Suchergebnisse - deep adversarial convolution autoencoder network*

Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network

Autoren: Lingfeng Qi Jiafang Pan Tianping Huang et al.

Quelle: Applied Sciences, Vol 15, Iss 12, p 6401 (2025)

Schlagwörter: remaining useful life prediction, reconstruction along projection pathway, deep adversarial convolution autoencoder network, joint domain adaptation loss, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/12/6401; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/e5b80ed94f004b17a3ca4adee50fd0fc

Adversarial Regularized Class Incremental Autoencoder Technique with Convolution Neural Network for Secure Clustering of Short Text in the High Dimensional Data

Autoren: null Kiruthika B, Dr. B. Srinivasan, Dr. P. Prabhusundhar

Quelle: Tuijin Jishu/Journal of Propulsion Technology. 44:5141-5151

Schlagwörter: 0203 mechanical engineering, 02 engineering and technology

A Visual Computing Unified Application Using Deep Learning and Computer Vision Techniques.

Autoren: B. J., Sowmya Meeradevi Seema, S. et al.

Quelle: International Journal of Interactive Mobile Technologies. 2024, Vol. 18 Issue 1, p59-74. 16p.

Schlagwörter: Deep learning, Computer vision, Machine learning, Generative adversarial networks, Artificial intelligence, Convolutional neural networks

On the use of Denoising Autoencoders and Deep Convolutional Adversarial Networks for Automated Removal of Date Stamps

Autoren: Anderson, Nicolas Paavola, Mikael Sognnes, Johnny

Verfügbarkeit: http://hdl.handle.net/11250/2618711

Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network.

Autoren: Qi, Lingfeng Pan, Jiafang Huang, Tianping et al.

Quelle: Applied Sciences (2076-3417); Jun2025, Vol. 15 Issue 12, p6401, 26p

Schlagwörter: REMAINING useful life, ROLLER bearings, AUTOENCODERS, ROTATING machinery, DATA distribution

A novel intelligent multicross domain fault diagnosis of servo motor-bearing system based on Domain Generalized Graph Convolution Autoencoder.

Autoren: Zhao, Xiaoli Hu, Yuanhao Liu, Jiahui et al.

Quelle: Structural Health Monitoring; May2025, Vol. 24 Issue 3, p1485-1499, 15p

Schlagwörter: AUTOENCODERS, FAULT diagnosis, DEEP learning, REPRESENTATIONS of graphs, ROLLER bearings

A QUANTUM CONVOLUTION AUTOENCODER FOR HANDWRITTEN LETTERS AND DIGITS: A CASE STUDY.

Autoren: SAWERWAIN, MAREK KOWAL, MAREK KORBICZ, JÓZEF

Quelle: International Journal of Applied Mathematics & Computer Science; 2025, Vol. 35 Issue 4, p651-666, 16p

基于约束对抗卷积自编码记忆融合网络的故障诊断. (Chinese)

Alternate Title: Fault diagnosis based on constraint adversarial convolution auto-encoding memory fusion network. (English)

Autoren: 刘建华 杨皓楠 何静 et al.

Quelle: Electric Machines & Control / Dianji Yu Kongzhi Xuebao; Jun2023, Vol. 27 Issue 6, p148-159, 12p

Schlagwörter: FAULT diagnosis, MULTISENSOR data fusion, DIAGNOSIS, GENERALIZATION, DEEP learning, MULTICASTING (Computer networks)

Precise detection of diabetic retinopathy using adaptive remora optimization algorithm with deep adversarial approach.

Autoren: Mondal, Sambit Sundar Mandal, Nirupama Singh, Krishna Kant

Quelle: Multimedia Tools & Applications; May2025, Vol. 84 Issue 18, p19883-19913, 31p

Schlagwörter: GENERATIVE adversarial networks, CURVELET transforms, OPTIMIZATION algorithms, ARTIFICIAL intelligence, DIABETIC retinopathy, DEEP learning

Enhanced defect detection in thermography through temporal denoising and deep feature extraction using a shallow convolution autoencoder.

Autoren: Prasanthi, Y. Naga Ghali, V. S. Vesala, G. T. et al.

Quelle: Insight: Non-Destructive Testing & Condition Monitoring; Mar2025, Vol. 67 Issue 3, p162-171, 10p

Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution Autoencoder and Artificial Neural Network.

Autoren: Zaman, Wasim Ahmad, Zahoor Kim, Jong-Myon

Quelle: Sensors (14248220); Feb2024, Vol. 24 Issue 3, p851, 21p

Schlagwörter: CENTRIFUGAL pumps, FAULT diagnosis, MACHINE learning, WAVELET transforms, DEEP learning, FEATURE extraction, FLUID pressure

Point Cloud Generation Using Deep Adversarial Local Features for Augmented and Mixed Reality Contents.

Autoren: Lim, Sohee Shin, Minwoo Paik, Joonki

Quelle: IEEE Transactions on Consumer Electronics; Feb2022, Vol. 68 Issue 1, p69-76, 8p

Schlagwörter: DEEP learning, POINT cloud, MIXED reality, AUGMENTED reality, GENERATIVE adversarial networks, AFFINE transformations

DRAN: Deep recurrent adversarial network for automated pancreassegmentation

Autoren: Yang Ning Li Zhong Caiming Zhang et al.

Quelle: IET Image Processing. 14:1091-1100

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Zugangs-URL: https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-ipr.2019.0399
https://dblp.uni-trier.de/db/journals/iet-ipr/iet-ipr14.html#NingHZZ20
https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2019.0399
https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0399
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-ipr.2019.0399
https://ieeexplore.ieee.org/document/9079705
http://dblp.uni-trier.de/db/journals/iet-ipr/iet-ipr14.html#NingHZZ20

DRAN: Deep recurrent adversarial network for automated pancreassegmentation.

Autoren: Ning, Yang Han, Zhongyi Zhong, Li et al.

Quelle: IET Image Processing (Wiley-Blackwell); May2020, Vol. 14, p1091-1100, 10p

A multi-stage deep adversarial network for video summarization with knowledge distillation.

Autoren: Sreeja, M. U. Kovoor, Binsu C.

Quelle: Journal of Ambient Intelligence & Humanized Computing; Aug2023, Vol. 14 Issue 8, p9823-9838, 16p

Tool wear condition monitoring across machining processes based on feature transfer by deep adversarial domain confusion network.

Autoren: Huang, Zhiwen Shao, Jiajie Zhu, Jianmin et al.

Quelle: Journal of Intelligent Manufacturing; Mar2024, Vol. 35 Issue 3, p1079-1105, 27p

Schlagwörter: DEEP learning, DISTRIBUTION (Probability theory), MACHINING, VIBRATIONAL spectra, INTELLIGENT buildings, MACHINERY

AMT-CDR: A Deep Adversarial Multi-Channel Transfer Network for Cross-Domain Recommendation.

Autoren: KEZHI LU QIAN ZHANG HUGHES, DANNY et al.

Quelle: ACM Transactions on Intelligent Systems & Technology; Aug2024, Vol. 15 Issue 4, p1-26, 26p

Schlagwörter: KNOWLEDGE graphs, KNOWLEDGE transfer, SOURCE code, LEARNING strategies, ELECTRONIC services, RECOMMENDER systems

Fortifying vision models: A comprehensive survey of defences against adversarial examples.

Autoren: Kumar, Siddheshwar Srivastava, Shashank Banerjea, Shashwati

Quelle: Applied Soft Computing. Dec2025:Part A, Vol. 185, pN.PAG-N.PAG. 1p.

Schlagwörter: Machine learning, Generative adversarial networks, Computer vision, Statistical reliability, Artificial neural networks, University research

Deep adversarial transfer neural network for fault diagnosis of wind turbine gearbox.

Autoren: Ma, Yuanchi Liu, Yongqian Yang, Zhiling et al.

Quelle: International Journal of Green Energy; 2023, Vol. 20 Issue 15, p1750-1762, 13p

Schlagwörter: FAULT diagnosis, WIND turbines, GEARBOXES, DEEP learning, WIND power plants, DATA distribution

Firma/Körperschaft: CASE Western Reserve University

Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning.

Autoren: Wang S Wu R Jia S et al.

Quelle: Magnetic resonance in medicine [Magn Reson Med] 2024 Aug; Vol. 92 (2), pp. 496-518. Date of Electronic Publication: 2024 Apr 16.

Publikationsart: Journal Article; Review

Info zur Zeitschrift: Publisher: Wiley Country of Publication: United States NLM ID: 8505245 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2594 (Electronic) Linking ISSN: 07403194 NLM ISO Abbreviation: Magn Reson Med Subsets: MEDLINE

MeSH-Schlagworte: Deep Learning* , Magnetic Resonance Imaging*/methods , Image Processing, Computer-Assisted*/methods , Neural Networks, Computer* , Algorithms*, Humans ; Supervised Machine Learning ; Brain/diagnostic imaging