Coupled phase oscillators are adopted as powerful platforms in studying synchrony behaviors emerged in various systems with rhythmic dynamics. Much attention has been focused on coupled time-continuous oscillators des...Coupled phase oscillators are adopted as powerful platforms in studying synchrony behaviors emerged in various systems with rhythmic dynamics. Much attention has been focused on coupled time-continuous oscillators described by differential equations. In this paper, we study the synchronization dynamics of networks of coupled circle maps as the discrete version of the Kuramoto model. Despite of its simplicity in mathematical form, it is found that discreteness may induce many interesting synchronization behaviors. Multiple synchronization and desynchronization transitions of both phases and frequencies are found with varying the coupling among circle-map oscillators. The mechanisms of these transitions are interpreted in terms of the mean-field approach, where collective bifurcation cascades are revealed for coupled circle-map oscillators.展开更多
“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素...“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No. 11875135)。
文摘Coupled phase oscillators are adopted as powerful platforms in studying synchrony behaviors emerged in various systems with rhythmic dynamics. Much attention has been focused on coupled time-continuous oscillators described by differential equations. In this paper, we study the synchronization dynamics of networks of coupled circle maps as the discrete version of the Kuramoto model. Despite of its simplicity in mathematical form, it is found that discreteness may induce many interesting synchronization behaviors. Multiple synchronization and desynchronization transitions of both phases and frequencies are found with varying the coupling among circle-map oscillators. The mechanisms of these transitions are interpreted in terms of the mean-field approach, where collective bifurcation cascades are revealed for coupled circle-map oscillators.
文摘“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。
