Viscoelastic parameter estimation based on spectral analysis

This paper introduces a new technique for the robust estimation of relaxation-time distribution in tissue. The main novelty is in the use of the phase of transfer functions calculated from a time series of strain measurements at multiple locations. Computer simulations with simulated measurement noise demonstrate the feasibility of the approach. An experimental apparatus and software were developed to confirm the simulations. The setup can be used both as a rheometer to characterize the overall mechanical properties of a material or as a vibro-elastography imaging device using an ultrasound system. The algorithms were tested on tissue mimicking phantoms specifically developed to exhibit contrast in elasticity and relaxation time. The phantoms were constructed using a combination of gelatin and a polyvinyl alcohol sponge to produce the desired viscoelastic properties. The tissue parameters were estimated and the elasticity and relaxation time of the materials have been used as complementary features to distinguish different materials. The estimation results are consistent with the rheometry, verifying that the relaxation time can be used as a complementary feature to elasticity to delineate the mechanical properties of the phantom.     

Sensitivity analysis of grating parameter estimation

An optimization method for the sensitivity of diffraction efficiency measurements is presented. I define the sensitivity as the estimation precision of the grating parameters. The optimization method called sensitivity analysis for fitting scans all the possible measurement configurations and selects the configuration that yields the best sensitivity. The scan is made over the domain of the experimental parameters of the arrangement, such as the azimuth angle of the grating and the orientation angles of the analyzer and the polarizer. These parameters can be freely varied, and among the multitude of possible combinations there is one configuration that provides optimum sensitivity. Comparison with experimental results reveals a qualitative agreement between theory and practice.     

基于NI-CompactRIO平台的水声信号参数估计技术实现

通过对水声信号进行参数估计可以获得信号的频率、制式和周期等参数信息,传统的水声信号参数估计的实现方法过程繁琐,工作量大,开发周期长且可靠性较低。采用美国国家仪器公司最新推出具有高实时性的嵌入式控制和采集平台NI-CompactRIO以及Labview图形化开发环境,在该平台上实现了谱线比值法测频、信号转发和匹配滤波三种参数估计算法,提高可靠性的同时也缩短了开发周期。     

粒子滤波参数估计方法在齿轮箱剩余寿命预测中的应用研究

针对非线性非高斯系统的剩余寿命（RUL）预测问题,本文提出了一种基于粒子滤波（PF）理论的设备剩余寿命预测方法。首先建立设备的非线性状态空间模型（含有未知的时变参数）,然后通过粒子滤波算法估计出设备状态的概率密度函数（PDF）,从而根据该PDF计算出设备的RUL。此外,计算设备RUL的期望值和95%置信区间,并对模型的预测效果进行评估,验证预测的有效性和准确性。最后通过齿轮箱的全寿命实验,对本文所提方法的有效性进行实例验证,将实验结果和传统的比例风险模型（PHM）预测结果对比分析,结果表明本文提出的剩余寿命预测方法要优于传统的PHM预测方法。     

音素基随机轨迹模型的理论机制和参数估计方法

随机轨迹模型是针对基本隐马尔可夫模型所隐含的三个不合理假设而提出的统计建模方法。本文介绍了随机轨迹模型相对于基本隐马尔可夫模型的优越性 ,综述了以音素为语音识别基元的随机轨迹模型的理论机制和参数估计方法     

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.     

基于独立分量分析的结构模态参数识别

简要介绍了独立分量分析的基本原理及算法,探讨了结构的正规坐标与独立分量的关系。分析认为,结构自由振动响应的振型分解可以看做是一个ICA问题。因此,可以把独立分量分析发展成为一种利用结构自由振动响应时域信号进行模态参数识别的方法。结合数值仿真算例及振动试验,验证了独立分量分析用于结构模态参数识别的有效性。结果表明,独立分量分析可以准确的从结构自振响应中,分离出各正规坐标,同时估计出各阶模态振型向量,适用于环境激励下的工作模态参数识别。     

Hierarchical pose classification based on human physiology for behaviour analysis

This study presents a new approach to classify human body poses by using angular constraints and variations of body joints. Although different classifications of the poses have been previously made, the proposed approach attempts to create a more comprehensive, accurate and extensible classification by integrating all possible poses based on angles of movement in human joints. The angular variations in all body joints can determine any possible poses. The joint angles from the body axis are computed in the three-dimensional space. In order to train and classify the pose in an automated manner, support vector machines (SVMs) were used. Experiments were carried out on both benchmark (CMU dataset) and in-house simulated (POSER dataset) poses to evaluate the performance of the proposed classification scheme.     

基于L型线阵的二维分布源参数估计方法

针对二维分布源信号波达方向估计问题,提出了一个新的二维分布源模型及其参数估计方法。将基于角度信号密度函数的一维相干分布源模型扩展至二维,推导了二维分布源的广义阵列方向向量。在L型线阵下,利用广义MUSIC算法,首先估计出俯仰角及其扩散参数,然后利用估计出来的俯仰角及扩散参数对方位角及扩散参数进行估计。仿真实验表明,所提出的二维相干分布源参数估计算法具有良好的定向精度。     

Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks

Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.     

Fault detection using parameter estimation

This paper presents a brief summary of fault-detection methods using parameter estimation techniques. An overview of the fault-detection system design methodology is first presented, followed by the principles of parameter-estimation fault-detection techniques. Applications from the field of industrial processes are given and finally a case study is described which applies the general techniques to the fault detection of D.C. motors using multiprocessor systems.     

基于主成分分析的温度对Flash存储器电参数一致性分析

针对温度对闪存(Flash)存储器电参数的一致性影响特性尚不清晰、一致性分析评价方法不明确等问题,提出采用主成分分析法对Flash存储器中的不同电参数进行一致性分析。实验结果表明:温度会对Flash存储器的电参数漂移产生影响,随着温度的升高或降低,Flash存储器电参数的主成分均值与方差具有相反的变化趋势。基于主成分分析法为Flash存储器的电参数一致性评价提供一种手段,温度对其电参数一致性的影响特性对保障产品的质量及改进设计具有指导意义。     

Gradient and parameter sensitivity estimation for systems evaluated using Monte Carlo analysis

The performance evaluation of many practical systems can be handled only through computationally intensive Monte Carlo simulation. Although a number of specialist techniques have been proposed, in general, estimation of the sensitivity of the outcome to changes in parameters involves duplicate simulations and finite differences for each parameter of interest. An approximate technique for gradient sensitivity estimation was outlined previously. It is appropriate when the performance function is uni-modal and relatively smooth in the region of interest. It generates all gradients simultaneously by converting Monte Carlo simulation run outcomes to an approximate analytic problem defined by a simplified response surface. The gradients then follow immediately. No extra simulation runs are required. Herein that approach is extended to non-Normal random variables and to the estimation of parameter sensitivities for random variable means and standard deviations. Some illustrative examples are given with comparisons to sensitivities computed by conventional Monte Carlo. The influence of constraint function(s) defining the admissible solution region is also considered.     

前后向时间序列模型联合估计的时变结构模态参数辨识

为提高时变结构模态参数辨识精度和抗噪声能力,提出一种前后向泛函向量时变自回归滑动平均（FS-VTARMA）时间序列模型联合估计的模态参数辨识方法。首先建立前后向FS-VTARMA模型联合估计的均方误差形式的费用函数,其次引入非平稳信号中前向模型和后向模型估计系数的近似共轭关系,再利用两步最小二乘法（2SLS）得到时变模型系数,最后把时变模型特征方程转换为广义特征值问题提取出模态参数。利用时变刚度系统非平稳振动信号验证该方法,结果表明：能有效地克服前向模型估计中模态参数一步延迟以及起始时刻无法准确获得,以及后向模型估计中模态参数一步超前以及终止时刻无法准确获得的缺点,具有更高的模态参数辨识精度和更强的抗噪声能力。     

基于贝叶斯估计的结构固有频率不确定性分析

在结构振动测试中,由于测试环境影响、试验设备、操作人员以及模态分析过程中的简化,试验获取的结构模态参数中包含了不确定因素。为了正确的估计结构物理参数的统计特性,在贝叶斯估计方法中选择共轭先验分布,结合试验数据导出后验分布,得到了结构固有频率均值及方差的估计值。对一个4层钢筋混凝土框架结构模型进行了振动测试。试验结果表明:在不能获得大量样本信息时,充分利用先验信息,结合现场测试数据,对频率参数进行估计,能够有效地减小均值与方差的可信区间长度,具有工程实用参考价值。     

Spin-dependent plasmonics based on interfering topological defects

Observation of spin-dependent plasmonics based on the interference of topological defects in the near-field is presented. We utilize the surface plasmons' scattering dynamics from localized vortex sources to create spinoptical devices as an ensemble of isolated nanoantennas to observe a "giant" spin-dependent plasmonic vortex and a spin-dependent plasmonic focusing lens. The spin-orbit point spread function, a spiral wavefront, is introduced, where the optical spin is a degree of freedom.     

Improved time-base for waveform parameter estimation

An improved gated oscillator time-base and associated auto-calibration algorithm for use in a high-accuracy sampling waveform acquisition system are described. The time-base architecture consists of a stable 100 MHz gated oscillator, 24-bit counter chain, and a clock period interpolator. The nominal, uncorrected linearity of the time-base is approximately ±30 ps. By using an iterative, sine-fit based algorithm, the linearity has been improved to <5 ps. Details of the performance and major sources of error of the time-base and correction algorithm in an equivalent time sampling system are also discussed     

Self-sensing magnetic bearings using parameter estimation

A signal processing technique is presented by which the position of a rotor supported in magnetic bearings may be deduced from the bearing current waveform. The bearing currents are presumed to be developed with a two-state switching amplifier which produces a substantial high-frequency switching ripple. This ripple is demodulated using a parameter estimation technique which extracts the length of the bearing air gap while rejecting the influence of amplifier voltage and duty-cycle variation. The performance of the estimator is evaluated both by simulation and experiment. The method is demonstrated to produce a sensor with bandwidth of at least 1 kHz and acceptably low feed-through of the bearing force     

Ultrasonic data compression via parameter estimation

Ultrasonic imaging in medical and industrial applications often requires a large amount of data collection. Consequently, it is desirable to use data compression techniques to reduce data and to facilitate the analysis and remote access of ultrasonic information. The precise data representation is paramount to the accurate analysis of the shape, size, and orientation of ultrasonic reflectors, as well as to the determination of the properties of the propagation path. In this study, a successive parameter estimation algorithm based on a modified version of the continuous wavelet transform (CWT) to compress and denoise ultrasonic signals is presented. It has been shown analytically that the CWT (i.e., time x frequency representation) yields an exact solution for the time-of-arrival and a biased solution for the center frequency. Consequently, a modified CWT (MCWT) based on the Gabor-Helstrom transform is introduced as a means to exactly estimate both time-of-arrival and center frequency of ultrasonic echoes. Furthermore, the MCWT also has been used to generate a phase x bandwidth representation of the ultrasonic echo. This representation allows the exact estimation of the phase and the bandwidth. The performance of this algorithm for data compression and signal analysis is studied using simulated and experimental ultrasonic signals. The successive parameter estimation algorithm achieves a data compression ratio of (1-5N/J), where J is the number of samples and N is the number of echoes in the signal. For a signal with 10 echoes and 2048 samples, a compression ratio of 96% is achieved with a signal-to-noise ratio (SNR) improvement above 20 dB. Furthermore, this algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements ranging from 10 to 60 dB for composite signals having SNR as low as -10 dB.     

Fast Bayesian approach for parameter estimation

This paper presents two techniques, i.e. the proper orthogonal decomposition (POD) and the stochastic collocation method (SCM), for constructing surrogate models to accelerate the Bayesian inference approach for parameter estimation problems associated with partial differential equations. POD is a model reduction technique that derives reduced‐order models using an optimal problem‐adapted basis to effect significant reduction of the problem size and hence computational cost. SCM is an uncertainty propagation technique that approximates the parameterized solution and reduces further forward solves to function evaluations. The utility of the techniques is assessed on the non‐linear inverse problem of probabilistically calibrating scalar Robin coefficients from boundary measurements arising in the quenching process and non‐destructive evaluation. A hierarchical Bayesian model that handles flexibly the regularization parameter and the noise level is employed, and the posterior state space is explored by the Markov chain Monte Carlo. The numerical results indicate that significant computational gains can be realized without sacrificing the accuracy. Copyright © 2008 John Wiley & Sons, Ltd.