城镇化水平时空演变研究对城市空间结构优化有重要意义。通过对DMSP/OLS和NPP/VIIRS夜间灯光遥感数据的饱和校正和一致性校正,构建了2000—2019年黄土高原夜间灯光遥感数据集,计算了黄土高原不同空间尺度的综合夜间灯光指数(compound ni...城镇化水平时空演变研究对城市空间结构优化有重要意义。通过对DMSP/OLS和NPP/VIIRS夜间灯光遥感数据的饱和校正和一致性校正,构建了2000—2019年黄土高原夜间灯光遥感数据集,计算了黄土高原不同空间尺度的综合夜间灯光指数(compound night light index,CNLI),并利用二分模型提取了黄土高原建成区面积,在此基础上,利用标准差椭圆等方法分析了其空间演变格局。结果表明:①基于夜间灯光指数构建的黄土高原CNLI与统计数据构建的城镇化综合发展水平指数(urbanization development index,UDI)及各分指标的相关系数均较高;②2000—2019年整个黄土高原及5个城市群CNLI值均呈现显著上升趋势,空间上呈从东南向西北递减的趋势;③基于二分模型提取的黄土高原建成区绝对误差和相对误差的均值分别为2.45 km^(2)和3.72%;④黄土高原建成区重心在2000—2019年期间呈现向东南方向移动的趋势,标准差椭圆的覆盖面积表现为显著下降的趋势(slope=0.0107 km^(2)/a,p<0.01),方位角的值由北偏东83.33°变为88.37°。研究结果可为黄土高原及生态脆弱区城镇化时空格局研究提供数据支持和方法借鉴。展开更多
基于GRACE(gravity recovery and climate experiment)观测数据和GLDAS(global land data assimilation system)同化数据,辅以趋势分析和随机森林等方法,对2002—2017年黄淮海平原地下水储量时空演变特征进行研究,并从降水量和不同部门...基于GRACE(gravity recovery and climate experiment)观测数据和GLDAS(global land data assimilation system)同化数据,辅以趋势分析和随机森林等方法,对2002—2017年黄淮海平原地下水储量时空演变特征进行研究,并从降水量和不同部门用水量等供需视角探究黄淮海平原地下水变化的影响因素。结果表明:(1)联合GRACE和GLDAS反演的黄淮海平原地下水储量与WGHM模型(WaterGAP Global Hydrology Model)输出的地下水储量具有较好的一致性,相关系数达0.76,相关性较高的区域分布在海河流域、淮河流域及山东半岛地区;(2)2002—2017年黄淮海平原地下水储量整体呈现持续下降趋势,下降速率为-9.36 mm/a,黄淮海平原地下水总损失体积达589亿~790亿m^(3);(3)黄淮海平原地下水储量变化具有明显季节性特征,呈现春季达到最大亏损、夏季缓慢回升、秋季达到最大盈余、冬季缓慢下降的季向循环模式;(4)空间上,除皖北、苏北地区呈略微上升趋势外,黄淮海平原大部分地区地下水呈现下降趋势,其中豫北及冀南等地区的下降趋势最为显著,降速高达-41.29 mm/a;(5)农业用水和工业用水是黄淮海平原地下水储量变化的主导因素,二者贡献率分别为27.04%和23.23%。展开更多
In order to improve the accuracy of rolling bearing fault diagnosis when the motor is running under non-stationary conditions,an AC motor rolling bearing fault diagnosis method was proposed based on heterogeneous data...In order to improve the accuracy of rolling bearing fault diagnosis when the motor is running under non-stationary conditions,an AC motor rolling bearing fault diagnosis method was proposed based on heterogeneous data fusion of current and infrared images.Firstly,VMD was used to decompose the motor current signal and extract the low-frequency component of the bearing fault signal.On this basis,the current signal was transformed into a two-dimensional graph suitable for convolutional neural network,and the data set was classified by convolutional neural network and softmax classifier.Secondly,the infrared image was segmented and the fault features were extracted,so as to calculate the similarity with the infrared image of the fault bearing in the library,and further the sigmod classifier was used to classify the data.Finally,a decision-level fusion method was introduced to fuse the current signal with the infrared image signal diagnosis result according to the weight,and the motor bearing fault diagnosis result was obtained.Through experimental verification,the proposed fault diagnosis method could be used for the fault diagnosis of motor bearing outer ring under the condition of load variation,and the accuracy of fault diagnosis can reach 98.85%.展开更多
文摘城镇化水平时空演变研究对城市空间结构优化有重要意义。通过对DMSP/OLS和NPP/VIIRS夜间灯光遥感数据的饱和校正和一致性校正,构建了2000—2019年黄土高原夜间灯光遥感数据集,计算了黄土高原不同空间尺度的综合夜间灯光指数(compound night light index,CNLI),并利用二分模型提取了黄土高原建成区面积,在此基础上,利用标准差椭圆等方法分析了其空间演变格局。结果表明:①基于夜间灯光指数构建的黄土高原CNLI与统计数据构建的城镇化综合发展水平指数(urbanization development index,UDI)及各分指标的相关系数均较高;②2000—2019年整个黄土高原及5个城市群CNLI值均呈现显著上升趋势,空间上呈从东南向西北递减的趋势;③基于二分模型提取的黄土高原建成区绝对误差和相对误差的均值分别为2.45 km^(2)和3.72%;④黄土高原建成区重心在2000—2019年期间呈现向东南方向移动的趋势,标准差椭圆的覆盖面积表现为显著下降的趋势(slope=0.0107 km^(2)/a,p<0.01),方位角的值由北偏东83.33°变为88.37°。研究结果可为黄土高原及生态脆弱区城镇化时空格局研究提供数据支持和方法借鉴。
文摘基于GRACE(gravity recovery and climate experiment)观测数据和GLDAS(global land data assimilation system)同化数据,辅以趋势分析和随机森林等方法,对2002—2017年黄淮海平原地下水储量时空演变特征进行研究,并从降水量和不同部门用水量等供需视角探究黄淮海平原地下水变化的影响因素。结果表明:(1)联合GRACE和GLDAS反演的黄淮海平原地下水储量与WGHM模型(WaterGAP Global Hydrology Model)输出的地下水储量具有较好的一致性,相关系数达0.76,相关性较高的区域分布在海河流域、淮河流域及山东半岛地区;(2)2002—2017年黄淮海平原地下水储量整体呈现持续下降趋势,下降速率为-9.36 mm/a,黄淮海平原地下水总损失体积达589亿~790亿m^(3);(3)黄淮海平原地下水储量变化具有明显季节性特征,呈现春季达到最大亏损、夏季缓慢回升、秋季达到最大盈余、冬季缓慢下降的季向循环模式;(4)空间上,除皖北、苏北地区呈略微上升趋势外,黄淮海平原大部分地区地下水呈现下降趋势,其中豫北及冀南等地区的下降趋势最为显著,降速高达-41.29 mm/a;(5)农业用水和工业用水是黄淮海平原地下水储量变化的主导因素,二者贡献率分别为27.04%和23.23%。
基金supported by National Natural Science Foundation of China(No.61903291)Shaanxi Province Key R&D Program(No.2022GY-134)。
文摘In order to improve the accuracy of rolling bearing fault diagnosis when the motor is running under non-stationary conditions,an AC motor rolling bearing fault diagnosis method was proposed based on heterogeneous data fusion of current and infrared images.Firstly,VMD was used to decompose the motor current signal and extract the low-frequency component of the bearing fault signal.On this basis,the current signal was transformed into a two-dimensional graph suitable for convolutional neural network,and the data set was classified by convolutional neural network and softmax classifier.Secondly,the infrared image was segmented and the fault features were extracted,so as to calculate the similarity with the infrared image of the fault bearing in the library,and further the sigmod classifier was used to classify the data.Finally,a decision-level fusion method was introduced to fuse the current signal with the infrared image signal diagnosis result according to the weight,and the motor bearing fault diagnosis result was obtained.Through experimental verification,the proposed fault diagnosis method could be used for the fault diagnosis of motor bearing outer ring under the condition of load variation,and the accuracy of fault diagnosis can reach 98.85%.
