随着地面无人平台(Unmanned Ground Vehicles,UGVs)在复杂作业环境中的潜在应用和战略价值日益凸显,确保其自主行为的安全性变得至关重要。提出一种结合系统理论过程分析(System-Theoretic Process Analysis,STPA)和Bow-Tie模型的地面...随着地面无人平台(Unmanned Ground Vehicles,UGVs)在复杂作业环境中的潜在应用和战略价值日益凸显,确保其自主行为的安全性变得至关重要。提出一种结合系统理论过程分析(System-Theoretic Process Analysis,STPA)和Bow-Tie模型的地面无人平台系统安全分析方法。围绕遥控操作地面无人平台系统安全,通过STPA方法识别UGV系统中的不安全控制行为及其潜在风险,并利用Bow-Tie模型分析从损失致因场景到可能事故后果的事件链,得到风险传播路径和风险扩散路径。最终,基于Bow-Tie分析结果确定主被动安全分级控制措施,并通过自主安全控制器实现了系统安全管理。展开更多
Based on previously released data,this paper first presented the criteria for recognizing bow echoes and divided their life cycle into three stages:the development stage,the mature stage,and the attenuation stage.Base...Based on previously released data,this paper first presented the criteria for recognizing bow echoes and divided their life cycle into three stages:the development stage,the mature stage,and the attenuation stage.Based on Doppler weather radar data during 2011-2020,43 bow echo events(including 54 individual bow echoes)in western South China were identified.The spatial and temporal distributions,formation and dissipation modes of these bow echoes,and the severe weather they caused were statistically analyzed.The results show that:(1)The bow echo events were unevenly distributed year-to-year,but all occurred from March to July,with the highest in April and May and the lowest in July.The period from night to early morning was found to be the main period for bow echo generation and intensification.(2)A banded area from Hechi City on the southeastern edge of the Yunnan-Guizhou Plateau to Wuzhou City in southeast Guangxi was identified as a high-incidence area of bow echoes.The length of bow echoes was correlated with their life cycle.(3)The origins of the bow echoes could be divided into five locations,most of which were in the eastern Yunnan-Guizhou Plateau.After entering western South China,their moving paths were categorized into three types,among which most bow echoes moved southeastward,generally because of the effect of cold air.Specifically,bow echoes generally moved eastward when cold air was weak or in the warm zone.Meanwhile,the fewest bow echoes moved northeastward.(4)Four modes of bow echo formation were identified:linearly organized,broken areal,linearly merging,and broken line.Dissipation could also be classified into four types.(5)The probability of convective weather generated by a bow echo was largest in the mature stage.展开更多
The outlet flow fields of a low-speed repeating-stage compressor with bowed stator stages are measured with five-hole probe under the near stall condition when the rotor/stator axial gap varies. The performances of th...The outlet flow fields of a low-speed repeating-stage compressor with bowed stator stages are measured with five-hole probe under the near stall condition when the rotor/stator axial gap varies. The performances of the straight stator stages are investigated and compared to those of the bowed stator stages. The results show that using bowed stator stages could alleviate the flow separation at both upper and low corners of the suction surface and the endwalls, and decrease the losses along the flow passage as well as the outlet flow angle. As the rotor/stator axial gap decreases, although the diffusion capacity of the compressor increases obviously, the outlet flow field in the straight stator stages deteriorates quickly. By contrast, little changes occur in the bowed stator stages, indicating that as the rotor/stator axial gap decreases, improved performance is achieved in the bowed stator stages.展开更多
针对传统BOW(Bag of Words)模型用于场景图像分类时的不足,通过引入关联规则的MFI(Maximum Frequent Itemsets)和Topology模型对其进行改进。为了突出同类图像的视觉单词,提取同类图像的MFI后,对其中频繁出现的视觉单词进行加权处理,增...针对传统BOW(Bag of Words)模型用于场景图像分类时的不足,通过引入关联规则的MFI(Maximum Frequent Itemsets)和Topology模型对其进行改进。为了突出同类图像的视觉单词,提取同类图像的MFI后,对其中频繁出现的视觉单词进行加权处理,增强同类图像的共有特征。同时,为了提高视觉词典的生成效率,利用Topology模型对原始模型进行分工并行处理。通过COREL和Caltech-256图像库的实验,证明改进后的模型提高了对场景图像的分类性能,并验证了其Topology模型的有效性和可行性。展开更多
文摘随着地面无人平台(Unmanned Ground Vehicles,UGVs)在复杂作业环境中的潜在应用和战略价值日益凸显,确保其自主行为的安全性变得至关重要。提出一种结合系统理论过程分析(System-Theoretic Process Analysis,STPA)和Bow-Tie模型的地面无人平台系统安全分析方法。围绕遥控操作地面无人平台系统安全,通过STPA方法识别UGV系统中的不安全控制行为及其潜在风险,并利用Bow-Tie模型分析从损失致因场景到可能事故后果的事件链,得到风险传播路径和风险扩散路径。最终,基于Bow-Tie分析结果确定主被动安全分级控制措施,并通过自主安全控制器实现了系统安全管理。
基金National Natural Science Foundation of China(52239006,41975001,41930972)Natural Science Foundation of Guangxi Zhuang Autonomous Region(2022GXNSFBA035565)。
文摘Based on previously released data,this paper first presented the criteria for recognizing bow echoes and divided their life cycle into three stages:the development stage,the mature stage,and the attenuation stage.Based on Doppler weather radar data during 2011-2020,43 bow echo events(including 54 individual bow echoes)in western South China were identified.The spatial and temporal distributions,formation and dissipation modes of these bow echoes,and the severe weather they caused were statistically analyzed.The results show that:(1)The bow echo events were unevenly distributed year-to-year,but all occurred from March to July,with the highest in April and May and the lowest in July.The period from night to early morning was found to be the main period for bow echo generation and intensification.(2)A banded area from Hechi City on the southeastern edge of the Yunnan-Guizhou Plateau to Wuzhou City in southeast Guangxi was identified as a high-incidence area of bow echoes.The length of bow echoes was correlated with their life cycle.(3)The origins of the bow echoes could be divided into five locations,most of which were in the eastern Yunnan-Guizhou Plateau.After entering western South China,their moving paths were categorized into three types,among which most bow echoes moved southeastward,generally because of the effect of cold air.Specifically,bow echoes generally moved eastward when cold air was weak or in the warm zone.Meanwhile,the fewest bow echoes moved northeastward.(4)Four modes of bow echo formation were identified:linearly organized,broken areal,linearly merging,and broken line.Dissipation could also be classified into four types.(5)The probability of convective weather generated by a bow echo was largest in the mature stage.
基金National Natural Science Foundation of China (50646021)Chinese Specialized Research Fund for the Doctoral Pro-gram of Higher Education (20060213007)
文摘The outlet flow fields of a low-speed repeating-stage compressor with bowed stator stages are measured with five-hole probe under the near stall condition when the rotor/stator axial gap varies. The performances of the straight stator stages are investigated and compared to those of the bowed stator stages. The results show that using bowed stator stages could alleviate the flow separation at both upper and low corners of the suction surface and the endwalls, and decrease the losses along the flow passage as well as the outlet flow angle. As the rotor/stator axial gap decreases, although the diffusion capacity of the compressor increases obviously, the outlet flow field in the straight stator stages deteriorates quickly. By contrast, little changes occur in the bowed stator stages, indicating that as the rotor/stator axial gap decreases, improved performance is achieved in the bowed stator stages.
文摘针对传统BOW(Bag of Words)模型用于场景图像分类时的不足,通过引入关联规则的MFI(Maximum Frequent Itemsets)和Topology模型对其进行改进。为了突出同类图像的视觉单词,提取同类图像的MFI后,对其中频繁出现的视觉单词进行加权处理,增强同类图像的共有特征。同时,为了提高视觉词典的生成效率,利用Topology模型对原始模型进行分工并行处理。通过COREL和Caltech-256图像库的实验,证明改进后的模型提高了对场景图像的分类性能,并验证了其Topology模型的有效性和可行性。
