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

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

Authors: Melo, Renato Finotti, Rafaelle Guedes, António et al.

Source: Applied Sciences (2076-3417); Mar2025, Vol. 15 Issue 5, p2662, 25p

Subject Terms: STRUCTURAL health monitoring, AUTOENCODERS, RAILROAD safety measures, RAILROAD trains, COMPARATIVE studies

Convolutional autoencoder-augmented sparse identification of noisy nonlinear fluid dynamics from unlabelled data.

Authors: Zhang, Meng Zeng, Fanfan Yousif, Mustafa Z. et al.

Source: Physics of Fluids; Sep2025, Vol. 37 Issue 9, p1-15, 15p

Subject Terms: FLUID dynamics, AUTOENCODERS, NONLINEAR mechanics, FLOW simulations, TURBULENT flow, METADATA, SPARSE approximations, NOISE control

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

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

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

An efficient model for diabetic detection using heuristic approach based serial cascaded convolutional ensemble network.

Authors: Bejugam, Santosh Kumar Vankara, Jyothi

Source: Artificial Intelligence Review; Oct2025, Vol. 58 Issue 10, p1-43, 43p

Estimation of Damaged Surface Images in Drilled Unidirectional Carbon Fiber Reinforced Polymer Sheets Using Convolutional Autoencoder and Multi-Layer Perceptron Decoder.

Authors: Kurniawan, Eddy Hur, Yong Chan Kim, Ji Hoon

Source: Fibers & Polymers; Dec2025, Vol. 26 Issue 12, p5647-5660, 14p

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

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)

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

Domain-Adaptive Direction of Arrival (DOA) Estimation in Complex Indoor Environments Based on Convolutional Autoencoder and Transfer Learning.

Authors: Shen, Lingyu Li, Jianfeng Pan, Jingjing et al.

Source: Sensors (14248220); May2025, Vol. 25 Issue 10, p2959, 24p

Subject Terms: AUTOENCODERS, DEEP learning, FEATURE extraction, SMART homes, WIRELESS communications

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

Multi-resolution reconstruction of longitudinal streambed footprints using embedded sparse convolutional autoencoders

Authors: Yang, Yifan Tang, Zihao 邵东 et al.

Contributors: Yang, Yifan Tang, Zihao 邵东 et al.

Subject Terms: Machine learning, Streambed footprint, Convolutional autoencoder, Multi-resolution reconstruction, RIVER, PROBABILITY, MODEL, Engineering, Geology, Water Resources, Civil, Geosciences, Multidisciplinary, 一类

Relation: JOURNAL OF HYDROLOGY; http://dspace.imech.ac.cn/handle/311007/100158; http://dspace.imech.ac.cn/handle/311007/100159

Availability: http://dspace.imech.ac.cn/handle/311007/100158
http://dspace.imech.ac.cn/handle/311007/100159
https://doi.org/10.1016/j.jhydrol.2025.132852

Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data.

Authors: Choi, YuRim Joe, Inwhee

Source: Electronics (2079-9292); Oct2024, Vol. 13 Issue 19, p3946, 29p

Subject Terms: ARTIFICIAL neural networks, REAL-time computing, ELECTRIC faults, FAULT diagnosis, SYSTEM analysis, ELECTRIC motors

Surrogate Modeling of Hydrogen-Enriched Combustion Using Autoencoder-Based Dimensionality Reduction.

Authors: Zhang, Lanfei Chu, Xu Ding, Siyu et al.

Source: Processes; Apr2025, Vol. 13 Issue 4, p1093, 21p

Subject Terms: AUTOENCODERS, JET nozzles, ENGINEERING design, FLUID flow, SPATIAL resolution, DEEP learning

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

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

Authors: Bergamasco, Luca Bovolo, Francesca Bruzzone, Lorenzo

Contributors: Bergamasco, Luca Bovolo, Francesca Bruzzone, Lorenzo

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

File Description: ELETTRONICO

Relation: ispartofbook:Proceedings of SPIE - The International Society for Optical Engineering; Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX 2024; volume:13196; serie:PROCEEDINGS OF SPIE; alleditors:Bruzzone, Lorenzo; https://hdl.handle.net/11572/444099

Availability: https://hdl.handle.net/11572/444099
https://doi.org/10.1117/12.3030005

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

Cascaded hybrid convolutional autoencoder network for spectral-spatial nonlinear hyperspectral unmixing.

Authors: Wu, Jinbin Zhao, Jiankang Long, Haihui

Source: International Journal of Remote Sensing; Dec2024, Vol. 45 Issue 24, p9267-9286, 20p

Subject Terms: AUTOENCODERS, DEEP learning, IMAGE reconstruction, SELF-perception, PHOTONS

Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.

Authors: Ogundokun, Roseline Oluwaseun Owolawi, Pius Adewale Tu, Chunling et al.

Source: Tomography: A Journal for Imaging Research; Aug2025, Vol. 11 Issue 8, p91, 22p

Subject Terms: AUTOENCODERS, ENSEMBLE learning, DEEP learning, FEATURE extraction, CANCER diagnosis, HISTOPATHOLOGY, MACHINE learning