Výsledky vyhledávání - 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

Autoři: Pu Yang Chenwan Wen Huilin Geng a další

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

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

Popis souboru: electronic resource

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

Přístupová URL adresa: https://doaj.org/article/57fd1ddf636d4ff7816cfd2cd17bcfcf

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

Autoři: Wang, Haozeng Li, Jiahuan

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

Témata: *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.

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

Zdroj: 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.

Autoři: Rabhi, Besma Elbaati, Abdelkarim Boubaker, Houcine a další

Zdroj: 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

Autoři: 胡智超 Hu, Jhih-Chao 陳文雄 教授 a další

Přispěvatelé: 胡智超 Hu, Jhih-Chao 陳文雄 教授 a další

Témata: 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

Popis souboru: 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

Dostupnost: 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.

Autoři: Xiong, Yihui Zuo, Renguang

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

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

Autoři: Shang, Zhiqiang Sun, Limin Xia, Ye a další

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

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

Denoising ambient seismic field correlation functions with convolutional autoencoders.

Autoři: Viens, Loïc Van Houtte, Chris

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

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

Geografický termín: JAPAN

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

Autoři: Roka, Sanjay Diwakar, Manoj

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

Témata: 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.

Autoři: Shi, Hongmei Chen, Jingcheng Si, Jin a další

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

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

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

Autoři: Li, Zikang Liu, Hao Zhao, Junbo a další

Zdroj: 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.

Autoři: Li, Chuankun Xu, Wei Zhao, Dongfeng a další

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

Témata: DEEP learning, ELECTRONIC data processing, CONVOLUTIONAL neural networks, SUPERVISED learning

Speech Enhancement Autoencoder with Hierarchical Latent Structure

Autoři: Koen Oostermeijer Chin-Hui Lee Qing Wang a další

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

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

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

Multilayer Fisher extreme learning machine for classification

Autoři: Jie Lai Xiaodan Wang Qian Xiang a další

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

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

Popis souboru: electronic resource

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

Přístupová URL adresa: https://doaj.org/article/d10de9068658478a9a68daf87ca6ce5e

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

Autoři: Testolin, Alberto Diamant, Roee

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

Témata: 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.

Autoři: Wang, Lei Rao, Hang Dong, Zhengcheng a další

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

Témata: AUTOENCODERS, DEEP learning, ROLLER bearings, DIAGNOSIS, ALGORITHMS, FEATURE extraction

Digital Image Forgery Detection Using Cyclic Symmetry Convolutional Neural Network.

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

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

Témata: 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.

Autoři: Wang, Yang Xiao, Hong Zhang, Zhihai a další

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

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

Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder.

Autoři: Lu, Haifei Zhou, Kedong He, Lei

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

Témata: 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.

Autoři: Akilandeswari, J. Jothi, G. Naveenkumar, A. a další

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

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