Suchergebnisse - "convolutional autoencoder(CAE)"

River Channel Microgeomorphic Feature Extraction and Potential Sandstorm Source Identification Method Based on a Convolutional Autoencoder Model

Autoren: Kecong Wu Lirong Chen Yalige Bai et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 2602-2617 (2025)

Schlagwörter: convolutional autoencoder (CAE), Ocean engineering, QC801-809, Geophysics. Cosmic physics, microgeomorphic feature, Anomaly values, TC1501-1800, Yarlung Zangbo River, sandstorm sources

Zugangs-URL: https://doaj.org/article/94424c5cfbc34885963f0ffe4a93a78c

Unsupervised Feature Extraction From Raw Data for Gesture Recognition With Wearable Ultralow-Power Ultrasound

Autoren: Sergei Vostrikov Matteo Anderegg Luca Benini et al.

Quelle: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 71:831-841

Schlagwörter: Male, Adult, Gestures, Signal Processing, Computer-Assisted, Hand, Armband, convolutional autoencoder (CAE), hand gesture recognition (HGR), wearable ultrasound (US), Pattern Recognition, Automated, Wearable Electronic Devices, Young Adult, Humans, Female, Algorithms, Ultrasonography, Unsupervised Machine Learning

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38787674

Multi-perspective Learning Based on Transformer for Stock Price Trend

Autoren: Xiliang Li Shuoru Chen Xiaoyan Qiao et al.

Quelle: International Journal of Computational Intelligence Systems, Vol 18, Iss 1, Pp 1-18 (2025)

Schlagwörter: Technical indicators, Multi-factor model, Transformer, Convolutional autoencoder (CAE), Stock trend prediction, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1875-6883

Zugangs-URL: https://doaj.org/article/f4ccb21d10bc421492b489485a652be0

ECG Identification Algorithm Based on CAE and Improved VGGNet

Autoren: YAN Jie, ZHANG Yefei, ZHANG Xianfei

Quelle: Jisuanji gongcheng, Vol 51, Iss 1, Pp 295-303 (2025)

Schlagwörter: electrocardiogram(ecg), ecg recognition, convolutional autoencoder(cae), residual network(resnet), signal preprocessing, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/20250132.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/77f8130553644b1db92b5a3276a49a8b

A study on hybrid-architecture deep learning model for predicting pressure distribution in 2D airfoils

Autoren: Jaehyun Yoon Jaehyeok Doh

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-15 (2025)

Schlagwörter: Convolutional autoencoder (CAE), Convolutional neural networks (CNN), Fully-connected neural networks (FNN), Computational fluid dynamics (CFD), Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/c93aa0848de6482c9c0328655d316b71

Multiscale Hierarchical Losses to Preserve Hidden-Layer Features for Unsupervised Change Detection

Autoren: Bergamasco, Luca Bovolo, Francesca Bruzzone, Lorenzo

Weitere Verfasser: Bergamasco, Luca Bovolo, Francesca Bruzzone, Lorenzo

Quelle: IEEE Transactions on Geoscience and Remote Sensing. 62:1-16

Schlagwörter: Convolutional autoencoder (CAE), deep learning (DL), hierarchical loss function, multiresolution, multitemporal analysis, unsupervised change detection (CD), unsupervised learning

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/11572/423695
https://ieeexplore.ieee.org/document/10623208
https://doi.org/10.1109/tgrs.2024.3438465

Robust sensor fault detection in wireless sensor networks using a hybrid conditional generative adversarial networks and convolutional autoencoder

Autoren: Khan, Rehan Saeed, Umer Koo, Insoo et al.

Weitere Verfasser: Khan, Rehan Saeed, Umer Koo, Insoo et al.

Schlagwörter: Wireless Sensor Networks (WSNs), Convolutional Autoencoder (CAE), Gramian Angular Field (GAF), Conditional Generative Adversarial Networks (Conditional GANs), sensor faults, Subject Categories::G420 Networks and Communications

Relation: https://ieeexplore.ieee.org/document/10919044; http://hdl.handle.net/10547/626621; IEEE Sensors Journal

Verfügbarkeit: http://hdl.handle.net/10547/626621
https://doi.org/10.1109/JSEN.2025.3547736

Surrogate Modeling of Hydrogen-Enriched Combustion Using Autoencoder-Based Dimensionality Reduction

Autoren: Lanfei Zhang Xu Chu Siyu Ding et al.

Quelle: Processes ; Volume 13 ; Issue 4 ; Pages: 1093

Schlagwörter: reduced order model (ROM), hydrogen-enriched combustion, convolutional autoencoder (CAE), fully connected autoencoder (FCAE), hyperparameter setting

Geographisches Schlagwort: agris

Dateibeschreibung: application/pdf

Relation: Process Control and Monitoring; https://dx.doi.org/10.3390/pr13041093

Verfügbarkeit: https://doi.org/10.3390/pr13041093

Unsupervised Feature Extraction From Raw Data for Gesture Recognition With Wearable Ultralow-Power Ultrasound

Autoren: Vostrikov, Sergei Anderegg, Matteo Benini, Luca et al.

Quelle: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 71 (7)

Schlagwörter: Armband, convolutional autoencoder (CAE), hand gesture recognition (HGR), wearable ultrasound (US)

Zugangs-URL: http://hdl.handle.net/20.500.11850/683989

Deep Learning Approach for Radar-Based People Counting

Autoren: Jae-Ho Choi Ji-Eun Kim Kyung-Tae Kim et al.

Weitere Verfasser: Jae-Ho Choi Ji-Eun Kim Kyung-Tae Kim et al.

Quelle: IEEE Internet of Things Journal. 9:7715-7730

Schlagwörter: CROWD, convolutional autoencoder (CAE), impulse radio ultra-wideband (IR-UWB) radar, radar-based people counting (RPC), Radar, Internet of Things, 02 engineering and technology, bidirectional recurrent neural network (Bi-RNN), TRACKING, Clutter, Deep learning (DL), 0202 electrical engineering, electronic engineering, information engineering, SMART CITIES, Feature extraction, Training, Radar clutter, Radar cross-sections

Zugangs-URL: https://ieeexplore.ieee.org/document/9540889

Ultrasonic Weld Quality Inspection Involving Strength Prediction and Defect Detection in Data-Constrained Training Environments

Autoren: Reenu Mohandas Patrick Mongan Martin Hayes et al.

Weitere Verfasser: Reenu Mohandas Patrick Mongan Martin Hayes et al.

Quelle: Sensors (Basel)
Sensors, Vol 24, Iss 20, p 6553 (2024)

Schlagwörter: convolutional autoencoder(CAE), autoencoder, convolutional autoencoder (CAE), Engineering, Chemical technology, Information and computing sciences, weld strength prediction, ultrasonic welding (USW), TP1-1185, Article, weld defect detection

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39460042
https://doaj.org/article/c15dc7289fb44e848984d3241b20483a
https://hdl.handle.net/10344/14126
https://doi.org/10.34961/researchrepository-ul.27979589

Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm

Autoren: Kasra Rafiezadeh Shahi Pedram Ghamisi Behnood Rasti et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 284-296 (2022)

Schlagwörter: convolutional autoencoder (CAE), Ocean engineering, remote sensing, Autoencoder (AE), QC801-809, Geophysics. Cosmic physics, 0211 other engineering and technologies, deep learning, multisensor data fusion, 02 engineering and technology, 0101 mathematics, TC1501-1800, 01 natural sciences

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/4609443/4609444/09638348.pdf
https://doaj.org/article/e78da2231b87497190e2ffcd669a023e

Ultrasonic weld quality inspection involving strength prediction and defect detection in data-constrained training environments

Autoren: Reenu Homandas Patrick Mongan Martin Hayes

Schlagwörter: Engineering, Information and computing sciences, weld strength prediction, autoencoder, weld defect detection, convolutional autoencoder(CAE), ultrasonic welding (USW)

Verfügbarkeit: https://doi.org/10.34961/researchrepository-ul.27979589.v1
https://figshare.com/articles/journal_contribution/Ultrasonic_weld_quality_inspection_involving_strength_prediction_and_defect_detection_in_data-constrained_training_environments/27979589

Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting

Autoren: Fu, Yuwei Chai, Hua Zhen, Zhao et al.

Weitere Verfasser: Fu, Yuwei Chai, Hua Zhen, Zhao et al.

Quelle: IEEE Transactions on Industry Applications. 57:3272-3281

Schlagwörter: ta222, 13. Climate action, solar PV power forecasting, sky image, 0202 electrical engineering, electronic engineering, information engineering, Convolutional autoencoder (CAE), minute time scale, fi=Sähkötekniikka|en=Electrical Engineering, 02 engineering and technology, spatiotemporal feature, 7. Clean energy

Dateibeschreibung: fi=kokoteksti; en=fulltext; true

Zugangs-URL: http://juuli.fi/Record/0385226121
https://doi.org/10.1109/TIA.2021.3072025
https://ieeexplore.ieee.org/document/9399287/

Canopy Averaged Chlorophyll Content Prediction of Pear Trees Using Convolutional Autoencoder on Hyperspectral Data

Autoren: Subir Paul Vinayaraj Poliyapram Nevrez Imamoglu et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 1426-1437 (2020)

Schlagwörter: convolutional autoencoder (CAE), pear orchard, QC801-809, Geophysics. Cosmic physics, 0211 other engineering and technologies, deep learning, 04 agricultural and veterinary sciences, 02 engineering and technology, Canopy averaged chlorophyll content (CACC), Ocean engineering, hyperspectral (HS) data, 0401 agriculture, forestry, and fisheries, Gaussian process regression (GPR), TC1501-1800

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/4609443/8994817/09050560.pdf
https://doaj.org/article/021ee32b52ce425c91b578ef0d36ccb1
https://ieeexplore.ieee.org/document/9050560/
https://ui.adsabs.harvard.edu/abs/2020IJSTA..13.1426P/abstract
http://eprints.iisc.ac.in/65531/
https://doi.org/10.1109/JSTARS.2020.2983000
https://dblp.uni-trier.de/db/journals/staeors/staeors13.html#PaulPIUNK20

RANet: Relationship Attention for Hyperspectral Anomaly Detection

Autoren: Yingzhao Shao Yunsong Li Li Li et al.

Quelle: Remote Sensing, Vol 15, Iss 23, p 5570 (2023)

Schlagwörter: anomaly detection, hyperspectral image, graph attention network (GAT), convolutional autoencoder (CAE), Science

Relation: https://www.mdpi.com/2072-4292/15/23/5570; https://doaj.org/toc/2072-4292; https://doaj.org/article/a6971d0c7fbd49b387ea1f8e7ba11002

Verfügbarkeit: https://doi.org/10.3390/rs15235570
https://doaj.org/article/a6971d0c7fbd49b387ea1f8e7ba11002

DOA Estimation Based on Convolutional Autoencoder in the Presence of Array Imperfections

Autoren: Dah-Chung Chang Yan-Ting Liu

Quelle: Electronics, Vol 12, Iss 3, p 771 (2023)

Schlagwörter: deep neural network, array imperfection, direction of arrival (DOA), autoencoder (AE), convolutional autoencoder (CAE), Electronics, TK7800-8360

Relation: https://www.mdpi.com/2079-9292/12/3/771; https://doaj.org/toc/2079-9292; https://doaj.org/article/3a534050891d4267a89745a21760832d

Verfügbarkeit: https://doi.org/10.3390/electronics12030771
https://doaj.org/article/3a534050891d4267a89745a21760832d

Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks

Autoren: Yu Liu Yuanbo Liu Yan Yang

Quelle: Entropy, Vol 25, Iss 1316, p 1316 (2023)

Schlagwörter: contact fatigue, feature extraction, health indexes (HIs), degradation prediction, temporal convolutional network (TCN), convolutional autoencoder (CAE), Science, Astrophysics, QB460-466, Physics, QC1-999

Relation: https://www.mdpi.com/1099-4300/25/9/1316; https://doaj.org/toc/1099-4300; https://doaj.org/article/359040aff2754f5fb3b921e464c19403

Verfügbarkeit: https://doi.org/10.3390/e25091316
https://doaj.org/article/359040aff2754f5fb3b921e464c19403

Quantification of respiratory effort magnitude in spontaneous breathing patients using Convolutional Autoencoders

Autoren: Ang, Christopher Yew Shuen Chiew, Yeong Shiong Vu, Lien Hong et al.

Weitere Verfasser: Ang, Christopher Yew Shuen Chiew, Yeong Shiong Vu, Lien Hong et al.

Quelle: Elements

Schlagwörter: Spontaneous breathing, Machine learning, Convolutional Autoencoder (CAE), Mechanical ventilation

Relation: https://scholarbank.nus.edu.sg/handle/10635/212801

Verfügbarkeit: https://scholarbank.nus.edu.sg/handle/10635/212801

Non-Contact Detection of Delamination in Composite Laminates Coated with a Mechanoluminescent Sensor Using Convolutional AutoEncoder

Autoren: Seogu Park Jinwoo Song Heung Soo Kim et al.

Quelle: Mathematics ; Volume 10 ; Issue 22 ; Pages: 4254

Schlagwörter: composite materials, Convolutional AutoEncoder (CAE), delamination, mechanoluminescent (ML) sensor, non-contact sensing, structural health monitoring

Dateibeschreibung: application/pdf

Relation: E: Applied Mathematics; https://dx.doi.org/10.3390/math10224254

Verfügbarkeit: https://doi.org/10.3390/math10224254