针对节点数量较多、功能类型多样的控制器局域网(controller area network,CAN)中存在的管理问题,基于其协议技术标准和OSEK/VDX规范,提出并实现了一种网络管理的改进策略。该策略可实现直接网络管理中CAN节点的分组管理和合并。实验结...针对节点数量较多、功能类型多样的控制器局域网(controller area network,CAN)中存在的管理问题,基于其协议技术标准和OSEK/VDX规范,提出并实现了一种网络管理的改进策略。该策略可实现直接网络管理中CAN节点的分组管理和合并。实验结果表明,这种网络管理机制能够快速构建网络管理的逻辑结构,提高了CAN网络管理效率。展开更多
提出了一种基于分组机制的位仲裁查询树(GBAQT,bit arbitration query tree based on grouping mechanism)算法。该算法根据标签ID自身特征分组,采用3位仲裁位来取代传统1位仲裁识别标签的方式,通过碰撞位信息得到传输数据,从而能避免...提出了一种基于分组机制的位仲裁查询树(GBAQT,bit arbitration query tree based on grouping mechanism)算法。该算法根据标签ID自身特征分组,采用3位仲裁位来取代传统1位仲裁识别标签的方式,通过碰撞位信息得到传输数据,从而能避免一些空闲时隙。算法的性能分析和仿真结果表明,GBAQT防碰撞算法具有较少的总时隙数,系统效率和时隙利用率也明显优于其他算法。展开更多
Human pose estimation is a critical research area in the field of computer vision,playing a significant role in applications such as human-computer interaction,behavior analysis,and action recognition.In this paper,we...Human pose estimation is a critical research area in the field of computer vision,playing a significant role in applications such as human-computer interaction,behavior analysis,and action recognition.In this paper,we propose a U-shaped keypoint detection network(DAUNet)based on an improved ResNet subsampling structure and spatial grouping mechanism.This network addresses key challenges in traditional methods,such as information loss,large network redundancy,and insufficient sensitivity to low-resolution features.DAUNet is composed of three main components.First,we introduce an improved BottleNeck block that employs partial convolution and strip pooling to reduce computational load and mitigate feature loss.Second,after upsampling,the network eliminates redundant features,improving the overall efficiency.Finally,a lightweight spatial grouping attention mechanism is applied to enhance low-resolution semantic features within the feature map,allowing for better restoration of the original image size and higher accuracy.Experimental results demonstrate that DAUNet achieves superior accuracy compared to most existing keypoint detection models,with a mean PCKh@0.5 score of 91.6%on the MPII dataset and an AP of 76.1%on the COCO dataset.Moreover,real-world experiments further validate the robustness and generalizability of DAUNet for detecting human bodies in unknown environments,highlighting its potential for broader applications.展开更多
近年来,面向黄河口的监测需求日益增大,如黄河入海流路改道至清水沟路以来,在新老河道的交汇处存在着丰富的地物类别,对于这些地物类别的检测识别研究有助于掌握生态环境状态,对于黄河口的湿地保护以及国家改善环境的战略支持具有重要...近年来,面向黄河口的监测需求日益增大,如黄河入海流路改道至清水沟路以来,在新老河道的交汇处存在着丰富的地物类别,对于这些地物类别的检测识别研究有助于掌握生态环境状态,对于黄河口的湿地保护以及国家改善环境的战略支持具有重要意义。因此,本文提出一种新的湿地高光谱图像分类方法,分双路分别提取图像的空谱特征并融合分类。光谱维采用分组预处理的双向长短期记忆网络(Bi-LSTM)有效学习光谱特征;空间维采用注意力加强的多尺度卷积网络有效增强所提取的空谱特征,使得分类结果更具准确性。本文实验应用覆盖黄河入海口新老河道交界处的成像光谱仪(Compact High Resolution Imaging Spectrometer,CHRIS)所采集的数据和黄河三角洲自然保护区滨海湿地高分5号传感器(GF-5)所采集的高光谱图像开展。结果表明:分组与双向长短期记忆网络(Bi-LSTM)的有效结合显著提升了网络性能,同其他监督分类方法相比提升约3%~8%,此外注意力机制的加入同比增加约3%,在使用1%的极少训练集下数据集CHRIS和GF-5的总体分类精度分别达到92.3%和86.11%。展开更多
文摘针对节点数量较多、功能类型多样的控制器局域网(controller area network,CAN)中存在的管理问题,基于其协议技术标准和OSEK/VDX规范,提出并实现了一种网络管理的改进策略。该策略可实现直接网络管理中CAN节点的分组管理和合并。实验结果表明,这种网络管理机制能够快速构建网络管理的逻辑结构,提高了CAN网络管理效率。
文摘提出了一种基于分组机制的位仲裁查询树(GBAQT,bit arbitration query tree based on grouping mechanism)算法。该算法根据标签ID自身特征分组,采用3位仲裁位来取代传统1位仲裁识别标签的方式,通过碰撞位信息得到传输数据,从而能避免一些空闲时隙。算法的性能分析和仿真结果表明,GBAQT防碰撞算法具有较少的总时隙数,系统效率和时隙利用率也明显优于其他算法。
基金supported by the Natural Science Foundation of Hubei Province of China under grant number 2022CFB536the National Natural Science Foundation of China under grant number 62367006the 15th Graduate Education Innovation Fund of Wuhan Institute of Technology under grant number CX2023579.
文摘Human pose estimation is a critical research area in the field of computer vision,playing a significant role in applications such as human-computer interaction,behavior analysis,and action recognition.In this paper,we propose a U-shaped keypoint detection network(DAUNet)based on an improved ResNet subsampling structure and spatial grouping mechanism.This network addresses key challenges in traditional methods,such as information loss,large network redundancy,and insufficient sensitivity to low-resolution features.DAUNet is composed of three main components.First,we introduce an improved BottleNeck block that employs partial convolution and strip pooling to reduce computational load and mitigate feature loss.Second,after upsampling,the network eliminates redundant features,improving the overall efficiency.Finally,a lightweight spatial grouping attention mechanism is applied to enhance low-resolution semantic features within the feature map,allowing for better restoration of the original image size and higher accuracy.Experimental results demonstrate that DAUNet achieves superior accuracy compared to most existing keypoint detection models,with a mean PCKh@0.5 score of 91.6%on the MPII dataset and an AP of 76.1%on the COCO dataset.Moreover,real-world experiments further validate the robustness and generalizability of DAUNet for detecting human bodies in unknown environments,highlighting its potential for broader applications.
文摘近年来,面向黄河口的监测需求日益增大,如黄河入海流路改道至清水沟路以来,在新老河道的交汇处存在着丰富的地物类别,对于这些地物类别的检测识别研究有助于掌握生态环境状态,对于黄河口的湿地保护以及国家改善环境的战略支持具有重要意义。因此,本文提出一种新的湿地高光谱图像分类方法,分双路分别提取图像的空谱特征并融合分类。光谱维采用分组预处理的双向长短期记忆网络(Bi-LSTM)有效学习光谱特征;空间维采用注意力加强的多尺度卷积网络有效增强所提取的空谱特征,使得分类结果更具准确性。本文实验应用覆盖黄河入海口新老河道交界处的成像光谱仪(Compact High Resolution Imaging Spectrometer,CHRIS)所采集的数据和黄河三角洲自然保护区滨海湿地高分5号传感器(GF-5)所采集的高光谱图像开展。结果表明:分组与双向长短期记忆网络(Bi-LSTM)的有效结合显著提升了网络性能,同其他监督分类方法相比提升约3%~8%,此外注意力机制的加入同比增加约3%,在使用1%的极少训练集下数据集CHRIS和GF-5的总体分类精度分别达到92.3%和86.11%。
