刚度主轴方位对模态耦合再生切削系统动态响应谐参数的影响

综合考虑再生切削效应和模态耦合效应,建立了随机扰动激励下两自由度切削加工系统的时滞动力学模型.分析了刚度主轴方位对切削稳定性叶瓣的影响.将切削系统的动态响应作为观测数据,基于互四阶累积量的谐参数联合估计ESPRIT方法,考察了刚度主轴方位对模态耦合再生切削系统动态响应的互功率、频率和相移等谐参数的影响,揭示了这些谐参数关于刚度主轴方位角的一些新依赖特征.     

基于GPD模型的车辆荷载效应极值估计

桥梁的服役安全问题一直广为人们关注。过去由于缺少实地通行车辆数据,新桥设计和旧桥评估都采用规范中的荷载模型。动态称重系统(WIM)能够提供准确的车辆信息,但利用WIM数据进行设计基准期内最大荷载效应估计时,又面临着概率模型在高分位点上不准确的问题。为解决这个问题,该文提出基于GPD模型的尾部拟合方法,以实现对基准期内车辆荷载效应极值较为准确的估计。该方法首先依据车辆荷载过程的特点对GPD模型的表达形式进行改进,然后利用最小均方差准则(MSE)对GPD模型中的参数进行估计,并对截尾GPD模型进行合理修正,从而得到修正的GPD模型。利用该模型,该文对国内某大桥实测车流量数据进行了系统分析和车辆荷载效应极值估计,并与规范方法进行了比较。结果表明:该文提出的基于GPD模型的计算方法能够合理预测未来车辆荷载效应的极值,而规范方法在中短评估期内对车辆荷载效应的估计存在偏低的风险。     

双向分类随机效应模型中方差分量的估计

双向分类随机效应模型是一类有着广泛应用背景的统计模型,其中对模型中方差参数的一种重要估计方法是方差分析估计.由于方差分析法得到估计的均方误差(MSE)并不是最小的,本文在一类新的估计族中提出了改进的ANOVA估计.结果表明新的估计比ANOVA具有较小的MSE,这种新的估计方法可推广到医学领域中常见的一般模型.     

刚度主轴方位对模态耦合再生切削系统动态响应谐参数的影响

摘 要：综合考虑再生切削效应和模态耦合效应，建立了随机扰动激励下两自由度切削加工系统的时滞动力学模型。分析了刚度主轴方位对切削稳定性叶瓣的影响。将切削系统的动态响应作为观测数据，基于互四阶累积量的谐参数联合估计ESPRIT方法，考察了刚度主轴方位对模态耦合再生切削系统动态响应的互功率、频率和相移等谐参数的影响，揭示了这些谐参数关于刚度主轴方位角的一些新依赖特征。     

基于双谱的正弦信号重构

双谱由于和傅里叶变换存在一定变换关系,也存在栅栏效应,这给基于双谱的频率估计带来误差。为克服该效应,提出了双谱幅值模平方重心法,该方法以双谱幅值谱线的局域重心作为频率的估计值。仿真结果表明该方法的估计性能良好,且复杂度相对简单、运算量较小、实时性较强。同时为充分利用采样数据,采用不同方式构造出两组数据,再对这两组数据分别做双谱分析并估计正弦信号的频率和初相,最后进行平均处理,从而降低了估计方差,改善了估计均值,提高了估计性能。     

一类分块混合效应模型的参数估计

研究一类分块混合效模型中部分固定效应向量的估计问题，为得到其可行的最佳线性无偏估计，提出了一种减约模型，并给出了由此减约模型得出的可行估计等于原模型下相应的最佳线性无偏估计的充分必要条件，并以实例说明文中结果。     

半参数回归模型拟极大似然估计的弱相合性

本文考虑一类固定设计的半参数回归模型,其误差为一阶自回归时间序列。用权函数及拟极大似然估计方法得到了一些参数及非参数的拟极大似然估计量,在适当的条件下,研究了它们的弱相合性,从而丰富了该类半参数回归模型的估计理论与方法。     

基于随机效应的可靠性试验设计分析

试验设计是改进产品质量的有效技术，通常假定试验输出服从正态分布且相互独立，通过拟合质量特性（试验输出）与影响因素（参数）的关系，优化产品或工艺参数，实现质量提升。然而，寿命数据通常是偏态的、截尾的，每个试验单元包含多个样本，因此基于分位值的研究更具实际意义和价值。多样本的试验单元给试验设计带来了组内差异，即参数估计中的随机效应。首先，基于此建立了随机效应可靠性模型，给出了基于随机效应的极大似然估计方法。然后，采用Gauss-Hermite积分求得了具有解析形式的似然函数。最后，以恒温器寿命试验为例，分析了影响寿命的显著因子，得到了具有稳健且高可靠性分位值的试验因子组合，可以有效降低恒温器早期失效，提升产品的可靠性。     

一种自适应EPF算法及其在光纤水听器中的应用

基于椭圆拟合的相位生成载波(Phase Generated Carrier,PGC)解调方法是消除非线性因素对光纤水听器PGC解调结果影响的一种有效手段,椭圆曲线参数的最优估计问题是实现该方法的关键。扩展卡尔曼粒子滤波(Extended Kalman Particle Filter,EPF)是解决此类非线性估计问题的一种常用的最优估计算法。但传统的EPF算法在用于常参数过程方程的参数或状态估计问题时,过程噪声的方差通常设置为一个常量,这使得算法难以兼顾收敛速度和估计精度,一定程度上限制了算法的整体性能。为了解决这个问题,文章对现有的EPF进行了改进,提出了一种自适应扩展卡尔曼粒子滤波(Adaptive Extended Kalman Particle Filter,AEPF)算法。模拟仿真和实验结果表明,文中所提出的AEPF算法能根据基于椭圆拟合的PGC解调方法有效地解调出待测声信号,相比EKF算法和EPF算法,AEPF算法的收敛速度和估计精度都得到了提升。此外,文章所提出的AEPF算法也适用于其他具有常参数过程方程的参数或状态估计问题,具有一定的通用性。     

基于MCNP的X光机模拟及足跟效应修正

为修正X射线管的足跟效应,根据常用的X射线管参数,基于MCNP建立了X光机仿真模型,模仿光机成像系统增感屏,建立了169个像素的探测器阵列,并利用该阵列探究了X射线管的足跟效应现象,采用多项式拟合分段修正的方法设计了足跟效应过滤片HEF,对足跟效应照度过高的区域进行匀整修正。通过平面过滤片与HEF的通量分布对比,发现HEF成功地将阴阳极轴线方向-12°至19°的范围修正到无过滤片时强度的60%,该区域通量的最大相对误差小于3%,实现较大匀整面积。通过模拟成像对比分析发现,经过HEF修正后的X光机,大大提高了物体分辨的能力。所提出的对特定X光管参数和成像阵列面足跟效应的修正方法具有一定的实用性与可推广性,对X光机的设计和使用具有一定的参考价值。     

Response Surface Models With Random Block Effects

In many experimental situations, a response surface design is divided into several blocks to control an extraneous source of variation. The traditional approach in most response surface applications is to treat the block effect as fixed in the assumed model. There are, however, situations in which it is more appropriate to consider the block effect as random. This article is concerned with inference about a response surface model in the presence of a random block effect. Since this model also contains fixed polynomial effects, it is considered to be a mixed-effects model. The main emphasis of the proposed analysis is on estimation and testing of the fixed effects. A two-stage mixed-model procedure is developed for this purpose. The variance components due to the random block effect and the experimental error are first estimated and then used to obtain the generalized least squares estimator of the fixed effects. This procedure produces the so-called Yates combined intra- and inter-block estimator. By contrast, the Yates intra-block estimator is the one obtained when the block effect is treated as fixed. In particular, if the response surface design blocks orthogonally, then the two estimators are shown to be identical. An experiment on bonding galvanized steel bars is used to motivate the problem and illustrate the results.     

Goodness-of-fit tests in mixed models

Mixed models, with both random and fixed effects, are most often estimated on the assumption that the random effects are normally distributed. In this paper we propose several formal tests of the hypothesis that the random effects and/or errors are normally distributed. Most of the proposed methods can be extended to generalized linear models where tests for non-normal distributions are of interest. Our tests are nonparametric in the sense that they are designed to detect virtually any alternative to normality. In case of rejection of the null hypothesis, the nonparametric estimation method that is used to construct a test provides an estimator of the alternative distribution.     

Postgrouped sampling method of estimation

A postgrouped sampling is considered for estimating the (finite or finite), population mean. Double sampling and an empirical-weighted estimator is used. Unbiasedness, variance and efficiency are considered. Its properties are discussed allowing the simple random sampling with replacement (SRSWR) design in the first phase, and in each stratum for the second phase. It is shown that for a fixed sample size in each postgroup, the variance of the proposed estimator with less prior information is asymptotically equivalent to the usual stratified estimator for fixed allocation. Some examples are provided for natural populations., The method is also extended to simple random sampling without replacement (SRSWOR) design in the first phase, and in each stratum for the second phase. Unbiased variance estimation is provided for both types of sampling designs.     

Optimal choice of test signals for linear channel estimation using second order statistics

A time-varying acoustic channel may be estimated by an appropriate inference using the output from a periodic test signal. In this paper it is shown how to do this in a way that takes full account of the past history of the background noise and the past history of the channel. An explicit formula is obtained for the optimal linear estimator that may be used for rapid channel estimation for a given test signal when we know the autocovariance or power spectrum of the interfering noise and the autocovariance of the echo channel variation. Given this closed formula for the optimal estimator of the channel impulse response, an efficient method for determining the optimal test signal, subject to a constraint on the test signal power, given the history of the channel and the noise, is developed. We show that if the second order statistics of the channel or the noise are known, then the optimal test signal is not white. The method includes an explicit formula for the optimal test signal given a fixed estimator. A model of channel variation which is realistic while having less complexity than a full second-order statistical model, and therefore is more amenable to robust estimation, is used in the experiments which illustrate the performance of the optimal test signals and the channel estimation method. Matrix calculus identities required for the derivation of this expression for the optimal estimator are stated and proved in the Appendixes 1 and 2.     

A pollution controlled hybrid interior error estimator for linear elastostatics

A hybrid error estimator using a priori interior region estimates in an a posteriori framework is presented for linear elastostatics problems in FEA. It is shown that local rates of convergence are augmented by this technique and global rates are not adversely affected. The effects of pollution for this estimator are explained and a pollution error estimator is derived using the concept of error loads. It is shown that pollution error estimation can improve the performance of both the conventional a posteriori and the hybrid techniques. A series of numerical results are presented which demonstrate the superior performance of the proposed method over previously published interior error estimation techniques. © 1998 John Wiley & Sons, Ltd.     

A smooth nonparametric approach to process capability

The determination of the capability of a stable process using the standard process capability indices requires that the quality characteristic of interest be normally distributed. Departures from normality can result in erroneous conclusions when using these indices. In this paper we propose assessing the capability of a process using a nonparametric estimator. This estimator is based on kernel estimation of the distribution function. Bandwidth selection for this method can be based either on a normal reference distribution or on a nonparametric estimate. An example is presented that applies this proposed method to non-normal process data. The performance of the resulting estimator is then compared with the sample proportion and a normal-based estimate in a simulation study. © 1998 John Wiley & Sons, Ltd.     

Semi-blind channel estimation for the uplink of multi-carrier code-division multiple access systems with timing offset

Timing offsets (TO) introduce misaligned interferences and phase errors, which degrade the performance of channel estimation. The authors propose a new semi-blind channel estimator that can cancel the TO effects on channel estimation and work well in the uplink of multi-carrier code-division multiple access systems in the presence of TO. The proposed channel estimator exploits the spreading sequence information instead of pilots to accomplish semi-blind estimation. Since the estimated channel coefficients are affected by the users? transmitted data, the effect of the users? data needs to be reduced. For this purpose, the authors also propose two smoothing algorithms, division smoothing and optimisation smoothing. In addition, to extend the new estimator to the system where the number of active users is too large, channel estimation over multiple symbols is discussed. The simulation results show that the proposed semi-blind estimator works better than a conventional estimator in the presence of TO, and its performance is very close to the conventional estimator in the absence of TO. Besides, the proposed estimator is compared with a sub-space estimator in simulation, and the results indicate that the new proposed estimator can achieve the same mean square error performance over the duration of a single symbol as the sub-space estimator over the duration of several symbols.     

Maximum-likelihood estimation of a laser system pointing parameters by use of return photon counts

A maximum-likelihood estimator used to determine boresight and jitter performance of a laser pointing system has been derived. The estimator is based on a Gaussian jitter model and uses a Gaussian far-field irradiance profile. The estimates are obtained using a set of return shots from the intended target. An experimental setup with a He-Ne laser and steering mirrors is used to study the performance of the proposed method. Both Monte Carlo simulations and experimental results demonstrate excellent performance of the estimator. Our study shows that boresight estimation is more challenging than jitter estimation when both quantities are estimated. Furthermore, their estimation performance improves with an increase in the number of shots. The experimental results are found to agree well with the simulation results.     

A Robust Phase I Exponentially Weighted Moving Average Chart for Dispersion

A Phase I estimator of the dispersion should be efficient under in‐control data and robust against contaminations. Most estimation methods proposed in the literature are either efficient or robust against either sustained shifts or scattered disturbances. In this article, we propose a new estimation method of the dispersion parameter, based on exponentially weighted moving average charting, which is efficient and robust to both types of unacceptable observations in Phase I. We compare the method with various existing estimation methods and show that the proposed method has the best overall performance if it is unknown what type of contaminations are present in Phase I. We also study the effect of the robust estimator from Phase I on the Phase II exponentially weighted moving average control chart performance. Copyright © 2014 John Wiley & Sons, Ltd.     

High Accuracy Network Cardinalities Estimation by Step Sampling Revision on GPU

Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors, such as the random parameters inherent in linear estimator and the random functions used to map a host to different linear estimators. These random factors cannot be reduced by merging multiple estimators, and existing algorithms cannot remove the deviation caused by such factors. In this paper, we regard the estimation deviation as a random variable and proposed a sampling method, recorded as the linear estimator array step sampling algorithm (L2S), to reduce the influence of the random deviation. L2S improves the accuracy of the estimated cardinalities by evaluating and remove the expected value of random deviation. The cardinality estimation algorithm based on the estimator array is a computationally intensive algorithm, which takes a lot of time when processing high-speed network data in a serial environment. To solve this problem, a method is proposed to port the cardinality estimating algorithm based on the estimator array to the Graphics Processing Unit (GPU). Experiments on real-world highspeed network traffic show that L2S can reduce the absolute bias by more than 22% on average, and the extra time is less than 61 milliseconds on average.