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Learning Laws for Deep Convolutional Neural Networks With Guaranteed Convergence 认领 引用 被引量：1
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作者 Sitan Li Chien Chern Cheah 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期170-185,共16页
Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empir... Convolutional neural networks(CNNs)have shown remarkable success across numerous tasks such as image classification,yet the theoretical understanding of their convergence remains underdeveloped compared to their empirical achievements.In this paper,the first filter learning framework with convergence-guaranteed learning laws for end-to-end learning of deep CNNs is proposed.Novel update laws with convergence analysis are formulated based on the mathematical representation of each layer in convolutional neural networks.The proposed learning laws enable concurrent updates of weights across all layers of the deep convolutional neural network and the analysis shows that the training errors converge to certain bounds which are dependent on the approximation errors.Case studies are conducted on benchmark datasets and the results show that the proposed concurrent filter learning framework guarantees the convergence and offers more consistent and reliable results during training with a trade-off in performance compared to stochastic gradient descent methods.This framework represents a significant step towards enhancing the reliability and effectiveness of deep convolutional neural network by developing a theoretical analysis which allows practical implementation of the learning laws with automatic tuning of the learning rate to guarantee the convergence during training. 展开更多
关键词 Convergence convolution neural networks(CNNs) end-to-end learning online learning
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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://gffzz188fe103f8f1460asb5uk505xppck6w5n.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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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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Efficient Video Emotion Recognition via Multi-Scale Region-Aware Convolution and Temporal Interaction Sampling 认领 引用
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作者 Xiaorui Zhang Chunlin Yuan +1 位作者 Wei Sun Ting Wang 《Computers, Materials & Continua》 SCIE EI 2026年第2期2036-2054,共19页
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-... Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition. 展开更多
关键词 Multi-scale region-aware convolution temporal interaction sampling video emotion recognition
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Dual Channel Graph Convolutional Networks via Personalized PageRank 认领 引用
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作者 Longlong Lin Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期221-223,共3页
Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representat... Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications. 展开更多
关键词 convolutional node feature similarity graph convolutional framework learning graph representations neural networks gnns networks graph personalized
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Enhanced Image Captioning via Integrated Wavelet Convolution and MobileNet V3 Architecture 认领 引用
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作者 Mo Hou Bin Xu Wen Shang 《Computers, Materials & Continua》 SCIE EI 2026年第2期897-915,共19页
Image captioning,a pivotal research area at the intersection of image understanding,artificial intelligence,and linguistics,aims to generate natural language descriptions for images.This paper proposes an efficient im... Image captioning,a pivotal research area at the intersection of image understanding,artificial intelligence,and linguistics,aims to generate natural language descriptions for images.This paper proposes an efficient image captioning model named Mob-IMWTC,which integrates improved wavelet convolution(IMWTC)with an enhanced MobileNet V3 architecture.The enhanced MobileNet V3 integrates a transformer encoder as its encoding module and a transformer decoder as its decoding module.This innovative neural network significantly reduces the memory space required and model training time,while maintaining a high level of accuracy in generating image descriptions.IMWTC facilitates large receptive fields without significantly increasing the number of parameters or computational overhead.The improvedMobileNet V3 model has its classifier removed,and simultaneously,it employs IMWTC layers to replace the original convolutional layers.This makes Mob-IMWTC exceptionally well-suited for deployment on lowresource devices.Experimental results,based on objective evaluation metrics such as BLEU,ROUGE,CIDEr,METEOR,and SPICE,demonstrate that Mob-IMWTC outperforms state-of-the-art models,including three CNN architectures(CNN-LSTM,CNN-Att-LSTM,CNN-Tran),two mainstream methods(LCM-Captioner,ClipCap),and our previous work(Mob-Tran).Subjective evaluations further validate the model’s superiority in terms of grammaticality,adequacy,logic,readability,and humanness.Mob-IMWTC offers a lightweight yet effective solution for image captioning,making it suitable for deployment on resource-constrained devices. 展开更多
关键词 Image caption wavelet convolution MobileNet V3 deep learning
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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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Multi-scale simplified residual convolutional neural network model for predicting compositions of binary magnesium alloys 认领 引用
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作者 Xu Qin Qinghang Wang +6 位作者 Xinqian Zhao Shouxin Xia Li Wang Jiabao Long Yuhui Zhang Yanfu Chai Daolun Chen 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2026年第1期117-123,共7页
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data... This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R2 compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems. 展开更多
关键词 Magnesium alloys Composition prediction Scanning electron microscope images Multi-scale simplified residual convolutional neural network
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Spatial-Temporal Graph Fusion with Dual-Scale Convolution for Traffic Flow Prediction 认领 引用
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作者 Dan Wang Mengyi Cui +1 位作者 Zhenhua Yu Yukang Liu 《Computers, Materials & Continua》 SCIE EI 2026年第6期1375-1396,共22页
Traffic flow prediction is of great importance in traffic planning,road resource management,and congestion mitigation.However,existing prediction have significant limitations in modeling multi-scale spatial-temporal f... Traffic flow prediction is of great importance in traffic planning,road resource management,and congestion mitigation.However,existing prediction have significant limitations in modeling multi-scale spatial-temporal features,particularly in capturing temporal periodicity and spatial dependency in dynamically evolving traffic networks.This paper proposes a novel framework of traffic flow prediction,referred to as Adaptive Graph Fusion Dual-scale Convolutional Network(AGFDCN),which integrates spatial-temporal dynamic graphs with dual-scale convolutional networks.Specifically,we introduce a Dual-Scale Temporal Network,which combines long-and short-term dilated causal convolutions with a temporal decay-aware attention mechanism to efficiently capture traffic patterns across multiple temporal scales.Furthermore,we design a Dynamic Adaptive Graph Module,which models complex spatial dependencies in traffic networks through an adaptive graph fusion mechanism and a dual-path attention-gated module.Finally,the temporal and spatial representations are integrated by employing a gated fusion mechanism,enhancing the overall prediction performance.Experimental results obtained based on three highway datasets(i.e.,PEMS04,PEMS07 and PEMS08)verify that the proposed model outperforms several state-of-the-art baselines in various evaluation metrics.Compared to the spatial-temporal graph model AGCRN with best performance in the baseline models,the proposed model exhibits significant improvements across all datasets:it achieves reduces of MAE by 42.07%and RMSE by 35.43%on PEMS04;MAE by 28.35%and RMSE by 29.28%on PEMS07;and MAE by 30.52%and RMSE by 30.73%on PEMS08,respectively,validating its effectiveness in modeling complex spatial-temporal traffic data and its robustness in handling sudden traffic changes. 展开更多
关键词 Dual-scale convolution dual-path attention-gated module adaptive graph fusion spatial-temporal dynamic graph traffic flow prediction
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Prediction of sea surface pCO2in the South China Sea using Spatiotemporal Convolutional LSTM model 认领 引用
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作者 Shuang LI Yu GAO +4 位作者 Jiannan GAO Yaqi ZHAO Peng HAO Jinbao SONG Chengcheng YU 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第1期19-35,共17页
The prediction of sea surface partial pressure of carbon dioxide(pCO2)in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate cha... The prediction of sea surface partial pressure of carbon dioxide(pCO2)in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO2in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R2)of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R2of 0.994.Feature importance analysis revealed that sea surface pCO2and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO2with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO2in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research. 展开更多
关键词 sea surface carbon dioxide South China Sea Spatiotemporal Convolutional Long Short-Term Memory(ST-ConvLSTM) deep learning
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Multi-Source Traffic Information Completion and Perception Method via Graph Convolutional Neural Networks in Intelligent Connected Transportation System 认领 引用
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作者 Pangwei Wang Jie Wang +2 位作者 Zipeng Wang Hangrui Dong Li Wang 《Computers, Materials & Continua》 SCIE EI 2026年第8期1417-1435,共19页
Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The ... Traffic holographic perception refers to the real-time,high-fidelity,and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors,including cameras,radars,and connected vehicle data.The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System(ITS).However,sensors are vulnerable to environmental interference,which can result in data loss at specific points or along arterial highways for certain periods,potentially undermining system safety and decision-making reliability.To address these challenges,a deep learning method based on Graph Convolutional Networks(GCN)and Gated Recurrent Units(GRU)is proposed,leveraging Artificial Intelligence(AI)and intelligent connected technologies for real-time acquisition of multi-sensor perception data.A feature-level fusion integrates multi-source perception data.GCN captures spatial dependencies from the road network topology,while GRU extracts temporal features from time series,enabling accurate imputation of missing traffic data.The method is evaluated at intelligent connected intersections in the Beijing High-level Autonomous Driving Demonstration Area.Results show that the accuracy of long-term traffic state completion reaches 89.36%,and the Root Mean Square Error(RMSE)is reduced by 17.2%compared to the Long Short-Term Memory(LSTM)baseline.This framework provides a practical solution for deploying traffic holographic perception technology in secure and trustworthy ITS. 展开更多
关键词 Intelligent transportation information security traffic information completion traffic holographic perception AI-driven edge computing graph convolutional neural network
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Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process 认领 引用 被引量：1
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作者 Ping Wu Yuxuan Ni +4 位作者 Huaimin Wang Xuguang Hu Zhenquan Wu Jian Jiang Yaowu Hu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第1期267-276,共10页
Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on li... Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on linear processes,leading to poor performance in dynamic nonlinear processes.In this paper,a novel quality-related fault detection method,named DiCAE-PLS,is developed by combining dynamic-inner convolutional autoencoder with PLS.In the proposed DiCAE-PLS method,latent features are first extracted through dynamic-inner convolutional autoencoder (DiCAE) to capture process dynamics and nonlinearity from process variables.Then,a PLS model is established to build the relationship between the extracted latent features and the final product quality.To detect quality-related faults,Hotelling's T2 statistic is employed.The developed quality-related fault detection is applied to the widely used industrial benchmark of the Tennessee. 展开更多
关键词 Partial least squares Dynamic-inner convolutional autoencoder Quality-related fault detection Neural networks Safety Dynamic modeling
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An Integrated Approach to Condition-Based Maintenance Decision-Making of Planetary Gearboxes: Combining Temporal Convolutional Network Auto Encoders with Wiener Process 认领 引用 被引量：1
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作者 Bo Zhu Enzhi Dong +3 位作者 Zhonghua Cheng Xianbiao Zhan Kexin Jiang Rongcai Wang 《Computers, Materials & Continua》 SCIE EI 2026年第1期661-686,共26页
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s... With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes. 展开更多
关键词 Temporal convolutional network autoencoder full lifecycle degradation experiment nonlinear Wiener process condition-based maintenance decision-making fault monitoring
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Autocorrelation-based convolutional neural network for reconstruction of noisy attosecond streaking traces 认领 引用
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作者 Xiaoxian Zhu Yitan Gao +2 位作者 Yiming Wang Kun Zhao Zhiyi Wei 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期432-438,共7页
Attosecond light sources serve as crucial tools for investigating the ultrafast electronic dynamics in matter with remarkable temporal resolution.Traditional methods face difficulties in accurately measuring attosecon... Attosecond light sources serve as crucial tools for investigating the ultrafast electronic dynamics in matter with remarkable temporal resolution.Traditional methods face difficulties in accurately measuring attosecond pulses,and the prevailing approach involves utilizing attosecond streak cameras coupled with inversion algorithms to reconstruct phase information.However,these algorithms often require multiple iterations and extensive computational time.This study investigates the utilization of autocorrelation graphs as inputs for a convolutional neural network(CNN)to invert streaking traces obtained by attosecond streak camera.We explore the noise resistance capability of autocorrelation within the CNN inversion and aim to provide a physical explanation for its effectiveness.The objective of this research is to enhance the accuracy and reliability of CNN inversion for attosecond streaking traces,enabling improved resilience against experimental noises. 展开更多
关键词 attosecond convolutional neural networks autocorrelation attosecond streaking camera phase retrieval noise robustness
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Interactive Dynamic Graph Convolution with Temporal Attention for Traffic Flow Forecasting 认领 引用
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作者 Zitong Zhao Zixuan Zhang Zhenxing Niu 《Computers, Materials & Continua》 SCIE EI 2026年第1期1049-1064,共16页
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In... Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods. 展开更多
关键词 Traffic flow prediction interactive dynamic graph convolution graph convolution temporal multi-head trend-aware attention self-attention mechanism
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Swin2FII:Fluid Image Inpainting Method Based on SwinV2 Transformer and Fast Fourier Convolution 认领 引用
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作者 LIN Chuchao ZOU Changjun +3 位作者 XU Hangbin MOU Yuanjin SHI Zhihua GE Zhiyu 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期929-941,I0010,共13页
Image inpainting is a crucial research area in computer vision.Despite significant advancements with deep learning methods,challenges such as information loss and weak adaptability remain.This paper introduces a Trans... Image inpainting is a crucial research area in computer vision.Despite significant advancements with deep learning methods,challenges such as information loss and weak adaptability remain.This paper introduces a Transformer-based image inpainting method named Swin2FII,which integrates SwinV2 Transformer and fast Fourier convolution structure to address information loss and bottleneck issues,significantly enhancing inpainting accuracy and expanding its application scope.Swin2FII incorporates a super-resolution model,enhancing feature extraction and information transmission through efficient reconstruction,thereby improving detail recovery and stability.We employ the Charbonnier loss function to address gradient explosion,accurately estimating low-frequency signals and enhancing the precision of detail and texture reconstruction.Furthermore,combining mixed-precision training and data augmentation significantly boosts the model's adaptability and generalization ability.Experimental results show that our Swin2FII method outperforms the existing techniques on multiple public datasets.Notably,it exhibits excellent generalization and performance in a variety of scenarios and mask scales.In addition,Swin2FII also demonstrates strong capabilities in fluid image inpainting and mural image inpainting tasks. 展开更多
关键词 computer vision image inpainting fast Fourier convolution Transformer
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Independent sampling and padding for Rayleigh-Sommerfeld diffraction based on scaled convolution approach 认领 引用
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作者 YANG Chen FU Xi-hong +1 位作者 FU Xin-peng BAYANHESHIG 《中国光学（中英文）》 EI CAS CSCD 北大核心 2026年第2期367-381,共15页
We propose a novel fast numerical calculation method for the Rayleigh-Sommerfeld diffraction integral,which is developed based on the existing scaled convolution method.This approach enables fast cal-culations for gen... We propose a novel fast numerical calculation method for the Rayleigh-Sommerfeld diffraction integral,which is developed based on the existing scaled convolution method.This approach enables fast cal-culations for general cases of off-axis scenarios where the sampling intervals and numbers of the input and observation planes are unequal.Additionally,it allows for arbitrary adjustment of the sampling interval of the impulse response function,facilitating a manual trade-off between computational load and accuracy.The er-rors associated with this method,which is equivalent to interpolation,primarily arise from the discontinuities of the sampling matrix of the impulse response function on its boundaries of periodic extension.To address this issue,we propose the concept of the padding function and its construction method,and evaluate its ef-fectiveness in enhancing computational accuracy.The feasibility of the proposed method is verified by nu-merical simulation and compared with the direct integration DI-method in a simplified scenario.It shows that the proposed method has good computational accuracy for the general case where the sampling interval of the input and observation plane is not equal under non-near-field diffraction,and when the diffraction distance is large,although the computational accuracy of the proposed method cannot exceed that of the DI-method,the computational amount can be significantly reduced with almost no effect on the computational accuracy.This method provides a general numerical calculation scheme of diffraction in the non-near field case for areas such as computational holography. 展开更多
关键词 Rayleigh-Sommerfeld diffraction scaled convolution padding function
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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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ECANet:Enhanced Convolutional Attention Network for Liver Segmentation 认领 引用
19
作者 Yuyan Ning Haiyun Huang +3 位作者 Legend Zhang Wei Wei Hao Quan Bo Yang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1155-1172,共18页
Hybrid CNN-Transformer models are widely used in medical image segmentation because they combine CNN-based local feature extraction with Transformer-based global context modeling.Despite their popularity,these models ... Hybrid CNN-Transformer models are widely used in medical image segmentation because they combine CNN-based local feature extraction with Transformer-based global context modeling.Despite their popularity,these models face several challenges,including computational complexity,noise blurring,and information loss.This paper proposes an enhanced convolutional attention network(ECANet)for liver segmentation.ECANet uses a U-shaped architecture with efficient channel-attention-based skip connections.Both the encoder and decoder are constructed using enhanced convolutional Transformer(ECT)blocks,where group convolution is integrated into the convolutional attention module for efficient Token embedding and channel disentanglement,and a Token-wise multi-layer perceptron(MLP)branch is incorporated into the wide-focus module to improve feature representation across channels.Deep supervision and a hybrid of Binary Cross-Entropy(BCE)and Dice loss are used to improve boundary accuracy.We evaluate the proposed model on the publicly available LiTS17 dataset.Experiments show that ECANet outperforms the compared CNN-based and CNN-Transformer baseline models on both quantitative and qualitative measures. 展开更多
关键词 Medical image segmentation convolutional transformer group convolution efficient channel attention deep supervision
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Multi-Label Classification Model Using Graph Convolutional Neural Network for Social Network Nodes 认领 引用
20
作者 Junmin Lyu Guangyu Xu +4 位作者 Feng Bao Yu Zhou Yuxin Liu Siyu Lu Wenfeng Zheng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期1235-1256,共22页
Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati... Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks. 展开更多
关键词 GNN social networks nodes multi-label classification model graphic convolution neural network coupling principle
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