| 1 |
Classification of Persian carpet patterns based on quantitativeaesthetic-relatedfeatures
Moghadam TS, Afjeh MG, Amirshahi SH
Color Research and Application, 46(1), 195, 2021 |
| 2 |
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 |
| 3 |
Shallow Versus Deep Neural Networks in Gear Fault Diagnosis
Cirrincione G, Kumar RR, Mohammadi A, Kia SH, Barbiero P, Ferretti J
IEEE Transactions on Energy Conversion, 35(3), 1338, 2020 |
| 4 |
A multi-feature extraction technique based on principal component analysis for nonlinear dynamic process monitoring
Guo LL, Wu P, Lou SW, Gao JF, Liu YC
Journal of Process Control, 85, 159, 2020 |
| 5 |
Information concentrated variational auto-encoder for quality-related nonlinear process monitoring
Zhu JZ, Shi HB, Song B, Tao Y, Tan S
Journal of Process Control, 94, 12, 2020 |
| 6 |
A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network
Wang SX, Chen HW
Applied Energy, 235, 1126, 2019 |
| 7 |
A time series clustering approach for Building Automation and Control Systems
Bode G, Schreiber T, Baranski M, Muller D
Applied Energy, 238, 1337, 2019 |
| 8 |
A hybrid deep learning-based neural network for 24-h ahead wind power forecasting
Hong YY, Rioflorido CLPP
Applied Energy, 250, 530, 2019 |
| 9 |
Using multivariate pattern segmentation to assess process performance and mine good operation conditions for dynamic chemical industry
Wang K, Chen JH, Song ZH
Chemical Engineering Science, 201, 339, 2019 |
| 10 |
Clustering district heat exchange stations using smart meter consumption data
Tureczek AM, Nielsen PS, Madsen H, Brun A
Energy and Buildings, 182, 144, 2019 |
