Exponentially weighted moving average—Moving average charts for monitoring the process mean

Abstract

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 ARL 1 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.

A New Nonparametric Tukey MA-EWMA Control Charts for Detecting Mean Shifts

“…ARL of all control charts comparing to the proposed chart would decreased. It was in line with the previous research [25] which discovered that if w value was higher, the detection efficiency of the proposed control chart would decrease when comparing to other control charts. In contrast, the higher value of w supported the better efficiency of MME chart.…”

“…This chart is a combination of the MA and EWMA control charts [24][25]. In the mathematical model developed for the MME chart design, the plot statistic of the MA chart is used as an input to the EWMA chart (equation 1).…”

An Efficient Routing Scheme for Intrabody Nanonetworks Using Artificial Bee Colony Algorithm

“…The presented scheme also employs the characteristics of the Artificial Bee Colony (ABC) algorithm that optimizes the fitness function for the selection of NBSs for improving the performance of IBNNs in terms of residual energy, end-to-end delay, and packet delivery ratio. In addition, the introduction of Exponential Weighted Moving Average (EWMA) in our proposed scheme improves diagnosis and treatment due to the characteristics of the EWMA approach for real-time monitoring applications [26]- [30]. Accordingly, the introduction of EWMA-Based Opportunistic Data Transmission (EWMA-ODT) and ABC algorithm -Based Query-Response Transmission (ABC-QRT) schemes in our proposed novel routing scheme achieves the ultimate objective of data collection with minimum delay and energy consumption.…”

BAFPN: An Optimization for YOLO

“…Experiments have verified that BAFPN can significantly improve the detection accuracy for YOLO networks. Exponential Moving Average (EMA) [3] is employed to optimize the model parameters. Pruning of the trained model can be achieved using the Batch Normalization (BN) [4] layer's coefficient-γ.…”