Výsledky vyhledávání - Temporal graph convolutional autoencoder*

Temporal Graph Convolutional Autoencoder based Fault Detection for Renewable Energy Applications

Autoři: Murshedul Arifeen Andrei Petrovski

Zdroj: 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS). :1-6

A Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder embedded pose for anomaly detection

Autoři: Honglei Zhu Pengjuan Wei Zhigang Xu

Zdroj: IET Computer Vision, Vol 18, Iss 3, Pp 405-419 (2024)

Témata: QA76.75-76.765, convolutional neural nets, feature extraction, Computer applications to medicine. Medical informatics, R858-859.7, 0202 electrical engineering, electronic engineering, information engineering, video surveillance, Computer software, 02 engineering and technology, pose estimation, computer vision

Přístupová URL adresa: https://doaj.org/article/41d360b7678b4b1285f23c36b7d97b4f

Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network

Autoři: Nanjun Li Faliang Chang Chunsheng Liu

Zdroj: Neurocomputing. 490:482-494

Témata: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Spatio-Temporal Graph Convolutional Autoencoder for Transonic Wing Pressure Distribution Forecasting

Autoři: Gabriele Immordino Andrea Vaiuso Andrea Da Ronch a další

Zdroj: Aerospace Science and Technology. 165:110516

Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting

Autoři: Immordino, Gabriele Vaiuso, Andrea Da Ronch, Andrea a další

Popis souboru: text

Relation: https://eprints.soton.ac.uk/504263/1/1-s2.0-S1270963825005875-main.pdf; Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea and Righi, Marcello (2025) Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting. Aerospace Science and Technology, 165, [110516]. (doi:10.1016/j.ast.2025.110516 ).

Dostupnost: https://eprints.soton.ac.uk/504263/

Memory Enhanced Spatial-Temporal Graph Convolutional Autoencoder for Human-Related Video Anomaly Detection

Autoři: Sibo Luo Shangshang Wang Yuan Wu a další

Zdroj: Lecture Notes in Computer Science ISBN: 9783031189128

Rolling Bearing Fault Diagnosis via Temporal-Graph Convolutional Fusion.

Autoři: Li, Fan Li, Yunfeng Wang, Dongfeng

Zdroj: Sensors (14248220); Jul2025, Vol. 25 Issue 13, p3894, 19p

Témata: FAULT diagnosis, FEATURE extraction, ROLLER bearings, GRAPH neural networks, CONVOLUTIONAL neural networks

Dataset for the paper 'Spatio-temporal Graph Convolutional Autoencoder for Transonic Wing Pressure Distribution Forecasting'

Autoři: Immordino, Gabriele orcid:0000-0003-2718- Vaiuso, Andrea a další

Přispěvatelé: Immordino, Gabriele orcid:0000-0003-2718- Vaiuso, Andrea a další

Relation: https://zenodo.org/records/17129717; oai:zenodo.org:17129717; https://doi.org/10.5281/zenodo.17129717

Dostupnost: https://doi.org/10.5281/zenodo.17129717
https://zenodo.org/records/17129717

Variable Structure Learning-Based Spatio-Temporal Graph Convolutional Networks for Chemical Process Quality Prediction with SHAP-Enhanced Interpretability.

Autoři: Tang, Siyuan Zhu, Zheren Zhou, Yuanqiang a další

Zdroj: Processes; Nov2025, Vol. 13 Issue 11, p3751, 22p

Bidirectional Spatial–Temporal Graph Convolutional Model: Traffic Flow Forecasting With Enhanced Extended Capabilities.

Autoři: Tan, Xiaogang Qian, Guoping Pei, Boyu a další

Zdroj: Journal of Advanced Transportation; 8/23/2025, Vol. 2025, p1-18, 18p

Témata: INTELLIGENT transportation systems, SPATIOTEMPORAL processes, BOX-Jenkins forecasting, TRAFFIC flow measurement, TRAFFIC estimation, GEOSPATIAL data

A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks.

Autoři: Francies, Mariam Labib Khalil, Abeer Twakol Amer, Hanan M. a další

Zdroj: Complex & Intelligent Systems; Oct2025, Vol. 11 Issue 10, p1-28, 28p

Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatiotemporal Forecasting

Autoři: Liang, Guojun, Postdoktor Tiwari, Prayag Nowaczyk, Sławomir a další

Zdroj: IEEE Transactions on Neural Networks and Learning Systems. 33(5):9524-9537

Témata: Diffusion process, graph neural networks (GNNs), spatiotemporal forecasting, variational graph autoencoders (VGAEs)

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-55718
https://doi.org/10.1109/tnnls.2024.3415149

Transient Stability Assessment of Power Systems Built upon Attention-Based Spatial–Temporal Graph Convolutional Networks.

Autoři: Nan, Yu Niu, Weiping Chang, Yong a další

Zdroj: Energies (19961073); Jul2025, Vol. 18 Issue 14, p3824, 18p

Témata: ELECTRIC power systems, GRAPH neural networks, MACHINE learning, SPATIOTEMPORAL processes, ELECTRIC power distribution grids, EMPIRICAL research, LOSS functions (Statistics), DYNAMIC stability

Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting.

Autoři: Singh, Vandana Sahana, Sudip Kumar Bhattacharjee, Vandana

Zdroj: Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 24, p11477, 18p

Témata: CONVOLUTIONAL neural networks, GRAPH neural networks, TRAFFIC estimation, TIME series analysis, TRAFFIC patterns

基于多头注意力时空图卷积网络的交通事故预测. (Chinese)

Alternate Title: Traffic accident prediction based on multi-head attention spatio-temporal graph convolutional network. (English)

Autoři: 姜天豪 王 瑞

Zdroj: Journal of Shanghai University / Shanghai Daxue Xuebao; Aug2025, Vol. 31 Issue 4, p678-690, 13p

Témata: SPATIAL ability, TRAFFIC accidents

A Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder embedded pose for anomaly detection.

Autoři: Zhu, Honglei Wei, Pengjuan Xu, Zhigang

Zdroj: IET Computer Vision (Wiley-Blackwell); Apr2024, Vol. 18 Issue 3, p405-419, 15p

Témata: TRANSFORMER models, INTRUSION detection systems (Computer security), HUMAN skeleton, COMPUTER vision, VIDEO surveillance, FEATURE extraction, ALGORITHMS

Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction

Autoři: Jiahui Zhao Zhibin Li Pan Liu a další

Zdroj: Journal of Transport and Land Use, Vol 17, Iss 1 (2024)

Témata: Transportation engineering, Keywords: Traffic demand prediction, Similarity-based Principle, Spatio-temporal Graph Convolutional Neural Network model, activity-based geographic information, prediction of bicycle sharing ridership, TA1001-1280, 0502 economics and business, 05 social sciences, Transportation and communications, HE1-9990

Přístupová URL adresa: https://doaj.org/article/912c74dba63f4108b6301fb6d75dafd7

STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

Autoři: Tanmay Randhavane Rohan Chandra Uttaran Bhattacharya a další

Zdroj: Proceedings of the AAAI Conference on Artificial Intelligence. 34:1342-1350

Témata: FOS: Computer and information sciences, Computer Science - Robotics, 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, Robotics (cs.RO)

Přístupová URL adresa: https://ojs.aaai.org/index.php/AAAI/article/download/5490/5346
http://arxiv.org/abs/1910.12906
https://arxiv.org/abs/1910.12906
http://ui.adsabs.harvard.edu/abs/2019arXiv191012906B/abstract
http://export.arxiv.org/pdf/1910.12906
https://arxiv.org/abs/1910.12906
https://dblp.uni-trier.de/db/conf/aaai/aaai2020.html#BhattacharyaMCR20
https://arxiv.org/pdf/1910.12906.pdf
http://dblp.uni-trier.de/db/journals/corr/corr1910.html#abs-1910-12906
https://ojs.aaai.org/index.php/AAAI/article/view/5490
https://aaai.org/ojs/index.php/AAAI/article/view/5490

Stgcn-pad: a spatial-temporal graph convolutional network for detecting abnormal pedestrian motion patterns at grade crossings.

Autoři: Song, Ge Qian, Yu Wang, Yi

Zdroj: Pattern Analysis & Applications; Mar2025, Vol. 28 Issue 1, p1-17, 17p

An Adaptive Spatiotemporal Decouple Graph Convolutional Network Based Quality‐Related Fault Detection Method for Complex Industrial Processes.

Autoři: Wu, Jiangxiao Peng, Kaixiang Zhang, Xueyi

Zdroj: International Journal of Adaptive Control & Signal Processing; Jul2025, Vol. 39 Issue 7, p1548-1566, 19p

Témata: SPATIOTEMPORAL processes, FAULT diagnosis, PROCESS control systems, ADAPTIVE control systems, GRAPH neural networks