Enhancing Software Process Management through Control Charts

In software development life cycle, Software Process Management (SPM) acts as a significant part throughout the execution of project. In this study, the application of control chart for analyzing the stability of software process and defects in the software product is discussed. This paper will discuss the analyzing impact or collision of rework effort, defect density, inspection performance and productivity by using control charts. This paper also explains the benefits and challenges of using control charts in software organization.

Some Group Runs Based Multivariate Control Charts for Monitoring the Process Mean Vector

In this article, we propose two control charts namely, the “Multivariate Group Runs’ (MV-GR-M)” and the “Multivariate Modified Group Runs’ (MV-MGR-M)” control charts, based on the multivariate normal processes, for monitoring the process mean vector. Methods to obtain the design parameters and operations of these control charts are discussed. Performances of the proposed charts are compared with some existing control charts. It is verified that, the proposed charts give a significant reduction in the out-of-control “Average Time to Signal” (ATS) in the zero state, as well in the steady state compared to the Hotelling’s T2 and the synthetic T2 control charts.

A Cause-Selecting Control Chart Method for Monitoring and Diagnosing Dependent Manufacturing Process Stages

Many industrial products are normally processed through multiple manufacturing process stages before it becomes a final product.Statistical process control techniques often utilize standard Shewhart control charts to monitor these process stages.If the process stages are independent,this is a meaningful procedure.However,they are not independent in many manufacturing scenarios.The standard Shewhart control charts can not provide the information to determine which process stage or group of process stages has caused the problems(i.e.,standard Shewhart control charts could not diagnose dependent manufacturing process stages).This study proposes a selective neural network ensemble-based cause-selecting system of control charts to monitor these process stages and distinguish incoming quality problems and problems in the current stage of a manufacturing process.Numerical results show that the proposed method is an improvement over the use of separate Shewhart control chart for each of dependent process stages,and even ordinary quality practitioners who lack of expertise in theoretical analysis can implement regression estimation and neural computing readily.

Computation of Average Run Length for Residual-Based T^2 Control Chart for Multivariate Autocorrelated Processes

The expression of residual is obtained according to its dynamic response to mean shift,then the distribution of T 2 statistic applied to the residual is derived,thus the probability of the T 2 statistic lying outside the control limit is calculated.The above-mentioned results are substituted into the infinite definition expression of the average run length(ARL),and then the final finite ARL expression is obtained.An example is used to demonstrate the procedures of the proposed method.In the comparative study,eight autocorrelated processes and four different mean shifts are performed,and the ARL values of the proposed method are compared with those obtained by simulation method with 50,000 replications.The accuracy of the proposed method can be illustrated through the comparative results.

Monitoring Saccharification Process in Brewery Industry Using Quality Control Charts

The aim of this study was to establish a control system for saccharification process using quality control charts. To achieve this goal, temperature, pH and brix were measured at 12 minutes intervals for 15 consecutive batches which took 2 hours each. The time variations for three process parameters were assessed to establish a good understanding of the saccharification process. The temperature varied between 58℃ and 62℃ while the pH decreased slowly due to oxidation, values of which varied between 5.7 and 5.0. Brix values increased linearly with time. The initial and final values of the three parameters varied from one batch to another. Of the three parameters, brix was not well represented on the quality control charts due to wide difference between initial and final values during saccharification. The final brix values varied between batches, from 10.6% to 11.6%. The control charts used in this study were X-bar and Range charts. The rules for interpreting control charts were implemented for both X-bar and R charts, results of which showed that the process was out of control, although some rules were not violated due to little number of batches studied. The values of for temperature and pH data (2.27℃ and 0.35, respectively) were lower compared to brix data (11.2%). The corresponding values of span between control limits, SPx and SPR for temperature and pH were also comparatively lower than those established from brix data. Due to larger values of for brix measurements, the corresponding control charts for brix were insensitive in identifying out-of-control points during saccharification process.

考虑测量误差的CUSUM控制图对维纳过程的监控设计

目的 针对医疗设备存在的退化加速现象,提出对退化过程进行监控的方法,旨在有效监测到真实情况中退化加速的现象并进行预警,降低设备运维成本。方法 无论是退化率恒定还是退化率加速的维纳过程,其增量概率分布都是独立于其他时间区间增量的,本文运用上侧累积和(Cumulative Sum,CUSUM)控制图监控策略,并分析两类测量误差(乘性测量误差和加性测量误差)对控制图监控效率的影响。结果 运用蒙特卡洛仿真得到不同加速系数和测量误差下的平均运行链长(Average Run Length,ARL)和运行链长标准差,结果表明对于退化率恒定的固定效应模型,CUSUM对退化加速的监控具有稳健性,ARL的降低率均维持在43%左右;而在退化加速的随机效应模型中,监控性能会随着乘性测量误差(当乘性误差>0.1后)的增加而下降。结论 CUSUM控制图对医疗设备退化加速现象具有良好的监控能力,随着退化率的加速系数的增加,控制图能够越早发现异常退化加速状态。

浅析饮用水水质监测实验室化学试剂分类及全流程化管理

饮用水水质监测实验室化学试剂的种类繁多,大多数都属于危险化学品范畴,实验过程中所产生危险废弃物的化学性质复杂,给实验室安全管理带来了一定的难度。本文基于GB/T 5750—2023《生活饮用水标准检验方法》中所涉及的化学试剂,依据GB 6944—2012《危险货物分类和品名编号》将化学试剂分为有机类、无机类和化学分析试剂3大类,并细分为盐类、酸类和醇类等16小类。根据化学试剂的危险性质,依据GB 12268—2012《危险货物品名表》,将化学试剂分为易燃易爆类、有毒有害类、压缩气体和液化气体、腐蚀类、放射性物质、一般化学试剂6大类。并结合实验室管理过程中存在的问题,提出从化学试剂的申购、采购、验收、保存、领用和处理全流程化管理,且对分过程采用流程图式管理,为试剂管理员和实验员提供详细的操作流程和科学的处理方法,提高实验室安全管理能力和规范化体系建设。

超高效液相色谱-质谱-一测多评法结合质量控制图在高温大曲生产过程品控分析中的应用

为了更好地监控不同生产环节下高温大曲的品质,对参与高温大曲发酵过程中重要风味成分前体氨基酸含量进行全面、准确的控制,该文通过收集制曲车间不同生产环节下高温大曲样品,采用超高效液相色谱-质谱-一测多评(ultra-high-performance liquid chromatography high-resolution mass spectrometry-quantitative analysis of multi-components by single-marker,UPLC-HRMS-QAMS)法测定9种重要风味成分前体氨基酸含量,同时,基于Minitab软件利用休哈特控制图中单值-移动极差控制图建立不同生产环节下高温大曲品质监测的质量控制图。结果表明,UPLC-HRMS-QAMS法具有良好线性关系(R^(2)>0.9970),待测氨基酸检出限、定量限低,可满足快速、准确定性定量分析高温大曲中多种重要风味成分前体氨基酸含量的目的,基于此,结合质量控制图可建立监测高温大曲生产过程中品质变化的单值-移动极差控制图,这为不同生产批次下高温大曲的品质监测提供了新思路,有助于提升高温大曲的质量控制水平,进而为制曲车间生产工艺提供技术参考,为实现企业优质优产提供数据支撑及技术保障。

基于PChart算法的哈萨克语句法分析

为研究哈萨克语的句法分析,研究自然语言处理的统计方法的句法分析。根据概率上下无关文法模型和Chart算法的特点,将概率引入Chart算法,提出一种PChart算法。利用PChart算法对哈萨克语进行句法分析,解决句法分析中哈萨克文法的获取和歧义消解问题,实现一个基于PChart算法的哈萨克语句法分析器,识别正确的哈萨克语句子。实验结果表明,该分析器在准确率、召回率等方面的性能都有所提高。

基于期待类型的Chart句法分析算法

Chart算法是目前句法分析中应用最广泛的算法之一,但该算法的计算效率仍有待提高。通过对两种常用Chart算法的分析,提出一种以由底向上的Chart算法为基础,结合自顶向下Chart算法的预测能力的算法。算法按严格从左到右、由底向上的方向进行,根据已有活动边的活动角色类型和句法规则,产生当前词位置上的期待类型表,并以此限制后续边的生成。对比实验的结果表明,分析速度较普通Chart算法提高了约24%,同时也减少了一半以上因边池溢出而导致的分析失败的语句。

The dynamic power management for embedded system with Poisson process

The mass of the embedded systems are driven by second batteries, not by wired power supply. So saving energy is one of the main design goals for embedded system. In this paper we present a new technique for modelling and solving the dynamic power management (DPM) problem for embedded systems with complex behavioural characteristics. First we model a power-managed embedded computing system as a controllable Flow Chart. Then we use the Poisson process for optimisation, and give the power management algorithm by the help of Dynamic Voltage Scaling (DVS) technology. At last we built the experi- mental model using the PXA 255 Processors. The experimental results showed that the proposed technique can achieve more than 12% power saving compared to other existing DPM techniques.

Construction of Control Charts for Monitoring Various Parameters Related to the Management of the COVID-19 Pandemic

To monitor the quality characteristics of a process, appropriate graphical and statistical tools must be used. These tools are capable of showing the evolution over time of the behavior of the quality characteristics (measurable or countable) and detecting situations that seem to present certain anomalies. The control chart is one of these tools widely used in quality management. In the process of managing the COVID-19 pandemic, this tool will make it possible to know at all times whether the parameters monitored such as the positivity rate, the recovery rate, and the mortality rate, are under control and to act accordingly. Monitoring cure and mortality rates will also show us the effectiveness of the treatments used.

Stochastic Model for Multiple Classes and Subclasses Simple Documents Processing

The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian ML×K/ML×K/1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. In fine, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.

Variable selection-based SPC procedures for high-dimensional multistage processes

Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring(SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average(MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length(ARL) for the detection of process mean shift.

New Method for Multivariate Statistical Process Monitoring

A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis (FDA) is used to derive a feature discriminant direction (FDD) between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts (XmR charts) are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit (UCL) and lower control limit (LCL) on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis (PCA)-based methods and can better identify step type faults on XmR charts.

Economic Design of  & S Control Charts Based on Taguchi's Loss Function and Its Optimization

Much research effort has been devoted to economic design of  & S control charts,however,there are some problems in usual methods.On the one hand,it is difficult to estimate the relationship between costs and other model parameters,so the economic design method is often not effective in producing charts that can quickly detect small shifts before substantial losses occur;on the other hand,in many cases,only one type of process shift or only one pair of process shifts are taken into consideration,which may not correctly reflect the actual process conditions.To improve the behavior of economic design of control chart,a cost & loss model with Taguchi's loss function for the economic design of  & S control charts is embellished,which is regarded as an optimization problem with multiple statistical constraints.The optimization design is also carried out based on a number of combinations of process shifts collected from the field operation of the conventional control charts,thus more hidden information about the shift combinations is mined and employed to the optimization design of control charts.At the same time,an improved particle swarm optimization(IPSO) is developed to solve such an optimization problem in design of  & S control charts,IPSO is first tested for several benchmark problems from the literature and evaluated with standard performance metrics.Experimental results show that the proposed algorithm has significant advantages on obtaining the optimal design parameters of the charts.The proposed method can substantially reduce the total cost(or loss) of the control charts,and it will be a promising tool for economic design of control charts.

Control charts for the Pareto distribution

The Pareto distribution plays an important role in various areas of researchIn this paper, the average run length(ARL) unbiased control charts, which monitor the shape and threshold parameters of the Pareto distribution respectively, are proposed when the incontrol parameters are knownThe effects of parameter estimation on the performance of the proposed control charts are also studiedResults show that the control charts with the estimated parameters are not suitable to be used in the known parameter case, thus the ARL-unbiased control charts for the shape and threshold parameters with the desired ARL0, which consider the variability of the parameter estimates, are further developedThe performance of the proposed control charts is investigated in terms of the ARLFinally, an example is given to illustrate the proposed control charts.

Automated Identification of Basic Control Charts Patterns Using Neural Networks

The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns;natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.

血液成分质量抽检统计过程控制方法的探讨

目的 探讨血站在血液成分质量抽检过程中,检测数据的分析方法。方法 收集2018—2022年本中心的血液成分质量抽检检测数据,利用统计过程控制原理,使用Mintab软件绘制控制图,对部分关键检测项目数据进行统计过程控制分析。结果 对血小板含量项目的合格率采用P控制图分析,检验出1个失控点。对血浆蛋白含量项目进行正态性检验,分析数据波动原因,寻找改进方向。对于血细胞比容项目,绘制均值极差控制图,检验出数据异常趋势,对数据做正态性检验出并进行过程能力分析,Cpk值为0.67。结论 运用统计过程控制方法,用P控制图分析血液成分质量抽检中的计数型数据,对计量型数据进行正态性检验,并利用均值极差控制图检验数据的异常趋势,对符合正态性分布的数据,计算其过程能力指数Cpk值,不失为监测采供血过程稳定性的有效方法。