DTCWT-based zinc fast roughing working condition identification

doi:10.1016/j.cjche.2018.06.028

Chin.J.Chem.Eng. ›› 2018, Vol. 26 ›› Issue (8): 1721-1726.DOI: 10.1016/j.cjche.2018.06.028

• Selected Papers from the 28th Chinese Process Control Conference • Previous Articles Next Articles

DTCWT-based zinc fast roughing working condition identification

Zhuo He¹, Zhaohui Tang¹, Zhihao Yan¹, Jinping Liu²

1 College of Information Science and Engineering, Central South University, Changsha 410083, China;
2 Key Laboratory of High Performance Computing and Stochastic Information Processing of Ministry of Education of China, College of Mathematics and Computer Science, Hunan Normal University, Changsha 410081, China

Received:2017-11-01 Revised:2018-04-20 Online:2018-09-21 Published:2018-08-28
Contact: Zhaohui Tang,E-mail address:zhtang@csu.edu.cn

DTCWT-based zinc fast roughing working condition identification

Zhuo He¹, Zhaohui Tang¹, Zhihao Yan¹, Jinping Liu²

1 College of Information Science and Engineering, Central South University, Changsha 410083, China;
2 Key Laboratory of High Performance Computing and Stochastic Information Processing of Ministry of Education of China, College of Mathematics and Computer Science, Hunan Normal University, Changsha 410081, China

通讯作者: Zhaohui Tang,E-mail address:zhtang@csu.edu.cn

Abstract

Abstract: The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process. The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform (DTCWT) is proposed for process monitoring of zinc fast roughing. Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification (iRFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.

Key words: DTCWT, Working condition, Integrated classification model, Zinc fast roughing

摘要： The surface texture of mineral flotation froth is well acknowledged as an important index of the flotation process. The surface texture feature closely relates to the flotation working conditions and hence can be used as a visual indicator for the zinc fast roughing working condition. A novel working condition identification method based on the dual-tree complex wavelet transform (DTCWT) is proposed for process monitoring of zinc fast roughing. Three-level DTCWT is implemented to decompose the froth image into different directions and resolutions in advance, and then the energy parameter of each sub-image is extracted as the froth texture feature. Then, an improved random forest integrated classification (iRFIC) with 10-fold cross-validation model is introduced as the classifier to identify the roughing working condition, which effectively improves the shortcomings of the single model and overcomes the characteristic redundancy but achieves higher generalization performance. Extensive experiments have verified the effectiveness of the proposed method.

关键词: DTCWT, Working condition, Integrated classification model, Zinc fast roughing

Zhuo He, Zhaohui Tang, Zhihao Yan, Jinping Liu. DTCWT-based zinc fast roughing working condition identification[J]. Chin.J.Chem.Eng., 2018, 26(8): 1721-1726.

Zhuo He, Zhaohui Tang, Zhihao Yan, Jinping Liu. DTCWT-based zinc fast roughing working condition identification[J]. Chinese Journal of Chemical Engineering, 2018, 26(8): 1721-1726.

References

[1] W.H. Gui, C.H. Yang, D.G. Xu, et al., Machine-vision-based online measuring and controlling technologies for mineral flotation:A review, Acta Automat. Sin. 39(11) (2013) 1879-1888.

[2] J.P. Liu, Z.H. Tang, J. Zhang, Q. Chen, P.F. Xu, W.Z. Liu, Visual perception based statistical modeling of complex grain image for product quality monitoring and supervision on assembly production line, PLoS One 11(3) (2016), e0146484..

[3] J.P. Liu, Z.H. Tang, P.F. Xu, W.Z. Liu, J. Zhang, J.Y. Zhu, Quality-related monitoring and grading of granulated products by Weibull-distribution modeling of visual images with semi-supervised learning, Sensors 16(12) (2016) 998.

[4] Z.H. Tang, M.L. Liu, J.P. Liu, et al., Performance recognition of antimony flotation based on froth image features and matter-element extension model, Control and Decision 30(8) (2015) 1485-1490.

[5] J. Zhang, Z.H. Tang, J.P. Liu, Recognition of flotation working conditions through froth image statistical modeling for performance monitoring, Miner. Eng. 86(2016) 116-129.

[6] X. Peng, T. Peng, L. Zhao, Y.P. Song, W.H. Gui, Working condition recognition based on an improved NGLDM and interval data-based classifier for the antimony roughing process, Miner. Eng. 86(2016) 1-9.

[7] Y.G. Xu, Z.P. Meng, G.L. Zhao, Study on compound fault diagnosis of rolling bearing based on dual-tree complex wavelet transform, Chin. J. Sci. Instrum. 35(2) (2014) 447-452.

[8] K. Selvakumar, Jovitha Jerome, Kumar Rajamani, Robust face identification using DTCWT and PCA subspace based sparse representation, Multimedia Tools Appl. 75(2016) 16073-16092.

[9] I.W. Selesnick, R.G. Baraniuk, N.G. Kingsbury, The dual-tree complex wavelet transform, IEEE Signal Process. Mag. 22(6) (2005) 123-151.

[10] N. Kingsbury, The dual-tree complex wavelet transform:A new technique for shiftinvariance and directional filters, Proceeding of the IEEE Digital Signal Processing Workshop; Bryce Canyon, UT, USA August 9-12, 1998, pp. 120-131.

[11] S. Ioannidou, V. Karathanassi, Investigation of the dual-tree complex and shift-invariant discrete wavelet transforms on Quickbird image fusion, IEEE Geosci. Remote Sens. Lett. 4(2007) 166-170.

[12] Leo Breiman, Random forests, Mach. Learn. J. 45(1) (2001) 5-32.

[13] I.H. Witten, E. Frank, M.A. Hall, Data Mining:Practical Machine Learning Tools and Techniques, 3rd ed. Morgan Kaufman, Boston, 2011.

[14] A.M. Molinaro, R. Simon, R.M. Pfeiffer, Prediction error estimation:a comparison of resampling methods, Bioinformatics 21(2005) 3301-3307.

[15] G.B. Huang, H.M. Zhou, X.J. Ding, et al., Extreme learning machines for regression and multiclass classification, IEEE Trans. Syst. Man Cybern. B Cybern. 42(3) (2011) 513-529.

DTCWT-based zinc fast roughing working condition identification

DTCWT-based zinc fast roughing working condition identification

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles 0

Metrics

Comments

[1]	Zheng Wang, Cheng Shao, Li Zhu. Soft-sensing modeling and intelligent optimal control strategy for distillation yield rate of atmospheric distillation oil refining process [J]. Chinese Journal of Chemical Engineering, 2019, 27(5): 1113-1124.
[2]	Shixin Gong, Cheng Shao, Li Zhu. Energy efficiency evaluation based on DEA integrated factor analysis in ethylene production [J]. , 2017, 25(6): 793-799.