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

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

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression.

Autoren: Bekiryazıcı T Damkacı M Aydemir G et al.

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

Publikationsart: Journal Article

Info zur Zeitschrift: 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-Schlagworte: Electrocardiography*/methods , Data Compression*/methods , Signal Processing, Computer-Assisted* , Neural Networks, Computer*, Humans ; Algorithms ; 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

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.

Autoren: 马良玉 黄日灏 段晓冲 et al.

Quelle: 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.

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

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

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

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

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

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

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

Autoren: Zuev VA Salmagambetova EG Djakov SN et al.

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

Publikationsart: Journal Article; Review

Info zur Zeitschrift: 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-Schlagworte: Electroencephalography*/methods , Epilepsy*/diagnosis , Epilepsy*/physiopathology , Video Recording*/methods, Humans ; Machine Learning

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

DLSIA: Deep Learning for Scientific Image Analysis

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

Quelle: Journal of Applied Crystallography. 57(2)

Schlagwörter: 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)

Dateibeschreibung: application/pdf

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

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

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

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

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

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: 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

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

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

Quelle: Sensors. 25(16)

Schlagwörter: 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

Dateibeschreibung: print

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

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

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

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

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

Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM.

Autoren: Zeng, Peipei Kang, Shuimiao Fan, Fan et al.

Quelle: Frontiers in Neuroinformatics; 2025, p1-15, 15p

Schlagwörter: ANOMALY detection (Computer security), HEART murmurs, SUPPORT vector machines, CONGENITAL heart disease, AUTOENCODERS

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

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

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

Schlagwörter: 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

Dateibeschreibung: application/pdf

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

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