| The global distribution and spread of the mobilized colistin resistance gene mcr-1 R Wang, L Van Dorp, LP Shaw, P Bradley, Q Wang, X Wang, L Jin, ... Nature communications 9 (1), 1179, 2018 | 568 | 2018 |
| A peptide encoded by circular form of LINC-PINT suppresses oncogenic transcriptional elongation in glioblastoma M Zhang, K Zhao, X Xu, Y Yang, S Yan, P Wei, H Liu, J Xu, F Xiao, H Zhou, ... Nature communications 9 (1), 4475, 2018 | 567 | 2018 |
| Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network H Liu, X Mi, Y Li Energy conversion and management 156, 498-514, 2018 | 447 | 2018 |
| Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction H Liu, H Tian, Y Li Applied Energy 98, 415-424, 2012 | 445 | 2012 |
| Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM H Liu, X Mi, Y Li Energy Conversion and Management 159, 54-64, 2018 | 419 | 2018 |
| Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks H Liu, H Tian, D Pan, Y Li Applied Energy 107, 191-208, 2013 | 351 | 2013 |
| A hybrid statistical method to predict wind speed and wind power H Liu, HQ Tian, C Chen, Y Li Renewable energy 35 (8), 1857-1861, 2010 | 325 | 2010 |
| A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks H Liu, C Chen, H Tian, Y Li Renewable energy 48, 545-556, 2012 | 320 | 2012 |
| Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks H Liu, H Tian, X Liang, Y Li Applied Energy 157, 183-194, 2015 | 283 | 2015 |
| Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network H Liu, X Mi, Y Li Energy Conversion and Management 166, 120-131, 2018 | 279 | 2018 |
| Data processing strategies in wind energy forecasting models and applications: A comprehensive review H Liu, C Chen Applied Energy 249, 392-408, 2019 | 269 | 2019 |
| Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods H Liu, C Chen, X Lv, X Wu, M Liu Energy Conversion and Management 195, 328-345, 2019 | 221 | 2019 |
| The 3D CoNi alloy particles embedded in N-doped porous carbon foams for high-performance microwave absorbers J Yan, Y Huang, C Chen, X Liu, H Liu Carbon 152, 545-555, 2019 | 210 | 2019 |
| Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine X Mi, H Liu, Y Li Energy conversion and management 180, 196-205, 2019 | 196 | 2019 |
| Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions H Liu, H Tian, Y Li, L Zhang Energy Conversion and Management 92, 67-81, 2015 | 193 | 2015 |
| Facile synthesis 3D porous MXene Ti3C2Tx@ RGO composite aerogel with excellent dielectric loss and electromagnetic wave absorption L Wang, H Liu, X Lv, G Cui, G Gu Journal of Alloys and Compounds 828, 154251, 2020 | 171 | 2020 |
| New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks H Liu, H Tian, X Liang, Y Li Renewable Energy 83, 1066-1075, 2015 | 165 | 2015 |
| Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression H Liu, X Mi, Y Li, Z Duan, Y Xu Renewable energy 143, 842-854, 2019 | 160 | 2019 |
| Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction Y Li, H Wu, H Liu Energy Conversion and Management 167, 203-219, 2018 | 157 | 2018 |
| Fault diagnosis of angle grinders and electric impact drills using acoustic signals A Glowacz, R Tadeusiewicz, S Legutko, W Caesarendra, M Irfan, H Liu, ... Applied Acoustics 179, 108070, 2021 | 147 | 2021 |
