| 1 |
In-process analysis of pharmaceutical emulsions using computer vision and artificial intelligence
Unnikrishnan S, Donovan J, Macpherson R, Tormey D
Chemical Engineering Research & Design, 166, 281, 2021 |
| 2 |
A Control Theoretic Look at Granger Causality: Extending Topology Reconstruction to Networks With Direct Feedthroughs
Dimovska M, Materassi D
IEEE Transactions on Automatic Control, 66(2), 699, 2021 |
| 3 |
On Passivity, Reinforcement Learning, and Higher Order Learning in Multiagent Finite Games
Gao BL, Pavel L
IEEE Transactions on Automatic Control, 66(1), 121, 2021 |
| 4 |
Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations
Osarogiagbon AU, Khan F, Venkatesan R, Gillard P
Process Safety and Environmental Protection, 147, 367, 2021 |
| 5 |
Daily natural gas consumption forecasting via the application of a novel hybrid model
Wei N, Li CJ, Peng XL, Li Y, Zeng FH
Applied Energy, 250, 358, 2019 |
| 6 |
Machine learning based prediction of metal hydrides for hydrogen storage, part I: Prediction of hydrogen weight percent
Rahnama A, Zepon G, Sridhar S
International Journal of Hydrogen Energy, 44(14), 7337, 2019 |
| 7 |
Machine learning based prediction of metal hydrides for hydrogen storage, part II : Prediction of material class
Rahnama A, Zepon G, Sridhar S
International Journal of Hydrogen Energy, 44(14), 7345, 2019 |
| 8 |
Machine learning applications in minerals processing: A review
McCoy JT, Auret L
Minerals Engineering, 132, 95, 2019 |
| 9 |
Infinite Time Horizon Maximum Causal Entropy Inverse Reinforcement Learning
Zhou ZY, Bloem M, Bambos N
IEEE Transactions on Automatic Control, 63(9), 2787, 2018 |
| 10 |
Negotiation context analysis in electricity markets
Pinto T, Vale Z, Sousa TM, Praca I
Energy, 85, 78, 2015 |
