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

Quantitative infrared thermography resolved leakage current problem in cathodic protection system

Leakage current problem can happen in Cathodic Protection (CP) system installation. It could affect the performance of underground facilities such as piping, building structure, and earthing system. Worse can happen is rapid corrosion where disturbance to plant operation plus expensive maintenance cost. Occasionally, if it seems, tracing its root cause could be tedious. The traditional method called line current measurement is still valid effective. It involves isolating one by one of the affected underground structures. The recent methods are Close Interval Potential Survey and Pipeline Current Mapper were better and faster. On top of the mentioned method, there is a need to enhance further by synthesizing with the latest visual methods. Therefore, this paper describes research works on Infrared Thermography Quantitative (IRTQ) method as resolution of leakage current problem in CP system. The scope of study merely focuses on tracing the root cause of leakage current occurring at the CP system lube base oil plant. The results of experiment adherence to the hypothesis drawn. Consequently, res

Graphical user interface (GUI) for supervisory control of computer intergtated manufacturing (CIM-70A) using SCADA

Supervisory Control system and the Acquisition Data or SCADA is generalization of effective plant monitoring and conU'ol system in meeting production needs etc. The aim of the study is to prepare a SCADA system for AS/RS, functional Mechatronics Educational Material which simulates to real-life production system. Graphical control buttons to the system will be design to perform single or multiple tasks. The software is form Citect Pty. Limited called Citect SCADA. This project will be discussed as it applied in a CIM-70A at Mechatronic Laboratory of UTHM. Designing a controlling and monitoring system not only for AS/RS but it is also a way providing up-to-date data. It will provide system operators with central or local control using clear, concise, resizable graphics pages (screens). Graphical control buttons to the system will be design to perform single or multiple tasks. In the last chapter, some methodologies for solving the problem as well as to improve the SCADA are proposed

Design Methodology of Modular-Ann Pattern Recognizer for Bivariate Quality Control

In quality control, monitoring unnatural variation (UV) in manufacturing process has become more challenging when dealing with two correlated variables (bivariate). The traditional multivariate statistical process control (MSPC) charts are only effective for triggering UV but unable to provide information towards diagnosis. In recent years, a branch of research has been focused on control chart pattern recognition (CCPR) technique. However, findings on the source of UV are still limited to sudden shifts patterns. In this study, a methodology to develop a CCPR scheme was proposed to identify various sources of UV based on shifts, trends, and cyclic patterns. The success factor for the scheme was outlined as a guideline for realizing accurate monitoring-diagnosis in bivariate quality control

Feature-based decision rules for control charts pattern recognition: A comparison between CART and QUEST algorithm

Control chart pattern (CCP) recognition can act as a problem identification tool in any manufacturing organization. Feature-based rules in the form of decision trees have become quite popular in recent years for CCP recognition. This is because the practitioners can clearly understand how a particular pattern has been identified by the use of relevant shape features. Moreover, since the extracted features represent the main characteristics of the original data in a condensed form, it can also facilitate efficient pattern recognition. The reported feature-based decision trees can recognize eight types of CCPs using extracted values of seven shape features. In this paper, a different set of seven most useful features is presented that can recognize nine main CCPs, including mixture pattern. Based on these features, decision trees are developed using CART (classification and regression tree) and QUEST (quick unbiased efficient statistical tree) algorithms. The relative performance of the CART and QUEST-based decision trees are extensively studied using simulated pattern data. The results show that the CART-based decision trees result in better recognition performance but lesser consistency, whereas, the QUEST-based decision trees give better consistency but lesser recognition performance

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

Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches

Because of the excellent performance on monitoring and controlling an autocorrelated process, the integration of statistical process control (SPC) and engineering process control (EPC) has drawn considerable attention in recent years. Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of a process, especially when the underlying process is autocorrelated. However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized. Effective recognition of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process. In practical situations, after compensating by EPC, the underlying disturbance patterns could be of any mixture types which are totally different from the original patterns. This study proposes the integration of support vector machine (SVM) and artificial neural network (ANN) approaches to recognize the disturbance patterns of the underlying disturbances. Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system

Portable marketing set

Folding table (Figure 8.1) that consist of chair or also known as “Portable Table” is an item that have been widely used all over the world. However, in Malaysia this concept of idea is still new and needs publicities. This portable table usually consist of a rectangular table and chairs around it. The development of the table is based from the dining table at home to gather family member and for eating. Folding tables are produced in many sizes, design and configuration and it can be made from plastic, metal, plastic and other material. Mostly special material will be used by engineer to produces the product

Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features

Control charts have been widely utilized for monitoring process variation in numerous applications. Abnormal patterns exhibited by control charts imply certain potentially assignable causes that may deteriorate the process performance. Most of the previous studies are concerned with the recognition of single abnormal control chart patterns (CCPs). This paper introduces an intelligent hybrid model for recognizing the mixture CCPs that includes three main aspects: feature extraction, classifier, and parameters optimization. In the feature extraction, statistical and shape features of observation data are used in the data input to get the effective data for the classifier. A multiclass support vector machine (MSVM) applies for recognizing the mixture CCPs. Finally, genetic algorithm (GA) is utilized to optimize the MSVM classifier by searching the best values of the parameters of MSVM and kernel function. The performance of the hybrid approach is evaluated by simulation experiments, and simulation results demonstrate that the proposed approach is able to effectively recognize mixture CCPs

Design optimization for the two-stage bivariate pattern recognition scheme

In manufacturing operations, unnatural process variation has become a major contributor to a poor quality product. Therefore, monitoring and diagnosis of variation is critical in quality control. Monitoring refers to the identification of process condition either it is running within in statistically in-control or out-of-control, whereas diagnosis refers to the identification of the source of out-of-control process. Selection of SPC scheme becomes more challenging when involving two correlated variables, which are known as bivariate quality control (BQC). Generally, the traditional SPC charting schemes were known to be effective in monitoring aspects, but there were unable to provide information towards diagnosis. In order to overcome this issue, many researches proposed an artificial neural network (ANN) - based pattern recognition schemes. Such schemes were mainly utilize raw data as input representation into an ANN recognizer, which resulted in limited performance. In this research, an integrated MEWMA-ANN scheme was investigated. The optimal design parameters for the MEWMA control chart have been studied. The study focused on BQC with variation in mean shifts (μ = ±0.75 ~ 3.00) standard deviations and cross correlation function (ρ = 0.1 ~ 0.9). The monitoring and diagnosis performances were evaluated based on the average run length (ARL0, ARL1) and recognition accuracy (RA) respectively. The selected optimal design parameters with λ=0.10, H=8.64 gave better performance among the other designs, namely, average run length, ARL1=3.24 ~ 16.93 (for out-of-control process) and recognition accuracy, RA=89.05 ~ 97.73%. For in-control process, design parameters with λ=0.40, H=10.31 parameter gave superior performance with ARL0 = 676.81 ~ 921.71, which is more effective in avoiding false alarm with any correlation