This research aimed to propose a newly-mixed control chart called the Exponentially Weighted Moving Average—Moving Average Chart (EWMA-MA) to detect the mean change in a process underlying symmetric and asymmetric distributions. The performance of the proposed control chart are compared with Shewhart, MA, EWMA, MA-EWMA and EWMA-MA control charts by using average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL) as the criteria for measuring efficiency which evaluated by using Monte Carlo simulation (MC), Moreover, the proposed control chart will be applied to real data. The results of performance comparison showed that the presented control charts performed better detection than the Shewhart, MA, and EWMA charts. However, the results of detection tended to be slower than those for the MA-EWMA chart. The value of ARL₁ for the mixed control chart depends on the parameters of the statistics for such control chart. The EWMA-MA chart is a variable following λ and the MA-EWMA chart is varied according to w. From applying the proposed control chart to the data for flow in the Nile River and data of the real GDP growth (%) in the Lebanese economy, it was found to be in accordance with the research results.

The mixed control chart is proposed to improve detection performance with fewer process shifts. In this study, we proposed the modified exponentially weighted moving average - moving average control chart (MMEM), a new mixed control chart for observing the changes in the process mean. Average run length, standard deviation of run length, and median run length can be used to examine the effectiveness of detecting changes in the proposed chart with Shewhart, Moving Average (MA), Modified Exponentially Weighted Moving Average (MEWMA), and Mixed Moving Average - Modified Exponentially Weighted Moving Average (MMME) control charts in parametric and nonparametric distributions that use Monte Carlo simulation. The results demonstrate that the proposed chart outperforms other control charts mostly in the detection of small-to-moderate shifts. To illustrate the application of the proposed chart, chemical process temperature data and dataset on survival times of a group of patients suffering from head and neck cancer disease and treated with radiotherapy were provided, and it was discovered that the proposed chart performs better than other control charts.

This paper presents new evidence about user perception of VoIP quality that is inconsistent with the general understanding of three codecs know as G.729, G.711 and G.722. The focus of the study is aimed at VoIP quality evaluation by Thai users that use the Thai language which is tonal. This study was conducted by using conversation-opinion tests. The results, called MOS-CQS, were then analyzed carefully. After the study and analysis, it has been found that the perception of subjects, who were Thai native speakers, to G.729, G.711 and G.722 is not significantly different.

Control charts are a type of statistical tool used to control a production process in order to obtain the quality products that can fulfill the demands of both the manufacturer and the consumers. In this paper, we propose the Tukey Moving Average-Exponentially Weighted Moving Average control chart (MME-TCC) to detect the change of average of the process with symmetric and asymmetric distribution and to compare the efficiency in detecting the change of the MME-TCC to the MA, MME, MEM, MA-TCC and MEM-TCC at the various change levels of the parameter. The criteria to measure the efficiency were average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL) which evaluated by using Monte Carlo simulation (MC), The research results showed that the proposed control chart has the highest efficiency in detecting the change when the change level was at $-0.75\le \delta \le 0.75$ . However, if the change of parameter increased ( $\delta \ge 1.00$ ), the MME had more efficiency. In the case where the observation was logistic distributions, the MA-TCC had more efficiency to detect the change. Moreover, from applying the proposed control chart to two sets of real data, the mine explosion period in the UK during 1875–1951 and data of diameter of the workpiece from an industrial factory, it was found that the MME-TCC was able to more quickly detect the change than the other control charts.

Control chart is a useful statistical tool for the production process control to maintain the product value at the standard. The objectives of this research were to propose the Tukey Moving Average - Double Exponentially Weighted Moving Average control chart (MMD-TCC chart) to detect the change of mean in the symmetric and non-symmetric distribution, and to compare the effectiveness of the change detection of MMD-TCC with that of MA, DEWMA, MMD, MDM, TCC, and MDM-TCC at the different parameter levels. ARL ₁ and MRL criteria were used to measure the efficiency by applying Monte Carlo (MC) simulation. Research results indicated that the change of mean in process under the control where ARL ₀ = 370 with MMD-TCC was more efficient to detect the change than other control charts unless it was the symmetric distribution with the change of parameter at ±1.50, ±2.00, ±3.00, ±4.00, and the non-symmetric distributions with the change of parameter at 3.00, 4.00 with MA control chart. Furthermore, by applying the proposed control chart to real data, it was found to be in accordance with the research results from simulation technique.