Výsledky vyhledávání - sparse convolutional autoencoder (((sace OR sae) OR space))

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Autoři: Bekiryazıcı T Damkacı M Aydemir G a další

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

Způsob vydávání: Journal Article

Informace o časopise: 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

Výrazy ze slovníku MeSH: 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.

Autoři: Liu, Yanming Liu, Jinglei

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

Témata: *DATA augmentation, *AUTOENCODERS

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

Autoři: Kong, Xuan Fan, Qipei Li, Jinzhao a další

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

Témata: *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.

Autoři: 马良玉 黄日灏 段晓冲 a další

Zdroj: 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.

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

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

Autoři: Hongyan Wang He Zhou Qinghua Guo a další

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

Témata: 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

Autoři: Renato Melo Rafaelle Finotti António Guedes a další

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

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

Geografické téma: agris

Popis souboru: application/pdf

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

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

DLSIA: Deep Learning for Scientific Image Analysis

Autoři: Roberts, Eric J Chavez, Tanny Hexemer, Alexander a další

Zdroj: Journal of Applied Crystallography. 57(2)

Témata: 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)

Popis souboru: application/pdf

Přístupová URL adresa: 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

Autoři: R. Vijay Anand G. Magesh I. Alagiri a další

Zdroj: Transactions on Emerging Telecommunications Technologies. 36

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

Autoři: Deng, Yangyu Liu, Yakun Zhang, Di a další

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

Témata: *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.

Autoři: Guo, Shuhan Zhang, Qin Fu, Xiaolong a další

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

Témata: *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

Autoři: Özalp, Elise Nóvoa, Andrea Magri, Luca

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

Přístupová URL adresa: 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

Autoři: Philippe Boutinaud Ami Tsuchida Alexandre Laurent a další

Zdroj: Frontiers in Neuroinformatics, Vol 15 (2021)

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

Popis souboru: electronic resource

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

Přístupová URL adresa: https://doaj.org/article/ed3360c8420941b78108600cebb4efc3

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

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

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

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

Temperature scaling unmixing framework based on convolutional autoencoder

Autoři: Jin Xu Mingming Xu Shanwei Liu a další

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

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

Popis souboru: electronic resource

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

Přístupová URL adresa: https://doaj.org/article/aa7533b7fbf447e1a8b51942e4c8c5f4

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

Autoři: Yang, Cheng Yuan, Yannan Wang, Fu a další

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

Témata: 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

Autoři: Yong-Jun Xie Shi-Hao Yu

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

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

Přístupová URL adresa: 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

Autoři: Melo, Renato da Silva Cury, Alexandre Abrahão http://lattes.cnpq.br/1092810690505352 a další

Přispěvatelé: Melo, Renato da Silva Cury, Alexandre Abrahão http://lattes.cnpq.br/1092810690505352 a další

Témata: 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

Popis souboru: application/pdf

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

Dostupnost: 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

Autoři: Kherad, M. Moayyedi, M. K. Fotouhi-Ghazvini, F. a další

Zdroj: Sensors. 25(16)

Témata: 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

Popis souboru: print

Přístupová URL adresa: 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

Autoři: Boutinaud, P. Tsuchida, A. Laurent, A. a další

Zdroj: Frontiers in Neuroinformatics

Popis souboru: application/pdf

Přístupová URL adresa: 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

Autoři: Rius, Ferran Plana Philipsen, Mark P. Tur, Josep Maria Mirats a další

Zdroj: 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

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

Popis souboru: application/pdf

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

Dostupnost: 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