Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier

Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-     

格兰·泰勒棱镜两类视场角的比较研究

以格兰·泰勒棱镜为例 ,推导了沿主截面和垂直主截面两正交方向的最大视场角 ,并在常用结构角下比较了两类视场角的大小及它们随波长的变化关系 ,得知沿主截面的视场角对光路调整影响较大     

陕西省产业结构的现状及其优化

分析陕西省产业结构的特点及其优化途径.认为陕西省产业结构的特点是第一产业总量缓步增长而比重下降,第二产业总量增长迅速,比重快速上升,而第三产业快速增长.陕西省产业结构趋向合理,但也存在着第二产业发展滞后,竞争力低下,缺乏集群效应和主导产业模糊等问题.产业结构的优化可通过技术创新改造传统产业,培育主导产业,发挥集群效应等途径实现.     

结构动力分析一种简易的直接积分法

本文利用方块脉冲函数优良的运算性质,提出一种结构动力分析简易的直接积分法——方块脉冲函数法,具有无条件稳定的积分格式,并表明方法的可行和有效.     

采用自举技术的不完全绝热电路

为了大规模集成电路的低能耗应用,提出了一种不完全绝热电路——自举能量回收逻辑电路(bootstrapenergyrecoverylogic,BERL)。该电路采用二相无交叠功率时钟。由于采用自举技术,使负载的冲放电过程不会产生非绝热损失,并且输出开关的导通电阻变小,使绝热损失降低。为了比较BERL电路与静态CMOS电路及PAL-2n绝热电路的能耗,设计了反相器链电路。Hspice软件仿真结果表明,BERL电路的工作频率可以超过400MHz。在10～100MHz下,BERL能耗只有静态CMOS电路的25%～33%。相对于PAL-2n电路,BERL也有较低的能耗。在200MHz下,BERL能耗只有PAL-2n的50%。负载越重,BERL电路的低能耗优势越明显。     

局部分析方法在可解群中的应用

局部分析方法是有限群理论最基本的方法,利用可解群的性质和有限群的基本定理,通过局部分析方法,研究了可解群和可解子群的一些性质.利用成分的性质及ThompsonA×B引理,得到了p-局部子群的相关结论.     

玻璃钢地下输水管的生产工艺

阐述了生产玻璃钢地下输水管道的工艺流程及管壁各层的设计内容及其作用,分析了各工艺中的工艺要点,并给出了生产工艺数据。     

结构力学计算机辅助教学模式的应用研究

针对结构力学这门课程,从教学改革的需要出发,分析了计算机辅助教学的必要性,介绍了CAI课件的研制与课堂应用效果。     

国有大旅行社市场渠道策略变革探讨

通过对近年来国有大旅行社渠道建设的现状分析,总结出国有大旅行社渠道变革的三大趋势,并分析这些变革趋势的动因。     

Samples Selection for Artificial Neural Network Training in Preliminary Structural Design

An artificial neural network (ANN) is applied in the preliminary structural design of reticulated shells. Major efforts are made to enhance the generalization ability of networks through well-selected training samples. Number-theoretic methods (NTMs) are adopted to generate samples with low discrepancy, i.e.uniformly scattered in the domain, where discrepancy is a quantitative measurement of the uniformity. The discrepancy of the NTM-based sample set is 1/6-1/7 that of samples with equal spacing. In a case study,networks trained by NTM-based samples are compared with those trained by equal-spaced samples in generalizing performance. The results show that both the computational precision and stability of the former ANNs are more satisfactory than those of the latter. It is concluded that the flexibility of ANNs in generalizing can be effectively increased by use of uniformly distributed training samples rather than simply piling data.More reliable uniformity should be obtained, however, through NTMs instead of equal-spaced samples.