Suchergebnisse - denoising autoencoder*

Conditioned fully convolutional denoising autoencoder for multi-target NILM

Autoren: García, Diego Pérez, Daniel Papapetrou, Panagiotis et al.

Quelle: Neural Computing & Applications. 37(17):10491-10505

Schlagwörter: Non-intrusive Load Monitoring, Energy Efficiency, Deep Convolutional Neural Networks, Interpretability, Multi-target NILM models, data- och systemvetenskap, Computer and Systems Sciences

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-237967
https://doi.org/10.1007/s00521-024-10552-0

Siamese Denoising Autoencoders for Enhancing Adversarial Robustness in Medical Image Analysis

Autoren: Jaesung Shim Kyuri Jo

Quelle: IEEE Access, Vol 13, Pp 86333-86343 (2025)

Schlagwörter: Adversarial examples, denoising autoencoder, deep learning, Siamese network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Zugangs-URL: https://doaj.org/article/d1728c8ede5b409d99fd3fd94f3d6b9b

A Channel Attention-Based Denoising Autoencoder U-Net and Classifier (UNA-DAEC) for Signal Identification Under Challenging SNR Conditions

Autoren: Olalekan Ebo, Ife Ajayi, Idowu Mroueh, Lina et al.

Weitere Verfasser: Olalekan Ebo, Ife Ajayi, Idowu Mroueh, Lina et al.

Quelle: IEEE Access, Vol 13, Pp 77317-77333 (2025)

Schlagwörter: denoising auto-encoder, [SPI] Engineering Sciences [physics], convolutional neural network, deep learning, Autoencoder, Electrical engineering. Electronics. Nuclear engineering, [INFO] Computer Science [cs], technology classification, time-frequency analysis, channel attention, low-power wireless area network, TK1-9971

Zugangs-URL: https://doaj.org/article/28eff17fa53b4f2e95198ac46dc3b9e6

Convolutional Autoencoder With Sequential and Channel Attention for Robust ECG Signal Denoising With Edge Device Implementation

Autoren: Alif Wicaksana Ramadhan Syifa Kushirayati Salsabila Aurellia et al.

Quelle: IEEE Access, Vol 13, Pp 54407-54422 (2025)

Schlagwörter: convolutional neural networks (CNN), signal denoising, ECG, Autoencoder, Electrical engineering. Electronics. Nuclear engineering, LSTM, TK1-9971

Zugangs-URL: https://doaj.org/article/6dac22e57f934cb1b8bc12995f41ee58

Prediction of Recurrence using a Stacked Denoising Autoencoder and Multifaceted Feature Analysis of Pretreatment MRI in Patients with Nasopharyngeal Carcinoma

Autoren: Yibin, Liu Xianwen, Wang Jiongyi, Li et al.

Quelle: Current Radiopharmaceuticals. 18:224-243

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40237056

Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data

Autoren: Timur Sattarov Marco Schreyer Damian Borth

Quelle: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. :2478-2489

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2508.00758

Ensemble Denoising Autoencoders Based on Broad Learning System for Time-Series Anomaly Detection

Autoren: Yuanxin Lin Zhiwen Yu Kaixiang Yang et al.

Quelle: IEEE Transactions on Neural Networks and Learning Systems. 36:13913-13926

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40126953

DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals

Autoren: Atik Faysal Taha Boushine Mohammad Rostami et al.

Quelle: IEEE Communications Letters. 29:1659-1663

Schlagwörter: Computer Science - Machine Learning

Zugangs-URL: http://arxiv.org/abs/2501.11538

Point-DAE: Denoising Autoencoders for Self-Supervised Point Cloud Learning

Autoren: Yabin Zhang Jiehong Lin Ruihuang Li et al.

Quelle: IEEE Transactions on Neural Networks and Learning Systems. 36:16279-16293

Schlagwörter: FOS: Computer and information sciences, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40279235
http://arxiv.org/abs/2211.06841

Electromagnetic signal denoising method based on inverse autoencoder and channel attention mechanism

Autoren: Nian Yu Mingjie Ji Chao Zhang

Quelle: 地球与行星物理论评, Vol 56, Iss 6, Pp 665-673 (2025)

Schlagwörter: magnetotelluric signal denoising, deep learning, inverse autoencoder, channel attention mechanism, Geophysics. Cosmic physics, QC801-809, Astrophysics, QB460-466

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2097-1893

Zugangs-URL: https://doaj.org/article/9338bd3b2229475f923ae6c716e5b4d9

Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering

Autoren: Mohammad Abboush Christoph Knieke Andreas Rausch

Quelle: Results in Engineering, Vol 27, Iss, Pp 105900-(2025)

Schlagwörter: Fault detection and clustering, Technology, Back-to-back testing, Deep denoising autoencoder, HIL testing, Real-time validation, Automotive software systems

Zugangs-URL: https://doaj.org/article/34c5052e622c4cb18ed2d1499b7ffeea

Advanced ECG signal denoising using deep learning architectures: convolutional neural networks, autoencoders, and generative adversarial networks

Autoren: Arjun Singh Vijoriya Yogesh Parmar Gaurav Varshney et al.

Quelle: IET Conference Proceedings. 2025:1760-1764

Remaining-capacity estimation of lithium-ion batteries using heatmap-based denoising autoencoder and vision transformer algorithm

Autoren: Yunseo Jang Joung-Hu Park

Quelle: Journal of Power Electronics. 25:1745-1760

Denoising Autoencoder and Histogram-GLCM Feature Fusion for Image-Based Classification of Locally Made Earthen Ceramic Pots

Autoren: Aljon L. Abines Aimee D. Molato

Quelle: 2025 IEEE Symposium on Industrial Electronics & Applications (ISIEA). :1-7

Robust Lithium-ion Battery SOH Estimation via Denoising Autoencoder-CNN-Transformer with Noisy Multi-Feature Input

Autoren: Shaohua Xie Daren Chen Guangzhong Dong

Quelle: 2025 IEEE 26th China Conference on System Simulation Technology and its Applications (CCSSTA). :942-947

Hybrid Transformer-Conv Autoencoder for Enhanced Denoising of Noisy Chest X-Ray Images

Autoren: V. Muthulakshmi K. Ramalakshmi

Quelle: 2025 6th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). :1470-1474

ASE-RAE: an attention-based squeeze-excitation residual autoencoder for medical image denoising.

Autoren: Babu, T. Murthy, H S Niranjana G, Surya et al.

Quelle: Imaging Science Journal. Nov2025, p1-18. 18p. 10 Illustrations.

Schlagwörter: *IMAGE denoising, *AUTOENCODERS, *RANDOM noise theory, *IMAGE analysis, *COMPUTER-aided diagnosis, *ARTIFICIAL neural networks, *SIGNAL denoising

Estimating Heart Rate Variability from Wrist-Worn Photoplethysmography Devices in Daily Activities: A Preliminary Convolutional Denoising Autoencoder Approach

Autoren: Gianluca Rho Nicola Carbonaro Marco Laurino et al.

Quelle: 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). :207-212

Schlagwörter: Wrist , Convolution , Time series analysis , Noise reduction , Autoencoders , Electrocardiography , Photoplethysmography , Reliability , Heart rate variability , Biomedical monitoring

Dateibeschreibung: application/pdf

Effect of noise reduction on PLSR modeling in near infrared spectroscopy using denoising autoencoder

Autoren: Özcan Çataltaş

Quelle: New Trends in Computer Sciences. 3:38-48

GENERATION: An Efficient Denoising Autoencoders-Based Approach for Amputated Image Reconstruction: An Efficient Denoising Autoencoders-Based Approach foAmputated Image Reconstruction

Autoren: Othman, Leila Ben Niloofar, Parisa Yahia, Sadok Ben

Quelle: Proceedings of the 16th International Conference on Agents and Artificial Intelligence. :1237-1244

Schlagwörter: Denoising Autoencoder, Missing Data Mechanism, 4. Education, Image Reconstruction, Amputation, Data Quality, Imputation