基于FPGA的可重构数控系统

利用FPGA丰富的逻辑资源及其内部可编程的特点,文章提出了可重构数控系统硬件设计方法及以FPGA为控制核心的数控系统设计方案,重点分析了对电机的速度控制及与PC机的串行通信等,开发了以FPGA为控制核心的经济型数控系统。其响应速度快,可重构性强,大大减少了系统的外围接口器件,同时有效降低了系统成本。     

基于ANSYS/LS DYNA的移动式压力容器动态碰撞仿真分析

运用有限元和瞬态动力学的有关理论和方法对移动式压力容器受碰撞后的安全性进行了研究。首先采用三维建模软件建立了移动式压力容器运输车的实体模型,然后分别利用有限元分析软件ANSYS和瞬态动力学仿真软件LS DYNA对模型进行前处理分析和求解计算,分别得到车速为60 km/h车尾受到碰撞时车架、压力容器罐体、组合支座及相关辅件的动态碰撞应力响应情况。计算结果表明：车架及连接螺栓失效,但有效降低了对压力容器罐体的冲击,基本保证了其安全性,并为今后的优化设计提供了理论支撑。     

Sample Splitting and Threshold Estimation

Threshold models have a wide variety of applications in economics. Direct applications include models of separating and multiple equilibria. Other applications include empirical sample splitting when the sample split is based on a continuously‐distributed variable such as firm size. In addition, threshold models may be used as a parsimonious strategy for nonparametric function estimation. For example, the threshold autoregressive model (TAR) is popular in the nonlinear time series literature. Threshold models also emerge as special cases of more complex statistical frameworks, such as mixture models, switching models, Markov switching models, and smooth transition threshold models. It may be important to understand the statistical properties of threshold models as a preliminary step in the development of statistical tools to handle these more complicated structures. Despite the large number of potential applications, the statistical theory of threshold estimation is undeveloped. It is known that threshold estimates are super‐consistent, but a distribution theory useful for testing and inference has yet to be provided. This paper develops a statistical theory for threshold estimation in the regression context. We allow for either cross‐section or time series observations. Least squares estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates (the threshold and the regression slopes) is developed. It is found that the distribution of the threshold estimate is nonstandard. A method to construct asymptotic confidence intervals is developed by inverting the likelihood ratio statistic. It is shown that this yields asymptotically conservative confidence regions. Monte Carlo simulations are presented to assess the accuracy of the asymptotic approximations. The empirical relevance of the theory is illustrated through an application to the multiple equilibria growth model of Durlauf and Johnson (1995).     

一种新的鲁棒自适应模糊控制器的设计及性能分析

针对一类连续时间非线性动态系统,根据李亚普诺夫分析方法设计了一种鲁棒自适应模糊控制器．这种自适应系统在受到有界的外部干扰或存在建模误差时具有鲁棒性．证明了控制算法是全局稳定的,跟踪误差可以收敛到零的一个邻域内     

关于极大η-单调映象的完全广义Fuzzy隐拟变分包含

本文介绍了一类关于极大,η-单调映象的完全广义拟变分包含问题,利用预解算子技巧研究了这类变分包含解的迭代算逼近,证明了解的存在性以及由算法生成的迭代序列的收敛性。     

非单调二元非线性算子方程组的迭代求解

利用锥与半序理论和混合单调算子理论 ,讨论 Banach空间中非单调二元非线性算子方程组解的存在性与唯一性 ,并给出收敛于方程组解的迭代序列和误差估计 ,改进和推广了混合单调算子方程和一元算子方程的某些相应结果 .     

T型非线性电阻网络功率转移图的图解法

本文论述了利用非线性电阻的伏安特性,作出T型非线性电阻网络中任一元件的功率转移图的图解法．     

一类地下水水质污染问题广义解的唯一性及对初值和自由项的连续依赖性

本文讨论了一类地下水水质污染问题,其数学模型为一组非线性双曲—抛物耦合方程组,并证明了在相应初边值条件下广义解的唯一性及对初值和自由项的连续依赖性．     

Statistical diagnostics for nonlinear regression models based on scale mixtures of skew-normal distributions

The purpose of this paper is to develop diagnostics analysis for nonlinear regression models (NLMs) under scale mixtures of skew-normal (SMSN) distributions introduced by Garay et al. [Nonlinear regression models based on SMSN distributions. J. Korean Statist. Soc. 2011;40:115–124]. This novel class of models provides a useful generalization of the symmetrical NLM [Vanegas LH, Cysneiros FJA. Assessment of diagnostic procedures in symmetrical nonlinear regression models. Comput. Statist. Data Anal. 2010;54:1002–1016] since the random terms distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as the skew-t, skew-slash, skew-contaminated normal distributions, among others. Motivated by the results given in Garay et al. [Nonlinear regression models based on SMSN distributions. J. Korean Statist. Soc. 2011;40:115–124], we presented a score test for testing the homogeneity of the scale parameter and its properties are investigated through Monte Carlo simulations studies. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. The newly developed procedures are illustrated considering a real data set.     

Structural Nonparametric Cointegrating Regression

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill‐posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near‐integrated time series, it is shown here that inverse and ill‐posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. It is further shown that use of augmented regression, as is common in linear cointegration modeling to address endogeneity, does not lead to bias reduction in nonparametric regression, but there is an asymptotic gain in variance reduction. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near‐integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required.