| Distributed energy management for networked microgrids using online ADMM with regret WJ Ma, J Wang, V Gupta, C Chen IEEE Transactions on Smart Grid 9 (2), 847-856, 2016 | 199 | 2016 |
| Stochastic stability of event-triggered anytime control DE Quevedo, V Gupta, WJ Ma, S Yüksel IEEE Transactions on Automatic Control 59 (12), 3373-3379, 2014 | 78 | 2014 |
| Optimal operation mode selection for a dc microgrid WJ Ma, J Wang, X Lu, V Gupta IEEE Transactions on Smart Grid 7 (6), 2624-2632, 2016 | 54 | 2016 |
| On distributed charging control of electric vehicles with power network capacity constraints WJ Ma, V Gupta, U Topcu 2014 American Control Conference, 4306-4311, 2014 | 53 | 2014 |
| Distributed charging control of electric vehicles using online learning WJ Ma, V Gupta, U Topcu IEEE Transactions on Automatic Control 62 (10), 5289-5295, 2016 | 36 | 2016 |
| Design of adaptive all-pass based notch filter for narrowband anti-jamming GPS system WJ Ma, WL Mao, FR Chang 2005 International Symposium on Intelligent Signal Processing and …, 2005 | 26 | 2005 |
| Risk-averse access point selection in wireless communication networks WJ Ma, C Oh, Y Liu, D Dentcheva, MM Zavlanos IEEE Transactions on Control of Network Systems 6 (1), 24-36, 2018 | 22 | 2018 |
| Input-to-state stability of hybrid systems with receding horizon control in the presence of packet dropouts WJ Ma, V Gupta Automatica 48 (8), 1920-1923, 2012 | 18 | 2012 |
| Anytime control using input sequences with Markovian processor availability DE Quevedo, WJ Ma, V Gupta IEEE Transactions on Automatic Control 60 (2), 515-521, 2014 | 14 | 2014 |
| Desynchronization of thermally-coupled first-order systems using economic model predictive control WJ Ma, V Gupta 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 278-283, 2012 | 14 | 2012 |
| Distributed charging control of electric vehicles using regret minimization WJ Ma, V Gupta, U Topcu 53rd IEEE Conference on Decision and Control, 4917-4923, 2014 | 10 | 2014 |
| Officelearn: An openai gym environment for reinforcement learning on occupant-level building’s energy demand response L Spangher, A Gokul, J Palakapilly, U Agwan, M Khattar, WJ Ma, ... Tackling Climate Change with Artificial Intelligence Workshop at NeurIPS 2020, 2020 | 7 | 2020 |
| Risk-averse sensor planning using distributed policy gradient WJ Ma, D Dentcheva, MM Zavlanos 2017 American Control Conference (ACC), 4839-4844, 2017 | 5 | 2017 |
| A study of estimation and communication tradeoff using an event-based approach C Yang, L Shi, WJ Ma 2013 Asilomar Conference on Signals, Systems and Computers, 32-36, 2013 | 4 | 2013 |
| Deep reinforcement learning for protein folding in the hydrophobic-polar model with pull moves H Yu, JS Schreck, WJ Ma Neural Information Processing Systems (NeurIPS): Workshop on Machine …, 2020 | 3 | 2020 |
| A fine-grained variant of the hierarchy of Lasserre WJ Ma, J Marecek, M Mevissen 2019 57th Annual Allerton Conference on Communication, Control, and …, 2019 | 3 | 2019 |
| Collaborative processing in distributed control for resource constrained systems WJ Ma, V Gupta, DE Quevedo IET Control Theory & Applications 11 (11), 1796-1806, 2017 | 1 | 2017 |
| Learning and optimization for smart power grids WJ Ma University of Notre Dame, 2015 | 1 | 2015 |
| Input-to-state stability of hybrid systems with receding horizon control in the presence of unreliable network packet dropouts WJ Ma, V Gupta 2012 American Control Conference (ACC), 1243-1247, 2012 | 1 | 2012 |
| An Algorithm for Control with Limited Processing and Communication Resources DE Quevedo, V Gupta, WJ Ma Cyber-Physical Systems: From Theory to Practice, 171-180, 2016 | | 2016 |
Daniel E. QuevedoQueensland University of TechnologyVerified email at qut.edu.au
