测井二次解释模型在储量套改中的应用

中国各油区储量套改一般采用岩心分析资料刻度测井资料的思路来建立二次解释模型，即应用数理统计的方法，建立测井和岩心分析资料之间的关系，然后应用这些关系进行定量解释和计算机处理。着重讨论了应用岩心分析和测井资料建立二次解释模型中的主要技术问题，即单元和多元线性回归分析、测井曲线校正及资料标准化方法和深度归位。利用胜利油区陈家庄地区的80多口井岩心、试油和测井数据，计算了主要的储层参数，平均绝对误差均低于1．3％，解释吻合程度达到了89％。所建立的测井二次解释模型的解释精度可满足储量套改的要求。     

复杂土坝的渗流安全分析评价

根据福华山土坝的渗流观测资料,建立了测压管水位的回归统计模型,定量分析了各影响量(水位分量、时效分量)对测压管水位的影响效应。在此基础上,结合观测资料,采用有限元法反演了大坝各填筑区的渗透系数,并综合评价了大坝的渗流性态。     

介孔-大孔结构无定形硅-铝催化剂的核磁共振研究

合成了具有介孔-大孔双模型分级结构的无定形硅-铝催化剂,利用硅和铝核的固体魔角核磁共振谱,考察了合成样品中硅和铝结构上的原子特征.     

Efficient simulation of hierarchical stochastic activity network models

Stochastic extensions to Petri nets have gained widespread acceptance as a method for describing the dynamic behavior of discrete-event systems. Both simulation and analytic methods have been proposed to solve such models. This paper describes a set of efficient procedures for simulating models that are represented as stochastic activity networks (SANs, a variant of stochastic Petri nets) and composed SAN-based reward models (SBRMs). Composed SBRMs are a hierarchical representation for SANs, in which individual SAN models can be replicated and joined together with other models, in an iterative fashion. The procedures exploit the hierarchical structure and symmetries introduced by the replicate operation in a composed SBRM to reduce the cost of future event list management. The procedures have been implemented as part of a larger performance-dependability modeling package known asUltraSAN, and have been applied to real, large-scale applications. This work was supported in part by the Digital Equipment Corporation Faculty Program: Incentives for Excellence.     

Effects of modeling means on properties of monitoring models of spot welding quality

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non-linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non-linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.     

Semiparametric Estimation in Time‐Series Regression with Long‐Range Dependence

Abstract. We consider semiparametric estimation in time‐series regression in the presence of long‐range dependence in both the errors and the stochastic regressors. A central limit theorem is established for a class of semiparametric frequency domain‐weighted least squares estimates, which includes both narrow‐band ordinary least squares and narrow‐band generalized least squares as special cases. The estimates are semiparametric in the sense that focus is on the neighbourhood of the origin, and only periodogram ordinates in a degenerating band around the origin are used. This setting differs from earlier studies on time‐series regression with long‐range dependence, where a fully parametric approach has been employed. The generalized least squares estimate is infeasible when the degree of long‐range dependence is unknown and must be estimated in an initial step. In that case, we show that a feasible estimate which has the same asymptotic properties as the infeasible estimate, exists. By Monte Carlo simulation, we evaluate the finite‐sample performance of the generalized least squares estimate and the feasible estimate.     

Simultaneous Spectrophotometric Determination of Three Components Including Deoxyschizandrin by Partial Least Squares Regression

1IntroductionWuweizi is a traditional Chinese herb,usually usedto cure cough,asthma,neurasthenic,hepatitisetc.Theactive ingredients in wuweizi are5%-22%lignan,themain components of which include deoxyschizandrin,schizandrin andγ-Schizandrin.Forthe purpos…     

均匀设计法分析制备过程对钴钼硫化物催化剂机械强度的影响

建立了CoMoP/Al2O3加氢精制催化剂机械强度及其可靠性在制备过程中的数学模型,采用均匀设计考察了浸渍、干燥、煅烧、硫化4个制备过程中,浸渍时间、干燥温度、干燥时间、煅烧温度、煅烧时间、硫化温度和硫化升温速率7个实验因子对催化剂强度均值、Weibull模量的影响,同时考察了这些因素对颗粒密度的影响。方差分析表明所有模型都是充足的。实验结果表明,硫化过程是影响催化剂强度均值的主要过程。在实验范围内提高硫化温度、降低硫化时的升温速率有利于提高催化剂的强度。影响催化剂Weibull模量的因素主要为各个制备过程的交互效应。要提高催化剂强度的可靠性就必须全面考虑催化剂制备的各个过程,特别是各个制备过程的交互效应。     

Logistic Regression Analysis of Customer Satisfaction Data

Variation exists in all processes. Significant work has been done to identify and remove sources of variation in manufacturing processes resulting in large returns for companies. However, business process optimization is an area that has a large potential return for a company. Business processes can be difficult to optimize due to the nature of the output variables associated with them. Business processes tend to have output variables that are binary, nominal or ordinal. Examples of these types of output include whether a particular event occurred, a customer's color preference for a new product and survey questions that assess the extent of the survey respondent's agreement with a particular statement. Output variables that are binary, nominal or ordinal cannot be modeled using ordinary least‐squares regression. Logistic regression is a method used to model data where the output is binary, nominal or ordinal. This article provides a review of logistic regression and demonstrates its use in modeling data from a business process involving customer feedback. Copyright © 2006 John Wiley & Sons, Ltd.