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

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Authors: Bekiryazıcı T Damkacı M Aydemir G et al.

Source: Journal of electrocardiology [J Electrocardiol] 2025 Nov-Dec; Vol. 93, pp. 154125. Date of Electronic Publication: 2025 Oct 10.

Publication Type: Journal Article

Journal Info: Publisher: Churchill Livingstone Country of Publication: United States NLM ID: 0153605 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-8430 (Electronic) Linking ISSN: 00220736 NLM ISO Abbreviation: J Electrocardiol Subsets: MEDLINE

MeSH Terms: Electrocardiography*/methods , Data Compression*/methods , Signal Processing, Computer-Assisted* , Neural Networks, Computer*, Humans ; Algorithms ; Autoencoder

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

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: *Transformer models, Convolutional neural networks, Generative adversarial networks, Fault diagnosis, Deep learning

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

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

DLSIA: Deep Learning for Scientific Image Analysis

Authors: Roberts, Eric J Chavez, Tanny Hexemer, Alexander et al.

Source: Journal of Applied Crystallography. 57(2)

Subject Terms: 3402 Inorganic Chemistry (for-2020), 34 Chemical Sciences (for-2020), 3406 Physical Chemistry (for-2020), 51 Physical Sciences (for-2020), 5104 Condensed Matter Physics (for-2020), Bioengineering (rcdc), Biomedical Imaging (rcdc), Machine Learning and Artificial Intelligence (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), deep learning, convolutional neural networks, X-ray scattering, tomography, data compression, X-ray scattering, convolutional neural networks, data compression, deep learning, tomography, 01 Mathematical Sciences (for), 02 Physical Sciences (for), 09 Engineering (for), Inorganic & Nuclear Chemistry (science-metrix), 3402 Inorganic chemistry (for-2020), 3406 Physical chemistry (for-2020), 5104 Condensed matter physics (for-2020)

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/966220w4
https://escholarship.org/content/qt966220w4/qt966220w4.pdf

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

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

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

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)

Application of a Convolutional Autoencoder to Half Space Radar Hrrp Recognition

Authors: Yong-Jun Xie Shi-Hao Yu

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

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

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

Out-of-roundness damage wheel idenficaon in railway vehicles using autoencoder models

Authors: Melo, Renato da Silva Cury, Alexandre Abrahão http://lattes.cnpq.br/1092810690505352 et al.

Contributors: Melo, Renato da Silva Cury, Alexandre Abrahão http://lattes.cnpq.br/1092810690505352 et al.

Subject Terms: Monitoramento de integridade estrutural, Ferrovias, Detecção de danos, Ovalização, Autocodificador esparso, Autocodificador convolucional, Autocodificador variacional, Structural health monitoring, Railways, Damage detection, Out-of-roundness, Sparse autoencoder, Convolutional autoencoder, Variational autoencoder, CNPQ::ENGENHARIAS

File Description: application/pdf

Relation: https://repositorio.ufjf.br/jspui/handle/ufjf/18552

Availability: https://repositorio.ufjf.br/jspui/handle/ufjf/18552

Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning

Authors: Kherad, M. Moayyedi, M. K. Fotouhi-Ghazvini, F. et al.

Source: Sensors. 25(16)

Subject Terms: Autoencoders, Control Of Dynamical Systems, Cyber-physical Systems, Deep Reinforcement Learning, Long Short-term Memory, Sensor-driven Modeling, Circular Cylinders, Computer Control Systems, Convolution, Dynamical Systems, Dynamics, Embedded Systems, Flow Control, Fuel Additives, Nonlinear Dynamical Systems, Real Time Control, Robust Control, Auto Encoders, Control Of Dynamical System, Cybe-physical Systems, High-dimensional, Higher-dimensional, Reinforcement Learning Agent, Reinforcement Learnings, Short Term Memory

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-73172
https://doi.org/10.3390/s25165149

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

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

Source: Frontiers in Neuroinformatics

File Description: application/pdf

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

Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data

Authors: Rius, Ferran Plana Philipsen, Mark P. Tur, Josep Maria Mirats et al.

Source: Rius, F P, Philipsen, M P, Tur, J M M, Moeslund, T B, Bahón, C A & Casas, M 2022, 'Autoencoders for Semi-Supervised Water Level Modeling in Sewer Pipes with Sparse Labeled Data', Water (Switzerland), vol. 14, no. 3, 333. https://doi.org/10.3390/w14030333

Subject Terms: Autoencoders and latent space, Data distribution, Self-supervised, Semi-supervised, Sparse data, Supervised, Water

File Description: application/pdf

Relation: info:eu-repo/semantics/altIdentifier/pissn/2073-4441

Availability: https://vbn.aau.dk/da/publications/65dbf42a-774e-45d2-906a-47e7bd52a17d
https://doi.org/10.3390/w14030333
https://vbn.aau.dk/ws/files/482740247/water_14_00333.pdf
https://www.scopus.com/pages/publications/85123710424