We design and construct a resonant photodetector(RPD)with a Q factor of 320.83 at the resonant frequency of38.5 MHz on the basis of a theoretical analysis.Compared with the existing RPD under the same conditions,the s...We design and construct a resonant photodetector(RPD)with a Q factor of 320.83 at the resonant frequency of38.5 MHz on the basis of a theoretical analysis.Compared with the existing RPD under the same conditions,the signalto-noise-ratio of the error signal is increased by 15 dB and the minimum operation power is reduced from-55 dBm to-70 dBm.By comparing the standard deviations of the stability curves,we confirm that the RPD has a dramatic improvement on ultralow power extraction.In virtue of the RPD,we have completed the demonstration of channel multiplexing quantum communication.展开更多
Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- a...Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.展开更多
为提升时间序列的聚类精度,提出一种融合优化可调Q因子小波变换的改进密度峰值聚类(improved density peaks clustering based on optimal tunable Q-factor wavelet transform,OTQWT-IDPC)算法,该算法利用可调Q因子小波变换的能量优化...为提升时间序列的聚类精度,提出一种融合优化可调Q因子小波变换的改进密度峰值聚类(improved density peaks clustering based on optimal tunable Q-factor wavelet transform,OTQWT-IDPC)算法,该算法利用可调Q因子小波变换的能量优化选择策略及改进粒子群优化算法确定的最佳Q因子分解时序信号,通过最优特征子带的能量、均值、标准差和模糊熵构建特征子空间,并采用主成分分析降低特征维度,以减少特征冗余。同时,考虑到距离较远而周围密集程度较大的K近邻样本对局部密度的贡献率,引入权重系数及K近邻重新定义DPC的局部密度,并利用共享最近邻描述样本间的相似性。在BONN癫痫脑电信号和CWRU滚动轴承数据集上进行对比实验,结果表明,该算法的聚类精度分别为95%、94%,且Jacarrd、FMI和F_(1)值指标均优于其他对比算法,证明了OTQWT-IDPC算法的有效性。展开更多
Low quality(Q) factor is often the limiting factor for high performance carbon nanotube(CNT) resonators. The most commonly used approach to enhance the Q factor of CNTs is to reduce/eliminate the intrinsic defects.Her...Low quality(Q) factor is often the limiting factor for high performance carbon nanotube(CNT) resonators. The most commonly used approach to enhance the Q factor of CNTs is to reduce/eliminate the intrinsic defects.Herein, we show surprisingly that hole defects of suitable size and position are able to enhance the Q factor of CNT, which strongly contradicts to the common notion that the presence of defects promote intrinsic dissipation via defects dissipation. By analyzing the strain distribution, we find that such abnormal enhancement in Q factor of defected CNT originates from a coupling competition mechanism between the atomic mismatch around defected atoms and the thermoelastic damping. Although the presence of holes will introduce an additional defect dissipation source, suitable holes are capable of reducing the energy dissipation arisen from the thermoelastic damping, through changing the spatial strain field of defected CNT. This coupling competition mechanism provides a new route for designing high performance CNT resonators via defects engineering.展开更多
针对滚动轴承在强背景噪声下周期性冲击特征难以提取的问题,提出一种基于周期增强的包络谱基尼系数(periodically enhanced Gini index of the envelope spettrum,PEGIES)和可调品质因子小波变换(tunable Q-factor wavelet transform,TQ...针对滚动轴承在强背景噪声下周期性冲击特征难以提取的问题,提出一种基于周期增强的包络谱基尼系数(periodically enhanced Gini index of the envelope spettrum,PEGIES)和可调品质因子小波变换(tunable Q-factor wavelet transform,TQWT)相结合的滚动轴承故障特征提取方法。首先,以PEGIES为TQWT分解效果的评判指标,事先设定品质因子Q和冗余度迭代范围区间,以中心频率比CFR为阈值指标及最大分解层数公式确定对应品质因子Q分解下的最佳分解层数J。通过网格搜索的方式确定最佳品质因子Q,得到其对应的TQWT分解重构子带,选取大于PEGIES均值的子带进行合并处理得到最佳分量,通过Hilbert包络解调得到故障特征。为验证方法的有效性,将XJTU-SY滚动轴承加速寿命实验数据集和DDS(drivetrain diagnostics simulator)实验台实测信号作为研究对象,结合仿真信号结果,与其他方法进行对比,证实本文所提方法能在一定程度上降低转频的干扰,具有更好的故障特征比,能实现更加准确的诊断。展开更多
We demonstrated a novel method to measure the unloaded quality factor(Q factor) of high-Q resonant cavities. This method was used to obtain data with low errors and calculate the unloaded Q factor. This procedure was ...We demonstrated a novel method to measure the unloaded quality factor(Q factor) of high-Q resonant cavities. This method was used to obtain data with low errors and calculate the unloaded Q factor. This procedure was more reliable than traditional methods. The data required for the method were near the resonant frequency,not at the half-power points of the reflection coefficient curve or Smith chart. We applied the new method to measure a resonant cavity with an unloaded Q factor of^100,000, obtaining good agreement between the measured and theoretical results.展开更多
We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute t...We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute the solution by solving several triangular systems. We give the first order error analysis to show that the method is backward stable. The method is more efficient than the backward stable method proposed by Chandrasekaran, Gu and Sayed.展开更多
The statistical analysis in Q-methodology is based on factor analysis followed by a factor rotation. Currently, the most common factor extraction methods are centroid and principal component extractions and the common...The statistical analysis in Q-methodology is based on factor analysis followed by a factor rotation. Currently, the most common factor extraction methods are centroid and principal component extractions and the common techniques for factor rotation are manual rotation and varimax rotation. However, there are some other factor extraction methods such as principal axis factoring and factor rotation methods such as quartimax and equamax which are not used by Q-users because they have not been implemented in any major Q-program. In this article we briefly explain some major factor extraction and factor rotation techniques and compare these techniques using three datasets. We applied principal component and principal axis factoring methods for factor extraction and varimax, equamax, and quartimax factor rotation techniques to three actual datasets. We compared these techniques based on the number of Q-sorts loaded on each factor, number of distinguishing statements on each factor, and excluded Q-sorts. There was not much difference between principal component and principal axis factoring factor extractions. The main findings of this article include emergence of a general factor and a smaller number of excluded Q-sorts based on quartimax rotation. Another interesting finding was that a smaller number of distinguishing statements for factors based on quartimax rotation compared to varimax and equamax rotations. These findings are not conclusive and further analysis on more datasets is needed.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.62027821,11654002,11874250,and 11804207)the National Key R&D Program of China(Grant No.2016YFA0301401)+3 种基金the Key R&D Program of Shanxi,China(Grant No.201903D111001)the Program for Sanjin Scholar of Shanxi Provincethe Program for Outstanding Innovative Teams of Higher Learning Institutions of Shanxi,Chinathe Fund for Shanxi“1331 Project”Key Subjects Construction,China。
文摘We design and construct a resonant photodetector(RPD)with a Q factor of 320.83 at the resonant frequency of38.5 MHz on the basis of a theoretical analysis.Compared with the existing RPD under the same conditions,the signalto-noise-ratio of the error signal is increased by 15 dB and the minimum operation power is reduced from-55 dBm to-70 dBm.By comparing the standard deviations of the stability curves,we confirm that the RPD has a dramatic improvement on ultralow power extraction.In virtue of the RPD,we have completed the demonstration of channel multiplexing quantum communication.
文摘Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.
文摘为提升时间序列的聚类精度,提出一种融合优化可调Q因子小波变换的改进密度峰值聚类(improved density peaks clustering based on optimal tunable Q-factor wavelet transform,OTQWT-IDPC)算法,该算法利用可调Q因子小波变换的能量优化选择策略及改进粒子群优化算法确定的最佳Q因子分解时序信号,通过最优特征子带的能量、均值、标准差和模糊熵构建特征子空间,并采用主成分分析降低特征维度,以减少特征冗余。同时,考虑到距离较远而周围密集程度较大的K近邻样本对局部密度的贡献率,引入权重系数及K近邻重新定义DPC的局部密度,并利用共享最近邻描述样本间的相似性。在BONN癫痫脑电信号和CWRU滚动轴承数据集上进行对比实验,结果表明,该算法的聚类精度分别为95%、94%,且Jacarrd、FMI和F_(1)值指标均优于其他对比算法,证明了OTQWT-IDPC算法的有效性。
基金supported by the National Natural Science Foundation of China (Grant Nos. 51605172 and 51775201)the Natural Science Foundation of Hubei Province (Grant No. 2016CFB191)+1 种基金the Fundamental Research Funds for the Central Universities (Grant No. 2015MS014)the Hong Kong Scholars Program (No. XJ2020043)。
文摘Low quality(Q) factor is often the limiting factor for high performance carbon nanotube(CNT) resonators. The most commonly used approach to enhance the Q factor of CNTs is to reduce/eliminate the intrinsic defects.Herein, we show surprisingly that hole defects of suitable size and position are able to enhance the Q factor of CNT, which strongly contradicts to the common notion that the presence of defects promote intrinsic dissipation via defects dissipation. By analyzing the strain distribution, we find that such abnormal enhancement in Q factor of defected CNT originates from a coupling competition mechanism between the atomic mismatch around defected atoms and the thermoelastic damping. Although the presence of holes will introduce an additional defect dissipation source, suitable holes are capable of reducing the energy dissipation arisen from the thermoelastic damping, through changing the spatial strain field of defected CNT. This coupling competition mechanism provides a new route for designing high performance CNT resonators via defects engineering.
文摘针对滚动轴承在强背景噪声下周期性冲击特征难以提取的问题,提出一种基于周期增强的包络谱基尼系数(periodically enhanced Gini index of the envelope spettrum,PEGIES)和可调品质因子小波变换(tunable Q-factor wavelet transform,TQWT)相结合的滚动轴承故障特征提取方法。首先,以PEGIES为TQWT分解效果的评判指标,事先设定品质因子Q和冗余度迭代范围区间,以中心频率比CFR为阈值指标及最大分解层数公式确定对应品质因子Q分解下的最佳分解层数J。通过网格搜索的方式确定最佳品质因子Q,得到其对应的TQWT分解重构子带,选取大于PEGIES均值的子带进行合并处理得到最佳分量,通过Hilbert包络解调得到故障特征。为验证方法的有效性,将XJTU-SY滚动轴承加速寿命实验数据集和DDS(drivetrain diagnostics simulator)实验台实测信号作为研究对象,结合仿真信号结果,与其他方法进行对比,证实本文所提方法能在一定程度上降低转频的干扰,具有更好的故障特征比,能实现更加准确的诊断。
基金supported by the National Key Research and Development Program of China(No.2016YFA0401902)
文摘We demonstrated a novel method to measure the unloaded quality factor(Q factor) of high-Q resonant cavities. This method was used to obtain data with low errors and calculate the unloaded Q factor. This procedure was more reliable than traditional methods. The data required for the method were near the resonant frequency,not at the half-power points of the reflection coefficient curve or Smith chart. We applied the new method to measure a resonant cavity with an unloaded Q factor of^100,000, obtaining good agreement between the measured and theoretical results.
文摘We present a numerical method for solving the indefinite least squares problem. We first normalize the coefficient matrix. Then we compute the hyperbolic QR factorization of the normalized matrix. Finally we compute the solution by solving several triangular systems. We give the first order error analysis to show that the method is backward stable. The method is more efficient than the backward stable method proposed by Chandrasekaran, Gu and Sayed.
文摘The statistical analysis in Q-methodology is based on factor analysis followed by a factor rotation. Currently, the most common factor extraction methods are centroid and principal component extractions and the common techniques for factor rotation are manual rotation and varimax rotation. However, there are some other factor extraction methods such as principal axis factoring and factor rotation methods such as quartimax and equamax which are not used by Q-users because they have not been implemented in any major Q-program. In this article we briefly explain some major factor extraction and factor rotation techniques and compare these techniques using three datasets. We applied principal component and principal axis factoring methods for factor extraction and varimax, equamax, and quartimax factor rotation techniques to three actual datasets. We compared these techniques based on the number of Q-sorts loaded on each factor, number of distinguishing statements on each factor, and excluded Q-sorts. There was not much difference between principal component and principal axis factoring factor extractions. The main findings of this article include emergence of a general factor and a smaller number of excluded Q-sorts based on quartimax rotation. Another interesting finding was that a smaller number of distinguishing statements for factors based on quartimax rotation compared to varimax and equamax rotations. These findings are not conclusive and further analysis on more datasets is needed.
基金supported by the National Natural Science Foundation of China(Grant No.41174117 and 41474109)the National Key Basic Research Development Program of China(Grant No.2013CB228606)
