As the maritime industry continues to thrive and maritime services diversify,the demand for highly reliable maritime communication systems has become increasingly prominent.However,harsh marine conditions pose signifi...As the maritime industry continues to thrive and maritime services diversify,the demand for highly reliable maritime communication systems has become increasingly prominent.However,harsh marine conditions pose significant challenges to communication systems.In this work,we propose a Maritime AutoEncoder(MAE)communication system based on Attention Mechanisms(AMs)and DenseBlock(namely AM-Dense-MAE).AM-Dense-MAE utilizes DenseBlock and long short-term memory to extract deep features and capture spatio-temporal relationships,addressing the issue of“long-term dependency”.Furthermore,the decoder incorporates spatial attention modules and convolutional block attention module to enhance the preservation of crucial information and suppress irrelevant data.We employ the Rician fading channel model to simulate maritime communication channels.A substantial volume of data is utilized for model training and parameter optimization.Simulation results demonstrate that,in comparison to the benchmarks,the proposed AM-Dense-MAE exhibits better block error rate performance under various signal-to-noise ratio conditions and showcases generalization capabilities across diverse parameter settings.展开更多
Earthquake early warning(EEW)is one of the important tools to reduce the hazard of earthquakes.In contemporary seismology,EEW is typically transformed into a fast classification of earthquake magnitude,i.e.,large magn...Earthquake early warning(EEW)is one of the important tools to reduce the hazard of earthquakes.In contemporary seismology,EEW is typically transformed into a fast classification of earthquake magnitude,i.e.,large magnitude earthquakes that require warning are in the positive category and vice versa in the negative category.However,the current standard information signal processing routines for magnitude fast classification are time-consuming and vulnerable to data imbalance.Therefore,in this study,Deep Learning(DL)algorithms are introduced to assist with EEW.For the three-component seismic waveform record of 7 s obtained from the China Earthquake Network Center(CENC),this paper proposes a DL model(EEWMagNet),which accomplishes the extraction of spatial and temporal features through DenseBlock with Bottleneck and Multi-Head Attention.Extensive experiments on Chinese field data demonstrate that the proposed model performs well in the fast classification of magnitude.Moreover,the comparison experiments demonstrate that the epicenter distance information is indispensable,and the normalization has a negative effect on the model to capture accurate amplitude information.展开更多
Accurate detection of citrus in the natural orchard is crucial for citrus-picking robots.However,it has become a challenging task due to the influence of illumination,severe shading of branches and leaves,as well as o...Accurate detection of citrus in the natural orchard is crucial for citrus-picking robots.However,it has become a challenging task due to the influence of illumination,severe shading of branches and leaves,as well as overlapping of citrus.To this end,a Dense-TRU-YOLO model was proposed,which integrated the Denseblock with the Transformer and used UNet++network as the neck structure.First of all,the Denseblock structure was incorporated into YOLOv5,which added shallow semantic information to the deep part of the network and improved the flow of information and gradients.Secondly,the deepest Cross Stage Partial Connections(CSP)bottleneck with the 3 convolutions module of the backbone was replaced by the CSP Transformer with 3 convolutions module,which increased the semantic resolution and improved the detection accuracy of occlusion.Finally,the neck of the original network was replaced by the combined structure of UNet++feature pyramid networks(UNet++-FPN),which not only added cross-weighted links between nodes with the same size but also enhanced the feature fusion ability between nodes with different sizes,making the regression of the network to the target boundary more accurate.Ablation experiments and comparison experiments showed that the Dense-TRU-YOLO can effectively improve the detection accuracy of citrus under severe occlusion and overlap.The overall accuracy,recall,mAP@0.5,and F1 were 90.8%,87.6%,90.5%,and 87.9%,respectively.The precision of Dense-TRU-YOLO was the highest,which was 3.9%,6.45%,1.9%,7.4%,3.3%,4.9%,and 9.9%higher than that of the YOLOv5-s,YOLOv3,YOLOv5-n,YOLOv4-tiny,YOLOv4,YOLOX,and YOLOF,respectively.In addition,the reasoning speed was 9.2 ms,1.7 ms,10.5 ms,and 2.3 ms faster than that of YOLOv3,YOLOv5-n,YOLOv4,and YOLOX.Dense TRU-YOLO is designed to enhance the accuracy of fruit recognition in natural settings and boost the detection capabilities for small targets at extended ranges.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51939001 and 62371085)the Fundamental Research Funds for the Central Universities(No.3132023514).
文摘As the maritime industry continues to thrive and maritime services diversify,the demand for highly reliable maritime communication systems has become increasingly prominent.However,harsh marine conditions pose significant challenges to communication systems.In this work,we propose a Maritime AutoEncoder(MAE)communication system based on Attention Mechanisms(AMs)and DenseBlock(namely AM-Dense-MAE).AM-Dense-MAE utilizes DenseBlock and long short-term memory to extract deep features and capture spatio-temporal relationships,addressing the issue of“long-term dependency”.Furthermore,the decoder incorporates spatial attention modules and convolutional block attention module to enhance the preservation of crucial information and suppress irrelevant data.We employ the Rician fading channel model to simulate maritime communication channels.A substantial volume of data is utilized for model training and parameter optimization.Simulation results demonstrate that,in comparison to the benchmarks,the proposed AM-Dense-MAE exhibits better block error rate performance under various signal-to-noise ratio conditions and showcases generalization capabilities across diverse parameter settings.
基金supported by Fundamental Research Funds for the Central Universities(N2217003)Joint Fund of Science&Technology Department of Liaoning Province,and State Key Laboratory of Robotics,China(2020-KF-12-11)+1 种基金National Natural Science Foundation of China(61902057,41774063)Science for Earthquake Resilience(XH21042).
文摘Earthquake early warning(EEW)is one of the important tools to reduce the hazard of earthquakes.In contemporary seismology,EEW is typically transformed into a fast classification of earthquake magnitude,i.e.,large magnitude earthquakes that require warning are in the positive category and vice versa in the negative category.However,the current standard information signal processing routines for magnitude fast classification are time-consuming and vulnerable to data imbalance.Therefore,in this study,Deep Learning(DL)algorithms are introduced to assist with EEW.For the three-component seismic waveform record of 7 s obtained from the China Earthquake Network Center(CENC),this paper proposes a DL model(EEWMagNet),which accomplishes the extraction of spatial and temporal features through DenseBlock with Bottleneck and Multi-Head Attention.Extensive experiments on Chinese field data demonstrate that the proposed model performs well in the fast classification of magnitude.Moreover,the comparison experiments demonstrate that the epicenter distance information is indispensable,and the normalization has a negative effect on the model to capture accurate amplitude information.
基金supported by the special research project of the Innovation and Development Center for Ideological and Political Work in Colleges and Universities(Wuhan Donghu University)under the Ministry of Education in 2024(Practice Research on the Whole-process Fine Cultivation of New Engineering Talents with New Qualities in the Context of New Productive Forces,Grant No.WHDHSZZX2024085)the Humanities and Social Sciences Research project of the Chongqing Education Commission in 2024(Theory and Practice Research on Digital Portraits Enabling Comprehensive Quality Evaluation of College Students,Grant No.24SKGH100)the general project of the“14th Five-Year Plan”for Education Science in Chongqing in 2024(Research and Practice on the Construction of an Intelligent Recommendation Employment System for Person-Job Matching Enabled by Digital Portraits,Grant No.K24YG2060081).
文摘Accurate detection of citrus in the natural orchard is crucial for citrus-picking robots.However,it has become a challenging task due to the influence of illumination,severe shading of branches and leaves,as well as overlapping of citrus.To this end,a Dense-TRU-YOLO model was proposed,which integrated the Denseblock with the Transformer and used UNet++network as the neck structure.First of all,the Denseblock structure was incorporated into YOLOv5,which added shallow semantic information to the deep part of the network and improved the flow of information and gradients.Secondly,the deepest Cross Stage Partial Connections(CSP)bottleneck with the 3 convolutions module of the backbone was replaced by the CSP Transformer with 3 convolutions module,which increased the semantic resolution and improved the detection accuracy of occlusion.Finally,the neck of the original network was replaced by the combined structure of UNet++feature pyramid networks(UNet++-FPN),which not only added cross-weighted links between nodes with the same size but also enhanced the feature fusion ability between nodes with different sizes,making the regression of the network to the target boundary more accurate.Ablation experiments and comparison experiments showed that the Dense-TRU-YOLO can effectively improve the detection accuracy of citrus under severe occlusion and overlap.The overall accuracy,recall,mAP@0.5,and F1 were 90.8%,87.6%,90.5%,and 87.9%,respectively.The precision of Dense-TRU-YOLO was the highest,which was 3.9%,6.45%,1.9%,7.4%,3.3%,4.9%,and 9.9%higher than that of the YOLOv5-s,YOLOv3,YOLOv5-n,YOLOv4-tiny,YOLOv4,YOLOX,and YOLOF,respectively.In addition,the reasoning speed was 9.2 ms,1.7 ms,10.5 ms,and 2.3 ms faster than that of YOLOv3,YOLOv5-n,YOLOv4,and YOLOX.Dense TRU-YOLO is designed to enhance the accuracy of fruit recognition in natural settings and boost the detection capabilities for small targets at extended ranges.
