Performance evaluation of a deep learning based wet coal image classification-Reference-Cited by-同舟云学术

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Performance evaluation of a deep learning based wet coal image classification

Published:2021-09 Issue: Volume:171 Page:107126
ISSN:0892-6875
Container-title:Minerals Engineering
language:en
Short-container-title:Minerals Engineering

Author:

Liu Yang,Zhang Zelin,Liu Xiang,Wang Lei,Xia Xuhui

Funder

Key Research and Development Project of Hainan Province

Major Technology Innovation of Hubei Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Mechanical Engineering,Geotechnical Engineering and Engineering Geology,General Chemistry,Control and Systems Engineering

Reference44 articles.

1. Baraboshkin, E.E., Ismailova, L.S., Orlov, D.M., Zhukovskaya, E.A., Kalmykov, G.A., Khotylev, O. V., Baraboshkin, E.Y., Koroteev, D.A., 2019. Deep convolutions for in-depth automated rock typing. arXiv 135, 104330. https://doi.org/10.1016/j.cageo.2019.104330.

2. Automatic characterisation of chars from the combustion of pulverised coals using machine vision;Chaves;Powder Technol.,2018

3. Coskun, M., Ucar, A., Yildirim, O., Demir, Y., 2017. Face recognition based on convolutional neural network. In: Proceedings of the International Conference on Modern Electrical and Energy Systems, MEES 2017. IEEE, pp. 376–379. https://doi.org/10.1109/MEES.2017.8248937.

4. Application of Raman spectroscopy to identify iron minerals commonly found in mine wastes;Das;Chem. Geol.,2011

5. Classification of coal and gangue under multiple surface conditions via machine vision and relief-SVM;Dou;Powder Technol.,2019

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