| A hybrid model for PM2. 5 forecasting based on ensemble empirical mode decomposition and a general regression neural network Q Zhou, H Jiang, J Wang, J Zhou Science of the Total Environment 496, 264-274, 2014 | 331 | 2014 |
| Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China J Wang, S Qin, Q Zhou, H Jiang Renewable Energy 76, 91-101, 2015 | 164 | 2015 |
| China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model J Wang, H Jiang, Q Zhou, J Wu, S Qin Renewable and Sustainable Energy Reviews 53, 1149-1167, 2016 | 119 | 2016 |
| Short‐Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm J Wang, Q Zhou, H Jiang, R Hou Mathematical Problems in Engineering 2015 (1), 619178, 2015 | 51 | 2015 |
| Bayesian inference and uncertainty quantification for medical image reconstruction with Poisson data Q Zhou, T Yu, X Zhang, J Li SIAM Journal on Imaging Sciences 13 (1), 29-52, 2020 | 28 | 2020 |
| An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems YMM Qingping Zhou, Wenqing Liu, Jinglai Li Inverse Problems, 2018 | 15 | 2018 |
| An experimental investigation of FNN model for wind speed forecasting using EEMD and CS J Wang, H Jiang, B Han, Q Zhou Mathematical Problems in Engineering 2015 (1), 464153, 2015 | 14 | 2015 |
| An optimized forecasting approach based on grey theory and Cuckoo search algorithm: a case study for electricity consumption in New South Wales P Jiang, Q Zhou, H Jiang, Y Dong Abstract and Applied Analysis 2014 (1), 183095, 2014 | 14 | 2014 |
| Nonlocal TV-Gaussian prior for Bayesian inverse problems with applications to limited CT reconstruction. D Lv, Q Zhou, JK Choi, J Li, X Zhang Inverse Problems & Imaging 14 (1), 2020 | 13 | 2020 |
| Spatiotemporal Variability of Human Disturbance Impacts on Ecosystem Services in Mining Areas S Liu, L Liu, J Li, Q Zhou, Y Ji, W Lai, C Long Sustainability 14 (13), 7547, 2022 | 7 | 2022 |
| A hybrid adaptive MCMC algorithm in function spaces Q Zhou, Z Hu, Z Yao, J Li SIAM/ASA Journal on Uncertainty Quantification 5 (1), 621-639, 2017 | 5 | 2017 |
| Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems Q Zhou, J Qian, J Tang, J Li Inverse Problems 40 (5), 055014, 2024 | 4 | 2024 |
| A comparative study of variational autoencoders, normalizing flows, and score-based diffusion models for electrical impedance tomography H Wang, G Xu, Q Zhou Journal of Inverse and Ill-posed Problems, 2024 | 4 | 2024 |
| Enhancing electrical impedance tomography reconstruction using learned half-quadratic splitting networks with Anderson acceleration G Xu, H Wang, Q Zhou Journal of Scientific Computing 98 (2), 49, 2024 | 3 | 2024 |
| GeoDILI: A Robust and Interpretable Model for Drug-Induced Liver Injury Prediction Using Graph Neural Network-Based Molecular Geometric Representation W Wu, J Qian, C Liang, J Yang, G Ge, Q Zhou, X Guan Chemical Research in Toxicology 36 (11), 1717-1730, 2023 | 1 | 2023 |
| A CVAE-within-Gibbs sampler for Bayesian linear inverse problems with hyperparameters J Yang, Y Niu, Q Zhou Computational and Applied Mathematics 42 (3), 138, 2023 | 1 | 2023 |
| MCDIP‐ADMM: Overcoming overfitting in DIP‐based CT reconstruction C Cheng, Q Zhou Expert Systems, e13440, 2023 | 1 | 2023 |
| Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler J Qian, Y Liu, J Yang, Q Zhou Computational Statistics & Data Analysis 196, 107930, 2024 | | 2024 |
| A MCMC method based on surrogate model and Gaussian process parameterization for infinite Bayesian PDE inversion Z Hu, H Wang, Q Zhou Journal of Computational Physics 507, 112970, 2024 | | 2024 |
| Anderson Accelerated Gauss-Newton-guided deep learning for nonlinear inverse problems with Application to Electrical Impedance Tomography Q Zhou, G Xu, Z Wen, H Wang arXiv preprint arXiv:2312.12693, 2023 | | 2023 |
Jinglai LiUniversity of Birmingham在 bham.ac.uk 的电子邮件经过验证
