Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to l...Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions （IMFs）, which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.展开更多
Based on wavelet packet decomposition （WPD） algorithm and Teager energy operator （TEO）, a novel gearbox fault detection and diagnosis method is proposed. Its process is expatiated after the principles of WPD and T...Based on wavelet packet decomposition （WPD） algorithm and Teager energy operator （TEO）, a novel gearbox fault detection and diagnosis method is proposed. Its process is expatiated after the principles of WPD and TEO modulation are introduced respectively. The preprocessed sigaaal is interpolated with the cubic spline function, then expanded over the selected basis wavelets. Grouping its wavelet packet components of the signal based on the minimum entropy criterion, the interpolated signal can be decomposed into its dominant components with nearly distinct fault frequency contents. To extract the demodulation information of each dominant component, TEO is used. The performance of the proposed method is assessed by means of several tests on vibration signals collected from the gearbox mounted on a heavy truck. It is proved that hybrid WPD-TEO method is effective and robust for detecting and diagnosing localized gearbox faults.展开更多
目的探索识别汉语语音情绪的有效识别特征。方法采用基于Teager能量算子(TEO)的非线性特征,通过马尔可夫模型法(HMM),从汉语语音中识别平静和生气、欢快、悲伤4种情绪。结果文本有关时,5个非线性特征:基于频域TEO的Mel倒谱系数(nonlinea...目的探索识别汉语语音情绪的有效识别特征。方法采用基于Teager能量算子(TEO)的非线性特征,通过马尔可夫模型法(HMM),从汉语语音中识别平静和生气、欢快、悲伤4种情绪。结果文本有关时,5个非线性特征:基于频域TEO的Mel倒谱系数(nonlinear frequency domain Mel,NFD-Mel)、基于幅频特性的Mel倒谱参数(am plitude and frequency property Mel,AF-Mel)、基于微分幅频特性的Mel倒谱参数(am plitude and frequency property Mel of differential,DAF_Mel)、基于幅度调制的子带倒谱参数(AM-based SBCC,AM_SBCC)及基于幅频调制的子带倒谱参数(AMFM-based SBCC,AMFM-SBCC)的情绪识别性能全部高于Mel频率倒谱参数(Mel-scaled cepstrum coefficients,MF-CC)。文本无关时,NFD-Mel、AF-Mel、DAF-Mel的识别率高于MFCC,AM_SBCC、AM FM-SBCC的情绪识别率低于MFCC。结论结合非线性TEO的识别特征NFD-Mel、AF-Mel、DAF-Mel可有效提高情绪识别性能。展开更多
针对变分模态分解(Variational mode decomposition,VMD)检测微电网中多类电能质量扰动信号时,其实时性差及多类信号难以统一处理的问题,提出一种参数优化的VMD与Teager能量算子(Teager energy operator,TEO)融合的微电网电能质量扰动...针对变分模态分解(Variational mode decomposition,VMD)检测微电网中多类电能质量扰动信号时,其实时性差及多类信号难以统一处理的问题,提出一种参数优化的VMD与Teager能量算子(Teager energy operator,TEO)融合的微电网电能质量扰动检测方法。针对VMD方法参数难确定的问题,利用天牛须搜索(Beetleantennaesearch,BAS)对VMD方法的最佳参数进行优化搜索。搜索过程以VMD分解后各本征模函数的包络熵极小值与VMD迭代次数的结合作为适应度函数。根据搜索结果设定VMD方法的最佳分解层数K和惩罚因子α,并运用参数优化VMD对扰动信号进行分解。针对扰动信号经分解后本征模函数的筛选问题,以包络熵为指标,选取包络熵较小值的本征模函数进行TEO解调分析,提取扰动信号的特征信息。仿真结果表明,融合算法能实现对微电网电能质量扰动的准确检测,并具有良好的抗噪性。展开更多
基于晶闸管换流器的特高压直流输电系统(ultra-high voltage direct current based on line commutated converter,LCC-UHVDC)的故障定位算法对智能电网的安全稳定运行起着重要作用。针对长距离特高压直流输电系统故障测距方法精准度低...基于晶闸管换流器的特高压直流输电系统(ultra-high voltage direct current based on line commutated converter,LCC-UHVDC)的故障定位算法对智能电网的安全稳定运行起着重要作用。针对长距离特高压直流输电系统故障测距方法精准度低、快速性差的问题,提出了一种基于变分模态分解法(variational mode decomposition,VMD)和Teager能量算子(Teager energy operator,TEO)的双端行波故障测距方法。首先,研究了LCC-UHVDC线路故障电压行波的传播特性。利用零模电压随线路传播衰减明显的特征,通过VMD算法提取采样点处零模电压行波的时频特性。针对VMD参数选择不当导致的模态混叠问题,利用K-L散度(Kullback-Leibler divergence)对提取的模态指标进行优化。然后采用TEO对分解后信号进行瞬时能量谱提取,精确标定波头到达时间,最后采用双端迭代测距法迭代求解故障距离。在PSCAD/EMTDC搭建±800 kV LCC-UHVDC仿真模型进行验证。结果表明,所提方法在不同故障位置、过渡电阻和故障类型下具有较强的鲁棒性。展开更多
随着大量新能源的接入,使得多端柔性直流系统(modular multilevel converter based multi-terminal direct current, MMC-MTDC)故障特征愈加复杂,快速准确的故障识别与测距是亟需解决的关键难题之一。为此,提出了一种风-光-储-蓄互补发...随着大量新能源的接入,使得多端柔性直流系统(modular multilevel converter based multi-terminal direct current, MMC-MTDC)故障特征愈加复杂,快速准确的故障识别与测距是亟需解决的关键难题之一。为此,提出了一种风-光-储-蓄互补发电站经柔性直流输电外送系统故障识别与测距方法。首先,搭建风-光-储-蓄互补发电站经柔直外送系统,在此基础上,提出了一种Teager能量算子能量熵的新方法,利用测量点正负极Teager能量算子能量熵的比值构建故障选极及区段识别判据。接着,针对已识别的故障线路,提出变分模态分解(variational mode decomposition, VMD)与Teager能量算子(teager energy operator, TEO)相结合的故障测距方法。最后,利用PSCAD/EMTDC进行仿真,结果表明所提识别方法可以准确判断故障所在线路,所提测距方法能在故障发生2 ms时间窗内实现故障测距,误差率不超过2.55%,并具有较高的耐过渡电阻能力。展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 60771033
文摘Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions （IMFs）, which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.
基金This project is supported by National Natural Science Foundation of China (No.50605065)Natural Science Foundation Project of CQ CSTC (No.2007BB2142)
文摘Based on wavelet packet decomposition （WPD） algorithm and Teager energy operator （TEO）, a novel gearbox fault detection and diagnosis method is proposed. Its process is expatiated after the principles of WPD and TEO modulation are introduced respectively. The preprocessed sigaaal is interpolated with the cubic spline function, then expanded over the selected basis wavelets. Grouping its wavelet packet components of the signal based on the minimum entropy criterion, the interpolated signal can be decomposed into its dominant components with nearly distinct fault frequency contents. To extract the demodulation information of each dominant component, TEO is used. The performance of the proposed method is assessed by means of several tests on vibration signals collected from the gearbox mounted on a heavy truck. It is proved that hybrid WPD-TEO method is effective and robust for detecting and diagnosing localized gearbox faults.
文摘目的探索识别汉语语音情绪的有效识别特征。方法采用基于Teager能量算子(TEO)的非线性特征,通过马尔可夫模型法(HMM),从汉语语音中识别平静和生气、欢快、悲伤4种情绪。结果文本有关时,5个非线性特征:基于频域TEO的Mel倒谱系数(nonlinear frequency domain Mel,NFD-Mel)、基于幅频特性的Mel倒谱参数(am plitude and frequency property Mel,AF-Mel)、基于微分幅频特性的Mel倒谱参数(am plitude and frequency property Mel of differential,DAF_Mel)、基于幅度调制的子带倒谱参数(AM-based SBCC,AM_SBCC)及基于幅频调制的子带倒谱参数(AMFM-based SBCC,AMFM-SBCC)的情绪识别性能全部高于Mel频率倒谱参数(Mel-scaled cepstrum coefficients,MF-CC)。文本无关时,NFD-Mel、AF-Mel、DAF-Mel的识别率高于MFCC,AM_SBCC、AM FM-SBCC的情绪识别率低于MFCC。结论结合非线性TEO的识别特征NFD-Mel、AF-Mel、DAF-Mel可有效提高情绪识别性能。
文摘针对变分模态分解(Variational mode decomposition,VMD)检测微电网中多类电能质量扰动信号时,其实时性差及多类信号难以统一处理的问题,提出一种参数优化的VMD与Teager能量算子(Teager energy operator,TEO)融合的微电网电能质量扰动检测方法。针对VMD方法参数难确定的问题,利用天牛须搜索(Beetleantennaesearch,BAS)对VMD方法的最佳参数进行优化搜索。搜索过程以VMD分解后各本征模函数的包络熵极小值与VMD迭代次数的结合作为适应度函数。根据搜索结果设定VMD方法的最佳分解层数K和惩罚因子α,并运用参数优化VMD对扰动信号进行分解。针对扰动信号经分解后本征模函数的筛选问题,以包络熵为指标,选取包络熵较小值的本征模函数进行TEO解调分析,提取扰动信号的特征信息。仿真结果表明,融合算法能实现对微电网电能质量扰动的准确检测,并具有良好的抗噪性。
文摘基于晶闸管换流器的特高压直流输电系统(ultra-high voltage direct current based on line commutated converter,LCC-UHVDC)的故障定位算法对智能电网的安全稳定运行起着重要作用。针对长距离特高压直流输电系统故障测距方法精准度低、快速性差的问题,提出了一种基于变分模态分解法(variational mode decomposition,VMD)和Teager能量算子(Teager energy operator,TEO)的双端行波故障测距方法。首先,研究了LCC-UHVDC线路故障电压行波的传播特性。利用零模电压随线路传播衰减明显的特征,通过VMD算法提取采样点处零模电压行波的时频特性。针对VMD参数选择不当导致的模态混叠问题,利用K-L散度(Kullback-Leibler divergence)对提取的模态指标进行优化。然后采用TEO对分解后信号进行瞬时能量谱提取,精确标定波头到达时间,最后采用双端迭代测距法迭代求解故障距离。在PSCAD/EMTDC搭建±800 kV LCC-UHVDC仿真模型进行验证。结果表明,所提方法在不同故障位置、过渡电阻和故障类型下具有较强的鲁棒性。
文摘随着大量新能源的接入,使得多端柔性直流系统(modular multilevel converter based multi-terminal direct current, MMC-MTDC)故障特征愈加复杂,快速准确的故障识别与测距是亟需解决的关键难题之一。为此,提出了一种风-光-储-蓄互补发电站经柔性直流输电外送系统故障识别与测距方法。首先,搭建风-光-储-蓄互补发电站经柔直外送系统,在此基础上,提出了一种Teager能量算子能量熵的新方法,利用测量点正负极Teager能量算子能量熵的比值构建故障选极及区段识别判据。接着,针对已识别的故障线路,提出变分模态分解(variational mode decomposition, VMD)与Teager能量算子(teager energy operator, TEO)相结合的故障测距方法。最后,利用PSCAD/EMTDC进行仿真,结果表明所提识别方法可以准确判断故障所在线路,所提测距方法能在故障发生2 ms时间窗内实现故障测距,误差率不超过2.55%,并具有较高的耐过渡电阻能力。
