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

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

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

The blockage ratio effect to the spray performances

Nozzle sprays are used in wide range of application. The used of nozzle application is depend on the spray characteristics, by which to suit the particular application. This project studies the effect of the air blockage ratio to the spray characteristics. This research conducted into two part which are experimental and simulation section. The experimental was conducted by using particle image velocimetry (PIV) method, and ANSYS software was used as tools for simulation section. There are two nozzles were tested at 1 bar pressure of water and air. Nozzle A (with blockage ratio 0.316) and nozzle B (blockage ratio 1.000). Both of the sprays performances generated by the nozzles was examined at 9 cm vertical line from 8 cm of the nozzle orifice. The validation result provided in the detailed analysis shows that the trend of graph velocity versus distance gives the good agreement within simulation and experiment. From result, nozzle A generated a wider spray angle and higher water droplet velocity which are 31.41 degree and 37.317 m/s compared to nozzle B which has produced 27.13 degree of spray penetration angle and 16.49 m/s water droplet velocity. As a conclusion, blockage ratio has affected the spray system by increasing the velocity of air inside the spray system. This is happened at a condition of 1 bar air pressure

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

Classification-based prediction of effective connectivity between timeseries with a realistic cortical network model

Effective connectivity measures the pattern of causal interactions between brain regions. Traditionally, these patterns of causality are inferred from brain recordings using either non-parametric, i.e., model-free, or parametric, i.e., model-based, approaches. The latter approaches, when based on biophysically plausible models, have the advantage that they may facilitate the interpretation of causality in terms of underlying neural mechanisms. Recent biophysically plausible neural network models of recurrent microcircuits have shown the ability to reproduce well the characteristics of real neural activity and can be applied to model interacting cortical circuits. Unfortunately, however, it is challenging to invert these models in order to estimate effective connectivity from observed data. Here, we propose to use a classification-based method to approximate the result of such complex model inversion. The classifier predicts the pattern of causal interactions given a multivariate timeseries as input. The classifier is trained on a large number of pairs of multivariate timeseries and the respective pattern of causal interactions, which are generated by simulation from the neural network model. In simulated experiments, we show that the proposed method is much more accurate in detecting the causal structure of timeseries than current best practice methods. Additionally, we present further results to characterize the validity of the neural network model and the ability of the classifier to adapt to the generative model of the data

Life jacket

Anyone who cannot swim well should wear life jacket whether they are in the water or around the water. Even those who are can swim well should wear the life jacket when they are doing activity such as swimming, fishing, boating or while doing any water-related activity. Life jacket is a kind of safety jacket that keeping the wearer float the in the water. The wearer may be in the conscious or unconscious condition but by wearing the life jacket we can minimize the risk of drowning because life jacket assist the wearer to keep floating in the water

Control Chart Pattern Recognition Using Small Window Size for Identifying Bivariate Process Mean Shifts

There are many traits in the manufacturing technology to assure the quality of products. One of the current practices aims for monitoring the in-process quality of small-lot production using Statistical Process Control (SPC), which requires small samples or small window sizes. In this study, the recognition performance of bivariate SPC pattern recognition scheme was investigated when dealing with small window sizes (less than 24). The framework of the scheme was constructed using an artificial neural network recognizer. The simulated SPC samples in different window sizes (8 ~ 24) and different change points (fixed and varies) were generated to study the recognition performance of the scheme based on mean square error (MSE) and classification accuracy (CA) measures. Two main findings have been suggested: (i) the scheme was superior when recognizing shift patterns with various change points compared to the shift patterns with fixed change point, with lower MSE and higher CA results, (ii) the scheme was more difficult to recognize smaller window size patterns with increasing MSE and decreasing CA trends, since these patterns provided insufficient information of unnatural variation. The outcome of this study would be helpful for industrial practitioners towards applying SPC for small-lot-production. &nbsp

Control Chart Pattern Recognition Using Small Window Size for Identifying Bivariate Process Mean Shifts

There are many traits in the manufacturing technology to assure the quality of products. One of the current practices aims for monitoring the in-process quality of small-lot production using Statistical Process Control (SPC), which requires small samples or small window sizes. In this study, the recognition performance of bivariate SPC pattern recognition scheme was investigated when dealing with small window sizes (less than 24). The framework of the scheme was constructed using an artificial neural network recognizer. The simulated SPC samples in different window sizes (8 ~ 24) and different change points (fixed and varies) were generated to study the recognition performance of the scheme based on mean square error (MSE) and classification accuracy (CA) measures. Two main findings have been suggested: (i) the scheme was superior when recognizing shift patterns with various change points compared to the shift patterns with fixed change point, with lower MSE and higher CA results, (ii) the scheme was more difficult to recognize smaller window size patterns with increasing MSE and decreasing CA trends, since these patterns provided insufficient information of unnatural variation. The outcome of this study would be helpful for industrial practitioners towards applying SPC for small-lot-production. &nbsp