针对Hilbert-Huang变换方法中由于信号经验模态分解(empirical mode decomposition,简称EMD)过程中所存在的端点效应问题,分析了现有数据延拓方式的利弊,并在基于斜率(slope based method,简称SBM)方法以及改进方法(improved slope base...针对Hilbert-Huang变换方法中由于信号经验模态分解(empirical mode decomposition,简称EMD)过程中所存在的端点效应问题,分析了现有数据延拓方式的利弊,并在基于斜率(slope based method,简称SBM)方法以及改进方法(improved slope based method,简称ISBM)的基础上提出了一种全新的基于斜率再优化(reoptimization slope based method,简称RO-SBM)方法用于信号序列的极值点延拓,然后对延拓后的数据进行EMD分解,得到相应的本征模函数(intrinsic mode function,简称IMF)分量。数值仿真结果表明,采用基于RO-SBM方法进行数据延拓,相比镜像延拓以及ISBM方法,可以更有效地抑制EMD中的端点效应问题,提升HHT方法的信号分析性能。通过基于RO-SBM方法进行数据延拓的HHT方法准确分离出了某转子系统的局部碰摩径向振动信号中所包含的故障特征分量,并将此方法成功应用于旋转机械故障诊断领域。展开更多
Since the “smart growth” was put forward in the late 90s, it has become an accepted design idea and concept in the field of urban design in the world, and has been deeply studied and applied. In order to better prom...Since the “smart growth” was put forward in the late 90s, it has become an accepted design idea and concept in the field of urban design in the world, and has been deeply studied and applied. In order to better promote “smart grown”, we set up an evaluation system, which consists of eleven indicators. In this paper, Oxford City and Fengzhen City are used as the objects of the study. Then smart growth evaluation model is established. The weight of the index is calculated by the entropy method. We use the model to evaluate the development plans of the two cities, from which to calculate the contribution of the indicators on the level of smart growth. Finally, we use the super-efficient data envelopment analysis model (DEA) to rank the importance of the indicators to the smart growth. The results show that the level of smart growth in Oxford is higher than that in Fengzhen. And “Multifunctional Building Density in Central City”, “The Density of Public Area in Central City” two indicators account for more than 36% weight. The contribution of the two indicators is also located in the top two indicators. Two cities focus on the direction of smart growth is also different. In summary, the differences between China and Western countries in urban planning are mainly focused on housing and public resources.展开更多
文摘针对Hilbert-Huang变换方法中由于信号经验模态分解(empirical mode decomposition,简称EMD)过程中所存在的端点效应问题,分析了现有数据延拓方式的利弊,并在基于斜率(slope based method,简称SBM)方法以及改进方法(improved slope based method,简称ISBM)的基础上提出了一种全新的基于斜率再优化(reoptimization slope based method,简称RO-SBM)方法用于信号序列的极值点延拓,然后对延拓后的数据进行EMD分解,得到相应的本征模函数(intrinsic mode function,简称IMF)分量。数值仿真结果表明,采用基于RO-SBM方法进行数据延拓,相比镜像延拓以及ISBM方法,可以更有效地抑制EMD中的端点效应问题,提升HHT方法的信号分析性能。通过基于RO-SBM方法进行数据延拓的HHT方法准确分离出了某转子系统的局部碰摩径向振动信号中所包含的故障特征分量,并将此方法成功应用于旋转机械故障诊断领域。
文摘Since the “smart growth” was put forward in the late 90s, it has become an accepted design idea and concept in the field of urban design in the world, and has been deeply studied and applied. In order to better promote “smart grown”, we set up an evaluation system, which consists of eleven indicators. In this paper, Oxford City and Fengzhen City are used as the objects of the study. Then smart growth evaluation model is established. The weight of the index is calculated by the entropy method. We use the model to evaluate the development plans of the two cities, from which to calculate the contribution of the indicators on the level of smart growth. Finally, we use the super-efficient data envelopment analysis model (DEA) to rank the importance of the indicators to the smart growth. The results show that the level of smart growth in Oxford is higher than that in Fengzhen. And “Multifunctional Building Density in Central City”, “The Density of Public Area in Central City” two indicators account for more than 36% weight. The contribution of the two indicators is also located in the top two indicators. Two cities focus on the direction of smart growth is also different. In summary, the differences between China and Western countries in urban planning are mainly focused on housing and public resources.
