| Granular prediction and dynamic scheduling based on adaptive dynamic programming for the blast furnace gas system J Zhao, T Wang, W Pedrycz, W Wang IEEE Transactions on Cybernetics 51 (4), 2201-2214, 2019 | 34 | 2019 |
| Multiseries featural LSTM for partial periodic time-series prediction: A case study for steel industry T Wang, H Leung, J Zhao, W Wang IEEE Transactions on Instrumentation and Measurement 69 (9), 5994-6003, 2020 | 33 | 2020 |
| Adaptive granulation-based prediction for energy system of steel industry T Wang, Z Han, J Zhao, W Wang IEEE transactions on cybernetics 48 (1), 127-138, 2016 | 26 | 2016 |
| Automatic modulation classification in impulsive noise: Hyperbolic-tangent cyclic spectrum and multibranch attention shuffle network J Ma, M Hu, T Wang, Z Yang, L Wan, T Qiu IEEE Transactions on Instrumentation and Measurement 72, 1-13, 2023 | 14 | 2023 |
| A dynamic scheduling framework for byproduct gas system combining expert knowledge and production plan T Wang, J Zhao, Q Xu, W Pedrycz, W Wang IEEE Transactions on Automation Science and Engineering 20 (1), 541-552, 2022 | 13 | 2022 |
| Granular-based multilayer spatiotemporal network with control gates for energy prediction of steel industry T Wang, J Zhao, Q Liu, W Wang IEEE Transactions on Instrumentation and Measurement 70, 1-12, 2021 | 9 | 2021 |
| Multi-layer encoding genetic algorithm-based granular fuzzy inference for blast furnace gas scheduling T Wang, J Zhao, C Sheng, W Wang, L Wang IFAC-PapersOnLine 49 (20), 132-137, 2016 | 7 | 2016 |
| A condition knowledge representation and feedback learning framework for dynamic optimization of integrated energy systems T Wang, J Zhao, H Leung, W Wang IEEE Transactions on Cybernetics, 2023 | 4 | 2023 |
| 基于分层粒度对比网络的钢铁燃气调度知识获取与建模 王天宇， 赵珺， 王伟， 王天鑫 自动化学报 48 (9), 2212-2222, 2022 | | 2022 |
