Subsurface structures in initial stage of FeSi2 growth studied by high-resolution Rutherford backscattering spectroscopy

The initial stage of iron silicide formation is investigated by high-resolution Rutherford backscattering spectroscopy. During the Fe deposition on Si(001) at 470 °C, the formation of FeSi₂ is confirmed by the surface peak analysis. Initially, FeSi₂ grows epitaxially so that one of the major crystallographic axes is parallel to the <111> axis of the Si substrate. With increasing Fe deposition, the deviation between the major crystallographic axis of the silicide region and Si<111> increases although the electron diffraction pattern is independent of the amount of Fe deposition. Therefore, the subsurface crystallographic structure of iron silicide is transformed from a cubic-like to a low-symmetry structure.     

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。     

A Ray-Tracing Analysis of ICRH in Tokamaks

Power deposition profiles generated by Ion Cyclotron Resonance Heating （ICRH） in non-circular tokamaks are studied using a ray-tracing technique. The simulation results for the Experimental Advanced Superconductor Tokamak （EAST） D-shaped plasma are presented. It is indicated that the spatial distributions of plasma parameters （plasma density, species temperature, minority ion concentration, etc.） have an significant influence on the power deposition profiles. The findings may be highly useful to the planned plasma heating and experiments in EAST.     

疲劳裂纹的灰色残差预测

借助灰色理论，建立了预测疲劳裂纹扩展的灰色残差预测模型，并应用此模型预测了某不锈钢构件腐蚀疲劳裂纹的扩展，得到了较高精度的预测结果，为疲劳裂纹预测提供了一种简易而可靠的新途径。     

中频反应溅射SiO2膜与直流溅射ITO膜的在线联镀

多数ITO透明导电玻璃生产线在实现SiO2膜与ITO膜在线联镀时，应用SiO2靶射频溅射沉积SiO2膜工艺和ITO靶直流溅射沉积ITO膜工艺，如果SiO2膜应用硅靶反应磁控溅射工艺，存在这种工艺是否可以与ITO靶直流溅射沉积ITO膜工艺在线联用以及如何实现联用的问题。作者对现有的生产线进行了改造设计、加工，做了大量实验、质谱分析和多项测试研究，成功地实现反应溅射SiO2膜与ITO膜在线联镀，做到SiO2镀膜室的工作状态的变化基本上不影响ITO镀膜室的工艺条件。     

低频天波传播预测与低电离层模式参数的测定

在理想化的均匀球面通过进一步地与具有突变边界的均匀各向同性同心球面电离层条件下的LF天波全波解的基础上，考虑由海陆之差引起的地面导电率的变化、大气和低电离层的不均匀性以及由地磁场引起的各向异性特性等因素的影响，推导出了较严格预测实际地与低电离层条件下场强与传播相延精确预测的完善公式。 给出了一些低电离层模式参数的实测结果。这些模式参数为参考高度h0、参考浓度N0与梯度参数α。由罗兰－C天波信号场强与相位的精细测量数据求得的。