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

Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps.

Authors: Altantawy, Doaa A. Yakout, Mohamed A.

Source: Journal of Intelligent Manufacturing. Jun2025, Vol. 36 Issue 5, p3359-3397. 39p.

Subject Terms: *MANUFACTURING processes, *INFORMATION processing, CONVOLUTIONAL neural networks, OPTIMIZATION algorithms, AUTOENCODERS

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.

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

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.

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

Authors: Boutinaud, Philippe Tsuchida, Ami Laurent, Alexandre et al.

Source: Frontiers in Neuroinformatics; 6/18/2021, Vol. 15, p1-21, 21p

Subject Terms: MAGNETIC resonance imaging, DEEP learning, ALGORITHMS, BASAL ganglia, YOUNG adults, VOXEL-based morphometry

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)

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder.

Authors: Obayashi, Wataru Aono, Hikaru Tatsukawa, Tomoaki et al.

Source: AIP Advances; Oct2021, Vol. 11 Issue 10, p1-6, 6p

Subject Terms: FLUID dynamics, FEATURE extraction, PROPER orthogonal decomposition, NAVIER-Stokes equations, CONVOLUTIONAL neural networks, UNSTEADY flow, COMPRESSIBLE flow

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

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

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

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

Lameness detection in dairy cows from overhead view: high-precision keypoint localization and multi-feature fusion classification.

Authors: Duan, Weijun Wang, Fang Li, Honghui et al.

Source: Frontiers in Veterinary Science. 2025, p1-19. 19p.

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

A 3D Sparse Autoencoder for Fully Automated Quality Control of Affine Registrations in Big Data Brain MRI Studies.

Authors: Thadikemalla, Venkata Sainath Gupta Focke, Niels K. Tummala, Sudhakar

Source: Journal of Digital Imaging; Feb2024, Vol. 37 Issue 1, p412-427, 16p

Subject Terms: RECEIVER operating characteristic curves, MAGNETIC resonance imaging, QUALITY control, DATA analytics, DESCRIPTIVE statistics, DIAGNOSIS of brain abnormalities

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

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder.

Authors: An, Yanling Hu, Shaohai Duan, Xiaoying et al.

Source: Frontiers in Computational Neuroscience; 10/18/2021, Vol. 15, p1-13, 13p

Subject Terms: EMOTION recognition, BRAIN-computer interfaces, SIGNAL convolution, DIFFERENTIAL entropy, CONVOLUTIONAL neural networks, ELECTROENCEPHALOGRAPHY, EMOTIONS, FEATURE extraction

Compression of Vehicle Trajectories with a Variational Autoencoder.

Authors: Rákos, Olivér Aradi, Szilárd Bécsi, Tamás et al.

Source: Applied Sciences (2076-3417); Oct2020, Vol. 10 Issue 19, p6739, 17p

Subject Terms: BEHAVIOR, SUPPORT vector machines, INTELLIGENT transportation systems, LANE changing, DATA compression, LOSSY data compression

Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images.

Authors: Nageswara Rao, B. Acharya, U. Rajendra Tan, Ru-San et al.

Source: Cognitive Neurodynamics; 5/19/2025, Vol. 19 Issue 1, p1-13, 13p

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

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder

Authors: Wataru Obayashi Hikaru Aono Tomoaki Tatsukawa et al.

Source: AIP Advances, Vol 11, Iss 10, Pp 105211-105211-6 (2021)

Subject Terms: Physics, QC1-999

File Description: electronic resource

Relation: https://doaj.org/toc/2158-3226

Access URL: https://doaj.org/article/7d4dc31e6e08442fba36ddd862379ce8

DSCAE: a denoising sparse convolutional autoencoder defense against adversarial examples.

Authors: Ye, Hongwei Liu, Xiaozhang Li, Chunlai

Source: Journal of Ambient Intelligence & Humanized Computing; Mar2022, Vol. 13 Issue 3, p1419-1429, 11p

A Novel Deep Neural Network-Based Framework for Subsurface Analysis in Non-Stationary Thermal Wave Imaging.

Authors: Swapna, M. N. Suman, M. Prasanthi, Y. N. et al.

Source: Sensing & Imaging; 9/5/2025, Vol. 26 Issue 1, p1-18, 18p

Adaptive Structured Latent Space Learning via Component-Aware Triplet Convolutional Autoencoder for Fault Diagnosis in Ship Oil Purifiers.

Authors: Chae, Sun Geu Yun, Gwang Ho Park, Jae Cheul et al.

Source: Processes; Sep2025, Vol. 13 Issue 9, p3012, 17p

Subject Terms: FAULT diagnosis, CONDITION-based maintenance, DIMENSIONAL reduction algorithms, OIL separators, DEEP learning, MECHANICAL vibration research, SHIPS, LOSS functions (Statistics)