| 1 |
Deep neural network based recursive feature learning for nonlinear dynamic process monitoring
Zhu JZ, Shi HB, Song B, Tan S, Tao Y
Canadian Journal of Chemical Engineering, 98(4), 919, 2020 |
| 2 |
A deep autoencoder feature learning method for process pattern recognition
Yu JB, Zheng XY, Wang SJ
Journal of Process Control, 79, 1, 2019 |
| 3 |
Novel performance prediction model of a biofilm system treating domestic wastewater based on stacked denoising auto-encoders deep learning network
Shi S, Xu GR
Chemical Engineering Journal, 347, 280, 2018 |
| 4 |
Denoising autoencoders for Non-Intrusive Load Monitoring: Improvements and comparative evaluation
Bonfigli R, Felicetti A, Principi E, Fagiani M, Squartini S, Piazza F
Energy and Buildings, 158, 1461, 2018 |
| 5 |
Automated feature learning for nonlinear process monitoring - An approach using stacked denoising autoencoder and k-nearest neighbor rule
Zhang ZH, Jiang T, Li SH, Yang YP
Journal of Process Control, 64, 49, 2018 |
| 6 |
Image denoising by a nonlinear control technique
Barbu T, Marinoschi G
International Journal of Control, 90(5), 1005, 2017 |
| 7 |
Reliability of multiresolution deconvolution for improving depth resolution in SIMS analysis
Boulakroune M
Applied Surface Science, 386, 24, 2016 |
| 8 |
Denoising of high-resolution single-particle electron-microscopy density maps by their approximation using three-dimensional Gaussian functions
Jonic S, Vargas J, Melero R, Gomez-Blanco J, Carazo JM, Sorzano COS
Journal of Structural Biology, 194(3), 423, 2016 |
| 9 |
Transfer learning for short-term wind speed prediction with deep neural networks
Hu QH, Zhang RJ, Zhou YC
Renewable Energy, 85, 83, 2016 |
| 10 |
Deconstructing principal component analysis using a data reconciliation perspective
Narasimhan S, BhattSystems N
Computers & Chemical Engineering, 77, 74, 2015 |
