Search Results - "denoising autoencoder (dae)"

Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder

Authors: Asri Rizki Yuliani Hilman Ferdinandus Pardede Ade Ramdan et al.

Source: Journal of Mechatronics, Electrical Power, and Vehicular Technology, Vol 15, Iss 1, Pp 93-104 (2024)

Subject Terms: neural network, remaining useful life (rul), denoising autoencoder (dae), lithium-ion (li-ion) battery, TJ1-1570, system robustness, Electrical engineering. Electronics. Nuclear engineering, Mechanical engineering and machinery, 7. Clean energy, TK1-9971

Access URL: https://doaj.org/article/045065a2412848b69e82bedb7c7174af

Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals

Authors: Ahmed Shaheen Liang Ye Chrishni Karunaratne et al.

Source: Sensors ; Volume 25 ; Issue 3 ; Pages: 801

Subject Terms: electrocardiogram (ECG), denoising autoencoder (DAE), convolutional neural network (CNN), self-ONN, U-Net, gated convolution, gated residual, attention, motion artifacts

File Description: application/pdf

Relation: Electronic Sensors; https://dx.doi.org/10.3390/s25030801

Availability: https://doi.org/10.3390/s25030801

Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

Authors: Fabrizio Romanelli Francesco Martinelli

Source: IEEE Access, Vol 11, Pp 111765-111788 (2023)

Subject Terms: machine learning (ML), denoising autoencoder (DAE), long short-term memory (LSTM), deep neural networks (DNNs), Electrical engineering. Electronics. Nuclear engineering, Convolutional neural networks (CNNs), artificial neural networks (ANNs), TK1-9971

Access URL: https://doaj.org/article/fa17c1d60b104e9f893f4e18153f2508

A series fault arc detection method based on denoising autoencoder and deep residual network

Authors: Jianyuan Wang Xue Li Yuhui Zhang

Source: Frontiers in Energy Research, Vol 12 (2024)

Subject Terms: deep residual networks (ResNet), denoising autoencoder (DAE), series arc fault, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, false color, 0210 nano-technology, abstract features, General Works

Access URL: https://doaj.org/article/b62d77040d8d416fbdd0b5921913d273

Integration Design of Portable ECG Signal Acquisition With Deep-Learning Based Electrode Motion Artifact Removal on an Embedded System

Authors: Yu-Syuan Jhang Szu-Ting Wang Ming-Hwa Sheu et al.

Source: IEEE Access, Vol 10, Pp 57555-57564 (2022)

Subject Terms: embedded system, 03 medical and health sciences, 0302 clinical medicine, ECG signal enhancement, denoising autoencoder (DAE), 0202 electrical engineering, electronic engineering, information engineering, deep learning, Electrocardiogram (ECG), Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, TK1-9971

Access URL: https://doaj.org/article/ed376052a46843e98c2b8e2b0e3aa667

Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis

Authors: Jun Liang Daoguang Liu Yinxiao Zhan et al.

Source: Actuators, Vol 13, Iss 11, p 440 (2024)

Subject Terms: process monitoring, fault diagnosis, canonical variate analysis (CVA), dynamics, denoising autoencoder (DAE), nonlinear process, Materials of engineering and construction. Mechanics of materials, TA401-492, Production of electric energy or power. Powerplants. Central stations, TK1001-1841

File Description: electronic resource

Relation: https://www.mdpi.com/2076-0825/13/11/440; https://doaj.org/toc/2076-0825

Access URL: https://doaj.org/article/12e18d4cc1334e0ba37224c8e9190b1c

Reducing MEG interference using machine learning

Authors: Sammi Hamdan Kyle DuBray Jordan Treutel et al.

Source: Machine Learning with Applications, Vol 12, Iss , Pp 100462- (2023)

Subject Terms: TensorFlow, MNE-python, Supervised learning, Denoising Autoencoder (DAE), REACT, Cybernetics, Q300-390, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666827023000154; https://doaj.org/toc/2666-8270

Access URL: https://doaj.org/article/de6cf2d86b0d4f65b29b6a45a11d27bc

Heart rate estimation for U-Net and LSTM models combining multiple attention mechanisms.

Authors: Li, Ahui Cai, Jun

Source: Med Eng Phys ; ISSN:1873-4030 ; Volume:145

Subject Terms: Denoising autoencoder (DAE), Long short-term memory network (LSTM), Photoplethysmography (PPG), Residual network (ResNet), U-Net architecture

Relation: https://doi.org/10.1016/j.medengphy.2025.104406; https://pubmed.ncbi.nlm.nih.gov/41176397

Availability: https://doi.org/10.1016/j.medengphy.2025.104406
https://pubmed.ncbi.nlm.nih.gov/41176397

A Multi-Level-Denoising Autoencoder Approach for Wind Turbine Fault Detection

Authors: Xin Wu Guoqian Jiang Xiao Wang et al.

Source: IEEE Access, Vol 7, Pp 59376-59387 (2019)

Subject Terms: multi-level-denoising autoencoder (MLD-AE), Wind turbines (WTs), denoising autoencoder (DAE), 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, 7. Clean energy, fault detection, multivariate data-driven, TK1-9971

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08705222.pdf
https://doaj.org/article/d52238a60f4245978c079965d465b3ad
https://ieeexplore.ieee.org/abstract/document/8705222
https://doi.org/10.1109/ACCESS.2019.2914731
https://dblp.uni-trier.de/db/journals/access/access7.html#WuJWXL19

A Deep Imbalanced Learning Framework for Transient Stability Assessment of Power System

Authors: Bendong Tan Jun Yang Yufei Tang et al.

Source: IEEE Access, Vol 7, Pp 81759-81769 (2019)

Subject Terms: ensemble cost-sensitive SDAE, transient stability of power system, feature patterns, denoising autoencoder (DAE), 0202 electrical engineering, electronic engineering, information engineering, G-mean, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Deep imbalanced learning, TK1-9971

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08740932.pdf
https://doaj.org/article/d5c26bb442104bc69becb9cec6dbf2fe
https://ieeexplore.ieee.org/document/8740932/
https://dblp.uni-trier.de/db/journals/access/access7.html#TanYTJXY19

Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information

Authors: Guoqian Jiang Ping Xie Haibo He et al.

Source: IEEE/ASME Transactions on Mechatronics. 23:89-100

Subject Terms: temporal information, 13. Climate action, denoising autoencoder (DAE), Complex mechatronics, wind turbines (WTs), 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, multivariate data driven, 7. Clean energy, fault detection

Access URL: https://ieeexplore.ieee.org/abstract/document/8059861
https://xplqa30.ieee.org/document/8059861/
https://digitalcommons.uri.edu/ele_facpubs/401/

Deep learning approach for active classification of electrocardiogram signals

Authors: Al Rahhal, M. M. Bazi, Yakoub Alhichri, H. et al.

Source: Information Sciences. 345:340-354

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, Active learning (AL), Deep neural network (DNN), Denoising autoencoder (DAE), ECG signal classification, Feature learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

File Description: application/pdf

Access URL: https://www.sciencedirect.com/science/article/abs/pii/S0020025516300184
https://hdl.handle.net/11572/154241
https://doi.org/10.1016/j.ins.2016.01.082

Table1_A series fault arc detection method based on denoising autoencoder and deep residual network.XLSX

Authors: Jianyuan Wang Xue Li Yuhui Zhang

Subject Terms: Nuclear Engineering, Carbon Sequestration Science, Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels), Carbon Capture Engineering (excl. Sequestration), Non-automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels), Chemical Engineering not elsewhere classified, Power and Energy Systems Engineering (excl. Renewable Power), Renewable Power and Energy Systems Engineering (excl. Solar Cells), Energy Generation, Conversion and Storage Engineering, Nuclear Engineering (incl. Fuel Enrichment and Waste Processing and Storage), Chemical Sciences not elsewhere classified, series arc fault, denoising autoencoder (DAE), abstract features, false color, deep residual networks (ResNet)

Relation: https://figshare.com/articles/dataset/Table1_A_series_fault_arc_detection_method_based_on_denoising_autoencoder_and_deep_residual_network_XLSX/25405225

Availability: https://doi.org/10.3389/fenrg.2024.1341281.s001
https://figshare.com/articles/dataset/Table1_A_series_fault_arc_detection_method_based_on_denoising_autoencoder_and_deep_residual_network_XLSX/25405225

Detección de fallos/anomalías en una planta industrial mediante técnicas de deep-learning y métodos de clustering

Authors: Ormazábal Gómez, Cristina Fuente Aparicio, María Jesús de la Universidad de Valladolid. Escuela de Ingenierías Industriales

Contributors: Ormazábal Gómez, Cristina Fuente Aparicio, María Jesús de la Universidad de Valladolid. Escuela de Ingenierías Industriales

Source: UVaDOC. Repositorio Documental de la Universidad de Valladolid
Universidad de Valladolid

Subject Terms: Autoencoder (AE), 3310.03 Procesos Industriales, K-NN (K- vecinos más cercanos), Planta Tennessee Eastman, Redes neuronales, Denoising Autoencoder (DAE), Análisis de Componentes Principales (PCA), Clustering

File Description: application/pdf

Access URL: https://uvadoc.uva.es/handle/10324/65527