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

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

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

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

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

Improving the Intelligibility of Electric and Acoustic Stimulation Speech Using Fully Convolutional Networks Based Speech Enhancement

Authors: Wang, Natalie Yu-Hsien Wang, Hsiao-Lan Sharon Wang, Tao-Wei et al.

Index Terms: Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, text

URL: http://arxiv.org/abs/1909.11912

Toward Informative Representations of Blood‐Based Infrared Spectra via Unsupervised Deep Learning.

Authors: Wegner, Corinna Zarandy, Zita I. Feiler, Nico et al.

Source: Journal of Biophotonics; Aug2025, Vol. 18 Issue 8, p1-12, 12p

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

ECG Signal Denoising Method Based on Disentangled Autoencoder.

Authors: Lin, Haicai Liu, Ruixia Liu, Zhaoyang

Source: Electronics (2079-9292); Apr2023, Vol. 12 Issue 7, p1606, 17p

Subject Terms: SIGNAL denoising, CONVOLUTIONAL neural networks, SIGNAL-to-noise ratio, NOISE control, ELECTROCARDIOGRAPHY

Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks.

Authors: Wang, Natalie Yu-Hsien Wang, Hsiao-Lan Sharon Wang, Tao-Wei et al.

Source: IEEE Transactions on Neural Systems & Rehabilitation Engineering; 2021, Vol. 29, p184-195, 12p

Subject Terms: CONVOLUTIONAL neural networks, ELECTRIC stimulation, INTELLIGIBILITY of speech, ACOUSTIC stimulation, DEEP learning, SPEECH perception, SPEECH enhancement

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.

Authors: Shankaranarayana, Sharath M. Ram, Keerthi Mitra, Kaushik et al.

Source: IEEE Journal of Biomedical & Health Informatics; Jul2019, Vol. 23 Issue 4, p1417-1426, 10p

Subject Terms: DEEP learning, OPTIC disc, FEATURE extraction, IMAGE color analysis, OPTIC nerve, DIAGNOSTIC imaging

A hybrid neuro-fuzzy prediction system with butterfly optimization algorithm for PM2.5 forecasting.

Authors: Bhanja, Samit Metia, Santanu Das, Abhishek

Source: Microsystem Technologies; Dec2022, Vol. 28 Issue 12, p2577-2592, 16p

Subject Terms: MATHEMATICAL optimization, CONVOLUTIONAL neural networks, FORECASTING, TIME series analysis, PREDICTION models, PARTICULATE matter, DEEP learning

Geographic Terms: SYDNEY (N.S.W.)

Enhanced Noise Reduction in Photoplethysmography Signals Using a Denoising Autoencoder.

Authors: Shin-Chi Lai Yao-Feng Liang Yi-Chang Zhu et al.

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

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