A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and...A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.展开更多
目的分析、评价基于健康生态学模型的健康管理对心血管病危险人群的干预研究。方法系统检索中国知网、中国生物医学文献数据库、万方数据库、维普中文科技期刊、PubMed、Proquest、Cochrane Library、Web of Science,收集有关基于健康...目的分析、评价基于健康生态学模型的健康管理对心血管病危险人群的干预研究。方法系统检索中国知网、中国生物医学文献数据库、万方数据库、维普中文科技期刊、PubMed、Proquest、Cochrane Library、Web of Science,收集有关基于健康生态学模型的健康管理对心血管病危险人群进行干预的研究,检索时限为建库至2021年9月。由2名研究者根据纳排标准独立筛选文献和提取数据,应用Cochrane Handbook5.1.0危险偏倚评估量表分析纳入文献的偏倚危险,采用RevMan 5.3统计软件对资料进行Meta分析。结果最终纳入8篇研究,合计样本量10545例。Meta分析结果显示,基于健康生态学模型的健康管理能有效降低心血管病危险人群的BMI(MD=-0.45,95%CI:-0.71~-0.2),但是在改善血压(收缩压,MD=-1.66,95%CI:-5.95~2.63;舒张压,MD=-0.84,95%CI:-1.75~0.06),提高心血管病危险人群中等身体活动水平(SMD=0.67,95%CI:-0.07~1.4)方面,差异均无统计学意义(P>0.05)。结论基于健康生态学模型的健康管理干预能改善心血管病危险人群的身体质量指数水平,但在中等程度活动水平、血压方面的干预效果还需进一步验证。展开更多
基金Project(51175159)supported by the National Natural Science Foundation of ChinaProject(2013WK3024)supported by the Science andTechnology Planning Program of Hunan Province,ChinaProject(CX2013B146)supported by the Hunan Provincial InnovationFoundation for Postgraduate,China
文摘A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.
