The New Neutrosophic Double and Triple Exponentially Weighted Moving Average Control Charts

The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average(NEWMA)chart.In this study,two control charts are designed under the uncertain environment or neutrosophic statistical interval system,when all observations are undermined,imprecise or fuzzy.These are termed neutrosophic double and triple exponentially weighted moving average(NDEWMA and NTEWMA)control charts.For the proficiency of the proposed chart,Monte Carlo simulations are used to calculate the run-length characteristics(such as average run length(ARL),standard deviation of the run length(SDRL),percentiles(P_(25),P_(50),P_(75)))of the proposed charts.The structures of the proposed control charts are more effective in detecting small shifts while these are comparable with the other existing charts in detecting moderate and large shifts.The simulation study and real-life implementations of the proposed charts show that the proposed NDEWMA and NTEWMA charts perform better in monitoring the process of road traffic crashes and electric engineering data as compared to the existing control charts.Therefore,the proposed charts will be helpful in minimizing the road accident and minimizing the defective products.Furthermore,the proposed charts are more acceptable and actual to apply in uncertain environment.

Parameters optimization for exponentially weighted moving average control chart using generalized regression neural network

As a useful alternative of Shewhart control chart, exponentially weighted moving average （EWMA） control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA （MEWMA） control charts, in which the optimal parameter pair （ λ, k） or （ λ, h ） was searched by using the generalized regression neural network （GRNN）. The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length （ARLo） and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.

Error Detection and Pattern Prediction Through Phase II Process Monitoring

The continuous monitoring of the machine is beneficial in improving its process reliability through reflected power function distribution.It is substantial for identifying and removing errors at the early stages of production that ultimately benefit the firms in cost-saving and quality improvement.The current study introduces control charts that help the manufacturing concerns to keep the production process in control.It presents an exponentially weighted moving average and extended exponentially weighted moving average and then compared their performance.The percentiles estimator and the modified maximum likelihood estimator are used to constructing the control charts.The findings suggest that an extended exponentially weighted moving average control chart based on the percentiles estimator performs better than exponentially weightedmoving average control charts based on the percentiles estimator and modified maximum likelihood estimator.Further,these results will help the firms in the early detection of errors that enhance the process reliability of the telecommunications and financing industry.

Nonlinear Statistical Process Monitoring Based on Control Charts with Memory Effect and Kernel Independent Component Analysis

A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.

Exponential gravitational search algorithm-based VM migration strategy for load balancing in cloud computing

With the advancement in the science and technology,cloud computing has become a recent trend in environment with immense requirement of infrastructure and resources.Load balancing of cloud computing environments is an important matter of concern.The migration of the overloaded virtual machines(VMs)to the underloaded VM with optimized resource utilization is the effective way of the load balancing.In this paper,a new VM migration algorithm for the load balancing in the cloud is proposed.The migration algorithm proposed(EGSA-VMM)is based on exponential gravitational search algorithm which is the integration of gravitational search algorithm and exponential weighted moving average theory.In our approach,the migration is done based on the migration cost and QoS.The experimentation of proposed EGSA-based VM migration algorithm is compared with ACO and GSA.The simulation of experiments shows that the proposed EGSA-VMM algorithm achieves load balancing and reasonable resource utilization,which outperforms existing migration strategies in terms of number of VM migrations and number of SLA violations.

Memory Based Scheme to Monitor Non-Random Small Shift Patterns in Manufacturing Process

Consistent high-quality and defect-free production is the demand of the day. The product recall not only increases engineering and manufacturing cost but also affects the quality and the reliability of the product in the eye of users. The monitoring and improvement of a manufacturing process are the strength of statistical process control. In this article we propose a process monitoring memory-based scheme for continuous data under the assumption of normality to detect small non-random shift patterns in any manufacturing or service process.The control limits for the proposed scheme are constructed. The in-control and out-of-control average run length(AVL) expressions have been derived for the performance evaluation of the proposed scheme. Robustness to non-normality has been tested after simulation study of the run length distribution of the proposed scheme, and the comparisons with Shewhart and exponentially weighted moving average(EWMA) schemes are presented for various gamma and t-distributions. The proposed scheme is effective and attractive as it has one design parameter which differentiates it from the traditional schemes. Finally, some suggestions and recommendations are made for the future work.