Výsledky vyhľadávania - sparse convolutional autoencoder ((sae OR cae))

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

Autori: Renato Melo Rafaelle Finotti António Guedes a ďalší

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

Predmety: 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

Popis súboru: electronic resource

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

Prístupová URL adresa: https://doaj.org/article/25623879dc094408963692cb2aa1b01c

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

Autori: Bergamasco, Luca Bovolo, Francesca

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

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

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

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

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

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

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

Autori: Yang, Cheng Yuan, Yannan Wang, Fu a ďalší

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

Predmety: 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

Autori: Kherad, M. Moayyedi, M. K. Fotouhi-Ghazvini, F. a ďalší

Zdroj: Sensors. 25(16)

Predmety: 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 súboru: print

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

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

Autori: Nageswara Rao, B. Acharya, U. Rajendra Tan, Ru-San a ďalší

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

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

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

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

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

Temperature scaling unmixing framework based on convolutional autoencoder

Autori: Jin Xu Mingming Xu Shanwei Liu a ďalší

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

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

Popis súboru: electronic resource

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

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

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

Autori: Zeng, Peipei Kang, Shuimiao Fan, Fan a ďalší

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

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

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

Autori: Sun, Yanan Xue, Bing Zhang, Mengjie a ďalší

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

Predmety: PARTICLE swarm optimization, CLASSIFICATION, COMPUTER architecture

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

Autori: An, Yanling Hu, Shaohai Duan, Xiaoying a ďalší

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

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

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

Autori: Zahra Sharifzadeh Jafari Sanaz Seyedin

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

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

Popis súboru: electronic resource

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

Prístupová URL adresa: https://doaj.org/article/09b910d7b3a44bcabfa7902391f7b0cb

EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder

Autori: Junxiu Liu Guopei Wu Yuling Luo a ďalší

Zdroj: Frontiers in Systems Neuroscience, Vol 14 (2020)

Predmety: EEG, emotion recognition, convolutional neural network, sparse autoencoder, deep neural network, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571

Popis súboru: electronic resource

Relation: https://www.frontiersin.org/article/10.3389/fnsys.2020.00043/full; https://doaj.org/toc/1662-5137

Prístupová URL adresa: https://doaj.org/article/418867ee4c3d4faebc2345d057d410f5

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

Autori: Zhang, Meng Zeng, Fanfan Yousif, Mustafa Z. a ďalší

Prispievatelia: Zhang, Meng Zeng, Fanfan Yousif, Mustafa Z. a ďalší

Zdroj: Physics of Fluids ; volume 37, issue 9 ; ISSN 1070-6631 1089-7666

Dostupnosť: https://doi.org/10.1063/5.0280421
https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/5.0280421/20705151/095177_1_5.0280421.pdf

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

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

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

Predmety: 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 súboru: application/pdf

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

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

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.

Autori: 马良玉 黄日灏 段晓冲 a ďalší

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.

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

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

Autori: Yang, Yifan Tang, Zihao 邵东 a ďalší

Prispievatelia: Yang, Yifan Tang, Zihao 邵东 a ďalší

Predmety: 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

Dostupnosť: 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

Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification

Autori: Vijay Anand, R. Magesh, G. Alagiri, I. a ďalší

Zdroj: Transactions on Emerging Telecommunications Technologies ; volume 36, issue 9 ; ISSN 2161-3915 2161-3915

Dostupnosť: https://doi.org/10.1002/ett.70224
https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.70224

Deep spectral unmixing framework via 3D denoising convolutional autoencoder.

Autori: Jia, Peiyuan Zhang, Miao Shen, Yi

Zdroj: IET Image Processing (Wiley-Blackwell); May2021, Vol. 15 Issue 7, p1399-1409, 11p

Predmety: HYPERSPECTRAL imaging systems, IMAGE denoising, IMAGE processing, PIXELS, GEOMETRIC analysis, DATA

Flight Test Sensor Fault Diagnosis Based on Data-Fusion and Machine Learning Method

Autori: Hongxin Wang Degang Xu Xin Wen a ďalší

Zdroj: IEEE Access, Vol 10, Pp 120013-120022 (2022)

Predmety: Convolutional neural network, fault classification, flight test data, fault diagnosis, sparse autoencoder, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis súboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/9928333/; https://doaj.org/toc/2169-3536

Prístupová URL adresa: https://doaj.org/article/f50ce16502da41ccae03b60fb2048516