A Sequential Shrinkage Estimating Method for Tobit Regression Model

In the applications of Tobit regression models we always encounter the data sets which contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “non-effective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model by using an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size based on Tobit regression model. Fixed design is considered for numerical simulation.

一种基于机器视觉的平面加工机床控制系统的设计

针对服装、包装等加工行业中须将人工测量的纸质图纸或模型样件的尺寸信息录入计算机并转换成电子加工图纸而导致的加工周期长、生产效率低的问题,提出了一种基于机器视觉的平面加工机床控制系统,以实现对纸质图纸或模型样件的快速检测。采用“ARM+DSP”方式搭建了主从式运动控制系统,设计了系统各部分功能模块。构建了“工控机+工业CCD (charge coupled device,电荷耦合器件)相机+光源控制”的视觉检测系统,结合FAWS (feature adaptive wavelet shrinkage,自适应特征的小波收缩)算法和麻雀搜索算法提出一种改进的FAWS算法进行图像降噪,并采用Canny算法进行图像边缘检测,实现图像轮廓的准确提取。设计了图像轮廓提取、轮廓数据转换为加工数据、数据通信等处理程序,实现了基于机器视觉的快速检测以及在系统加工过程中的人机交互。最后,对系统进行了实验测试,对实际加工效果进行了评价。结果表明,采用所研制的平面加工机床控制系统不仅能显著提高生产效率,而且能减小图像轮廓的误差。其性能稳定可靠,具有一定的工程实用价值。