Výsledky vyhľadávania - Residual learning convolutional denoising autoencoder

Enhanced Noise Reduction in Photoplethysmography Signals Using a Denoising Autoencoder.

Autori: Shin-Chi Lai Yao-Feng Liang Yi-Chang Zhu a ďalší

Zdroj: Sensors & Materials; 2025, Vol. 37 Issue 11, Part 3, p5123-5139, 17p

Predmety: PHOTOPLETHYSMOGRAPHY, CONVOLUTIONAL neural networks, NOISE control, SIGNAL processing, SIGNAL denoising, REAL-time computing, MEDICAL technology, WEARABLE technology

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

Autori: Hongmei Shi Jingcheng Chen Jin Si a ďalší

Zdroj: Sensors, Vol 20, Iss 20, p 5734 (2020)

Predmety: intelligent fault diagnosis, residual learning, dilated pyramid network, fully convolutional denoising autoencoder, noise robustness, Chemical technology, TP1-1185

Popis súboru: electronic resource

Relation: https://www.mdpi.com/1424-8220/20/20/5734; https://doaj.org/toc/1424-8220

Prístupová URL adresa: https://doaj.org/article/995f8a47d49c4e059e88abcfd858ecc7

An improved autoencoder for denoising acoustic emission signals in rock fracturing.

Autori: Wang, Tingting Qin, Yifan Zhao, Wanchun a ďalší

Zdroj: Nondestructive Testing & Evaluation; Jun2025, Vol. 40 Issue 6, p2572-2597, 26p

Predmety: ACOUSTIC emission, AUTOENCODERS, STANDARD deviations, DEEP learning, SIGNAL-to-noise ratio, EMISSION control, ACOUSTIC emission testing

MRI Rician Noise Reduction Using Recurrent Convolutional Neural Networks

Autori: Javier Gurrola-Ramos Teresa Alarcon Oscar Dalmau a ďalší

Prispievatelia: Javier Gurrola-Ramos Teresa Alarcon Oscar Dalmau a ďalší

Zdroj: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Universitat Politècnica de València (UPV)
IEEE Access, Vol 12, Pp 128272-128284 (2024)

Predmety: Denoising, recurrent convolutional neural network, convolutional neural network, gated recurrent units, Computational modeling, Convolutional neural network, Autoencoder, Gated recurrent units, MRI denoising, TK1-9971, Kernel, Magnetic resonance imaging, Recurrent neural networks, Encoding, denoising, CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL, Training, Recurrent convolutional neural network, Convolutional neural networks, Electrical engineering. Electronics. Nuclear engineering, Noise reduction

Popis súboru: application/pdf

Prístupová URL adresa: https://riunet.upv.es/handle/10251/211402
https://doaj.org/article/4f30db8383fe42408584bc78991adcc5
https://hdl.handle.net/10251/211402
https://doi.org/10.1109/access.2024.3446791

Network security situation assessment method based on ACDAE-ResBiGRU.

Autori: Li, Binyong Zhang, Jie

Zdroj: Maintenance & Reliability / Eksploatacja i Niezawodność; 2025, Vol. 27 Issue 4, p1-19, 19p

Predmety: COMPUTER network security, FEATURE extraction, SITUATIONAL awareness, RISK assessment, RECURRENT neural networks, CLASSIFICATION, ARTIFICIAL neural networks, SIGNAL denoising

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Autori: Shuang Mei Yudan Wang Guojun Wen

Zdroj: Sensors, Vol 18, Iss 4, p 1064 (2018)

Predmety: fabric defect detection, unsupervised learning, deep neural network, convolutional denoising autoencoder, Gaussian pyramid, Chemical technology, TP1-1185

Popis súboru: electronic resource

Relation: http://www.mdpi.com/1424-8220/18/4/1064; https://doaj.org/toc/1424-8220

Prístupová URL adresa: https://doaj.org/article/5e389f2bca1348e780347c16d402a82d

Denoising and Baseline Correction Methods for Raman Spectroscopy Based on Convolutional Autoencoder: A Unified Solution.

Autori: Han, Ming Dang, Yu Han, Jianda

Zdroj: Sensors (14248220); May2024, Vol. 24 Issue 10, p3161, 15p

Predmety: RAMAN spectroscopy, CONVOLUTIONAL neural networks, IMAGE denoising, NOISE control, SIGNAL processing

Robust correlation mapping of train-induced stresses for high-speed railway bridge using convolutional denoising autoencoder.

Autori: Niu, Jin Li, Zhonglong Zhuo, Yi a ďalší

Zdroj: Structural Health Monitoring; Mar2023, Vol. 22 Issue 2, p832-845, 14p

Predmety: RAILROAD bridges, STRUCTURAL health monitoring, TRUSS bridges, DEEP learning, DATA integrity, MISSING data (Statistics), SENSOR networks, HIGH speed trains, FEATURE extraction

Research on Loosening Fault Diagnosis Method of Escalator Drive Mainframe Anchor Bolts Based on Improved High-Strength Denoising RCDAE Model.

Autori: Chen, Dongdong Chen, Minghui Lang, Binxin a ďalší

Zdroj: Sensors (14248220); Sep2025, Vol. 25 Issue 17, p5219, 23p

Predmety: ANCHOR bolts (Structural engineering), FAULT diagnosis, ESCALATORS, SIGNAL denoising, LOSS functions (Statistics), FEATURE extraction, NOISE

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments.

Autori: Yahang Qin Zhenni Li Shengli Xie a ďalší

Zdroj: Sensors (14248220); 3/15/2024, Vol. 24 Issue 6, p1-23, 23p, 1 Color Photograph, 2 Diagrams, 4 Charts, 8 Graphs

Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network

Autori: Ting Yan Zhenpeng Yan Lili Liu a ďalší

Zdroj: Frontiers in Computational Neuroscience, Vol 16 (2023)

Predmety: glioblastoma multiforme, deep learning, survival prediction, denoising autoencoder, time-dependent ROC curve, Cox proportional hazard regression, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Popis súboru: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fncom.2022.916511/full; https://doaj.org/toc/1662-5188

Prístupová URL adresa: https://doaj.org/article/1b901e4cde1f4d28b246bcd3028ee0b6

A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation.

Autori: Wang, Youming Wang, Yue

Zdroj: Applied Intelligence; Oct2023, Vol. 53 Issue 19, p22682-22699, 18p

Predmety: REMAINING useful life, DEEP learning, CONVOLUTIONAL neural networks, TURBOFAN engines, SUPERVISED learning, STANDARD deviations, FEATURE extraction

Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise

Autori: Deshan Feng Xiangyu Wang Xun Wang a ďalší

Zdroj: Remote Sensing, Vol 13, Iss 9, p 1761 (2021)

Predmety: GPR, noise attenuation, Gaussian spike impulse noise, deep convolutional denoising autoencoders (CDAEs), deep convolutional denoising autoencoders with network structure optimization (CDAEsNSO), Science

Popis súboru: electronic resource

Relation: https://www.mdpi.com/2072-4292/13/9/1761; https://doaj.org/toc/2072-4292

Prístupová URL adresa: https://doaj.org/article/c42d57a98e7f4b01ba98105ec0458aaa

Detection and Classification of Knee Injuries from MR Images Using the MRNet Dataset with Progressively Operating Deep Learning Methods

Autori: Ali Can Kara Fırat Hardalaç

Zdroj: Machine Learning and Knowledge Extraction, Vol 3, Iss 4, Pp 1009-1029 (2021)

Predmety: deep learning, transfer learning, ResNet50, convolutional neural networks, denoising autoencoders, knee injuries, Computer engineering. Computer hardware, TK7885-7895

Popis súboru: electronic resource

Relation: https://www.mdpi.com/2504-4990/3/4/50; https://doaj.org/toc/2504-4990

Prístupová URL adresa: https://doaj.org/article/ea3f6c9907a041e98bce9443ac500037

Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition.

Autori: Guo, Jiajun Ji, Qingge Shan, Guangwei

Zdroj: IET Image Processing (Wiley-Blackwell); 1/10/2024, Vol. 18 Issue 1, p233-246, 14p

Predmety: SIGNAL denoising, COMPUTER vision

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

Autori: Rabhi, Besma Elbaati, Abdelkarim Boubaker, Houcine a ďalší

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

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

Autori: Shi, Hongmei Chen, Jingcheng Si, Jin a ďalší

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

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

Dual Channel Residual Learning for Denoising Path Tracing.

Autori: Liu, Ping Ji, Hangyu

Zdroj: International Journal of Image & Graphics; Jan2025, Vol. 25 Issue 1, p1-20, 20p

RESIDUAL LEARNING BASED IMAGE DENOISING AND COMPRESSION USING DNCNN.

Autori: Shaliniswetha, Savaram Mahaboob, Shaik Taj

Zdroj: ICTACT Journal on Image & Video Processing; Nov2022, Vol. 13 Issue 2, p2817-2824, 8p

Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine

Autori: Ping Zhou Gang Chen Mingwei Wang a ďalší

Zdroj: Remote Sensing, Vol 12, Iss 22, p 3762 (2020)

Predmety: sediment classification, acoustic backscatter image, stacked denoising auto encoder, extreme learning machine, Parzen window, particle swarm optimization, Science

Popis súboru: electronic resource

Relation: https://www.mdpi.com/2072-4292/12/22/3762; https://doaj.org/toc/2072-4292

Prístupová URL adresa: https://doaj.org/article/ebf6cc1095aa4a4680ea7f7335bdeeb3