Difference Detection Between Two Images for Image Monitoring

In manufacturing industries, images are commonly used for quality control purposes. In such applications, if the quality of the products is good, then their images should be all similar to the image of a good-quality product. Therefore, comparison of images is a fundamental task in image-based quality control. This problem, however, is complicated in the sense that (1) observed images often contain noise, and (2) the related images need to be geometrically matched up first because images of different products could be geometrically mismatched because the relative positions between a camera and different products are often not exactly the same. The first issue requires a statistical method that can remove noise, and the second issue is related to the so-called image registration problem in the image processing literature. In this article, we propose effective methods for detecting difference between two images of products, and our proposed methods can accommodate both noise and geometric mismatch mentioned above. Theoretical results and numerical examples show that they can work effectively in applications.     

爆破机理的推墙假说研究

在长期的露天矿台阶爆破实践中，发现前排孔抵抗线过小是爆破产生极底的主要原因之一。作者在几个基本命题的基础上，提出了爆破机理的推墙假说，及以此为理论基础的前排孔加密爆破技术，推墙假说认为，两孔间的贯穿是两孔中炸药的爆炸力向自由面合推墙效应的结果，且要求岩墙厚度适度，均匀，有充足的补偿空间，且施力均衡。而大区微差爆破则是把一堵厚岩墙分成若干个薄岩墙，然后分别推倒，所以，微差挤压爆破不如前排孔退后且加密。还指出前排孔加密爆破技术应采用三角形布孔，排间起爆，以便达到合推的效果。     

On “Critical Period Hypothesis” and demonstrate the irrationality of English learning begins in the Early Childhood in China

This paper first introduces the “Critical Period Hypothesis”(CPH) in L2 acquisition and the view of its supporters. However, it raised the opposite views from many other scholars and made them using th...     

A simultaneous sample-and-filter strategy for robust multi-structure model fitting

In many robust model fitting methods, obtaining promising hypotheses is critical to the fitting process. However the sampling process unavoidably generates many irrelevant hypotheses, which can be an obstacle for accurate model fitting. In particular, the mode seeking based fitting methods are very sensitive to the proportion of good/bad hypotheses for fitting multi-structure data. To improve hypothesis generation for the mode seeking based fitting methods, we propose a novel sample-and-filter strategy to (1) identify and filter out bad hypotheses on-the-fly, and (2) use the remaining good hypotheses to guide the sampling to further expand the set of good hypotheses. The outcome is a small set of hypotheses with a high concentration of good hypotheses. Compared to other sampling methods, our method yields a significantly large proportion of good hypotheses, which greatly improves the accuracy of the mode seeking-based fitting methods.     

安徽某铁精粉细度合格率影响因素分析

以某选矿厂2013年4月份的生产数据为依据,研究了生产过程中原矿处理量、自磨浓度、球磨浓度、分级机溢流浓度以及矿石品位对铁精粉细度合格率的影响。通过研究发现,原矿处理量、自磨浓度、球磨浓度的变化对铁精粉细度合格率影响较小,而分级机溢流浓度和矿石品位的变化对铁精粉细度合格率影响较大,铁精粉细度合格率大致随着溢流浓度和铁矿石品位的增大而减小。     

基于高斯混合势化概率假设密度的脉冲多普勒雷达多目标跟踪算法

为在新兴的随机有限集(RFS)框架下充分利用多普勒信息跟踪杂波环境下的多目标,该文提出基于高斯混合势化概率假设密度(GM-CPHD)的脉冲多普勒雷达多目标跟踪(MTT)算法.该算法在标准GM-CPHD基础上,在使用位置量测更新状态后,再利用多普勒量测进行序贯更新,可获得更精确的似然函数和状态估计.仿真结果验证了该算法的有效性,表明在GM-CPHD基础上引入目标的多普勒信息可有效抑制杂波,显著改善跟踪性能.     

Geometric precision analysis for Additive Manufacturing processes: A comparative study

Additive Manufacturing (AM) has recently attracted increasing attention among manufacturing industries. This class of technologies is capable of creating parts with complex shapes and intricate structures. However, the poor geometric quality of the parts they produced is a major constraint in wide industrial adoption. Currently available analytical techniques based on classic measurement equipment could fail in analyzing the process parameters based on AM-created parts because of the layer-by-layer fabrication process. In this article, we introduce a novel three-dimensional point-cloud-based analytical toolset, volumetric data analysis (VDA), for AM-oriented metrological and experimental analysis. Each step of the VDA is discussed in detail. A high dimensional hypothesis testing procedure is proposed to compare the geometric precision of the part samples from two printing settings. New visualization tools for deviation diagnostics are presented to aid in interpreting and comparing the process outputs. The proposed methods are illustrated with a real experiment to compare the effects of different layer thicknesses in a filament deposition modeling printing process.     

Model validation under epistemic uncertainty

This paper develops a methodology to assess the validity of computational models when some quantities may be affected by epistemic uncertainty. Three types of epistemic uncertainty regarding input random variables - interval data, sparse point data, and probability distributions with parameter uncertainty - are considered. When the model inputs are described using sparse point data and/or interval data, a likelihood-based methodology is used to represent these variables as probability distributions. Two approaches - a parametric approach and a non-parametric approach - are pursued for this purpose. While the parametric approach leads to a family of distributions due to distribution parameter uncertainty, the principles of conditional probability and total probability can be used to integrate the family of distributions into a single distribution. The non-parametric approach directly yields a single probability distribution. The probabilistic model predictions are compared against experimental observations, which may again be point data or interval data. A generalized likelihood function is constructed for Bayesian updating, and the posterior distribution of the model output is estimated. The Bayes factor metric is extended to assess the validity of the model under both aleatory and epistemic uncertainty and to estimate the confidence in the model prediction. The proposed method is illustrated using a numerical example.     

基于高斯混合带势概率假设密度滤波器的未知杂波下多机动目标跟踪算法

多模型的随机有限集(RFS)类方法是一类有效的多机动目标跟踪算法,但是现有算法都假定杂波统计特性先验已知,不适用于未知杂波背景。该文以高斯混合带势概率假设密度滤波器(GMCPHDF)为基础,提出一种未知杂波下的多机动目标跟踪算法。该算法对目标和杂波分别独立建模,通过最优高斯(BFG)估计方法对真实目标的强度函数进行预测,从而使多目标强度函数独立于机动目标的运动模型,实现各时刻真实目标的强度函数、杂波源期望个数以及真实目标和杂波源的混合势分布的迭代。仿真结果表明,该算法能够有效地联合估计多机动目标状态以及杂波期望个数。