| Prediction of the transient stability boundary using the lasso J Lv, M Pawlak, UD Annakkage IEEE Transactions on Power Systems 28 (1), 281-288, 2012 | 68 | 2012 |
| Addressing the conditional and correlated wind power forecast errors in unit commitment by distributionally robust optimization X Zheng, K Qu, J Lv, Z Li, B Zeng IEEE Transactions on Sustainable Energy 12 (2), 944-954, 2020 | 50 | 2020 |
| Prediction of the transient stability boundary based on nonparametric additive modeling J Lv, M Pawlak, UD Annakkage IEEE Transactions on Power Systems 32 (6), 4362-4369, 2017 | 38 | 2017 |
| Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms J Lv, X Zheng, M Pawlak, W Mo, M Miśkowicz Renewable Energy 177, 181-192, 2021 | 32 | 2021 |
| True random number generator using GPUs and histogram equalization techniques JJM Chan, B Sharma, J Lv, G Thomas, R Thulasiram, P Thulasiraman 2011 IEEE International Conference on High Performance Computing and …, 2011 | 20 | 2011 |
| Additive modeling and prediction of transient stability boundary in large-scale power systems using the Group Lasso algorithm J Lv, M Pawlak International Journal of Electrical Power & Energy Systems 113, 963-970, 2019 | 15 | 2019 |
| Statistical testing for load models using measured data J Lv, M Pawlak, UD Annakkage, B Bagen Electric Power Systems Research 163, 66-72, 2018 | 12 | 2018 |
| Transient stability assessment in large-scale power systems based on the sparse single index model J Lv Electric Power Systems Research 184, 106291, 2020 | 10 | 2020 |
| Transient stability assessment in large-scale power systems using sparse logistic classifiers J Lv International Journal of Electrical Power & Energy Systems 136, 107626, 2022 | 9 | 2022 |
| Power system oscillation mode prediction based on the lasso method W Mo, J Lv, M Pawlak, UD Annakkage, H Chen IEEE Access 8, 101068-101078, 2020 | 6 | 2020 |
| Nonparametric specification testing for Hammerstein systems M law Pawlak, J Lv IFAC-PapersOnLine 48 (28), 392-397, 2015 | 6 | 2015 |
| On semiparametric identification of MISO Hammerstein systems M Pawlak, J Lv 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP …, 2011 | 4 | 2011 |
| Machine learning techniques for large-scale system modeling J Lv University of Manitoba (Canada), 2011 | 4 | 2011 |
| On identification of multivariate Hammerstein systems J Lv, M Pawlak CCECE 2010, 1-4, 2010 | 4 | 2010 |
| Nonparametric testing for Hammerstein systems M Pawlak, J Lv IEEE Transactions on Automatic Control 67 (9), 4568-4584, 2022 | 3 | 2022 |
| Bandwidth selection for kernel generalized regression neural networks in identification of hammerstein systems J Lv, M Pawlak Journal of Artificial Intelligence and Soft Computing Research 11 (3), 181-194, 2021 | 3 | 2021 |
| Prediction of daily maximum ozone levels using lasso sparse modeling method J Lv, X Xu arXiv preprint arXiv:2010.08909, 2020 | 1 | 2020 |
| Analysis of Large Scale Power Systems via LASSO Learning Algorithms M Pawlak, J Lv Artificial Intelligence and Soft Computing: 18th International Conference …, 2019 | 1 | 2019 |
| Identification of MISO nonlinear systems via the semiparametric approach J Lv, M Pawlak 2011 IEEE International Conference on Acoustics, Speech and Signal …, 2011 | 1 | 2011 |
| Power System Online Sensitivity Identification Based on Lasso Algorithm W Mo, J Lv, M Pawlak, UD Annakkage, H Chen, Y Chen 2020 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2020 | | 2020 |
