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New Parallel Interference Cancellation for Convolutionally Coded CDMA Systems 认领 引用
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作者 Xu Guo-xiong Gan Liang-cai Huang Tian-xi 《Wuhan University Journal of Natural Sciences》 EI CAS 2004年第1期78-82,共5页
Based on BCJR algorithm proposed by Bahlet al and linear soft decision feedback,a reduced-complexity parallel interference cancellation(simplified PIC)for convolutionally coded DS CDMA systems is proposed.By computer ... Based on BCJR algorithm proposed by Bahlet al and linear soft decision feedback,a reduced-complexity parallel interference cancellation(simplified PIC)for convolutionally coded DS CDMA systems is proposed.By computer simulation,we compare the simplified PIC with the exact PIC.It shows that the simplified PIC can achieve the performance close to the exact PIC if the mean values of coded symbols are linearly computed in terms of the sum of initiala prior log-likelihood rate(LLR)and updateda prior LLR,while a significant performance loss will occur if the mean values of coded symbols are linearly computed in terms of the updateda prior LLR only.Meanwhile,we also compare the simplified PIC with MF receiver and conventional PICs.The simulation results show that the simplified PIC dominantly outperforms the MF receiver and conventional PICs,at signal-noise rate(SNR)of 7 dB,for example,the bit error rate is about 10-4for the simplified PIC,which is far below that of matched-filter receiver and conventional PIC. 展开更多
关键词 convolutionally coded CDMA parallel interference cancellation BCJR
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Visual Detection Algorithms for Counter-UAV in Low-Altitude Air Defense 认领 引用 被引量：1
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作者 Minghui Li Hongbo Li +1 位作者 Jiaqi Zhu Xupeng Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第3期823-844,共22页
To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target... To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target Detection YOLO)model for anti-drone object detection,based on the YOLOv8 architecture.To overcome the limitations of existing methods in detecting small objects within complex backgrounds,we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set,aiming to reduce the model’s computational complexity.To improve multi-scale feature fusion,we construct a Multi-Branch Feature Pyramid Network(MB-FPN)that employs a cross-level feature fusion strategy to enhance the model’s representation of small objects.Additionally,a shared detail-enhanced detection head is introduced to address the large size variations of Unmanned Aerial Vehicle(UAV)targets,thereby improving detection performance across different scales.Experimental results demonstrate that the proposed model achieves consistent improvements across multiple benchmarks.On the Det-Fly dataset,it improves precision by 3%,recall by 5.6%,and mAP50 by 4.5%compared with the baseline,while reducing parameters by 21.2%.Cross-validation on the VisDrone dataset further validates its robustness,yielding additional gains of 3.2%in precision,6.1%in recall,and 4.8%in mAP50 over the original YOLOv8.These findings confirm the effectiveness of the proposed algorithm in enhancing UAV detection performance under complex scenarios. 展开更多
关键词 Small target detection anti-drone yolov8 shared convolution feature fusion network
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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://gffzz188fe103f8f1460asknkkvu6c6fpw6quw.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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WiFi-Based Indoor Intrusion Detection via Two-Level Gait Feature Fusion Model 认领 引用
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作者 Lijun Cui Yongjie Niu +3 位作者 Yuxiang Sun Xiaokang Gu Jing Guo Pengfei Xu 《Computers, Materials & Continua》 SCIE EI 2026年第7期370-387,共18页
Indoor intrusion detection is essential for various applications,including security systems and smart homes.Recently,WiFi-based detection has gained popularity due to its low cost and non-invasive nature.Current Chann... Indoor intrusion detection is essential for various applications,including security systems and smart homes.Recently,WiFi-based detection has gained popularity due to its low cost and non-invasive nature.Current Channel State Information(CSI)based frameworks primarily use deep learning to extract gait signatures;however,their performance depends heavily on extensive labeled datasets.These methods struggle to differentiate between unlabeled and labeled data that exhibit similar features.To address this challenge,we propose a novel Two-level Feature Fusion model for Indoor Intrusion Detection(TFF-IID)utilizing commercial WiFi CSI.The model adopts a two-level structure to learn rich feature representations and introduces a Transformer with multi-head self-attention alongside a multi-scale convolution module to process sensor data.Additionally,it incorporates a self-supervised learning module to capture general normality patterns.Based on this architecture,TFF-IID achieves accurate intrusion detection using only CSI.Empirical evaluations on a private gait dataset demonstrate that TFF-IID achieves an intrusion detection accuracy of 73.5%and an F1-score of 76.2%across 10 unauthorized subjects.Moreover,cross-scenario assessments verify that the proposed model maintains high efficiency and robustness in environments characterized by diverse spatial layouts and multipath complexities.Furthermore,TFF-IID outperforms the best baseline by 19.7%and 25.7%in accuracy and F1-score,respectively. 展开更多
关键词 Intrusion detection transformer feature fusion WIFI signal CSI multi-scale convolution module
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Attention-enhanced multi-time scale LSTM for soft sensor modeling of corn starch liquefaction 认领 引用
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作者 Yu Zhuang Zhongyi Zhang +5 位作者 Jin Tao Yi Li Fan Li Yu Wang Lei Zhang Jian Du 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第1期132-144,共13页
Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling... Data-driven deep learning modeling has been increasingly applied to quality prediction in complex chemical processes.However,the data show complex temporal features due to different residence times and strong coupling relationships among chemical entities.This study proposes a multi-scale temporal feature extraction module to extract local dynamic temporal features across different time scales and combines it with long short-term memory(LSTM)networks to capture global temporal patterns,thereby taking full advantage of available data.In addition,variable-wise channel attention is integrated into the model to enhance attention on the essential parts of the feature maps and improve predictive performance.Furthermore,by analyzing the attention weights,the model quickly identifies the key variables that significantly affect the predictions.Finally,the model is applied to a real corn starch liquefaction process and achieves an accurate product quality prediction with an R2 value of 0.9392,which represents a 4%to 9%improvement over traditional models and demonstrates the superiority of the proposed approach. 展开更多
关键词 Multi-scale dilated causal convolution Neural networks Soft sensor Systems engineering attention mechanism Biochemical engineering
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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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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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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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基于空洞因果卷积的学生成绩预测及分析方法 认领 引用
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作者 赖英旭 张亚薇 +1 位作者 庄俊玺 刘静 《北京工业大学学报》 CAS CSCD 北大核心 2026年第3期252-267,共16页
针对使用循环神经网络对学生长序列行为数据进行特征提取存在梯度消失或爆炸、长期依赖关系提取能力不足、深度学习模型缺乏可解释性等问题,提出一种面向长序列数据的空洞因果卷积(dilated causal convolution,DCC)成绩预测及分析方法... 针对使用循环神经网络对学生长序列行为数据进行特征提取存在梯度消失或爆炸、长期依赖关系提取能力不足、深度学习模型缺乏可解释性等问题,提出一种面向长序列数据的空洞因果卷积(dilated causal convolution,DCC)成绩预测及分析方法。首先,采用生成对抗网络(generative adversarial network,GAN)生成符合少数类学生原始行为数据分布规律的新样本,并将新样本加入学生数据集中以达到均衡数据集的目的;然后,提出一种基于DCC的成绩预测模型,DCC和门控循环单元(gated recurrent unit,GRU)相结合的结构提高了模型对长序列数据依赖关系的提取能力;最后,使用沙普利加性解释(Shapley additive explanations,SHAP)方法并结合三因素理论对影响学生成绩的因素进行重要性分析和解释。在公开数据集上的实验结果表明,在成绩预测任务中提出的方法与基线方法相比,加权F1分数提高了约6个百分点,并进一步验证了所提方法中关键模块的有效性和模型的泛化能力。此外,通过对比优秀学生和风险学生的学习特点发现,良好的学习习惯、课堂学习的主动性以及不同行为环境等因素会对学生成绩产生重要影响。 展开更多
关键词 学生成绩预测 空洞因果卷积(dilated causal convolution,DCC) 不均衡数据 生成对抗网络(generative adversarial network,GAN) 沙普利加性解释(Shapley additive explanations,SHAP)方法 成绩影响因素分析
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Research on Steel Surface Defect Detection Algorithm Based on YOLOv11-ODF 认领 引用
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作者 Zhengxiang Ma Xiaofei Ma +2 位作者 Xiaoliang Liu Heng Zhang Weichao Yu 《Structural Durability & Health Monitoring》 EI 2026年第4期417-436,共20页
Steel surface defect detection is a key technology for ensuring the quality of steel products and the automation of production.However,in actual industrial scenarios,the complex texture background of steel surfaces of... Steel surface defect detection is a key technology for ensuring the quality of steel products and the automation of production.However,in actual industrial scenarios,the complex texture background of steel surfaces often leads to lowrecognition of tiny defect features and easy confusion,and the full extraction and fusion ofmulti-scale features remain challenging.To address these issues,this paper proposes a lightweight and high-precision detection model based on the improved YOLOv11n,named YOLOv11-ODF.Firstly,in the backbone network,a C3k2_ODConv module integrating full-dimensional convolution(ODConv)is constructed,which enhances the model’s ability to capture subtle defect features through multi-dimensional dynamic weights,and combines the C2PSA attention mechanism to optimize the feature representation in both spatial and channel dimensions.Secondly,in the feature fusion network(Head),an OD_WT_Fuse module is designed to replace the traditional fusion method,effectively improving the efficiency of cross-scale information transmission and semantic consistency.In addition,an anisotropic strip spatial pyramid pooling(ASSPPF)module is designed to further expand the receptive field and enhance the robustness of detecting irregular multi-scale defects.Experimental results show that on the NEU-DET dataset,the mAP@0.5 of YOLOv11-ODF reaches 77.1%,significantly improving by 3.2% compared to the original YOLOv11 model;the precision and recall increase by 1.6% and 4.6%,respectively,significantly reducing the missed detection rate of tiny defects.While achieving significant performance improvements,the model parameters only increase by 0.9 M,achieving an excellent balance between detection accuracy and computational efficiency,providing an effective technical solution for high-quality real-time automatic detection in industrial environments. 展开更多
关键词 Steel surface defect detection YOLOv11n full-dimensional convolution feature fusion NEU-DET dataset
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A transfer learning framework for the real-time detection of atmospheric gravity waves from All-Sky Airglow Imager 认领 引用 被引量：1
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作者 YuBin He QingChen Xu +6 位作者 YaJun Zhu QinZeng Li Cui Tu Bing Cai Wei Yuan XinYing Wang Feng Wei 《Earth and Planetary Physics》 EI CSCD 2026年第3期454-462,共9页
Atmospheric gravity waves(AGWs)observed by the All-Sky Airglow Imager(ASAI)require accurate identification for the study of atmospheric coupling mechanisms and space weather prediction.However,the traditional manual s... Atmospheric gravity waves(AGWs)observed by the All-Sky Airglow Imager(ASAI)require accurate identification for the study of atmospheric coupling mechanisms and space weather prediction.However,the traditional manual screening methods and existing machine learning approaches do not meet the demands of practical station monitoring,which has significantly impeded climatological statistical research based on AGWs.Therefore,a real-time detection framework for ground-based airglow gravity waves that integrates transfer learning with adaptive image preprocessing has been proposed.By employing wavelength-adaptive median filtering and multiscale fusion,the framework effectively suppresses stellar noise while preserving weak gravity wave features.The model utilizes an EfficientNet-B3(convolutional neural network)backbone enhanced with a deformable convolutional layer,trained via a two-stage strategy:A frozen phase prevents overfitting by locking the lower level feature extractor,and a fine-tuning phase optimizes the deformable convolution through cosine annealing and layered optimization.This approach improves both feature transfer efficiency and gravity wave detection sensitivity.The resulting lightweight model achieves 91.2%accuracy with millisecond-level inference speed(23 ms per frame). 展开更多
关键词 atmospheric gravity waves All-Sky Airglow Imager real-time detection multiscale fusion transfer learning EfficientNet-B3 deformable convolution
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Improving ECAPA-TDNN Performance with Coordinate Attention 认领 引用 被引量：1
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作者 LIU Shuanghong SONG Zhida HE Liang 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期241-247,共7页
The current mainstream networks,such as squeeze and excitation residual neural network(SE-ResNet)and emphasized channel attention,propagation and aggregation based time delay neural network(ECAPATDNN),enhance the capa... The current mainstream networks,such as squeeze and excitation residual neural network(SE-ResNet)and emphasized channel attention,propagation and aggregation based time delay neural network(ECAPATDNN),enhance the capability of speaker embedding extractors to extract more discriminative speaker embeddings by incorporating squeeze and excitation(SE)attention within the convolutional blocks.However,the SE attention focuses solely on encoding inter-channel information,overlooking the importance of spatial positional information and time-frequency information,which are crucial for the model’s performance.In this paper,we first experimentally compare the effectiveness of several mainstream attention mechanisms in the computer vision domain for the ECAPA-TDNN model.Next,we focus on the substantial improvements that coordinate attention(CA)brings to the ECAPA-TDNN model.The introduction of CA can help the model embed time-frequency information into the channel representation.Even without using AS-Norm,our proposed model achieves relative reductions of about 5.3%equal error rate(EER)and 5.5%minimum detection cost function(minDCF)on both the Voxceleb-O and Voxceleb-H test sets compared to the ECAPA-TDNN baseline model.In addition,the EER is relatively reduced by 9.46%on the CN-Celeb1 test set.This result strongly demonstrates that the CA module can effectively improve the generalization ability of the ECAPA-TDNN model. 展开更多
关键词 speaker verification convolution attention mechanism coordinate attention(CA) domain generalization ECAPA-TDNN model
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Detection of vitreoretinal interface abnormalities from OCT images based on DS-YOLOv7 network 认领 引用
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作者 Xin Zhou Ying Fan +6 位作者 Gaowei Li Menghan Li Weifang Zhu Dehui Xiang Xinjian Chen Xun Xu Fei Shi 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第2期102-115,共14页
Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreou... Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreous detachment(PVD)and epiretinal membrane(ERM).They can cause severe damage to the structure and function of the retina,leading to permanent vision loss.Therefore,fully automated detection of abnormalities at the VRI is crucial for the management of high myopia.This paper presents a DS-YOLOv7 network aimed at accurately identifying abnormalities,including partial PVD,complete PVD,and ERM from retinal OCT images.Built upon the YOLOv7 network,the proposed model integrates the advanced dynamic snake convolution(DSConv)module to capture the curvilinear characteristics of lesions,and the mixture of attention and convolution(ACMix)module to improve the precision and robustness of feature extraction through effective fusion of self-attention mechanisms and convolution.Moreover,the introduction of the efficient complete intersection-over-union(ECIoU)loss function further enhances the coordinate regression capability of the model.Threefold cross-validation on a dataset with 1973 OCT B-scans from 46 patients shows that the DS-YOLOv7 achieved superior performance in vitreoretinal interface abnormality detection,with mAP@0.5 of 0.714,mAP@0.75 of 0.438,and mAP@0.5:0.95 of 0.424.The proposed model can provide an accurate and efficient diagnostic tool for patients with high myopia. 展开更多
关键词 YOLOv7 dynamic snake convolution mixture of attention and convolution optical coherence tomography vitreoretinal interface abnormality
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Video Super-Resolution via Effective Spatio-Temporal Alignment Network 认领 引用
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作者 Bin Guo Xin Wang +5 位作者 Hao Wen Yuhong Fu Jinxing Li Hui Ma Haoqian Wang Yong Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期726-738,共13页
Extracting spatio-temporal cues from neighbouring frames is challenging in video super-resolution(VSR).Although deformable alignment-based VSR methods have shown promise in aligning neighbouring frames with the refere... Extracting spatio-temporal cues from neighbouring frames is challenging in video super-resolution(VSR).Although deformable alignment-based VSR methods have shown promise in aligning neighbouring frames with the reference frame,most existing methods rely on one or a few traditional convolutions to estimate motion offsets for spatio-temporal alignment,restricting receptive field size and alignment accuracy.To address these limitations,we propose an effective spatio-temporal alignment network(ESTA-Net)for VSR.The core component of our method is the group convolution-based alignment module(GCBAM),which utilises cascaded group convolutions to learn offsets across both the original and downsampled resolutions.By employing group convolutions rather than traditional convolutions,GCBAM enables the deformable alignment to achieve a wider receptive field with lower computational cost,thereby improving the accuracy of offset estimation.Additionally,the bi-scale alignment strategy within GCBAM enhances robustness to complex and large-scale motions.Furthermore,we introduce an attention-based feature enhancement module(AFEM)to refine the aligned features,focusing on critical details to improve reconstruction quality.Extensive experiments on standard benchmarks show that our ESTA-Net achieves superior VSR performance against other advanced methods,while maintaining a good equilibrium between model size and performance. 展开更多
关键词 group convolution motion offsets spatio‐temporal alignment video super‐resolution
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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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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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VulSCP: Automated Code Vulnerability Detection via Sequential Convolution and Parallel Attention Mechanism 认领 引用
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作者 Zhe Wang Yu Yan +3 位作者 Junqi Tong Yijun Lin Dechun Yin Xiaoliang Zhao 《Computers, Materials & Continua》 SCIE EI 2026年第8期1057-1081,共25页
As software applications grow increasingly large and complex,traditional code vulnerability detection methods struggle with performance and efficiency.Although code visualization-based algorithms have demonstrated eff... As software applications grow increasingly large and complex,traditional code vulnerability detection methods struggle with performance and efficiency.Although code visualization-based algorithms have demonstrated effectiveness in capturing sparse features and complex workflows in large-scale source code,their capacity to extract global semantic information and intricate long-range dependencies remains limited.Recent large language model(LLM)-based approaches have shown promising accuracy by leveraging rich contextual information,but their high computational cost often limits practical efficiency.To address these challenges,we propose VulSCP,a new framework that integrates sequential convolution with a parallel attention mechanism.Specifically,VulSCP first constructs a semantically weighted graph from the source code,then employs sequential convolution to extract local vulnerability-related features,and finally enhances the global feature representation through parallel attention.Experimental results on large-scale C/C++function-level datasets show that VulSCP achieves an accuracy of 85.14%and a false positive rate of 17.25%,outperforming the best baseline in accuracy by 1.73 percentage points and reducing the false positive rate by 3.38 percentage points.Moreover,while maintaining high detection accuracy,VulSCP achieves a low average inference time of 1.89 s per sample,showing favorable efficiency compared with the evaluated LLM-based methods.These results suggest that VulSCP is a promising approach for vulnerability detection in large and complex software systems,offering a favorable balance between accuracy and efficiency.The source code of VulSCP is publicly available at http://gffzz188fe103f8f1460asknkkvu6c6fpw6quw.ffgz.tsg.suse.edu.cn/Hwzx-ZeL/VulSCP. 展开更多
关键词 Code vulnerability detection sequential convolution parallel attention program dependency graph semantic graph representation
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Linear Variable Convolution and Cross Attention LSTM Method for Radar Echo Extrapolation 认领 引用
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作者 ZHANG Wang HE Guang-xin +1 位作者 LEI Lei LUO Jing-jia 《Journal of Tropical Meteorology》 SCIE CAS CSCD 2026年第2期119-131,共13页
Radar echo extrapolation is critical in short-term weather forecasting.To enhance the accuracy and adaptability of radar echo prediction,this paper proposes a novel method that integrates a linear variable convolution... Radar echo extrapolation is critical in short-term weather forecasting.To enhance the accuracy and adaptability of radar echo prediction,this paper proposes a novel method that integrates a linear variable convolution(LVC)module and a cross-attention(CA)mechanism into the spatiotemporal long short-term memory(ST-LSTM)framework,named LVC-LSTM.The LVC module enables dynamic adjustment of the convolutional sampling shape,allowing the network to capture the irregular and evolving structures of radar echoes more accurately,thereby offering improved flexibility and representation capability compared with traditional and deformable convolutions.The CA mechanism introduces a pyr-amid-based CA structure that incorporates cross-scale embeddings and hierarchical attention blocks,effectively capturing multi-scale features and long-and short-range spatial dependencies inherent in radar echo dynamics.Experimental results using a real-world radar echo dataset demonstrated that the LVC-LSTM model outperformed comparative models across multiple evaluation metrics,including the critical success index,heidke skill score,root mean square error,mean absolute error,and structural similarity index,indicating its strong potential for operational radar echo extrapolation. 展开更多
关键词 long-and short-term memory networks precipitation forecasting radar echo extrapolation linear variable convolution cross-attention mechanism
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