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Unsupervised Domain Adaptation for Grade Prediction of Froth Flotation Based on Wasserstein Distance and Transformer

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Abstract

In a zinc flotation process, the concentrate grade is an important indicator which cannot be measured online. To ensure the stability of the process, deep learning has been widely used for the prediction of concentrate grade. However, grade prediction based on deep learning leads to the dataset bias problem, which caused by illumination change, environment noise, etc. A popular network for addressing this problem is the domain adversarial neural network (DANN), which cannot predict the domain label according to data distribution and extract global information. To overcome these limitations, we propose a new method, the wasserstein-transformer domain adversarial neural network (WT-DANN), which uses transformer to extract global features and calculates domain loss with wasserstein distance. We evaluated our approach on public dataset (Office31) and flotation dataset. On the Office31 dataset, WT-DANN achieved a better cross-domain accuracy than that of DANN. For the flotation dataset, WT-DANN's cross-domain prediction accuracy was 60.7%, compared to DANN's accuracy of 51.2%.

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References

  1. I. Jovanović, and I. Miljanović, Miner. Eng. 70, 228 (2015).

    Article  Google Scholar 

  2. I. Jovanović, I. Miljanović, and T. Jovanović, Miner. Eng. 84, 34 (2015).

    Article  Google Scholar 

  3. J. Luo, Z. Tang, H. Zhang, and Y. Xie, IEEE Trans. Instrum. Meas. 70, 1 (2021).

    Google Scholar 

  4. Y. Xie, J. Wu, D. Xu, C. Yang, and W. Gui, IEEE Trans. Ind. Electron. 64(5), 4199 (2016).

    Article  Google Scholar 

  5. B.J. Shean, and J.J. Cilliers, Int. J. Miner. Process. 100(3–4), 57 (2011).

    Article  Google Scholar 

  6. Z. Tang, C. Zhu, and J. Liu, Appl. Res. Comput. 28(10), 3934 (2011).

    Google Scholar 

  7. T.G. Dietterich, Neural Comput. 10(7), 1895 (1998).

    Article  Google Scholar 

  8. Y. Jiang, J. Fan, T. Chai, J. Li, and F.L. Lewis, IEEE Trans. Ind. Inform. 14(5), 1974 (2017).

    Article  Google Scholar 

  9. S.H. Morar, M.C. Harris, and D.J. Bradshaw, Miner. Eng. 36, 31 (2012).

    Article  Google Scholar 

  10. C. Marais, and C. Aldrich, IFAC Proc. Vol. 43(9), 104 (2010).

    Article  Google Scholar 

  11. J. Li, T. Chai, F.L. Lewis, J. Fan, Z. Ding, and J. Ding, IEEE Trans. Ind. Electron. 65(5), 4092 (2017).

    Article  Google Scholar 

  12. J.T. McCoy, and L. Auret, Miner. Eng. 132, 95 (2019).

    Article  Google Scholar 

  13. A. Jahedsaravani, M.H. Marhaban, and M. Massinaei, Miner. Eng. 69, 137 (2014).

    Article  Google Scholar 

  14. Z. Li, and W. Gui, Asian J. Control 20(6), 2223 (2018).

    Article  Google Scholar 

  15. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, and F. Li, Int. J. Comput. Vis. 115(3), 211 (2015).

    Article  MathSciNet  Google Scholar 

  16. A. Torralba and A.A. Efros, Unbiased look at dataset bias, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2011), pp. 1521–1528

  17. M. Ghifary, W.B. Kleijn, M. Zhang, D. Balduzzi, and W. Li, Deep reconstruction-classification networks for unsupervised domain adaptation, in European Conference on Computer Vision (2016), pp. 597–613

  18. E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko, Simultaneous deep transfer across domains and tasks, in Proceedings of the IEEE International Conference on Computer Vision (2015), pp. 4068–4076

  19. X. Wang, L. Li, W. Ye, M. Long, and J. Wang, Transferable attention for domain adaptation, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1 (2019), p. 5345

  20. Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviollete, M. Marchand, and V. Lempitsky, J. Mach. Learn. Res. 17(1), 2096 (2016).

    Google Scholar 

  21. M. Long, Y. Cao, Z. Cao, and M.I. Jordan, IEEE Trans. Pattern. Anal. 41(12), 3071 (2018).

    Article  Google Scholar 

  22. H. Yan, Y. Ding, P. Li, Q. Wang, and W. Zuo, Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2272–2281

  23. M. Long, H. Zhu, J. Wang, and M.I. Jordan, Deep transfer learning with joint adaptation networks, in International Conference on Machine Learning (2017), pp. 2208–2217

  24. J. Shen, Y. Qu, W. Zhang, and Y. Yu, Wasserstein distance guided representation learning for domain adaptation, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)

  25. N. Courty, R. Flamary, A. Habrard, and A. Rakotomamonjy, in Advances in Neural Information Processing Systems (2017), pp. 3733–3742

  26. B.B. Damodaran, B. Kellenberger, R. Flamary, D. Tuia, and N. Courty. Deepjdot: deep joint distribution optimal transport for unsupervised domain adaptation, in Proceedings of the European Conference on Computer Vision (2018), pp. 447–463

  27. C.Y. Lee, T. Batra, M.H. Baig, and D. Ulbricht, Sliced Wasserstein discrepancy for unsupervised domain adaptation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019), pp. 10285–10295

  28. E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, Adversarial discriminative domain adaptation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp 7167–7176

  29. Z. Pei, Z. Cao, M. Long, and J. Wang, Multi-adversarial domain adaptation, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018), pp. 3934–3941

  30. C. Villani, Optimal Transport: Old and New (Springer, Berlin, 2009).

    Book  Google Scholar 

  31. K. Saenko, B. Kulis, M. Fritz, and T. Darrell, Adapting visual category models to new domains, in Proceeding of the 11th European Conference on Computer Vision (2010), pp. 213–226

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Acknowledgements

This work was supported by the State Key Program of National Natural Science Foundation of China (Grant No. 62233018) and the Natural Science Foundation of Hunan Province, China (Grant No. 2021JJ30862).

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Lihui Cen, Xuanpu Li, Xiaofang Chen, Yongfang Xie & Zhaohui Tang

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  1. Lihui Cen
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  2. Xuanpu Li
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  3. Xiaofang Chen
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  4. Yongfang Xie
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  5. Zhaohui Tang
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Correspondence to Xiaofang Chen.

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Cen, L., Li, X., Chen, X. et al. Unsupervised Domain Adaptation for Grade Prediction of Froth Flotation Based on Wasserstein Distance and Transformer. JOM 76, 2362–2371 (2024). https://doi.org/10.1007/s11837-024-06446-0

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