Modified One-Sided EWMA Charts without- and with Variable Sampling Intervals for Monitoring a Normal Process

Much research has been conducted on two-sided Exponentially Weighted Moving Average (EWMA) control charts, while less work has been devoted to the one-sided EWMA charts. Traditional one-sided EWMA charts involve resetting the EWMA statistic to the target whenever it falls below or above the target, or truncating the observations above or below the target and further applying the EWMA statistic to the truncated samples. In order to further improve the performance of traditional one-sided EWMA mean (X¯) charts, this paper studies the performance of the Modified One-sided EWMA (MOEWMA) X¯ charts to monitor a normally distributed process. The Monte-Carlo simulation method is used to obtain the zero- and steady-state Run Length (RL) properties of the proposed control charts. Through extensive simulations and comparisons with other charts, it is shown that the proposed MOEWMA X¯ charts compare favorably with some existing competing charts. Moreover, by attaching the variable sampling intervals (VSI) feature to the MOEWMA X¯ charts, it is shown that the VSI MOEWMA charts outperform the corresponding charts without the VSI feature. Finally, a real data example from manufacturing process shows the implementation of the proposed one-sided charts.

A Mathematical Model of Parallel Programs and an Approach Based on it to Verification of MPI Programs

The paper presents a new mathematical model of parallel programs, on the basis of which it is possible, in particular, to verify parallel programs presented on a certain subset of the parallel programming interface MPI. This model is based on the concepts of a sequential and distributed process. A parallel program is modeled as a distributed process in which sequential processes communicate by asynchronously sending and receiving messages over channels. The main advantage of the described model is the ability to simulate and verify parallel programs that generate an indefinite number of sequential processes. The proposed model is illustrated by the application of verification of the matrix multiplication MPI program.

Bootstrap beta control chart for monitoring proportion data

PurposeAs an alternative to the standard p and np charts along with their various modifications, beta control charts are used in the literature for monitoring proportion data. These charts in general use average of proportions to set up the control limits assuming in-control parameters known. The purpose of the paper is to propose a control chart for detecting shift(s) in the percentiles of a beta distributed process monitoring scheme when in-control parameters are unknown. Such situations arise when specific percentile of proportion of conforming or non-conforming units is the quality parameter of interest.Design/methodology/approachParametric bootstrap method is used to develop the control chart for monitoring percentiles of a beta distributed process when in-control parameters are unknown. Extensive Monte Carlo simulations are conducted for various combinations of percentiles, false-alarm rates and sample sizes to evaluate the in-control performance of the proposed bootstrap control charts in terms of average run lengths (ARL). The out-of-control behavior and performance of the proposed bootstrap percentile chart is thoroughly investigated for several choices of shifts in the parameters of beta distribution. The proposed chart is finally applied to two skewed data sets for illustration.FindingsThe simulated values of in-control ARL are found to be closer to the theoretical results implying that the proposed chart for percentiles performs well with both positively and negatively skewed data. Also, the out-of-control ARL values for the percentiles decrease sharply with both downward and upward small, medium and large shifts in the parameters. The phenomenon indicates that the chart is effective in detecting shifts in the parameters. However, the speed of detection of shifts varies depending on the type of shift, the parameters and the percentile being considered. The proposed chart is found to be effective in comparison to the Shewhart-type chart and bootstrap-based unit gamma chart.Originality/valueIt is worthwhile to mention that the beta control charts proposed in the literature use average of proportion to set up the control limits. However, in practice, specific percentile of proportion of conforming or non-conforming items should be more useful as the quality parameter of interest than average. To the best of our knowledge, no research addresses beta control chart for percentiles of proportion in the literature. Moreover, the proposed control chart assumes in-control parameters to be unknown, and hence captures additional variability introduced into the monitoring scheme through parameter estimation. In this sense, the proposed chart is original and unique.

Scalable Architecture and Structure of Modules for Distributed Process Control System in Industrial Greenhouse Comp

With the growth of the population, the issue of food supply of cities with high-quality agricultural crops becomes urgent. Supply problems arising from this can be solved with the use of industrial greenhouse complexes with artificial lighting and groundless technologies. The development of these complexes makes the task of developing a control system to automate the cultivation processes urgent. Real industrial greenhouse complexes have a significant number of operations with the direct participation of personnel, which can be automated: control of the greenhouse microclimate, lighting, watering and preparation of the nutrient solution composition. This paper presents the architecture of a distributed control system for industrial greenhouse complexes. The system is built on a modular basis and is divided into three levels. The developed architecture is based on the use of standard modules, which makes the control system flexible and scalable. The paper also presents the basic design ratios, with the help of which it is possible to determine the required number of modules for the three levels of the proposed architecture. The use of wireless data transmission between modules based on LoRa technology allows you to abandon the laying of an information bus and at the same time deploy the system over large areas. Control of the system and its parameters is possible through direct human interaction with the interface of the control module or through remote interaction through the cloud. The architecture includes 3 types of executive modules, one combined sensor module and a control module. Each of the executive modules functions according to a given algorithm, and its parameters are controlled by a control module, based on a given growing program and information from sensors. This feature allows you to increase the reliability of the system and continue working in the event of a loss of communication with the cloud, as well as to exclude emergencies in the event of a loss of communication between the modules. The developed solutions make it possible to adapt the proposed control system for greenhouse complexes of various configurations and growing principles.

How do I bite thee? Let me count the ways: Heterogeneity and temperature dependence in Ae. aegypti biting habits drive individual mosquito arbovirus transmission potential

In models of mosquito-borne transmission, biting rate is the most influential parameter, and understanding the role of temperature on this process is important for assessing the transmission potential of arboviruses like dengue (DENV). Further, the process of biting is assumed to be relatively homogeneous across individuals, with time-between-bites described by an exponentially distributed process. However, these assumptions have not been addressed through laboratory experimentation. We experimentally determined daily biting habits of Ae. aegypti at three temperatures (24°C, 28°C, and 32°C) and determined that there was a high degree of individual heterogeneity in biting habits (number of bites, timing of bites, etc.). There was a significantly higher proportion of females that bit at 28°C compared to both 24°C and 32°C. We further explored the consequences of biting heterogeneity using an individual-based model designed to examine whether a particular biting profile determines whether a mosquito is more or less likely to 1) become exposed given a single index case of DENV and 2) transmit to a susceptible individual. Our results indicate that biting is heterogeneous among individuals and this heterogeneity affects transmission potential of DENV. Understanding individual-level heterogeneity in biting is important and may suggest a role for high-frequency biters as significant contributors to the transmission of DENV.

SACF of the Errors of Stationary Time Series Models in the Presence of a Large Additive Outlier

In this paper, the limiting behaviour of the Sample Autocorrelation Function(SACF) of the errors {et} of First-Order Autoregressive (AR(1)), First-Order Moving Average (MA(1)) and First Order Autoregressive First-Order Moving Average (ARMA(1,1)) stationary time series models in the presence of a large Additive Outlier(AO) is discussed. It is found that the errors which are supposed to be uncorrelated due to either white noise process or normally distributed process are not so in the presence of a large additive outlier. The SACF of the errors follows a particular pattern based on the time series model. In the case of AR(1) model, at lag 1, the contaminated errors {et} are correlated, whereas at higher lags, they are uncorrelated. But in the MA(1) and ARMA(1,1) models, the contaminated errors {et} are correlated at all the lags. Furthermore it is observed that the intensity of correlations depends on the parameters of the respective models.