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。     

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.     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

模具钢干铣削试验及参数优化研究

为了对铣削力做进一步的研究,以及预测铣削参数的改变对铣削力变化的影响,文章建立了铣削力预测模型,引 入了PSO优化算法。试验采用正交设计方法,干式铣削SKD61模具钢;KISTLER测力仪测量铣削力;HRsoft_DW数采软 件采集试验数据,并对数据进行极差分析。研究结果表明每齿进给量是铣削参数中影响铣削力最为主要的因素。研究 验证了PSO算法对铣削参数优化问题具有有效性。     

搅拌釜内双层改进型INTER-MIG桨流场实验研究

为得到双层INTER-MIG桨搅拌釜内流场特征,文章采用粒子图像测速技术(PIV)对直径为430 mm的双层改进 型INTER-MIG桨搅拌槽内流场进行了实验研究。分析了搅拌转速、相位角及轴向位置对搅拌槽内流场分布的影响。结 果表明：整个流场表现出较强轴流特性,底桨右端形成一个漩涡;转速增加引起桨叶附近湍动增强,但整体流型变化不 大;当r/R <0.48时,桨叶间轴流效果显著,有利于釜底流体向上抬升,增强整体混合效果;当r/R>0.48时,桨叶右端在 近壁面处会形成上下2股流动流体,有助于形成局部区域循环。实验结果可为工业搅拌过程提供工艺参考。     

口述史的复调价值及其实现

布罗代尔认为,理想的历史应该是多声部的。现代意义上的口述史的出现使基于文献书写的历史的单声部特征显露无遗。口述史不再是现代社会“沉默的大多数”发出的声音。以口头陈述为基础的口述史的叙事和以文献为基础的历史的书写都同样是有关历史事实的观念的反映。各具独立旋律的叙事与书写理应建立起和谐的关系而形成复调历史。这种复调历史并非杜赞奇基于话语分析方法,以解构民族国家单线叙事为目的的复线历史。口述史与历史二者之间互补互益的差异使反思性知识与批判性知识可以对过去的历史进行检讨并对历史事实进行更为深刻的理解。借鉴历史研究方法及滥觞于研究无文字民族的民族志方法是解决口述史实践中存在的诸多问题的重要路径。     

魏晋六朝“自然”论文学观的确立——论《文心雕龙》的“文道自然”观

以"自然"论文学,汉朝<淮南子>已有记载,但只言片语,语焉不详,直到魏晋鸿篇<文心雕龙>才有了真正意义上的"自然"论文学观.刘勰以"自然之道"来阐释文学创作的根本原则,从哲学本体论高度论证"文"之产生与存在的必然性、"文"与"质"的本质统一关系,并以此为核心探讨了文学创作之发生、文学风格之生成、创作自由之实现等一系列重要问题,建立了相当系统化的"文道自然"观.     

股票供给曲线凸现向后弯曲与理性投资决策

股票市场价格的高低及变动是对股票内在价值高低及变动的综合反映和表现,其价格的形成直接取决于市场的供求关系,而影响和调节股票供求量的并不是单位股票的绝对价格水平的高低及变动,它是股票作为投资品的单位内在价值的市场价格水平的高低及变动。由于股票收入效应和替代效应的存在又使股票供给曲线向后弯曲提升了股票的内在价值,这一表象成为引起股价波动背后的主要理性因素,从而为投资决策提供理性依据。