Search Results - Stack Convolutional Denoising Autoencoder

Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network

Authors: Pu Yang Chenwan Wen Huilin Geng et al.

Source: Machines, Vol 9, Iss 12, p 360 (2021)

Subject Terms: intelligent fault diagnosis, stacked pruning sparse denoising autoencoder, convolutional neural network, anti-noise, Mechanical engineering and machinery, TJ1-1570

File Description: electronic resource

Relation: https://www.mdpi.com/2075-1702/9/12/360; https://doaj.org/toc/2075-1702

Access URL: https://doaj.org/article/57fd1ddf636d4ff7816cfd2cd17bcfcf

A Multi-Dimensional Evaluation Method of Collaborative Learning Quality in Higher Education Based on Random Forest.

Authors: Wang, Haozeng Li, Jiahuan

Source: International Journal of High Speed Electronics & Systems. Dec2025, Vol. 34 Issue 4, p1-21. 21p.

Subject Terms: *ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, RANDOM forest algorithms, AUTOENCODERS, COLLABORATIVE learning

SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising.

Authors: Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam

Source: IET Image Processing (Wiley-Blackwell). Dec2019, Vol. 13 Issue 14, p2778-2789. 12p.

Multi-lingual handwriting recovery framework based on convolutional denoising autoencoder with attention model.

Authors: Rabhi, Besma Elbaati, Abdelkarim Boubaker, Houcine et al.

Source: Multimedia Tools & Applications; Mar2024, Vol. 83 Issue 8, p22295-22326, 32p

具批量正規化之卷積去雜訊自編碼器性能分析 ; Performance Analysis of Convolutional Denoising Autoencoder with BatchNormalization

Authors: 胡智超 Hu, Jhih-Chao 陳文雄 教授 et al.

Contributors: 胡智超 Hu, Jhih-Chao 陳文雄 教授 et al.

Subject Terms: 卷積神經網路, 卷積去雜訊自編碼器, 批量正規化, 堆疊卷積去雜訊自編碼器, Convolutional Neural Network, Convolutional Denoising Autoencoder, Batch Normalization, Stack Convolutional Denoising Autoencoder

File Description: 130 bytes; text/html

Relation: http://ir.ncnu.edu.tw:8080/handle/310010000/15237; http://ir.ncnu.edu.tw:8080/bitstream/310010000/15237/1/index.html

Availability: http://ir.ncnu.edu.tw:8080/handle/310010000/15237
http://ir.ncnu.edu.tw:8080/bitstream/310010000/15237/1/index.html

Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder.

Authors: Xiong, Yihui Zuo, Renguang

Source: Mathematical Geosciences; Apr2022, Vol. 54 Issue 3, p623-644, 22p

Vibration-based damage detection for bridges by deep convolutional denoising autoencoder.

Authors: Shang, Zhiqiang Sun, Limin Xia, Ye et al.

Source: Structural Health Monitoring; Jul2021, Vol. 20 Issue 4, p1880-1903, 24p

Subject Terms: STRUCTURAL health monitoring, DEEP learning, MOVING average process, QUALITY control charts, TEMPERATURE effect, PROPERTY damage

Denoising ambient seismic field correlation functions with convolutional autoencoders.

Authors: Viens, Loïc Van Houtte, Chris

Source: Geophysical Journal International; Mar2020, Vol. 220 Issue 3, p1521-1535, 15p

Subject Terms: SURFACE waves (Seismic waves), SEISMIC wave velocity, SURFACE of the earth, RELATIVE velocity, AUTOMORPHISMS, PROPORTIONAL hazards models, METROPOLITAN areas

Geographic Terms: JAPAN

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance.

Authors: Roka, Sanjay Diwakar, Manoj

Source: Journal of Electronic Imaging; May/Jun2023, Vol. 32 Issue 3, p33015-033015-23, 1p

Subject Terms: VIDEO surveillance, ANOMALY detection (Computer security), IMAGE denoising, OPTICAL flow, CONVOLUTIONAL neural networks, DEEP learning

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder.

Authors: Shi, Hongmei Chen, Jingcheng Si, Jin et al.

Source: Sensors (14248220); Oct2020, Vol. 20 Issue 20, p5734, 1p

Subject Terms: ROLLER bearings, PYRAMIDS, FAULT diagnosis, VIBRATION tests, DEEP learning, ALGORITHMS, SIGNAL denoising

A Power System Disturbance Classification Method Robust to PMU Data Quality Issues.

Authors: Li, Zikang Liu, Hao Zhao, Junbo et al.

Source: IEEE Transactions on Industrial Informatics; Jan2022, Vol. 18 Issue 1, p130-142, 13p

Anomaly identification with few labeled data in the distillation process based on semisupervised ladder networks.

Authors: Li, Chuankun Xu, Wei Zhao, Dongfeng et al.

Source: Process Safety Progress; Jun2021, Vol. 40 Issue 2, p1-10, 10p

Subject Terms: DEEP learning, ELECTRONIC data processing, CONVOLUTIONAL neural networks, SUPERVISED learning

Speech Enhancement Autoencoder with Hierarchical Latent Structure

Authors: Koen Oostermeijer Chin-Hui Lee Qing Wang et al.

Source: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :671-675

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://dblp.uni-trier.de/db/conf/icassp/icassp2021.html#OostermeijerD0L21
https://ieeexplore.ieee.org/document/9414695

Multilayer Fisher extreme learning machine for classification

Authors: Jie Lai Xiaodan Wang Qian Xiang et al.

Source: Complex & Intelligent Systems, Vol 9, Iss 2, Pp 1975-1993 (2022)

Subject Terms: Extreme learning machine, Deep learning, Representation learning, Autoencoder, Fisher criterion, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64

File Description: electronic resource

Relation: https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053

Access URL: https://doaj.org/article/d10de9068658478a9a68daf87ca6ce5e

Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets †.

Authors: Testolin, Alberto Diamant, Roee

Source: Sensors (14248220); 2020, Vol. 20 Issue 10, p2945-2945, 1p

Subject Terms: DYNAMIC programming, NOISE measurement, OBJECT tracking (Computer vision), VITERBI decoding, DETECTION alarms, ACOUSTIC reflection

Automatic fault diagnosis of rolling bearings under multiple working conditions based on unsupervised stack denoising autoencoder.

Authors: Wang, Lei Rao, Hang Dong, Zhengcheng et al.

Source: Structural Health Monitoring; Sep2024, Vol. 23 Issue 5, p3084-3104, 21p

Subject Terms: AUTOENCODERS, DEEP learning, ROLLER bearings, DIAGNOSIS, ALGORITHMS, FEATURE extraction

Digital Image Forgery Detection Using Cyclic Symmetry Convolutional Neural Network.

Authors: S., Shashikala G. K., Ravikumar

Source: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 1, p368-379, 12p

Subject Terms: CONVOLUTIONAL neural networks, FEATURE selection, DIGITAL images, FEATURE extraction, AUTOENCODERS

Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering.

Authors: Wang, Yang Xiao, Hong Zhang, Zhihai et al.

Source: Computer-Aided Civil & Infrastructure Engineering; 2/4/2025, Vol. 40 Issue 4, p503-522, 20p

Subject Terms: TRANSPORTATION noise, SIGNAL denoising, PATTERN perception, FAULT diagnosis, LATENT variables, MACHINE learning, EMPIRICAL research

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder.

Authors: Lu, Haifei Zhou, Kedong He, Lei

Source: Electronics (2079-9292); Jun2024, Vol. 13 Issue 12, p2403, 14p

Subject Terms: SIGNAL denoising, SIGNAL-to-noise ratio, ROLLER bearings, FAULT diagnosis, RESEARCH personnel

Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people.

Authors: Akilandeswari, J. Jothi, G. Naveenkumar, A. et al.

Source: Multimedia Tools & Applications; Jan2022, Vol. 81 Issue 3, p3483-3514, 32p

Subject Terms: CONVOLUTIONAL neural networks, PEOPLE with visual disabilities, RADIAL basis functions, DEEP learning, VISION disorders, RAILROAD stations