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Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction 认领 引用 被引量：15
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作者 Jihua Ye Shengjun Xue Aiwen Jiang 《Digital Communications and Networks》 SCIE CSCD 2022年第3期343-350,共8页
Traffic flow prediction is an important part of the intelligent transportation system. Accurate multi-step traffic flow prediction plays an important role in improving the operational efficiency of the traffic network... Traffic flow prediction is an important part of the intelligent transportation system. Accurate multi-step traffic flow prediction plays an important role in improving the operational efficiency of the traffic network. Since traffic flow data has complex spatio-temporal correlation and non-linearity, existing prediction methods are mainly accomplished through a combination of a Graph Convolutional Network (GCN) and a recurrent neural network. The combination strategy has an excellent performance in traffic prediction tasks. However, multi-step prediction error accumulates with the predicted step size. Some scholars use multiple sampling sequences to achieve more accurate prediction results. But it requires high hardware conditions and multiplied training time. Considering the spatiotemporal correlation of traffic flow and influence of external factors, we propose an Attention Based Spatio-Temporal Graph Convolutional Network considering External Factors (ABSTGCN-EF) for multi-step traffic flow prediction. This model models the traffic flow as diffusion on a digraph and extracts the spatial characteristics of traffic flow through GCN. We add meaningful time-slots attention to the encoder-decoder to form an Attention Encoder Network (AEN) to handle temporal correlation. The attention vector is used as a competitive choice to draw the correlation between predicted states and historical states. We considered the impact of three external factors (daytime, weekdays, and traffic accident markers) on the traffic flow prediction tasks. Experiments on two public data sets show that it makes sense to consider external factors. The prediction performance of our ABSTGCN-EF model achieves 7.2%–8.7% higher than the state-of-the-art baselines. 展开更多
关键词 Multi-step traffic flow prediction Graph convolutional network External factors Attentional encoder network Spatiotemporal correlation
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Lightweight Multiscale Spatio-Temporal Graph Convolutional Network for Skeleton-Based Action Recognition 认领 引用
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作者 Zhiyun Zheng Qilong Yuan +2 位作者 Huaizhu Zhang Yizhou Wang Junfeng Wang 《Big Data Mining and Analytics》 EI CSCD 2025年第2期310-325,共16页
Using skeletal information to model and recognize human actions is currently a hot research subject in the realm of Human Action Recognition(HAR).Graph Convolutional Networks(GCN)have gained popularity in this discipl... Using skeletal information to model and recognize human actions is currently a hot research subject in the realm of Human Action Recognition(HAR).Graph Convolutional Networks(GCN)have gained popularity in this discipline due to their capacity to efficiently process graph-structured data.However,it is challenging for current models to handle distant dependencies that commonly exist between human skeleton nodes,which hinders the development of algorithms in related fields.To solve these problems,the Lightweight Multiscale Spatio-Temporal Graph Convolutional Network(LMSTGCN)is proposed.Firstly,the Lightweight Multiscale Spatial Graph Convolutional Network(LMSGCN)is constructed to capture the information in various hierarchies,and multiple inner connections between skeleton joints are captured by dividing the input features into a number of subsets along the channel direction.Secondly,the dilated convolution is incorporated into the temporal convolution to construct Lightweight Multiscale Temporal Convolutional Network(LMTCN),which allows to obtain a wider receptive field while keeping the size of the convolution kernel unchanged.Thirdly,the Spatio-Temporal Location Attention(STLAtt)module is used to identify the most informative joints in the sequence of skeletal information at a specific frame,hence improving the model’s ability to extract features and recognize actions.Finally,multi-stream data fusion input structure is used to enhance the input data and expand the feature information.Experiments on three public datasets illustrate the effectiveness of the proposed network. 展开更多
关键词 Human Action Recognition(HAR) skeleton data Graph Convolutional Network(GCN) attention mechanism
Modularized Graph Convolutional Network 认领 引用
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作者 Tiantian He Zhixuan Duan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期737-739,共3页
Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighb... Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighbor aggregation are fixed, leading to the limited capability of capturing diverse relationships among nodes for representation learning. Conventional GCNs always learn node representations in the graph according to the weights computed from the graph Laplacian, consequently overlooking the similarity and group cohesiveness of node features. 展开更多
关键词 graph convolution networks gcns capturing diverse relationships nodes representation learning modularized graph convolution network neighbor aggregation graph neural network modularized graph convolution network mgcn graph convolutional network
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Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System 认领 引用
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作者 Pangwei Wang Jie Wang +2 位作者 Zipeng Wang Hangrui Dong Li Wang 《Computers, Materials & Continua》 SCIE EI 2026年第8期1417-1435,共19页
Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The ... Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System(ITS).However,sensors are vulnerable to environmental interference,which can result in data loss at specific points or along arterial highways for certain periods,potentially undermining system safety and decision-making reliability.To address these challenges,a deep learning method based on Graph Convolutional Networks(GCN)and Gated Recurrent Units(GRU)is proposed,leveraging Artificial Intelligence(AI)and intelligent connected technologies for real-time acquisition of multi-sensor perception data.A feature-level fusion integrates multi-source perception data.GCN captures spatial dependencies from the road network topology,while GRU extracts temporal features from time series,enabling accurate imputation of missing traffic data.The method is evaluated at intelligent connected intersections in the Beijing High-level Autonomous Driving Demonstration Area.Results show that the accuracy of long-term traffic state completion reaches 89.36%,and the Root Mean Square Error(RMSE)is reduced by 17.2%compared to the Long Short-Term Memory(LSTM)baseline.This framework provides a practical solution for deploying traffic holographic perception technology in secure and trustworthy ITS. 展开更多
关键词 Intelligent transportation information security traffic information completion traffic holographic perception AI-driven edge computing graph convolutional neural network
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Dual Channel Graph Convolutional Networks via Personalized PageRank 认领 引用
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作者 Longlong Lin Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期221-223,共3页
Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representat... Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications. 展开更多
关键词 convolutional node feature similarity graph convolutional framework learning graph representations neural networks gnns networks graph personalized
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Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network 认领 引用 被引量：1
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作者 Pongsakon Promsawat Weerapan Sae-dan +2 位作者 Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期579-607,共29页
The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u... The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets. 展开更多
关键词 Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal
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Spatio-Temporal Graph Neural Networks with Elastic-Band Transform for Solar Radiation Prediction 认领 引用 被引量：1
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作者 Guebin Choi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期848-872,共25页
This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically def... This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series.Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks(STGNNs).However,such definitions are prone to generating spurious correlations due to the dominance of periodic structures.To address this limitation,we adopt the Elastic-Band Transform(EBT)to decompose solar radiation into periodic and amplitude-modulated components,which are then modeled independently with separate graph neural networks.The periodic component,characterized by strong nationwide correlations,is learned with a relatively simple architecture,whereas the amplitude-modulated component is modeled with more complex STGNNs that capture climatological similarities between regions.The predictions from the two components are subsequently recombined to yield final forecasts that integrate both periodic patterns and aperiodic variability.The proposed framework is validated with multiple STGNN architectures,and experimental results demonstrate improved predictive accuracy and interpretability compared to conventional methods. 展开更多
关键词 Spatio-temporal graph neural network(STGNN) elastic-band transform(EBT) solar radiation fore-casting spurious correlation time series decomposition
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A Graph-Based Spatio-Temporal Attention Network for Stress-Strain Behavior Prediction of Copper-Based Composites 认领 引用
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作者 Chuhan Zhang Jinguo You +5 位作者 Jialin Xu Mingqian Li Xiaofeng Chen Jingmei Tao Caiju Li Jianhong Yi 《Computers, Materials & Continua》 SCIE EI 2026年第7期407-422,共16页
With the rapid development of artificial intelligence and data-driven modeling,deep learning has become an effective tool for analyzing scientific discovery such as predicting material behaviors.Graphene-reinforced co... With the rapid development of artificial intelligence and data-driven modeling,deep learning has become an effective tool for analyzing scientific discovery such as predicting material behaviors.Graphene-reinforced copper-based composites,which exhibit excellent mechanical,electrical,and thermal properties,have attracted extensive attention in advanced engineering applications;however,accurate prediction of their stress-strain behavior still relies heavily on computationally expensive molecular dynamics simulations or experiments.In this work,we propose a Graph-based Spatio-Temporal Attention Network,termed GraphSTAN,for stress-strain behavior prediction of copper-based composites.Specifically,atomic-scale initial microstructures are encoded as graphs and integrated with static physical parameters.A topology-aware spatio-temporal feature interaction mechanism is introduced to effectively couple structural representations with stress-strain time-series dynamics,enabling accurate prediction of full stress-strain evolution.Moreover,a multi-features dataset is constructed based on LAMMPS molecular dynamics simulations,consisting of 596 independent simulation samples corresponding to distinct combinations of microstructural configurations,loading conditions,and stress-strain time series.Experimental results demonstrate that GraphSTAN effectively predicts full stress-strain curves and achieves the higher performance of R2,MAE and RMSE for yield strength and Young’s modulus,respectively,significantly outperforming baseline methods. 展开更多
关键词 Temporal convolutional network graph attention network copper-based composites mechanical property prediction stress-strain behavior
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Advanced High-Order Graph Convolutional Networks With Assorted Time-Frequency Transforms 认领 引用
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作者 Ling Wang Ye Yuan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期394-408,共15页
A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spa... A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance. 展开更多
关键词 Dynamic graph(DG)learning ensemble graph representation learning high-order graph convolution network(HGCN) time-frequency transform tensor product
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HGS-ATD:A Hybrid Graph Convolutional Network-GraphSAGE Model for Anomaly Traffic Detection 认领 引用
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作者 Zhian Cui Hailong Li Xieyang Shen 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第1期33-50,共18页
With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a ... With network attack technology continuing to develop,traditional anomaly traffic detection methods that rely on feature engineering are increasingly insufficient in efficiency and accuracy.Graph Neural Network(GNN),a promising Deep Learning(DL)approach,has proven to be highly effective in identifying intricate patterns in graph⁃structured data and has already found wide applications in the field of network security.In this paper,we propose a hybrid Graph Convolutional Network(GCN)⁃GraphSAGE model for Anomaly Traffic Detection,namely HGS⁃ATD,which aims to improve the accuracy of anomaly traffic detection by leveraging edge feature learning to better capture the relationships between network entities.We validate the HGS⁃ATD model on four publicly available datasets,including NF⁃UNSW⁃NB15⁃v2.The experimental results show that the enhanced hybrid model is 5.71%to 10.25%higher than the baseline model in terms of accuracy,and the F1⁃score is 5.53%to 11.63%higher than the baseline model,proving that the model can effectively distinguish normal traffic from attack traffic and accurately classify various types of attacks. 展开更多
关键词 anomaly traffic detection graph neural network deep learning graph convolutional network
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A Bridge Transformer Network With Deep Graph Convolution for Hyperspectral Image Classification 认领 引用
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作者 Yuquan Gan Siyu Wu +3 位作者 Chang Su Nan Xiang Zhijie Xu Yushan Pan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期464-482,共19页
Transformers have been widely applied to hyperspectral image classification,leveraging their self-attention mechanism for powerful global modelling.However,two key challenges remain as follows:excessive memory and com... Transformers have been widely applied to hyperspectral image classification,leveraging their self-attention mechanism for powerful global modelling.However,two key challenges remain as follows:excessive memory and computational costs from calculating correlations between all tokens(especially as image size or spectral bands increase)and limited ability to model local boundary information due to lacking explicit enhancement mechanisms.This paper proposes a novel method,bridge transformer network fused with deep graph convolution(BTDGC),to address these issues.The framework includes three components as follows:a double random masking mechanism(DRMM)that forces the model to infer masked features from context during training,a bridge transformer(BT)module with bridge tokens for cross-region feature interaction and a Deep Graph Convolutional Pooling(DGCP)module that preserves spatial topology while aggregating hierarchical information.Experiments on standard hyperspectral datasets show BTDGC outperforms mainstream methods in classification accuracy and robustness,effectively balancing global modelling and local boundary representation.The code is available at http://gffzz188fe103f8f1460asbcu6cb50wqpn6un5.ffgz.tsg.suse.edu.cn/jenny3489/BTDGC. 展开更多
关键词 convolution graph convolutional network masking mechanism transforms
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State Space Guided Spatio-Temporal Network for Efficient Long-Term Traffic Prediction 认领 引用
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作者 Guangyu Huo Chang Su +2 位作者 Xiaoyu Zhang Xiaohui Cui Lizhong Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第2期1242-1264,共23页
Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation... Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks,requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize trafficmanagement and enhance urban mobility and sustainability.However,traditional predictivemodels struggle to capture long-term temporal dependencies and are computationally intensive,limiting their practicality in real-time.Moreover,many approaches overlook the periodic characteristics inherent in traffic data,further impacting performance.To address these challenges,we introduce ST-MambaGCN,a State-Space-Based Spatio-Temporal Graph Convolution Network.Unlike conventionalmodels,ST-MambaGCN replaces the temporal attention layer withMamba,a state-space model that efficiently captures long-term dependencies with near-linear computational complexity.The model combines Chebyshev polynomial-based graph convolutional networks(GCN)to explore spatial correlations.Additionally,we incorporate a multi-temporal feature capture mechanism,where the final integrated features are generated through the Hadamard product based on learnable parameters.This mechanism explicitly models shortterm,daily,and weekly traffic patterns to enhance the network’s awareness of traffic periodicity.Extensive experiments on the PeMS04 and PeMS08 datasets demonstrate that ST-MambaGCN significantly outperforms existing benchmarks,offering substantial improvements in both prediction accuracy and computational efficiency for long-term traffic flow prediction. 展开更多
关键词 State space model long-term traffic flow prediction graph convolutional network multi-time scale analysis emerging applications at intelligent networks
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MSSTGCN: Multi-Head Self-Attention and Spatial-Temporal Graph Convolutional Network for Multi-Scale Traffic Flow Prediction 认领 引用 被引量：2
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作者 Xinlu Zong Fan Yu +1 位作者 Zhen Chen Xue Xia 《Computers, Materials & Continua》 SCIE EI 2025年第2期3517-3537,共21页
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ... Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks. 展开更多
关键词 Graph convolutional network traffic flow prediction multi-scale traffic flow spatial-temporal model
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Occluded Gait Emotion Recognition Based on Multi-Scale Suppression Graph Convolutional Network 认领 引用 被引量：1
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作者 Yuxiang Zou Ning He +2 位作者 Jiwu Sun Xunrui Huang Wenhua Wang 《Computers, Materials & Continua》 SCIE EI 2025年第1期1255-1276,共22页
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac... In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods. 展开更多
关键词 KNN interpolation multi-scale temporal convolution suppression graph convolutional network gait emotion recognition human skeleton
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Container cluster placement in edge computing based on reinforcement learning incorporating graph convolutional networks scheme 认领 引用 被引量：1
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作者 Zhuo Chen Bowen Zhu Chuan Zhou 《Digital Communications and Networks》 SCIE EI CSCD 2025年第1期60-70,共11页
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat... Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods. 展开更多
关键词 Edge computing Network virtualization Container cluster Deep reinforcement learning Graph convolutional network
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Fault Identification Method for In-Core Self-Powered Neutron Detectors Combining Graph Convolutional Network and Stacking Ensemble Learning 认领 引用 被引量：2
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作者 LIN Weiqing LU Yanzhen +1 位作者 MIAO Xiren QIU Xinghua 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第5期1018-1027,共10页
Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification ... Self-powered neutron detectors(SPNDs)play a critical role in monitoring the safety margins and overall health of reactors,directly affecting safe operation within the reactor.In this work,a novel fault identification method based on graph convolutional networks(GCN)and Stacking ensemble learning is proposed for SPNDs.The GCN is employed to extract the spatial neighborhood information of SPNDs at different positions,and residuals are obtained by nonlinear fitting of SPND signals.In order to completely extract the time-varying features from residual sequences,the Stacking fusion model,integrated with various algorithms,is developed and enables the identification of five conditions for SPNDs:normal,drift,bias,precision degradation,and complete failure.The results demonstrate that the integration of diverse base-learners in the GCN-Stacking model exhibits advantages over a single model as well as enhances the stability and reliability in fault identification.Additionally,the GCN-Stacking model maintains higher accuracy in identifying faults at different reactor power levels. 展开更多
关键词 self-powered neutron detector(SPND) graph convolutional network(GCN) Stacking ensemble learning fault identification
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Prediction of mechanical properties of cross-linked polymer interface by graph convolution network 认领 引用
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作者 Xintianyang Wang Lijuan Liao +1 位作者 Chenguang Huang Xianqian Wu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期404-424,共21页
Machine learning models have made significant advances in the establishment of structure-property relationships.However,it is still a challenge to predict the mechanical properties of the adhesive interface due to the... Machine learning models have made significant advances in the establishment of structure-property relationships.However,it is still a challenge to predict the mechanical properties of the adhesive interface due to the complexity and randomness of the polymer topologies.In this paper,we employed a graph convolutional network(GCN)model to predict the mechanical properties of a specific cross-linked polymer interfacial system,including yield strength(σy),ultimate strength(σu),failure strain(εu),and fracture toughness(Γ)utilizing molecular dynamics simulations.The results showed that the adopted GCN model can predict the mechanical properties with over 88%accuracy.Furthermore,the prediction performances for εu and σu are better than those for Γ and σy,with R2~0.73 for εu,R2~0.64 for σu,R2~0.51 for Γ,and R2~0.43 for σy.It is worth noting that the GCN model with the sum aggregator slightly outperforms that with the mean aggregator,and that models with linear regression and fully connected neural network regression provide similar predictions.The influence of input node features on prediction performance was also investigated.It was observed that the node closeness centrality is an important graph parameter in prediction.Specifically,node closeness centrality presents a more significant influence on the global mechanical properties of the adhesive interface,such as εu,σu,and Γ.Additionally,sensitivity analysis demonstrated that appropriate hyperparameters can improve computational efficiency without losing accuracy on a restricted set of data.This paper demonstrated the capacity of the GCN model to predict the mechanical properties of the adhesive interface with diverse topologies and provided a possible pathway for improving the mechanical properties of the adhesive interface by tailoring polymer structures in the future. 展开更多
关键词 Graph convolutional network Molecular dynamic Adhesive interface Mechanical properties Topology structure
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Curvature-driven shifts of the Potts transition on spherical Fibonacci graphs:A graph-convolutional transfer-learning study 认领 引用
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作者 Zheng Zhou Xu-Yang Hou Hao Guo 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期495-508,共14页
We investigate the ferromagnetic q-state Potts model on spherical Fibonacci graphs.These graphs are constructed by embedding quasi-uniform sites on a sphere and defining interactions via a chord-distance cutoff chosen... We investigate the ferromagnetic q-state Potts model on spherical Fibonacci graphs.These graphs are constructed by embedding quasi-uniform sites on a sphere and defining interactions via a chord-distance cutoff chosen so as to yield a network approximating four-neighbor connectivity.By combining Swendsen-Wang cluster Monte Carlo simulations with graph convolutional networks(GCNs),which operate directly on the adjacency structure and node spins,we develop a unified phase-classification framework applicable to both regular planar lattices and curved,irregular spherical graphs.Benchmarks on planar lattices demonstrate an efficient transfer strategy:after a fixed binarization of Potts spins into an effective Ising variable,a single GCN pre-trained on the Ising model can localize the transition region for different q values without retraining.Applying this strategy to spherical graphs,we find that curvature-and defect-induced connectivity irregularities induce only modest shifts in the inferred transition temperatures relative to their planar counterparts.Further analysis shows that the curvature-induced shift of the critical temperature is most pronounced at small q and diminishes rapidly as q increases.This trend is consistent with the physical picture that,in two dimensions,the Potts model undergoes a transition from a continuous phase transition to a weakly first-order one for q>4,accompanied by a pronounced reduction in the correlation length. 展开更多
关键词 q-state Potts model spherical fibonacci graphs graph convolutional networks(GCNs) critical temperature
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Graph Convolution Network with EEG-EMG Fusion for Upper Limb Motion Intention Recognition 认领 引用
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作者 ZHENG Luzhou ZHAO Changchen +2 位作者 ZHANG Chao CHENG Shichao ZHANG Jianhai 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第1期12-23,共12页
With the continuous advancement of sensors and algorithms,an increasing number of deep learning methods have been applied to fine-grained upper limb motion intention recognition using multimodal physiological signals.... With the continuous advancement of sensors and algorithms,an increasing number of deep learning methods have been applied to fine-grained upper limb motion intention recognition using multimodal physiological signals.However,effectively and quantifiably integrating correlations between electroencephalogram(EEG)and electromyogram(EMG)signal channels as well as within EEG signal channels as a clue to improve performance remained challenging.In this paper,we proposed a novel framework that achieved accurate prediction of upper limb motion intentions via fusing EEG and EMG signals.Firstly,the raw input signals were fed into the feature extraction module,respectively,enabling feature decomposition in the channel dimension.Secondly,the graph convolution module with learnable edge weights was proposed to adaptively learn correlations between different modalities.Thirdly,we designed a self-attention graph pooling module that employed the self-attention mechanism to compute the attention score for each node as the basis for pooling.Compared with calculation methods using the mean or maximum value,this approach was more likely to retain nodes with stronger correlations to motor intentions.Finally,the prediction results were obtained through a classifier.We validated the effectiveness of our method on a publicly available multimodal upper limb dataset,achieving an accuracy of 93.17%. 展开更多
关键词 motion intention recognition graph convolution network(GCN) electroencephalogram(EEG) electromyogram(EMG) multi-modality
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Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks 认领 引用
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作者 Wasim Khan Afsaruddin Mohd +3 位作者 Mohammad Suaib Mohammad Ishrat Anwar Ahamed Shaikh Syed Mohd Faisal 《Data Science and Management》 EI CSCD 2025年第2期137-146,共10页
In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study in... In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism,thereby creating a more nuanced and efficient method for anomaly detection in complex networks.The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data.This is further bolstered by deep residual learning,which is employed to model intricate nonlinear connections directly from input data.A pivotal innovation in our approach is the incorporation of a residual-based attention mech-anism.This mechanism dynamically adjusts the importance of nodes based on their residual information,thereby significantly enhancing the sensitivity of the model to subtle anomalies.Furthermore,we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data.This mapping is the key to our model’s ability to pinpoint anomalies with greater precision.An extensive experimental setup was used to validate the efficacy of the proposed model.Using attributed social network datasets,we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection.The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks. 展开更多
关键词 Anomaly detection Deep learning Hypersphere learning Residual modeling Graph convolution network Attention mechanism
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