利用2008—2012年台站资料、NCEP(National Centers for Environ-mental Prediction)-FNL(Final Operational Global Analysis)1°×1°再分析资料,将近5年经过渤海持续发展的黄河气旋分为夏季型和春季型,采用动态合成法对...利用2008—2012年台站资料、NCEP(National Centers for Environ-mental Prediction)-FNL(Final Operational Global Analysis)1°×1°再分析资料,将近5年经过渤海持续发展的黄河气旋分为夏季型和春季型,采用动态合成法对两类气旋的结构和黄渤海海域的热力、动力、水汽等影响因子进行对比分析。结果表明:经过渤海时,夏季型气旋主要伴随大范围的强降水,而春季型气旋主要形成强风区。春夏季黄河气旋均为冷暖交汇的斜压性结构,但夏季型有偏暖中心,斜压性弱于春季型。春季高空急流位于气旋南部,其左侧正涡度区维持气旋的深厚,且气旋后部高空动量下传与锋面二级环流及平坦海面配合有利于气旋低层大风迅速增强。夏季高空急流位于气旋北部,高空强辐散区和低层辐合区配置加强了气旋中的上升运动,有利于气旋强降水和凝结潜热释放。气旋发展阶段,扰动位能向动能的转化,支持气旋动能的维持与加强。湿位涡计算显示,夏季气旋中有深厚的干空气下沉,干湿梯度强,尺度大,有利于气旋的强降水,春季气旋中干湿梯度小,分布零散,对应降水强度和范围均小。黄渤海为气旋主要水汽输送通道,夏季海温相对春季高,水汽充沛,春季水汽辐合量仅为夏季三分之一。海洋下垫面作用对春季气旋影响大,在夏季作用不明显。夏季海面潜热加热影响为主,春季感热加热影响明显。展开更多
Deep learning algorithms are the basis of many artificial intelligence applications.Those algorithms are both computationally intensive and memory intensive,making them difficult to deploy on embedded systems.Thus var...Deep learning algorithms are the basis of many artificial intelligence applications.Those algorithms are both computationally intensive and memory intensive,making them difficult to deploy on embedded systems.Thus various deep learning accelerators(DLAs)are proposed and applied to achieve better performance and lower power consumption.However,most deep learning accelerators are unable to support multiple data formats.This research proposes the MW-DLA,a deep learning accelerator supporting dynamic configurable data-width.This work analyzes the data distribution of different data types in different layers and trains a typical network with per-layer representation.As a result,the proposed MW-DLA achieves 2X performance and more than 50%memory requirement for AlexNet with less than 5.77%area overhead.展开更多
文摘利用2008—2012年台站资料、NCEP(National Centers for Environ-mental Prediction)-FNL(Final Operational Global Analysis)1°×1°再分析资料,将近5年经过渤海持续发展的黄河气旋分为夏季型和春季型,采用动态合成法对两类气旋的结构和黄渤海海域的热力、动力、水汽等影响因子进行对比分析。结果表明:经过渤海时,夏季型气旋主要伴随大范围的强降水,而春季型气旋主要形成强风区。春夏季黄河气旋均为冷暖交汇的斜压性结构,但夏季型有偏暖中心,斜压性弱于春季型。春季高空急流位于气旋南部,其左侧正涡度区维持气旋的深厚,且气旋后部高空动量下传与锋面二级环流及平坦海面配合有利于气旋低层大风迅速增强。夏季高空急流位于气旋北部,高空强辐散区和低层辐合区配置加强了气旋中的上升运动,有利于气旋强降水和凝结潜热释放。气旋发展阶段,扰动位能向动能的转化,支持气旋动能的维持与加强。湿位涡计算显示,夏季气旋中有深厚的干空气下沉,干湿梯度强,尺度大,有利于气旋的强降水,春季气旋中干湿梯度小,分布零散,对应降水强度和范围均小。黄渤海为气旋主要水汽输送通道,夏季海温相对春季高,水汽充沛,春季水汽辐合量仅为夏季三分之一。海洋下垫面作用对春季气旋影响大,在夏季作用不明显。夏季海面潜热加热影响为主,春季感热加热影响明显。
基金the National Key Research and Development Program of China(No.2017YFA0700900,2017YFA0700902,2017YFA0700901,2017YFB1003101)the National Natural Science Foundation of China(No.61472396,61432016,61473275,61522211,61532016,61521092,61502446,61672491,61602441,61602446,61732002,61702478,61732020)+4 种基金Beijing Natural Science Foundation(No.JQ18013)the National Basic Research Program of China(No.2015CB358800)National Science and Technology Major Project(No.2018ZX01031102)the Transformation and Transfer of Scientific and Technological Achievements of Chinese Academy of Sciences(No.KFJ-HGZX-013)Strategic Priority Research Program of Chinese Academy of Science(No.XDB32050200).
文摘Deep learning algorithms are the basis of many artificial intelligence applications.Those algorithms are both computationally intensive and memory intensive,making them difficult to deploy on embedded systems.Thus various deep learning accelerators(DLAs)are proposed and applied to achieve better performance and lower power consumption.However,most deep learning accelerators are unable to support multiple data formats.This research proposes the MW-DLA,a deep learning accelerator supporting dynamic configurable data-width.This work analyzes the data distribution of different data types in different layers and trains a typical network with per-layer representation.As a result,the proposed MW-DLA achieves 2X performance and more than 50%memory requirement for AlexNet with less than 5.77%area overhead.
