实验室的质量监督(上)

质量监督是实验室保持人员能力,进行自我完善,日常渐进的重要手段,是持续改进的重要组成部分,也是实验室管理工作的难点,本文给出质量监督的目的、对象、方法、记录等.     

FINITE GROUPS WHOSE AUTOMORPHISM GROUP HAS ORDER CUBEFREE

ＦＩＮＩＴＥＧＲＯＵＰＳＷＨＯＳＥＡＵＴＯＭＯＲＰＨＩＳＭＧＲＯＵＰＨＡＳＯＲＤＥＲＣＵＢＥＦＲＥＥＬＩＳＨＩＲＯＮＧＡｂｓｔｒａｃｔＬｅｔＧｄｅｎｏｔｅａｆｉｎｉｔｅｇｒｏｕｐ．Ｉｔｉｓｓｈｏｗｎｔｈａｔｉｆ｜Ａｕｔ（Ｇ）｜ｉｓｃｕｂｅｆｒｅ...     

一类非线性Schr(o)dinger方程的守恒差分法与Fourier谱方法

考察了一类带导数项的非线性Schrodinger方程的周期边值问题，提出了一种守恒的差分格式，在空间方向上采用Fourier谱方法，证明了格式的稳定性和收敛性．数值试验得到了与理论分析一致的结果．     

Sparse polynomials, redundant bases, gauss periods, and efficient exponentiation of primitive elements for small characteristic finite fields

Gauss periods give an exponentiation algorithm that is fast for many finite fields but slow for many other fields. The current paper presents a different method for construction of elements that yield a fast exponentiation algorithm for finite fields where the Gauss period method is slow or does not work. The basic idea is to use elements of low multiplicative order and search for primitive elements that are binomial or trinomial of these elements. Computational experiments indicate that such primitive elements exist, and it is shown that they can be exponentiated fast.     

Neural Computation of the MoM Matrix Elements for Planar Configurations

In this paper a new artificial neural network (ANN) based model for the calculation of the method of moments (MoM) matrix elements is presented. Training sets that characterize the matrix elements are first constructed. These sets are then utilized to effectively train two radial basis function (RBF) neural networks to accurately estimate all the elements of the MoM matrix for any mesh used. The potential of the proposed approach is demonstrated in the case of a narrow microstrip line. The current distribution on the microstrip line produced by the trained RBF networks agrees very well with the exact distribution. In addition, the proposed ANN model is much faster than the conventional MoM procedure.