An energy matching method for battery electric vehicle and hydrogen fuel cell vehicle based on source energy consumption rate

The smart cities development requires reducing energy consumption and using as much renewable energy as possible, so the widespread use of new energy vehicles is a very important measure. In this work, for the energy system configuration and energy efficiency balance of new energy vehicles, we propose an energy matching method to study its energy efficiency from the view point for energy life cycle. Nowadays, new energy vehicles mainly include battery electric vehicles (BEV) and hydrogen fuel cell vehicles (HFCEV). Firstly, we proposed the Source to Range (STR) model. Then, based on STR model, we used energy efficiency analysis chart to visually represent the conversion, delivery and consumption of the vehicle energy life cycle. Furthermore, we proposed a Source Energy Consumption Rate (SECR), which is used to evaluate the vehicles energy efficiency. Finally, based on STR model, we obtained the dividing line of the same SECR for new energy vehicles and equivalent fuel vehicles, which provides constraints on the vehicle energy system design. The results show that STR model can provide an effective tool for energy matching and energy efficiency analysis of new energy vehicles, and has a reference for product development of new energy vehicles.     

Dynamic Profile Monitoring for Flooding Prognosis in Packed Columns

In the chemical industry, real‐time flooding prognosis is a necessity for packed‐column operation because the flooding phenomenon interferes with the performance of production systems. In this work, the profile monitoring technique is utilized to capture the dynamic behavior of pressure drop, which is an important indicator for the flooding phenomenon. In each moving window, the pressure drop signals are described by using an exponential generalized autoregressive conditional heteroskedastic model. The onset of the flooding phenomenon is then indicated by changes in model parameters. Moreover, to efficiently capture the process change, a nonparametric approach is utilized to establish a statistical control chart. Experimental and comparison results show the advantages of the proposed method.     

Statistical testing for sufficient control chart performances during monitoring of grouped processes

With ISO 7870-8, a standardized application of charting techniques for short runs and small mixed batches was presented in 2017. Similar to various scientific approaches, it requires that sample values from grouped processes follow nearly identical distributions. In practice, however, there tend to be differences between distribution parameters. Moreover, equal parameters do not ensure that distributions are properly aligned to the center line and control limits of the chart. These facts can lead to undesired control chart performances which can be expressed by average run lengths (ARL) during in-control and out-of-control conditions. In this work, a statistical test for sufficient control chart performances during monitoring of grouped processes based on preliminary samples is proposed. Control chart performances are defined as sufficient when they deviate within acceptable ranges from usual performances during single process monitoring in mass production. The ARL resulting from estimated distributions and planned production sequences is used as test statistic and calculated via the Markov chain approach. Exemplary tests are executed for scenarios with individuals and cumulated sum (CUSUM) charts. A simulative determination of error rates resulting from the ARL-based testing demonstrates its effectiveness in testing for sufficient control chart performances compared to an indirect testing with Levene's test and a one-way analysis of variance (ANOVA).     

An artificial neural network approach to inhomogeneous soil slope stability predictions based on limit analysis methods

The assessment of soil slope stability is an important task in geotechnical designs. This study uses finite element upper bound (UB) and lower bound (LB) limit analysis (LA) methods to investigate inhomogeneous soil slope stability on the basis of the conventional Mohr–Coulomb parameters. The obtained stability numbers are presented in inhomogeneous soil slope stability charts. In order to minimize manual reading errors when using the chart solutions, an artificial neural network (ANN) is employed to develop a stability assessment tool for the slopes investigated in this paper. The slope stability analysis using the ANN-based tool is convenient, and the predictions it provides are highly accurate.     

基于Delphi 7.0和MapObjects 2.3编写控制点管理系统

对于基础测绘而言,控制点的重要性不言而喻。随着测绘范围的逐年加大,积累起来的控制点越来越多,这就要求我们要改变已有的管理方式,寻求一种新的管理方式。基于此,本文对快捷方便地管理控制点进行了研究,采用Delphi 7.0和MapObjects 2.3进行开发,较好完成了对控制点的查询、添加、删除等操作,使得图形与数据的结合,能直观地显示在"屏"上,达到了管理控制点的目的。     

Process capability analysis chart with the application of Cpm

Process capability analysis (PCA) is a highly effective means of assessing the process ability of product that meets specifications. The process capability analysis chart (PCAC/C_pk ) evaluates the capabilities of multiprocess products together with nominal-the-best specifications, larger-the-better and smaller-the-better specifications. This study proposes process capability analysis chart (PCAC/C_pm ) to consider process yield and expected process loss. A new generated estimator for C_pm is proposed and the properties of statistical estimator and hypothesis test are discussed. A practical example was given for application.     

用Excel绘制自动打点常规控制图

目的:用Excel绘制自动打点常规控制图,它对检测过程进行分析评价,使过程维持在仅受随机性因素影响的受控状态,以达到控制质量的目的。方法:用Excel的函数和插入折线图表功能绘制自动打点常规控制图。应用:用Excel绘制自动打点常规质控图,能及时发现失控并予以纠正。结论:用Excel绘制自动打点常规质控图,对小型实验室来说通用性强,经济实惠,便于推广。     

Adaptive nonparametric CUSUM scheme for detecting unknown shifts in location

In this paper, we provide a sequential rank-based adaptive nonparametric cumulative sum control chart for detecting a range of shifts in the location parameter. This chart is a self-starting scheme, and thus can be used to monitor processes at the start-up stages rather than having to wait for the accumulation of sufficiently large calibration samples. It does not require any prior knowledge of the underlying distribution. The choice of the chart parameters is studied and a simulation study demonstrates that the proposed control chart not only performs robustly for different distributions, but also efficiently detects various magnitudes of shifts. An illustrative example is also given to introduce the implementation of this proposed control chart.     

Synthetic double sampling np control chart for attributes

The quality of a product, based on the number of non-conforming items can be controlled using the np chart. This paper proposes a synthetic double sampling (DS) np chart which comprises two sub-charts, i.e. the DS np and conforming run length (CRL) sub-charts. For the zero-state case, the synthetic DS np chart surpasses its standard counterpart, i.e. the synthetic np and the basic DS np chart, and other np type charts like the standard np, combined synthetic and np (Syn-np), variable sample size (VSS) np, exponentially weighted moving average (EWMA) np and cumulative sum (CUSUM) np charts, for detecting increases in the fraction of non-conforming items p, for most shift sizes. The synthetic DS np chart also performs reasonably well in the steady-state case in comparison with other charts mentioned above. Thus, among the competing charts, the synthetic DS np chart stands out as one of the best charts.     

Alternative Hotelling's T2 Charts using Winsorized Modified One‐Step M‐estimator

Hotelling's T² chart is a popular tool for monitoring statistical process control. However, this chart is sensitive in the presence of outliers. To alleviate the problem, this paper proposed alternative Hotelling's T² charts for individual observations using robust location and scale matrix instead of the usual mean vector and the covariance matrix, respectively. The usual mean vector in the Hotelling T² chart is replaced by the winsorized modified one‐step M‐estimator (MOM) whereas the usual covariance matrix is replaced by the winsorized covariance matrix. MOM empirically trims the data based on the shape of the data distribution. This study also investigated on the different trimming criteria used in MOM. Two robust scale estimators with highest breakdown point, namely S_n and T_n were selected to suit the criteria. The upper control limits for the proposed robust charts were calculated based on simulated data. The performance of each control chart is based on the false alarm and the probability of outlier's detection. In general, the performance of an alternative robust Hotelling's T² charts is better than the performance of the traditional Hotelling's T² chart. Copyright © 2012 John Wiley & Sons, Ltd.