| 1 |
Hybrid deep neural model for hourly solar irradiance forecasting
Huang X, Li Q, Tai YH, Chen ZQ, Zhang J, Shi JS, Gao BX, Liu WM
Renewable Energy, 171, 1041, 2021 |
| 2 |
Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines
Leite GDP, da Cunha GTM, dos Santos JG, Araujo AM, Rosas PAC, Stosic T, Stosic B, Rosso OA
Renewable Energy, 164, 1183, 2021 |
| 3 |
Damage characterization of carbon/epoxy composites using acoustic emission signals wavelet analysis
Khamedi R, Abdi S, Ghorbani A, Ghiami A, Erden S
Composite Interfaces, 27(1), 111, 2020 |
| 4 |
Black tea classification employing feature fusion of E-Nose and E-Tongue responses
Banerjee MB, Roy RB, Tudu B, Bandyopadhyay R, Bhattacharyya N
Journal of Food Engineering, 244, 55, 2019 |
| 5 |
Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks
Liu H, Mi XW, Li YF
Energy Conversion and Management, 155, 188, 2018 |
| 6 |
Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network
Liu H, Mi XW, Li YF
Energy Conversion and Management, 166, 120, 2018 |
| 7 |
An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm
Liu H, Mi XW, Li YF
Renewable Energy, 123, 694, 2018 |
| 8 |
An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization
Yin H, Dong Z, Chen YL, Ge JF, Lai LL, Vaccaro A, Meng AN
Energy Conversion and Management, 150, 108, 2017 |
| 9 |
Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm
Mi XW, Liu H, Li YF
Energy Conversion and Management, 151, 709, 2017 |
| 10 |
Study on the natural gas pipeline safety monitoring technique and the time-frequency signal analysis method
Qu ZG, Wang YF, Yue HH, An Y, Wu LQ, Zhou WB, Wang HY, Su ZC, Li J, Zhang Y, Wang LK, Yang XL, Cai YC, Yan DX
Journal of Loss Prevention in The Process Industries, 47, 1, 2017 |
