杀菌剂苯菌灵的结构鉴定及其核磁共振研究

用红外、质谱、核磁共振等手段确证了江阴农药厂合成的苯菌灵的化学结构,并进行了该化合物核磁共振谱线的归属。     

BP神经网络在致密砂岩储层识别中的应用

川西须家河组地层岩性复杂,属于超致密低孔渗储层,所以储层识别是该地层天然气勘探中所面临的关键问题和难点之一。针对常规储层识别准确率不高的状况,提出利用BP神经网络进行储层的气水干层识别,利用模糊聚类和产层测试结果标定建模样本,采取随机抽样形成建模集与测试集,建立BP神经网络模型对23口井的储层进行气水干层预测,正确率达77.9%以上,明显提高了该地区的测井解释精度,并提供了一种准确率较高的储层预测方法。     

Determination of Flow Curves at High Strain Rates using the Electromagnetic Forming Process and an Iterative Finite Element Simulation Scheme

The knowledge of reliable material parameters is an essential requirement for the numerical analysis and planning of forming processes, especially when using high speed forming processes in industry. Therefore, a method was developed and implemented to determine the relationship between yield stress, plastic strain, and strain rate of aluminium alloys at very high strain rates of up to 10,000 s-1 by using the electromagnetic forming process (EMF) of tubular specimens. To determine the associated flow curves, an inverse engineering strategy combining an online measurement technique and multi-physical coupled finite element simulations is used. The results are discussed with regard to the anisotropic behaviour of specific aluminium alloys.     

测井相识别模型及其在东海油气勘探中的应用

文章提出一种测井相识别模型,该模型用常规测井信息,定量地确定测井相在钻遇地层中的空间位置,建立测井相。配合东海油气勘探工作,用该识别模型对苏堤构造带的钻遇地层进行了测井相分析。     

Solving regularized least squares with qualitatively controlled adaptive cross‐approximated matrices

The adaptive cross‐approximation (ACA) technique is applied to accelerating an inverse‐problem solver that estimates charge distribution on a dielectric spacer. The ACA generates an approximated system‐matrix that enables us to carry out high‐speed inverse calculation. We designed an approximation procedure based on ACA with some additional concepts, that is, (a) partitioning of matrix based on algebraic information, (b) approximation quality control based on matrix norms, and so on. The tested solver (LSQR for regularized least squares) with ACA demonstrates about 10 times faster performance than that without ACA. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 159(3): 10–18, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20326     

Inference for pth‐order random coefficient integer‐valued autoregressive processes

Abstract. A pth‐order random coefficient integer‐valued autoregressive [RCINAR(p)] model is proposed for count data. Stationarity and ergodicity properties are established. Maximum likelihood, conditional least squares, modified quasi‐likelihood and generalized method of moments are used to estimate the model parameters. Asymptotic properties of the estimators are derived. Simulation results on the comparison of the estimators are reported. The models are applied to two real data sets.     

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     

陆相碎屑岩储层隔夹层成因、特征及其识别

陆相储层隔夹层是形成储层流体流动非均质的主要因素之一，也是储层精细表征的重要内容，油田勘探开发实践表明，陆相储层隔夹层主要可分为泥质隔夹层、钙质隔夹层和物性隔夹层三大类。根据这种分类，深入分析和总结了这三类隔夹层成因、岩性特征、分布特征和测井识别特征，并比较其区别，这对以后新老区块隔夹层研究有参考和适用价值。     

陆相断陷湖盆中层序边界的形成机理及其识别特征研究

陆相断陷湖盆中层序边界是由于沉积物表面高出沉积基准面或盆地基准面、沉积物发生剥蚀或无沉积作用所形成的，其产生机理主要有边界断层停止活动、断块翘倾活动、湖盆整体抬升和湖平面下降。层序边界在地震特征上、测井曲线上、钻井剖面中的岩性、岩相特征、古生物组合、微量元素的变化上均有明显的特征。利用这些特征，在济阳坳陷下第三系中识别出9个三级层序。     

Step response classification for model-based autotuning via polygonal curve approximation

A model-based autotuning method consists of an identification and a regulator tuning phase. To achieve satisfactory performance and robustness, it is advisable that both phases be tailored a priori to the characteristics of the observed process dynamics. Such characteristics include, but are not limited to, the model structure. For example, overdamped and underdamped models with the same pole-zero structure are parametrised and controlled in different ways. Step response data, that are typically used for the identification phase in the autotuning context, can also be pre-processed to reveal those characteristics. This paper presents a step response classification method suitable for the above purpose. The method is based on a polygonal curve approximation technique for data pre-processing, followed by a neural network classifier. Only normalised I/O data are employed, so that the neural network can be trained off-line with simulated data. Simulation results are reported to show the effectiveness of the proposed classification method in terms of the achievable tuning results.