{'fraction_non_alphanumeric': 0.021133525456292025, 'fraction_numerical': 0.0038424591738712775, 'mean_word_length': 4.887005649717514, '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}}
Diopter measurement based on an artificial neural network.

The measurement of the diopters of spectacle lenses using an artificial neural network is proposed in this paper. A diopter is measured by obtaining the distances between the spots imaged by a charge-coupled device (CCD) during a Hartmann test. Backpropagation (BP) and a radial basis function (RBF) algorithm are applied to train the BP and RBF neural networks, respectively. A set of -20D to 20D spectacle lenses is used to test the proposed method. When comparing the diopter measurement results of the RBF neural network with those of the BP neural network, the former exhibited higher stability and lower errors. In addition, the errors of diopter measurement are not against the system error measured by an auto-focimeter due to the increase of curvature of spectacle lenses. These results indicated that RBF neural network has better performance in diopter detection, and it can overcome the system error caused by the change of lens curvature in auto-focimeter measurement.