底水油藏高含水水平井注N_2实验与参数优化

在深入调研N2吞吐工艺的基础上,针对塔河油田实际情况建立了塔河9区TK907井模型,最大程度地还原了储层的真实情况。首先,进行衰竭式生产,当压力降到20 MPa时,进行底水驱形成底水脊进;当底水侵入到水平段、含水率上升到一定程度后,注入N2进行吞吐。实验结果表明,对底水油藏中高含水水平井进行N2吞吐后,能使采收率提高10.0~20.0百分点。同时得到以下认识:1)N2吞吐具有良好的压脊作用;2)吞吐中有明显的毛细管力滞后现象;3)含水率为80%时,是注入N2的最佳时期;4)在不超过地层破裂压力的情况下,注气量、注气速度和焖井时间这3个参数越大越好;5)影响N2吞吐效果的因素从大到小排序为注气量、焖井时间、注入速度、注气时机、地层压力恢复水平。     

砷化镓激光器脊型波导的制备

本文主要研究了用湿法腐蚀的方法制备砷化镓激光器脊型波导,具体研究了砷化镓（GaAs）、铟镓磷（ InGaP）、铝镓铟磷（AlGaInP）材料的腐蚀。本文还成功地配制了能同时腐蚀铟镓磷和铝镓铟磷材料的腐蚀液。我们还分析了腐蚀时间和腐蚀浓度对脊型形状和脊型深度的影响。最后,在合适的条件下,本文得到了合适的脊型深度和光滑的脊型表面,而且得到的脊型的陡直度很小。这些结果表明这些腐蚀液将有用于激光器的制备。     

热轧带钢局部高点的原因分析及改进措施

对热轧带钢在冷轧时产生局部高点的原因进行了分析,并提出了相应的改进措施.改进后,精轧机的轧辊磨损量减少了约120～150μm,不均匀磨损减少了40～60μm,从而解决了冷轧料局部高点问题.     

基于小波分析的光栅信号质量评估方法

依据谐波分析法给出的评定信号质量指标的准则，提出了基于小波分析的光栅信号质量评估方法，并讨论了此方法的特征与优点。与谐波分析法相比较，小波分析在时域和频域同时具有良好的局部化性质，每次小波变换所取的都是原信号的最大可能近似值，信号的失真度小，通过变更尺度，可以对信号更细微的瞬态特征进行研究，并最大可能地除去噪声，较快地得到理想的质量评定效果(通常只需3至5次变换即可)。在此基础上，给出了相应的小波分析评定光栅信号质量的实验方法。     

紫坪铺水利枢纽工程溢洪道挑坎体形研究

挑流鼻坎体型设计和它的变化具有很大的灵活性，结合紫坪铺水利枢纽工程溢洪道的挑坎特性，应用CFX软件对其挑流鼻坎的体型进行了改进优化，经模型试验的验证，挑流鼻坎上的水舌分布均匀、挑距增大，而且对下游的冲刷深度减小。     

The predictive power of ridge regression: Some quasi-simulation results: Correction to Cattin.

Reports an error in the original article by Philippe Cattin (Journal of Applied Psychology, 1981, Vol. 66, No. 3, pp. 282-290). The fifth sentence in the first paragraph on page 284 contains an error. The sentence should read: "For intermediate values of k?, the ridge regression weights are 'weighted sums' of the OLS regression weights [not models] and of the zero-order sample correlations and tend to decrease in absolute value as k? increases." (The following abstract of this article originally appeared in record 1981-27117-001.) Reviews research indicating that ridge regression tends to improve the mean square error of prediction obtained with ordinary least squares (OLS) regression in a wide range of conditions. MMPI profiles of 861 psychiatric patients and diagnoses from 29 psychologists, as collected by P. E. Meehl (see record 1960-04396-001), were used to examine the gains in cross-validated multiple correlation obtained with ridge regression compared with OLS and equal weights as a function of sample size and ridge constant. A simple formula for estimating the ridge constant was also evaluated. Results that are related to recent developments concerning the use of Bayesian regression procedures show that ridge regression improves both the mean square error of prediction and the cross-validated multiple correlation obtained with OLS when the ratio sample size to number of predictor variables is relatively small. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Sigma-Pi-Sigma Neural Network (SPSNN)

This letter presents a sigma-pi-sigma neural network (SPSNN) structure. The SPSNN can learn to implement static mapping that multilayer neural networks and radial basis function networks usually do. The output of the SPSNN has the sum of product-of-sum form , where x _j's are inputs, N _v is the number of inputs, f _nij() is a function to be generated through the network training, and K is the number of pi-sigma network (PSN) which is the basic building block for SPSNN. A linear memory array can be used to implement f _nij (). The function f _nij (x _j ) can be expressed as , where B _ijk() is a single-variable basis function, w _nijk's are weight values stored in memory, N _q is the quantized element number for x _j , and N _e is the number of basis functions in the neighborhood used for storing information for x _j. If all B _ijk()'s are Gaussian functions, the new neural network degenerates to a Gaussian function network. This paper focuses on the use of overlapped rectangular pulses as the basis functions. With such basis functions, will equal either zero or w _nijk, and the computation of f _nij (x _j) becomes a simple addition of retrieved w _nijk's. The new neural network structure demonstrates excellent learning convergence characteristics and requires small memory space. It has merits over multilayer neural networks, radial basis function networks and CMAC. This revised version was published online in June 2006 with corrections to the Cover Date.     

一种基于机器学习的指纹纹路方向计算方法

纹路方向是指纹图像的基本特征,而方向计算是指纹识别的基础,特征提取和匹配的过程中都需要用到方向.目前大多数纹路方向计算方法都是基于像素之间的灰度关系的.提出了一种用神经网络学习纹路方向的方法.对于正确的纹路方向,该网络的响应值较大;对于错误的纹路方向,该网络的响应值较小.计算指纹图像的方向场时,对于每个纹路图像块,计算网络在各个方向上的响应值,基于每个图像块在每个方向上的响应值可以计算出整个图像的方向场.该方法比现有方法更能正确地计算指纹图像方向场.     

聚集数据线性模型中广义岭估计相对效率问题

对于聚集数据的线性模型,给出了参数的广义岭估计及改进的广义岭估计的定义,并提出了广义岭估计及改进的广义岭估计在推广的欧氏模之比意义下及加权欧氏模之比意义下的二种相对效率。分别对广义岭估计及改进的广义岭估计,得到了两种相对效率的上界。