On Turán type inequality with doubling weights and A weights

Let Hn be the set of real algebraic polynomials of degree n, whose zeros all lie in the interval [-1,1]. The well known Turán type inequalities tell us that forf（x）∈Hn, it holds ‖f＇‖≥C√n‖f‖. This note deals with the weighted Turán type inequalities with the weights having inner singularities under L^p norm for 0〈p≤∞. Our results essentially extend the result of Wang and Zhou （2002）, and the method used in this paper is simpler and more direct than that of Wang and Zhou （2002）. The results and methods have their own values in approximation theory and computation.

The New Neutrosophic Double and Triple Exponentially Weighted Moving Average Control Charts

The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are designed under the uncertain environment or neutrosophic statistical interval system,when all observations are undermined,imprecise or fuzzy.These are termed neutrosophic double and triple exponentially weighted moving average(NDEWMA and NTEWMA)control charts.For the proficiency of the proposed chart,Monte Carlo simulations are used to calculate the run-length characteristics(such as average run length(ARL),standard deviation of the run length(SDRL),percentiles(P_(25),P_(50),P_(75)))of the proposed charts.The structures of the proposed control charts are more effective in detecting small shifts while these are comparable with the other existing charts in detecting moderate and large shifts.The simulation study and real-life implementations of the proposed charts show that the proposed NDEWMA and NTEWMA charts perform better in monitoring the process of road traffic crashes and electric engineering data as compared to the existing control charts.Therefore,the proposed charts will be helpful in minimizing the road accident and minimizing the defective products.Furthermore,the proposed charts are more acceptable and actual to apply in uncertain environment.

Weighted (<i>λ</i>, <i>μ</i>)-Ideal Statistical Convergence and Strongly Weighted (<i>λ</i>, <i>μ</i>)-Ideal Convergence of Double Sequences of Fuzzy Numbers

The paper aims to investigate different types of weighted ideal statistical convergence and strongly weighted ideal convergence of double sequences of fuzzy numbers. Relations connecting ideal statistical convergence and strongly ideal convergence have been investigated in the environment of the newly defined classes of double sequences of fuzzy numbers. At the same time, we have examined relevant inclusion relations concerning weighted (λ, μ)-ideal statistical convergence and strongly weighted (λ, μ)-ideal convergence of double sequences of fuzzy numbers. Also, some properties of these new sequence spaces are investigated.

New Double Integrals Weighted Cebysev Type Inequalities

In the last years, the theory of integral inequalities are playing a very significant role in all fields of mathematics, many monographs have been devoted to this subject and present a very active and attractive field of research, the applications of integral inequalities have known a great development in many branches of mathematics in statistics, differential equations and numerical integration, The aim of this paper is to establish new extension of the weighted montgomery identity for double integrals then used it to establish new t^eby^evtype inequalities.

Apply the Double-Weight Neural Network to Dynamic Power Management

This paper expounds a data-fitting algorithm for the double-weight neural network,and presents a new algorithm for the system's power management on the base of that.The double-weight neural network learns knowledge from the past idle periods of the system,and predicts the lengths of the coming idle periods.As a result of that,the system can switch its running states and re- duce the power dissipation according to the predictive values.The results of the experiments prove that this algorithm shows a better performance in increasing the right rate of shutting down and reducing the power consumption than other traditional ones.

Composition operators on weighted Bergman spaces induced by doubling weights

Given a doubling weightωon the unit disk D,let A_(ω)^(p) be the space of all the holomorphic functions f,where∥f∥A_(ω)^(p):=(∫_(D)|f(z)|_(p)ω(z)dA(z))^(1/p)<∞.We completely characterize the topological connectedness of the set of composition operators on A_(ω)^(p).As an application,we construct an interesting example which reveals that two composition operators on A_(α)^(p) in the same path component may fail to have a compact difference and give a negative answer to the Shapiro-Sundberg question in the(standard)weighted Bergman space.In addition,we completely describe the central compactness of any finite linear combinations of composition operators on A_(ω)^(p) in three terms:a Julia-Carathéodory-type function-theoretic characterization,a power-type characterization,and a Carleson-type measure-theoretic characterization.

A group decision making method based on double hesitant linguistic preference relations

A simple decision method is proposed to solve the group decision making problems in which the weights of decision organizations are unknown and the preferences for alternatives are provided by double hesitant linguistic preference relations. First, double hesitant linguistic elements are defined as representing the uncertain assessment information in the process of group decision making accurately and comprehensively, and the double hesitant linguistic weighted averaging operator is developed based on the defined operational laws for double hesitant linguistic elements. Then, double hesitant linguistic preference relations are defined and a means to objectively determine the weights of decision organizations is put forward using the standard deviation of scores of preferences provided by the individual decision organization for altematives. Finally the correlation coefficient between the scores of preferences and the scores of preferences are provided by the other decision organizations. Accordingly, a group decision method based on double hesitant linguistic preference relations is proposed, and a practical example of the Jiudianxia reservoir operation alternative selection is used to illustrate the practicability and validity of the method. Finally, the proposed method is compared with the existing methods. Comparative results show that the proposed method can deal with the double hesitant linguistic preference information directly, does not need any information transformation, and can thus reduce the loss of original decision information.

THE EXISTENCE OF A NONTRIVIAL WEAK SOLUTION TO A DOUBLE CRITICAL PROBLEM INVOLVING A FRACTIONAL LAPLACIAN IN R^N WITH A HARDY TERM

In this paper,we consider the existence of nontrivial weak solutions to a double critical problem involving a fractional Laplacian with a Hardy term:(−Δ)s u−γu|x|2s=|u|2∗s(β)−2 u|x|β+[Iμ∗Fα(⋅,u)](x)fα(x,u),u∈H˙s(R n),(0.1)(1)where s∈(0,1),0≤α,β<2s<n,μ∈(0,n),γ<γH,Iμ(x)=|x|−μ,Fα(x,u)=|u(x)|2#μ(α)|x|δμ(α),fα(x,u)=|u(x)|2#μ(α)−2 u(x)|x|δμ(α),2#μ(α)=(1−μ2n)⋅2∗s(α),δμ(α)=(1−μ2n)α,2∗s(α)=2(n−α)n−2s andγH=4 sΓ2(n+2s4)Γ2(n−2s4).We show that problem(0.1)admits at least a weak solution under some conditions.To prove the main result,we develop some useful tools based on a weighted Morrey space.To be precise,we discover the embeddings H˙s(R n)↪L 2∗s(α)(R n,|y|−α)↪L p,n−2s2 p+pr(R n,|y|−pr),(0.2)(2)where s∈(0,1),0<α<2s<n,p∈[1,2∗s(α))and r=α2∗s(α).We also establish an improved Sobolev inequality,(∫R n|u(y)|2∗s(α)|y|αdy)12∗s(α)≤C||u||θH˙s(R n)||u||1−θL p,n−2s2 p+pr(R n,|y|−pr),∀u∈H˙s(R n),(0.3)(3)where s∈(0,1),0<α<2s<n,p∈[1,2∗s(α)),r=α2∗s(α),0<max{22∗s(α),2∗s−12∗s(α)}<θ<1,2∗s=2nn−2s and C=C(n,s,α)>0 is a constant.Inequality(0.3)is a more general form of Theorem 1 in Palatucci,Pisante[1].By using the mountain pass lemma along with(0.2)and(0.3),we obtain a nontrivial weak solution to problem(0.1)in a direct way.It is worth pointing out that(0.2)and 0.3)could be applied to simplify the proof of the existence results in[2]and[3].

WEIGHTED ESTIMATES FOR COMMUTATORS OF MULTILINEAR CALDERóN-ZYGMUND OPERATORS WITH NON-DOUBLING MEASURES

Let μ be a nonnegative Radon measure on Rd, which only satisfies the polynomial growth condition. Under this assumption, the authors obtain some weighted weaktype estimates for the commutators generated by the multilinear CalderSn-Zygmund op- erators and RBMO（μ） functions.

APPROXIMATION RATES OF ERROR DISTRIBUTION OF DOUBLE KERNEL ESTIMATES OF CONDITIONAL DENSITY

In this paper, the normal approximation rate and the random weighting approximation rate of error distribution of the kernel estimator of conditional density function f(y|x) are studied. The results may be used to construct the confidence interval of f(y|x) .

空气中双称重法砝码体积测量方法实验研究

以质量1 g~5 kg的标准砝码为对象,开展了空气中双称重法和静水力学法砝码体积测量比对实验研究。提出一种辅助砝码盘的砝码体积测量方法,解决了1~5 g小砝码体积测量不能直接加载到秤盘上的问题。利用Monte Carlo法评定了砝码体积测量的不确定度,结果发现双称重法和静水力学法测得的砝码体积及其不确定度一致性很好,砝码标称质量在1 g~1 kg之间时砝码体积相对不确定度随着标称质量增加而减小,质量超过1 kg时相对不确定度随着标称质量增加而增加。

基于超图理论的中医方剂网络药对挖掘方法

随着复杂网络研究的深入发展,利用复杂网络理论对中药药方数据进行建模分析已成为研究中药配伍规律的重要方法之一。然而,传统的“药-药”网络结构只基于“点-边”关系,无法体现方剂和中药之间的高阶交互关系,从而影响药对挖掘的准确率和效果。针对该问题,提出了一种基于超图的方药超网络建模方法来刻画方剂与中药之间的高阶关联关系,以方剂为超边、方剂中的中药为节点构建方药超网络,对比传统“药-药”网络和方药超网络的统计特性发现,方药超网络节点超度分布更加符合幂律分布特征,具有小世界和无标度特性,证明了方药超网络建模方法具有合理性和可行性;基于方药超网络提出一种融合多因素的双权重度中心性指标(double weight degree centrality,DWDC)挖掘方剂中的常见中药,引入超边属性对节点特性的影响,以网络的脆弱性和鲁棒性为依据,通过各算法之间的对比实验,验证所提出方法能够更有效筛选出方剂超网络中的常见中药;再将DWDC指标推广到二阶常见药对挖掘,提出高阶指标H-DWDC,基于真实数据集的实验结果表明,该方法能够有效地从海量数据中挖掘出常见药对。

基于WMIAEKF的锂离子电池SOC与容量联合估算

精确的荷电状态(SOC)估算对可靠的电池管理系统来说十分关键。基于二阶等效电路模型,提出了一种加权多新息自适应扩展卡尔曼滤波(WMIAEKF)算法,该算法可以解决传统多新息算法中误差累积的问题,从而提高SOC估算精度。实验仿真结果表明,所提算法比传统的自适应扩展卡尔曼(AEKF)以及多新息自适应扩展卡尔曼(MIAEKF)精度要高,最大误差控制在1.15%以内。此外,基于该算法提出了一种改进的多时间尺度双卡尔曼滤波算法,其中,WMIAEKF用于SOC估算,AEKF用于容量估算,两者结合对电池的SOC和容量进行实时的联合估算。所提算法能够对电池SOC以及容量进行较精确的估计,在新欧洲行驶工况(NEDC)下,SOC估算误差始终控制在1.2%,并且在面对错误容量初始值时也能保持较好的鲁棒性。

远程光纤传感网中滤波降噪方法的优化

为实现远程光纤传感网络在噪声干扰条件下的高精度检测传输,提出一种优化差分(optimized difference,OD)的降噪方法。基于移动平均法构建新差分算法,并结合低通算法、中值算法预判边缘信息及预测信号数值;采用双重判别弥补边缘信息误判的缺陷,优化准确定位边缘信息的能力;融合加权平均法与卡尔曼滤波器(Kalman filter,KF),优化信号预测的精度。实验验证结果表明:采用该方法进行数据预处理将使系统能够正常检测数据,测量精度可达0.76%,提高检测系统的抗噪性能。与先进小波阈值降噪方法的对比实验结果表明:斜率优化方法在≥10 dB噪声干扰下,测量精度相对提高0.62倍以上。

PSM⁃DID在政策评价中的应用现状与改进方法

倾向得分匹配-双重差分模型(PSM⁃DID)是政策评估及因果推断中最为流行的方法之一.但是在实际应用中,该方法面临着控制变量在处理组样本和控制组样本之间非平衡性的挑战.传统基于均值差异t检验的平衡性检验容易产生片面和误导性的结论,使得后续因果推断产生偏误.为克服上述问题,本文对传统的平衡性检验提出以下改进:一是推荐更全面的多维度的平衡性测度指标,便于在匹配后更严谨地比较处理组和控制组的平衡性;二是提出了适用于非平衡样本的新估计方法:倾向得分匹配-逆概率加权-双重差分(PSM⁃IPW⁃DID),该方法结合了倾向得分匹配(PSM)克服样本自选择内生性及对非平衡样本稳健的优势和逆概率加权(inverse probability weighting,IPW)利用全样本信息的长处,在不进一步删除样本的情况下得到一种更稳健的双重差分估计方法.数据模拟和应用实例显示,本文提出的新方法能更全面、客观地评价宏观、微观政策的作用,得到更为可信的因果推断.

地基微波辐射计资料的质量控制及同化应用研究

针对地基微波辐射计反演资料的质量问题,利用北京区域7个站点的RPG-HATPRO型微波辐射计反演的温湿数据,在传统质量控制方法的基础上,通过增加双权重检查和偏差订正方法,设计了新的质量控制方案,而后针对质量控制后的温湿廓线开展了同化应用研究,结果表明:只有个别站点的反演温度误差较大,反演温度误差大的站点在质量控制前最大均方根误差为8℃,但在质量控制后均方根误差下降到4℃以内,误差下降50%以上,而对于反演温度误差小的站点,质量控制只对高层数据有所改善;进一步分析表明所有站点的反演相对湿度误差都比较大,在质量控制前反演的相对湿度均方根误差最大甚至达到50%,但在质量控制后所有站点反演的相对湿度均方根误差都降到20%以内,误差显著降低,同时经过质量控制后的反演温湿数据误差平均偏差降为0左右,误差满足高斯分布,符合同化的要求。同化试验表明:与控制试验相比,同化质量控制后的地基微波辐射计反演温湿廓线资料能有效改善模式对所有量级的6 h累积降水量预报,无论是降水中心位置预报还是降水量级预报,都使得模式降水预报更加趋近实况,同化试验还表明同化对温湿场有较大影响,使得模式温湿场更加合理,能有效提高模式的预报准确率。

制造型企业碳减排信用风险防控研究:基于深度学习与压力测试

应对气候变化是推动经济高质量发展和生态文明建设的重要抓手,企业的碳减排信用风险关系到投资者、银行和政府等利益相关者的资金保障。因此,有效预警中国制造型企业碳减排信用风险成为当前的研究热点。首先基于熵权TOPSIS与耦合协调度模型验证制造型企业碳减排信用风险预警的必要性;其次,通过与多模型对比,证明MLP深度学习模型在企业碳减排信用风险预警中的优越性;最后,通过压力测试方法确定利益相关者的重点防控企业和行业类型。结果表明:第一,多数制造型企业处于中度、高度协调度耦合阶段,即企业“碳排放水平”因素会显著影响自身的碳减排信用风险;第二,制造型企业碳减排信用风险对“预期碳税力度τ”具有敏感性,而碳税对“石油加工、炼焦及核燃料加工业”的冲击最为明显;第三,本文提出的MLP深度学习模型预测效果最佳,其预测准确率达到98.60%,相较于其他模型平均提升了15.57%,充分表明了该模型的可行性和实用价值。

基于数据挖掘的新旧动能转换成效监测体系研究

采用目前方法构建新旧动能转换成效监测体系时,没有对监测数据进行去噪处理,导致方法的监测精度和敏感度较低。为此,提出基于数据挖掘的新旧动能转换成效监测体系研究方法。首先确定构建新旧动能转换成效监测体系的具体步骤,其次在双树复小波原理的基础上对监测数据进行去噪处理,利用数据挖掘方法构建新旧动能转换成效监测指标体系,并对监测指标进行标准化处理,最后结合熵权法和非线性加权模型完成新旧动能转换成效的监测。实验结果表明所提方法的监测精度和敏感度较高。

时变对抗环境中基于双层加权估计的雷达干扰决策方法

如何在目标雷达策略实时变化的战场对抗环境中准确估计出干扰策略的收益,是有效评估干扰策略效果并不断优化干扰策略的关键之一。针对这一问题,在常见的收益估计方法的基础上,提出了一种时变对抗环境中基于双层加权估计的雷达干扰决策方法。该方法通过交叉验证、对收益均值进行加权估计和加权更新来估计所选策略平均收益,并根据策略权重和最大估计值选取最优策略。仿真实验表明,该方法对所选策略平均收益的估计值总体更接近于真实的最优策略收益均值,并能够根据对抗环境的变化,识别最优策略,并逐步提高对最优策略的选取概率。