| 1 |
A hybrid model combining mechanism with semi-supervised learning and its application for temperature prediction in roller hearth kiln
Chen JY, Gui WH, Dai JY, Jiang ZH, Chen N, Li X
Journal of Process Control, 98, 18, 2021 |
| 2 |
Field data analysis and risk assessment of gas kick during industrial deepwater drilling process based on supervised learning algorithm
Yin QS, Yang J, Tyagi M, Zhou X, Hou XX, Cao BH
Process Safety and Environmental Protection, 146, 312, 2021 |
| 3 |
Determination of bubble sizes in bubble column reactors with machine learning regression methods
Thesseling C, Grunewald M, Biessey P
Chemical Engineering Research & Design, 163, 47, 2020 |
| 4 |
Some Aspects of Combining Data and Models in Process Engineering
Heese R, Nies J, Bortz M
Chemie Ingenieur Technik, 92(7), 856, 2020 |
| 5 |
Online state-of-health prediction of lithium-ion batteries with limited labeled data
Yu JS, Yang J, Wu Y, Tang DY, Dai J
International Journal of Energy Research, 44(14), 11345, 2020 |
| 6 |
Machine learning methods to assist energy system optimization
Perera ATD, Wickramasinghe PU, Nik VM, Scartezzini JL
Applied Energy, 243, 191, 2019 |
| 7 |
Machine learning methods to assist energy system optimization
Perera ATD, Wickramasinghe PU, Nik VM, Scartezzini JL
Applied Energy, 243, 191, 2019 |
| 8 |
Simultaneous fault detection and isolation using semi-supervised kernel nonnegative matrix factorization
Zhai LR, Jia QL
Canadian Journal of Chemical Engineering, 97(12), 3025, 2019 |
| 9 |
Machine learning for locating organic matter and pores in scanning electron microscopy images of organic-rich shales
Wu YK, Misra S, Sondergeld C, Curtis M, Jernigen J
Fuel, 253, 662, 2019 |
| 10 |
Stability of Stochastic Approximations With "Controlled Markov" Noise and Temporal Difference Learning
Ramaswamy A, Bhatnagar S
IEEE Transactions on Automatic Control, 64(6), 2614, 2019 |
