该试验研究了武威3个酿酒葡萄种植区土壤中8种重金属元素,运用单因子污染指数及内梅罗综合污染指数对酿酒葡萄种植土壤中重金属污染进行评价,并借助主成分分析/绝对主成分分数(principal component analysis/absolute principal compone...该试验研究了武威3个酿酒葡萄种植区土壤中8种重金属元素,运用单因子污染指数及内梅罗综合污染指数对酿酒葡萄种植土壤中重金属污染进行评价,并借助主成分分析/绝对主成分分数(principal component analysis/absolute principal component score,PCA/APCS)受体模型对重金属来源进行了解析。结果表明,武威3个酿酒葡萄种植区土壤8种重金属元素的含量均低于国家标准,但以甘肃省土壤背景值为评价依据时,结果显示3个区域均存在轻度污染或少量样点重度污染。PCA/APCS受体模型显示A区主要可分为自然源(包含少量大气沉降源和农业活动源)及农业活动源;B区分为自然源、农业活动源和工业源;C区分为自然源与农业活动结合源以及交通源。农业活动源是各种植区主要重金属污染源,Cd为其特征元素,且各区Cd的不同来源贡献率空间差异较大。此外,本试验所测8种重金属元素含量虽均低于国家标准,但易受人为活动影响且来源复杂,应加强控制,合理耕作,保障土壤环境质量和酿酒葡萄品质,进而保证所生产葡萄酒的品质。展开更多
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
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.
