Search Results - sparse convolutional autoencoder (((sae OR sage) OR space))~

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

Authors: Liu, Yanming Liu, Jinglei

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

Subject Terms: *DATA augmentation, *AUTOENCODERS

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

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

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

Subject Terms: *AUTOENCODERS, *MACHINE learning, *AERODYNAMIC measurements, *ATMOSPHERIC models, *DETECTORS

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

Authors: Hongyan Wang He Zhou Qinghua Guo et al.

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

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

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

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

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

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

Authors: Renato Melo Rafaelle Finotti António Guedes et al.

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

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

Subject Geographic: agris

File Description: application/pdf

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

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

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

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

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

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

Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges.

Authors: Zuev VA Salmagambetova EG Djakov SN et al.

Source: Journal of medical systems [J Med Syst] 2025 Oct 20; Vol. 49 (1), pp. 142. Date of Electronic Publication: 2025 Oct 20.

Publication Type: Journal Article; Review

Journal Info: Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 7806056 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-689X (Electronic) Linking ISSN: 01485598 NLM ISO Abbreviation: J Med Syst Subsets: MEDLINE

MeSH Terms: Electroencephalography*/methods , Epilepsy*/diagnosis , Epilepsy*/physiopathology , Video Recording*/methods, Humans ; Machine Learning

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review.

Authors: Kalu, Kingsley Ataguba, Grace Onifade, Oyepeju et al.

Source: Nutrients. Oct2025, Vol. 17 Issue 20, p3223. 33p.

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

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

Source: Transactions on Emerging Telecommunications Technologies. 36

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

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

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

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

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

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

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

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

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

Authors: Özalp, Elise Nóvoa, Andrea Magri, Luca

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

Access URL: http://arxiv.org/abs/2508.08729

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

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

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

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

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

Authors: Philippe Boutinaud Ami Tsuchida Alexandre Laurent et al.

Source: Frontiers in Neuroinformatics, Vol 15 (2021)

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/ed3360c8420941b78108600cebb4efc3

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

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

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

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

基于改进生成对抗网络和 Swin Transformer 的样本不均衡轴承故障诊断.

Alternate Title: Bearing fault diagnosis based on improved generative adversarial network and Swin Transformer under imbalanced sample conditions.

Authors: 马良玉 黄日灏 段晓冲 et al.

Source: Journal of Nanjing University of Information Science & Technology (Natural Science Edition) / Nanjing Xinxi Gongcheng Daxue Xuebao (ziran kexue ban). Jul2025, Vol. 17 Issue 4, p528-537. 10p.

Subject Terms: *CONVOLUTIONAL neural networks, *GENERATIVE adversarial networks, *TRANSFORMER models, *FAULT diagnosis, *DEEP learning

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

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

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

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

Temperature scaling unmixing framework based on convolutional autoencoder

Authors: Jin Xu Mingming Xu Shanwei Liu et al.

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

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/aa7533b7fbf447e1a8b51942e4c8c5f4

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

Authors: Junxiu Liu Guopei Wu Yuling Luo et al.

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

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

File Description: electronic resource

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

Access URL: https://doaj.org/article/418867ee4c3d4faebc2345d057d410f5

Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors.

Authors: Yang, Cheng Yuan, Yannan Wang, Fu et al.

Source: Quality & Reliability Engineering International; Mar2024, Vol. 40 Issue 2, p819-837, 19p

Subject Terms: FAULT location (Engineering), NUCLEAR reactors, NUCLEAR reactor control, FAULT diagnosis, ELECTRONIC equipment, LINE drivers (Integrated circuits)