Search Results - "Deep convolutional autoencoder"

Studies on 1D Electronic Noise Filtering Using an Autoencoder

Authors: Marcelo Bender Perotoni Lincoln Ferreira Lucio

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

Subject Terms: 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

Access URL: https://doaj.org/article/433d3f19e0de4cbe926d08080a3f240e

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

Authors: Nana Han Wending Miao Mingzhi Li et al.

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

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

Access URL: 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

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

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

Subject Terms: 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

File Description: 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

Availability: 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

Authors: Neha Agarwal Rajendra Pandey P. Smitha Rajagopal

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

Subject Terms: 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)

Access URL: https://doaj.org/article/1c2ac034097640d995a3e4e2bb99191b

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

Authors: Hongshan Zhao Yangyang Cui Wei Song et al.

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

Subject Terms: 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

Access URL: 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

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

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

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/3a6a57479b654726a1809ba432bb2e46

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

Authors: Nana Han Wending Miao Mingzhi Li et al.

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

Availability: 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

Authors: Suryeom Jo Changhyup Park Dong-Woo Ryu et al.

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

Subject Terms: 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

Availability: 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

Authors: Bardia Yousefi Satoru Kawakita Arya Amini et al.

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

Subject Terms: 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

Availability: 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

Authors: Bardia Yousefi Hamed Akbari Michelle Hershman et al.

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

Subject Terms: 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

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

Deep Convolutional Clustering-Based Time Series Anomaly Detection

Authors: Gavneet Singh Chadha Intekhab Islam Andreas Schwung et al.

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

Subject Terms: 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

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

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

Authors: Sadrawi, M Lin, YT Lin, CH et al.

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

File Description: 1 - 20; Electronic

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

Availability: 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

Authors: Muammar Sadrawi Yin-Tsong Lin Chien-Hung Lin et al.

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

Subject Terms: 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

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

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

Authors: Sadrawi, M Lin, Y-T Lin, C-H et al.

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

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

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

Authors: Galán Mercant, Alejandro Ortiz-García, Andrés Herrera Viedma, Enrique et al.

Subject Terms: 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

Availability: 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

Authors: Caleb Vununu Kwang-Seok Moon Suk-Hwan Lee et al.

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

Subject Terms: 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

Availability: 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

Authors: T. Kadeethum F. Ballarin Y. Choi et al.

Source: Advances in Water Resources. 160:104098

Subject Terms: 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

Access URL: 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

Authors: Abdolghader, Pedram Pegoraro, Adrian F. Ridsdale, Andrew et al.

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

Subject Terms: hyperspectral image denoising, stimulated raman microscopy, deep convolutional autoencoder

Access URL: https://ieeexplore.ieee.org/document/9166931

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Contributors: Abbod, Maysam Shieh, Jiann-Shing

Subject Terms: 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

File Description: image/jpeg

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

Availability: 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

Authors: Lin, Hongbo Liu, Chang Wang, Shigang et al.

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

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