屏蔽人体模型对电磁波吸收的数值模拟研究

本文用时域有限差分法计算了三类电磁特性不同的材料屏蔽下三层无限长椭圆柱人体模型对电磁波的吸收。结果表明 ,功率密度相同 ,频率在 0 .2～ 6GHz之间的平面电磁波照射下 ,模型吸收的能量、能量在体内的分布及两者对入射电磁波频率的敏感程度严重地受到外加屏蔽的影响。考察了屏蔽材料的电磁特性与其屏蔽效能之间的关系。三类材料的比较显示 ,对人体电磁辐射防护 ,耗散材料优于非耗散材料 ,导电材料优于非导电材料。     

基于相关性的人机系统可靠性模型

本文在剖析人机系统中操作者行为特征的基础上,考虑到操作者行为的自适应性、自学习能力及其行为形成主因子,着重结合操作者行为的相关性-感知作用于判断决策后影响到动作,建立了人机系统中操作者行为模式,并推导出计算操作者可靠度的公式,求出了相应参数.最后进行了实例量度,同时定量辨识了行为相关性对人可靠性的制约程度,以期从人机系统可靠性角度为人机系统的优化设计提供新的依据.     

基于双因子认证技术的网络身份识别

本文提出了一种基于动态双因子认证技术的网络身份识别方法,用户每次登录的口令是利用系统时间和用户ID通过MD5加密算法计算得到,其通过MD5加密后生成的登录口令是随机的,这将进一步提高开放网络环境下身份识别的可靠性和安全性。同时,本文还提出网络身份识别技术可以应用到各类网站的服务器上,用来完成对用户身份的识别,以提高网络系统的安全性。     

应用遗传神经网络研究低渗透储层成岩储集相—以胜利渤南油田三区沙河街组为例

以渤南油田三区沙河街组为例,应用遗传人工神经网络模式识别方法,开展了低渗透储层成岩储集相的研究。该工作是在储集层沉积相、成岩作用研究的基础上,选用流动层带指标、孔隙度、渗透率、粒度中值、泥质含量、孔喉半径均值和变异系数等7项参数,采用神经网络模式识别方法,通过建立遗传神经网络的学习及预测模型,对渤南油田三区沙河街组进行了成岩储集相识别,识别出4类成岩储集相:不稳定组分强溶解次生孔隙成岩储集相、碳酸盐胶结物溶解次生孔隙成岩储集相、强压实强胶结残余粒间孔成岩储集相和极强压实强胶结致密成岩储集相。Ⅰ类储集相的储集性能最好,Ⅳ类最差,为非储层或差储层。     

Awards for Distinguished Contributions to Psychology in the Public Interest: 1986.

The awards for Distinguished Contributions to Psychology in the Public Interest recognize persons who, in the opinion of the Committee on Public Interest Awards, have advanced psychology as a science and/or profession by a single extraordinary achievement or a lifetime of outstanding contributions in the public interest. These contributions might include the identification or solution of significant social problems; unusual initiative or dedication to activity in the public interest; or the integration of the science and/or profession of psychology with social action in a manner beneficial to all. The Committee is authorized to present two awards per year, each with a prize of $2,000. The second award, for which recipients may not have held a Ph.D. for more than 15 years, was established to recognize the large number of excellent psychologists who have made significant contributions to the public interest during the early stages of their careers. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

地震勘探技术在焉耆盆地勘探开发中的合理应用探讨

地震勘探技术应用可以分为以二维地震研究构造为主的地震勘探初级阶段、以地震地层学为主的地震勘探中级阶段、以三维地震油藏静态描述为主的地震勘探高级阶段、以地震储层研究为主的开发地震妆级阶段和以四维地震进行油藏监测为主的开发地震高级阶段。针对5个不同阶段的地质条件和地质任务，选择使用合理的地震勘探技术是有效提高勘探开发效益的重要途径。焉耆盆地的勘探开发体现了以地震勘探为主的思路，在勘探开发过程中重视了针对不同勘探开发阶段和不同的地质 地震技术，因此，在早期勘探开发中取得了很好的经济效益。     

神经网络在缺陷识别中的应用

为了准确、快速地进行缺陷识别，介绍了一种新型的前馈神经网络模型，即径向基概率神经网络。与以往的算法相比，该方法具有分类识别精度高且速度快的优点。仿真获得了很好的结果。     

池27井机抽排水采气效果分析

介绍了池27井实施机抽排水工艺取得累计排水13 334.4m3的明显效果.分析了五次检泵的原因.提出了对池27井机抽排水的四点认识.     

Solving the small sample size problem in face recognition using generalized discriminant analysis

The goal of face recognition is to distinguish persons via their facial images. Each person's images form a cluster, and a new image is recognized by assigning it to the correct cluster. Since the images are very high-dimensional, it is necessary to reduce their dimension. Linear discriminant analysis (LDA) has been shown to be effective at dimension reduction while preserving the cluster structure of the data. It is classically defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular restricts its application to datasets in which the dimension of the data does not exceed the sample size. For face recognition, however, the dimension typically exceeds the number of images in the database, resulting in what is referred to as the small sample size problem. Recently, the applicability of LDA has been extended by using the generalized singular value decomposition (GSVD) to circumvent the nonsingularity requirement, thus making LDA directly applicable to face recognition data. Our experiments confirm that LDA/GSVD solves the small sample size problem very effectively as compared with other current methods.