| 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 novel hybrid deep neural network model for short-term electricity price forecasting
Huang CJ, Shen YM, Chen YH, Chen HC
International Journal of Energy Research, 45(2), 2511, 2021 |
| 3 |
FibeR-CNN: Expanding Mask R-CNN to improve image-based fiber analysis
Frei M, Kruis FE
Powder Technology, 377, 974, 2021 |
| 4 |
Enhanced spectrum convolutional neural architecture: An intelligent leak detection method for gas pipeline
Ning FL, Cheng ZH, Meng D, Duan S, Wei J
Process Safety and Environmental Protection, 146, 726, 2021 |
| 5 |
Gas leak detection in galvanised steel pipe with internal flow noise using convolutional neural network
Song YJ, Li SZ
Process Safety and Environmental Protection, 146, 736, 2021 |
| 6 |
Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks
Kang R, Park B, Eady M, Ouyang Q, Chen KJ
Applied Microbiology and Biotechnology, 104(7), 3157, 2020 |
| 7 |
A process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network
Gao XR, Yang F, Feng EB
Canadian Journal of Chemical Engineering, 98(6), 1280, 2020 |
| 8 |
Color reproduction in virtual lip makeup using a convolutional neural network
Kim MC, Lee JH
Color Research and Application, 45(6), 1190, 2020 |
| 9 |
Simulated production of OH, HO2, CH2O, and CO2 during dilute fuel oxidation can predict 1st-stage ignition delays
Buras ZJ, Safta C, Zador J, Sheps L
Combustion and Flame, 216, 472, 2020 |
| 10 |
Combustion regime identification from machine learning trained by Raman/Rayleigh line measurements
Wan KD, Hartl S, Vervisch L, Domingo P, Barlow RS, Hasse C
Combustion and Flame, 219, 268, 2020 |
