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Multi-scale simplified residual convolutional neural network model for predicting compositions of binary magnesium alloys 认领 引用
1
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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MSSTGCN: Multi-Head Self-Attention and Spatial-Temporal Graph Convolutional Network for Multi-Scale Traffic Flow Prediction 认领 引用 被引量：2
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作者 Xinlu Zong Fan Yu +1 位作者 Zhen Chen Xue Xia 《Computers, Materials & Continua》 SCIE EI 2025年第2期3517-3537,共21页
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ... Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks. 展开更多
关键词 Graph convolutional network traffic flow prediction multi-scale traffic flow spatial-temporal model
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An Adaptive Multi-Scale Dilated Convolution Network for Real-Time Road Black Ice Detection 认领 引用
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作者 Sun-Kyoung Kang Yeonwoo Lee 《Computers, Materials & Continua》 SCIE EI 2026年第8期1742-1768,共27页
Black ice formation on road surfaces presents a serious hazard due to its low visibility and high slipperiness,underscoring the critical need for timely and accurate detection in intelligent transportation systems.In ... Black ice formation on road surfaces presents a serious hazard due to its low visibility and high slipperiness,underscoring the critical need for timely and accurate detection in intelligent transportation systems.In this paper,we propose AdaMsDCNet,an adaptive multi-scale dilated convolution network designed for real-time black-ice semantic segmentation on resource-constrained edge platforms,applying a Convolutional Neural Network(CNN)with an adaptive Multi-Scale Dilated Convolution(MsDC)feature fusion encoder-decoder architecture.The key concept of AdaMsDCNet is to employ an encoder-decoder architecture with parallel multi-scale dilated convolutional paths that adjust dilation rates at different encoder depths using a systematic 4→2→1 progression,optimally capturing a wide range of receptive fields while mitigating checkerboard artifacts.The encoder dynamically fuses features from multiple dilation rates at each stage,enhancing segmentation accuracy.Simultaneously,the decoder uses transposed convolutions and skip connections to preserve fine spatial details.Experimental validation on a proprietary thermal infrared dataset of 1156 annotated images show that AdaMsDCNet_9 achieves 96.47%mIoU,95.48%Black-Ice IoU,97.55%Precision,97.82%Recall,and 97.69%F1-Score,outperforming U-Net(+26.78 pp mIoU,+29.88 pp Recall),DeepLabv3+(+2.82 pp mIoU),and LinkNet(+1.08 pp mIoU)while requiring only 1.86M parameters and maintaining real-time inference speeds of 3.94~5.63 FPS on the NVIDIA Jetson Nano embedded GPU.Ablation studies confirm the benefits of adaptive dilation,parallel feature fusion,and controlled channel growth for the accuracy–efficiency trade-off.Limitations including dataset generalization to uncontrolled outdoor conditions and the evaluation of imbalance-aware loss functions are identified as directions for future work. 展开更多
关键词 CNN multi-scale dilation convolution feature fusion black ice detection
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Occluded Gait Emotion Recognition Based on Multi-Scale Suppression Graph Convolutional Network 认领 引用
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作者 Yuxiang Zou Ning He +2 位作者 Jiwu Sun Xunrui Huang Wenhua Wang 《Computers, Materials & Continua》 SCIE EI 2025年第1期1255-1276,共22页
In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accurac... In recent years,gait-based emotion recognition has been widely applied in the field of computer vision.However,existing gait emotion recognition methods typically rely on complete human skeleton data,and their accuracy significantly declines when the data is occluded.To enhance the accuracy of gait emotion recognition under occlusion,this paper proposes a Multi-scale Suppression Graph ConvolutionalNetwork(MS-GCN).TheMS-GCN consists of three main components:Joint Interpolation Module(JI Moudle),Multi-scale Temporal Convolution Network(MS-TCN),and Suppression Graph Convolutional Network(SGCN).The JI Module completes the spatially occluded skeletal joints using the(K-Nearest Neighbors)KNN interpolation method.The MS-TCN employs convolutional kernels of various sizes to comprehensively capture the emotional information embedded in the gait,compensating for the temporal occlusion of gait information.The SGCN extracts more non-prominent human gait features by suppressing the extraction of key body part features,thereby reducing the negative impact of occlusion on emotion recognition results.The proposed method is evaluated on two comprehensive datasets:Emotion-Gait,containing 4227 real gaits from sources like BML,ICT-Pollick,and ELMD,and 1000 synthetic gaits generated using STEP-Gen technology,and ELMB,consisting of 3924 gaits,with 1835 labeled with emotions such as“Happy,”“Sad,”“Angry,”and“Neutral.”On the standard datasets Emotion-Gait and ELMB,the proposed method achieved accuracies of 0.900 and 0.896,respectively,attaining performance comparable to other state-ofthe-artmethods.Furthermore,on occlusion datasets,the proposedmethod significantly mitigates the performance degradation caused by occlusion compared to other methods,the accuracy is significantly higher than that of other methods. 展开更多
关键词 KNN interpolation multi-scale temporal convolution suppression graph convolutional network gait emotion recognition human skeleton
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Modularized Graph Convolutional Network 认领 引用
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作者 Tiantian He Zhixuan Duan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期737-739,共3页
Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighb... Dear Editor,This letter presents a novel graph neural network, namely modularized graph convolution network(MGCN), to address the underexplored issue in graph convolution networks(GCNs), wherein the weights for neighbor aggregation are fixed, leading to the limited capability of capturing diverse relationships among nodes for representation learning. Conventional GCNs always learn node representations in the graph according to the weights computed from the graph Laplacian, consequently overlooking the similarity and group cohesiveness of node features. 展开更多
关键词 graph convolution networks gcns capturing diverse relationships nodes representation learning modularized graph convolution network neighbor aggregation graph neural network modularized graph convolution network mgcn graph convolutional network
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Multi-scale keypoints detection and motion features extraction in dairy cows using ResNet101-ASPP network 认领 引用
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作者 Saisai Wu Shuqing Han +5 位作者 Jing Zhang Guodong Cheng Yali Wang Kai Zhang Mingming Han Jianzhai Wu 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第5期2028-2040,共13页
Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,charact... Detecting keypoints in dairy cows aims to locate and track the motion trajectories of the body's joints,which plays a crucial role in behavior analysis and lameness detection.However,real farming scenarios,characterized by occlusions and large variations in object scale may result in poor detection results.Therefore,we introduce the atrous spatial pyramid pooling(ASPP) module into the shallow layers network of ResNet101,designed to improve the multi-scale feature extraction capability of the model.The ASPP module enhances the robustness of recognition for different dimensional sizes and occluded keypoints using different dilatation rates in the parallel atrous convolutional layers to expand the model's receptive field.Furthermore,seven types of motion features,including tracking up,gait symmetry,step height balance,motion speed variability,head swing amplitude,head-neck slope and back curvature are extracted simultaneously by monitoring and tracking the motion trajectory of distinct keypoints.Several of these features represent innovative extraction models and attributes,first proposed in this study.Multiple models are trained and tested on datasets containing 2,385 frames for ablation experiments.The experiments show that,in comparison with the ResNet50,MobileNet_v2_1.0,and EfficientNet-b0backbone networks,the training error and test error of ResNet101 are reduced by 4.04-30.12 pixels and 3.81-28.14 pixels.Therefore,ResNet101 is used as the benchmark for subsequent model improvement by adding the ASPP module.The training error and test error of the ResNet101-ASPP network are reduced by 0.27 and 0.24 pixels,respectively,compared to the benchmark network.The prediction confidence improves by 1.65-2.50% at three different dairy cow object scales.In addition,the keypoints under different occlusion conditions improve considerably,especially for small-scale keypoints,demonstrating the capability of the ASPP module for multi-scale feature extraction.By analyzing the distribution of the seven features and health,mild lameness,and severe lameness in dairy cows,it is shown that all the different features play an important role in distinguishing between different levels of lameness. 展开更多
关键词 dairy cows multi-scale keypoints detection ResNet101-ASPP network motion features
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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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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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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-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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Learning Laws for Deep Convolutional Neural Networks With Guaranteed Convergence 认领 引用
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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 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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Brief application notes for vision transformer (ViT) and convolutional neural network (CNN) in medical imaging 认领 引用
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作者 Wei Kitt Wong Melinda Melinda 《Medical Data Mining》 CAS 2026年第2期34-42,共9页
In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in... In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in medical imaging applications,they operate based on fundamentally different computational principles.This report attempts to provide brief application notes on ViTs and CNNs,particularly focusing on scenarios that guide the selection of one architecture over the other in practical medical implementations.Generally,CNNs rely on convolutional kernels,localized receptive fields,and weight sharing,enabling efficient hierarchical feature extraction.These properties contribute to strong performance in detecting spatially constrained patterns such as textures,edges,and anatomical boundaries,while maintaining relatively low computational requirements.ViTs,on the other hand,decompose images into smaller segments referred to as tokens and employ self-attention mechanisms to model relationships across the entire image.This global modeling capability allows ViTs to capture long-range dependencies that may be difficult for convolution-based architectures to learn.However,ViTs typically achieve optimal performance when trained on extremely large datasets or when supported by extensive pretraining,as their reduced inductive bias requires greater data exposure to learn robust representations.This report briefly examines the architectural structure,underlying mathematical foundations,and relative performance characteristics of CNNs and ViTs,drawing upon recent findings from contemporary research.Emphasis is placed on understanding how differences in data availability,computational resources,and task requirements influence model effectiveness across medical imaging domains.Most importantly,the report serves as a concise application guide for practitioners seeking informed implementation decisions between these two influential deep learning frameworks. 展开更多
关键词 convolutional neural network vision transformer comparative study medical imaging
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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
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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A Multi-Scale Graph Neural Networks Ensemble Approach for Enhanced DDoS Detection 认领 引用
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作者 Noor Mueen Mohammed Ali Hayder Seyed Amin Hosseini Seno +2 位作者 Hamid Noori Davood Zabihzadeh Mehdi Ebady Manaa 《Computers, Materials & Continua》 SCIE EI 2026年第4期1216-1242,共27页
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t... Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist. 展开更多
关键词 DDoS detection graph neural networks multi-scale learning ensemble learning network security stealth attacks network graphs
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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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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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AMVT-NMN:Adaptive Multi-Scale Vision Transformer with Neuromorphic Memory Networks for Enhanced Lung Cancer Detection 认领 引用
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作者 Wariyo Godana Arero Yaqin Zhao +6 位作者 Mudasir Ahmad Wani Pir Noman Ahmad Kashish Ara Shakil Sadique Ahmad Sidrak Habtemariam Teredda Merhawit Berhane Teklu Longwen Wu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1236-1262,共27页
Lung cancer accounts for the highest number of cancer deaths globally,underscoring the urgent need for early and precise detection to enhance patient outcomes.While deep learning has made remarkable strides in analyzi... Lung cancer accounts for the highest number of cancer deaths globally,underscoring the urgent need for early and precise detection to enhance patient outcomes.While deep learning has made remarkable strides in analyzing medical images,current approaches face a fundamental challenge.They cannot adequately capture detailed local patterns and broader contextual relationships within lung Computed tomography(CT)scans.To address this limitation,we introduce AMVT-NMN(adaptive multi-scale vision transformer with neuromorphic memory networks),which combines three complementary mechanisms.The dynamic adaptive kernel networks component intelligently adjusts receptive field sizes based on input characteristics,enabling flexible feature capture across multiple scales.The neuromorphic contextual memory attention module draws inspiration from how human memory systems process information,maintaining a dynamic record of diagnostically relevant patterns to inform current predictions.The hierarchical cross-scale fusion mechanism with learnable weights synthesizes information from different resolution levels through adaptive weighting.Testing on the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases(IQOTHNCCD)dataset demonstrates strong performance:97.9%accuracy,96.5%sensitivity,98.7%specificity,and 99.2%Area under the Curve-Receiver Operating Characteristic(AUC-ROC).These results surpass existing methods such as CNN-GD,which achieved 97.2%accuracy.Notably,the high specificity translates to fewer false alarms,potentially reducing unnecessary biopsies and follow-up imaging outcomes that matter considerably in clinical practice.Result of AMVT-NMN generalization to the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI),Lung nodule analysis(LUNA16),and Non-Small Cell Lung Cancer(NSCLC)-Radiomics datasets showed AUCs of 96.5%,92.8%,and 97.2%,respectively.Ablation experiments confirm that each architectural element of AMVT-NMN contributes meaningfully to overall performance.Five-fold cross-validation yielded consistent results(97.71±0.57%),indicating reliable performance across different patient subsets.The memory-augmented design shows particular promise for handling diagnostically ambiguous cases.It is focused on pattern recognition and computational intelligence,which is useful for coping with uncertain information in intelligent diagnosis systems,meeting the growing trend for trusted artificial intelligence(AI)in decision-making. 展开更多
关键词 Adaptive multi-scale vision transformer dynamic adaptive kernel networks hierarchical cross-scale fusion lung cancer detection neuromorphic memory networks
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MSA-ConvNeXt:Predicting Magnetism of Doped Two-Dimensional Nanomaterials via Multi-Scale Convolution and Attention Mechanisms 认领 引用
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作者 Yuxuan Feng Lili Liang +1 位作者 Guanglu Sun Yanrui Wei 《Computers, Materials & Continua》 SCIE EI 2026年第9期354-368,共15页
In doped two-dimensional nanomaterials,magnetism is one of the important physical properties.By introducing foreign doping atoms or molecules,the electronic structure of the material can be effectively regulated,leadi... In doped two-dimensional nanomaterials,magnetism is one of the important physical properties.By introducing foreign doping atoms or molecules,the electronic structure of the material can be effectively regulated,leading to changes in magnetic behavior.Currently,magnetic property prediction has achieved considerable results with the help of traditional CNNs,but there are still obvious limitations:(1)The feature extraction of dopant sites is constrained by fixed receptive fields,making it difficult to characterize local structural perturbations in the vicinity of dopant atoms and their spatial influence propagating to surrounding regions;(2)CNNs lack the capability to model long-range dependencies between non-neighboring atoms and their chemical bonds,thereby weakening the representation of long-range interactions within the material.In this study,we propose Multi-Scale and Attention ConvNeXt(MSA-ConvNeXt)based on multi-scale convolution and attention mechanisms,which consists of the following two core modules:(1)The Multi-scale Convolution Attention Block(MCAB),which models local structural perturbations around dopant atoms and their spatial effects via parallelmulti-scale convolutions.It uses a serial channel and spatial attention mechanism to adaptively recalibrate multi-scale features,highlighting the response of doping related regions and enhancing the ability to express dopant-site information;(2)The Visual Geometry Group–Swin Transformer(VGG-Swin)architecture extracts structural features of dopant sites using VGG convolutions to prevent the attenuation of structural information during global relationship modeling.Subsequently,the Swin Transformer is introduced,which uses the self-attention mechanism to dynamically weight and globally associate features at different spatial locations,in order to depict the long-range correlations between non-neighboring atoms and their chemical bonds with the dopant-site.Experiments conducted on a doped two-dimensional nanomaterial dataset constructed from the CMR database demonstrate that the proposed model outperforms existing methods in terms of accuracy and F1-score.Specifically,MSA-ConvNeXt achieves an accuracy of 91.66%,representing an improvement of 1.65%over the next best model.In addition,all experimental results are averaged over multiple independent runs(with five different random seeds),demonstrating the stability and reliability of themodel’s performance.Ablation studies further validate the effectiveness of each module design. 展开更多
关键词 Doped two-dimensional nanomaterials magnetic property prediction multi-scale convolution attention mechanism ConvNeXt
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