松花蛋中痕量铅的方波溶出伏安法测定

用方波溶出伏安法测定松花蛋中痕量铅,在抗坏血酸存在下,以0.1 mol/L硝酸溶液为底液,玻碳电极为工作电极,测得铅的溶出峰电位是-0.48 V。峰高与铅浓度在1.00×10-4~1.00×10-6mol/L范围内呈良好的线性关系,铅的检出限为5.00×10-8mol/L,回收率为90%~108%。此法干扰较少,易于掩蔽,灵敏度较高,适用于松花蛋等食品中微量铅的测定。     

L-Fuzzy拓扑和空间的系列性质

本文研究了L-Fuzzy拓扑和空间的系列性质,揭示了L-Fuzzy拓扑和空间与分明拓扑和空间之间的内在联系.     

二维声子晶体的弹性波禁带及其影响因素

选取了5种不同材料组分和2组不同截面形状散射体的声子晶体作为研究对象,利用平面波展开法研究了二维固相双组分正方点阵声子晶体中z向剪切波的频率带结构,探讨了材料特性和截面形状对禁带的影响．结果表明：填充物的密度和弹性模量比基体的大时,容易产生弹性波禁带,材料的密度比剪切模量更能影响弹性波禁带的宽度,圆截面散射体的结构在总体上比正方形截面更容易产生弹性波禁带．     

Bernoulli多项式与幂和多项式的关系

研究了Bernoulli多项式与幂和多项式的关系,给出了用幂和表示Bernoulli多项式的一个公式,得到了关于Bernoulli多项式的形式上非常对称的两个恒等式.     

Application of meshless method to electromagnetic NDT

0 Introduction Finite element methods have been successfully pliedfor solving different kinds of problemsinelectrom netic NDT. The domain under study is divided into mesh of element inthis method.For complicated geom tries or in variable geometry problemsthe mesh present high computational cost due to the reevaluation of e newgeometry. Meshless methods are nowproven as a robust te nique to study field problems in which large geometri deformations areto be modeled,since such methods av the on…     

基于Origin的牛顿环实验数据处理方法

用Origin软件对牛顿环实验的数据进行线性拟合与作图,拟合的结果与所作图形均与传统的最小二乘法进行数据处理的结果相吻合.实验表明：利用软件处理的实验数据与作图,具有简洁、快捷与直观等特点,避免了人为因素所造成的误差.     

孪生组合恒等式(十七)——Fibonacci幂级数与Lucas幂级数类型

笔者提出一对孪生幂级数,即Fibonacci幂级数∑∞n=0fnkxn与Lucas幂级数∑∞n=0lnkxn,这里fn为Fibonacci数,ln为Lucas数,k为正整数.它们有相同的收敛区间,应用代入法求出它们的级数和,从而获得孪生组合恒等式.此外,证明孪生数集{fnk}与{lnk}有相同的递推关系式.     

基于递推最小二乘改进算法的洪水预报模型研究

由递推最小二乘算法估算出的自回归系数在一定条件下具有最佳的统计特性,但在实际应用中,这种方法往往难以动态地把握水文现象的动态特性.为提高自回归洪水预报模型的精度,分别用衰减记忆、有限记忆及2种算法相结合的方法对基本的递推最小二乘算法进行改进,并利用这几种改进算法对白马寺水文站的实测径流序列进行了模拟演算.结果表明,这3种改进的递推最小二乘算法,都可以使自回归洪水预报模型取得较好的预报效果,但实际应用时应根据不同预报的侧重点选择相应的算法.     

基于最小二乘法的电力系统状态估计算法研究

针对现行电力系统状态估计算法中存在的各种发展潮流,重点剖析了被广泛应用的基于最小二乘法的状态估计的基本原理和改进后的几种状态估计方法,阐明了各算法的应用优势和存在的不足。对于存在不良信息系统进一步提高算法的精度和估算速度的发展方向提出了新的思想。     

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.