Some exact expressions for the mean and higher moments of functions of sample moments

Examples of exact expressions for the moments (mainly of the mean) of functions of sample moments are given. These provide checks on alternative developments such as asymptotic series for n, and simulation processes. Exact expressions are given for the mean of the square of the sample coefficient of variation, particularly in uniform sampling; Frullani integrals studied by G. H. Hardy arise. It should be kept in mind that exact results for (joint) moment generating functions (mgfs) are of interest as they produce a means of obtaining exact results for (cross) moments—including moments with negative indices. Thus an exact expression for the joint mgf of the 1st two noncentral moments can be used to obtain the mean of the (c.v.)² (but not for the mean of the c..). A general expression is given for the moment generating function of the sample variance. The limitations of Fisher's symbolic formula for the characteristic function of sample moments (or more general statistics) are noted.This research was sponsored by the Applied Mathematical Sciences Research program, Office of Energy Research, U. S. Department of Energy under contract DE-AC0584OR21400 with the Martin Marietta Energy Systems. Inc.     

Proof of some conjectures on the mean-value of Titchmarsh series — III

With some applications in view, the following problem is solved in some special case which is not too special. LetF(s) =Σ _n ^∞ =1a_n λ _n ^−s be a generalized Dirichlet series with 1 =λ ₁ <λ ₂ < …,λ _n≤Dn, andλ _n+1 -λ _n ≥D ^{− 1} λ _n+1 ^{− a} where α>0 andD(≥ 1) are constants. Then subject to analytic continuation and some growth conditions, a lower bound is obtained for . These results will be applied in other papers to appear later.     

On vector-valued random Fourier series

Consider a (complex) Banach spaceX, such thatX C_O, and vectors(X _i ) _i ofX. Consider an independent standard normal sequence(g _i ) _i . Then if anX-valued random Fourier series _{|k| n} e ^ikt g _k x _k satisfies

Discussion of Some Problems About Nonlinear Time Series Prediction Using v-Support Vector Machine

Some problems in using v-support vector machine (v-SVM) for the prediction of nonlinear time series are discussed. The problems include selection of various net parameters, which affect the performance of prediction, mixture of kernels, and decomposition cooperation linear programming v-SVM regression, which result in improvements of the algorithm. Computer simulations in the prediction of nonlinear time series produced by Mackey-Glass equation and Lorenz equation provide some improved results.     

磁致冷材料钆硅锗系合金中Si和Gd的重量法联测的研究

参考了HClO4脱水重量法测定Si及草酸盐重量法测定Gd的方法,建立了钆硅锗系合金中主成分Si和Gd联合测定的方法,并详细考查了共存元素及杂质元素对测定的影响。合金共存元素Ge在HCl的存在下,绝大部分可以GeCl4的形式被蒸发,达到与被测元素分离的目的。熔样过程引入的杂质元素Ni可与被测元素Gd,用氨水沉淀分离,残留Ni不足以影响Gd的测定。方法回收率在98.5%以上,Si和Gd的测量相对标准偏差分别在2.6%~3.0%和0.4%~0.5%。     

Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, Rt has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an Rt estimation by adjusting the data produced by the output layer of the NN on the related Rt estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the Rt as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the Rt. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively.     

高阶常微分方程的动态演化建模

提出了采用高阶常微分方程模型代替传统时序分析中所用的ARMA模型来实现一维时间序列的建模和预报．设计的将遗传程序设计与遗传算法相嵌套的动态演化建模算法,用遗传程序设计优化模型结构,以遗传算法优化模型参数,边收集数据边建模边预报,首次成功地实现了时间序列实时预报的程序自动化．两个时间序列的应用实例表明采用此算法可获得较好的实时预报效果．     

氨吸收式串联型动力/制冷复合循环

本文提出了一种新型的采用氨水混合工质的动力／制冷复合循环,该循环以中低温工业余热或燃气轮机排气为热源,将动力、制冷子循环采用串联方式连接,基础循环工质为氨质量浓度为0．27的氨水溶液,热源为365℃／104．3 kPa 的热空气,透平进气参数为350℃／3750 kPa,,以热效率η1、(火用)效率η2作为评价准则,模拟计算表明本循环的热效率η1 为17．8％,炯效率η2为45％,通过与其它有代表性的分供系统及联供系统进行热力性能方面的比较,表明本循环η1、η2均有不同程度提高。     

混沌时间序列基于邻域点的非线性多步自适应预测

根据流形理论，利用混沌时间序列中某点邻域内最近几点的P次迭代像，提出了一种多步自 适应预测算法.仿真说明，这种算法使得预测速度成倍提高，而预测稳定后得到的误差均方 根序列呈指数增长趋势，这个指数就是该混沌时间序列的Lyapunov指数. 关键词： 混沌时间序列 邻域 非线性自适应预测 Lyapunov指数     

用时滞和阶跃序列组合生成网格多涡卷蔡氏混沌吸引子

提出了在规范型蔡氏电路中生成两种不同类型网格多涡卷混沌吸引子的新方法.与现有文献报道仅构造同一类型非线性函数产生多涡卷混沌吸引子的主要差别在于,这种方法能在一个蔡氏电路中同时构造时滞序列和阶跃序列,并通过其不同的组合方式来扩展相空间中指标2的鞍焦平衡点,从而生成两种不同类型的网格多涡卷混沌吸引子.理论分析、数值模拟和电路实验结果证实了该方法的可行性. 关键词： 规范型蔡氏电路 网格多涡卷混沌吸引子 时滞序列和阶跃序列 电路实现