稳压电源的自适应去火弧技术

针对传统基于继电器控制的稳压电源在带负载工作时,档位切换常常伴随火弧现象的问题,设计了一种可广泛 应用于此类稳压电源上的自适应去火弧技术：使用光耦和多级运放完成对零点和电压电流的采样;基于单片机的10量, 实现对继电器的驱动和控制;基于RCC自激震荡原理为系统提供稳定电源;设计了稳压器的完整软件系统程序,并在软 件中应用自适应去火弧算法。实验结果表明：自适应去火弧技术,能够有效地规避火弧现象的产生,并能够根据条件作 出调整。采用自适应去火弧技术的稳压系统达到了设计目标,延长了系统工作寿命。     

基于高斯图的截面数据精确分段及重构

针对反求工程中截面重构时由于分段点提取不精确造成重构结果不理想的问题,提出一种分段点精确提取方 法。基于截面数据的法向量信息构建高斯图,将分段点确定在一个区间内,根据区间端点将截面数据分为不同的特征 段;优先重构直线特征,再根据约束重构自由特征,并在自由特征重构过程中建立动态搜索模型,用二次插值法动态搜索 精确分段点。实验表明该方法提高了分段点的提取精度和截面重构的精度,重构的结果很好地还原了产品模型的初始 设计意图。     

基于G1连续的圆弧与B样条分段区间确定及重构方法

在逆向工程中,截面曲线重构质量的高低决定了能否更好地反映物体的初始设计意图。高精度分段点的提取则 是提高重构质量的关键。针对圆弧与B样条曲线重构过程中,无法确定分段点所在区间问题,提出了圆弧与B样条线 性化处理方法,并结合基于数理统计原理的分段点区间确定办法和基于黄金分割法的重构方法,提取了圆弧与B样条间 的高精度分段点。实例证明该方法能在现存数据之间搜寻精度更高的分段点。该方法有效地解决了圆弧与B样条高精 度分段点提取问题。     

Local influence diagnostics for incomplete overdispersed longitudinal counts

We develop local influence diagnostics to detect influential subjects when generalized linear mixed models are fitted to incomplete longitudinal overdispersed count data. The focus is on the influence stemming from the dropout model specification. In particular, the effect of small perturbations around an MAR specification are examined. The method is applied to data from a longitudinal clinical trial in epileptic patients. The effect on models allowing for overdispersion is contrasted with that on models that do not.     

计算机网络技术应用下的外语词汇教学模式研究

鉴于词汇教学在整个外语教学中的重要性及目前外语词汇教学所面临的问题,创建系统性外语词汇教学模式,并与计算机网络技术的应用相结合,来促进教师的外语词汇教学,进而提高学生的词汇学习效率。系统性外语词汇教学模式涉及三个主要教学阶段：课前预习、课堂教学和课后复习,而这三个教学阶段将与一系列新设计的词汇认知过程进行有机的结合。同时,还将尝试通过计算机网络技术的应用来优化新创建的系统性外语词汇教学模式。教师可以利用计算机网络技术为学生提供丰富的学习资源,并帮助他们充分运用各种词汇学习策略,从而调动学生对重点词汇学习的兴趣,进而提升教学效果。     

省域经济洼地区域中心城市发展战略研究——以江苏淮安为例

省域经济洼地地区是实现区域协调发展的重要板块和关键区域。以江苏省淮安市为例,从战略角度审视淮安建设成为苏北重要中心城市的意义和基础,并提出淮安在区域经济和流域经济层面上两个维度的发展定位和三个方向的发展路径。通过纵向融入京沪高铁和京杭运河两条复合发展轴,横向建设淮河流域生态经济带,打造苏皖在江北地区的跨界合作示范区等战略,推动淮安苏北重要中心城市的建设和省域经济协调发展格局的形成。     

Zero-inflated sum of Conway-Maxwell-Poissons (ZISCMP) regression

While excess zeros are often thought to cause data over-dispersion (i.e. when the variance exceeds the mean), this implication is not absolute. One should instead consider a flexible class of distributions that can address data dispersion along with excess zeros. This work develops a zero-inflated sum-of-Conway-Maxwell-Poissons (ZISCMP) regression as a flexible analysis tool to model count data that express significant data dispersion and contain excess zeros. This class of models contains several special case zero-inflated regressions, including zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), zero-inflated binomial (ZIB), and the zero-inflated Conway-Maxwell-Poisson (ZICMP). Through simulated and real data examples, we demonstrate class flexibility and usefulness. We further utilize it to analyze shark species data from Australia's Great Barrier Reef to assess the environmental impact of human action on the number of various species of sharks.     

Improved two-parameter estimators for the negative binomial and Poisson regression models

Negative binomial regression (NBR) and Poisson regression (PR) applications have become very popular in the analysis of count data in recent years. However, if there is a high degree of relationship between the independent variables, the problem of multicollinearity arises in these models. We introduce new two-parameter estimators (TPEs) for the NBR and the PR models by unifying the two-parameter estimator (TPE) of Özkale and Kaç?ranlar [The restricted and unrestricted two-parameter estimators. Commun Stat Theory Methods. 2007;36:2707–2725]. These new estimators are general estimators which include maximum likelihood (ML) estimator, ridge estimator (RE), Liu estimator (LE) and contraction estimator (CE) as special cases. Furthermore, biasing parameters of these estimators are given and a Monte Carlo simulation is done to evaluate the performance of these estimators using mean square error (MSE) criterion. The benefits of the new TPEs are also illustrated in an empirical application. The results show that the new proposed TPEs for the NBR and the PR models are better than the ML estimator, the RE and the LE.