Indicators for measuring satisfaction towards design quality of buildings

Design quality is an important component in measuring satisfaction towards total product quality (TPQ) of buildings, the product of construction projects. Design Quality Indicator (DQI), developed by the Construction Industry Council (CIC) in the UK looking at three quality fields, i.e. functionality, build quality, and impact of building in measuring the quality of design embodied in the buildings through feedback and perceptions of all stakeholders involved in the production and use of buildings. Design quality is always a major concern in the Malaysian construction industry. With inspiration from this DQI, this study was carried out to identify indicators for measuring the satisfaction towards design quality of buildings and to evaluate the suitability of the indicators for application in the context of Malaysian construction industry. Through literature survey, 32 indicators of design quality were identified and grouped into the three design quality fields. A questionnaire survey was carried out among Malaysian construction professionals (architects, engineers, quantity surveyors, contractors and developers) to assess the identified design quality indicators in terms of their relevance and significance in the context of construction industry in Malaysia. The survey reveals that access, natural lighting, access and use, structure element, landscape, finishes, location, external environment, urban and social integration and noise are among the design quality indicators that were perceived as the most important to be looked at. In overall, all the indicators are relevant for adoption in the Malaysian construction industry to measure the satisfaction towards design quality of buildings

A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes

Control charts are considered as powerful tools in detecting any shift in a process. Usually, the Shewhart control chart is used when data follows the symmetrical property of a normal distribution. In practice, the data from the industry may follow a non-symmetrical distribution or an unknown distribution. The average run length (ARL) is a significant measure to assess the performance of the control chart. The ARL may mislead when the statistic is computed from an asymmetric distribution. To handle this issue, in this paper, an ARL-unbiased hybrid exponentially weighted moving average proportion (HEWMA-p) chart is proposed for monitoring the process variance for a non-normal distribution or an unknown distribution. The efficiency of the proposed chart is compared with the existing chart in terms of ARLs. The proposed chart is more efficient than the existing chart in terms of ARLs. A real example is given for the illustration of the proposed chart in the industry.11Ysciescopu

Efficient Auxiliary Information Based Exponentially Weighted Moving Coefficient of Variation Control Chart using Hybrid Estimator : An Application to Monitor NPK Fertilizer

In this era, manufacturing sectors should ensure the quality of their production process and products. They must reduce the variability that occurs in their operation. Coefficient variation control charts have become important statistical Process Control (SPC) tools for monitoring processes when the process mean linear function with the standard deviation. In recent years, auxiliary information-based-CV control charts using memory type structure have been investigated to enhance the sensitivity of control charts. Auxiliary information is selected when the variable remains stable during the monitoring period. Nevertheless, the AIB statistic is constructed based on lognormal transformation, and no research investigated the memory type CV chart using estimator of AIB-CV from the combination of ratio and regression form called hybrid form. This research proposes a hybrid auxiliary information-based exponentially weighted moving coefficient of variation (Hybrid AIB-EWMCV) control chart for detecting small to moderate shifts in the CV process. The Average Run Length (ARL) simulation shows that increasing the level of correlation and sample sizes enhances the detection ability of the control chart. Also, the proposed chart performs well than existing chart. A real dataset from fertilizer manufacturing is implemented to explain the condition of the process by using a Hybrid AIB-EWMCV control chart

Method of lines and runge-kutta method in solving partial differential equation for heat equation

Solving the differential equation for Newton’s cooling law mostly consists of several fragments formed during a long time to solve the equation. However, the stiff type problems seem cannot be solved efficiently via some of these methods. This research will try to overcome such problems and compare results from two classes of numerical methods for heat equation problems. The heat or diffusion equation, an example of parabolic equations, is classified into Partial Differential Equations. Two classes of numerical methods which are Method of Lines and Runge-Kutta will be performed and discussed. The development, analysis and implementation have been made using the Matlab language, which the graphs exhibited to highlight the accuracy and efficiency of the numerical methods. From the solution of the equations, it showed that better accuracy is achieved through the new combined method by Method of Lines and Runge-Kutta method

Mixed control charts using EWMA Statistics

In this paper, two mixed control charts are designed for process monitoring when the quality characteristic of interest follows a normal distribution. The mixed control chart starts with monitoring the number of non-conforming items but switches to monitoring using exponentially weighted moving average (EWMA) statistic or hybrid EWMA statistic when the decision is indeterminate with the attribute data. The average run lengths are calculated to evaluate the performance of the proposed control charts according to the mean shift. The performance of both control charts is compared with each other and with the existing control chart. Simulation study is given to demonstrate the efficiency of the proposed control charts.1150Ysciescopu

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Multivariate Ewma Models and Monitoring Health Surveillance during a Pandemic

We examine a common problem is biological analytics and surveillance in health care. These methods can improve greatly the process of monitoring health data to assess changes in the likelihood of Pandemics and disease incidence in a world where medical knowledge is still largely in an embryonic period. Based on an illustration, we suggest that multivariate exponential moving-average (MEWMA) control charts are suitable in many cases where detection and inspection of several or more variables over a lengthy period of testing provide for the best analysis of data leading to pre-­diagnostic and diagnostic therapy. Though these methods came from the control of quality and continuous improvement in lean manufacturing and service operations, these methods are useful if not a vital application in the analysis of health care and therapeutic data. The indications from this study corroborate earlier findings by others that MEWMA methods fit the diagnostic activity under study. Unfortunately Pandemic Analysis is using oversimplified techniques in analyzing data secure by diagnostic tests which can easily be improved especially in the use modern day analytics based on quality control methods used in other disciplines

Detection Sensitivity of a Modified EWMA Control Chart with a Time Series Model with Fractionality and Integration

Among the many statistical process control charts, the modified exponentially weighted moving average (EWMA) control chart has been designed to swiftly detect a small shift in a process parameter. Herein, we propose two explicit formulas for the average run length (ARL) for integrated moving average (IMA) and fractional integrated moving average (FIMA) models combined with the modified EWMA control chart for time series prediction. The application of the suggested control chart procedures depends on the residuals of the IMA and FIMA models. The performance of the control chart with both models is evaluated by using the ARL. Explicit formulas for the ARL for the two models with the modified EWMA statistic are derived and their precision is compared with the numerical integral equation method. The explicit formulas could accurately predict the true ARL while markedly decreasing the computational processing time compared to the numerical integration method. The capabilities of the IMA and FIMA models with the modified EWMA control chart were studied by varying g times the last term and exponential smoothing parameter λ, with the relative mean index being used to evaluate these situations. The results show that the modified EWMA control chart with either model performed better than the original EWMA control chart. Furthermore, the modified EWMA control chart with either model was highly efficient when g increased and λ was small. Two applications involving energy commodity prices are used to illustrate the efficacies of the proposed approaches, the results of which were in accordance with the experimental study. Doi: 10.28991/ESJ-2022-06-05-015 Full Text: PD

Forward Intensity Model Monitoring Using Multivariate Exponential Weighted Moving Average Scheme

We propose a parameter monitoring method for the forward intensity model – the default probability prediction model of the Credit Research Initiative (CRI). We review the relative statistical process control scheme in the field of engineering. Based on this, we propose a new Multivariate Exponential Weighted Moving Average (MEWMA) scheme to monitor the forward intensity model monthly. This new chart might be applied to identify and diagnose the out-of-control (OC) parameters in real time as the data updating, which reduces the cost of recalculating all parameters and improve the operational and calculational efficiency of the default prediction models in practical application