代表性论文：
[1] Z. Q. Lv*, Y. H. Fan, T. Sha, et al., A large-scale open image dataset for deep learning-enabled intelligent sorting and analyzing of raw coal, Scientific Data, 2025.
[2] Z. Q. Lv, W. D. Wang*, K. H. Zhang, et al., A high-confidence instance boundary regression approach and its application in coal-gangue separation, Engineering Applications of Artificial Intelligence, 2024.
[3] Z. Q. Lv*, Y. Cui, K. H. Zhang, et al., Investigating comparisons on the coal and gangue in various scenarios using multidimensional image features, Minerals Engineering, 2023.
[4] Z. Q. Lv, W. D. Wang*, K. H. Zhang, et al., A synchronous detection-segmentation method for oversized gangue on a coal preparation plant based on multi-task learning, Minerals Engineering, 2022.
[5] Z. Q. Lv, W. D. Wang*, Z. Q. Xu, et al., Fine-grained object detection method using attention mechanism and its application in coal-gangue detection, Applied Soft Computing, 2021.
[6] R. Tian, Z. Q. Lv*, M. J. Sun, et al., A Tracking-Based Burst Bubble Recognition in Flotation Using Deep Graph Neural Network, IEEE Transactions on Industrial Informatics, 2025.
[7] Y. H. Fan, Z. Q. Lv*, Y. Song, et al., A precise flotation bubble size measurement method based on boundary probability regression, IEEE Transactions on Instrumentation and Measurement, 2025.
[8] Y. Cui, Z. Q. Lv*, Y. Gao, et al., A strongly supervised hyperspectral unmixing framework for precise mineral composition and coal ash content estimation, Engineering Applications of Artificial Intelligence, 2025.
[9] M. J. Sun, Z. Q. Lv*, Z. Q. Xu, et al., Utilizing spatio-temporal feature fusion in videos for detecting the fluidity of coal water slurry, International Journal of Mining Science and Technology, 2024.
[10] Y. H. Fan, Z. Q. Lv*, Y. Song, et al., Optimizing flotation froth image segmentation via parallel branch network and hybrid loss supervision, Minerals Engineering, 2024.
