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。     

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。     

炸药爆炸事故的综合后果模型

讨论爆炸冲击波对建筑物的破坏作用，比较冲击波破坏半径和爆炸火球引燃木材半径的相对大小，提出炸药地面爆炸后果预测模型。该模型同时考虑爆炸冲击波、火球热辐射、房屋倒塌、爆源间面积、爆源间防爆程度、人员和财产分布状部等因素对事故后果的影响。模拟计算结果与事故的实际损伤情况基本相符。     

某型杀伤枪榴弹引信弹道炸原因分析及再设计思考

通过对某型杀伤枪榴弹引信所发生的三次弹道炸的原因进行分析，提出用延期索替代延期药盘，并对有关机构进行再设计的对策。     

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.     

海上靶场角度量录取系统的研制

本文详细叙述了海上靶场角度量录取糸统的组成、测试原理及其功能设计，简要介绍了该系统在靶扬的应用情况．     

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.     

多属性神经网络地震反演在NB油田水平井钻探中的应用

NB油田位于渤海海域,为新近系河流相稠油油田,储层横向分布不稳定,砂体厚度薄、连通性较差,油水关系复杂,开发设计部署了大量水平井。选择神经网络反演方法,建立地震属性与储层参数的非线性关系,进而根据其关系反演得到储层参数数据体,利用该数据体沿层切片提取储层预测信息,研究储层发育规律,指导该油田水平井的部署和钻探,取得了较好的效果,降低了钻探风险。用神经网络地震反演方法进行储层预测研究,指导水平井钻探,在渤海海域尚属首次。