为了评估同化时间序列叶面积指数(leaf area index,LAI)和蒸散发(evapotranspiration,ET)产品对冬小麦产量估测的有效性和适用性,该文选择陕西省关中平原冬小麦为研究对象,以SWAP为作物生长动态模型,利用冬小麦关键生育期的遥感观测和S...为了评估同化时间序列叶面积指数(leaf area index,LAI)和蒸散发(evapotranspiration,ET)产品对冬小麦产量估测的有效性和适用性,该文选择陕西省关中平原冬小麦为研究对象,以SWAP为作物生长动态模型,利用冬小麦关键生育期的遥感观测和SWAP模拟LAI、ET趋势变化信息构建代价函数,以SCE-UA作为优化算法最小化代价函数,重新初始化SWAP模型中的出苗日期和灌溉量2个参数。重点比较了基于向量夹角和一阶差分2种代价函数的冬小麦单产估测精度。结果表明,同化MODIS LAI和ET后,冬小麦产量的估测精度比未同化精度(r=0.57,RMSE=1 192 kg/hm2)有显著提高,并且基于向量夹角代价函数法同化策略的单产估测精度(r=0.75,RMSE=494 kg/hm2)高于一阶差分代价函数法(r=0.73,RMSE=667 kg/hm2)的估测精度。该方法为其他区域的水分胁迫模式下遥感与作物模型双变量数据同化提供了参考。展开更多
Complete and reliable field traffic data is vital for the planning, design, and operation of urban traf- fic management systems. However, traffic data is often very incomplete in many traffic information systems, whic...Complete and reliable field traffic data is vital for the planning, design, and operation of urban traf- fic management systems. However, traffic data is often very incomplete in many traffic information systems, which hinders effective use of the data. Methods are needed for imputing missing traffic data to minimize the effect of incomplete data on the utilization. This paper presents an improved Local Least Squares （LLS） ap- proach to impute the incomplete data. The LLS is an improved version of the K Nearest Neighbor （KNN） method. First, the missing traffic data is replaced by a row average of the known values. Then, the vector angle and Euclidean distance are used to select the nearest neighbors. Finally, a regression step is used to get weights of the nearest neighbors and the imputation results. Traffic flow volume collected in Beijing was analyzed to compare this approach with the Bayesian Principle Component Analysis （BPCA） imputation ap- proach. Tests show that this approach provides slightly better performance than BPCA imputation to impute missing traffic data.展开更多
目的探讨Lorenz散点图(LPs)矢量角的价值,及其联合B线斜率在提高心律失常诊断效能方面的作用。方法回顾性分析119例室性期前收缩(室早组)、97例室上性期前收缩(室上早组)、52例二度Ⅰ型房室传导阻滞(二度Ⅰ型组)和54例二度Ⅱ型房室/窦...目的探讨Lorenz散点图(LPs)矢量角的价值,及其联合B线斜率在提高心律失常诊断效能方面的作用。方法回顾性分析119例室性期前收缩(室早组)、97例室上性期前收缩(室上早组)、52例二度Ⅰ型房室传导阻滞(二度Ⅰ型组)和54例二度Ⅱ型房室/窦房传导阻滞(二度Ⅱ型组)患者的LPs,测量B线斜率及矢量角,比较各组间的差异。采用受试者工作特征曲线分析B线斜率、矢量角及两者联合在组间的诊断效能并使用MedCalc软件进行统计学比较。使用组内相关系数(ICC)、Bland-Altman图评估B线斜率、矢量角的观察者内和观察者间测量的一致性。结果室早组与室上早组、二度Ⅰ型组与二度Ⅱ型组间比较差异均有统计学意义(P<0.05)。B线斜率、矢量角以及两者联合鉴别室性与室上性期前收缩的曲线下面积(AUC)分别为0.81、0.84、0.87,鉴别二度Ⅰ型与二度Ⅱ型房室/窦房传导阻滞的AUC分别为0.76、0.78、0.80。矢量角的ICC优于B线斜率(观察者内0.99 vs 0.98、观察者间0.97 vs 0.96)。结论矢量角可用于鉴别心律失常类型,且具有较好的观察者内及观察者间一致性。其联合B线斜率诊断心律失常具有较高准确率,为临床诊疗提供了新的参考依据。展开更多
基金Partially supported by the National High-Tech Research and Development (863) Program of China (Nos. 2009AA11Z206 and 2011AA110401)the National Natural Science Foundation of China (Nos. 60721003 and 60834001)Tsinghua University Innovation Research Program (No. 2009THZ0)
文摘Complete and reliable field traffic data is vital for the planning, design, and operation of urban traf- fic management systems. However, traffic data is often very incomplete in many traffic information systems, which hinders effective use of the data. Methods are needed for imputing missing traffic data to minimize the effect of incomplete data on the utilization. This paper presents an improved Local Least Squares （LLS） ap- proach to impute the incomplete data. The LLS is an improved version of the K Nearest Neighbor （KNN） method. First, the missing traffic data is replaced by a row average of the known values. Then, the vector angle and Euclidean distance are used to select the nearest neighbors. Finally, a regression step is used to get weights of the nearest neighbors and the imputation results. Traffic flow volume collected in Beijing was analyzed to compare this approach with the Bayesian Principle Component Analysis （BPCA） imputation ap- proach. Tests show that this approach provides slightly better performance than BPCA imputation to impute missing traffic data.
文摘目的探讨Lorenz散点图(LPs)矢量角的价值,及其联合B线斜率在提高心律失常诊断效能方面的作用。方法回顾性分析119例室性期前收缩(室早组)、97例室上性期前收缩(室上早组)、52例二度Ⅰ型房室传导阻滞(二度Ⅰ型组)和54例二度Ⅱ型房室/窦房传导阻滞(二度Ⅱ型组)患者的LPs,测量B线斜率及矢量角,比较各组间的差异。采用受试者工作特征曲线分析B线斜率、矢量角及两者联合在组间的诊断效能并使用MedCalc软件进行统计学比较。使用组内相关系数(ICC)、Bland-Altman图评估B线斜率、矢量角的观察者内和观察者间测量的一致性。结果室早组与室上早组、二度Ⅰ型组与二度Ⅱ型组间比较差异均有统计学意义(P<0.05)。B线斜率、矢量角以及两者联合鉴别室性与室上性期前收缩的曲线下面积(AUC)分别为0.81、0.84、0.87,鉴别二度Ⅰ型与二度Ⅱ型房室/窦房传导阻滞的AUC分别为0.76、0.78、0.80。矢量角的ICC优于B线斜率(观察者内0.99 vs 0.98、观察者间0.97 vs 0.96)。结论矢量角可用于鉴别心律失常类型,且具有较好的观察者内及观察者间一致性。其联合B线斜率诊断心律失常具有较高准确率,为临床诊疗提供了新的参考依据。
