Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using sa...Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus展开更多
As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.I...As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.展开更多
This paper offers a positive research result of TIP before 16 strong earthquakes in North and Southwest China and their nearby areas since 1979 by using improved algorithm M8.The result showed that 14 of them were det...This paper offers a positive research result of TIP before 16 strong earthquakes in North and Southwest China and their nearby areas since 1979 by using improved algorithm M8.The result showed that 14 of them were determined to occur within the times of increased probability.TIP precaution occupies about 37% of the total space-time domain.That means we have made quite good results of intermediate-term prediction of strong earthquakes.So the method could be used as one of the useful means of the intermediate-term prediction of strong earthquakes.展开更多
本文以玉米叶斑病检测为研究对象,针对现有目标检测模型普遍存在的计算复杂度高、部署困难等问题,基于you only look once version 8(YOLOv8)算法提出了一种改进方案.构建轻量化注意力模块提升特征提取能力,引入SIoU损失函数优化目标框...本文以玉米叶斑病检测为研究对象,针对现有目标检测模型普遍存在的计算复杂度高、部署困难等问题,基于you only look once version 8(YOLOv8)算法提出了一种改进方案.构建轻量化注意力模块提升特征提取能力,引入SIoU损失函数优化目标框定位精度,采用基于BatchNorm层的模型剪枝策略降低计算复杂度.在RobFlow玉米病虫害数据集上的实验结果表明,改进后的模型检测精度达到88.8%,较原始YOLOv8算法提升了0.7个百分点;同时模型参数量和计算量分别减少33.1%和31.4%,推理速度提升20.5%.该方法在保持较高检测精度的同时,显著提升了效率,为农作物病虫害智能检测提供了新的技术思路.展开更多
文摘Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus
文摘As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.
基金This project was sponsored by the National Science Foundation, China
文摘This paper offers a positive research result of TIP before 16 strong earthquakes in North and Southwest China and their nearby areas since 1979 by using improved algorithm M8.The result showed that 14 of them were determined to occur within the times of increased probability.TIP precaution occupies about 37% of the total space-time domain.That means we have made quite good results of intermediate-term prediction of strong earthquakes.So the method could be used as one of the useful means of the intermediate-term prediction of strong earthquakes.
文摘本文以玉米叶斑病检测为研究对象,针对现有目标检测模型普遍存在的计算复杂度高、部署困难等问题,基于you only look once version 8(YOLOv8)算法提出了一种改进方案.构建轻量化注意力模块提升特征提取能力,引入SIoU损失函数优化目标框定位精度,采用基于BatchNorm层的模型剪枝策略降低计算复杂度.在RobFlow玉米病虫害数据集上的实验结果表明,改进后的模型检测精度达到88.8%,较原始YOLOv8算法提升了0.7个百分点;同时模型参数量和计算量分别减少33.1%和31.4%,推理速度提升20.5%.该方法在保持较高检测精度的同时,显著提升了效率,为农作物病虫害智能检测提供了新的技术思路.
