Search Results - sparse convolutional autoencoder ((((scae OR sae) OR sage) OR cae))

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)

SCAE—Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples.

Authors: Eraliev, Oybek Lee, Kwang-Hee Lee, Chul-Hee

Source: Sensors (14248220); Jul2024, Vol. 24 Issue 14, p4661, 17p

Subject Terms: RECIPROCATING pumps, FAULT diagnosis, HIERARCHICAL Bayes model, CONVOLUTIONAL neural networks, DEEP learning, MACHINE learning, FEATURE extraction, DIAGNOSIS

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

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

Authors: Cheng Yang Yannan Yuan Fu Wang et al.

Source: Quality and Reliability Engineering International. 40:819-837

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

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

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

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

Unsupervised sparse convolutional autoencoder for multi-class change detection in hyperspectral images

Authors: Bergamasco, Luca Bovolo, Francesca

Source: Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX. :22

Subject Terms: Convolutional Autoencoder, Hyperspectral images, Multi-class Change Detection, Spectral Unmixing, Unsupervised Change Detection

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

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

Authors: Renato Melo Rafaelle Finotti António Guedes et al.

Source: Applied Sciences, Vol 15, Iss 5, p 2662 (2025)

Subject Terms: structural health monitoring, railways, damage detection, out of roundness, sparse autoencoder, convolutional autoencoder, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/5/2662; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/25623879dc094408963692cb2aa1b01c

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.

Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network †.

Authors: Lai, Trinh Thuc Tran, Tuan Phong Cho, Jaehyuk et al.

Source: Applied Sciences (2076-3417); Sep2023, Vol. 13 Issue 18, p10090, 19p

Subject Terms: WIRELESS sensor networks, DATA recovery, NOISE, NOISE control

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

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

A Novel Multi-Task and Ensembled Optimized Parallel Convolutional Autoencoder and Transformer for Speech Emotion Recognition

Authors: Zahra Sharifzadeh Jafari Sanaz Seyedin

Source: AUT Journal of Electrical Engineering, Vol 56, Iss 2, Pp 213-226 (2024)

Subject Terms: speech emotion recognition, mel frequency cepstral coefficients, autoencoder, transformer, multi-task deep learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://eej.aut.ac.ir/article_5336_945e73b38cfede1308072d0f634d7969.pdf; https://doaj.org/toc/2588-2910; https://doaj.org/toc/2588-2929

Access URL: https://doaj.org/article/09b910d7b3a44bcabfa7902391f7b0cb

A radar emitter structural identification method for complex conditions based on compressed sensing and an autoencoder.

Authors: Song, Yu Zhang, Wei Yang, Yaozu et al.

Source: Nonlinear Dynamics; Aug2025, Vol. 113 Issue 15, p19159-19178, 20p

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

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

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

Authors: Serag Mohamed Akila Elbrus Imanov Khaled Almezhghwi

Source: Diagnostics, Vol 13, Iss 13, p 2199 (2023)

Subject Terms: chest pneumonia, classification, unsupervised learning, convolution, autoencoder, variational, Medicine (General), R5-920

File Description: electronic resource

Relation: https://www.mdpi.com/2075-4418/13/13/2199; https://doaj.org/toc/2075-4418

Access URL: https://doaj.org/article/3f5772b5ed7f4a0e867c5884e5bbc516

A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification.

Authors: Sun, Yanan Xue, Bing Zhang, Mengjie et al.

Source: IEEE Transactions on Neural Networks & Learning Systems; Aug2019, Vol. 30 Issue 8, p2295-2309, 15p

Subject Terms: PARTICLE swarm optimization, CLASSIFICATION, COMPUTER architecture