A real-time visual inspection system for discrete surface defects of rail heads Q Li, S Ren IEEE Transactions on Instrumentation and Measurement 61 (8), 2189-2199, 2012 | 340 | 2012 |
Temperature Dependent Scattering Rates at the Fermi Surface of Optimally Doped T Valla, AV Fedorov, PD Johnson, Q Li, GD Gu, N Koshizuka Physical review letters 85 (4), 828, 2000 | 296 | 2000 |
A hybrid thresholding algorithm for cloud detection on ground-based color images Q Li, W Lu, J Yang Journal of atmospheric and oceanic technology 28 (10), 1286-1296, 2011 | 272 | 2011 |
A hierarchical extractor-based visual rail surface inspection system J Gan, Q Li, J Wang, H Yu IEEE Sensors Journal 17 (23), 7935-7944, 2017 | 202 | 2017 |
A Visual Detection System for Rail Surface Defects Q Li, S Ren IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and …, 2012 | 154 | 2012 |
A coarse-to-fine model for rail surface defect detection H Yu, Q Li, Y Tan, J Gan, J Wang, Y Geng, L Jia IEEE Transactions on Instrumentation and Measurement 68 (3), 656-666, 2018 | 149 | 2018 |
A kernel-power-density-based algorithm for channel multipath components clustering R He, Q Li, B Ai, YLA Geng, AF Molisch, V Kristem, Z Zhong, J Yu IEEE Transactions on Wireless Communications 16 (11), 7138-7151, 2017 | 145 | 2017 |
Multi-component graph convolutional collaborative filtering X Wang, R Wang, C Shi, G Song, Q Li Proceedings of the AAAI conference on artificial intelligence 34 (04), 6267-6274, 2020 | 141 | 2020 |
Clustering enabled wireless channel modeling using big data algorithms R He, B Ai, AF Molisch, GL Stuber, Q Li, Z Zhong, J Yu IEEE Communications Magazine 56 (5), 177-183, 2018 | 122 | 2018 |
Surface defect detection via entity sparsity pursuit with intrinsic priors J Wang, Q Li, J Gan, H Yu, X Yang IEEE Transactions on Industrial Informatics 16 (1), 141-150, 2019 | 108 | 2019 |
RECOME: A new density-based clustering algorithm using relative KNN kernel density Y Geng, Q Li, R Zheng, F Zhuang, R He, N Xiong Information Sciences 436, 13-30, 2018 | 79 | 2018 |
Application of XGBoost algorithm in the optimization of pollutant concentration J Li, X An, Q Li, C Wang, H Yu, X Zhou, Y Geng Atmospheric Research 276, 106238, 2022 | 76 | 2022 |
Friction between a viscoelastic body and a rigid surface with random self-affine roughness Q Li, M Popov, A Dimaki, AE Filippov, S Kürschner, VL Popov Physical review letters 111 (3), 034301, 2013 | 71 | 2013 |
Rail inspection meets big data: Methods and trends Q Li, Z Zhong, Z Liang, Y Liang 2015 18th International Conference on Network-Based Information Systems, 302-308, 2015 | 65 | 2015 |
Mid-infrared conductivity from mid-gap states associated with charge stripes CC Homes, JM Tranquada, Q Li, AR Moodenbaugh, DJ Buttrey Physical Review B 67 (18), 184516, 2003 | 65 | 2003 |
Lightnet: A dual spatiotemporal encoder network model for lightning prediction Y Geng, Q Li, T Lin, L Jiang, L Xu, D Zheng, W Yao, W Lyu, Y Zhang Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 62 | 2019 |
A fast template matching-based algorithm for railway bolts detection Y Dou, Y Huang, Q Li, S Luo International Journal of Machine Learning and Cybernetics 5, 835-844, 2014 | 59 | 2014 |
Thin cloud detection of all-sky images using Markov random fields Q Li, W Lu, J Yang, JZ Wang IEEE Geoscience and remote sensing letters 9 (3), 417-421, 2011 | 58 | 2011 |
A cyber-enabled visual inspection system for rail corrugation Q Li, Z Shi, H Zhang, Y Tan, S Ren, P Dai, W Li Future Generation Computer Systems 79, 374-382, 2018 | 53 | 2018 |
Unraveling the Symmetry of the Hole States near the Fermi Level in the Superconductor Y Zhu, AR Moodenbaugh, G Schneider, JW Davenport, T Vogt, Q Li, G Gu, ... Physical review letters 88 (24), 247002, 2002 | 53 | 2002 |