Suchergebnisse - Temporal Graph Convolutional Autoencoder

Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network von Li, Nanjun, Chang, Faliang, Liu, Chunsheng

“… Our network is established on a Spatial-temporal Graph Convolutional …”
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Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting von Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea, Righi, Marcello

“… This study presents a framework for predicting unsteady transonic wing pressure distributions due to pitch and plunge movement, integrating an autoencoder architecture with graph convolutional …”
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Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection von Ren, Zhiyu, Li, Xiaojie, Peng, Jing, Chen, Ken, Tan, Qushan, Wu, Xi, Shi, Canghong

“… In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network …”
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Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting von Khodayar, Mahdi, Mohammadi, Saeed, Khodayar, Mohammad E., Wang, Jianhui, Liu, Guangyi

“… This probabilistic data generation model, i.e., convolutional graph autoencoder (CGAE), is devised based on the localized first-order approximation …”
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Temporal Graph Convolutional Autoencoder based Fault Detection for Renewable Energy Applications von Arifeen, Murshedul, Petrovski, Andrei

“… To address this issue, we propose an autoencoder model that uses a temporal graph convolutional layer to detect faults in the energy generation process …”
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A Comprehensive Survey on Graph Neural Networks von Wu, Zonghan, Pan, Shirui, Chen, Fengwen, Long, Guodong, Zhang, Chengqi, Yu, Philip S.

“… -Euclidean domains and are represented as graphs with complex relationships and interdependency between objects …”
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A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder von Ni, Yanchun, Jin, Qiyuan, Hu, Rui

“… This paper proposes an autoencoder model integrating Temporal Convolutional Networks (TCN) and Graph Attention Networks (GAT …”
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Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays von Arifeen, Murshedul, Petrovski, Andrei, Hasan, Md Junayed, Noman, Khandaker, Navid, Wasib Ul, Haruna, Auwal

“… This paper introduces a deep learning model that combines a graph convolutional network with a variational autoencoder to diagnose faults in solar arrays …”
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DeGTeC: A deep graph-temporal clustering framework for data-parallel job characterization in data centers von Liang, Yi, Chen, Kaizhong, Yi, Lan, Su, Xing, Jin, Xiaoming

“… The DeGTeC framework is constructed mainly based on two autoencoders, i.e., TaskAE and JobAE. TaskAE and JobAE contain spectral graph convolutional network …”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data von Choi, Seung-Hwan, An, Dawn, Lee, Inho, Lee, Suwoong

“… This paper proposes a deep learning-based anomaly detection method using time-series vibration and current data, which were obtained from endurance tests on …”
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Topology and FDIA identification in distribution system state estimation using a data-driven approach von Raghuvamsi, Y., Batchu, Sreenadh, Teeparthi, Kiran

“… To address these issues, a novel denoising autoencoder (DAE) is developed with the use of graph-based temporal convolutional layers in the encoder and decoder stages …”
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Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids von Takiddin, Abdulrahman, Ismail, Muhammad, Atat, Rachad, Davis, Katherine R., Serpedin, Erchin

“… Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The …”
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Uncertainty-aware probabilistic travel demand prediction for mobility-on-demand services von Peng, Tao, Gao, Jie, Cats, Oded

“… •Spatial-temporal deep learning framework for probabilistic MoD demand prediction …”
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Matrix Completion with Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference von Do, Tien Huu, Minh Nguyen, Duc, Tsiligianni, Evaggelia, Aguirre, Angel Lopez, Panzica La Manna, Valerio, Pasveer, Frank, Philips, Wilfried, Deligiannis, Nikos

“… We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders …”
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An Integrated Distributed Fault Diagnosis Framework for Large-Scale Industrial Processes Based on Spatio-Temporal Causal Analysis von Hua, Dongjie, Dong, Jie, Peng, Kaixiang, Simani, Silvio

“… Second, an embedded time convolutional network-based autoencoder is designed to extract spatio-temporal features simultaneously …”
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Graph neural networks: Historical backgrounds, present revolutions, and conventionalization for the future von Maghdid, Sozan S., Rashid, Tarik A., Askar, Shavan K.

“… –temporal graph neural networks (STGNNs), recurrent-based GNNs (RecGNNs), and graph autoencoders (GAEs …”
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Recurrent graph convolutional multi-mesh autoencoder for unsteady transonic aerodynamics von Massegur, David, Da Ronch, Andrea

“… This work presents a geometric-deep-learning multi-mesh autoencoder framework to predict the spatial and temporal evolution of aerodynamic loads for a finite-span wing undergoing different types of motion …”
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Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising von Zhou, Kanglei, Shum, Hubert P. H., Li, Frederick W. B., Liang, Xiaohui

“… Our solution is the Multi-task Spatial-Temporal Graph Auto-Encoder (Multi-STGAE), a model that accurately denoises and predicts hand motion by exploiting the inter-dependency of both tasks …”
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A Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder embedded pose for anomaly detection von Zhu, Honglei, Wei, Pengjuan, Xu, Zhigang

“… ‐based video anomaly detection in recent years. The spatio‐temporal graph convolutional network has been proven to be effective in modelling the spatio …”
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A graph-based semi-supervised approach to classification learning in digital geographies von Liu, Pengyuan, De Sabbata, Stefano

“… As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday …”
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