Suchergebnisse - sparse convolutional autoencoder (sae)

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

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

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

Autoren: Junxiu Liu Guopei Wu Yuling Luo et al.

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

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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/418867ee4c3d4faebc2345d057d410f5

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

Type 2 diabetes prediction method based on dual-teacher knowledge distillation and feature enhancement.

Autoren: Zhao, Jian Gao, Hanlin Sun, Lei et al.

Quelle: Scientific Reports. 1/2/2025, Vol. 15 Issue 1, p1-15. 15p.

Schlagwörter: *CONVOLUTIONAL neural networks, *TYPE 2 diabetes, *AUTOENCODERS, *MEDICAL care costs, *DIABETES

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)

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

Autoren: Hongxin Wang Degang Xu Xin Wen et al.

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

Schlagwörter: fault classification, sparse autoencoder, 0202 electrical engineering, electronic engineering, information engineering, Convolutional neural network, flight test data, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, fault diagnosis, TK1-9971

Zugangs-URL: https://doaj.org/article/f50ce16502da41ccae03b60fb2048516

Fusion of Local and Global Feature Representation With Sparse Autoencoder for Improved Melanoma Classification

Autoren: Himanshu K, Gajera Deepak Ranjan, Nayak Mukesh A, Zaveri

Quelle: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). :5051-5054

Schlagwörter: 03 medical and health sciences, Skin Neoplasms, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Humans, Neural Networks, Computer, 02 engineering and technology, Melanoma, Skin

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/36085953

Diagnosis of unbalanced bearing fault samples based on cycle generative adversarial networks.

Autoren: Huang, Rihao Ma, Liangyu Hu, Jingchen et al.

Quelle: Neural Computing & Applications. Jul2025, Vol. 37 Issue 19, p13447-13467. 21p.

Schlagwörter: *CONVOLUTIONAL neural networks, *TRANSFORMER models, *GENERATIVE adversarial networks, *ARTIFICIAL intelligence, *FAULT diagnosis, *ROLLER bearings

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: *CONVOLUTIONAL neural networks, *GENERATIVE adversarial networks, *TRANSFORMER models, *FAULT diagnosis, *DEEP learning

Deep Learning-Based Magnetic Resonance Image Segmentation and Classification for Alzheimer's Disease Diagnosis.

Autoren: Manochandar, T. Diderot, P. Kumaraguru

Quelle: International Journal of Image & Graphics. May2025, Vol. 25 Issue 3, p1-17. 17p.

Schlagwörter: *CONVOLUTIONAL neural networks, *IMAGE analysis, *IMAGE recognition (Computer vision), *IMAGE segmentation, *ALZHEIMER'S disease

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

Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN

Autoren: Chunyu Xu Hong Wang

Quelle: Applied Sciences, Vol 12, Iss 633, p 633 (2022)

Schlagwörter: convolutional neural network, convolution kernel, local binary patterns, sparse autoencoder, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/12/2/633; https://doaj.org/toc/2076-3417; https://doaj.org/article/2284785b658a4daa810370cf9e55fc6e

Verfügbarkeit: https://doi.org/10.3390/app12020633
https://doaj.org/article/2284785b658a4daa810370cf9e55fc6e

Hyperspectral Image: A Fusion-Based Extraction and Classification Utilizing Centernet Technique.

Autoren: Rajyalakshmi, Ch Sanjeevaiah, K. Jose, Bibin K et al.

Quelle: International Journal of Image & Graphics. Jun2025, p1. 28p.

Schlagwörter: *HYPERSPECTRAL imaging systems, *DEEP learning, *IMAGE recognition (Computer vision), *CENTROID, *ARTIFICIAL neural networks, *AUTOENCODERS, *FEATURE extraction

Automated Detection and Localization of Myocardial Infarction With staked Sparse Autoencoder and TreeBagger

Autoren: Zhang, Jieshuo Lin, Feng Xiong, Peng et al.

Schlagwörter: Electrocardiograph, myocardial infarction, sparse autoencoder, bagged decision tree, deep learning networks, CONVOLUTIONAL NEURAL-NETWORK, ECG SIGNALS, CLASSIFICATION, SELECTION, Computer Science, Engineering, Telecommunications, Information Systems, Electrical & Electronic

Relation: IEEE ACCESS; http://ir.ia.ac.cn/handle/173211/26039; http://ir.ia.ac.cn/handle/173211/26040

Verfügbarkeit: http://ir.ia.ac.cn/handle/173211/26039
http://ir.ia.ac.cn/handle/173211/26040
https://doi.org/10.1109/ACCESS.2019.2919068

Shelf Commodity Identification Method Based on Hybrid Fully Convolutional Automatic Encoder

Autoren: Aofeng Cheng Guodong Chen Zheng Wang

Quelle: IEEE Access, Vol 7, Pp 169899-169907 (2019)

Schlagwörter: autoencoder, 0202 electrical engineering, electronic engineering, information engineering, Fully convolutional neural network, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, object identification, shelf regulation, TK1-9971

Zugangs-URL: https://doaj.org/article/430ef5e3edfc4c1d8c85e459deb5fba4
https://dblp.uni-trier.de/db/journals/access/access7.html#ChengCW19

ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data.

Autoren: Almuqhim, Fahad Saeed, Fahad

Quelle: Frontiers in Computational Neuroscience; 4/8/2021, Vol. 15, pN.PAG-N.PAG, 10p

Schlagwörter: AUTISM spectrum disorders, FUNCTIONAL magnetic resonance imaging, BRAIN imaging, NEURAL development, FEATURE extraction

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

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

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

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

Towards COVID-19 detection and classification using optimal efficient Densenet model on chest X-ray images.

Autoren: Hilali-Jaghdam, Inès Elhag, Azhari A. Ben Ishak, Anis et al.

Quelle: Alexandria Engineering Journal. Aug2024, Vol. 101, p136-146. 11p.

Schlagwörter: Optimization algorithms, COVID-19 pandemic, Convolutional neural networks, Deep learning, Polymerase chain reaction, X-rays

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

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

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

Schlagwörter: PARTICLE swarm optimization, CLASSIFICATION, COMPUTER architecture