The use of Nassi-Shneiderman charts and supporting tools in software engineering education

In 1973 Nassi and Shneiderman proposed a new form of flowchart which was advocated as being better suited to the practices of structured programming. It is argued that Nassi-Shneiderman charts (N-S charts) provide a convenient common framework for the communication and practice of other widely accepted ideas concerning software development. They can for example be used to promote top-down design, structured implementation, thorough testing and graphical documentation. This paper relates experiences using N-S charts in a University educational environment and describes some tools which have been developed to support use of the charts.     

S-57电子海图数据读取和自动多模式分层显示

电子海图显示与信息系统(Electronic Chart Display and Information System,简称ECDIS)是以数字形式储存的海图并与多种传感设备、图形显示终端等高度融合的船舶航行综合信息处理系统,而S-57是电子海图国际通用的数据格式。在Linux/Fedora系统下,针对于S-57格式,研究其数据结构模型,从最基本的点、线、面出发,读取海图数据并根据时间和天气数据实现S-57海图数据自动多模式显示,并提出了一种根据物标重要性进行分层显示的方法。     

结构化控制流程图（N－S图）的改进与模块化程序设计图的设想

本文简要回顾了结构化程序设计和Ｎ－Ｓ图的产生,不难看出：Ｎ－Ｓ图是应结构化程序设计的需要而产生的。近年来模块化结构化程序设计思想有了新的发展,本文正是为了适应新的情况,在反复推敲与使用的基础上,第一,提出了结构化控制流程图的改进方案,它比Ｎ－Ｓ图有更高的集成度,表示能力更强,使得用图形表示算法更方便;第二,提出了各种模块图的方案,它可以使模块化程序设计图形化,合起来就构成了一套完整的模块化结构化程序设计盒图系统,简称ＭＳ图。     

Optimal linear combination of Poisson variables for multivariate statistical process control

In this paper we analyze the monitoring of p Poisson quality characteristics simultaneously, developing a new multivariate control chart based on the linear combination of the Poisson variables, the LCP control chart. The optimization of the coefficients of this linear combination (and control limit) for minimizing the out-of-control ARL is constrained by the desired in-control ARL. In order to facilitate the use of this new control chart the optimization is carried out employing user-friendly Windows© software, which also makes a comparison of performance between this chart and other schemes based on monitoring a set of Poisson variables; namely a control chart on the sum of the variables (MP chart), a control chart on their maximum (MX chart) and an optimized set of univariate Poisson charts (Multiple scheme). The LCP control chart shows very good performance. First, the desired in-control ARL (ARL₀) is perfectly matched because the linear combination of Poisson variables is not constrained to integer values, which is an advantage over the rest of charts, which cannot in general match the required ARL₀ value. Second, in the vast majority of cases this scheme signals process shifts faster than the rest of the charts.     

Design of attribute control charts for multistage production systems-based on cost criteria

This paper develops a method to design attribute control charts, p-charts and c-charts, for multistage production systems based on quality cost criteria. The production systems are structured and formulated as a dynamic programming model. Each productions station and its inspection station is viewed as a combined station and is treated as the stage in the dynamic programming model. The model has quality distribution as the state variable and the stage transformation of this model is also developed. the stage return of this dynamic programming model is the quality cost of implementing an attribute control chart to that stage and the decision variables are the control chart parameters, n-sample size and control limits. Methods to combine quality distributions for finding type I and type II errors are then developed. As the dynamic programming model has quality distribution as state variable, conventional methods of solving dynamic programming problems are not applicable. A method, for solving this model, is thus developed based on branch and bound approach. The method itself may be applied to other dynamic programming problems where conventional methods are inefficient or not applicable. An example of the foundry process of cylinder liners is included to illustrate the developed method.     

Statistical computation and analyses for attribute events

This article studies the monitoring of the attribute events based on statistical computation and analyses. The size of an attribute event is an integer rather than a continuous variable. For example, the detection of a product lot containing defectives is an attribute event, the size of which is the number of defectives found in this lot. While many control charts have been developed for monitoring the time interval (T) between the occurrences of an event, many other attribute charts may be employed to examine the size (C) of the event. However, these two types of control charts have been investigated and applied separately with limited syntheses in Statistical Process Control (SPC). This article presents a single SPC chart (called the rate chart for attribute, or rate chart in short) for simultaneously monitoring the time interval T and size Cof an attribute event based on the ratio between C and T. Our studies show that the new chart is more effective for detecting the out-of-control status of the attribute event compared with an individual t chart or an individual c chart, as well as a combined chart. More profound is that the rate chart performs more uniformly than other charts for detecting both T shift and C shift, as well as the joint shift in T and C. The rate chart has demonstrated its potential for both manufacturing systems and non-manufacturing sectors (e.g., supply chain management, office administration and health care industry), especially for the latter.     

Comparative study of the performance of the CuSum and EWMA control charts

This work presents a comparative study of the performance of the cumulative sum (CuSum), as well as the exponentially weighted moving average (EWMA) control charts. The objective of this research is to verify when CuSum and EWMA control charts do the best control region, in order to detect small changes in the process average. Starting from the data of a productive process, several series were simulated. CuSum and EWMA control charts were used to determine the average run length (ARL) to detect a condition out of control. ARL found by each chart which was then, compared. It was observed that the CuSum control chart practically did not sign points out of control for the levels of variation between ±1.0 standard deviation. For these variation levels the EWMA control chart was more efficient than CuSum. Among the parameters EWMA control chart the ones with constant λ=0.10 and 0.05, with the respective control limits L=2.814 and 2.625, were the ones that detected larger number of altered positions.     

A nonparametric exponentially weighted moving average signed-rank chart for monitoring location

Nonparametric control charts can provide a robust alternative in practice to the data analyst when there is a lack of knowledge about the underlying distribution. A nonparametric exponentially weighted moving average (NPEWMA) control chart combines the advantages of a nonparametric control chart with the better shift detection properties of a traditional EWMA chart. A NPEWMA chart for the median of a symmetric continuous distribution was introduced by Amin and Searcy (1991) using the Wilcoxon signed-rank statistic (see Gibbons and Chakraborti, 2003). This is called the nonparametric exponentially weighted moving average Signed-Rank (NPEWMA-SR) chart. However, important questions remained unanswered regarding the practical implementation as well as the performance of this chart. In this paper we address these issues with a more in-depth study of the two-sided NPEWMA-SR chart. A Markov chain approach is used to compute the run-length distribution and the associated performance characteristics. Detailed guidelines and recommendations for selecting the chart’s design parameters for practical implementation are provided along with illustrative examples. An extensive simulation study is done on the performance of the chart including a detailed comparison with a number of existing control charts, including the traditional EWMA chart for subgroup averages and some nonparametric charts i.e. runs-rules enhanced Shewhart-type SR charts and the NPEWMA chart based on signs. Results show that the NPEWMA-SR chart performs just as well as and in some cases better than the competitors. A summary and some concluding remarks are given.     

The application of control chart for defects and defect clustering in IC manufacturing based on fuzzy theory

c-Chart was frequently used to monitor wafer defects during IC manufacturing. The clustering degree of defect on a wafer will increase along with the area of wafer gradually enlarging. The defect clustering causes the Poisson-based c-chart to exhibit many false alarms. Although several revised control charts have been developed to reduce the number of false alarms, those control charts still have some disadvantages in practical use. This study proposes a control chart that applies fuzzy theory and engineering experience to monitor wafer defects with the consideration of defect clustering. The proposed control chart is simpler and more rational than those revised c-charts. Finally, a case study of an IC company, owing to the HsinChu Scientific part at Taiwan, is used to demonstrate and verify the rationality and effectiveness.     

A control chart pattern recognition system using a statistical correlation coefficient method

This paper presents a control chart pattern recognition system using a statistical correlation coefficient method. Pattern recognition techniques have been widely applied to identify unnatural patterns in control charts. Most of them are capable of recognizing a single unnatural pattern for different abnormal types. However, before an unnatural pattern occurs, a change point from normal to abnormal may appear at any point in control charts for most practical cases. Moreover, concurrent patterns where two unnatural patterns simultaneously exist may also occur in a control chart pattern recognition system. Our statistical correlation coefficient approach is a simple mechanism for recognizing these unnatural control chart patterns with good performance. This approach is also an effective method for the control chart pattern recognition without a tedious training process.     

A hybrid fuzzy adaptive sampling - Run rules for Shewhart control charts

In crisp run control rules, usually it is stated that a process moves very sharply from in-control condition to out-of-control act. This causes an increase in both false-alarm rate and control chart sensitivity. Moreover, the classical run control rules are not implemented on an intelligent sampling strategy that changes control charts’ parameters to reduce error probability when the process appears to have a shift in parameter values. This paper presents a new hybrid method based on a combination of fuzzified sensitivity criteria and fuzzy adaptive sampling rules, which make the control charts more sensitive and proactive while keeping false alarms rate acceptably low. The procedure is based on a simple strategy that includes varying control chart parameters (sample size and sample interval) based on current fuzzified state of the process and makes inference about the state of process based on fuzzified run rules. Furthermore, in this paper, the performance of the proposed method is examined and compared with both conventional run rules and adaptive sampling schemes.     

A combined synthetic and np scheme for detecting increases in fraction nonconforming

The applications of attribute control charts cover a wide variety of manufacturing processes in which quality characteristics cannot be measured on a continuous numerical scale or even a quantitative scale. The np control chart is an attribute chart used to monitor the fraction nonconforming p of a process. This chart is effective for detecting large process shifts in p. The attribute synthetic chart is also proposed to detect p shifts. It utilizes the information about the time interval or the Conforming Run Length (CRL) between two nonconforming samples. During the implementation of a synthetic chart, a sample is classified as nonconforming if the number d of nonconforming units falls beyond a warning limit. Unlike the np chart, the synthetic chart is more powerful to detect small and moderate p shifts. This article proposes a new scheme, the Syn-np chart, which comprises a synthetic chart and an np chart. Since the Syn-np chart has both the strength of the synthetic chart for quickly detecting small p shifts and the advantage of the np chart of being sensitive to large p shifts, it has a better and more uniform overall performance. Specifically, it is more effective than the np chart and synthetic chart by 73% and 31%, respectively, in terms of Weighted Average of Average Time to Signal (WAATS) over a wide range of p shifts under different conditions.     

Economic design of time-between-events control chart system

This article presents the economic design of the control chart system consisting of several individual control charts based on time-between-events (TBE) data for monitoring multistage manufacturing processes. The design algorithm considers all the TBE charts within a system in an integrative and optimal manner. Numerical studies show that the proposed design algorithm improves the performance characteristics (in terms of profit) considerably. The proposed control chart system is easy to understand and operate, and thus the floor operators can utilize and understand it as easily as for the traditional system.     

Extended maximum generally weighted moving average control chart for monitoring process mean and variability

In this paper, we propose an extended control chart, called the maximum generally weighted moving average (MaxGWMA) control chart, to simultaneously detect both increases and decreases in the mean and/or variability of a process. Simulations are performed to evaluate the average run length, standard deviation of the run length, and diagnostic abilities of the MaxGWMA and maximum exponentially weighted moving average (MaxEWMA) charts. An extensive comparison reveals that the MaxGWMA control chart is more sensitive than the MaxEWMA control chart.     

Applying a coupled expert system to quality control charts

Quality control charts are a common tool used for control of a manufacturing process. Expert systems can serve as a valuable tool to automate the different phases in the use of quality control charts. The authors have developed a coupled expert system to select a proper control chart, optimize the design of the selected control chart, analyze the chart to identify an out-of-control process and suggest the reason(s) for an out-of-control process. The intent of this paper is to discuss the main features of this coupled expert system.

An expert system shell called PCPLUS helps a user select the most appropriate control chart and identify the reason(s) for a process to be out-of-control. FORTRAN was used to implement the modules for design, plotting, and analysis of the selected control chart.     

On the performance of the conditional decision procedure in geometric charts

To improve the performance of control charts the conditional decision procedure (CDP) incorporates a number of previous observations into the chart’s decision rule. It is expected that charts with this runs rule are more sensitive to shifts in the process parameter. To signal an out-of-control condition more quickly some charts use a headstart feature. They are referred as charts with fast initial response (FIR). The CDP chart can also be used with FIR. In this article we analyze and compare the performance of geometric CDP charts with and with no FIR. To do it we model the CDP charts with a Markov chain and find closed-form ARL expressions. We find the conditional decision procedure useful when the fraction p of nonconforming units deteriorates. However the CDP chart is not very effective for signaling decreases in p.     

Control chart based on likelihood ratio for monitoring linear profiles

A control chart based on the likelihood ratio is proposed for monitoring the linear profiles. The new chart which integrates the EWMA procedure can detect shifts in either the intercept or the slope or the standard deviation, or simultaneously by a single chart which is different from other control charts in literature for linear profiles. The results by Monte Carlo simulation show that our approach has good performance across a wide range of possible shifts. We show that the new method has competitive performance relative to other methods in literature in terms of ARL, and another feature of the new chart is that it can be easily designed. The application of our proposed method is illustrated by a real data example from an optical imaging system.     

A neural network-based procedure for the monitoring of exponential mean

Control charts are widely used for both manufacturing and service industries. Cumulative sum (CUSUM) charts are known to be very sensitive in detecting small shifts in the mean. In this paper, we propose a neural network as an alternative approach to CUSUM charts when monitoring exponential mean. The performance of neural network was evaluated by estimating the average run lengths (ARLs) using simulation. The results obtained with simulated data suggest that control scheme based on neural network is significantly more sensitive to process shifts than CUSUM charts. This research also examines the feasibility of using CUSUM chart and neural network together in detecting process mean shifts. The results indicate that using the two methods in combination is more effective than using the methods separately.     

用于监控过程均值的DMHWMA控制图

在已经存在的多元均匀加权移动平均(Multivariate Homogeneously Weighted Moving Average, MHWMA)控制图的基础上,提出了一种用于监控过程均值的双多元均匀加权移动平均(Double Multivariate Homogeneously Weighted Moving Average,DMHWMA)控制图。并且与现有的控制图一样,DMHWMA控制图的平均运行长度只与偏移系数有关。通过蒙特卡罗模拟实验,比较了在不同程度的过程均值偏移下DMHWMA控制图与MHWMA控制图、多元指数加权移动平均(Multivariate Exponentially Weighted Moving Average,MEWMA)控制图、多元累积和(Multivariate Cumulation Sum, MCUSUM)控制图的平均运行长度特性。结论表明,不论过程均值发生多大程度的偏移,DMHWMA控制图的对过程均值变化的监控敏感度都明显优于其他控制图。并且通过一个实例数据验证了上述模拟实验结论的正确性。