Search Results - fully convolutional denoising autoencoder*

Conditioned fully convolutional denoising autoencoder for multi-target NILM

Authors: García, Diego Pérez, Daniel Papapetrou, Panagiotis et al.

Source: Neural Computing & Applications. 37(17):10491-10505

Subject Terms: Non-intrusive Load Monitoring, Energy Efficiency, Deep Convolutional Neural Networks, Interpretability, Multi-target NILM models, data- och systemvetenskap, Computer and Systems Sciences

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-237967
https://doi.org/10.1007/s00521-024-10552-0

Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders

Authors: Hsin-Tien Chiang Yi-Yen Hsieh Szu-Wei Fu et al.

Source: IEEE Access, Vol 7, Pp 60806-60813 (2019)

Subject Terms: Electrocardiography, fully convolutional network, denoising autoencoders, signal denoising, 0206 medical engineering, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, artificial neural networks, TK1-9971

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08693790.pdf
https://doaj.org/article/81f46f96755b468bacc652dd28c48c93
https://ieeexplore.ieee.org/document/8693790
https://dblp.uni-trier.de/db/journals/access/access7.html#ChiangHFHTC19

Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders

Authors: Kechris, Christodoulos Delitzas, Alexandros Matsoukas, Vasileios et al.

Source: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). :890-893

Subject Terms: Signal Processing (eess.SP), FOS: Computer and information sciences, 0301 basic medicine, Computer Science - Machine Learning, Signal-To-Noise Ratio, Machine Learning (cs.LG), Protein Transport, 03 medical and health sciences, 0302 clinical medicine, Cell Movement, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, FOS: Electrical engineering, electronic engineering, information engineering, Neurons and Cognition (q-bio.NC), Neural Networks, Computer, Electrical Engineering and Systems Science - Signal Processing, Noise

Access URL: http://arxiv.org/pdf/2109.08945
https://pubmed.ncbi.nlm.nih.gov/34891433
http://arxiv.org/abs/2109.08945

Conditioned Fully Convolutional Denoising Autoencoder for Energy Disaggregation

Authors: Diego García Daniel Pérez Panagiotis Papapetrou et al.

Source: IFIP Advances in Information and Communication Technology ISBN: 9783031341700

Conditioned Fully Convolutional Denoising Autoencoder for Energy Disaggregation

Authors: García, Diego Pérez, Daniel Papapetrou, Panagiotis et al.

Index Terms: energy dissagregation, NILM, convolutional neural networks, Computer Sciences, Datavetenskap (datalogi), Conference paper, info:eu-repo/semantics/conferenceObject, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-225171
IFIP Advances in Information and Communication Technology, 1868-4238

Cross-Technology Interference Mitigation Using Fully Convolutional Denoising Autoencoders

Authors: Chi-Cheng Lee Yu Tsao Chi-Lun Lin et al.

Source: GLOBECOM 2020 - 2020 IEEE Global Communications Conference. :1-6

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

Access URL: https://scholar.nycu.edu.tw/zh/publications/cross-technology-interference-mitigation-using-fully-convolutiona
https://dblp.uni-trier.de/db/conf/globecom/globecom2020.html#LinLLT20

Fully convolutional denoising autoencoder for 3D scene reconstruction from a single depth image

Authors: Palla, Alessandro Moloney, David Fanucci, Luca

Source: 2017 4th International Conference on Systems and Informatics (ICSAI). :566-575

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

Access URL: https://arpi.unipi.it/handle/11568/887780
https://dblp.uni-trier.de/db/conf/icsai/icsai2017.html#PallaMF17
https://ieeexplore.ieee.org/document/8248355/
https://hdl.handle.net/11568/887780
https://ieeexplore.ieee.org/document/8248355
https://doi.org/10.1109/ICSAI.2017.8248355

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

Authors: Hongmei Shi Jingcheng Chen Jin Si et al.

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

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/995f8a47d49c4e059e88abcfd858ecc7

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

Authors: Wang, Tingting Qin, Yifan Zhao, Wanchun et al.

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

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

ECG Compression and Denoising based on Fully Convolutional Autoencoder

完全卷積自動編碼器應用在心電圖訊號的壓縮與除噪

Authors: Hsin-Tien Chiang 江欣恬

Thesis Advisors: Hsin-Tien Chiang 江欣恬

File Description: 82

Availability: http://ndltd.ncl.edu.tw/handle/6e327t

Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition.

Authors: Guo, Jiajun Ji, Qingge Shan, Guangwei

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

Subject Terms: SIGNAL denoising, COMPUTER vision

Single channel audio source separation using convolutional denoising autoencoders

Authors: Grais, Emad M Plumbley, Mark D

Source: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). :1265-1269

Subject Terms: FOS: Computer and information sciences, Sound (cs.SD), I.5, I.4.3, I.2.6, H.5.5, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 68T01, Computer Science - Sound

File Description: text

Access URL: http://arxiv.org/pdf/1703.08019
http://arxiv.org/abs/1703.08019
https://arxiv.org/abs/1703.08019
https://core.ac.uk/display/84589145
https://surrey.eprints-hosting.org/841860/
https://doi.org/10.1109/GlobalSIP.2017.8309164
https://epubs.surrey.ac.uk/841860/
https://dblp.uni-trier.de/db/journals/corr/corr1703.html#GraisP17
https://surrey.eprints-hosting.org/841860/

Conditioned fully convolutional denoising autoencoder for multi-target NILM.

Authors: Ingenieria de Sistemas y Automatica García Pérez, Diego Pérez López, Daniel et al.

Index Terms: Ingenierías, Ingeniería industrial, info:eu-repo/semantics/article

URL: https://hdl.handle.net/10612/23166
https://doi.org/10.1007/s00521-024-10552-0
info:eu-repo/grantAgreement/AEI/Programa Estatal de I+D+i Orientada a los Retos de la Sociedad/PID2020-117890RB-I00/ES/ TECNICAS DE MODELADO INTELIGENTE BASADO EN DATOS APLICADAS A INSTALACIONES INDUSTRIALES PARA LA MEJORA DE LA EFICIENCIA ENERGETICA
info:eu-repo/grantAgreement /AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2020-115401GB-I00/ES/ HERRAMIENTAS DE ANALITICA VISUAL PARA EL ESTUDIO DE PROBLEMAS COMPLEJOS EN INGENIERIA Y BIOMEDICINA

Single Channel Audio Source Separation using Convolutional Denoising Autoencoders

Authors: Grais, Emad M. Plumbley, Mark D.

Index Terms: Computer Science - Sound, 68T01, H.5.5, I.5, I.2.6, I.4.3, text

URL: http://arxiv.org/abs/1703.08019

Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders

Authors: Prakash, Mangal Krull, Alexander Jug, Florian

Index Terms: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, text

URL: http://arxiv.org/abs/2006.06072

Convolutional Deep Denoising Autoencoders for Radio Astronomical Images

Authors: Gheller, Claudio Vazza, Franco

Index Terms: Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2110.08618

Fault detection and recognition of multivariate process based on feature learning of one-dimensional convolutional neural network and stacked denoised autoencoder.

Authors: Zhang, Chengyi Yu, Jianbo Wang, Shijin

Source: International Journal of Production Research. Apr2021, Vol. 59 Issue 8, p2426-2449. 24p. 2 Color Photographs, 3 Diagrams, 15 Charts, 4 Graphs.

Subject Terms: *ARTIFICIAL neural networks, *MANUFACTURING processes, CONVOLUTIONAL neural networks, SIGNAL convolution, FAULT diagnosis, SIGNAL processing, DEEP learning, CONVEYOR belts

ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks

Authors: Wang, Kuan-Chen Liu, Kai-Chun Peng, Sheng-Yu et al.

Index Terms: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2210.13271

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

Authors: Wang, Youming Wang, Yue

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

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

Transfer learning for denoising the echolocation clicks of finless porpoise (Neophocaena phocaenoides sunameri) using deep convolutional autoencoders.

Authors: Yang, Wuyi Chang, Wenlei Song, Zhongchang et al.

Source: Journal of the Acoustical Society of America. Aug2021, Vol. 150 Issue 2, p1243-1250. 8p.

Subject Terms: *PORPOISES, *IMAGE denoising, *ECHOLOCATION (Physiology), *TOOTHED whales