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
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
Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life d...Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.展开更多
The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries an...The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.展开更多
Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the...Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network(SGCCN),a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships,utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction.The SGCCN integrates spectral and spatial features through a self-attention fusion network,significantly improving classification accuracy and efficiency.The proposed method outperforms existing techniques,demonstrating its effectiveness in handling the challenges associated with HSI data.展开更多
Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges ...Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.展开更多
Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,...Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.展开更多
Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which ignores the distinct impacts from different neighbors when aggregating their features to update a node’s representation....Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which ignores the distinct impacts from different neighbors when aggregating their features to update a node’s representation. Disentangled GCNs have been proposed to divide each node’s representation into several feature units. However, current disentangling methods do not try to figure out how many inherent factors the model should assign to help extract the best representation of each node. This paper then proposes D^(2)-GCN to provide dynamic disentanglement in GCNs and present the most appropriate factorization of each node’s mixed features. The convergence of the proposed method is proved both theoretically and experimentally. Experiments on real-world datasets show that D^(2)-GCN outperforms the baseline models concerning node classification results in both single- and multi-label tasks.展开更多
In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limi...In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limitations in handling complex spatiotemporal patterns.To address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data.Onsite aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the AMTGCN.The results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,respectively.This indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.展开更多
Purpose-Human behavior recognition poses a pivotal challenge in intelligent computing and cybernetics,significantly impacting engineering and management systems.With the rapid advancement of autonomous systems and int...Purpose-Human behavior recognition poses a pivotal challenge in intelligent computing and cybernetics,significantly impacting engineering and management systems.With the rapid advancement of autonomous systems and intelligent manufacturing,there is an increasing demand for precise and efficient human behavior recognition technologies.However,traditional methods often suffer from insufficient accuracy and limited generalization ability when dealing with complex and diverse human actions.Therefore,this study aims to enhance the precision of human behavior recognition by proposing an innovative framework,dynamic graph convolutional networks with multi-scale position attention(DGCN-MPA)to sup.Design/methodology/approach-The primary applications are in autonomous systems and intelligent manufacturing.The main objective of this study is to develop an efficient human behavior recognition framework that leverages advanced techniques to improve the prediction and interpretation of human actions.This framework aims to address the shortcomings of existing methods in handling the complexity and variability of human actions,providing more reliable and precise solutions for practical applications.The proposed DGCN-MPA framework integrates the strengths of convolutional neural networks and graph-based models.It innovatively incorporates wavelet packet transform to extract time-frequency characteristics and a MPA module to enhance the representation of skeletal node positions.The core innovation lies in the fusion of dynamic graph convolution with hierarchical attention mechanisms,which selectively attend to relevant features and spatial relationships,adjusting their importance across scales to address the variability in human actions.Findings-To validate the effectiveness of the DGCN-MPA framework,rigorous evaluations were conducted on benchmark datasets such as NTU-RGB+D and Kinetics-Skeleton.The results demonstrate that the framework achieves an F1 score of 62.18%and an accuracy of 75.93%on NTU-RGB+D and an F1 score of 69.34%and an accuracy of 76.86%on Kinetics-Skeleton,outperforming existing models.These findings underscore the framework’s capability to capture complex behavior patterns with high precision.Originality/value-By introducing a dynamic graph convolutional approach combined with multi-scale position attention mechanisms,this study represents a significant advancement in human behavior recognition technologies.The innovative design and superior performance of the DGCN-MPA framework contribute to its potential for real-world applications,particularly in integrating behavior recognition into engineering and autonomous systems.In the future,this framework has the potential to further propel the development of intelligent computing,cybernetics and related fields.展开更多
Although significant progress has been made in micro-expression recognition,effectively modeling the intricate spatial-temporal dynamics remains a persistent challenge owing to their brief duration and complex facial ...Although significant progress has been made in micro-expression recognition,effectively modeling the intricate spatial-temporal dynamics remains a persistent challenge owing to their brief duration and complex facial dynamics.Furthermore,existing methods often suffer from limited gen-eralization,as they primarily focus on single-dataset tasks with small sample sizes.To address these two issues,this paper proposes the cross-domain spatial-temporal graph convolutional network(GCN)(CDST-GCN)model,which comprises two primary components:a siamese attention spa-tial-temporal branch(SASTB)and a global-aware dynamic spatial-temporal branch(GDSTB).Specifically,SASTB utilizes a contrastive learning strategy to project macro-and micro-expressions into a shared,aligned feature space,actively addressing cross-domain discrepancies.Additionally,it integrates an attention-gated mechanism that generates adaptive adjacency matrices to flexibly model collaborative patterns among facial landmarks.While largely preserving the structural paradigm of SASTB,GDSTB enhances the feature representation by integrating global context extracted from a pretrained model.Through this dual-branch architecture,CDST-GCN success-fully models both the global and local spatial-temporal features.The experimental results on CASME II and SAMM datasets demonstrate that the proposed model achieves competitive perfor-mance.Especially in more challenging 5-class tasks,the accuracy of the model on CASME II dataset is as high as 80.5%.展开更多
Modeling and analysis of complex social networks is an important topic in social computing.Graph convolutional networks(GCNs)are widely used for learning social network embeddings and social network analysis.However,r...Modeling and analysis of complex social networks is an important topic in social computing.Graph convolutional networks(GCNs)are widely used for learning social network embeddings and social network analysis.However,real-world complex social networks,such as Facebook and Math,exhibit significant global structural and dynamic characteristics that are not adequately captured by conventional GCN models.To address the above issues,this paper proposes a novel graph convolutional network considering global structural features and global temporal dependencies(GSTGCN).Specifically,we innovatively design a graph coarsening strategy based on the importance of social membership to construct a dynamic diffusion process of graphs.This dynamic diffusion process can be viewed as using higher-order subgraph embeddings to guide the generation of lower-order subgraph embeddings,and we model this process using gate recurrent unit(GRU)to extract comprehensive global structural features of the graph and the evolutionary processes embedded among subgraphs.Furthermore,we design a new evolutionary strategy that incorporates a temporal self-attention mechanism to enhance the extraction of global temporal dependencies of dynamic networks by GRU.GSTGCN outperforms current state-of-the-art network embedding methods in important social networks tasks such as link prediction and financial fraud identification.展开更多
Automatically extracting Drug-Drug Interactions (DDIs) from text is a crucial and challenging task, particularly when multiple medications are taken concurrently. In this study, we propose a novel approach, called Enh...Automatically extracting Drug-Drug Interactions (DDIs) from text is a crucial and challenging task, particularly when multiple medications are taken concurrently. In this study, we propose a novel approach, called Enhanced Attention-driven Dynamic Graph Convolutional Network (E-ADGCN), for DDI extraction. Our model combines the Attention-driven Dynamic Graph Convolutional Network (ADGCN) with a feature fusion method and multi-task learning framework. The ADGCN effectively utilizes entity information and dependency tree information from biomedical texts to extract DDIs. The feature fusion method integrates User-Generated Content (UGC) and molecular information with drug entity information from text through dynamic routing. By leveraging external resources, our approach maximizes the auxiliary effect and improves the accuracy of DDI extraction. We evaluate the E-ADGCN model on the extended DDIExtraction2013 dataset and achieve an F1-score of 81.45%. This research contributes to the advancement of automated methods for extracting valuable drug interaction information from textual sources, facilitating improved medication management and patient safety.展开更多
Accurately predicting the State of Health(SOH)of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems,such as electric vehicles and renewable energy grids.The ...Accurately predicting the State of Health(SOH)of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems,such as electric vehicles and renewable energy grids.The intricate battery degradation process is influenced by evolving spatial and temporal interactions among health indicators.Existing methods often fail to capture the dynamic interactions between health indicators over time,resulting in limited predictive accuracy.To address these challenges,we propose a novel framework,Dynamic Graph Learning with Spatial-Temporal Fusion Attention(DGL-STFA),which transforms health indicator series time-data into time-evolving graph representations.The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns,a self-attention mechanism to construct dynamic adjacency matrices that adapt over time,and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation.This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies,enhancing SOH prediction accuracy.Extensive experiments were conducted on the NASA and CALCE battery datasets,comparing this framework with traditional time-series prediction methods and other graph-based prediction methods.The results demonstrate that our framework significantly improves prediction accuracy,with a mean absolute error more than 30%lower than other methods.Further analysis demonstrated the robustness of DGL-STFA across various battery life stages,including early,mid,and end-of-life phases.These results highlight the capability of DGL-STFA to accurately predict SOH,addressing critical challenges in advancing battery health monitoring for energy storage applications.展开更多
Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservat...Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservation in buildings.Traditional performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently,which impedes accurate predictions.To overcome these challenges,this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network(GCN)enhanced by association rules.The distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data.A real-time graph is created by analyzing the correlation between various sensor variables in the current operating data.This graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of GCN.The effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller system.Comparative analysis with state-of-the-art methods highlights the superior performance of the proposed method.展开更多
Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowled...Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowledge graph reasoning is more challenging due to its temporal nature.In essence,within each time step in a dynamic knowledge graph,there exists structural dependencies among entities and relations,whereas between adjacent time steps,there exists temporal continuity.Based on these structural and temporal characteristics,we propose a model named“DKGR-DR”to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs.In addition,we construct a static attribute graph to represent entities’inherent properties.DKGR-DR is capable of modeling both dynamic and static aspects of entities,enabling effective entity prediction and relation prediction.We conduct experiments on ICEWS05-15,ICEWS18,and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance.展开更多
文摘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.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘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.
基金supported,in part,by the National Nature Science Foundation of China under Grant Numbers 62272236,62376128in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401.
文摘Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.
基金supported by the China Scholarship Council and the CERNET Innovation Project under grant No.20170111.
文摘The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.
基金supported in part by the National Natural Science Foundations of China(No.61801214)the Postgraduate Research Practice Innovation Program of NUAA(No.xcxjh20231504)。
文摘Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network(SGCCN),a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships,utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction.The SGCCN integrates spectral and spatial features through a self-attention fusion network,significantly improving classification accuracy and efficiency.The proposed method outperforms existing techniques,demonstrating its effectiveness in handling the challenges associated with HSI data.
基金supported by the National Natural Science Foundation of China(61975020,62171053)。
文摘Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.
文摘Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.
基金supported by the National Natural Science Foundation of China(Grant Nos.62141214 and 62272171).
文摘Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which ignores the distinct impacts from different neighbors when aggregating their features to update a node’s representation. Disentangled GCNs have been proposed to divide each node’s representation into several feature units. However, current disentangling methods do not try to figure out how many inherent factors the model should assign to help extract the best representation of each node. This paper then proposes D^(2)-GCN to provide dynamic disentanglement in GCNs and present the most appropriate factorization of each node’s mixed features. The convergence of the proposed method is proved both theoretically and experimentally. Experiments on real-world datasets show that D^(2)-GCN outperforms the baseline models concerning node classification results in both single- and multi-label tasks.
基金funded by the National Key Research and Development Program of China:Sino-Malta Fund 2022“Autonomous Biomimetic Underwater Vehicle for Digital Cage Monitoring”(Grant No.2022YFE0107100).
文摘In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limitations in handling complex spatiotemporal patterns.To address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data.Onsite aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the AMTGCN.The results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,respectively.This indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.
基金supported by the Guangxi University Young and middle-aged Teachers Basic Ability Improvement Project(No.:2023KY1692)Guilin University of Information Technology 2022 Research Project(No.:XJ202207)。
文摘Purpose-Human behavior recognition poses a pivotal challenge in intelligent computing and cybernetics,significantly impacting engineering and management systems.With the rapid advancement of autonomous systems and intelligent manufacturing,there is an increasing demand for precise and efficient human behavior recognition technologies.However,traditional methods often suffer from insufficient accuracy and limited generalization ability when dealing with complex and diverse human actions.Therefore,this study aims to enhance the precision of human behavior recognition by proposing an innovative framework,dynamic graph convolutional networks with multi-scale position attention(DGCN-MPA)to sup.Design/methodology/approach-The primary applications are in autonomous systems and intelligent manufacturing.The main objective of this study is to develop an efficient human behavior recognition framework that leverages advanced techniques to improve the prediction and interpretation of human actions.This framework aims to address the shortcomings of existing methods in handling the complexity and variability of human actions,providing more reliable and precise solutions for practical applications.The proposed DGCN-MPA framework integrates the strengths of convolutional neural networks and graph-based models.It innovatively incorporates wavelet packet transform to extract time-frequency characteristics and a MPA module to enhance the representation of skeletal node positions.The core innovation lies in the fusion of dynamic graph convolution with hierarchical attention mechanisms,which selectively attend to relevant features and spatial relationships,adjusting their importance across scales to address the variability in human actions.Findings-To validate the effectiveness of the DGCN-MPA framework,rigorous evaluations were conducted on benchmark datasets such as NTU-RGB+D and Kinetics-Skeleton.The results demonstrate that the framework achieves an F1 score of 62.18%and an accuracy of 75.93%on NTU-RGB+D and an F1 score of 69.34%and an accuracy of 76.86%on Kinetics-Skeleton,outperforming existing models.These findings underscore the framework’s capability to capture complex behavior patterns with high precision.Originality/value-By introducing a dynamic graph convolutional approach combined with multi-scale position attention mechanisms,this study represents a significant advancement in human behavior recognition technologies.The innovative design and superior performance of the DGCN-MPA framework contribute to its potential for real-world applications,particularly in integrating behavior recognition into engineering and autonomous systems.In the future,this framework has the potential to further propel the development of intelligent computing,cybernetics and related fields.
基金funded in part by the National Natural Science Foundation of China(Nos.62322111,62271289,62501186)the Natural Science Fund for Outstanding Young Scholars of Shandong Province(No.ZR2022YQ60)+4 种基金the Research Fund for the Taishan Scholar Project of Shandong Province(No.tsqn202306064)the Natural Science Fund for Distinguished Young Scientists of ShandongProvince(No.ZR2024JQ007)Shenzhen Science and Technology Program(No.JCYJ20240813101228036)Jinan“20 Terms of New Universities”Funding Project(No.202333035)the Fundamental Research funds for theCentral Universities(No.3072025CFJ0805).
文摘Although significant progress has been made in micro-expression recognition,effectively modeling the intricate spatial-temporal dynamics remains a persistent challenge owing to their brief duration and complex facial dynamics.Furthermore,existing methods often suffer from limited gen-eralization,as they primarily focus on single-dataset tasks with small sample sizes.To address these two issues,this paper proposes the cross-domain spatial-temporal graph convolutional network(GCN)(CDST-GCN)model,which comprises two primary components:a siamese attention spa-tial-temporal branch(SASTB)and a global-aware dynamic spatial-temporal branch(GDSTB).Specifically,SASTB utilizes a contrastive learning strategy to project macro-and micro-expressions into a shared,aligned feature space,actively addressing cross-domain discrepancies.Additionally,it integrates an attention-gated mechanism that generates adaptive adjacency matrices to flexibly model collaborative patterns among facial landmarks.While largely preserving the structural paradigm of SASTB,GDSTB enhances the feature representation by integrating global context extracted from a pretrained model.Through this dual-branch architecture,CDST-GCN success-fully models both the global and local spatial-temporal features.The experimental results on CASME II and SAMM datasets demonstrate that the proposed model achieves competitive perfor-mance.Especially in more challenging 5-class tasks,the accuracy of the model on CASME II dataset is as high as 80.5%.
基金supported by the National Natural Science Foundation of China(Nos.62002063 and U21A20472)Natural Science Foundation of Fujian Province(Nos.2020J05112 and 2022J01118)+1 种基金National Key Research and Development Plan of China(No.2021YFB3600503)Major Science and Technology Project of Fujian Province(No.2021HZ022007).
文摘Modeling and analysis of complex social networks is an important topic in social computing.Graph convolutional networks(GCNs)are widely used for learning social network embeddings and social network analysis.However,real-world complex social networks,such as Facebook and Math,exhibit significant global structural and dynamic characteristics that are not adequately captured by conventional GCN models.To address the above issues,this paper proposes a novel graph convolutional network considering global structural features and global temporal dependencies(GSTGCN).Specifically,we innovatively design a graph coarsening strategy based on the importance of social membership to construct a dynamic diffusion process of graphs.This dynamic diffusion process can be viewed as using higher-order subgraph embeddings to guide the generation of lower-order subgraph embeddings,and we model this process using gate recurrent unit(GRU)to extract comprehensive global structural features of the graph and the evolutionary processes embedded among subgraphs.Furthermore,we design a new evolutionary strategy that incorporates a temporal self-attention mechanism to enhance the extraction of global temporal dependencies of dynamic networks by GRU.GSTGCN outperforms current state-of-the-art network embedding methods in important social networks tasks such as link prediction and financial fraud identification.
基金supported by the National Natural Science Foundation of China(No.62476025)the Shaanxi Provincial Department of Science and Technology Projects(No.2013K06-39).
文摘Automatically extracting Drug-Drug Interactions (DDIs) from text is a crucial and challenging task, particularly when multiple medications are taken concurrently. In this study, we propose a novel approach, called Enhanced Attention-driven Dynamic Graph Convolutional Network (E-ADGCN), for DDI extraction. Our model combines the Attention-driven Dynamic Graph Convolutional Network (ADGCN) with a feature fusion method and multi-task learning framework. The ADGCN effectively utilizes entity information and dependency tree information from biomedical texts to extract DDIs. The feature fusion method integrates User-Generated Content (UGC) and molecular information with drug entity information from text through dynamic routing. By leveraging external resources, our approach maximizes the auxiliary effect and improves the accuracy of DDI extraction. We evaluate the E-ADGCN model on the extended DDIExtraction2013 dataset and achieve an F1-score of 81.45%. This research contributes to the advancement of automated methods for extracting valuable drug interaction information from textual sources, facilitating improved medication management and patient safety.
基金sponsored by the National Key Research and Development Program of China(No.2023YFB4606200)Key Program of Science and Technology of Yunnan Province,China (No.202302AB080020)Key Project of Shanghai Zhangjiang National Independent hnovation Demonstration Zone,China(No.ZJ2021-2D-006).
文摘Accurately predicting the State of Health(SOH)of lithium-ion batteries is a critical challenge to ensure their reliability and safety in energy storage systems,such as electric vehicles and renewable energy grids.The intricate battery degradation process is influenced by evolving spatial and temporal interactions among health indicators.Existing methods often fail to capture the dynamic interactions between health indicators over time,resulting in limited predictive accuracy.To address these challenges,we propose a novel framework,Dynamic Graph Learning with Spatial-Temporal Fusion Attention(DGL-STFA),which transforms health indicator series time-data into time-evolving graph representations.The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns,a self-attention mechanism to construct dynamic adjacency matrices that adapt over time,and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation.This combination enables DGL-STFA to effectively model both dynamic spatial relationships and long-term temporal dependencies,enhancing SOH prediction accuracy.Extensive experiments were conducted on the NASA and CALCE battery datasets,comparing this framework with traditional time-series prediction methods and other graph-based prediction methods.The results demonstrate that our framework significantly improves prediction accuracy,with a mean absolute error more than 30%lower than other methods.Further analysis demonstrated the robustness of DGL-STFA across various battery life stages,including early,mid,and end-of-life phases.These results highlight the capability of DGL-STFA to accurately predict SOH,addressing critical challenges in advancing battery health monitoring for energy storage applications.
基金supported in part by the Science and Technology Innovation Program of Hunan Province(No.2022RC1090)in part by the National Natural Science Foundation of China(No.62173349)+2 种基金in part by the Natural Science Foundation of Hunan Province(No.2022J20076)in part by the Innovation Driven Projection of Central South University(No.2023CXQD073)in part by the Major Program of Xiangjiang Laboratory(No.22XJ01005).
文摘Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservation in buildings.Traditional performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently,which impedes accurate predictions.To overcome these challenges,this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network(GCN)enhanced by association rules.The distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data.A real-time graph is created by analyzing the correlation between various sensor variables in the current operating data.This graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of GCN.The effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller system.Comparative analysis with state-of-the-art methods highlights the superior performance of the proposed method.
基金supported byNationalNatural Science Foundation of China(GrantNos.62071098,U24B20128)Sichuan Science and Technology Program(Grant No.2022YFG0319).
文摘Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowledge graph reasoning is more challenging due to its temporal nature.In essence,within each time step in a dynamic knowledge graph,there exists structural dependencies among entities and relations,whereas between adjacent time steps,there exists temporal continuity.Based on these structural and temporal characteristics,we propose a model named“DKGR-DR”to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs.In addition,we construct a static attribute graph to represent entities’inherent properties.DKGR-DR is capable of modeling both dynamic and static aspects of entities,enabling effective entity prediction and relation prediction.We conduct experiments on ICEWS05-15,ICEWS18,and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance.
