Constitutive equations of 1060 pure aluminum based on modified double multiple nonlinear regression model

In order to study the work-ability and establish the optimum hot formation processing parameters for industrial 1060 pure aluminum, the compressive deformation behavior of pure aluminum was investigated at temperatures of 523?823 K and strain rates of 0.005?10 s?1 on a Gleeble?1500 thermo-simulation machine. The influence rule of processing parameters (strain, strain rate and temperature) on flow stress of pure aluminum was investigated. Nine analysis factors consisting of material parameters and according weights were optimized. Then, the constitutive equations of multilevel series rules, multilevel parallel rules and multilevel series &parallel rules were established. The correlation coefficients (R) are 0.992, 0.988 and 0.990, respectively, and the average absolute relative errors (AAREs) are 6.77%, 8.70% and 7.63%, respectively, which proves that the constitutive equations of multilevel series rules can predict the flow stress of pure aluminum with good correlation and precision.

Effect of Correlation Level on the Use of Auxiliary Variable in Double Sampling for Regression Estimation

While an auxiliary information in double sampling increases the precision of an estimate and solves the problem of bias caused by non-response in sample survey, the question is that, does the level of correlation between the auxiliary information x and the study variable y ease in the accomplishment of the objectives of using double sampling? In this research, investigation was conducted through empirical study to ascertain the importance of correlation level between the auxiliary variable and the study variable to maximally accomplish the importance of auxiliary variable(s) in double sampling. Based on the Statistics criteria employed, which are minimum variance, coefficient of variation and relative efficiency, it was established that the higher the correlation level between the study and auxiliary variable(s) is, the better the estimator is.

Seasonal responses of monthly mean sea levels in the Bohai Sea to hydrometeorological forcing and their double screening regression models

-In this paper, the maximum entropy spectral, the cross-spectral and the frequency response analyses are madeon the basis of the data of monthly mean sea levels at coastal stations in the Bohai Sea during 1965-1986. The results show that the annual fluctuations of the monthly mean sea levels in the Bohai Sea are the results of the coupling response of seasonal variations of the marine hydrometeorological factors. Furthermore, the regression prediction equation is obtained by using the double screening stepwise regression analysis method . Through the prediction test , it is proved that the obtained results are desirable.

Intelligent process expert database of double pulse MIG welding of AI-Si alloy

Based on double pulse welding process characteristics, expert database structure and work flow are designed. Further, multiple outstanding specifications of 1.0 ram-diameter wire are obtained through a large number of experiments. By making non-linear regression analysis on these groups of standards, the relationship between average welding current and other pulse parameters can be found out. Polynomial regression equation is set up for further realization of＂ parameter estimation function of the expert database. Finally, the preliminary developed expert database is tested. The result indicates that the unified adjusting and parameters estimation of the expert database leads to stable welding process and good weld appearance.

Double Penalized Quantile Regression for the Linear Mixed Effects Model

This paper proposes a double penalized quantile regression for linear mixed effects model,which can select fixed and random effects simultaneously.Instead of using two tuning parameters,the proposed iterative algorithm enables only one optimal tuning parameter in each step and is more efficient.The authors establish asymptotic normality for the proposed estimators of quantile regression coefficients.Simulation studies show that the new method is robust to a variety of error distributions at different quantiles.It outperforms the traditional regression models under a wide array of simulated data models and is flexible enough to accommodate changes in fixed and random effects.For the high dimensional data scenarios,the new method still can correctly select important variables and exclude noise variables with high probability.A case study based on a hierarchical education data illustrates a practical utility of the proposed approach.

Bayesian quantile semiparametric mixed-effects double regression models

Semiparametric mixed-effects double regression models have been used for analysis of longitu-dinal data in a variety of applications,as they allow researchers to jointly model the mean and variance of the mixed-effects as a function of predictors.However,these models are commonly estimated based on the normality assumption for the errors and the results may thus be sensitive to outliers and/or heavy-tailed data.Quantile regression is an ideal alternative to deal with these problems,as it is insensitive to heteroscedasticity and outliers and can make statistical analysis more robust.In this paper,we consider Bayesian quantile regression analysis for semiparamet-ric mixed-effects double regression models based on the asymmetric Laplace distribution for the errors.We construct a Bayesian hierarchical model and then develop an efficient Markov chain Monte Carlo sampling algorithm to generate posterior samples from the full posterior dis-tributions to conduct the posterior inference.The performance of the proposed procedure is evaluated through simulation studies and a real data application.

“双减”背景下河北省中小学生焦虑抑郁症状现状

目的:调查“双减”背景下河北省中小学生焦虑抑郁症状的影响因素、关键症状及容易导致共病的症状,为制定中小学生焦虑抑郁症状预防与干预工作提供参考依据。方法:使用一般资料调查表、抑郁症状群量表(PHQ-9)、广泛性焦虑量表(GAD-7)对河北省6个地区48所的4413名中小学进行问卷调查。结果:中小学生焦虑和抑郁症状检出率分别为13.7%、10.4%,共病检出率为6.3%。二元Logistic回归结果显示,焦虑和抑郁症状的共同危险因素为女生、住宿、感到学习压力大、受过欺凌和睡不着觉(P<0.05),其中焦虑症状的危险因素为初中和独生子女(P<0.01),抑郁症状的危险因素为城镇、双亲家庭、每日上网时间>2小时和对学校感到不适应(P<0.05)。网络结构中预期影响较高的症状为GAD4“很难放松”(Z=1.09)、PHQ6“感觉自己糟糕或让其他人失望”(Z=1.06)、PHQ1“兴趣下降”(Z=1.05);桥梁预期影响较高的3个症状分别为PHQ9“存在消极观念”(Z=0.54)、PHQ1“兴趣下降”(Z=0.53)、GAD7“害怕将有可怕的事发生”(Z=0.46)。结论:“双减”背景下河北省焦虑和抑郁症状的检出率较低,但仍有待进一步降低。二者共同危险因素为女生、住宿、感到学习压力大、受过欺凌和睡不着觉,其中焦虑症状的危险因素为初中和独生子女,抑郁症状的危险因素城镇、双亲家庭、每日上网时间>2小时和对学校感到不适应。焦虑抑郁症状网络中的关键症状为GAD4、PHQ6、PHQ1,传递风险较高的桥梁症状为PHQ9、PHQ1、GAD7。

基于SVM的富水岩溶区双线盾构隧道下穿既有线临界安全距离研究

双线盾构隧道在极复杂的岩溶地质中下穿既有线施工时,会对地层造成较大扰动,从而增加既有地表沉降过大或坍塌的风险,严重威胁既有线的运营安全。同时,隧道穿越富水岩溶区域时,需要保持一定距离以避免与溶洞相交,确保双线盾构隧道围岩的稳定性。利用FlAC^(3D)软件建立不同工况组合下双线盾构隧道与左侧溶洞的临界安全距离数值模型,分析不同地下水位对既有线沉降和围岩塑性区范围的影响,并采用正交试验和多元线性回归方法建立双线盾构隧道在不同围岩等级、地下水埋深、开挖隧道埋深和溶洞孔径组合下与溶洞的临界安全距离计算公式。此外,还基于SVM模型对正交试验结果进行机器学习,得到可以预测岩溶地区双线盾构隧道临界安全距离的智能预测系统,实现双线盾构隧道下穿既有线临界安全距离的非线性智能预测。结果表明:当地下水位位于既有线以上时,其水位越高,既有线的沉降量越大、围岩塑性区范围越大,地下水对盾构隧道与溶洞的临界安全距离有很大影响,应用SVM模型和多元线性回归方法确定双线盾构隧道与溶洞之间的临界安全距离是行之有效的。

两维异质性面板分位数模型的双惩罚回归方法

文章关注系数具有两维异质性结构的面板分位数模型,基于SCAD惩罚函数和MCP惩罚函数提出双惩罚最小加权绝对偏差目标函数,同时进行参数估计和两维异质性结构识别。利用ADMM算法求解目标函数,并使用BIC信息准则通过网格搜索选择最优调节参数。根据蒙特卡洛模拟结果验证了所提方法的有限样本性质,最后使用实际数据检验了其应用效果。研究结果表明:所提出的方法能够准确识别两维异质性结构,并且Post估计量的参数估计精确度接近于Oracle估计量。

外置耗能角钢的摇摆-自复位桥梁双柱墩地震易损性分析

为评估设置耗能角钢的摇摆-自复位桥梁双柱墩的抗震能力,本文建立该结构的有限元分析模型,分别定义和量化双柱墩不同损伤状态和损伤指标。本文从太平洋地震工程研究中心选取16条近断层地震动,基于增量动力分析,得到该结构的地震响应。基于最小二乘法,对得到的增量动力分析曲线进行对数回归分析,计算结构不同损伤状态的超越概率及易损性曲线。研究表明:当地面峰值加速度为0.1 g时,外置耗能角钢与耗能钢筋屈服的超越概率分别为34%和22%;当峰值加速度为0.4 g时,外置耗能角钢与耗能钢筋失效的超越概率分别为6%和11%,而预应力筋未屈服;当峰值加速度为1.0 g时,外置耗能角钢和耗能钢筋失效的概率分别达到85%和91%,桥墩发生倒塌的概率为46%。

黄河水沙监测数据分析模型

为研究黄河小浪底水库下游某水文站水沙通量的变化规律和“调水调沙”效果,以2016-2022年水位、水流量与含沙量的实际监测数据为依据,估算出年均径流量325亿立方米,年均排沙量2亿吨.分别使用双对数回归分析、M-K检验、小波分析、R/S分析对水沙通量的敏感性、突变性、周期性、趋势性进行了分析,得出结论:水位每增加1%,含沙量就增加37.63%,含沙量关于水位非常敏感;水沙通量均在2018年3月发生了突变;水沙通量均以18月为主周期振荡;水通量、沙通量在未来2年分别是增长趋势和下降趋势.使用傅里叶级数叠加线性函数预测未来2年的水沙通量,并建立了0-1规划模型对今后2年的监测方案进行了优化.通过比较“调水调沙”前后水沙通量、河底高程的变化,证明了“调水调沙”效果非常显著.

计及风力发电机转速安全约束的DFIG一次调频模型预测控制策略

针对传统方法控制双馈感应发电机(DFIG)的调频效果过于依赖参数整定的问题,该文提出了一种基于模型预测控制(MPC),计及转速安全约束的风力发电机参与一次调频控制策略。该方法结合风力发电机动力学模型与频率响应模型建立预测模型,通过线性回归方法变参考值将风力发电机转速的稳定性纳入考虑。相较于虚拟惯性控制等方法将系统频率偏差与转子动能直接关联,该文方法通过建立全新的预测模型,实时统筹考虑整个系统的状态信息,无需反复调整参数,在考虑转速安全性的同时兼顾全局性能。最后,通过实际算例分析验证了该文调频策略的有效性以及相对现有方法的优越性。

改性双基推进剂机械感度的预测方法

为了对改性双基推进剂的机械感度进行预测,通过BAM撞击装置和摩擦感度仪对其进行试验,得到了其临界撞击能量E和临界摩擦载荷F_(s);基于单质炸药氧平衡指数与撞击感度的相关性探究了改性双基推进剂氧平衡指数与机械感度之间的关系,并将改性双基推进剂看作硝基含能化合物,选取氧平衡指数、N—NO_(2)、O—NO_(2)、—COOR和苯环作为分子结构描述符,进行多元线性回归建立机械感度预测模型并计算了改性双基推进剂的E和F_(s),随机选取检验样本对模型进行检验。结果表明,氧平衡指数OB_(100)与E和F_(s)均呈现较好的线性关系,可以对改性双基推进剂的机械感度进行判断;基于分子结构描述符建立的E和F_(s)预测模型具有较高的相关系数,分别为0.963和0.983;同时其检验样本的均方根误差(RMSE)与平均绝对百分误差(MAPE)分别为0.40J和10.27%与8.33N和10.83%。

双参数Tweedie机器学习模型及其精算应用

Tweedie回归是保险损失预测和风险定价的主要工具之一。为充分利用大数据、物联网、机器学习等技术促进保险业的数字化转型,实现更加精准的风险识别和风险定价,本文将传统的Tweedie广义线性模型推广到双参数形式,并结合机器学习算法,提出双参数Tweedie梯度提升树模型和双参数Tweedie组合神经网络模型。基于我国一家保险公司的车联网大数据,提取了新的驾驶行为风险因子。通过实证研究检验了双参数Tweedie梯度提升树和双参数Tweedie组合神经网络在风险识别以及风险定价中的有效性,为促进我国保险业数字化转型提供了一种新的模型和方法。

基于双脉冲LIBS结合偏最小二乘回归的稀土元素定量分析

激光诱导击穿光谱(LIBS)是一种以激光为激发源的等离子体发射光谱分析技术,已有将其用于稀土元素的定量分析研究,但由于稀土矿基体差异大、元素含量低,定量分析灵敏度和准确度仍有待提高。通过使用单激光分束构造双脉冲LIBS系统,并结合偏最小二乘回归(PLSR)算法实现对稀土矿石样品中的稀土元素La、Dy、Yb和Y的定量分析。结果表明,双脉冲LIBS结合PLSR可建立更加稳定的定标模型,与常规基本定标法相比,La、Dy、Yb和Y元素的相对均方根预测误差(RMSEP)从0.0061%、0.0037%、0.0045%、0.0280%降低至0.0044%、0.0016%、0.0029%、0.0134%,平均相对预测误差(AREP)从10.88%、15.27%、6.42%、17.20%降低至6.67%、3.62%、4.10%、7.98%。因此,双脉冲LIBS结合PLSR方法可以有效地提高LIBS对稀土矿石中稀土元素的定量分析能力。

区域双碳目标与预测研究

以区域双碳目标为主线,综合运用了德尔菲法、差异性分析法和多元线性回归模型,深入研究和实施可持续发展与低碳经济的策略。德尔菲法汇集多领域专家意见,建立区域双碳目标共识,为碳排放规划提供基础;差异性分析法用于识别不同地区的碳排放特点、趋势和关键领域,为个性化碳排放路径的设计提供依据;多元线性回归模型解析碳排放与各影响因素的关系,为制定决策提供数据支持。通过整合3种方法,为实现区域双碳目标提供综合性工具,促进可持续发展和碳排放的有机结合。这一综合方法不仅有助于实施碳排放目标,还可以适应不同地区的需求,为未来可持续发展提供理论依据。

超高维数据下部分线性可加分位数回归模型的变量选择

在超高维数据中,一方面,协变量的维数可能远远大于样本量,甚至随着样本量以指数级的速度增长;另一方面,超高维数据通常是异质的,协变量对条件分布中心的影响可能与他们对尾部的影响大不相同,甚至会出现重尾以及异常点的复杂情况。文章在协变量维度发散且为超高维的情况下研究了部分线性可加分位数回归模型的变量选择和稳健估计问题。首先,为了实现模型的稀疏性和非参数光滑性,引入了一种非凸Atan双惩罚,并采用分位迭代坐标下降算法来解决所提方法的优化问题。在选择适当正则化参数的情况下,证明了所提双惩罚估计量的理论性质。其次,通过模拟研究对所提方法的性能进行验证。模拟结果表明,所提方法比其他惩罚方法具有更好的表现,尤其是在数据存在重尾的情况下。最后,通过基于癌症筛查病人血液样本数据的实证来验证所提方法的实用性。

“双碳”目标下中国能源需求未来走势预测分析

“双碳”目标下准确掌握能源需求对社会经济发展至关重要。文章采用灰色相对关联度对各变量间的相关性进行定量刻画,并诊断自变量是否存在明显多重共线性;运用逐步回归分析法精细化分析能源需求影响因素,挖掘能源需求有效驱动因素,构建“最佳”能源需求函数模型。结果表明:(1)能源价格、消费水平和经济发展水平是中国能源需求的有效驱动因素。(2)基于有效影响因素建立的“最佳”逐步回归双对数能源需求函数模型具有良好的预测性能,可用于中国能源中长期需求预测,预测结果可作为科学制定能源政策的重要参考依据。(3)未来20年中国能源需求增长量主要来源于清洁能源需求量的增长,到2040年中国能源需求量约62.6亿吨标准煤,年均增长率约0.8%。

Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussian process regression

Lithium-ion batteries(LIBs)are widely used in transportation,energy storage,and other fields.The prediction of the remaining useful life(RUL)of lithium batteries not only provides a reference for health management but also serves as a basis for assessing the residual value of the battery.In order to improve the prediction accuracy of the RUL of LIBs,a two-phase RUL early prediction method combining neural network and Gaussian process regression(GPR)is proposed.In the initial phase,the features related to the capacity degradation of LIBs are utilized to train the neural network model,which is used to predict the initial cycle lifetime of 124 LIBs.The Pearson coefficient’s two most significant characteristic factors and the predicted normalized lifetime form a 3D space.The Euclidean distance between the test dataset and each cell in the training dataset and validation dataset is calculated,and the shortest distance is considered to have a similar degradation pattern,which is used to determine the initial Dual Exponential Model(DEM).In the second phase,GPR uses the DEM as the initial parameter to predict each test set’s early RUL(ERUL).By testing four batteries under different working conditions,the RMSE of all capacity estimation is less than 1.2%,and the accuracy percentage(AP)of remaining life prediction is more than 98%.Experiments show that the method does not need human intervention and has high prediction accuracy.

“双减”背景下中小学生抑郁影响因素研究——基于我国六省10698位学生的调查数据分析

分析“双减”背景下中小学生抑郁影响因素,可为“双减”相关的理论与实践工作提供一定的借鉴。我国六省10698位学生的调查数据显示:“双减”背景下,中小学生抑郁总体状况较好;学业焦虑、校园欺凌、每天写作业时间、每天参加校外培训时间等显著正向影响抑郁;对未来的信心、自尊、父母情感支持、亲子关系、师生关系等显著负向影响抑郁。对此,需加强家校合作,及时干预中小学生抑郁,持续执行“双减”政策,建立良好的亲子关系、和谐的师生关系。