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     

OPGW应用问题的探讨

根据多年的光纤复合架空地线(0PGW)应用经验与实践，从0PGW结构及组成、光纤余长、线路配合、金具配套、0PGW安装等方面，对0PGW应用中的若干问题进行详细的探讨。     

Oil yield and compressibility modeling during microscale expression: Criteria for linseed breeding

Seven linseed varieties were harvested at seven times before complete maturity. Microscale oilseed expressions were subsequently carried out on it. A pressure of 10 MPa was applied for 1 h under uniaxial stress. The results were characterized in terms of oil, water and mucilage content, harvest date and oil yield. The piston displacement versus time (creep curves) was described by a four‐Kelvin‐Voigt elements viscoelastic model. Parameters were derived from the model, such as the mechanical properties of seeds (compressibility moduli), showing that the viscoelastic model fits well the experimental data (R² ≈ 0.999). The modeling parameters were combined with some biochemical data in order to predict the kinetics of oil extraction.     

Spline-regression estimation of the influence of exposure dose on cancer probability

Spline regression is used to analyze the influence of radiation on the cancer probability in a group of participants of Chernobyl accident mitigation, depending on the exposure dose. A new method is proposed to approximate modified polygons by linear splines with two nodes. An algorithm for the identification of a transition point is outlined. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 168–176, May–June 2006.     

大庆油田深层岩石力学特性参数的试验研究

通过对岩石可钻性、硬度、抗压强度等参数的测量 ,对大庆油田深井不同深度、不同层位、不同岩性的具有广泛代表性的岩心进行试验。并通过线性回归分析 ,建立了适用性较广的钻井岩石力学特性参数预测模型 ,为深井钻头选型提供了依据。     

Comparative study on adaptive rational macromodels of highly resonant structures

Two macromodeling techniques using rational bases are investigated to accurately predict the natural frequencies of highly resonant microwave structures. Three methods are proposed and compared to calculate pole‐free solutions to the Thiele continued fraction and vector fitting pole‐residue models of the characteristic equation det[Z(s)]. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

Consistency of robust estimators in multi-structural visual data segmentation

A theoretical framework is presented to study the consistency of robust estimators used in vision problems involving extraction of fine details. A strong correlation between asymptotic performance of a robust estimator and the asymptotic bias of its scale estimate is mathematically demonstrated where the structures are assumed to be linear corrupted by Gaussian noise. A new measure for the inconsistency of scale estimators is defined and formulated by deriving the functional forms of four recent high-breakdown robust estimators. For each estimator, the inconsistency measures are numerically evaluated for a range of mutual distances between structures and inlier ratios, and the minimum mutual distance between the structures, for which each estimator returns a non-bridging fit, is calculated.     

分偏移距动校正方法的改进

在地震资料处理中，速度分析和动校正对资料品质的影响很大。由于动校正速度是随偏移距变化而变化的，而常规速度分析并没有考虑这一因素，动校正效果往往不是很理想，从而影响资料的叠加和成像效果。分偏移距动校方法是在不同偏移距上采用不同的速度进行动校正。但由于是在不同偏移距组上分别独立进行速度分析和动校正，因此不能将不同偏移距组的同一反射同相轴校正到同一to层位上。这样就需要进行繁琐的后续处理，而且还会降低同相轴的连续性和平滑性。为此，对该方法进行了一些改进，即固定to在不同的分偏移距段进行速度扫描，段与段之间的衔接问题采用平滑处理解决。理论模型分析表明，采取上述改进措施后，动校正效果得到了一定提高，改善了同相轴的连续性和平滑性。     

Climate-related electricity demand-side management in oil-exporting countries—the case of the United Arab Emirates

The oil crisis of the 1970s has increased the concern about the continuity of oil imports flow to major oil-importing developed countries. Numerous policy measures including electricity demand-side management (DSM) programs have been adopted in such countries. These measures aim at reducing the growing need for electricity power that increases the dependency on imported foreign oil and damages the environment. On the other hand, the perception that energy can be obtained at very low cost in oil-rich countries led to less attention being paid to the potential of DSM policies in these countries. This paper discusses such potential using the case of the United Arab Emirates (UAE). Since air conditioning is a major source of electric energy consumption, the relationship between climate conditions and electric energy consumption is considered. An electricity demand model is constructed using time series techniques. The fitted model seems to represent these relationships rather well. Forecasts for electricity consumption using the estimated model indicate that a small reduction in cooling degrees requirement might induce a significant reduction in electric energy demand. Hence, a DSM program is proposed with policy actions to include, among others, measures to reduce cooling degrees requirement.     

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.