• Laser & Optoelectronics Progress
  • Vol. 58, Issue 12, 1215002 (2021)
Jin Zhang, Yipeng Liao*, Shiyuan Chen, and Weixing Wang
Author Affiliations
  • College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China
  • show less
    DOI: 10.3788/LOP202158.1215002 Cite this Article Set citation alerts
    Jin Zhang, Yipeng Liao, Shiyuan Chen, Weixing Wang. Flotation Dosing State Recognition Based on Multiscale CNN Features and RAE-KELM[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1215002 Copy Citation Text show less
    References

    [1] Kaartinen J, Hätönen J, Hyötyniemi H et al. Machine-vision-based control of zinc flotation: a case study[J]. Control Engineering Practice, 14, 1455-1466(2006). http://www.sciencedirect.com/science/article/pii/S0967066105002716

    [2] Liu J P, Gui W H, Tang Z H et al. Dynamic bubble-size-distribution-based health status analysis of reagent-addition in froth flotation process[J]. Control Theory & Applications, 30, 492-502(2013).

    [3] Wu J, Xie Y F, Yang C H et al. Froth-image-features-driven control strategy for addition rates of reagents in stibium rougher flotation[J]. Control Theory & Applications, 32, 1599-1606(2015).

    [4] Yao Q L, Hu X, Lei H. Object detection in remote sensing images using multiscale convolutional neural networks[J]. Acta Optica Sinica, 39, 1128002(2019).

    [5] Yan M J, Su X Y. Hyperspectral image classification based on three-dimensional dilated convolutional residual neural network[J]. Acta Optica Sinica, 40, 1628002(2020).

    [6] Zhu R F, Ma J Y, Li Z Q et al. Domestic multispectral image classification based on multilayer perception convolutional neural network[J]. Acta Optica Sinica, 40, 1528003(2020).

    [7] Fu Y, Aldrich C. Flotation froth image recognition with convolutional neural networks[J]. Minerals Engineering, 132, 183-190(2019). http://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsRW5nTmV3UzIwMjEwMzAyEiAwY2NiMzZiMzQ2MDc0ZmJmZDEyNTlkYzk1YWJhOWY0YxoIaDNmOGI1dnQ%3D

    [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). http://www.sciencedirect.com/science/article/pii/S0892687517302510

    [9] Duan M X, Li K L, Yang C Q et al. A hybrid deep learning CNN-ELM for age and gender classification[J]. Neurocomputing, 275, 448-461(2018). http://smartsearch.nstl.gov.cn/paper_detail.html?id=683e602a8260f6d87345553d74a46c08

    [10] Wang J, Song Y F, Ma T L. Mexican hat wavelet kernel ELM for multiclass classification[J]. Computational Intelligence and Neuroscience, 2017, 7479140(2017).

    [11] Dass R. Speckle noise reduction of ultrasound images using BFO cascaded with Wiener filter and discrete wavelet transform in homomorphic region[J]. Procedia Computer Science, 132, 1543-1551(2018). http://www.sciencedirect.com/science/article/pii/S1877050918308500

    [12] Liao Y P, Wang W X, Fu H D et al. Flotation foam image NSCT multi-scale enhancement with fractional differential[J]. Journal of South China University of Technology (Natural Science Edition), 46, 92-102(2018).

    [13] Li Z M, Gui W H, Zhu J Y. Fault detection in flotation processes based on deep learning and support vector machine[J]. Journal of Central South University, 26, 2504-2515(2019). http://link.springer.com/article/10.1007/s11771-019-4190-8

    [14] Easley G, Labate D, Lim W Q. Sparse directional image representations using the discrete Shearlet transform[J]. Applied and Computational Harmonic Analysis, 25, 25-46(2008). http://www.sciencedirect.com/science/article/pii/s1063520307000875

    [15] Wang Y, Lin S. Energy feature finger-knuckle-print recognition based on NSST and Tetrolet[J]. Laser & Optoelectronics Progress, 58, 0210019(2021).

    [16] Prasad R, Deo R C, Li Y et al. Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition[J]. Geoderma, 330, 136-161(2018).

    [17] Cheng Y S, Zhao D W, Wang Y B et al. Multi-label learning with kernel extreme learning machine autoencoder[J]. Knowledge-Based Systems, 178, 1-10(2019). http://www.sciencedirect.com/science/article/pii/S0950705119301613

    [18] Sun W, He Y J, Chang H. Forecasting fossil fuel energy consumption for power generation using QHSA-based LSSVM model[J]. Energies, 8, 939-959(2015). http://www.ingentaconnect.com/content/doaj/19961073/2015/00000008/00000002/art00013

    [19] Lyu X, Hu Z Q, Zhou H L et al. Application of improved MCKD method based on QGA in planetary gear compound fault diagnosis[J]. Measurement, 139, 236-248(2019). http://www.sciencedirect.com/science/article/pii/S0263224119301897

    [20] Li J Q, Yang C H, Zhu H Q et al. Improved image enhancement method for flotation froth image based on parameter extraction[J]. Journal of Central South University, 20, 1602-1609(2013).

    Jin Zhang, Yipeng Liao, Shiyuan Chen, Weixing Wang. Flotation Dosing State Recognition Based on Multiscale CNN Features and RAE-KELM[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1215002
    Download Citation