Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

曲柄滑块机构最优解的解析计算方法

用优化设计方法,以曲柄滑块机构最小传动角为最大作为目标函数,给出了最优解的解析计算方法,从而得到机构的最佳设计。     

Explosive Random‐Coefficient AR(1) Processes and Related Asymptotics for Least‐Squares Estimation

Abstract. Large sample properties of the least‐squares and weighted least‐squares estimates of the autoregressive parameter of the explosive random‐coefficient AR(1) process are discussed. It is shown that, contrary to the standard AR(1) case, the least‐squares estimator is inconsistent whereas the weighted least‐squares estimator is consistent and asymptotically normal even when the error process is not necessarily Gaussian. Conditional asymptotics on the event that a certain limiting random variable is non‐zero is also discussed.     

CXFTV2: A Fortran subroutine for the discrete least squares convex approximation

A Fortan subroutine calculates the least squares approximation to n data values containing random errors subject to non-negative second divided differences (convexity). The method employs a dual active set quadratic programming technique that allows several concavities of an iterate to be corrected simultaneously, which is a distinctive feature of this calculation. A B-spline representation of the iterates reduces each active set calculation to an unconstrained minimization with fewer variables that requires only O(n) computer operations. Details in these techniques including the data structure that establishes the implementation of the method are specified. Numerical testing on a variety of data sets indicates that the subroutine is particularly efficient, terminating after a small number of active set changes, the subroutine being suitable for large numbers of data. A numerical example and its output is provided to help the use of the software.     

次生伽马能谱分析方法的实验研究

本文指出一种地化学闭合模型的热中子瞬发活化伽马分析方法，它是地球化学测井的核心技术，标准谱的获得，基于标准谱的加权最小二乘解谱方法、灵敏度刻度及地球化学闭合模型是这一方法的关键环节。     

初至波走时层析成像中的正则化方法

 波场的等效性、观测数据的有限性和观测误差等因素导致地球物理反演本身具有严重的多解性，降低这种多解性的一种重要途径就是对反演过程进行约束。传统的初至波走时层析成像方法，通常是在外部对模型空间施加显式的约束和平滑处理，这样就会破坏模型参数与传播算子之间的相关性，难以达到有效利用先验信息的目的, 甚至会影响反演的稳定性。针对这一问题，本文将先验信息分为三类，在此基础上阐述了通过正则化手段将各种先验信息纳入到反演方程当中，替代传统的外部约束模式。理论试验结果表明，正则化方法比外部约束方法有效地提高了反演的精度和分辨率；将该正则化方法应用于实际二维资料处理，也得到了比较好的反演结果。     

Performance analysis of the forgetting factor RLS algorithm

An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ²/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.     

光时域反射计的设计与实现

文章介绍了一种光时域反射计的设计和实现,详细介绍了各个硬件模块和使用FPGA实现数据等效采样和消噪处理的方法.模块配备LAN和RS232接口,方便集成以及分布式部署,利于推广应用,从而带来较好的经济、社会和管理效益.     

磁场信号检测误差分析及补偿算法

针对载体自身磁场特性及三轴磁传感器误差特性建立了地磁导航干扰误差补偿模型,通过对模型进行简化变形,利用系统辨识的最小二乘估计算法(LS)求取误差特性参数.数据分析表明:该方法简单易实现,简化了复杂的求解过程,基本达到了误差补偿的要求.