高强混凝土高温性能研究进展

对近年来高强混凝土高温性能的研究成果进行了综述与分析,包括热工性、高温下和高温后力学性能以及高强高性能混凝土的爆裂性能。其中,高强混凝土热工性能方面的研究成果较少且研究结果离散性较大;高强混凝土力学性能方面的研究主要集中于抗压强度、抗拉强度、变形性能、弹性模量、泊松比、应力 应变关系等,而对高强混凝土结构、构件高温力学性能和温度场分析方面的研究较少;高强高性能混凝土爆裂方面的研究多集中于爆裂规律和爆裂抑制措施,对爆裂机理和微观结构的研究较少。本文分别从3个方面指出了高强混凝土高温性能研究中存在的问题和今后的研究方向,为深入研究高强混凝土的高温性能提供参考。     

我国企业FDI逆向知识转移效能评估模型构建

我国企业在发达国家FDI激增成为近年来的趋势,以创新资产寻求为动机的FDI更关注子公司逆向知识转移的效能。对影响逆向知识转移的因素已有很多学者进行了深入研究,在进行梳理的基础上,总结归纳出影响逆向知识转移效能的四大类因素,即母公司因素、子公司因素、双方关系因素、知识因素,运用网络层次分析法赋予各因素权重,构建可定量评估逆向知识转移效能的模型,最后据此提出我国企业提高逆向知识转移效能的建议。     

论电子场景下现代科学教育的重塑

历史上的科学教育经历了由“专才教育”向“通识教育”再到“通才教育”转变的过程。“通才教育”在现代社会发挥着重要的人才培养功能,但 IT(电子信息)技术在现代科学教育中所形成的特定“电子场景”使得科学教育致力于追求普遍显性知识的“通识教育”,一定程度上缺失了隐性意会知识的养成,而后者是现代“通才教育”所必需的。现代科学教育应该重塑“通才教育”,其本质是一种显性知识与隐性知识相结合的 STS 教育。     

双电机驱动自同步振动磨动力响应研究

以双电机驱动自同步振动磨为研究对象,对其进行简化处理,建立力学模型。通过对其施加X,y方向的简谐激 振力,建立无阻尼系统强迫振动和有阻尼系统强迫振动的数学模型,在此基础上,确定Rayleigh阻尼的常数α和β,利用 有限元ANSYS软件进行瞬态动力学分析,研究其在动载荷作用下的变形情况。研究结果表明：在工况频率16 Hz下,节 点运动轨迹更接近圆形,距理想粉碎效果的圆形轨迹较近,粉碎效果较好,进入稳定期的时间较快;在共振频率42 Hz 下,节点轨迹振幅更大,进入稳定期较慢,阻尼对振动的衰减作用较强;不同阻尼比对振动的衰减作用成正比关系。因 此,可以通过控制阻尼比等参数获得更有利的研磨效果,提高研磨效率。     

分布式室内移动机器人的定位与导航

针对当前移动机器人定位与导航成本较高的问题,提出了一种低成本、高性能的分布式导航方案。以性价比较 高的RPLIDAR激光传感器为核心,采用分布式软件的设计思想,设计了传感器的软件接口;采用DWA方法进行局部路 径规划。设计实现了移动机器人的定位与导航。实验结果表明该方案具有可行性与高效性。     

多相感应电机任意非正弦气隙磁密的磁动势分析与应用

多相感应电机转矩密度的提高依赖于非正弦气隙磁密行波的合理构建,由于传统的电流相位、幅值控制策略无 法给出期望的非正弦磁密行波,因此文章研究了控制多相感应电机定子相电流中基波和谐波幅值、相位、频率的大小来 构建期望的气隙磁密波形的方法、首先基于单根载流导体产生的气隙磁动势波形,推导了任意相数、不对称、包含任意 高次谐波电流波形激励的具有典型绕组结构的多相感应电机的磁动势分布表达式。根据以上磁动势分析结论,多相电 机可通过变电流相序变极。针对气隙磁密行波逼近方波波形的案例,根据所提出的磁动势分析方法分析和预测了理想 空载励磁电流的波形。将预测波形和方波供电下的励磁电流波形进行了比较,从FFT分析结果来看,仍需微调各次谐波 电压相位,从而实现各次电流相位完全一致。     

New Ridge Regression Estimator in Semiparametric Regression Models

In the context of ridge regression, the estimation of shrinkage parameter plays an important role in analyzing data. Many efforts have been put to develop the computation of risk function in different full-parametric ridge regression approaches using eigenvalues and then bringing an efficient estimator of shrinkage parameter based on them. In this respect, the estimation of shrinkage parameter is neglected for semiparametric regression model. Not restricted, but the main focus of this approach is to develop necessary tools for computing the risk function of regression coefficient based on the eigenvalues of design matrix in semiparametric regression. For this purpose the differencing methodology is applied. We also propose a new estimator for shrinkage parameter which is of harmonic type mean of ridge estimators. It is shown that this estimator performs better than all the existing ones for the regression coefficient. For our proposal, a Monte Carlo simulation study and a real dataset analysis related to housing attributes are conducted to illustrate the efficiency of shrinkage estimators based on the minimum risk and mean squared error criteria.     

Robust Inference for Near-Unit Root Processes with Time-Varying Error Variances

The autoregressive Cauchy estimator uses the sign of the first lag as instrumental variable (IV); under independent and identically distributed (i.i.d.) errors, the resulting IV t-type statistic is known to have a standard normal limiting distribution in the unit root case. With unconditional heteroskedasticity, the ordinary least squares (OLS) t statistic is affected in the unit root case; but the paper shows that, by using some nonlinear transformation behaving asymptotically like the sign as instrument, limiting normality of the IV t-type statistic is maintained when the series to be tested has no deterministic trends. Neither estimation of the so-called variance profile nor bootstrap procedures are required to this end. The Cauchy unit root test has power in the same 1/T neighborhoods as the usual unit root tests, also for a wide range of magnitudes for the initial value. It is furthermore shown to be competitive with other, bootstrap-based, robust tests. When the series exhibit a linear trend, however, the null distribution of the Cauchy test for a unit root becomes nonstandard, reminiscent of the Dickey-Fuller distribution. In this case, inference robust to nonstationary volatility is obtained via the wild bootstrap.     

Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation

Inverse probability weighting (IPW) and multiple imputation are two widely adopted approaches dealing with missing data. The former models the selection probability, and the latter models data distribution. Consistent estimation requires correct specification of corresponding models. Although the augmented IPW method provides an extra layer of protection on consistency, it is usually not sufficient in practice as the true data‐generating process is unknown. This paper proposes a method combining the two approaches in the same spirit of calibration in sampling survey literature. Multiple models for both the selection probability and data distribution can be simultaneously accounted for, and the resulting estimator is consistent if any model is correctly specified. The proposed method is within the framework of estimating equations and is general enough to cover regression analysis with missing outcomes and/or missing covariates. Results on both theoretical and numerical investigation are provided.     

Some maximum-indifference estimators for the slope of a univariate linear model

As known, the least-squares estimator of the slope of a univariate linear model sets to zero the covariance between the regression residuals and the values of the explanatory variable. To prevent the estimation process from being influenced by outliers, which can be theoretically modelled by a heavy-tailed distribution for the error term, one can substitute covariance with some robust measures of association, for example Kendall's tau in the popular Theil–Sen estimator. In a scarcely known Italian paper, Cifarelli [(1978), ‘La Stima del Coefficiente di Regressione Mediante l'Indice di Cograduazione di Gini’, Rivista di matematica per le scienze economiche e sociali, 1, 7–38. A translation into English is available at http://arxiv.org/abs/1411.4809 and will appear in Decisions in Economics and Finance] shows that a gain of efficiency can be obtained by using Gini's cograduation index instead of Kendall's tau. This paper introduces a new estimator, derived from another association measure recently proposed. Such a measure is strongly related to Gini's cograduation index, as they are both built to vanish in the general framework of indifference. The newly proposed estimator is shown to be unbiased and asymptotically normally distributed. Moreover, all considered estimators are compared via their asymptotic relative efficiency and a small simulation study. Finally, some indications about the performance of the considered estimators in the presence of contaminated normal data are provided.