Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

荆州长江公路大桥运营阶段健康观测浅析

介绍了荆州长江公路大桥运营阶段健康观测的内容，对健康观测的结果进行了初步分析，为大桥的工作状况评估、养护与管理等工作提供了依据，同时也可供其他同类桥梁运营期健康观测参考。     

Data visualization and analysis within a Hydrologic Information System: Integrating with the R statistical computing environment

This paper presents a prototype software system for visualization and analysis of hydrologic data that provides interoperability between the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the R statistical computing environment. By linking these two systems within a single desktop software application, an integrated hydrologic data management and analysis environment has been created that simplifies the process used by scientists and engineers to find, access, organize, and analyze the hydrologic data needed in modeling and managing hydrologic and environmental systems. The implementation of this work is a software plug-in for the CUAHSI HIS HydroDesktop software system called HydroR. We describe the design, graphical user interface, and implementation of the HydroR plug-in. An example application of HydroR is presented in which total suspended solids concentrations are modeled for the Little Bear River using a regression developed from turbidity and total suspended solids observations downloaded from the CUAHSI HIS using HydroDesktop. Finally, we conclude with a summary of our experience in developing interoperability between HIS and R and suggest future developments that can extend the capabilities we have developed.     

Assessing robustness of factor ranking for supersaturated designs

Supersaturated designs can potentially be a beneficial tool for efficiently exploring a large number of factors with a moderately sized design. However, because more factors are being considered than there are runs, the stability of the identified factors depends heavily on effect sparsity and the lack of highly influential observations. A helpful tool for the analysis of supersaturated designs is least absolute shrinkage and selection operation (LASSO), which is useful when the effects of many explanatory variables are sparse in a high‐dimensional dataset. To understand the impact of individual observations on the selected factors, the LASSO influence plot was created. This paper describes an application of this plot and its variants that can be used to identify influential points, increase understanding of the impact of individual observations on model parameters, and the robustness of results in analyses with supersaturated designs. These graphical methods can serve as a complement to other regression diagnostics techniques in the LASSO regression setting.     

Confidence intervals in repeated-measures designs: The number of observations principle.

Since the publication of Loftus and Masson’s (1994) method for computing confidence intervals (CIs) in repeated-measures (RM) designs, there has been uncertainty about how to apply it to particular effects in complex factorial designs. Masson and Loftus (2003) proposed that RM CIs for factorial designs be based on number of observations rather than number of participants. However, determining the correct number of observations for a particular effect can be complicated, given the variety of effects occurring in factorial designs. In this paper the authors define a general “number of observations” principle, explain why it obtains, and provide step-by-step instructions for constructing CIs for various effect types. The authors illustrate these procedures with numerical examples. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

中高精度捷联惯导系统的无转台标定

阐述了一种无转台标定中高精度惯导系统的方法,依据正交轴上加速度计输出矢量和等于重力的原理,标定出加速度计输出模型中相关参数。对于陀螺参数的标定,先通过翻转法得到陀螺标度因数的粗略值,再通过观测导航系统速度误差,求出陀螺相关安装误差并修正陀螺标度因数,最后根据静止时陀螺输出矢量相对关系得到陀螺零位。仿真和实际试验验证了这种标定方法的可行性。     

Geometric Charts with Estimated Control Limits

The geometric control chart has been shown to be more effective than p and np‐charts for monitoring the proportion of nonconforming items, especially for high‐quality Bernoulli processes. When implementing a geometric control chart, the in‐control proportion nonconforming is typically unknown and must be estimated. In this article, we used the standard deviation of the average run length (SDARL) and the standard deviation of the average number of inspected items to signal, SDARL*, to show that much larger phase I sample sizes are needed in practice than implied by previous research. The SDARL (or SDARL*) was used because practitioners would estimate the control limits based on different phase I samples. Thus, there would be practitioner‐to‐practitioner variability in the in‐control ARL (or ARL*). In addition, we recommend a Bayes estimator for the in‐control proportion nonconforming to take advantage of practitioners' knowledge and to avoid estimation problems when no nonconforming items are observed in the phase I sample. If the in‐control proportion nonconforming is low, then the required phase I sample size may be prohibitively large. In this case, we recommend an approach to identify a more informative continuous variable to monitor. Copyright © 2012 John Wiley & Sons, Ltd.     

随机时滞网络控制系统的后退时域估计

本文考虑具有随机观测时滞系统的后退时域估计问题.首先,针对随机时滞网络控制系统,运用观测重组技术,将带有时滞的观测方程转化为无时滞观测方程,得到一组新的无时滞观测序列.在此基础上,运用线性最小方差无偏估计理论,推导出后退时域估计器的批形式公式和迭代形式公式,并给出稳定性分析.通过具体的仿真实例,对比现有卡尔曼滤波器,验证了所提出的后退时域估计器具有更好的跟踪能力.