X-ray bright points(XBPs)are small-scale brightenings in the solar corona.Their counterparts in the lower atmosphere,however,are poorly investigated.In this paper,we study the counterparts of XBPs in the upper chromos...X-ray bright points(XBPs)are small-scale brightenings in the solar corona.Their counterparts in the lower atmosphere,however,are poorly investigated.In this paper,we study the counterparts of XBPs in the upper chromosphere where the Hαline center is formed.The XBPs were observed by the X-ray Telescope(XRT)aboard the Hinode spacecraft during the observing plan(HOP0124)in August 2009,coordinated with the Solar Magnetic Activity Research Telescope(SMART)in the Kwasan and Hida Observatory,Kyoto University.It is found that there are 77 Hαbrightenings in the same field of view of XRT,and among 57 XBPs,29 have counterparts in the Hαchannel.We found three types of relationship:Types a,b and c,corresponding to XBPs appearing first,Hαbrightenings occurring first and no respective correspondence between them.Most of the strong XBPs belong to Type a.The Hαcounterparts generally have double-kernel structures associated with magnetic bipoles and are cospatial with the footpoints of the XBP loops.The average lag time is~3 minutes.This implies that for Type a the heating,presumably through magnetic reconnection,occurs first in the solar upper atmosphere and then goes downwards along the small-scale magnetic loops that comprise the XBPs.In this case,the thermal conduction plays a dominant role over the non-thermal heating.Only a few events belong to Type b,which could happen when magnetic reconnection occurs in the chromosphere and produces an upward jet which heats the upper atmosphere and causes the XBP.About half of the XBPs belong to Type c.Generally they have weak emission in SXR.About 62%Hαbrightenings have no corresponding XBPs.Most of them are weak and have single structures.展开更多
From the observed vector magnetic fields by the Solar Optical Telescope/ Spectro-Polarimeter aboard the satellite Hinode, we have examined whether or not the quiet Sun magnetic fields are non-potential, and how the G-...From the observed vector magnetic fields by the Solar Optical Telescope/ Spectro-Polarimeter aboard the satellite Hinode, we have examined whether or not the quiet Sun magnetic fields are non-potential, and how the G-band filigrees and Ca II network bright points （NBPs） are associated with the magnetic non-potentiality. A sizable quiet region in the disk center is selected for this study. The new findings by the study are as follows. （1） The magnetic fields of the quiet region are obviously non-potential. The region-average shear angle is 40°, the average vertical current is 0.016A m^-2, and the average free magnetic energy density, 2.7× 10^2erg cm^-3. The magnitude of these non-potential quantities is comparable to that in solar active regions. （2） There are overall correlations among current helicity, free magnetic energy and longitudinal fields. The magnetic non-potentiality is mostly concentrated in the close vicinity of network elements which have stronger longitudinal fields. （3） The filigrees and NBPs are magnetically characterized by strong longitudinal fields, large electric helicity, and high free energy density. Because the selected region is away from any enhanced network, these new results can generally be applied to the quiet Sun. The findings imply that stronger network elements play a role in high magnetic non-potentiality in heating the solar atmosphere and in conducting the solar wind.展开更多
目的柑橘是我国最常见的水果之一,目前多以人工采摘为主,成本高、效率低等问题严重制约规模化生产,因此柑橘自动采摘成为近年的研究热点。但是,柑橘生长环境复杂、枝条形态各异、枝叶和果实互遮挡严重,如何精准实时地定位采摘点成为自...目的柑橘是我国最常见的水果之一,目前多以人工采摘为主,成本高、效率低等问题严重制约规模化生产,因此柑橘自动采摘成为近年的研究热点。但是,柑橘生长环境复杂、枝条形态各异、枝叶和果实互遮挡严重,如何精准实时地定位采摘点成为自动采摘的关键。通过构建级联混合网络模型,提出了一种通用且高效的柑橘采摘点自动精准定位方法。方法构建团簇框生成模型和枝条稀疏实例分割模型,对两者进行级联混合实现实时柑橘采摘点定位。首先,构建柑橘果实检测网络,提出团簇框生成模型,该模型通过特征提取、果实检测框生成和DBSCAN(density-based spatial clustering of applications with noise)果实密度聚类,实时地生成图像内果实数目最多的团簇框坐标;然后,提出融合亮度先验的枝条稀疏分割模型,该模型以团簇框内的图像作为输入,有效降低背景枝条的干扰,通过融合亮度先验的稀疏实例激活图,实时地分割出与果实相连接枝条实例;最后基于分割结果搜索果实采摘点定位坐标。结果经过长时间户外采集制作了柑橘果实检测数据集CFDD(citrus fruit detection dataset)和柑橘枝条分割数据集CBSD(citrus branch segmentation dataset)。这两个数据集由成熟果实、未成熟果实组成,包含晴天、阴天、顺光和逆光等挑战,总共37000幅图像。在该数据集上本文方法的采摘点定位精准度达到了95.77%,帧率(frames per second,FPS)达到了28.21帧/s。结论本文方法在果实采摘点定位方面取得较好进展,能够快速且准确地获取柑橘采摘点,并且提供配套的机械臂采摘设备可供该采摘点定位算法的落地使用,为柑橘产业发展提供有力支持。展开更多
基金supported by the National Basic Research Program of China (Grant No.2011CB811402)the National Natural Science Foundation of China (Grant Nos.10878002,10610099,10933003,10673004,10073005,10403003,and 11025314)
文摘X-ray bright points(XBPs)are small-scale brightenings in the solar corona.Their counterparts in the lower atmosphere,however,are poorly investigated.In this paper,we study the counterparts of XBPs in the upper chromosphere where the Hαline center is formed.The XBPs were observed by the X-ray Telescope(XRT)aboard the Hinode spacecraft during the observing plan(HOP0124)in August 2009,coordinated with the Solar Magnetic Activity Research Telescope(SMART)in the Kwasan and Hida Observatory,Kyoto University.It is found that there are 77 Hαbrightenings in the same field of view of XRT,and among 57 XBPs,29 have counterparts in the Hαchannel.We found three types of relationship:Types a,b and c,corresponding to XBPs appearing first,Hαbrightenings occurring first and no respective correspondence between them.Most of the strong XBPs belong to Type a.The Hαcounterparts generally have double-kernel structures associated with magnetic bipoles and are cospatial with the footpoints of the XBP loops.The average lag time is~3 minutes.This implies that for Type a the heating,presumably through magnetic reconnection,occurs first in the solar upper atmosphere and then goes downwards along the small-scale magnetic loops that comprise the XBPs.In this case,the thermal conduction plays a dominant role over the non-thermal heating.Only a few events belong to Type b,which could happen when magnetic reconnection occurs in the chromosphere and produces an upward jet which heats the upper atmosphere and causes the XBP.About half of the XBPs belong to Type c.Generally they have weak emission in SXR.About 62%Hαbrightenings have no corresponding XBPs.Most of them are weak and have single structures.
基金supported by the National Natural Science Foundation of China (10873020, 10703007, G10573025, 40674081, 10603008, 10733020 and 40890161)the Chinese Academy of Sciences Project KJCX2-YW-T04the National Basic Research Program of China(G2006CB806303)
文摘From the observed vector magnetic fields by the Solar Optical Telescope/ Spectro-Polarimeter aboard the satellite Hinode, we have examined whether or not the quiet Sun magnetic fields are non-potential, and how the G-band filigrees and Ca II network bright points （NBPs） are associated with the magnetic non-potentiality. A sizable quiet region in the disk center is selected for this study. The new findings by the study are as follows. （1） The magnetic fields of the quiet region are obviously non-potential. The region-average shear angle is 40°, the average vertical current is 0.016A m^-2, and the average free magnetic energy density, 2.7× 10^2erg cm^-3. The magnitude of these non-potential quantities is comparable to that in solar active regions. （2） There are overall correlations among current helicity, free magnetic energy and longitudinal fields. The magnetic non-potentiality is mostly concentrated in the close vicinity of network elements which have stronger longitudinal fields. （3） The filigrees and NBPs are magnetically characterized by strong longitudinal fields, large electric helicity, and high free energy density. Because the selected region is away from any enhanced network, these new results can generally be applied to the quiet Sun. The findings imply that stronger network elements play a role in high magnetic non-potentiality in heating the solar atmosphere and in conducting the solar wind.
文摘目的柑橘是我国最常见的水果之一,目前多以人工采摘为主,成本高、效率低等问题严重制约规模化生产,因此柑橘自动采摘成为近年的研究热点。但是,柑橘生长环境复杂、枝条形态各异、枝叶和果实互遮挡严重,如何精准实时地定位采摘点成为自动采摘的关键。通过构建级联混合网络模型,提出了一种通用且高效的柑橘采摘点自动精准定位方法。方法构建团簇框生成模型和枝条稀疏实例分割模型,对两者进行级联混合实现实时柑橘采摘点定位。首先,构建柑橘果实检测网络,提出团簇框生成模型,该模型通过特征提取、果实检测框生成和DBSCAN(density-based spatial clustering of applications with noise)果实密度聚类,实时地生成图像内果实数目最多的团簇框坐标;然后,提出融合亮度先验的枝条稀疏分割模型,该模型以团簇框内的图像作为输入,有效降低背景枝条的干扰,通过融合亮度先验的稀疏实例激活图,实时地分割出与果实相连接枝条实例;最后基于分割结果搜索果实采摘点定位坐标。结果经过长时间户外采集制作了柑橘果实检测数据集CFDD(citrus fruit detection dataset)和柑橘枝条分割数据集CBSD(citrus branch segmentation dataset)。这两个数据集由成熟果实、未成熟果实组成,包含晴天、阴天、顺光和逆光等挑战,总共37000幅图像。在该数据集上本文方法的采摘点定位精准度达到了95.77%,帧率(frames per second,FPS)达到了28.21帧/s。结论本文方法在果实采摘点定位方面取得较好进展,能够快速且准确地获取柑橘采摘点,并且提供配套的机械臂采摘设备可供该采摘点定位算法的落地使用,为柑橘产业发展提供有力支持。
