针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数...针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数据先验信息的问题,引入相空间重构算法将原始缺失数据映射到高维空间,利用数据间的关联性和结构特征,为后续数据恢复算法提供先验知识;结合对数与Sigmoid函数构建变步长基础函数,并利用等比项提高前期步长,构建自适应变步长SVT算法,克服传统SVT在大规模数据情况下计算效率低的问题.结合多项公用电力负荷数据集及多种常用电力负荷数据恢复算法进行对比实验分析,结果表明,改进SVT算法可获得更好的数据恢复效果,收敛速度、精度以及稳定性得到提升,具有较强的工程实用性.展开更多
This paper describes the capability of remote sensing in the monitoring of rangeland vegetation productivities and dynamics in the foothill areas of Uzbekistan, in order to enhance the sustainable utilization of natur...This paper describes the capability of remote sensing in the monitoring of rangeland vegetation productivities and dynamics in the foothill areas of Uzbekistan, in order to enhance the sustainable utilization of natural resources. Seasonal productivity, including above-ground biomass, density, coverage, foliar chlorophyll, and carotene content, was measured for the Artemisia diffusa, the dominant species of the study area. The Normalized Difference Vegetation Index (NDVI), extracted from time-series Landsat TM5 satellite images, was used to obtain pertinent data regarding vegetation coverage and potential productivities. Seasonal precipitation was found to be a key factor in governing soil moisture in the semi-arid foothill rangelands, which directly influence the dynamics of plants and productivities. Precipitation and soil moisture determine the length of the plant growing season and further influence NDVI values. We found that time-series NDVI was significantly correlated with the seasonal green and total above-ground biomass of vegetation and coverage of Artemisia diffusa, soil moisture, and changeable nitrogen. We also found that the foliar chlorophylls of Artemisia diffusa was significantly correlated with the green above-ground biomass (r = 0.44, P < 0.05). The results can contribute to further monitoring of ecosystem health and habitat conditions using remote sensing (RS) as an accurate tool in large rangeland areas.展开更多
文摘针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数据先验信息的问题,引入相空间重构算法将原始缺失数据映射到高维空间,利用数据间的关联性和结构特征,为后续数据恢复算法提供先验知识;结合对数与Sigmoid函数构建变步长基础函数,并利用等比项提高前期步长,构建自适应变步长SVT算法,克服传统SVT在大规模数据情况下计算效率低的问题.结合多项公用电力负荷数据集及多种常用电力负荷数据恢复算法进行对比实验分析,结果表明,改进SVT算法可获得更好的数据恢复效果,收敛速度、精度以及稳定性得到提升,具有较强的工程实用性.
文摘This paper describes the capability of remote sensing in the monitoring of rangeland vegetation productivities and dynamics in the foothill areas of Uzbekistan, in order to enhance the sustainable utilization of natural resources. Seasonal productivity, including above-ground biomass, density, coverage, foliar chlorophyll, and carotene content, was measured for the Artemisia diffusa, the dominant species of the study area. The Normalized Difference Vegetation Index (NDVI), extracted from time-series Landsat TM5 satellite images, was used to obtain pertinent data regarding vegetation coverage and potential productivities. Seasonal precipitation was found to be a key factor in governing soil moisture in the semi-arid foothill rangelands, which directly influence the dynamics of plants and productivities. Precipitation and soil moisture determine the length of the plant growing season and further influence NDVI values. We found that time-series NDVI was significantly correlated with the seasonal green and total above-ground biomass of vegetation and coverage of Artemisia diffusa, soil moisture, and changeable nitrogen. We also found that the foliar chlorophylls of Artemisia diffusa was significantly correlated with the green above-ground biomass (r = 0.44, P < 0.05). The results can contribute to further monitoring of ecosystem health and habitat conditions using remote sensing (RS) as an accurate tool in large rangeland areas.
