Control charts for monitoring of process variance are developed based on Shewhart, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts for mean. In all these variance control charts, log transformation of the sample variance is used. The design procedure of this chart is complex and it is poorly understood by the industry. In this paper a EWMA chart for monito...

The drawbacks to multivariate charting schemes is their inability to identify which variable was the source of the signal. The multivariate exponentially weighted moving average (MEWMA) developed by Lowry, et al (1992) is an example of a multivariate charting scheme whose monitoring statistic is unable to determine which variable caused the signal. In this paper, the run length performance of m...

The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are seriallycorrelated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, s...

1. Unlike X -R and Individuals charts (without the Western Electric Handbook rules which aim to increase sensitivity), all of the data collected over time may be used to determine the control status of a process. 2. The EWMA is often superior to the CUSUM charting technique for detecting "larger" shifts. 3. EWMA schemes may be applied for monitoring standard deviations in addition to the proces...

in many computer vision applications, segmenting and extraction of moving objects in video sequences is an essential task. background subtraction, by which each input image is subtracted from the reference image, has often been used for this purpose. in this paper, we offer a novel background-subtraction technique for real-time dynamic background generation using color images that are taken fro...