{'fraction_non_alphanumeric': 0.02456499488229273, 'fraction_numerical': 0.0010235414534288639, 'mean_word_length': 5.434210526315789, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Fast and accurate temperature extraction via general regression neural network for BOTDA sensors

Curve fitting algorithm is traditionally utilized for Brillouin optical time domain analyzer (BOTDA) temperature extraction, with high time cost. To improve the extraction speed, general regression neural network (GRNN) is introduced into BOTDA temperature extraction. As a feed-forward network, only one parameter is needed to be learned in GRNN and its structure is self-adaptive for various samples with different scale, making it easier to train. The performance of GRNN is investigated in simulation and experiment different signal-to-noise ratios, pump pulse widths, and frequency scanning steps. The results show that, with the similar or better accuracy, GRNN achieves faster processing speed which is 7 times over curve fitting methods. The fast processing speed, and high extraction accuracy make GRNN approach a potential way of real-time BOTDA temperature extraction.