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- W4384831752 abstract "Aiming at the problem of low recognition accuracy of coal and gangue under wetting conditions, a new idea of feature extraction for recognition of coal and gangue was proposed based on grayscale distribution morphology. The surface imaging experiments under wetting conditions were carried out and three features such as zone brightness varying with location, bright irregular zones and bright curve zones were found in the obtained images. By studying the causes of these features, the distribution morphology of great grayscale value was selected as features for recognition of coal and gangue. The sub-image was sampled from the whole image by the algorithm of sliding window traversal and its occupied degree by great grayscale value was used to quantify distribution morphology. The maximum width of the sliding window, which was full of great grayscale value, was extracted as feature value of distribution morphology of great grayscale value, which was expressed as A. The recognition experiments of coal and gangue were carried out based on the statistical features of grayscale, the deep learning algorithm (Alex-Net) and A, respectively. The results showed that the maximum recognition accuracy was only 46.26% based on the statistical features of grayscale and the recognition accuracy was 83.0% based on the deep learning algorithm (Alex-Net), while the recognition accuracy reached 97.5% based on A. Such result demonstrates that the proposed feature extraction method can effectively improve the recognition accuracy of coal and gangue under wetting conditions." @default.
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- W4384831752 date "2023-10-01" @default.
- W4384831752 modified "2023-10-17" @default.
- W4384831752 title "A novel feature extraction method for recognition of coal and gangue under wetting conditions" @default.
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- W4384831752 doi "https://doi.org/10.1016/j.powtec.2023.118825" @default.
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