Suchergebnisse - sparse convolutional autoencoder ((sae OR space))

Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering.

Autoren: Liu, Yanming Liu, Jinglei

Quelle: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Feb2025, Vol. 29 Issue 3, p1419-1435. 17p.

Schlagwörter: *DATA augmentation, *AUTOENCODERS

Deep-Learning-Based Dictionary Construction for MIMO Radar Detection in Complex Scenes

Autoren: Hongyan Wang He Zhou Qinghua Guo et al.

Quelle: IEEE Internet of Things Journal. 11:12419-12434

Schlagwörter: nonlinear correction, Multiple-input multiple-output (MIMO) radar, transceiving space-time resource allocation, sparse representation, complex-valued convolutional autoencoder

Reconstructing flow field from sparse sensor data: A deep learning framework combining autoencoder and cross-attention mechanism.

Autoren: Kong, Xuan Fan, Qipei Li, Jinzhao et al.

Quelle: Physics of Fluids. Sep2025, Vol. 37 Issue 9, p1-12. 12p.

Schlagwörter: *AUTOENCODERS, *MACHINE learning, *AERODYNAMIC measurements, *ATMOSPHERIC models, *DETECTORS

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Autoren: Bekiryazıcı, Tahir Damkacı, Mehmet Aydemir, Gürkan et al.

Quelle: Journal of Electrocardiology. Nov2025, Vol. 93, pN.PAG-N.PAG. 1p.

Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models

Autoren: Renato Melo Rafaelle Finotti António Guedes et al.

Quelle: Applied Sciences ; Volume 15 ; Issue 5 ; Pages: 2662

Schlagwörter: structural health monitoring, railways, damage detection, out of roundness, sparse autoencoder, convolutional autoencoder, variational autoencoder

Geographisches Schlagwort: agris

Dateibeschreibung: application/pdf

Relation: Civil Engineering; https://dx.doi.org/10.3390/app15052662

Verfügbarkeit: https://doi.org/10.3390/app15052662

Hyperspectral Image: A Fusion-Based Extraction and Classification Utilizing Centernet Technique.

Autoren: Rajyalakshmi, Ch Sanjeevaiah, K. Jose, Bibin K et al.

Quelle: International Journal of Image & Graphics. Jun2025, p1. 28p.

Schlagwörter: *HYPERSPECTRAL imaging systems, *DEEP learning, *IMAGE recognition (Computer vision), *CENTROID, *ARTIFICIAL neural networks, *AUTOENCODERS, *FEATURE extraction

Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification

Autoren: R. Vijay Anand G. Magesh I. Alagiri et al.

Quelle: Transactions on Emerging Telecommunications Technologies. 36

DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network.

Autoren: Guo, Shuhan Zhang, Qin Fu, Xiaolong et al.

Quelle: Digital Signal Processing. Jan2026:Part D, Vol. 168, pN.PAG-N.PAG. 1p.

Schlagwörter: *DIRECTION of arrival estimation, *CONVOLUTIONAL neural networks, *SPARSE matrices, *MULTIPLE Signal Classification, *FEATURE extraction, *COVARIANCE matrices, *ESTIMATION theory

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

Autoren: Philippe Boutinaud Ami Tsuchida Alexandre Laurent et al.

Quelle: Frontiers in Neuroinformatics, Vol 15 (2021)

Schlagwörter: perivascular space, deep learning, U-net, MRI, brain cohort, segmentation, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fninf.2021.641600/full; https://doaj.org/toc/1662-5196

Zugangs-URL: https://doaj.org/article/ed3360c8420941b78108600cebb4efc3

A novel intelligent model for high-precision reconstruction of dam-break free surface profiles from sparse monitoring data.

Autoren: Deng, Yangyu Liu, Yakun Zhang, Di et al.

Quelle: Physics of Fluids. Feb2025, Vol. 37 Issue 2, p1-28. 28p.

Schlagwörter: *PROPER orthogonal decomposition, *WATER levels, *AUTOENCODERS, *FREE surfaces, *FLOOD control

Real-time forecasting of chaotic dynamics from sparse data and autoencoders

Autoren: Özalp, Elise Nóvoa, Andrea Magri, Luca

Schlagwörter: 37N10, 65P20, Chaotic Dynamics, FOS: Physical sciences, Chaotic Dynamics (nlin.CD)

Zugangs-URL: http://arxiv.org/abs/2508.08729

Application of a Convolutional Autoencoder to Half Space Radar Hrrp Recognition

Autoren: Yong-Jun Xie Shi-Hao Yu

Quelle: 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). :48-53

Schlagwörter: 2. Zero hunger, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Zugangs-URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8521306
https://ieeexplore.ieee.org/document/8521306/

Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia.

Autoren: Akila, Serag Mohamed Imanov, Elbrus Almezhghwi, Khaled

Quelle: Diagnostics (2075-4418); Jul2023, Vol. 13 Issue 13, p2199, 14p

Schlagwörter: MACHINE learning, PNEUMONIA, ANOMALY detection (Computer security), LUNG infections, DIAGNOSIS, KLEBSIELLA infections

EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder

Autoren: Junxiu Liu Guopei Wu Yuling Luo et al.

Quelle: Frontiers in Systems Neuroscience, Vol 14 (2020)

Schlagwörter: EEG, emotion recognition, convolutional neural network, sparse autoencoder, deep neural network, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Dateibeschreibung: electronic resource

Relation: https://www.frontiersin.org/article/10.3389/fnsys.2020.00043/full; https://doaj.org/toc/1662-5137

Zugangs-URL: https://doaj.org/article/418867ee4c3d4faebc2345d057d410f5

Type 2 diabetes prediction method based on dual-teacher knowledge distillation and feature enhancement.

Autoren: Zhao, Jian Gao, Hanlin Sun, Lei et al.

Quelle: Scientific Reports. 1/2/2025, Vol. 15 Issue 1, p1-15. 15p.

Schlagwörter: *CONVOLUTIONAL neural networks, *TYPE 2 diabetes, *AUTOENCODERS, *MEDICAL care costs, *DIABETES

Temperature scaling unmixing framework based on convolutional autoencoder

Autoren: Jin Xu Mingming Xu Shanwei Liu et al.

Quelle: International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103864- (2024)

Schlagwörter: Hyperspectral unmixing, Convolutional autoencoder, Sparsity constraint, Temperature scaling, Equal-frequency binning, Physical geography, GB3-5030, Environmental sciences, GE1-350

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1569843224002188; https://doaj.org/toc/1569-8432

Zugangs-URL: https://doaj.org/article/aa7533b7fbf447e1a8b51942e4c8c5f4

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

Autoren: Boutinaud, P. Tsuchida, A. Laurent, A. et al.

Quelle: Frontiers in Neuroinformatics

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/21.11116/0000-000B-12F0-1
http://hdl.handle.net/21.11116/0000-000B-12EE-5

Unsupervised learning with sparse space-and-time autoencoders

Autoren: Graham, Benjamin

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/1811.10355

Diagnosis of unbalanced bearing fault samples based on cycle generative adversarial networks.

Autoren: Huang, Rihao Ma, Liangyu Hu, Jingchen et al.

Quelle: Neural Computing & Applications. Jul2025, Vol. 37 Issue 19, p13447-13467. 21p.

Schlagwörter: *CONVOLUTIONAL neural networks, *TRANSFORMER models, *GENERATIVE adversarial networks, *ARTIFICIAL intelligence, *FAULT diagnosis, *ROLLER bearings

3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

Autoren: Philippe Boutinaud Ami Tsuchida Alexandre Laurent et al.

Quelle: Front Neuroinform
Frontiers in Neuroinformatics, Vol 15 (2021)

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, segmentation, deep learning, Neurosciences. Biological psychiatry. Neuropsychiatry, perivascular space, U-net, brain cohort, MRI, RC321-571, Neuroscience

Zugangs-URL: https://oskar-bordeaux.fr/bitstream/20.500.12278/110227/1/BPH_FN_2021_Boutinaud.pdf
https://www.frontiersin.org/articles/10.3389/fninf.2021.641600/pdf
https://pubmed.ncbi.nlm.nih.gov/34262443
https://doaj.org/article/ed3360c8420941b78108600cebb4efc3
https://biorxiv.org/content/10.1101/2020.11.25.397364v1.full.pdf
https://www.biorxiv.org/content/10.1101/2020.11.25.397364v1