Suchergebnisse - autoencoder network

A Novel PAPR Reduction Scheme Based on Deep Autoencoder Network for FBMC Systems

Autoren: Xing Cheng Deli Chen Shuaishuai Li et al.

Quelle: IEEE Access, Vol 13, Pp 68948-68958 (2025)

Schlagwörter: peak to average power ratio, denoising autoencoder network, deep learning, Filter bank multicarrier, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Zugangs-URL: https://doaj.org/article/678c4448230241fc87fe49f3e61edf69

Autoencoder artificial neural network for accelerated forward and inverse design of locally resonant acoustic metamaterials.

Autoren: Jiang, Yongfeng Li, Zheng Ren, Jianwei et al.

Quelle: Journal of Applied Physics. 2/7/2025, Vol. 137 Issue 5, p1-15. 15p.

Schlagwörter: *ARTIFICIAL neural networks, *AUTOENCODERS, *TRANSMISSION of sound, *METAMATERIALS, *COMPUTER simulation

Self-supervised autoencoder network for robust heart rate extraction from noisy photoplethysmogram: Applying blind source separation to biosignal analysis.

Autoren: Webster MB Lee D Lee J

Quelle: Computers in biology and medicine [Comput Biol Med] 2025 Dec; Vol. 199, pp. 111319. Date of Electronic Publication: 2025 Nov 21.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE

MeSH-Schlagworte: Photoplethysmography*/methods , Heart Rate*/physiology , Signal Processing, Computer-Assisted* , Neural Networks, Computer*, Humans ; Male ; Female ; Adult ; Algorithms ; Middle Aged ; Databases, Factual ; Autoencoder

A Noise-Resilient Gearbox Fault Diagnosis Method Using Gaborlet Atlas-Guided Autoencoder Network

Autoren: Tao Lu Liang Zhao Jinrui Wang et al.

Quelle: IEEE Sensors Journal. 25:34049-34056

Enhanced Short-Term GHI Prediction using Cloud-Aware Deep Autoencoder Network

Autoren: Kalpalathika N Jumin Salih Lakshmi P et al.

Quelle: 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA). :890-896

Prediction of Transmission Spectrum From Photonic Energy Band via Autoencoder Network

Autoren: Wenqin Mo Yaling Zhu Yuanxing Yin et al.

Quelle: IEEE Photonics Technology Letters. 37:737-740

A graph autoencoder network to measure the geometric similarity of drainage networks in scaling transformation

Autoren: Yu, Huafei Ai, Tinghua Yang, Min et al.

Weitere Verfasser: Yu, Huafei Ai, Tinghua Yang, Min et al.

Quelle: International Journal of Digital Earth. 16(1):1828-1852

Schlagwörter: Natural Sciences, Earth and Related Environmental Sciences, Other Earth Sciences (including Geographical Information Science), Naturvetenskap, Geovetenskap och relaterad miljövetenskap, Annan geovetenskap (Här ingår: Geografisk informationsvetenskap), Physical Geography, Naturgeografi

Zugangs-URL: https://doi.org/10.1080/17538947.2023.2212920

Pixel-Level and Global Similarity-Based Adversarial Autoencoder Network for Hyperspectral Unmixing

Autoren: Wei Tao Haiyang Zhang Shan Zeng et al.

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 7064-7082 (2025)

Schlagwörter: Ocean engineering, Wasserstein generative adversarial network (WGAN), QC801-809, Hyperspectral image unmixing, Geophysics. Cosmic physics, u-shaped network, similarity, TC1501-1800, swin transformer

Zugangs-URL: https://doaj.org/article/14e629a71323448c97213b8e7d3d8c78

Accurate Open Set Radio Frequency Fingerprinting Identification Enabled by a Novel Hierarchical Autoencoder Network

Autoren: Xiangqi Kong Zitong Zhang Junjie Sun et al.

Quelle: 2025 IEEE International Workshop on Radio Frequency and Antenna Technologies (iWRF&AT). :174-179

Enhanced water quality index prediction using a dual-key convolutional transformer autoencoder network integrating with poplar optimization

Autoren: R. Maruthamuthu S. Balamurugan N. Naveen Kumar et al.

Quelle: Franklin Open, Vol 13, Iss , Pp 100370- (2025)

Schlagwörter: Water quality index, Water quality class, Dual-key convolutional transformer autoencoder network, Poplar optimization algorithm, Technology

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2773186325001586; https://doaj.org/toc/2773-1863

Zugangs-URL: https://doaj.org/article/bc6cba3cdab040f6a98286e4f3d6d2b8

Proximal Mapping-Incorporated Deep Autoencoder Network with Momentum Acceleration Method for Image Denoising

Autoren: Yang Zhao Ming Li Meng Huang et al.

Quelle: 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). :1322-1327

Cross Attention with Deep Convolutional Recurrent Autoencoder Network Intrusion Detection System in Internet of Things

Quelle: International Journal of Intelligent Engineering and Systems. 18:338-350

A multistage graph-based autoencoder network with global-local features for hyperspectral unmixing

Autoren: Hua Dong Xiaohua Zhang Hongyun Meng et al.

Quelle: International Journal of Remote Sensing. 46:3709-3735

A Novel Adam Hypertuned Artificial Neural Network Using Autoencoder Network Approach to Improve Software Defect Prediction Accuracy

Autoren: S. Thenmozhi PM. Shanthi

Quelle: Journal of Neonatal Surgery. 14:437-453

Image Dehazing Using a Convolutional Autoencoder Network with Integrated Convolutional Block Attention

Autoren: Rastegar, Homayoun Khotanlou, Hassan

Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition

Autoren: Hosam Abduljalil Ahmed Elhayek Abdullah Marish Ali et al.

Quelle: AI, Vol 5, Iss 3, Pp 1695-1708 (2024)

Schlagwörter: human activity analysis, Electronic computers. Computer science, graph autoencoder, deep learning, graph convolutional networks, QA75.5-76.95, skeleton-based human action recognition

Zugangs-URL: https://doaj.org/article/6db5e7cb49744e039fc486429393c144

Convolutional autoencoder network lithology recognition based on scratch tests

Autoren: Suling Wang Zhihui Ren Kangxing Dong et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-16 (2025)

Schlagwörter: Continental shale, Lithology recognition, Scratch tests, Recognition size, CAE network, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/f7f862af682241f488ef0ed868100a5f

JEDAN: Joint Euclidean Distance and Autoencoder Network for Robust Out-of-Distribution Detection in Radar-Based Hand Gesture Recognition

Autoren: Muhammad Ghufran Janjua Kevin Kaiser Thomas Stadelmayer et al.

Quelle: IEEE Access, Vol 12, Pp 196364-196381 (2024)

Schlagwörter: Artificial neural networks, gesture recognition, autoencoders, deep learning, Electrical engineering. Electronics. Nuclear engineering, FMCW radar, out-of-distribution detection, TK1-9971

Zugangs-URL: https://doaj.org/article/641fb84417c543d8ab07bcd5e1cf8b5e

Signal Enhancement for Downhole Microseismic Data Using Improved Attention Mechanism Based on Autoencoder Network

Autoren: Wenxuan Ge Qinghui Mao Wei Zhou et al.

Quelle: IEEE Access, Vol 12, Pp 156390-156400 (2024)

Schlagwörter: autoencoder network, first arrival picking, improved attention mechanism, Microseismic signal denoising, polarization analysis, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Zugangs-URL: https://doaj.org/article/2c58241bbbcf44c5824ba514c4ba0f3f

Mitigating the Concurrent Interference of Electrode Shift and Loosening in Myoelectric Pattern Recognition Using Siamese Autoencoder Network

Autoren: Ge Gao Xu Zhang Xiang Chen et al.

Quelle: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3388-3398 (2024)

Schlagwörter: Male, Adult, Support Vector Machine, Gestures, Electromyography, electrode shift, electrode loosening, Reproducibility of Results, Siamese autoencoder network, RM1-950, surface electromyography, Pattern Recognition, Automated, Young Adult, Myoelectric pattern recognition, Medical technology, Humans, Female, Therapeutics. Pharmacology, Neural Networks, Computer, R855-855.5, Electrodes, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39196739
https://doaj.org/article/cc98e516e69945a6b12fa2043960d7c9