地震资料精细处理和解释在徐家围子油田的应用

以地震资料精细处理和解释在大庆长垣外围徐家围子油田开发中的成功应用为例，论述了地震地质综合描述技术在地质情况复杂的低、特低渗透油田开发中的应用效果。     

Entropy,Free Volume,and Cooperative Relaxation

The high frequency end of the relaxation spectrum for polymer molecules involves the rotation of the segmental bonds. This fast relaxation process, however, cannot take place easily in the condensed state crowded by the densely packed conformers, necessitating the slower cooperatively synchronous relaxation. As the temperature is lowered, the domain of cooperativity grows towards the infinite size at the Kauzmann zero entropy temperature, though actually the system deviates from the equilibrium as the glass transition intervenes typically at 50 K above that temperature. The excess enthalpy and entropy drop faster than predicted by the rotational isomeric states which would reach zero only at 0 K. The real ΔC_P is greater than that of the RIS value. The actual volume in excess of the crystalline lattice volume, however, points towards zero at 0 K. Thus, a polymer with higher T_g typically exhibits a lower density and modulus in the glassy state. Since the configurational entropy associated with the free volume is proportional to the logarithm of the latter, the Kauzmann temperature can be scaled by ln M, where M is the algebraic average of the conformer molecular weight. The temperature dependence of the most dominant, i.e., the largest equilibrium domain size will result in the Adam-Gibbs and Vogel equations for the characteristic relaxation time. The cooperative domain distribution leads to the relaxation spectrum that follows a power law. The relationship between the characteristic relaxation time and the rate of physical aging is derived.     

地震数据油气预测中的属性优化方法

本文简单介绍了智能信息处理中新出现的RoughSet（RS）理论及属性选择方法，从双相介质地震波传播理论角度，探讨了地震数据油气预测属性优化原理，提出了基于RS理论的地震数据油气预测属性优化方法。实际应用表明；本方法速度快、易实现，而且在优选属性、最大程度地减少提取地震属性种数、提高分类正确率等方面，明显优于其它方法。本方法将成为地震数据油气预测的一种有效手段。     

Measurement and prediction of pressure drop in two-phase flow

Experimental data for air–water two-phase co-current flow in two different pipe diameters were used to test the prediction of pressure drop by a number of existing theories and correlations. Several models are shown to be useful for prediction, particularly with the stratified regimes which have proved difficult to handle in the past. The model suggested by Olujic proved to be of particular value.     

A NEW ALGORITHM FOR ADAPTIVE LATTICE FILTERAND ITS APPLICATION IN THE SPEECH LINEARPREDICTIVE SYNTHESIS

In this paper, an adaptive line spectral pair filter is derived from an adaptive lattice filter. A least-mean-square(LMS) type adaptive algorithm used to calculate directly the line spectral pair(LSP) coefficients on a stage-by-stage basis is presented. Experimental results show that the algorithm has higher convergence rate and lower misadjustment as compared with the other algorithms. The LSP coefficients calculated by the algorithm have been used to carry out speech linear predictive synthesis, resulting in better results than PARCOR coefficients.     

对洪水分级的浅见

洪水的等级划分应考虑流量、水位、防洪能力及灾害等因素,综合这些因素把洪水分成5个等级。1、2级洪水,洪水流量小,水位低,基本无灾;3级洪水,洪水流量较大或水位较高,有一定灾害;4级洪水,为大洪水或高水位,有较大灾害;5级洪水,为特大洪水或高水位,灾情严重。     

铝－锂合金中δ’（Al＿3Li）沉淀相时效粗化行为及体积分数的测定

对经固溶处理的二种Ａｌ－Ｌｉ合金（１．９４ｗｔ．％Ｌｉ和２．７５ｗｔ．％Ｌｉ）的时效过程进行了研究。结果表明：δ’的生长速率遵从Ｏｓｔｗａｒｄ粗化动力学。１．９４ｗｔ．％Ｌｉ合金δ’产生于时效过程中，而２．７５ｗｔ．％Ｌｉ合金的δ’产生于时效的前期阶段。前者的表现体积分数明显大于根据相图估算的数伍，真实体积分数则与此值接近；后者的体积分数低于根据相图估算的数值，这可能归咎于δ’固相线的位置、统计学误差及Ｌｉ更易在时效过程中贫化。     

大藤峡水利枢纽及在珠江防洪与水资源综合利用中的地位与作用

大藤峡水利枢纽是珠江流域西江干流上的控制性水利枢纽工程，在流域防洪及水资源方面具有不可替代的作用，同时具有发电、航运与灌溉等综合利用效益。国务院在对《珠江流域防洪规划》的批复中要求抓紧做好前期工作．积极促进大藤峡水利枢纽建设。本文对大藤峡水利枢纽及在珠江防洪与水资源综合利用中的地位与作用进行了简要的介绍。     

动态模糊神经网络在大坝变形预报中的应用

针对静态模糊神经网络的局限性,提出了在线动态建模的模糊神经网络方法.当新增样本进入训练集之后,根据新样本对模型的贡献大小,在已有模型的基础上进行动态修正,这样可以减少建模的计算时间.新方法实现了增加样本而矩阵阶数不增加,避免了矩阵求逆运算,理论上可以提高计算效率.实例表明动态模糊神经网络方法是可行的,可实现持久预报,具有较强的适应能力和较高的预报精度,可应用于在线实时变形预报及相关领域.     

Network traffic prediction based on a new time series model

Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory‐shortening technique and an adaptive‐order selection method originally developed by this study. Compared to the conventional one‐step ahead prediction using traditional Box–Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright © 2005 John Wiley & Sons, Ltd.