| Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data F Jia, Y Lei, J Lin, X Zhou, N Lu Mechanical systems and signal processing 72, 303-315, 2016 | 1697 | 2016 |
| Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization F Jia, Y Lei, N Lu, S Xing Mechanical Systems and Signal Processing 110, 349-367, 2018 | 495 | 2018 |
| A deep learning scheme for motor imagery classification based on restricted Boltzmann machines N Lu, T Li, X Ren, H Miao IEEE transactions on neural systems and rehabilitation engineering 25 (6 …, 2016 | 475 | 2016 |
| Deep learning for fall detection: Three-dimensional CNN combined with LSTM on video kinematic data N Lu, Y Wu, L Feng, J Song IEEE journal of biomedical and health informatics 23 (1), 314-323, 2018 | 310 | 2018 |
| Mixture correntropy for robust learning B Chen, X Wang, N Lu, S Wang, J Cao, J Qin Pattern Recognition 79, 318-327, 2018 | 141 | 2018 |
| Dynamic frequency feature selection based approach for classification of motor imageries J Luo, Z Feng, J Zhang, N Lu Computers in biology and medicine 75, 45-53, 2016 | 78 | 2016 |
| Motor imagery EEG classification based on ensemble support vector learning J Luo, X Gao, X Zhu, B Wang, N Lu, J Wang Computer methods and programs in biomedicine 193, 105464, 2020 | 72 | 2020 |
| A label description space embedded model for zero-shot intelligent diagnosis of mechanical compound faults S Xing, Y Lei, S Wang, N Lu, N Li Mechanical Systems and Signal Processing 162, 108036, 2022 | 62 | 2022 |
| Deep partial transfer learning network: A method to selectively transfer diagnostic knowledge across related machines B Yang, CG Lee, Y Lei, N Li, N Lu Mechanical Systems and Signal Processing 156, 107618, 2021 | 62 | 2021 |
| Transfer relation network for fault diagnosis of rotating machinery with small data N Lu, H Hu, T Yin, Y Lei, S Wang IEEE Transactions on Cybernetics 52 (11), 11927-11941, 2021 | 59 | 2021 |
| Adaptive knowledge transfer by continual weighted updating of filter kernels for few-shot fault diagnosis of machines S Xing, Y Lei, B Yang, N Lu IEEE Transactions on Industrial Electronics 69 (2), 1968-1976, 2021 | 55 | 2021 |
| Toward optimal feature and time segment selection by divergence method for EEG signals classification J Wang, Z Feng, N Lu, J Luo Computers in biology and medicine 97, 161-170, 2018 | 40 | 2018 |
| Feature extraction by common spatial pattern in frequency domain for motor imagery tasks classification J Wang, Z Feng, N Lu 2017 29th Chinese control and decision conference (CCDC), 5883-5888, 2017 | 39 | 2017 |
| Transferable common feature space mining for fault diagnosis with imbalanced data N Lu, T Yin Mechanical systems and signal processing 156, 107645, 2021 | 37 | 2021 |
| A novel power line inspection robot with dual-parallelogram architecture and its vibration suppression control D Yang, Z Feng, X Ren, N Lu Advanced Robotics 28 (12), 807-819, 2014 | 37 | 2014 |
| Feature subset and time segment selection for the classification of EEG data based motor imagery J Wang, Z Feng, X Ren, N Lu, J Luo, L Sun Biomedical Signal Processing and Control 61, 102026, 2020 | 35 | 2020 |
| An advanced bispectrum features for EEG-based motor imagery classification L Sun, Z Feng, N Lu, B Wang, W Zhang Expert Systems with Applications 131, 9-19, 2019 | 32 | 2019 |
| Motor imagery classification via combinatory decomposition of ERP and ERSP using sparse nonnegative matrix factorization N Lu, T Yin Journal of neuroscience methods 249, 41-49, 2015 | 32 | 2015 |
| Numerical potential field and ant colony optimization based path planning in dynamic environment N Lv, Z Feng 2006 6th World Congress on Intelligent Control and Automation 2, 8966-8970, 2006 | 32 | 2006 |
| Spatio-temporal discrepancy feature for classification of motor imageries J Luo, Z Feng, N Lu Biomedical Signal Processing and Control 47, 137-144, 2019 | 29 | 2019 |
