@ARTICLE{Li_Xin_A_2023,
 author={Li, Xin and Wang, Shuang and He, Lei and Luo, Qisheng},
 volume={vol. 39},
 number={No 1},
 journal={Gospodarka Surowcami Mineralnymi - Mineral Resources Management},
 pages={109-129},
 howpublished={online},
 year={2023},
 publisher={Komitet Zrównoważonej Gospodarki Surowcami Mineralnymi PAN},
 publisher={Instytut Gospodarki Surowcami Mineralnymi i Energią PAN},
 abstract={In order to explore the impact of coal and gangue particle size changes on recognition accuracy and to improve the single particle size of coal and gangue identification accuracy of sorting equipment, this study established a database of different particle sizes of coal and gangue through image gray and texture feature extraction, using a relief feature selection algorithm to compare different particle size of coal and gangue optimal features of the combination, and to identify the points and particle size of coal and gangue. The results show that the optimal features and number of coal and gangue are different with different particle sizes. Based on visible-light coal and gangue separation technology, the change of coal and gangue particle size cause fluctuations in the recognition accuracy, and the fluctuation of recognition accuracy will gradually decrease with increases in the number of features. In the process of particle size classification, if the training model has a single particle size range, the recognition accuracy of each particle size range is low, with the highest recognition accuracy being 98% and the average recognition rate being only 97.2%. The method proposed in this paper can effectively improve the recognition accuracy of each particle size range. The maximum recognition accuracy is 100%, the maximum increase is 4%, and the average recognition accuracy is 99.2%. Therefore, this method has a high practical application value for the separation of coal and gangue with single particle size.},
 type={Article},
 title={A study on the influence of particle size on the identification accuracy of coal and gangue},
 URL={http://journals.pan.pl/Content/126719/PDF/Li%20i%20inni.pdf},
 doi={10.24425/gsm.2023.144634},
 keywords={particle size, gray feature, texture feature, support vector machine, coal and gangue identification},
}