A 3.8-kin Coupled Ice-Ocean Model （C1OM） was implemented to successfully reproduce many observed phenomena in the Beaufort and Chukchi seas, including the Bering-inflow-originated coastal current that splits into th...A 3.8-kin Coupled Ice-Ocean Model （C1OM） was implemented to successfully reproduce many observed phenomena in the Beaufort and Chukchi seas, including the Bering-inflow-originated coastal current that splits into three branches： Alaska Coastal Water （ACW） , Central Channel, and Herald Valley branches. Other modeled phenomena include the Beaufort Slope Current （BSC） , the Beaufort Gyre, the East Siberian Current （ ESC）, mesoscale eddies, seasonal landfast ice, sea ice ridging, shear, and deformation. Many of these downscaling processes can only be captured by using a high-resolution CIOM, nested in a global climate model. The seasonal cycles for sea ice concentration, thickness, velocity, and other variables are well reproduced with Solid validation by satellite measurements. The seasonal cycles for upper ocean dynamics and thermodynamics are also well reproduced, which include the formation of the cold saline layer due to the injection of salt during sea ice formation, the BSC, and the subsurface upwelling in winter that brings up warm, even more saline Atlantic Water along the shelfbreak and shelf along the Beaufort coast.展开更多
随着无人机的大规模普及,倾斜摄影测量技术应用越来越广泛,但是由于其技术特点,在数据采集过程中受角度、遮蔽等因素的影响,导致三维模型极易出现拉花、扭曲等问题。基于此,探讨了融合机载激光雷达(Light Laser Detection and Ranging,L...随着无人机的大规模普及,倾斜摄影测量技术应用越来越广泛,但是由于其技术特点,在数据采集过程中受角度、遮蔽等因素的影响,导致三维模型极易出现拉花、扭曲等问题。基于此,探讨了融合机载激光雷达(Light Laser Detection and Ranging,LiDAR)、地面数据采集和倾斜摄影技术的方法,以提高高层建筑物三维建模的精度和细节。机载LiDAR技术通过激光雷达扫描获取点云数据,提供建筑物的几何形状和细节信息,倾斜摄影技术可以提供建筑物纹理信息,与机载LiDAR技术结合可以提高建模的真实感和精度。点面三维激光扫描仪能够有效补充前两项技术的缺失,如补充细节数据。研究融合三种数据源的信息,综合利用它们的优势,为城市规划、建设和管理提供更准确、可靠的数据支持。展开更多
SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an ...SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an earthquake forecasting model. The main function of this system is to analyze and process the deformation, fluid, electromagnetic and other geophysical field observing data from ground-based observation, as well as space-based observation. Combined station and earthquake distributions, geological structure and other information, this system can provide a basic software platform for earthquake forecasting research based on spatiotemporal fusion. The hierarchical station tree for data sifting and the interaction mode have been innovatively developed in this SeisGuard system to improve users’ working efficiency. The data storage framework designed according to the characteristics of different time series can unify the interfaces of different data sources, provide the support of data flow, simplify the management and usage of data, and provide foundation for analysis of big data. The final aim of this development is to establish an effective earthquake forecasting model combined all available information from ground-based observations to space-based observations.展开更多
基金supports from the University of Alaska Costal Marine Institute(CMI) and Minerals Management Service(MMS) and IARC/JAMSTEC Cooperative Agreementsupported by NSF OPP Project ARC-0712673 awarded to Yanling Yu and Hajo Eicken (PIs) and Jia Wang(co-PI).This is GLERL Contribution No.1497
文摘A 3.8-kin Coupled Ice-Ocean Model （C1OM） was implemented to successfully reproduce many observed phenomena in the Beaufort and Chukchi seas, including the Bering-inflow-originated coastal current that splits into three branches： Alaska Coastal Water （ACW） , Central Channel, and Herald Valley branches. Other modeled phenomena include the Beaufort Slope Current （BSC） , the Beaufort Gyre, the East Siberian Current （ ESC）, mesoscale eddies, seasonal landfast ice, sea ice ridging, shear, and deformation. Many of these downscaling processes can only be captured by using a high-resolution CIOM, nested in a global climate model. The seasonal cycles for sea ice concentration, thickness, velocity, and other variables are well reproduced with Solid validation by satellite measurements. The seasonal cycles for upper ocean dynamics and thermodynamics are also well reproduced, which include the formation of the cold saline layer due to the injection of salt during sea ice formation, the BSC, and the subsurface upwelling in winter that brings up warm, even more saline Atlantic Water along the shelfbreak and shelf along the Beaufort coast.
文摘随着无人机的大规模普及,倾斜摄影测量技术应用越来越广泛,但是由于其技术特点,在数据采集过程中受角度、遮蔽等因素的影响,导致三维模型极易出现拉花、扭曲等问题。基于此,探讨了融合机载激光雷达(Light Laser Detection and Ranging,LiDAR)、地面数据采集和倾斜摄影技术的方法,以提高高层建筑物三维建模的精度和细节。机载LiDAR技术通过激光雷达扫描获取点云数据,提供建筑物的几何形状和细节信息,倾斜摄影技术可以提供建筑物纹理信息,与机载LiDAR技术结合可以提高建模的真实感和精度。点面三维激光扫描仪能够有效补充前两项技术的缺失,如补充细节数据。研究融合三种数据源的信息,综合利用它们的优势,为城市规划、建设和管理提供更准确、可靠的数据支持。
文摘识别非驾驶行为是提高驾驶安全性的重要手段之一。目前基于骨架序列和图像的融合识别方法具有计算量大和特征融合困难的问题。针对上述问题,本文提出一种基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别模型(skeleton-image based behavior recognition network,SIBBR-Net)。SIBBR-Net通过基于多尺度图的图卷积网络和基于局部视觉及注意力机制的卷积神经网络,充分提取运动和外观特征,较好地平衡了模型表征能力和计算量间的关系。基于手部运动的特征双向引导学习策略、自适应特征融合模块和静态特征空间上的辅助损失,使运动和外观特征间互相引导更新并实现自适应融合。最终在Drive&Act数据集进行算法测试,SIBBR-Net在动态标签和静态标签条件下的平均正确率分别为61.78%和80.42%,每秒浮点运算次数为25.92G,较最优方法降低了76.96%。
文摘SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an earthquake forecasting model. The main function of this system is to analyze and process the deformation, fluid, electromagnetic and other geophysical field observing data from ground-based observation, as well as space-based observation. Combined station and earthquake distributions, geological structure and other information, this system can provide a basic software platform for earthquake forecasting research based on spatiotemporal fusion. The hierarchical station tree for data sifting and the interaction mode have been innovatively developed in this SeisGuard system to improve users’ working efficiency. The data storage framework designed according to the characteristics of different time series can unify the interfaces of different data sources, provide the support of data flow, simplify the management and usage of data, and provide foundation for analysis of big data. The final aim of this development is to establish an effective earthquake forecasting model combined all available information from ground-based observations to space-based observations.
