[1] Quintanilla P, Neethling S J, Brito-Parada P R. Modelling for froth flotation control: a review[J]. Minerals Engineering, 162, 106718(2021).

[2] Gui W H, Yang C H, Xu D G et al. Machine-vision-based online measuring and controlling technologies for mineral flotation: a review[J]. Acta Automatica Sinica, 39, 1879-1888(2013).

[3] Bartolacci G, Pelletier P,, Tessier J, et al. Application of numerical image analysis to process diagnosis and physical parameter measurement in mineral processes: part I: flotation control based on froth textural characteristics[J]. Minerals Engineering, 19, 734-747(2006).

[4] Ai M X, Xie Y F, Xu D G et al. Data-driven flotation reagent changing evaluation via union distribution analysis of bubble size and shape[J]. The Canadian Journal of Chemical Engineering, 96, 2616-2626(2018).

[5] Yang C H, Ren H F, Gui W H et al. Performance recognition using texture credit distributed SVM for froth flotation process[J]. Chinese Journal of Scientific Instrument, 32, 2205-2209(2011).

[6] Liang X M, Tian T, Liu W T et al. Coal slime flotation condition identification based on fusion of froth image features[J]. Computer Simulation, 38, 385-389(2021).

[7] Ai M X, Xie Y F, Tang Z H et al. Deep learning feature-based setpoint generation and optimal control for flotation processes[J]. Information Sciences, 578, 644-658(2021).

[8] Fu Y H, Aldrich C. Froth image analysis by use of transfer learning and convolutional neural networks[J]. Minerals Engineering, 115, 68-78(2018).

[9] Zhang J, Liao Y P, Chen S Y et al. Flotation dosing state recognition based on multiscale CNN features and RAE-KELM[J]. Laser & Optoelectronics Progress, 58, 1215002(2021).

[10] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth 16x16 words: transformers for image recognition at scale[EB/OL]. https://arxiv.org/abs/2010.11929

[11] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C], 6000-6010(2017).

[12] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).

[13] Xu P C, Zhao J X, Zhang J. Identification of intrinsically disordered protein regions based on deep neural network-VGG16[J]. Algorithms, 14, 107-113(2021).

[14] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[15] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[16] Selvaraju R R, Cogswell M, Das A et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C], 618-626(2017).

[17] Cao B F, Xie Y F, Gui W H et al. Coordinated optimization setting of reagent dosages in roughing-scavenging process of antimony flotation[J]. Journal of Central South University, 25, 95-106(2018).

[18] Ai M X, Xie Y F, Xie S W et al. Fuzzy association rule-based set-point adaptive optimization and control for the flotation process[J]. Neural Computing and Applications, 32, 14019-14029(2020).

[19] Shen H, Meng Q H, Liu Y B. Facial expression recognition by merging multilayer features of lightweight convolutional networks[J]. Laser & Optoelectronics Progress, 58, 0610005(2021).

[20] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C], 448-456(2015).

[21] Zhang J J, Tang Y Q, Yang Z X et al. Shoe type recognition algorithm based on attention mechanism[J]. Laser & Optoelectronics Progress, 59, 0215004(2022).

[22] Srivastava N, Hinton G E, Krizhevsky A et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 15, 1929-1958(2014).

[23] Garg B, Sharma G K. A quality-aware energy-scalable gaussian smoothing filter for image processing applications[J]. Microprocessors and Microsystems, 45, 1-9(2016).

[24] Jindal A, Gnaneshwar D, Sawhney R et al. Leveraging BERT with mixup for sentence classification (student abstract)[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 13829-13830(2020).

[25] Yun S, Han D, Chun S et al. CutMix: regularization strategy to train strong classifiers with localizable features[C], 6022-6031(2019).

[26] Lu Y N, Chen B C, Chen D G et al. Recognition algorithm of strip steel surface defects based on attention model[J]. Laser & Optoelectronics Progress, 58, 1410014(2021).