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A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings 认领 引用 被引量：12
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作者 Ding Yunhao Jia Minping 《Journal of Southeast University(English Edition)》 EI CAS 2019年第4期417-423,共7页
Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on ... Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data. 展开更多
关键词 fault diagnosis deep learning convolutional auto-encoder multi-scale convolutional kernel feature extraction
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Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder 认领 引用
2
作者 Xiaoxiong Feng Jianhua Liu 《Journal of Sensor Technology》 2023年第4期69-85,共17页
To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features e... To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion. 展开更多
关键词 Multi-Mode Data Fusion Coupling Convolutional Auto-Encoder Adaptive Optimization Deep Learning
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Temporal Convolutional Auto-encoder for Residential Non-intrusive Load Monitoring 认领 引用
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作者 Weicheng Liu Jianfeng Zhao +2 位作者 Yue Yu Xiao-Ping Zhang Ying Xue 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2026年第3期1208-1220,共13页
Non-intrusive load monitoring(NILM)enables disaggregation of appliance power usage through data gathered from the incoming power line.This disaggregation assists householders in reducing electricity expenses and aids ... Non-intrusive load monitoring(NILM)enables disaggregation of appliance power usage through data gathered from the incoming power line.This disaggregation assists householders in reducing electricity expenses and aids system operators in implementing demand-side responses.However,despite advancements in power measurement,processing household power data remains a significant challenge.Existing methods struggle to accurately disaggregate appliances with brief operation times and exhibit large errors in load curve regression tasks.To address these issues,a novel time-power hybrid(TPH)algorithm is proposed to enhance the accuracy of dataset preprocessing for NILM model training.Additionally,the temporal convolutional auto-encoder model(TCAE)is introduced to improve disaggregation performance.Both the TPH and TCAE are evaluated using a public dataset in comparison to other representative methods.The results demonstrate that the proposed TPH algorithm exhibits superior load activation extraction performance.The TCAE model displays significant advantages in both curve regression and load ON/OFF detection for various appliances.Remarkably,the TCAE achieves a detection precision of 99.9%,particularly for appliances with short operation times,which other models are unable to accomplish.Furthermore,the TCAE necessitates less computational space compared to existing models. 展开更多
关键词 Demand-side response dilated causal convolution load monitoring supervised learning
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Hamiltonian Reduction Using a Convolutional Auto-Encoder Coupled to a Hamiltonian Neural Network 认领 引用
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作者 Raphaël Côte Emmanuel Franck +2 位作者 Laurent Navoret Guillaume Steimer Vincent Vigon 《Communications in Computational Physics》 SCIE CSCD 2025年第2期315-352,共38页
The reduction of Hamiltonian systems aims to build smaller reduced models,valid over a certain range of time and parameters,in order to reduce computing time.By maintaining the Hamiltonian structure in the reduced mod... The reduction of Hamiltonian systems aims to build smaller reduced models,valid over a certain range of time and parameters,in order to reduce computing time.By maintaining the Hamiltonian structure in the reduced model,certain longterm stability properties can be preserved.In this paper,we propose a non-linear reduction method for models coming from the spatial discretization of partial differential equations:it is based on convolutional auto-encoders and Hamiltonian neural networks.Their training is coupled in order to learn the encoder-decoder operators and the reduced dynamics simultaneously.Several test cases on non-linear wave dynamics show that the method has better reduction properties than standard linear Hamiltonian reduction methods. 展开更多
关键词 Hamiltonian dynamics model order reduction convolutional auto-encoder Hamiltonian neural network non-linear wave equations shallow water equation
Ship Magnetic Field Modeling and Extrapolation Based on a Convolutional Neural Network 认领 引用 被引量：1
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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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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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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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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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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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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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A Convolutional Neural Network-Based Deep Support Vector Machine for Parkinson’s Disease Detection with Small-Scale and Imbalanced Datasets 认领 引用
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作者 Kwok Tai Chui Varsha Arya +2 位作者 Brij B.Gupta Miguel Torres-Ruiz Razaz Waheeb Attar 《Computers, Materials & Continua》 SCIE EI 2026年第1期1410-1432,共23页
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d... Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested. 展开更多
关键词 Convolutional neural network data generation deep support vector machine feature extraction generative artificial intelligence imbalanced dataset medical diagnosis Parkinson’s disease small-scale dataset
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Fusion for Classification of COVID-19 Patients Using a Discriminative Shape-Texture Convolutional Neural Network with X-Ray Images and Speech Signal Processing 认领 引用
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作者 R.Venkatesh A.Shenbagarajan G.Shenbagalakshmi 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1899-1917,共19页
Accurate and quick diagnosis of COVID-19 remains a global challenge,as conventional RT-PCR testing suffers from high false-negative rates,delays,and limited availability.Chest X-ray imaging,while more accessible,requi... Accurate and quick diagnosis of COVID-19 remains a global challenge,as conventional RT-PCR testing suffers from high false-negative rates,delays,and limited availability.Chest X-ray imaging,while more accessible,requires expert interpretation and is prone to human error.Although several deep learning methods have been applied,many face issues of overfitting,insufficient feature selection,and reduced reliability.In order to overcome these constraints,we suggest a hybrid framework,COVID-19-DSCNN-XISSP,which integrates a Deep Spatial Convolutional Neural Network(DSCNN)for robust spatial excision of features with the Exponential Incremental Social Ski Driver with Sine Cosine Algorithm(XISSP)for optimal feature selection.This strategy enhances classification accuracy while reducing computational overhead.Extensive experiments on benchmark COVID-19 chest X-ray datasets demonstrate that the proposed model outperforms existing approaches in terms of accuracy,precision,and robustness.The findings highlight the potential of COVID-19-DSCNN-XISSP as an economical,automated,and expandable tool for assisting radiologists in early and reliable detection of COVID-19,thereby supporting improved clinical decision-making and pandemic control. 展开更多
关键词 Bayesian boundary trend filtering COVID-19 Shape-texture discriminative convolutional neural network Lung area specific COVID-19 Xray dataset Multi‐hypothesis fuzzy‐matching radon transform Snow ablation optimizer
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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ECANet:Enhanced Convolutional Attention Network for Liver Segmentation 认领 引用
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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
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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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Classification Method of Lower Limbs Motor Imagery Based on Functional Connectivity and Graph Convolutional Network 认领 引用
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作者 Yang Liu Qi Lu +2 位作者 Junjie Wu Huaichang Yin Shiwei Cheng 《Computers, Materials & Continua》 SCIE EI 2026年第3期1674-1689,共16页
The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied... The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied in many fields,including rehabilitation.However,the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex,making it difficult to distinguish their features.Therefore,classifying lower limbs motor imagery is more challenging.In this study,we propose a feature extraction method based on functional connectivity,which utilizes phase-locked values to construct a functional connectivity matrix as the features of the left and right legs,which can effectively avoid the problem of physiological representations of the left and right lower limbs being too close to each other during movement.In addition,considering the topology and the temporal characteristics of the electroencephalogram(EEG),we designed a temporal-spatial convolutional network(TSGCN)to capture the spatiotemporal information for classification.Experimental results show that the accuracy of the proposed method is higher than that of existing methods,achieving an average classification accuracy of 73.58%on the internal dataset.Finally,this study explains the network mechanism of left and right foot MI from the perspective of graph theoretic features and demonstrates the feasibility of decoding lower limb MI. 展开更多
关键词 Brain-computer interface lower limb motor imagery functional connectivity temporal-spatial convolutional network
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Tensor Low-Rank Orthogonal Compression for Convolutional Neural Networks 认领 引用
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作者 Yaping He Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期227-229,共3页
Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression... Dear Editor,The letter proposes a tensor low-rank orthogonal compression(TLOC)model for a convolutional neural network(CNN),which facilitates its efficient and highly-accurate low-rank representation.Model compression is crucial for deploying deep neural network(DNN)models on resource-constrained embedded devices. 展开更多
关键词 model compression convolutional neural network cnn which tensor low rank orthogonal compression deep neural network dnn models embedded devices convolutional neural networks
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