Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback com...Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback combination of the agent's own state and neighbors' output,which can achieve exponential output consensus through intermittent communication. The controller is obtained by solving two linear matrix equations, and Zeno behavior is excluded.展开更多
While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used imag...While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.展开更多
针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现...针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现了硬件时间戳获取,避免了由于协议栈软件处理延时产生的不确定性;针对PTP时钟晶振老化导致的时间同步偏差及网络延迟抖动问题,采用迭代方法优化了本地时钟频率调节算法,提高了频率校正精度。经实际测试,主从时钟偏差的RMS(root mean square)优于20 ns,提升了时钟同步精度。展开更多
针对视觉结构类似导致的文种相似性问题,基于局部三值模式的相邻共生矩阵(co-occurrence of adjacent local ternary patterns,CoALTP)提出一种具有判别性和鲁棒性的局部三值模式的相邻共生矩阵(discriminant and robust co-occurrence ...针对视觉结构类似导致的文种相似性问题,基于局部三值模式的相邻共生矩阵(co-occurrence of adjacent local ternary patterns,CoALTP)提出一种具有判别性和鲁棒性的局部三值模式的相邻共生矩阵(discriminant and robust co-occurrence of adjacent local ternary patterns,DRCoALTP)方法,用于获取图像纹理。计算文档图像的相邻稀疏局部三值模式(adjacent sparse local ternary patterns,ASLTP),将采样点数量设定为8,以便获得详细的局部纹理,设计出一种基于自适应中值滤波思想的半自适应阈值方法,用于提取灰度图像中心像素周边对角邻域像素的编码值。ASLTP在邻域像素位置存放稀疏局部三值模式(local ternary patterns,LTP)的值,提取灰度共生矩阵(gray-level co-occurrence matrix,GLCM),从4个方向统计使用ASLTP后灰度图像像素之间的频率关系。该算法在阿拉伯文、俄文、简体中文、哈萨克文、藏文、蒙古文、土耳其文、维吾尔文、英文、吉尔吉斯斯坦文和塔吉克斯坦文11个文种的自建印刷体文档图像数据集中验证。试验结果表明,相较于基线和先进的纹理方法,改进后的方法更具判别性,平均识别准确率为99.14%。为改善CoALTP方法可能产生低效分类特征的问题,提出半自适应阈值方法,有效提高识别率并抑制噪声。此外,针对算法产生的高维特征,采用基于均方差的特征选择方法,通过支持向量机(support vector machine,SVM)分类器特征选择后,识别速度提高284%,对11个文种的平均识别准确率达99.44%。展开更多
基金supported by the National Science and Technology Innovation 2030-Major Program(2022ZD 0115403)the National Natural Science Foundation of China(61991414)+1 种基金Chongqing Natural Science Foundation(CSTB2023NSCQJQX0018)Beijing Natural Science Foundation(L221005)
文摘Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback combination of the agent's own state and neighbors' output,which can achieve exponential output consensus through intermittent communication. The controller is obtained by solving two linear matrix equations, and Zeno behavior is excluded.
文摘While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.
文摘针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现了硬件时间戳获取,避免了由于协议栈软件处理延时产生的不确定性;针对PTP时钟晶振老化导致的时间同步偏差及网络延迟抖动问题,采用迭代方法优化了本地时钟频率调节算法,提高了频率校正精度。经实际测试,主从时钟偏差的RMS(root mean square)优于20 ns,提升了时钟同步精度。
文摘针对视觉结构类似导致的文种相似性问题,基于局部三值模式的相邻共生矩阵(co-occurrence of adjacent local ternary patterns,CoALTP)提出一种具有判别性和鲁棒性的局部三值模式的相邻共生矩阵(discriminant and robust co-occurrence of adjacent local ternary patterns,DRCoALTP)方法,用于获取图像纹理。计算文档图像的相邻稀疏局部三值模式(adjacent sparse local ternary patterns,ASLTP),将采样点数量设定为8,以便获得详细的局部纹理,设计出一种基于自适应中值滤波思想的半自适应阈值方法,用于提取灰度图像中心像素周边对角邻域像素的编码值。ASLTP在邻域像素位置存放稀疏局部三值模式(local ternary patterns,LTP)的值,提取灰度共生矩阵(gray-level co-occurrence matrix,GLCM),从4个方向统计使用ASLTP后灰度图像像素之间的频率关系。该算法在阿拉伯文、俄文、简体中文、哈萨克文、藏文、蒙古文、土耳其文、维吾尔文、英文、吉尔吉斯斯坦文和塔吉克斯坦文11个文种的自建印刷体文档图像数据集中验证。试验结果表明,相较于基线和先进的纹理方法,改进后的方法更具判别性,平均识别准确率为99.14%。为改善CoALTP方法可能产生低效分类特征的问题,提出半自适应阈值方法,有效提高识别率并抑制噪声。此外,针对算法产生的高维特征,采用基于均方差的特征选择方法,通过支持向量机(support vector machine,SVM)分类器特征选择后,识别速度提高284%,对11个文种的平均识别准确率达99.44%。
