由随机过程的一次实现估计输入数据的方差

目的　为了满足正则化方法求解反问题之需要 ,提出了一种由随机过程的一次物理实现估计原始数据方差的新方法 .方法　通过假设一个随机误差的分布 ,然后通过数值仿真得到了方差估算的公式 .结果与结论　用不同的随机测量过程对估算方法进行了验证 .结果表明 ,由该方法及公式估算得到的方差具有较高的精度 ,能比较准确地反映随机误差的内在统计规律     

Using sine wave histograms to estimate analog-to-digital converterdynamic error functions

This paper describes a new method for developing analog-to-digital converter (ADC) error function models using modified sinewave histogram methods. The error models may be used to digitally compensate for nonlinearities introduced by the converter. The histogram modification involves sorting of converter output samples based upon an estimated associated input derivative signal. This error model is based upon a previously unpublished result which shows that sinewave histograms yield distinctly different expected errors for each state based upon input signal slope associated with each output sample. This result thus provides a dynamic dependence for expected errors measured by means of histogram methods. Sorted sinewave histograms are used to estimate slope dependent expected errors at each ADC output state (code). The method provides improved error representation by providing error basis functions for every output code. Simulated results prove that this method removes all slope dependent errors for complex ADC architectures while experimental results for an 8-bit 200 MSPS ADC yielded more than 10 dB improvement in spurious-free-dynamic-range (SFDR) over the full Nyquist band. The new method is thus shown to possess wideband dynamic error character     

A Direct Measurement Method of Frequency Responses of the Smart Power IC

Smart power ICs are widely used in universal input power supplies. This paper presents a detailed analysis and scheme design for real-time small-signal modeling method for smart power IC. First, in this method, the switching duty cycle is converted into a voltage output signal based on the operational characteristics of the power IC. The power IC's frequency response is then obtained by measuring the duty cycle output signal and the power IC's control input signal. The modeling system is referred to as sampling the duty cycle of the power IC with a zero-order hold output. Using least-square identification, the smart power IC's dynamic model and transfer function are synthesized from the measurement data. The results of the modeling experiment are discussed, and practical guidelines and error amplifier (EA) design examples are presented for both continuous and discontinuous conduction modes (CCM and DCM). The effectiveness and practicality are verified through experiments.      

Validity of uniform quantization error model for sinusoidal signalswithout and with dither

A technique is presented for determining the probability density function (PDF) and variance of the quantization error of a sinusoidal signal applied to a uniform quantizer, namely, an ideal analog-digital converter. The results are the basis for determining the validity of the uniform quantization noise model for this class of signals. When dither is used it tends to decorrelate quantization error and the input signal. The effect of added uniform dither on the PDF of the quantizer input is investigated, as well as the PDF of the quantization error corresponding to the dithered sinusoid. The results prove the validity of the uniform quantization model in this case     

Extracting vertical winds from simulated clouds with ground-based coherent Doppler lidar

The performance of mean velocity estimators is determined by computer simulations for solid-state coherent Doppler lidar measurements of wind fields at a cloud interface with deterministic profiles of velocity and aerosol backscatter. Performance of the velocity estimates is characterized by the standard deviation about the estimated mean and the bias referenced to the input velocity. A new class of estimators are required for cloud conditions, as traditional techniques result in biased estimates. We consider data with high signal energy that produces negligible random outliers.     

Analysis and Modeling of Inertial Sensors Using Allan Variance

It is well known that inertial navigation systems can provide high-accuracy position, velocity, and attitude information over short time periods. However, their accuracy rapidly degrades with time. The requirements for an accurate estimation of navigation information necessitate the modeling of the sensors' error components. Several variance techniques have been devised for stochastic modeling of the error of inertial sensors. They are basically very similar and primarily differ in that various signal processings, by way of weighting functions, window functions, etc., are incorporated into the analysis algorithms in order to achieve a particular desired result for improving the model characterizations. The simplest is the Allan variance. The Allan variance is a method of representing the root means square (RMS) random-drift error as a function of averaging time. It is simple to compute and relatively simple to interpret and understand. The Allan variance method can be used to determine the characteristics of the underlying random processes that give rise to the data noise. This technique can be used to characterize various types of error terms in the inertial-sensor data by performing certain operations on the entire length of data. In this paper, the Allan variance technique will be used in analyzing and modeling the error of the inertial sensors used in different grades of the inertial measurement units. By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial-sensor output data. Being a directly measurable quantity, the Allan variance can provide information on the types and magnitude of the various error terms. This paper covers both the theoretical basis for the Allan variance for modeling the inertial sensors' error terms and its implementation in modeling different grades of inertial sensors.     

Noise-Enhanced Detection of Subthreshold Signals With Carbon Nanotubes

Electrical noise can help pulse-train signal detection at the nanolevel. Experiments on a single-walled carbon nanotube transistor confirmed that a threshold exhibited stochastic resonance (SR) for finite-variance and infinite-variance noise: small amounts of noise enhanced the nanotube detector's performance. The experiments used a carbon nanotube field-effect transistor to detect noisy subthreshold electrical signals. Two new SR hypothesis tests in the Appendix also confirmed the SR effect in the nanotube transistor. Three measures of detector performance showed the SR effect: Shannon's mutual information, the normalized correlation measure, and an inverted bit error rate compared the input and output discrete-time random sequences. The nanotube detector had a threshold-like input-output characteristic in its gate effect. It produced little current for subthreshold digital input voltages that fed the transistor's gate. Three types of synchronized white noise corrupted the subthreshold Bernoulli sequences that fed the detector. The Gaussian, the uniform, and the impulsive Cauchy noise combined with the random input voltage sequences to help the detector produce random output current sequences. The experiments observed the SR effect by measuring how well an output sequence matched its input sequence. Shannon's mutual information used histograms to estimate the probability densities and computed the entropies. The correlation measure was a scalar inner product of the input and output sequences. The inverted bit error rate computed how often the bits matched between the input and output sequences. The observed nanotube SR effect was robust: it persisted even when infinite-variance Cauchy noise corrupted the signal stream. Such noise-enhanced signal processing at the nanolevel promises applications to signal detection in wideband communication systems and biological and artificial neural networks     

一种分段平稳随机信号的参数识别方法

基于振动的结构健康监测的前提是从振动测试信号中提取结构模态参数。随机子空间方法是近年来发展起来的一种线性系统辨识方法，可以有效地从环境激励的结构响应信号中提取结构模态参数。随机子空间识别方法的应用前提是输人满足白噪声的假定，输出信号应当是平稳信号。论文对随机子空间方法的使用前提进行了拓展。将非平稳信号划分为分段平稳随机信号进行处理，为非平稳信号的研究提供一种新的分析方法。基本思想是将在现场采集的结构输出信号进行分段，各段信号应满足稳定的条件，即分段平稳。将各段信号用随机子空间结合稳定图进行识别，然后将所有各段所识别的模态参数再一次用稳定图方法进行分析，得出结构的模态参数。最后用-3跨连续梁的数值模型进行验证，结果表明用随机子空间方法结合两次稳定图可以有效地识别分段平稳的随机信号。     

Validation and error estimation of computational models

This paper develops a Bayesian methodology for assessing the confidence in model prediction by comparing the model output with experimental data when both are stochastic. The prior distribution of the response is first computed, which is then updated based on experimental observation using Bayesian analysis to compute a validation metric. A model error estimation methodology is then developed to include model form error, discretization error, stochastic analysis error (UQ error), input data error and output measurement error. Sensitivity of the validation metric to various error components and model parameters is discussed. A numerical example is presented to illustrate the proposed methodology.     

Importance Sampling for Reliability Evaluation With Stochastic Simulation Models

Importance sampling has been used to improve the efficiency of simulations where the simulation output is uniquely determined, given a fixed input. We extend the theory of importance sampling to estimate a system’s reliability with stochastic simulations. Thanks to the advance of computing power, stochastic simulation models are employed in many applications to represent a complex system behavior. A stochastic simulation model generates stochastic outputs at the same input. Given a budget constraint on total simulation replications, we develop a new approach, which we call stochastic importance sampling, which efficiently uses stochastic simulations with unknown output distribution. Specifically, we derive the optimal importance sampling density and allocation procedure that minimize the variance of an estimator. Application to a computationally intensive aeroelastic wind turbine simulation demonstrates the benefits of the proposed approach. Supplementary materials for this article are available online.     

阵元信号相幅非一致性对波束形成的影响

在波束形成技术中,由于各输入通道相幅的不一致性和阵元位置的误差,而将影响波束形成性能.本文以小型舷侧阵为研究对象,首先介绍了含有误差分量的波束模型,并推导了含有误差分量的波束输出能量模型.然后分别从阵元的相位误差、幅度误差和位置误差三个方面着手,理论分析和计算机仿真相结合,详细介绍了它们对波束形成的影响,对工程应用具有现实的指导意义.     

Challenges in constructing time series models from process data

Correlated input and output sequences of industrial processes are often autocorrelated. Analysts who wish to construct models for such processes from input and output sequences alone must be careful as the autocorrelations in individual time series can masquerade as cross‐correlations. Several different models may appear equally plausible, depending on how the process had been operated. In this paper we study the input and output sequences from two industrial case studies using various time series tools. The results of our study illustrate that in feedback situations, where the current input is adjusted—either automatically or manually—on the basis of past output, different models may fit the data equally well. Which of these models describe the actual transfer function is unclear, unless a known dither signal can be added to the input to allow for an unambiguous identification of the transfer function. This problem is related to spurious regression and is of interest to econometricians who often deal with time series where feedback, known or unknown, may be responsible for misleading interpretations. Copyright © 2010 John Wiley & Sons, Ltd.     

An adaptive spectral Galerkin stochastic finite element method using variability response functions

A methodology is proposed in this paper to construct an adaptive sparse polynomial chaos (PC) expansion of the response of stochastic systems whose input parameters are independent random variables modeled as random fields. The proposed methodology utilizes the concept of variability response function in order to compute an a priori low‐cost estimate of the spatial distribution of the second‐order error of the response, as a function of the number of terms used in the truncated Karhunen–Loève (KL) expansion. This way the influence of the response variance to the spectral content (correlation structure) of the random input is taken into account through a spatial variation of the truncated KL terms. The criterion for selecting the number of KL terms at different parts of the structure is the uniformity of the spatial distribution of the second‐order error. This way a significantly reduced number of PC coefficients, with respect to classical PC expansion, is required in order to reach a uniformly distributed target second‐order error. This results in an increase of sparsity of the coefficient matrix of the corresponding linear system of equations leading to an enhancement of the computational efficiency of the spectral stochastic finite element method. Copyright © 2015 John Wiley & Sons, Ltd.     

线性修正下的神经网络盲均衡算法

研究了一种利用横向滤波器对神经网络进行线性修正的盲均衡算法。在神经网络的输入层之前加入一个横向滤波器,以横向滤波器的节点输出作为神经网络盲均衡器的输入,利用常数模代价函数分别得到横向滤波器和神经网络盲均衡器的瞬时输出误差,将瞬时误差加权处理作为调节误差分别对横向滤波器和神经网络盲均衡器的权系数进行调节,算法实现了对非凸性误差曲面进行线性和非线性寻优的组合。计算机仿真证明提出的算法有效提高了神经网络盲均衡算法的收敛速度,降低了稳态剩余误差,具有更好的实用性和均衡性能。     

Estimating the Expected Probability of Misclassification for a Rule Based on the Linear Discriminant Function: Univariate Normal Case

The problem is to estimate the average probability of misclassifying an observation from a given population in the context of the two group classification problem when populations are univariate normal with unknown means and common known variance, and the rule is based on the linear discriminant function. Several estimators are compared with respect to asymptotic MSE and with respect to the distribution of the absolute error between estimator and parameter, and conclusions drawn about the best estimators.     

随机参数诱导交流积分器中的随机共振研究

研究了具有随机参数的交流积分器中的随机共振现象。基于线性系统理论,得到了系统输出信噪比的表达式。研究发现,输出信噪比是噪声强度、噪声相关率以及系统参数的非单调函数。信噪比随着激励信号频率的增大而增大,随着信号直流分量的增大而减小。     

Stochastic free vibration analyses of composite shallow doubly curved shells – A Kriging model approach

This paper presents the Kriging model approach for stochastic free vibration analysis of composite shallow doubly curved shells. The finite element formulation is carried out considering rotary inertia and transverse shear deformation based on Mindlin’s theory. The stochastic natural frequencies are expressed in terms of Kriging surrogate models. The influence of random variation of different input parameters on the output natural frequencies is addressed. The sampling size and computational cost is reduced by employing the present method compared to direct Monte Carlo simulation. The convergence studies and error analysis are carried out to ensure the accuracy of present approach. The stochastic mode shapes and frequency response function are also depicted for a typical laminate configuration. Statistical analysis is presented to illustrate the results using Kriging model and its performance.     

Low-order AR models for mean and maximum frequency estimation in the context of Doppler color flow mapping

Autoregressive (AR) techniques are investigated by developing mean and maximum frequency estimators suitable for use in Doppler color flow mapping systems, where they are most needed. The estimators are based on low-order (for computational efficiency) AR models applied to complex signals whose real and imaginary parts are the in-phase and quadrature components of the analytical Doppler signal, respectively. A large number of simulated data sequences generated by a sinusoidal computer model and having different number of samples, spectral shapes, bandwidths, and signal-to-noise ratios are used to examine the performance (bias and variance) of the estimators in a systematic manner. Comparisons are made with the established autocorrelation technique, whose output is shown to be identical to one of the AR mean frequency estimators described.     

基于周期势函数的自适应二阶欠阻尼随机共振信号增强方法

针对滚动轴承微弱故障振动信号在噪声环境下故障特征难以提取的问题,提出一种基于周期势函数的自适应二阶欠阻尼随机共振信号增强方法。采用粒子群算法对系统参数和阻尼系数的自适应匹配,实现对多个拟增强频段的随机共振,更加适用于工程实际中多故障信号提取。数据库考题检验和工程实验验证表明:1)该方法明显提高了输出信噪比,故障特征频率处主峰突出,边带干扰少,方便故障的机器判读,误判率低;2)随着噪声强度的增加,虽然输出信噪比有所降低,但该方法的检测效果仍优于基于周期势函数的自适应一阶随机共振方法的检测效果;3)该方法对噪声的适应性更强,在噪声环境下对于微弱故障信号的提取有着明显优势。     

Characterization of Frequency Stability: A Transfer Function Approach and Its Application to Measurements via Filtering of Phase Noise

Frequency stability of high-quality signal sources is characterized in the Fourier frequency domain by the spectral density Sy(f) of the fractional instantaneous frequency deviation y(t), and in the time domain by the Allan variance ?y2(?). Two well-known types of measuring apparatus used to evaluate these parameters are analog spectrum analyzers and digital electronic counters, respectively. A detailed analysis of the structure of the relation between ?y2(?) and Sy(f) shows that it is possible to define a variance, i.e., a time-domain measure, by its transfer function in the Fourier frequency domain, even when no corresponding measurement sequence exists in the time domain. Two different kinds of variance are then defined, which possess different properties for white and flicker phase noises. One of these variances is an estimate of the Allan variance. These variances may be measured by a suitable filtering of phase noise at the output of a phase detector.