Phase-rectified signal averaging for the detection of quasi-periodicities and the prediction of cardiovascular risk

We present the phase-rectified signal averaging (PRSA) method as an efficient technique for the study of quasi-periodic oscillations in noisy, nonstationary signals. It allows the assessment of system dynamics despite phase resetting and noise and in relation with either increases or decreases of the considered signal. We employ the method to study the quasi-periodicities of the human heart rate based on long-term ECG recordings. The center deflection of the PRSA curve characterizes the average capacity of the heart to decelerate (or accelerate) the cardiac rhythm. It can be measured by a central wavelet coefficient which we denote as deceleration capacity (DC). We find that decreased DC is a more precise predictor of mortality in survivors of heart attack than left ventricular ejection fraction, the current "gold standard" risk predictor. In addition, we discuss the dependence of the DC parameter on age and on diabetes.     

基于聚合经验模态分解方法的OJ 287 射电流量变化周期分析

BL Lac 天体OJ 287 是一个重要的Blazar天体, 光变具有准周期特点. 本文从密歇根大学射电天文台数据库收集了OJ 287射电波段4.8, 8.0和 14.5 GHz超过30年时间的观测数据. 由于天文观测资料的复杂性, 一些传统寻找周期的方法不是太理想. 近几年迅速发展起来的聚合经验模态分解(EEMD) 特别适合于具有非线性和非平稳动态变化特性的Blazar天体光变规律的研究. 运用EEMD方法对OJ 287 的3个射电波段流量分别进行多时间尺度分解, 各获得6个代表不同时间尺度局部特征信息的本征模态函数分量和一个趋势项. 研究结果表明: 4.8 GHz射电流量变化的主要周期是18.9, 11.9, 5.7 和2.4年; 8.0 GHz 的主要周期是12.2, 5.2和2.4 年; 14.5 GHz的主要周期是21.8, 12.0, 4.3和 2.4 年. 尽管3个波段流量变化主要周期大小有一定的差别, 但是存在着近似相同的变化周期12.0 和2.4年, 说明这3个波段可能来源于相同的辐射机制, 也证实了OJ 287 射电波段和光学波段具有相同的长光变周期12.0 年. 这些结论表明: EEMD 作为一种全新的非线性、非平稳信号处理方法, 可以更精确地提取Blazar天体中不同光变周期的分量. 关键词： OJ 287 光变周期 聚合经验模态分解     

利用慢特征分析法提取层次结构系统中的外强迫

在大量真实的动力系统中,外部驱动力总是随时间发生变化,正是这种变化导致了非平稳行为的产生.因此,从此类系统的观测数据中提取和分析外强迫(也称驱动力)信号引起了人们越来越多的关注.慢特征分析法(slow feature analysis,SFA)是从非平稳时间序列中提取外强迫信息的一种有效算法.在其基础上利用变参数的Logistic映射产生的非平稳时间序列,通过数值试验进一步讨论了该方法的应用前景,并发展了一些相应的分析技术.试验结果表明,对于模型中包含两个时变驱动力参数的系统,经过一次SFA处理之后,可以进一步利用子波分析技术检索出外强迫信号中的两个参数;对于模型中有两个叠加驱动力层次的三层动力系统,可先通过一次SFA处理,提取出次慢层外强迫信号,对该信号进行二次SFA处理,可提取出最慢层外强迫信号.     

Angular pseudomomentum theory for the generalized nonlinear Schrödinger equation in discrete rotational symmetry media

We develop a complete mathematical theory for the symmetrical solutions of the generalized nonlinear Schrödinger equation based on the concept of angular pseudomomentum. We consider the symmetric solitons of a generalized nonlinear Schrödinger equation with a nonlinearity depending on the modulus of the field. We provide a rigorous proof of a set of mathematical results justifying that these solitons can be classified according to the irreducible representations of a discrete group. Then we extend this theory to non-stationary solutions and study the relationship between angular momentum and pseudomomentum. We illustrate these theoretical results with numerical examples.     

Multiresolution analysis of fluctuations in non-stationary time series through discrete wavelets

We illustrate the efficacy of a discrete wavelet based approach to characterize fluctuations in non-stationary time series. The present approach complements the multifractal detrended fluctuation analysis (MF-DFA) method and is quite accurate for small size data sets. As compared to polynomial fits in the MF-DFA, a single Daubechies wavelet is used here for detrending purposes. The natural, built-in variable window size in wavelet transforms makes this procedure well suited for non-stationary data. We illustrate the working of this method through the analysis of binomial multifractal model. For this model, our results compare well with those calculated analytically and obtained numerically through MF-DFA. To show the efficacy of this approach for finite data sets, we also do the above comparison for Gaussian white noise time series of different sizes. In addition, we analyze time series of three experimental data sets of tokamak plasma and also spin density fluctuations in 2D Ising model.     

Frequency analysis with coupled nonlinear oscillators

We present a method to obtain the frequency spectrum of a signal with a nonlinear dynamical system. The dynamical system is composed of a pool of adaptive frequency oscillators with negative mean-field coupling. For the frequency analysis, the synchronization and adaptation properties of the component oscillators are exploited. The frequency spectrum of the signal is reflected in the statistics of the intrinsic frequencies of the oscillators. The frequency analysis is completely embedded in the dynamics of the system. Thus, no pre-processing or additional parameters, such as time windows, are needed. Representative results of the numerical integration of the system are presented. It is shown, that the oscillators tune to the correct frequencies for both discrete and continuous spectra. Due to its dynamic nature the system is also capable to track non-stationary spectra. Further, we show that the system can be modeled in a probabilistic manner by means of a nonlinear Fokker-Planck equation. The probabilistic treatment is in good agreement with the numerical results, and provides a useful tool to understand the underlying mechanisms leading to convergence.     

Measuring time dependent volatility and cross-sectional correlation in Australian equity returns

In this study we examine the time-dependent nature of volatility and cross-correlation of Australian equity returns data. Volatility and correlation estimates are calculated using methods that allow for non-stationary behaviour. By averaging the estimates across the entire data set we show that the correlation in ASX stock returns displays evidence of significant time-dependent behaviour. We also find that the volatility estimates do not display similar non-stationary patterns.     

基于遗传神经网络的旋转机械故障预测方法研究

许多大型旋转机械运行工况恶劣,非平稳、非线性特征明显,以及各种突发性、偶然性因素的影响,给基于振动信号处理的状态预测和状态维护分析带来困难。神经网络以其强大的处理非线性系统的能力在故障预测中得到广泛的应用,但由于其在追求高精度训练目标时易陷入局部极值,且收敛速度慢甚至发散。针对这个问题,提出了采用遗传算法对神经网络连接权值和阈值进行优化,这样不仅发挥了神经网络广泛的映射特性也使遗传算法的全局搜索优势尽显无疑。通过组合这两种算法,在提升网络学习的准确度方面,优点尤其突出,最终提高对旋转机械故障预测和寿命估计的性能,这在某环境模拟试验系统动力风机的轴承磨损故障预测中得到了验证。     

Collocation Methods for Hyperbolic Partial Differential Equations with Singular Sources

A numerical study is given on the spectral methods and the high order WENO finite difference scheme for the solution of linear and nonlinear hyperbolic partial differential equations with stationary and non-stationary singular sources. The singular source term is represented by the $δ$-function. For the approximation of the $δ$-function, the direct projection method is used that was proposed in [6]. The $δ$-function is constructed in a consistent way to the derivative operator. Nonlinear sine-Gordon equation with a stationary singular source was solved with the Chebyshev collocation method. The $δ$-function with the spectral method is highly oscillatory but yields good results with small number of collocation points. The results are compared with those computed by the second order finite difference method. In modeling general hyperbolic equations with a non-stationary singular source, however, the solution of the linear scalar wave equation with the non-stationary singular source using the direct projection method yields non-physical oscillations for both the spectral method and the WENO scheme. The numerical artifacts arising when the non-stationary singular source term is considered on the discrete grids are explained.     

小波分析方法在合肥光源逐束团测量系统中的应用

介绍了小波分析方法在逐束团束流位置测量系统中的应用。小波分析方法在高频处频率窗口较宽,具有较高的时间分辨力,使用小波分析可分离并提取信号的振荡成分及基线漂移成分,各成分在时间轴上的位置与原信号相同,原有的线性关系保持不变,在处理非平稳信号时不会造成信号明显劣化,如幅度失真和相位偏差。基于小波分解和重构的时间序列多分辨力滤波处理非平稳信号时不会造成信号明显劣化,保证了追踪束团振荡强度、相位、频率和振荡模式随时间变化的结果更真实可信。在合肥光源中,小波分析方法成功用于横向振荡振幅包络的提取及增长率、阻尼率的计算,也可用于提取横向振荡振幅包络及计算增长率和阻尼率,为机器研究、束流诊断和逐束团反馈系统调试提供了准确的依据。     

基于希尔伯特-黄变换的近红外脑功能成像信号分析

近红外光谱技术(Near-infrared spectroscopy,NIRS)已被广泛应用于无损大脑功能检测,然而传统时频分析方法并不适用于非稳态、非线性的血氧信号。在视觉刺激实验中,采集被试前额叶的血氧信号,分别比较了傅里叶谱分析法、小波谱分析法和希尔伯特黄变换(Hilbert-Huang transform,HHT)方法在近红外脑功能成像信号分析中应用的结果。实验表明基于希尔伯特黄变换的谱分析方法优于其它两种方法,并且使近红外光谱研究可以采用事件相关设计的实验,为形成近红外光谱信号分析的标准方法开辟了新的途径。     

RETRACTED: A Hybrid Nonlinear Active Noise Control Method Using Chebyshev Nonlinear Filter

Investigations into active noise control (ANC) technique have been conducted with the aim of effective control of the low-frequency noise. In practice, however, the performance of currently available ANC systems degrades due to the effects of nonlinearity in the primary and secondary paths, primary noise and louder speaker. This paper proposes a hybrid control structure of nonlinear ANC system to control the non-stationary noise produced by the rotating machinery on the nonlinear primary path. A fast version of ensemble empirical mode decomposition is used to decompose the non-stationary primary noise into intrinsic mode functions, which are expanded using the second-order Chebyshev nonlinear filter and then individually controlled. The convergence of the nonlinear ANC system is also discussed. Simulation results demonstrate that proposed method outperforms the FSLMS and VFXLMS algorithms with respect to noise reduction and convergence rate.     

A random telegraph signal of Mittag-Leffler type

A general method is presented to explicitly compute autocovariance functions for non-Poisson dichotomous noise based on renewal theory. The method is specialized to a random telegraph signal of Mittag-Leffler type. Analytical predictions are compared to Monte Carlo simulations. Non-Poisson dichotomous noise is non-stationary and standard spectral methods fail to describe it properly as they assume stationarity.     

Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring

Accessing difficulties and harsh environments require more advanced condition monitoring techniques to ensure the high availability of offshore wind turbines. Empirical mode decomposition (EMD) has been shown to be a promising technique for meeting this need. However, EMD was developed for one-dimensional signals, unable to carry out an information fusion function which is of importance to reach a reliable condition monitoring conclusion. Therefore, bivariate empirical mode decomposition (BEMD) is investigated in this paper to assess whether it could be a better solution for wind turbine condition monitoring. The effectiveness of the proposed technique in detecting machine incipient fault is compared with EMD and a recently developed wavelet-based ‘energy tracking’ technique. Experiments have shown that the proposed BEMD-based technique is more convenient than EMD for processing shaft vibration signals, and more powerful than EMD and wavelet-based techniques in terms of processing the non-stationary and nonlinear wind turbine condition monitoring signals and detecting incipient mechanical and electrical faults.     

基于噪声辅助非均匀采样复数据经验模态分解的混沌信号降噪

鉴于非均匀采样复数据经验模态分解(NSBEMD)相对传统分解方法的优势和噪声的NSBEMD特性,提出了一种基于噪声辅助NSBEMD的混沌信号自适应降噪方法.该方法首先以含噪混沌信号和高斯白噪声分别为实、虚部来构造复数据并进行NSBEMD,然后根据虚部各IMF的能量来估算实部各IMF中包含的噪声能量,最后根据噪声能量的估计值对实部IMF进行奇异值分解(SVD)降噪.噪声估计实验验证了噪声能量估计方法的可行性,而Lorenz信号和太阳黑子月平均数的降噪实验则表明,相对于现有EMD降噪方法,本文方法能够进一步消除噪声,更清晰地恢复出混沌吸引子的拓扑结构.     

基于HHT变换的起动电动机特征信号时频分析

针对车辆起动电动机电气和机械故障发生时特征信号的时变不平稳特性,进行了时频域分析处理,提出了利用现代信号处理方法对故障信号提取特征向量的方法,主要对起动电动机的电枢和轴承故障进行诊断。在构建电机故障测试实验平台的基础上,利用破坏性实验构造了故障类型,测取了电枢电流和振动信号,分别采用小波分析理论和HHT变换对信号进行分析,通过分解再重构的方式将信号分解成了频率由高到低的不同分量,并获得了故障的特征频率,提取了特征向量。实验结果表明,基于HHT变换的现代信号处理方法在处理时变非平稳信号方面比小波分析理论更具有自适应性,更易识别。     

A suggested statistical test for measuring bivariate nonlinear dependence

We devise a new asymptotic statistical test to assess independence in bivariate continuous distributions. Our approach is based on the Cramér–von Mises test, in which the empirical process is viewed as the Kullback–Leibler divergence, that is, as the distance between the data under the independence hypothesis and the data empirically observed. We derive the theoretical characteristic function of the limit distribution of the test statistic and find the critical values through computer simulation. A Monte Carlo experiment is considered as assessing the validation and power performance of the test by assuming a bivariate nonlinear dependence structure with fat tails. Two extra examples, respectively, consider stationary and conditionally nonstationary series. Results confirm that our suggested test is consistent and powerful in the presence of bivariate nonlinear dependence even if the environment is non-Gaussian. Our case is illustrated with high-frequency data from stocks listed on the NYSE that recently experienced so-called mini-flash crashes.     

Breakdown of waves described by exact solutions of the Thomas-Fermi model

The dynamics of large localized repulsive clouds is examined by means of exact non-stationary solutions of the one-dimensional Thomas-Fermi model. The nonlinear flattening of the cloud peak, the wave breakdown at the cloud peripheries, and the condensate velocity distributions are thus described. Our solutions, which can contain an arbitrary amount of free parameters, show the nonlinear evolution of an arbitrary initial wave form. A unique procedure for analyzing these solutions is presented. The difference between our breakdown matter wave solutions and the well known Riemann shock waves is stressed. Received 22 March 2002 Published online 9 July 2002     

基于Hilbert-Huang变换的水声信号特征提取及分类技术

水下目标噪声信号是一种典型的非线性非平稳随机信号，Hilbert-Huang变换较传统方法在处理此类信号时具有很大优势，据此本文提出了一种基于Hilbert-Huang变换的水下目标噪声特征提取方法。本文将环境噪声当作一类目标，利用上述方法对涉及两种航行船舶、海洋生物和海洋环境噪声的四类目标信号的特征进行了详细分析，最后对各类目标噪声信号进行了分类识别实验。实验结果表明基于Hilbert-Huang变换提取的特征对各类目标具有较好的可分性，达到了较高的识别率。     

小波分析在遥测信号处理中的应用

综述了小波分析的发展现状,通过对比小波变换与短时傅里叶变换之间的差异,指出小波分析是一种多分辨分析方法,特别适合于非平稳信号的分析与处理,并且应用该方法对遥测速变信号进行处理,取得了较好的效果。