In recent years, the exponential-weighted-moving-average (EWMA) statistic based controllers are popular in semiconductor manufacturing. However, the single EWMA controller is not su3cient for compensating for the wear-out process. Thus, a double EWMA controller was proposed to enhance the capability for controlling the drifting process. In the literatures, in the solution of the double EWMA con...

The exponentially weighted moving average (EWMA) control chart has been widely studied as a tool for monitoring normal processes due to its simplicity and efficiency. However, relatively little attention has been paid to EWMA charts for monitoring Poisson processes. This paper extends EWMA charts to Poisson processes with emphasis on quick detection of increases in Poisson rate. Both cases with...

Process monitoring through control charts is a quite popular practice in statistical process control. This study is planned for monitoring the process dispersion parameter using exponentially weighted moving average (EWMA) control chart scheme. Most of the EWMA dispersion charts that have been proposed are based on the assumption that the parent distribution of the quality characteristic is nor...

A quality-control chart based on exponentially weighted moving averages (EWMA) has, in the past few years, become a popular tool for controlling inaccuracy in industrial quality control. In this paper, I explain the principles of this technique, present some numerical examples, and by computer simulation compare EWMA with other control charts currently used in clinical chemistry. The EWMA chart...

In this paper, a comparative study of performance of some parametric and nonparametric EWMA control charts for detecting the process mean shift is considered. In section 2, we briefly describe each of the various EWMA models that are taken into account in our present study, Viz. the Parametric X -EWMA and Nonparametric Grouped Signed Rank (GSR)-EWMA, for the purpose of comparative study. In Sec...

Control charts are extensively used in processes and are very helpful in determining the special cause variations so that a timely action may be taken to eliminate them. One of the charting procedures is the Shewhart-type control charts, which are used mainly to detect large shifts. Two alternatives to the Shewhart-type control charts are the cumulative (CUSUM) control charts and the exponentia...

Combining an EWMA chart with a Shewhart chart is traditionally recommended as a means of providing good protection against both small and large shifts in the process mean. Capizzi and Masarotto have proposed an EWMA chart with a variable smoothing constant (AEWMA) for the same purpose. In this paper, we optimize the designs of the AEWMA and of the combined EWMA-Shewhart schemes with regard to p...

The multi-chart consists of several CUSUM or EWMA charts with different reference values that are used simultaneously to detect anticipated process changes. We not only prove that the chart can quickly achieve the asymptotic optimal bound, but also give an integral equation to determine the reference values to arrive at optimality. Simulation results are used to verify the theoretical optimal p...

Several core network protocols and applications adjust their operation dynamically based on current network conditions. TCP and IEEE 802.11 are notable examples, both of which periodically adapt the retransmission timeout and the contention window size depending on the average round trip time and the degree of collisions, respectively. Consequently, accurate network state estimation is critical...

Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts are famous statistical tools, to handle special causes and to bring the process back in statistical control. Shewhart charts are useful to detect large shifts, whereas EWMA and CUSUM are more sensitive for small to moderate shifts. In this study, we propose a new control chart, named mixed CUSUM-EWMA chart...