Výsledky vyhledávání - "deep convolutional autoencoder"

Studies on 1D Electronic Noise Filtering Using an Autoencoder

Autoři: Marcelo Bender Perotoni Lincoln Ferreira Lucio

Zdroj: Knowledge, Vol 4, Iss 4, Pp 571-581 (2024)

Témata: noise generator, Electronic computers. Computer science, noise filtering, 0202 electrical engineering, electronic engineering, information engineering, QA75.5-76.95, 02 engineering and technology, denoise, 01 natural sciences, deep convolutional autoencoder, 0105 earth and related environmental sciences

Přístupová URL adresa: https://doaj.org/article/433d3f19e0de4cbe926d08080a3f240e

Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition

Autoři: Nana Han Wending Miao Mingzhi Li a další

Zdroj: Frontiers in Earth Science, Vol 13 (2025)

Témata: health monitoring, Science, rainfall, slope displacement, multi-source data fusion, deep convolutional autoencoder

Přístupová URL adresa: https://doaj.org/article/4440996c733b4eaf8040edb8e0c3f0dd

Clustering of spatio-temporal precipitation patterns in the Legal Amazon using deep convolutional autoencoder ; Clusterização de padrões espaço-temporais de precipitação na Amazônia Legal via deep convolutional autoencoder

Autoři: Silva, Vander Augusto Oliveira da Texeira, Raphael Barros

Zdroj: Ciência e Natura; Vol. 47 (2025): Publicação contínua; e85042 ; Ciência e Natura; v. 47 (2025): Publicação contínua; e85042 ; 2179-460X ; 0100-8307

Témata: Machine learning, Deep convolutional autoencoder, Clustering, Pattern recognition, Precipitation time series, Aprendizado de máquina, Autoencoder convolucional profundo, Agrupamentos, Reconhecimento de padrões, Séries temporais de precipitação

Popis souboru: application/pdf; text/html

Relation: https://periodicos.ufsm.br/cienciaenatura/article/view/85042/65968; https://periodicos.ufsm.br/cienciaenatura/article/view/85042/66553; https://periodicos.ufsm.br/cienciaenatura/article/view/85042

Dostupnost: https://periodicos.ufsm.br/cienciaenatura/article/view/85042
https://doi.org/10.5902/2179460X85042

RESEARCH ON IIOT SECURITY: NOVEL MACHINE LEARNING-BASED INTRUSION DETECTION USING TCP/IP PACKETS

Autoři: Neha Agarwal Rajendra Pandey P. Smitha Rajagopal

Zdroj: Proceedings on Engineering Sciences, Vol 5, Iss S1, Pp 63-68 (2023)

Témata: tcp/ip packet, 03 medical and health sciences, internet of things (iot), 0302 clinical medicine, intrusion detection system (ids), industrial systems, and splinted decision tree (hdca-sdt), hybrid deep convolutional autoencoder, security, TA1-2040, Engineering (General). Civil engineering (General)

Přístupová URL adresa: https://doaj.org/article/1c2ac034097640d995a3e4e2bb99191b

A Method of Complementing Missing Power Data in Low-Voltage Stations Based on Improved Deep Convolutional Self-Encoding Network

Autoři: Hongshan Zhao Yangyang Cui Wei Song a další

Zdroj: IEEE Access, Vol 10, Pp 57565-57573 (2022)

Témata: missing data completion, low-voltage power distribution station area, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Intelligent distribution network, deep convolutional autoencoder, residual learning, TK1-9971

Přístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09552884.pdf
https://doaj.org/article/a4c67e65742e4066be43c318d163e47d
https://ieeexplore.ieee.org/document/9552884

Self-supervised Deep Clustering Algorithm Based on Self-attention

Autoři: HAN Jie, CHEN Jun-fen, LI Yan, ZHAN Ze-cong

Zdroj: Jisuanji kexue, Vol 49, Iss 3, Pp 134-143 (2022)

Témata: deep convolutional autoencoder, image clustering, features representation, self-attention, computational complexity, Computer software, QA76.75-76.765, Technology (General), T1-995

Popis souboru: electronic resource

Relation: https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-3-134.pdf; https://doaj.org/toc/1002-137X

Přístupová URL adresa: https://doaj.org/article/3a6a57479b654726a1809ba432bb2e46

Image 1_Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition.jpeg

Autoři: Nana Han Wending Miao Mingzhi Li a další

Témata: Solid Earth Sciences, multi-source data fusion, deep convolutional autoencoder, slope displacement, rainfall, health monitoring

Dostupnost: https://doi.org/10.3389/feart.2025.1531857.s001
https://figshare.com/articles/figure/Image_1_Integrating_multi-source_monitoring_data_and_deep_convolutional_autoencoder_technology_for_slope_failure_pattern_recognition_jpeg/28237844

Adaptive Surrogate Estimation with Spatial Features Using a Deep Convolutional Autoencoder for CO 2 Geological Sequestration

Autoři: Suryeom Jo Changhyup Park Dong-Woo Ryu a další

Zdroj: Energies, Vol 14, Iss 2, p 413 (2021)

Témata: deep convolutional autoencoder, deep learning, spatial parameter, latent feature, surrogate model, data integration, Technology

Relation: https://www.mdpi.com/1996-1073/14/2/413; https://doaj.org/toc/1996-1073; https://doaj.org/article/b3db498b72064c3cb866bd14b6dac3b0

Dostupnost: https://doi.org/10.3390/en14020413
https://doaj.org/article/b3db498b72064c3cb866bd14b6dac3b0

Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics

Autoři: Bardia Yousefi Satoru Kawakita Arya Amini a další

Zdroj: Journal of Clinical Medicine, Vol 10, Iss 3100, p 3100 (2021)

Témata: COVID-19 detection, deep convolutional autoencoder (ConvAE), 2D U-Net model, imaging biomarker, deep-learning features, deep latent space radiomics, Medicine

Relation: https://www.mdpi.com/2077-0383/10/14/3100; https://doaj.org/toc/2077-0383; https://doaj.org/article/ed58cfeb1ab04dc9821d3e46241ab42e

Dostupnost: https://doi.org/10.3390/jcm10143100
https://doaj.org/article/ed58cfeb1ab04dc9821d3e46241ab42e

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography

Autoři: Bardia Yousefi Hamed Akbari Michelle Hershman a další

Zdroj: Applied Sciences, Vol 11, Iss 3248, p 3248 (2021)

Témata: breast cancer, thermography, sparse deep convolutional autoencoder, matrix factorization, dimensionality reduction, thermomics, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/11/7/3248; https://doaj.org/toc/2076-3417; https://doaj.org/article/3c70757effa743f3b9bef5e4d264eb2f

Dostupnost: https://doi.org/10.3390/app11073248
https://doaj.org/article/3c70757effa743f3b9bef5e4d264eb2f

Deep Convolutional Clustering-Based Time Series Anomaly Detection

Autoři: Gavneet Singh Chadha Intekhab Islam Andreas Schwung a další

Zdroj: Sensors, Vol 21, Iss 5488, p 5488 (2021)

Témata: unsupervised learning, deep convolutional autoencoder, top-K K-means clustering, anomaly detection, Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/21/16/5488; https://doaj.org/toc/1424-8220; https://doaj.org/article/60cecf0efafc4ace9ccc79ed188b6384

Dostupnost: https://doi.org/10.3390/s21165488
https://doaj.org/article/60cecf0efafc4ace9ccc79ed188b6384

Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

Autoři: Sadrawi, M Lin, YT Lin, CH a další

Témata: non-invasive system, hemodynamics, electrocardiography, arterial blood pressure, central venous pressure, pulmonary arterial pressure, intracranial pressure, deep convolutional autoencoder

Popis souboru: 1 - 20; Electronic

Relation: Sensors (Basel, Switzerland); 6264; https://bura.brunel.ac.uk/handle/2438/24610; https://doi.org/10.3390/s21186264

Dostupnost: https://bura.brunel.ac.uk/handle/2438/24610
https://doi.org/10.3390/s21186264

Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

Autoři: Muammar Sadrawi Yin-Tsong Lin Chien-Hung Lin a další

Zdroj: Sensors, Vol 20, Iss 3829, p 3829 (2020)

Témata: photoplethysmography, continuous arterial blood pressure, systolic blood pressure, diastolic blood pressure, deep convolutional autoencoder, genetic algorithm, Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/20/14/3829; https://doaj.org/toc/1424-8220; https://doaj.org/article/5ef0804cdd4a43289aedae3b138c5074

Dostupnost: https://doi.org/10.3390/s20143829
https://doaj.org/article/5ef0804cdd4a43289aedae3b138c5074

Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

Autoři: Sadrawi, M Lin, Y-T Lin, C-H a další

Témata: continuous arterial blood pressure, systolic blood pressure, diastolic blood pressure, genetic algorithm, photoplethysmography, deep convolutional autoencoder

Přístupová URL adresa: http://bura.brunel.ac.uk/handle/2438/21163

Assessing Physical Activity and Functional Fitness Level Using Convolutional Neural Networks.

Autoři: Galán Mercant, Alejandro Ortiz-García, Andrés Herrera Viedma, Enrique a další

Témata: Redes neuronales (Informática), Ejercicio físico - Evaluación, Ancianos - Condición física, Physical activity, Functional fitness, Deep learning, Inertial signal, Deep Convolutional Autoencoder/sep Convolutional Network

Relation: https://hdl.handle.net/10630/28103

Dostupnost: https://hdl.handle.net/10630/28103
https://doi.org/10.1016/j.knosys.2019.104939

A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals

Autoři: Caleb Vununu Kwang-Seok Moon Suk-Hwan Lee a další

Zdroj: Sensors, Vol 18, Iss 8, p 2634 (2018)

Témata: machine fault diagnosis, sound and acoustic processing, pattern recognition, machine learning, deep convolutional autoencoder, deep learning, smart factory, artificial neural network, Chemical technology, TP1-1185

Relation: http://www.mdpi.com/1424-8220/18/8/2634; https://doaj.org/toc/1424-8220; https://doaj.org/article/5bf54295e092442a9404ca46176c2b69

Dostupnost: https://doi.org/10.3390/s18082634
https://doaj.org/article/5bf54295e092442a9404ca46176c2b69

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Autoři: T. Kadeethum F. Ballarin Y. Choi a další

Zdroj: Advances in Water Resources. 160:104098

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Deep convolutional autoencoder, Reduced order modeling, Numerical Analysis (math.NA), Data-driven, 01 natural sciences, Machine Learning (cs.LG), Computational Engineering, Finance, and Science (cs.CE), Finite element, 0103 physical sciences, FOS: Mathematics, Mathematics - Numerical Analysis, 0101 mathematics, Computer Science - Computational Engineering, Finance, and Science, Non-intrusive, Nonlinear problem

Přístupová URL adresa: http://arxiv.org/abs/2107.11460
https://hdl.handle.net/10807/202829
https://doi.org/10.1016/j.advwatres.2021.104098

Hyperspectral Stimulated Raman Scattering microscopy image denoising via a Deep Convolutional Autoencoder

Autoři: Abdolghader, Pedram Pegoraro, Adrian F. Ridsdale, Andrew a další

Zdroj: 2020 Photonics North (PN). :1-1

Témata: hyperspectral image denoising, stimulated raman microscopy, deep convolutional autoencoder

Přístupová URL adresa: https://ieeexplore.ieee.org/document/9166931

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Přispěvatelé: Abbod, Maysam Shieh, Jiann-Shing

Témata: automated dietary monitoring, eating detection, eating timing error analysis, biomedical signal processing, smart eyeglasses, wearable health monitoring, artificial neural network, joint moment prediction, extreme learning machine, Hill muscle model, online input variables, Review, ECG, Signal Processing, Machine Learning, Cardiovascular Disease, Anomaly Detection, photoplethysmography, motion artifact, independent component analysis, multi-wavelength, continuous arterial blood pressure, systolic blood pressure, diastolic blood pressure, deep convolutional autoencoder, genetic algorithm, electrocardiography, vectorcardiography, myocardial infarction, long short-term memory

Popis souboru: image/jpeg

Relation: ONIX_20220506_9783036538877_92; https://mdpi.com/books/pdfview/book/5368

Dostupnost: https://directory.doabooks.org/handle/20.500.12854/81026
https://hdl.handle.net/20.500.12854/81026
https://mdpi.com/books/pdfview/book/5368

Attention mechanism-based deep denoiser for desert seismic random noise suppression

Autoři: Lin, Hongbo Liu, Chang Wang, Shigang a další

Témata: seismic explorations, random noise suppression, deep convolutional autoencoder network, attention mechanism, low signal-to-noise ratio

Přístupová URL adresa: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-6efc455c-5479-4f38-ad63-96fee9d0a7b3