Výsledky vyhledávání - Spatial-temporal graph conventional network algorithm

Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial–temporal proximity data from Build2Vec Autor Abdelrahman, Mahmoud M., Chong, Adrian, Miller, Clayton

“… This research aims to build upon an existing vector-based spatial model, called Build2Vec, for predicting spatial–temporal occupants…”
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A Novel Attention-Based Dynamic Multi-Graph Spatial-Temporal Graph Neural Network Model for Traffic Prediction Autor Diao, Chunyan, Zhang, Dafang, Liang, Wei, Jiang, Man, Li, Kuanching

“… However, the existing spatial-temporal prediction algorithms are based on graph convolution to capture global or heterogeneous relationships, and simpler graph convolution models cannot accurately…”
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ST-ReGE: A Novel Spatial-Temporal Residual Graph Convolutional Network for CVD Autor Zhang, Huaicheng, Liu, Wenhan, Chang, Sheng, Wang, Hao, He, Jin, Huang, Qijun

“… However, conventional DL models typically merely focus on temporal features when analyzing Euclidean data…”
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On hourly prediction of PM2.5 using spatial–temporal graph convolutional network Autor Ren, Zhenxing, Ji, Xinxin

“… for dangerous air pollutants. Conventional forecasting models are limited due to the extraction of spatial and spatiotemporal features between variables, emphasizing more on the temporal characteristics of the variables…”
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Bidirectional Spatial–Temporal Graph Convolutional Model: Traffic Flow Forecasting With Enhanced Extended Capabilities Autor Tan, Xiaogang, Qian, Guoping, Pei, Boyu, Long, Kejun

“…–temporal expanded graph convolutional model (Bi‐STEGCM) to traffic flow forecasting. This addresses the limitations of conventional models, particularly in capturing spatial features and managing missing or anomalous data. The Bi…”
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Building energy consumption prediction for campus accommodation buildings based on spatial temporal graph convolution networks Autor Qiao, Qingyao, Xu, Ziqing, Ren, Congyang, Yunusa-Kaltungo, Akilu, Cheung, Clara

“…The Net Zero Building (NZB) strategy has been regarded as the fundamental pathway to achieve sustainable cities and communities and to amend climate change…”
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Evaluating Method of Lower Limb Coordination Based on Spatial-Temporal Dependency Networks Autor Qin, Xuelin, Sang, Huinan, Wu, Shihua, Chen, Shishu, Chen, Zhiwei, Ren, Yongjun

“…, and the requirement for multiple markers. While 3D pose estimation algorithms combined with ordinary cameras offer an alternative, their accuracy often deteriorates under significant body occlusion…”
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Enhancement of traffic forecasting through graph neural network-based information fusion techniques Autor Ahmed, Shams Forruque, Kuldeep, Sweety Angela, Rafa, Sabiha Jannat, Fazal, Javeria, Hoque, Mahfara, Liu, Gang, Gandomi, Amir H.

“… To improve forecasting accuracy and capture complex interactions within transportation networks, information fusion approaches are crucial for traffic predictions based on graph neural networks (GNNs…”
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Toward the Age in Forwarding: A Deep Reinforcement Learning Enabled Routing Mechanism for Large-Scale Satellite Networks via Spatial-Temporal Graph Neural Networks Autor Gao, Ronghao, Zhang, Bo, Zhang, Qinyu, Yang, Zhihua

“…)-assisted Spatial-Temporal Graph Neural Network (STGNN) is well-designed to extract the topological features both in temporal…”
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HyperLAC: Hypergraph-based Large-scale Alert Classification with spatial-temporal context enhancement Autor Zhang, Shilong, Luo, Zian, Ren, Zehua, Zhu, Yumeng, Zhang, Haichuan, Liu, Yang

“…•We introduce AIHC, an efficient hypergraph clustering algorithm for security event extraction…”
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A hybrid GNN–vanilla vision transformer model for IoT-based soil and crop forecasting Autor Mallick, Shrabani, Suhas, S., John, Tegil J., Mallick, Soubhagya Ranjan, Chaudhary, Neha, Tumma, Shyam Sunder, Behera, Aurobinda

“…In this work, we propose a Graph Neural Network (GNN) and Vanilla Transformer-based hybrid model for IoT driven soil and crop prediction…”
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When Dynamic Causality Comes to Graph-Temporal Neural Network Autor Wang, Haowei, Pan, Yicheng, Ma, Meng, Wang, Ping

“…Spatial-temporal data forecasting is a core task in many applications, and traffic forecasting is a typical example…”
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Transformer based models with hierarchical graph representations for enhanced climate forecasting Autor Ramu, T. Bhargava, Kocherla, Raviteja, Sirisha, G. N. V. G., Chetana, V. Lakshmi, Sagar, P. Vidya, Balamurali, R., Boddu, Nanditha

“…–2017, consisting of 1,500 daily records). The model integrates three key components: Spatial-Temporal Fusion Module (STFM…”
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CNGAT: A Graph Neural Network Model for Radar Quantitative Precipitation Estimation Autor Peng, Xuan, Li, Qian, Jing, Jinrui

“… In this article, we propose a graph neural network (GNN)-based RQPE model named categorical node graph attention network (CNGAT…”
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A Traffic Flow Prediction Model Based on Dynamic Graph Convolution and Adaptive Spatial Feature Extraction Autor Li, Weijun, Yang, Guoliang, Xiong, Zhangyou, Zhu, Xiaojuan, Ma, Xinyu

“…–temporal dependencies. Unlike conventional models relying on static graph structures that often break real-world symmetry relationships, our approach introduces two key innovations respecting the dynamic symmetry of traffic networks…”
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Dynamic Hypergraph Neural Networks for Flotation Condition Recognition Based on Group-View Composite Features Autor Wang, Shuai, Wang, Kang, Li, Xiaoli

“… To address these challenges, we propose group-view multipath residual neural network (GVResNet)-EvolveHGNN, a novel framework for robust feature extraction and spatial-temporal modeling in flotation video sequences…”
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Topology-Compressed Data Delivery in Large-Scale Heterogeneous Satellite Networks: An Age-Driven Spatial-Temporal Graph Neural Network Approach Autor Gao, Ronghao, Zhang, Bo, Zhang, Qinyu, Yang, Zhihua

“…In Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO…”
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Leveraging deep learning for robust EEG analysis in mental health monitoring Autor Liu, Zixiang, Zhao, Juan

“… of cognitive and emotional conditions. Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks…”
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Multi-Branch Multi-Scale Channel Fusion Graph Convolutional Networks With Transfer Cost for Robotic Tactile Recognition Tasks Autor Zhang, Yupo, Li, Xiaoyu, Fang, Senlin, Liu, Yiwen, Wang, Jingnan, Yuan, Bo, Yi, Zhengkun

“…) to extract spatial-temporal features. It is the first LSTM-GCN network with a multi-branch structure that utilizes multi-feature scale and multi-channel fusion…”
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Enhancing power quality through the integration of hybrid renewable energy sources and multi-level inverters with unified power quality conditioner Autor Bharathi, S. Lakshmi Kanthan, Manjula, A., Veeramanikandan, P.

“…) and a Spike-Induced Graph Neural Network (SIGNN), referred to as the HOA–SIGNN method. The primary objective is to mitigate the effects of nonlinear, unbalanced, and critical load conditions by optimizing the 2DOF-PIDF…”
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