Generally weighted moving average monitoring schemes: Overview and perspectives

An overview of monitoring schemes from a class called generally weighted moving average (GWMA) is provided. A GWMA scheme is an extended version of the exponentially weighted moving average (EWMA) scheme with an additional adjustment parameter that introduces more flexibility in the GWMA model as it adjusts the kurtosis of the weighting function so that the GWMA scheme can be designed such that it has an advantage over the corresponding EWMA scheme in the detection of certain shift values efficiently. The parametric and distribution-free GWMA schemes to monitor various quality characteristics and its existing enhanced versions (ie, double GWMA, composite Shewhart-GWMA, mixed GWMA-CUSUM, and mixed CUSUM-GWMA) have better performance than their corresponding EWMA counterparts in many situations; hence, all such existing research works discussing GWMA-related schemes (ie, 66 publications in total) are documented and categorized in such a manner that it is easy to identify research gaps. Finally, a number of possible future research ideas are provided.     

Progressive mean control chart is not a special case of an exponentially weighted moving average control chart

The progressive mean (PM) statistic is based on a simple idea of accumulating information of each subgroup by calculating the average progressively. Its weighting structure is based on a subgroup number that changes arithmetically, which makes the PM chart unique and efficient compared with the existing classical memory control charts. In a recent article (see reference 1), it was claimed that the PM chart is a special case of the exponentially weighted moving average (EMWA) chart. In this article, it is shown that even though the PM statistic can be written in the form of an EWMA statistic, the variance of the PM statistic is different from that of the EWMA statistic. Consequently, the limits of the PM chart are different from that of the EWMA chart. Therefore, it is found that the PM chart is not a special case of the EWMA chart; hence, the claim in reference 1 is incorrect. Furthermore, it is pointed out in this paper that no adaptive property in the weighting parameter of the PM statistic exists, further contradicting the claim in reference 1.     

基于移动窗口法的城市遥感生态质量评价研究

为进一步提高城市生态监测的精确度，在前人开展的城市生态遥感监测研究的基础上引入“区域尺度”的概念，充分考虑小区域范围内不同地物间的交互作用，并利用移动窗口模型(Moving Window-Remote Sensing Ecology Index, MW-RSEI)对沈阳市浑南区的遥感影像进行逐像元分析。实验结果表明，MW-RSEI模型和生态遥感监测模型（Remote Sensing Ecology Index, RSEI)在整体生态评价趋势上表现出一致性。但MW-RSEI模型对城市生态中的细节区域表征更明显。考虑到建筑及裸地周围植被的影响，其较差生态区域的占比仅为6%，生态评价为优的区域占比为11%。研究区生态评价结果表现得较为连续，并未出现明显的断层现象。该结果具有现实意义且与实际生态分布更为符合。MW-RSEI模型在城市生态监测中更为适用，可为相关部门提供一定的技术参考。     

On a marching level-set method for extended discontinuous Galerkin methods for incompressible two-phase flows: Application to two-dimensional settings

In this work a solver for two-dimensional, instationary two-phase flows on the basis of the extended discontinuous Galerkin (extended DG/XDG) method is presented. The XDG method adapts the approximation space conformal to the position of the interface. This allows a subcell accurate representation of the incompressible Navier-Stokes equations in their sharp interface formulation. The interface is described as the zero set of a signed-distance level-set function and discretized by a standard DG method. For the interface, resp. level-set, evolution an extension velocity field is used and a two-staged algorithm is presented for its construction on a narrow-band. On the cut-cells a monolithic elliptic extension velocity method is adapted and a fast-marching procedure on the neighboring cells. The spatial discretization is based on a symmetric interior penalty method and for the temporal discretization a moving interface approach is adapted. A cell agglomeration technique is utilized for handling small cut-cells and topology changes during the interface motion. The method is validated against a wide range of typical two-phase surface tension driven flow phenomena in a 2D setting including capillary waves, an oscillating droplet and the rising bubble benchmark.     

SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection. As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection. In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.     

一种基于证据推理的视频运动目标融合检测算法

为了解决复杂场景下传统的运动目标检测问题, 利用证据推理—–谨慎有序加权平均方法(COWA-ER), 提出一种综合使用混合高斯、均值滤波和码本的多方法融合的检测方法. 该融合检测算法以上述3 种检测方法为准则建立一个多准则决策框架, 通过双阈值检测法来表征检测过程中的不确定性, 最终利用COWA-ER 方法进行决策级融合, 实现多种方法的优势互补. 实验表明, 所提出的融合检测算法具有更理想的目标检测效果, 能有效应对诸如阴影及光照突变等问题对检测性能的影响.

     

Using surrogate models for efficient optimization of simulated moving bed chromatography

A new approach of using computationally cheap surrogate models for efficient optimization of simulated moving bed (SMB) chromatography is presented. Two different types of surrogate models are developed to replace the detailed but expensive full-order SMB model for optimization purposes. The first type of surrogate is built through a coarse spatial discretization of the first-principles process model. The second one falls into the category of reduced-order modeling. The proper orthogonal decomposition (POD) method is employed to derive cost-efficient reduced-order models (ROMs) for the SMB process. The trust-region optimization framework is proposed to implement an efficient and reliable management of both types of surrogates. The framework restricts the amount of optimization performed with one surrogate and provides an adaptive model update mechanism during the course of optimization. The convergence to an optimum of the original optimization problem can be guaranteed with the help of this model management method. The potential of the new surrogate-based solution algorithm is evaluated by examining a separation problem characterized by nonlinear bi-Langmuir adsorption isotherms. By addressing the feed throughput maximization problem, the performance of each surrogate is compared to that of the standard full-order model based approach in terms of solution accuracy, CPU time and number of iterations. The quantitative results prove that the proposed scheme not only converges to the optimum obtained with the full-order system, but also provides significant computational advantages.     

醋丙乳液HG-498的合成及性能

采用半连续种子乳液聚合方法,合成了一种高性能醋酸乙烯-丙烯酸丁酯-新癸酸乙烯酯三元共聚乳液(简称醋丙乳液)。探讨了乳化剂的配比、单体的用量、聚合温度、引发剂的用量和单体滴定时间对产品性能的影响。确定了聚合配方,优化了聚合工艺。实验结果表明,丙烯酸丁酯为单体总量的25%、阴离子型与非离子型乳化剂的质量比为1∶1、聚合温度为80℃、单体滴定时间为1.5~2 h时,乳液综合性能良好。同时对胶体稳定性进行了相关表征:乳液外观为乳白色且泛蓝光,胶体冷冻24 h后,无破乳现象发生,无悬浮颗粒,pH值6.4,固含量49.7%,黏度116 Pa s,黏均分子质量为36 872左右。·     

一种高效过滤亚微米级细颗粒的新液固过滤技术——表层过滤

论述一种新的可高效过滤液体中亚微米级细颗粒的表层过滤技术,分析了表层过滤技术的特点、过滤模式、表层过滤基本条件和设计计算中所必需的数学模型。介绍了表层过滤技术在我国工业生产上推广应用的概况。