Suchergebnisse - denoising-stacked autoencoder

Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation

Autoren: Jiapeng Lv Zihao Chen Xiaoli Luan et al.

Quelle: The Canadian Journal of Chemical Engineering. 101:2693-2703

Schlagwörter: 02 engineering and technology, 0204 chemical engineering, 0210 nano-technology

Fault Diagnosis of Rotating Equipment Unbalance Problem Based on Denoising Stacked Autoencoders

Autoren: Peijian Zeng Nankai Lin Jianghao Lin et al.

Quelle: Lecture Notes in Computer Science ISBN: 9789819756711

Partial Discharge Patterns Recognition of GIS with Denoising-stacked Autoencoder Networks

Autoren: Tingliang Liu Yiming Zhao Junjie Jiang et al.

Quelle: 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE). :1815-1818

Schlagwörter: 0103 physical sciences, 01 natural sciences

Zugangs-URL: https://ieeexplore.ieee.org/document/9136370

Fault Diagnosis of High-Voltage Circuit Breakers Using Hilbert-Huang Transform and Denoising-Stacked Autoencoder

Autoren: Song Dongbo Cai Mengyi Jing Yan et al.

Quelle: 2019 4th International Conference on Power and Renewable Energy (ICPRE). :228-232

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9034783
http://xplorestaging.ieee.org/ielx7/9028046/9034534/09034783.pdf?arnumber=9034783

A denoising stacked autoencoders for transient electromagnetic signal denoising

Autoren: Xuben Wang Kecheng Chen Fanqiang Lin et al.

Quelle: Nonlinear Processes in Geophysics, Vol 26, Pp 13-23 (2019)

Schlagwörter: QC801-809, 13. Climate action, Science, Physics, QC1-999, Geophysics. Cosmic physics, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 01 natural sciences, 0105 earth and related environmental sciences

Dateibeschreibung: application/pdf

Zugangs-URL: https://npg.copernicus.org/articles/26/13/2019/npg-26-13-2019.pdf
https://npg.copernicus.org/articles/26/13/2019/
https://doaj.org/article/02a1d2686874451dbdd4be885934f9ee
https://npg.copernicus.org/articles/26/13/2019/npg-26-13-2019.pdf
https://www.nonlin-processes-geophys-discuss.net/npg-2018-39/npg-2018-39.pdf
https://www.nonlin-processes-geophys.net/26/13/2019/npg-26-13-2019.pdf
http://ui.adsabs.harvard.edu/abs/2019NPGeo..26...13L/abstract
https://npg.copernicus.org/articles/26/13/2019/

Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network

Autoren: Singh, Prateek Sharma, Ambalika Maiya, Shreesha

Index Begriffe: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, text

URL: http://arxiv.org/abs/2202.05177

Automated atrial fibrillation classification based on denoising stacked autoencoder and optimized deep network

Autoren: Prateek Singh Ambalika Sharma Shreesha Maiya

Quelle: Expert Systems with Applications. 233:120975

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Image and Video Processing (eess.IV), 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2202.05177

Denoising stacked autoencoders for transient electromagnetic signal denoising

Autoren: F. Lin K. Chen X. Wang et al.

Quelle: Nonlinear Processes in Geophysics, Vol 26, Pp 13-23 (2019)

Schlagwörter: Science, Physics, QC1-999, Geophysics. Cosmic physics, QC801-809

Dateibeschreibung: electronic resource

Relation: https://www.nonlin-processes-geophys.net/26/13/2019/npg-26-13-2019.pdf; https://doaj.org/toc/1023-5809; https://doaj.org/toc/1607-7946

Zugangs-URL: https://doaj.org/article/02a1d2686874451dbdd4be885934f9ee

A novel modulation classification method in cognitive radios using higher-order cumulants and denoising stacked sparse autoencoder

Autoren: Xu Zhu Takeo Fujii

Quelle: 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). :1-5

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Building feature space of extreme learning machine with sparse denoising stacked-autoencoder

Autoren: Lele Cao Wenbing Huang Fuchun Sun

Quelle: Neurocomputing. 174:60-71

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/journals/ijon/ijon174.html#CaoHS16
https://dl.acm.org/doi/10.1016/j.neucom.2015.02.096
https://dl.acm.org/citation.cfm?id=2852657
http://www.sciencedirect.com/science/article/pii/S0925231215011674
https://www.sciencedirect.com/science/article/pii/S0925231215011674

Decision letter for 'Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation'

Review for 'Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation'

Review for 'Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation'

Author response for 'Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation'

Autoren: null Jiapeng Lv null Zihao Chen null Xiaoli Luan et al.

Decision letter for 'Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation'

Review for 'Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation'

Review for 'Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation'

Denoising stacked autoencoders‐based near‐infrared quality monitoring method via robust samples evaluation.

Autoren: Lv, Jiapeng Chen, Zihao Luan, Xiaoli et al.

Quelle: Canadian Journal of Chemical Engineering; May2023, Vol. 101 Issue 5, p2693-2703, 11p

Schlagwörter: ROBUST statistics, STATISTICAL learning, INFRARED spectra, MULTICOLLINEARITY, FEATURE extraction, XYLENOL, IMAGE denoising, NEAR infrared spectroscopy

Comments on 'A denoising stacked autoencoders for transient electromagnetic signal denoising'

Denoising stacked autoencoders for transient electromagnetic signal denoising.

Autoren: Lin, Fanqiang Chen, Kecheng Wang, Xuben et al.

Quelle: Nonlinear Processes in Geophysics; 2019, Vol. 26 Issue 1, p13-23, 11p

Schlagwörter: SIGNAL denoising, RANDOM noise theory, NOISE control, MULTIPLE correspondence analysis (Statistics), ELECTROMAGNETIC pulses, WAVELET transforms, DEEP learning