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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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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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Spectral graph convolution networks for microbialite lithology identification based on conventional well logs 认领 引用
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作者 Ke-Ran Li Jin-Min Song +9 位作者 Han Wang Hai-Jun Yan Shu-Gen Liu Yang Lan Xin Jin Jia-Xin Ren Ling-Li Zhao Li-Zhou Tian Hao-Shuang Deng Wei Chen 《Petroleum Science》 SCIE EI CAS CSCD 2025年第4期1513-1533,共21页
Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,... Machine learning algorithms are widely used to interpret well logging data.To enhance the algorithms'robustness,shuffling the well logging data is an unavoidable feature engineering before training models.However,latent information stored between different well logging types and depth is destroyed during the shuffle.To investigate the influence of latent information,this study implements graph convolution networks(GCNs),long-short temporal memory models,recurrent neural networks,temporal convolution networks,and two artificial neural networks to predict the microbial lithology in the fourth member of the Dengying Formation,Moxi gas field,central Sichuan Basin.Results indicate that the GCN model outperforms other models.The accuracy,F1-score,and area under curve of the GCN model are 0.90,0.90,and 0.95,respectively.Experimental results indicate that the time-series data facilitates lithology prediction and helps determine lithological fluctuations in the vertical direction.All types of logs from the spectral in the GCN model and also facilitates lithology identification.Only on condition combined with latent information,the GCN model reaches excellent microbialite classification resolution at the centimeter scale.Ultimately,the two actual cases show tricks for using GCN models to predict potential microbialite in other formations and areas,proving that the GCN model can be adopted in the industry. 展开更多
关键词 Graph convolution network Mirobialite Lithology forecasting Well log
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Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems 认领 引用
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作者 Sang-min Lee Namgi Kim 《Computers, Materials & Continua》 SCIE EI 2024年第2期1897-1914,共18页
Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ... Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets. 展开更多
关键词 Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems
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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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Recommendation System Based on Perceptron and Graph Convolution Network 认领 引用
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作者 Zuozheng Lian Yongchao Yin Haizhen Wang 《Computers, Materials & Continua》 SCIE EI 2024年第6期3939-3954,共16页
The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combinatio... The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms. 展开更多
关键词 Recommendation system graph convolution network attention mechanism multi-layer perceptron
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Mu-Net:Multi-Path Upsampling Convolution Network for Medical Image Segmentation 认领 引用 被引量：5
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作者 Jia Chen Zhiqiang He +3 位作者 Dayong Zhu Bei Hui Rita Yi Man Li Xiao-Guang Yue 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第4期73-95,共23页
Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of... Medical image segmentation plays an important role in clinical diagnosis,quantitative analysis,and treatment process.Since 2015,U-Net-based approaches have been widely used formedical image segmentation.The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps.However,the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information.More high-level information can make the segmentationmore accurate.In this paper,we propose MU-Net,a novel,multi-path upsampling convolution network to retain more high-level information.The MU-Net mainly consists of three parts:contracting path,skip connection,and multi-expansive paths.The proposed MU-Net architecture is evaluated based on three different medical imaging datasets.Our experiments show that MU-Net improves the segmentation performance of U-Net-based methods on different datasets.At the same time,the computational efficiency is significantly improved by reducing the number of parameters by more than half. 展开更多
关键词 Medical image segmentation MU-Net(multi-path upsampling convolution network) U-Net clinical diagnosis encoder-decoder networks
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Semantic-aware graph convolution network on multi-hop paths for link prediction 认领 引用 被引量：1
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作者 彭斐 CHEN Shudong +2 位作者 QI Donglin YU Yong TONG Da 《High Technology Letters》 EI CAS 2023年第3期269-278,共10页
Knowledge graph(KG) link prediction aims to address the problem of missing multiple valid triples in KGs. Existing approaches either struggle to efficiently model the message passing process of multi-hop paths or lack... Knowledge graph(KG) link prediction aims to address the problem of missing multiple valid triples in KGs. Existing approaches either struggle to efficiently model the message passing process of multi-hop paths or lack transparency of model prediction principles. In this paper,a new graph convolutional network path semantic-aware graph convolution network(PSGCN) is proposed to achieve modeling the semantic information of multi-hop paths. PSGCN first uses a random walk strategy to obtain all-hop paths in KGs,then captures the semantics of the paths by Word2Sec and long shortterm memory(LSTM) models,and finally converts them into a potential representation for the graph convolution network(GCN) messaging process. PSGCN combines path-based inference methods and graph neural networks to achieve better interpretability and scalability. In addition,to ensure the robustness of the model,the value of the path thresholdKis experimented on the FB15K-237 and WN18RR datasets,and the final results prove the effectiveness of the model. 展开更多
关键词 knowledge graph(KG) link prediction graph convolution network(GCN) knowledge graph completion(KGC) multi-hop paths semantic information
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A Nonlinear Spatiotemporal Optimization Method of Hypergraph Convolution Networks for Traffic Prediction 认领 引用
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作者 Difeng Zhu Zhimou Zhu +3 位作者 Xuan Gong Demao Ye Chao Li Jingjing Chen 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期3083-3100,共18页
Traffic prediction is a necessary function in intelligent transporta-tion systems to alleviate traffic congestion.Graph learning methods mainly focus on the spatiotemporal dimension,but ignore the nonlinear movement o... Traffic prediction is a necessary function in intelligent transporta-tion systems to alleviate traffic congestion.Graph learning methods mainly focus on the spatiotemporal dimension,but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments.There exist two issues:1)deep integration of the spatiotempo-ral information and 2)global spatial dependencies for structural properties.To address these issues,we propose a nonlinear spatiotemporal optimization method,which introduces hypergraph convolution networks(HGCN).The method utilizes the higher-order spatial features of the road network captured by HGCN,and dynamically integrates them with the historical data to weigh the influence of spatiotemporal dependencies.On this basis,an extended Kalman filter is used to improve the accuracy of traffic prediction.In this study,a set of experiments were conducted on the real-world dataset in Chengdu,China.The result showed that the proposed method is feasible and accurate by two different time steps.Especially at the 15-minute time step,compared with the second-best method,the proposed method achieved 3.0%,11.7%,and 9.0%improvements in RMSE,MAE,and MAPE,respectively. 展开更多
关键词 Intelligent transportation systems traffic prediction hypergraph convolution networks spatiotemporal optimization
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Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks 认领 引用
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作者 Yunchang Liu Fei Wan Chengwu Liang 《Computers, Materials & Continua》 SCIE EI 2024年第3期4343-4361,共19页
Traffic flow prediction plays a key role in the construction of intelligent transportation system.However,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very challenging.Most of... Traffic flow prediction plays a key role in the construction of intelligent transportation system.However,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very challenging.Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes.However,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial features.This paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic flow.By combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic data.Experiments on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms. 展开更多
关键词 Intelligent transportation graph convolutional network traffic flow DTW algorithm attention mechanism
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TC-Net:A Modest&Lightweight Emotion Recognition System Using Temporal Convolution Network 认领 引用
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作者 Muhammad Ishaq Mustaqeem Khan Soonil Kwon 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期3355-3369,共15页
Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines.Speech Emotion Recognition(SER)is one of the critical sources for human evaluatio... Speech signals play an essential role in communication and provide an efficient way to exchange information between humans and machines.Speech Emotion Recognition(SER)is one of the critical sources for human evaluation,which is applicable in many real-world applications such as healthcare,call centers,robotics,safety,and virtual reality.This work developed a novel TCN-based emotion recognition system using speech signals through a spatial-temporal convolution network to recognize the speaker’s emotional state.The authors designed a Temporal Convolutional Network(TCN)core block to recognize long-term dependencies in speech signals and then feed these temporal cues to a dense network to fuse the spatial features and recognize global information for final classification.The proposed network extracts valid sequential cues automatically from speech signals,which performed better than state-of-the-art(SOTA)and traditional machine learning algorithms.Results of the proposed method show a high recognition rate compared with SOTAmethods.The final unweighted accuracy of 80.84%,and 92.31%,for interactive emotional dyadic motion captures(IEMOCAP)and berlin emotional dataset(EMO-DB),indicate the robustness and efficiency of the designed model. 展开更多
关键词 Affective computing deep learning emotion recognition speech signal temporal convolutional network
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Self-attention Based Multimodule Fusion Graph Convolution Network for Traffic Flow Prediction 认领 引用
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作者 Lijie Li Hongyang Shao +1 位作者 Junhao Chen Ye Wang 《国际计算机前沿大会会议论文集》 2022年第1期3-16,共14页
With rapid economic development,the per capita ownership of automobiles in our country has begun to rise year by year.More researchers have paid attention to using scientific methods to solve traffic flow problems.Tra... With rapid economic development,the per capita ownership of automobiles in our country has begun to rise year by year.More researchers have paid attention to using scientific methods to solve traffic flow problems.Traffic flow prediction is not simply affected by the number of vehicles,but also contains various complex factors,such as time,road conditions,and people flow.However,the existing methods ignore the complexity of road conditions and the correlation between individual nodes,which leads to the poor performance.In this study,a deep learning model SAMGCN is proposed to effectively capture the correlation between individual nodes to improve the performance of traffic flow prediction.First,the theory of spatiotemporal decoupling is used to divide each time of each node into finer particles.Second,multimodule fusion is used to mine the potential periodic relationships in the data.Finally,GRU is used to obtain the potential time relationship of the three modules.Extensive experiments were conducted on two traffic flow datasets,PeMS04 and PeMS08 in the Caltrans Performance Measurement System to prove the validity of the proposed model. 展开更多
关键词 Flow prediction Temporal-spatial correlation Graph convolution network Self-attention mechanism
STTG-net:a Spatio-temporal network for human motion prediction based on transformer and graph convolution network 认领 引用 被引量：2
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作者 Lujing Chen Rui Liu +3 位作者 Xin Yang Dongsheng Zhou Qiang Zhang Xiaopeng Wei 《Visual Computing for Industry,Biomedicine,and Art》 EI 2022年第1期224-238,共15页
In recent years,human motion prediction has become an active research topic in computer vision.However,owing to the complexity and stochastic nature of human motion,it remains a challenging problem.In previous works,h... In recent years,human motion prediction has become an active research topic in computer vision.However,owing to the complexity and stochastic nature of human motion,it remains a challenging problem.In previous works,human motion prediction has always been treated as a typical inter-sequence problem,and most works have aimed to capture the temporal dependence between successive frames.However,although these approaches focused on the effects of the temporal dimension,they rarely considered the correlation between different joints in space.Thus,the spatio-temporal coupling of human joints is considered,to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network(GCN)(STTG-Net).The temporal transformer is used to capture the global temporal dependencies,and the spatial GCN module is used to establish local spatial correlations between the joints for each frame.To overcome the problems of error accumulation and discontinuity in the motion prediction,a revision method based on fusion strategy is also proposed,in which the current prediction frame is fused with the previous frame.The experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction methods.The effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset. 展开更多
关键词 Human motion prediction Transformer Gragh convolutional network
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MDCN:Modified Dense Convolution Network Based Disease Classification in Mango Leaves 认领 引用
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作者 Chirag Chandrashekar K.P.Vijayakumar +1 位作者 K.Pradeep A.Balasundaram 《Computers, Materials & Continua》 SCIE EI 2024年第2期2511-2533,共23页
The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accu... The most widely farmed fruit in the world is mango.Both the production and quality of the mangoes are hampered by many diseases.These diseases need to be effectively controlled and mitigated.Therefore,a quick and accurate diagnosis of the disorders is essential.Deep convolutional neural networks,renowned for their independence in feature extraction,have established their value in numerous detection and classification tasks.However,it requires large training datasets and several parameters that need careful adjustment.The proposed Modified Dense Convolutional Network(MDCN)provides a successful classification scheme for plant diseases affecting mango leaves.This model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning techniques.The data loader also builds mini-batches for training the models to reduce training time.Finally,optimization approaches help increase the overall model’s efficiency and lower computing costs.MDCN employed on the MangoLeafBD Dataset consists of a total of 4,000 images.Following the experimental results,the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput. 展开更多
关键词 Leaf disease detection deep convolutional neural networks transfer learning optimization MangoLeafBD Dataset
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PSTGCN:A Taxi Flow Prediction Model Period-Based Spatial-Temporal Graph Convolution Network 认领 引用
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作者 Zhiqiang Lv Qu Hao +1 位作者 Jianbo Li Lei You 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第3期1706-1721,共16页
Efficient urban traffic management and intelligent urban planning are pivotal drivers for the development of modern cities.Among them,accurate prediction of taxi flow,as a core component of the urban transportation sy... Efficient urban traffic management and intelligent urban planning are pivotal drivers for the development of modern cities.Among them,accurate prediction of taxi flow,as a core component of the urban transportation system,has significant implications for traffic management,resource allocation,and the daily lives of citizens.However,practical taxi flow prediction faces numerous challenges,particularly in fully utilizing periodic features.To address the issue of capturing periodic characteristics in urban taxi flow prediction research,this study proposes a prediction model named Period-based Spatial-Temporal Graph Convolution Network(PSTGCN).In the PSTGCN,graph convolutional techniques are employed in the spatial convolution module to capture both local and global spatial correlations of taxi flow,effectively integrating the two.This further enhances the understanding of flow interaction between different areas.Furthermore,in the temporal convolution module,PSTGCN transforms the one-dimensional time series data into two-dimensional data,enabling the simultaneous analysis of both the within-period changes in taxi flow and the patterns of variation between different periods.This significantly strengthens the capability to capture flow periodic features.Lastly,this study extensively validates the predictive performance of the PSTGCN model.Experimental results demonstrate its significant advantages over various existing benchmark models in terms of prediction effectiveness.Moreover,the model's excellent generalization ability is showcased through validation using multiple real-world datasets. 展开更多
关键词 urban planning taxi flow periodic feature spatial-temporal model Graph Convolution Network(GCN)
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Skeleton Split Strategies for Spatial Temporal Graph Convolution Networks 认领 引用
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作者 Motasem S.Alsawadi Miguel Rio 《Computers, Materials & Continua》 SCIE EI 2022年第6期4643-4658,共16页
Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the ... Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events.A skeleton representation of the human body has been proven to be effective for this task.The skeletons are presented in graphs form-like.However,the topology of a graph is not structured like Euclideanbased data.Therefore,a new set of methods to perform the convolution operation upon the skeleton graph is proposed.Our proposal is based on the Spatial Temporal-Graph Convolutional Network(ST-GCN)framework.In this study,we proposed an improved set of label mapping methods for the ST-GCN framework.We introduce three split techniques(full distance split,connection split,and index split)as an alternative approach for the convolution operation.The experiments presented in this study have been trained using two benchmark datasets:NTU-RGB+D and Kinetics to evaluate the performance.Our results indicate that our split techniques outperform the previous partition strategies and aremore stable during training without using the edge importance weighting additional training parameter.Therefore,our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments. 展开更多
关键词 Skeleton split strategies spatial temporal graph convolutional neural networks skeleton joints action recognition
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Large Language Model Assisted Interpretability in Graph Convolution Network for AGC Dispatch 认领 引用
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作者 Xiaoshun Zhang Kun Zhang +1 位作者 Zhengxun Guo Penggen Wang 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2026年第2期632-643,共12页
Automatic generation control(AGC)dispatch is the key task of secondary frequency regulation for interconnected grids.To generate a high-quality dispatch solution,numerous machine learning techniques,such as reinforcem... Automatic generation control(AGC)dispatch is the key task of secondary frequency regulation for interconnected grids.To generate a high-quality dispatch solution,numerous machine learning techniques,such as reinforcement learning and graph convolutional networks(GCNs),have been developed for AGC dispatch.However,they are challenging to apply to a real-world power grid due to their weak interpretability.Hence,this work proposes a novel approach to large language model(LLM)-assisted interpretability in GCN for AGC dispatch.Firstly,the impact of input features(e.g.,the total regulation command and the regulation capacities of various resources)on dispatch solutions is assessed quantitatively using Shapley additive explanations(SHAP)for global interpretability.Then,local interpretability for GCN is achieved using an LLM-assisted,model-agnostic local interpretable model-agnostic explanations(LIME),which can provide actionable insights into the model's decision logic.SHAP shows that the top eight features drive decisions,while the rest average just 15.8%of the leading feature's contribution.Unit outputs correlate positively with their own history and negatively with others.Swapping LIME's linear model for a decision tree boosts multiple metrics by over 50%.Experimental results further confirm that this method not only clearly uncovers the relationships between input features and AGC dispatch outputs,but also faithfully reconstructs the GCN's decision logic across different dispatch scenarios. 展开更多
关键词 AGC dispatch graph convolution network interpretability large language model
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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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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://gffzz188fe103f8f1460asw0p55xwpbq6c6vvn.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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Ship Magnetic Field Modeling and Extrapolation Based on a Convolutional Neural Network 认领 引用
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作者 Ao Zhou Yadong Zhang +3 位作者 Wentie Yang Zuoshuai Wang Jianxun Wang Zhiwei Chen 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期536-549,共14页
Accurate modeling of ship magnetic fields is important for predicting their spatial distribution to improve the magnetic stealth effect of ships.This study proposes an extrapolation model for ship magnetic fields base... Accurate modeling of ship magnetic fields is important for predicting their spatial distribution to improve the magnetic stealth effect of ships.This study proposes an extrapolation model for ship magnetic fields based on genetic algorithms and convolutional neural networks(CNNs).The magnetic probe position matrix of the traditional equivalent source is utilized as input,and the three-directional components of the magnetic field measured by the probes are employed as output.The extrapolation model for ship magnetic fields is obtained through iterative training and fitting with CNNs.Variables such as the number of magnetic dipoles,the distance between magnetic dipoles,the size and quantity of convolutional kernels,batch size,learning rate,and L2 regularization coefficient are optimized to boost the accuracy of the extrapolation model for magnetic fields.The fitting accuracy of the extrapolation model for ship magnetic fields is used as the optimization objective.Based on a finite element simulation model of ship magnetic fields,the accuracy and robustness of the CNN algorithm under different magnetic field conditions are validated using the known standard depth plane,the unknown depth at 1.125 times the standard depth plane,and the unknown depth at 1.25 times the standard depth plane.Results show that,after optimization,the fitting error for the magnetic field extrapolation model based on CNN is 1.50%for the standard depth plane,1.63%for the unknown depth at 1.125 times the standard depth plane,and 2.36%for the unknown depth at 1.25 times the standard depth plane.The error remains below 5%under varying magnetic field conditions.When a random measurement error of 0%-5%is introduced for the magnetic probes,the prediction error at 1.25 times the standard depth plane is 2.30%;with a random error of 0%-10%,the prediction error is 4.95%.This approach significantly improves the accuracy and robustness of magnetic field extrapolation,which makes it an effective and feasible method for ship magnetic field modeling. 展开更多
关键词 Shipboard magnetic field Convolutional neural network Genetic algorithm Equivalent source method Magnetic field extrapolation
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