Suchergebnisse - Stacked denoising variational autoencoder model

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification.

Autoren: Johnsen, Timothy K. Bi, Xiangyu Chou, Chunwei et al.

Quelle: Water Resources Research; Oct2025, Vol. 61 Issue 10, p1-23, 23p

Schlagwörter: EVAPOTRANSPIRATION, AUTOENCODERS, STOCHASTIC analysis, MACHINE learning, HYDROLOGICAL forecasting, NOISE control, TELEMETRY

Intelligent fault prediction of rolling bearing based on gate recurrent unit and hybrid autoencoder.

Autoren: Che, Changchang Wang, Huawei Fu, Qiang et al.

Quelle: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (Sage Publications, Ltd.); Mar2021, Vol. 235 Issue 6, p1106-1114, 9p

Deep learning based photovoltaic generation on time series load forecasting.

Autoren: Loganathan, Umasankar Nagarajan, Geetha Gopinath, Srimathy et al.

Quelle: Bulletin of Electrical Engineering & Informatics; Oct2024, Vol. 13 Issue 5, p3757-3769, 13p

Schlagwörter: RECURRENT neural networks, DEEP learning, TIME series analysis, FORECASTING, PERCENTILES

Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening.

Autoren: Yi, Yaoyang¹ (AUTHOR) ns2ciw@163.com

Quelle: PLoS ONE. 6/30/2025, Vol. 20 Issue 6, p1-34. 34p.

Schlagwörter: *LONG short-term memory, *HILBERT-Huang transform, *FLEXIBLE work arrangements, *STANDARD deviations, *CARBON offsetting, *CARBON pricing

Health condition identification for rolling bearing using a multi-domain indicator-based optimized stacked denoising autoencoder.

Autoren: Yan, Xiaoan Liu, Ying Jia, Minping

Quelle: Structural Health Monitoring; Sep2020, Vol. 19 Issue 5, p1602-1626, 25p

Schlagwörter: ROLLER bearings, DEEP learning, AUTOMATIC identification, LINEAR operators, MATHEMATICAL optimization, BIOLOGICALLY inspired computing, ALGORITHMS

A novel deep learning‐based technique for driver drowsiness detection.

Autoren: Mukherjee, Prithwijit Roy, Anisha Halder

Quelle: Human Factors & Ergonomics in Manufacturing & Service Industries; Nov2024, Vol. 34 Issue 6, p667-684, 18p

Schlagwörter: GALVANIC skin response, WAKEFULNESS, FEATURE extraction, DROWSINESS, TRAFFIC accidents

Conditional Generative Denoising Autoencoder.

Autoren: Karatsiolis, Savvas Schizas, Christos N.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Oct2020, Vol. 31 Issue 10, p4117-4129, 13p

Schlagwörter: MONTE Carlo method, MARKOV chain Monte Carlo, SIGNAL denoising, SUPERVISED learning

Loop closure detection of visual SLAM based on variational autoencoder.

Autoren: Shibin Song Fengjie Yu Xiaojie Jiang et al.

Quelle: Frontiers in Neurorobotics; 2024, p1-13, 13p

Schlagwörter: DEEP learning, IMAGE representation, HANDICRAFT

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.

Autoren: Addo, Daniel Zhou, Shijie Jackson, Jehoiada Kofi et al.

Quelle: Diagnostics (2075-4418); Nov2022, Vol. 12 Issue 11, p2569, 24p

Schlagwörter: X-ray imaging, DEEP learning, COVID-19, COVID-19 pandemic, MACHINE learning

Variational Autoencoder for Image-Based Augmentation of Eye-Tracking Data.

Autoren: Elbattah, Mahmoud Loughnane, Colm Guérin, Jean-Luc et al.

Quelle: Journal of Imaging; May2021, Vol. 7 Issue 5, p1-14, 14p

Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance.

Autoren: Roka, Sanjay Diwakar, Manoj

Quelle: Journal of Electronic Imaging; May/Jun2023, Vol. 32 Issue 3, p33015-033015-23, 1p

Schlagwörter: VIDEO surveillance, ANOMALY detection (Computer security), IMAGE denoising, OPTICAL flow, CONVOLUTIONAL neural networks, DEEP learning

Neighborhood Geometric Structure-Preserving Variational Autoencoder for Smooth and Bounded Data Sources.

Autoren: Chen, Xingyu Wang, Chunyu Lan, Xuguang et al.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Aug2022, Vol. 33 Issue 8, p3598-3611, 14p

Schlagwörter: STATISTICAL smoothing, FACIAL expression, LATENT variables, NEIGHBORHOODS

Deep Convolutional Autoencoders for Robust Flow Model Calibration Under Uncertainty in Geologic Continuity.

Autoren: Jiang, Anyue Jafarpour, Behnam

Quelle: Water Resources Research; Nov2021, Vol. 57 Issue 11, p1-37, 37p

Schlagwörter: DEEP learning, LATENT variables, CALIBRATION, SUBSURFACE drainage, CONTINUITY, HYDRAULIC conductivity, PRINCIPAL components analysis, TURBO codes

Intelligent fault prediction of rolling bearing based on gate recurrent unit and hybrid autoencoder

Autoren: Changchang Che Huawei Wang Qiang Fu et al.

Quelle: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 235:1106-1114

Schlagwörter: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 6. Clean water

Zugangs-URL: https://journals.sagepub.com/doi/10.1177/0954406220941037
http://journals.sagepub.com/doi/10.1177/0954406220941037

Efficient and expressive high-resolution image synthesis via variational autoencoder-enriched transformers with sparse attention mechanisms.

Autoren: Tang, Bingyin Feng, Fan

Quelle: Journal of Electronic Imaging; May/Jun2024, Vol. 33 Issue 3, p33002-33002-15, 1p

Schlagwörter: VOCABULARY

Target localization and defect detection of distribution insulators based on ECA‐SqueezeNet and CVAE‐GAN.

Autoren: Zhang, Chao Liu, Yu Liu, Honggang

Quelle: IET Image Processing (Wiley-Blackwell); 11/13/2024, Vol. 18 Issue 13, p3864-3877, 14p

Schlagwörter: GENERATIVE adversarial networks, COMPUTER vision, ELECTRIC insulators & insulation, IMAGE registration, COMPUTER networking equipment

Data-driven two-layer visual dictionary structure learning.

Autoren: Yu, Xiangchun Yu, Zhezhou Wu, Lei et al.

Quelle: Journal of Electronic Imaging; Mar/Apr2019, Vol. 28 Issue 2, p1-15, 15p

Schlagwörter: OBJECT tracking (Computer vision), CONTENT-based image retrieval

K-Means Clustering Optimizing Deep Stacked Sparse Autoencoder.

Autoren: Bi, Yandong Wang, Peng Guo, Xuchao et al.

Quelle: Sensing & Imaging; 2/10/2019, Vol. 20 Issue 1, p1-1, 1p

Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering.

Autoren: Wang, Yang Xiao, Hong Zhang, Zhihai et al.

Quelle: Computer-Aided Civil & Infrastructure Engineering; 2/4/2025, Vol. 40 Issue 4, p503-522, 20p

Schlagwörter: TRANSPORTATION noise, SIGNAL denoising, PATTERN perception, FAULT diagnosis, LATENT variables, MACHINE learning, EMPIRICAL research

A Hybrid Cluster Variational Autoencoder Model for Monitoring the Multimode Blast Furnace System.

Autoren: Chen, Chenyu Cai, Jinhui

Quelle: Processes; Sep2023, Vol. 11 Issue 9, p2580, 13p

Schlagwörter: BLAST furnaces, PRINCIPAL components analysis, DIRECT-fired heaters, GAUSSIAN mixture models