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A dual attention-based deep learning model for lithology identificationwhile drilling 认领 引用 被引量：3
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作者 Jie Chen Zhen Gui +6 位作者 Yichao Rui Xusheng Zhao Xiaokang Pan Qingfeng Wang Yuanyuan Pu Zheng Li Maoyi Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1177-1192,共16页
Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex ge... Lithology identificationwhile drilling technology can obtain rock information in real-time.However,traditional lithology identificationmodels often face limitations in feature extraction and adaptability to complex geological conditions,limiting their accuracy in challenging environments.To address these challenges,a deep learning model for lithology identificationwhile drilling is proposed.The proposed model introduces a dual attention mechanism in the long short-term memory(LSTM)network,effectively enhancing the ability to capture spatial and channel dimension information.Subsequently,the crayfishoptimization algorithm(COA)is applied to optimize the model network structure,thereby enhancing its lithology identificationcapability.Laboratory test results demonstrate that the proposed model achieves 97.15%accuracy on the testing set,significantlyoutperforming the traditional support vector machine(SVM)method(81.77%).Field tests under actual drilling conditions demonstrate an average accuracy of 91.96%for the proposed model,representing a 14.31%improvement over the LSTM model alone.The proposed model demonstrates robust adaptability and generalization ability across diverse operational scenarios.This research offers reliable technical support for lithology identification while drilling. 展开更多
关键词 Lithology identificationwhile drilling Deep learning Dual attention mechanism Metaheuristic algorithm Field applications
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Research on Low Visibility Forecast Model of Sea Fog in Beibu Gulf Based on Attention Mechanism-Embedded LSTM Deep Learning 认领 引用 被引量：1
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作者 ZHENG Feng-qin LI Jie +1 位作者 JIN Long LU Qian-qian 《Journal of Tropical Meteorology》 SCIE CAS CSCD 2026年第2期176-185,共10页
To address the complexities associated with forecasting low-probability,low-visibility fog events and the underlying nonlinear interdependencies among various influencing variables,we present an attention mechanism-em... To address the complexities associated with forecasting low-probability,low-visibility fog events and the underlying nonlinear interdependencies among various influencing variables,we present an attention mechanism-em-bedded long short-term memory(ATT-LSTM)deep learning model for sea fog visibility hazard prediction.This archi-tecture seamlessly incorporates ATT into the conventional LSTM neural network framework.This integration enables the model to adaptively assign weights to the input features,thereby distinguishing between salient and non-salient variables.This targeted allocation enhances the contribution of considerable factors within the LSTM forecasting algorithm,opti-mizes input data,and assigns varying levels of attention to each variable.Consequently,the model substantially mitigates prediction errors in multivariate scenarios.An empirical analysis employing an independent dataset encompassing 303 foggy days over a biennial period confirmed the superior performance of the proposed ATT-LSTM model.Comparative evaluations with LSTM,logistic classification regression,and support vector machine classification regression models revealed that the ATT-LSTM model achieved a recall rate of 37%,a precision rate of 48%,an accuracy rate of 91%,and a threat score(TS)of 0.26.Among the assessed methodologies,the ATT-LSTM model outperformed the others in terms of recall,accuracy,and TS metrics.These findings confirm that the ATT-LSTM model offers a potent and innovative deep learning approach for enhancing the accuracy of low-visibility sea fog hazard predictions. 展开更多
关键词 deep learning low visibility attention mechanism prediction model low-probability event
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Coordinate Attention Based Wide&Deep Fusion Network for Typhoon Track Prediction 认领 引用 被引量：1
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作者 Guangxin He Jiadian Wang +2 位作者 Jie Feng Jingjia Luo Xun Li 《Tropical Cyclone Research and Review》 2026年第2期307-321,共15页
Accurate tropical cyclone track forecasting is of crucial importance for effective disaster risk management in coastal regions.These storms are recognized as one of nature's most hazardous weather phenomena.Howeve... Accurate tropical cyclone track forecasting is of crucial importance for effective disaster risk management in coastal regions.These storms are recognized as one of nature's most hazardous weather phenomena.However,traditional typhoon track prediction methods face certain limitations in handling complex meteorological data and multi-dimensional features.To improve prediction accuracy,this paper proposes an improved Wide&Deep framework.This framework utilizes a Stacked Two-layer Long Short-Term Memory(S2-LSTM)encoder in the Wide branch to model the sequential features of typhoon tracks,and incorporates a Multi-ConvGRU in the Deep branch to handle three-dimensional meteorological data.Following a dynamic/static feature decomposition,the S2-LSTM encoder processes a subset of dynamic features,effec-tively integrating typhoon position,wind speed,and rate-of-change information from multiple time steps to better capture the temporal evolution patterns of typhoon movement.Furthermore,a Coordinate Attention(CA)module is introduced,which captures positional dependencies along the vertical and horizontal dimensions within the feature maps through a factorized average pooling operation.This enhances the model's ability in identifying key areas within atmospheric fields,thus ensuring improved spatial precision in typhoon track forecasting.The model's efficacy was assessed with the 2020-2024 typhoon track dataset published by the China Meteorological Administration(CMA)and the ERA5 reanalysis product maintained by the European Centre for Medium-Range Weather Forecasts(ECMWF).The findings indicate how the two introduced enhancements to the model lead to higher precision for deep learning-based forecasts to a certain extent. 展开更多
关键词 Typhoon track prediction Wide&Deep framework S2-LSTM encoder Coordinate attention Multi-ConvGRU
基于Attention U2-Net的巷道围岩钻孔采动裂隙抗干扰识别研究 认领 引用
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作者 单鹏飞 康佳星 +4 位作者 来兴平 代晶晶 许慧聪 李杰宇 惠聪 《煤炭学报》 EI CAS CSCD 北大核心 2026年第2期1052-1067,共16页
采动裂隙演化特征是量化巷道围岩动力显现特征的关键依据之一。为了降低光照不均、噪声等对围岩钻孔成像的干扰以及孔内采动裂隙边缘模糊、形态多变等对采动裂隙识别的不利影响,提出基于Attention U2-Net的巷道围岩钻孔采动裂隙抗干... 采动裂隙演化特征是量化巷道围岩动力显现特征的关键依据之一。为了降低光照不均、噪声等对围岩钻孔成像的干扰以及孔内采动裂隙边缘模糊、形态多变等对采动裂隙识别的不利影响,提出基于Attention U2-Net的巷道围岩钻孔采动裂隙抗干扰识别方法。利用自主研发的巷道围岩态势全息感知装备来全天候实时采集高分辨率围岩钻孔采动裂隙影像,结合注入噪声、直方图均衡化调节、HSV中V通道色彩扰动与裂隙灰度三维投影等多种增强手段来提高非理想成像条件下图像数据环境泛化能力;通过在基准模型U2-Net中融合单通道注意力(SE、ECA)、空间注意力(CBAM)与全局多通道注意力(DANet)及组合注意力(CBAM+ECA)等机制,增强对低可见度裂隙等非理想采集环境下裂隙的感知与提取能力;在训练阶段采用深度监督复合损失函数(Dice+BCE)嵌入基准模型U2-Net的6个网络输出端,促进基准模型U2-Net以及Attention U2-Net模型的稳定训练与快速收敛,从而缓解小目标裂隙梯度消失与不连续问题。巷道围岩钻孔采动裂隙抗干扰识别实验结果表明:Attention U2-Net模型的IoU提升至83.1%、F1达到92.6%、EMA降至0.052,相较基准模型U-Net和U2-Net,训练阶段的收敛步长提前21轮次与10轮次,F1提高8.4%、4.0%。Attention U2-Net模型训练收敛更快,裂隙边缘检测、细长裂隙提取与复杂纹理分割能力更强,为准确分析围岩钻孔采动裂隙演化特征以及巷道围岩动力显现特征提供了可靠技术支撑。 展开更多
关键词 采动裂隙 损失函数 注意力机制 Attention U2-Net CBAM+ECA
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基于双通道CNN-LSTM-Attention预测模型的晋华炉煤气化过程操作优化 认领 引用 被引量：4
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作者 韩永明 李帅 +2 位作者 耿志强 汪京培 王孟志 《控制与决策》 EI CSCD 北大核心 2026年第4期1110-1121,共12页
煤气化过程具有强非线性、强耦合以及多目标冲突等特点,传统基于机理模型的操作优化方法难以达到高效且稳健的效果.晋华炉作为我国煤气化工艺中应用广泛的典型炉型,其运行优化亟需智能化建模和决策支持.鉴于此,提出一种基于双通道卷积-... 煤气化过程具有强非线性、强耦合以及多目标冲突等特点,传统基于机理模型的操作优化方法难以达到高效且稳健的效果.晋华炉作为我国煤气化工艺中应用广泛的典型炉型,其运行优化亟需智能化建模和决策支持.鉴于此,提出一种基于双通道卷积-长短期记忆网络-注意力机制(CNN-LSTM-Attention)预测模型的晋华炉操作优化方法.预测模型使用双通道结构融合工艺特征和历史序列信息,并利用层次化注意力机制提升关键特征的表达能力.在氢气、一氧化碳比例预测任务中,所构建双通道CNN-LSTM-Attention模型分别取得0.9322和0.9637的判定系数,显示出良好的精度和鲁棒性.在此基础上,结合粒子群优化算法,将预测模型作为代理模型对关键操作变量进行智能寻优.实验结果表明,优化方案相较于原始工况氢气比例提高了1.22%,一氧化碳比例提高了1.51%,总体有效气含量提升了1.38%.该研究为晋华炉气化过程的智能建模和工况优化提供了有效支撑,对煤气化典型炉型的高效运行具有重要参考价值. 展开更多
关键词 晋华炉 操作优化 LSTM CNN 注意力机制 粒子群算法
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A node importance prediction algorithm based on graph attention and contrastive learning 认领 引用
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作者 Jun Ai Yuming Zhang +2 位作者 Zhan Su Chenye Guo Mingsong Li 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期671-684,共14页
In complex network analysis,node ranking is vital for propagation prediction,structural optimization,and intervention strategy design,yet existing methods often fail to effectively integrate community information in d... In complex network analysis,node ranking is vital for propagation prediction,structural optimization,and intervention strategy design,yet existing methods often fail to effectively integrate community information in dynamic settings.To address this,this paper proposes a node ranking method that combines graph attention mechanisms with contrastive learning.Community detection is employed to extract node-level community features,and a joint embedding module is designed to fuse global and local structures,thereby incorporating community information into node representations.Based on this,a multi-layer graph attention network adaptively learns node and neighborhood features,while contrastive learning mitigates interference from dynamic evolution and strengthens the model's ability to capture multi-scale structural differences.Experiments on multiple dynamic network datasets show that the proposed method significantly outperforms existing approaches in ranking accuracy,particularly in networks with higher average degrees and clearer community structures.These results validate the effectiveness of the method in enhancing feature representation and modeling multi-scale dynamic node influence. 展开更多
关键词 node importance community features graph attention contrastive learning
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基于UNGO-CNN-BiLSTM-Self Attention的烧结混合料含水量预测模型研究 认领 引用
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作者 周亚罗 姚飞宇 +1 位作者 刘文广 张瑞成 《中国有色冶金》 CAS 北大核心 2026年第3期163-177,共15页
针对目前烧结机混合料含水量预测精度不足与收敛速度慢等问题,本文提出一种基于改进北方苍鹰优化算法(UNGO)的CNN-BiLSTM-Self Attention混合预测模型。模型首先利用卷积神经网络(CNN)提取输入特征的空间关联信息,再通过双向长短期记忆... 针对目前烧结机混合料含水量预测精度不足与收敛速度慢等问题,本文提出一种基于改进北方苍鹰优化算法(UNGO)的CNN-BiLSTM-Self Attention混合预测模型。模型首先利用卷积神经网络(CNN)提取输入特征的空间关联信息,再通过双向长短期记忆网络(BiLSTM)捕捉混合料含水量的时序依赖特征,并引入自注意力机制(Self Attention)以强化关键特征权重分配和长距离依赖建模,从而提升预测的稳定性与精度。为降低模型复杂度并提高运行效率,采用Spearman相关系数对输入特征进行筛选,剔除相关性较低的变量。针对传统优化算法易陷入局部最优的问题,在北方苍鹰算法基础上引入混沌初始化与非线性正余弦衰减策略,显著增强了全局寻优与局部搜索能力,实现了学习率、神经元个数、正则化系数及注意力键值等超参数的自适应优化。实验以河北某大型钢厂烧结生产数据为样本,结果表明,在相同数据条件下,CNN-BiLSTM-Self Attention模型的误差指标MSE、RMSE、MAE分别为0.0575、0.2398和0.1923,R2达到0.7908,均优于CNN、LSTM、CNN-LSTM及CNN-BiLSTM等对比模型,表明CNN-BiLSTM-Self Attention模型具有更强的特征表达与时序建模能力。经UNGO算法优化后,相较原模型,MSE、RMSE、MAE分别降低至0.0224、0.1498和0.1185,R2达到0.9184,整体性能优于NGO、PSO和WOA等算法优化结果,表现出更快的收敛速度和更高的预测精度。进一步与UNGO-Transformer、UNGO-BP及UNGO-XGBoost等主流预测模型对比,所提模型在预测精度、泛化能力和鲁棒性方面均具有更优表现。综上,UNGO-CNN-BiLSTM-Self Attention模型在结构设计与参数优化层面均展现出良好的综合性能,可有效实现烧结混合料含水量的高精度预测,为烧结过程的智能控制与能效优化提供了可靠的技术支撑。 展开更多
关键词 烧结混合料 含水量预测 CNN-BILSTM-Self Attention 改进北方苍鹰算法(UNGO) 自注意力机制(Self Attention) Tent混沌初始化策略 Spearman相关系数
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基于SPPF-Attention和C2f-DAB的铁路扣件图像检测算法 认领 引用 被引量：1
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作者 孟建军 姚丽青 +2 位作者 吕德芳 石锐 李怡璇 《科学技术与工程》 EI 北大核心 2026年第9期3859-3866,共8页
铁路扣件巡检作业过程中,由于病害扣件特征不明显、背景对比度较复杂等多种原因,病害钢轨扣件缺陷检测速度较慢,精度较低。为了更快、更精准地识别铁路扣件,提出了基于改进YOLOv10的铁路扣件图像检测算法,在SPPF(spatial pyramid poolin... 铁路扣件巡检作业过程中,由于病害扣件特征不明显、背景对比度较复杂等多种原因,病害钢轨扣件缺陷检测速度较慢,精度较低。为了更快、更精准地识别铁路扣件,提出了基于改进YOLOv10的铁路扣件图像检测算法,在SPPF(spatial pyramid pooling-fast)模块引入注意力机制(SPPF-Attention),通过特征重标定提升模型对关键区域的关注度,构建病害扣件检测模型;在C2f模块中嵌入双注意力块(C2f-DAB),该双注意力块串联了通道-空间注意力和并行注意力,进一步增强模型对扣件多维度特征的提取能力。实验结果表明,在自建铁路扣件数据集上,原始YOLOv10模型的平均精度为88.1%,加入注意力机制后提升至89.3%,引入双注意力块的比原模型提升2.1%。三种方案的运行速度分别为42、41和39帧/s,均满足铁路实时检测需求。通过对比实验验证了注意力机制和双注意力块网络模型在铁路扣件检测任务中的有效性,为相关领域的研究提供了理论依据和实践参考。 展开更多
关键词 改进YOLOv10 铁路扣件检测 SPPF模块 注意力机制 双注意力块
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BAID:A Lightweight Super-Resolution Network with Binary Attention-Guided Frequency-Aware Information Distillation 认领 引用
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作者 Jiajia Liu Junyi Lin +3 位作者 Wenxiang Dong Xuan Zhao Jianhua Liu Huiru Li 《Computers, Materials & Continua》 SCIE EI 2026年第2期1190-1208,共19页
Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such ... Single Image Super-Resolution(SISR)seeks to reconstruct high-resolution(HR)images from lowresolution(LR)inputs,thereby enhancing visual fidelity and the perception of fine details.While Transformer-based models—such as SwinIR,Restormer,and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information,these methods often suffer from substantial computational and memory overhead,which limits their deployment on resource-constrained edge devices.To address these challenges,we propose a novel lightweight super-resolution network,termed Binary Attention-Guided Information Distillation(BAID),which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter count whilemaintaining strong reconstruction performance.The network combines a high–low frequency decoupling strategy with a local–global attention sharing mechanism,enabling efficient compression of redundant computations through binary attention guidance.At the core of the architecture lies the Attention-Guided Distillation Block(AGDB),which retains the strengths of the information distillation framework while introducing a sparse binary attention module to enhance both inference efficiency and feature representation.Extensive×4 superresolution experiments on four standard benchmarks—Set5,Set14,BSD100,and Urban100—demonstrate that BAID achieves Peak Signal-to-Noise Ratio(PSNR)values of 32.13,28.51,27.47,and 26.15,respectively,with only 1.22 million parameters and 26.1 G Floating-Point Operations(FLOPs),outperforming other state-of-the-art lightweight methods such as Information Multi-Distillation Network(IMDN)and Residual Feature Distillation Network(RFDN).These results highlight the proposed model’s ability to deliver high-quality image reconstruction while offering strong deployment efficiency,making it well-suited for image restoration tasks in resource-limited environments. 展开更多
关键词 Single image super-resolution lightweight network binary attention information distillation
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Attention-guided multi-mission GNSS-R integration for enhanced global soil moisture retrieval 认领 引用
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作者 Wei Banand Xiaohong Zhang Qingyun Wang +3 位作者 Cuixian Lu Zhuo Wang Wei Ban Xiaohong Zhang 《Satellite Navigation》 SCIE EI CSCD 2026年第1期279-298,I0006,共20页
Soil Moisture(SM)monitoring using Global Navigation Satellite System-Reflectometry(GNSS-R)has often relied on observations from a single source,thereby resulting in underutilizing abundant reflected signal resources.M... Soil Moisture(SM)monitoring using Global Navigation Satellite System-Reflectometry(GNSS-R)has often relied on observations from a single source,thereby resulting in underutilizing abundant reflected signal resources.Moreover,aggregating multi-source observations without considering their heterogeneous characteristics can lead to suboptimal retrieval performance.To address these limitations,this study proposes a dual-branch attention-fusion Transformer model for global SM retrieval by integrating GNSS-R observations from the complementary Tianmu-1 and Fengyun-3 missions.The proposed model learns mission-specific feature representations and adaptively fuses them through attention-based fusion mechanism,thereby enabling effective exploitation of cross-mission comple mentary information.The integrated Tianmu-1+Fengyun-3 Level-1 dataset provides an average monthly temporal coverage of 79.7%globally.Using Soil Moisture Active Passive(SMAP)SM products as the reference,the proposed approach achieves correlation coefficient of 0.88 and root mean square error(RMSE)of 0.053 m3/m3,outperforming the conventional feature-concatenation and feature-merged baselines.Independent validation against measure ments from International Soil Moisture Network further confirms reliable performance,yielding an average correlation coefficient of 0.67 and unbiased RMSE of 0.041 m3/m3.In addition,Extended Triple Collocation(ETC)analysis dem onstrates satisfactory retrieval accuracy,with average ETC-derived correlation coefficient of 0.75 and random error standard deviation of 0.030 m3/m3.Overall,the results of this study demonstrate the effectiveness of the proposed attention-guided multi-mission SM retrieval framework and highlight its potential for enhancing spatiotemporal con tinuity of GNSS-R-based hydrological monitoring. 展开更多
关键词 Global Navigation Satellite System-Reflectometry Soil moisture Tianmu-1 Fengyun-3 Attention mechanism
基于CNN-BiLSTM-Attention的电动矿用车辆动力电池故障诊断研究 认领 引用
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作者 彭倩 曲家璇 +1 位作者 韩锋钢 付俊 《河南科技学院学报（自然科学版）》 2026年第3期33-44,共12页
目的提出CNN-BiLSTM-Attention融合方法,旨在解决复杂矿山场景下动力电池的故障诊断问题.方法采集电动矿用车辆动力电池故障数据,并对采集到的故障数据通过斯皮尔曼算法进行相关性分析,确定影响故障的8个重要因素;以此作为模型的输入,通... 目的提出CNN-BiLSTM-Attention融合方法,旨在解决复杂矿山场景下动力电池的故障诊断问题.方法采集电动矿用车辆动力电池故障数据,并对采集到的故障数据通过斯皮尔曼算法进行相关性分析,确定影响故障的8个重要因素;以此作为模型的输入,通过CNN挖掘故障数据与故障类型之间的局部特征;基于BiLSTM对CNN输出的特征数据和原始数据进行双向特征提取组合;将Attention与CNN-BiLSTM融合过滤次要特征信息来提高电动矿用车辆动力电池的故障诊断精度.结果采用CNN-BiLSTM-Attention方法开展动力电池故障诊断,准确率可达98.3%.结论与CNN、CNN-LSTM、CNN-BiLSTM相比,CNN-BiLSTM-Attention可有效提高故障诊断准确率,分别提升12.2%、6.9%、4.5%. 展开更多
关键词 CNN BiLSTM attention 动力电池 故障诊断
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Landslide image recognition based on residual networks with transfer learning and spatial-channel dual attention mechanism 认领 引用
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作者 ZHAO Cheng YU Jiacheng +3 位作者 XIE Yongfei CHEN Huiguan XING Jinquan NIU Jialun 《Journal of Mountain Science》 SCIE CSCD 2026年第7期3155-3169,共15页
Landslides are one of the most significant types of geological disasters worldwide.However,current landslide disaster recognition models generally face issues such as low training efficiency,reliance on large amounts ... Landslides are one of the most significant types of geological disasters worldwide.However,current landslide disaster recognition models generally face issues such as low training efficiency,reliance on large amounts of samples,and vague feature extraction.To address these problems,this paper proposes an enhanced intelligent recognition model based on an improved residual network.Taking remote sensing images from landslide-prone areas in Bijie City,Guizhou Province,China as the research subject,we implemented data augmentation techniques to expand the datasets,subsequently partitioning it into training and validation subsets at a73:27 ratio.During model training,a transfer learning strategy was employed to improve training efficiency through the utilization of ImageNet pre-trained weights.The Res Net50 network architecture was enhanced through the integration of channel attention and spatial attention mechanisms,enabling more effective extraction of critical landslide features.Comparative analysis indicates that after introducing the attention mechanisms,the enhanced model achieved an increase of 1.49% in accuracy,0.45% in precision,1.52% in F1 score,and 0.0297 in Kappa coefficient on the validation set.Notably,recall improved by 2.59%,indicating that the improved model has enhanced landslide disaster recognition capability and overall performance.This study successfully coupled the transfer learning strategy with a dual-attention mechanism into the ResNet50 architecture,allowing the rapid construction of an efficient recognition model under limited sample conditions,significantly improving the comprehensive performance and generalisation ability of the landslide recognition model. 展开更多
关键词 Attention mechanism Convolutional neural network Transfer Learning Landslide disaster identification
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YOLO-VSF:An Improved YOLO Model by Incorporating Attention Mechanism for Object Detection in Traffic Scenes 认领 引用
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作者 MIAO Jun GONG Shaocui +4 位作者 DENG Yongqiang LIANG Hao LI Juanjuan QI Honggang ZHANG Maoxuan 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期334-347,共14页
Intelligent transportation and autonomous driving systems have made urgent demands on the techniques with high performance on object detection in traffic scenes.This paper proposes an improved object detection model Y... Intelligent transportation and autonomous driving systems have made urgent demands on the techniques with high performance on object detection in traffic scenes.This paper proposes an improved object detection model YOLO-VSF over the YOLOv4 model,which is a representative work with excellent performance among YOLO series of object detection models.The main improvement measures include:The backbone feature extraction network CSPDarknet53 of YOLOv4 is replaced with VGG16 to improve the feature extraction capability;SENet attention mechanism is incorporated to improve the salient and correlation feature representation capability;Focal Loss is integrated into the loss function to overcome the sample imbalance problem.In addition,the detection performance of small targets is improved by increasing the resolution of input images.Experimental results show that on the VanJee traffic image dataset provided by Beijing VanJee Technology Co.,Ltd.,the proposed YOLO-VSF model achieves an average mean accuracy(mAP)of 92.21 percentage points,which improves the mAP by 3.04 percentage points compared with the YOLOv4 model while maintaining the detection speed of the original model.On the UA-DETRAC dataset,the average accuracy of YOLO-VSF is close to that of the latest YOLOv7 model with the number of parameters reduced by 1.329×107.The proposed method can provide a support for object detection in traffic scenes. 展开更多
关键词 object detection traffic scenes backbone network attention mechanism Focal Loss
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DANet:CSI Feedback for Massive MIMO Systems Based on Dual Attention Mechanism 认领 引用
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作者 Li Jun Wang Yukai +3 位作者 Zhang Zhichen He Bo Zheng Wenjing Lin Fei 《China Communications》 SCIE EI CSCD 2026年第2期285-297,共13页
In massive multiple-input multiple-output(MIMO)systems utilizing frequency division duplexing,optimizing system performance requires user equipment(UE)to compress downlink channel state information(CSI)and transmit it... In massive multiple-input multiple-output(MIMO)systems utilizing frequency division duplexing,optimizing system performance requires user equipment(UE)to compress downlink channel state information(CSI)and transmit it to the base station(BS).As the number of antennas increases,there is a significant rise in the overhead related to CSI feedback,posing considerable challenges to the precise acquisition of CSI by the BS.Existing approaches to CSI feedback utilizing deep learning techniques face challenges such as significant feedback overhead and limited precision in the reconstruction process.This study presents a novel lightweight CSI feedback framework known as the dual attention neural network(DANet).Within the DANet architecture,a dual attention module(DAM)is designed to enhance the network's performance.This DAM includes both channel attention blocks and spatial attention blocks.The channel attention blocks direct the model's focus toward channel features rich in information content while simultaneously suppressing less significant features.This approach enables the extraction of temporal correlations within the CSI matrix.The spatial attention block aids in extracting the correlation between the delay domain and the angle domain in the CSI matrix.By enhancing neural network performance,the DAM reduces information dispersion while enhancing the representation of global interactions.Simulation results demonstrate that DANet exhibits superior normalized mean square error and cosine similarity with comparable complexity compared to existing advanced CSI feedback methods. 展开更多
关键词 CSI feedback deep learning dual attention module(DAM) massive MIMO
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Highly biased DNA sequence reconstruction in DNA storage with multi-scale attention mechanism and contrast learning 认领 引用
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作者 Xue Li Yanfen Zheng +6 位作者 Qi Shao Jiadong Wang Wei Li Bin Wang Shihua Zhou Ben Cao Pan Zheng 《Synthetic and Systems Biotechnology》 SCIE CSCD 2026年第2期422-432,共11页
Base substitution,insertion,and deletion errors due to inherent technical constraints inducing unavoidable sequencing inaccuracies,limiting access to high-quality raw data and biological knowledge.To address this,we p... Base substitution,insertion,and deletion errors due to inherent technical constraints inducing unavoidable sequencing inaccuracies,limiting access to high-quality raw data and biological knowledge.To address this,we propose a deep sequence reconstruction model based on the multi-scale attention mechanism and contrastive learning(MACL),designed to enhance DNA sequence reconstruction under highly error rate conditions.The multi-scale attention mechanism includes base scale,inter-sequence and intra-sequence scale.First,the MSA Transformer fully extracts both global and local features of the base scale from the dimensions of the rows and columns.Furthermore,for the errors between sequences and the substitution errors within sequences,MACL proposes Inter-Sequence and Intra-Sequence Multi-Head Attention Mechanisms,respectively,and handles the insertion and deletion errors through the convolution module.In order to maximize the consistency of positive sample pairs in the representation space,we introduce contrastive learning and design a negative sample construction method and data augmentation that are more suitable for substitution errors in sequencing channels.Experiments on real-world DNA storage and viral genome datasets demonstrate that MACL significantly outperforms existing methods in reconstructing the DNA sequence.In particular,when combined with RS codes,MACL can losslessly reconstruct medical images in highly biased sequence(base error rate=5%)in DNA storage.In summary,the MACL introduces a novel approach to DNA sequence reconstruction in highly error rate conditions,laying a solid foundation for practical applications in DNA storage and genomics research. 展开更多
关键词 DNA sequence reconstruction DNA storage Noisy sequencing channels Multi-scale attention Contrast learning
Rutting depth prediction model for asphalt pavements based on a dual branch spatiotemporal attention network 认领 引用
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作者 Jun Hao Yuhan Weng +2 位作者 Le Li Zhenzhen Xing Lili Pei 《Digital Transportation and Safety》 2026年第1期32-41,共10页
Existing asphalt pavement rutting prediction models suffer from large long-term prediction errors due to their reliance on laboratory parameters and simplified assumptions.To address this issue,a dual-branch spatio-te... Existing asphalt pavement rutting prediction models suffer from large long-term prediction errors due to their reliance on laboratory parameters and simplified assumptions.To address this issue,a dual-branch spatio-temporal attention network model(DSAN)is proposed.The model is constructed by fusing temporal convolutional networks(TCN),long short-term memory networks(LSTM),and multi-head attention mechanisms to form parallel feature extraction branches for hierarchical spatio-temporal modeling.Validation is conducted based on full-scale pavement loop test results of eight typical asphalt pavement structures(AC layer thickness:12–52 cm)under 80 million equivalent standard axle loads(ESALs)accumulated during 2017–2023.Results show that the DSAN model significantly outperforms comparative models in prediction accuracy,breaking through the generalization bottleneck of traditional models for different layer thickness structures.This study provides an efficient solution for long-term performance prediction of asphalt pavements. 展开更多
关键词 Asphalt pavement Multiple branches Spatiotemporal attention Rutting prediction Rutting depth
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A Hierarchical Attention Framework for Business Information Systems:Theoretical Foundation and Proof-of-Concept Implementation 认领 引用
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作者 Sabina-Cristiana Necula Napoleon-Alexandru Sireteanu 《Computers, Materials & Continua》 SCIE EI 2026年第2期2055-2088,共34页
Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contempo... Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems. 展开更多
关键词 Attention mechanisms business information systems theoretical framework enterprise architecture complex systems hierarchical attention
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An Attention-Based 6D Pose Estimation Network for Weakly Textured Industrial Parts 认领 引用
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作者 Song Xu Liang Xuan +1 位作者 Yifeng Li Qiang Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第2期2148-2166,共19页
The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly fa... The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly faces two challenges:one is the loss of information and interference caused by occlusion and stacking in the sorting scenario,the other is the difficulty of feature extraction due to the weak texture of industrial parts.To address the above problems,this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts,namely CB-PVNet.On the one hand,the voting scheme can predict the keypoints of affected pixels,which improves the accuracy of keypoint localization even in scenarios such as weak texture and partial occlusion.On the other hand,the attention mechanism can extract interesting features of the object while suppressing useless features of surroundings.Extensive comparative experiments were conducted on both public datasets(including LINEMOD,Occlusion LINEMOD and T-LESS datasets)and self-made datasets.The experimental results indicate that the proposed network CB-PVNet can achieve accuracy of ADD(-s)comparable to state-of-the-art using only RGB images while ensuring real-time performance.Additionally,we also conducted robot grasping experiments in the real world.The balance between accuracy and computational efficiency makes the method well-suited for applications in industrial automation. 展开更多
关键词 Industrial robots pose estimation industrial parts attention mechanism weak texture
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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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Attention-Enhanced ResNet-LSTM Model with Wind-Regime Clustering for Wind Speed Forecasting 认领 引用
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作者 Weiqi Mao Enbo Yu +1 位作者 Guoji Xu Xiaozhen Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期781-811,共31页
Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning... Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting. 展开更多
关键词 Wind speed prediction residual network transfer learning long short-term memory attention mechanism
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