外，所提出的方法在训练数据集未覆盖的温度下产生了令人满意的估计，并且对随机噪声不敏感。