Arcing High Impedance Fault Detection Using Real Coded Genetic Algorithm

Safety and reliability are two of the most important aspects of electric power supply systems. Sensitivity and robustness to detect and isolate faults can influence the safety and reliability of such systems. Overcurrent relays are generally used to protect the high voltage feeders in distribution systems. Downed conductors, tree branches touching conductors, and failing insulators often cause high-impedance faults in overhead distribution systems. The levels of currents of these faults are often much smaller than detection thresholds of traditional ground fault detection devices, thus reliable detection of these high impedance faults is a real challenge. With modern signal processing techniques, special hardware and software can be used to significantly improve the reliability of detection of certain types of faults. This paper presents a new method for detecting High Impedance Faults (HIF) in distribution systems using real coded genetic algorithm (RCGA) to analyse the harmonics and phase angles of the fault current signals. The method is used to discriminate HIFs by identifying specific events that happen when a HIF occurs

MRI brain classification using support vector machine

The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Magnetic resonance imaging (MRI) is an imaging technique that has played an important role in neuroscience research for studying brain images. Classification is an important part in retrieval system in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. In this paper, we have obtained the feature related to MRI images using discrete wavelet transformation. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed

Improvement of strength and water absorption of Interlocking Compressed Earth Bricks (ICEB) with addition of Ureolytic Bacteria (UB)

Interlocking Compressed Earth Brick (ICEB) are cement stabilized soil bricks that allow for dry stacked construction. This characteristic resulted to faster the process of building walls and requires less skilled labour as the bricks are laid dry and lock into place. However there is plenty room for improving the interlocking bricks by increase its durability. Many studies have been conducted in order to improve the durability of bricks by using environmentally method. One of the methods is by introducing bacteria into bricks. Bacteria in brick induced calcite precipitation (calcite crystals) to cover the voids continuously. Ureolytic Bacteria (UB) was used in this study as a partial replacement of limestone water with percentage of 1%, 3% and 5%. Enrichment process was done in soil condition to ensure the survivability of UB in ICEB environment. This paper evaluates the effect of UB in improving the strength and water absorption properties of ICEB and microstructure analysis. The results show that addition of 5% UB in ICEB indicated positive results in improving the ICEB properties by 15.25% in strength, 14.72% in initial water absorption and 14.68% reduction in water absorption. Precipitation of calcium carbonate (CaCo3) in form of calcite can be distinguish clearly in microstructure analysis

Generalization Bounds in the Predict-then-Optimize Framework

The predict-then-optimize framework is fundamental in many practical settings: predict the unknown parameters of an optimization problem, and then solve the problem using the predicted values of the parameters. A natural loss function in this environment is to consider the cost of the decisions induced by the predicted parameters, in contrast to the prediction error of the parameters. This loss function was recently introduced in Elmachtoub and Grigas (2017) and referred to as the Smart Predict-then-Optimize (SPO) loss. In this work, we seek to provide bounds on how well the performance of a prediction model fit on training data generalizes out-of-sample, in the context of the SPO loss. Since the SPO loss is non-convex and non-Lipschitz, standard results for deriving generalization bounds do not apply. We first derive bounds based on the Natarajan dimension that, in the case of a polyhedral feasible region, scale at most logarithmically in the number of extreme points, but, in the case of a general convex feasible region, have linear dependence on the decision dimension. By exploiting the structure of the SPO loss function and a key property of the feasible region, which we denote as the strength property, we can dramatically improve the dependence on the decision and feature dimensions. Our approach and analysis rely on placing a margin around problematic predictions that do not yield unique optimal solutions, and then providing generalization bounds in the context of a modified margin SPO loss function that is Lipschitz continuous. Finally, we characterize the strength property and show that the modified SPO loss can be computed efficiently for both strongly convex bodies and polytopes with an explicit extreme point representation.Comment: Preliminary version in NeurIPS 201

Development of Ultra-Wideband (UWB)Horn Antenna Using Approximation Method

This paper presents a design of an ultra wide-band (UWB) horn antenna for microwave imaging radar system. The key purpose of the study is to design a horn antenna which is used in medical imaging system. The proposed antenna operates within 3.1-10.6 GHz as it is the band allocated for medical industry usage. The antenna known as a directional antenna which is supported by rectangular waveguide. The horn antenna is purposely chosen to design in order to increase the directivity of the antenna within 15-20 dB and achieve higher gain and wider bandwidth as possible. This horn antenna is capable to produce return loss as minimum as possible. The antenna is designed and simulated using CST Microwave Studio. The simulation results show that the pyramidal horn antenna structure exhibits low VSWR as well as good radiation pattern over 3.1-10.6 GHz frequency ban

Antimicrobial activities of marine fungi from Malaysia

Copyright 2011 Elsevier B.V., All rights reserved.Peer reviewedPublisher PD

Ventricular arrhythmias classification and onset determination system

Accurately differentiating between ventricular fibrillation (VF) and ventricular tachycardia (VT) episodes is crucial in preventing potentially fatal missed interpretations that could lead to needless shock to the patients, resulting in damaging the heart. Apart from accurately classifying between VT and VF, the predetermination of the onset of the ventricular arrhythmias is also important in order to allow for more efficient monitoring of patients and can potentially save one’s life. Thus, this research intends to focus on developing a system called Classification and Onset Determination System (CODS) that is able to classify, track and monitor ventricular arrhythmias by using a method called Second Order Dynamic Binary Decomposition (SOD-BD) technique. Two significant characteristics (the natural frequency and the input parameter) were extracted from Electrocardiogram (ECG) signals that are provided by Physiobank database and analyzed to find the significant differences for each ventricular arrhythmia types and classify the ECGs accordingly (N, VT and VF). The outcome from these ECG extractions was also used to locate the onset of ventricular arrhythmia that is useful to predict the occurrence of the heart abnormalities. All the ECGs analysis, parameters extraction, classification techniques, and the CODS are developed using LabVIEW software

Potential dopant in photocatalysis process for wastewater treatment-a review

Nowadays, too much pollution has happened around us, and one of them is water pollution, which each day has become more severe and worse. One of the sources of water pollution comes from the industry that has used dyes either excessively or not. In case of that, the wastewater needs to be treated before released to the river or environment. In this paper, a review of the wastewater treatment using dopants such as nitrogen and magnesium, will be discussed

Assessment of drought impacts on vegetation health: a case study in Kedah

Prolonged drought in the early of 2014 has caused Malaysia to experience water supply shortage which directly affects both health and growth of vegetation. Thus this study aims to assess the risk vegetation areas that were impacted during 2014's drought by integrating the Standardized Precipitation Index (SPI) and Normalized Differentiation Vegetation Index (NDVI) methods. These two methods were able to assess the risk areas for the vegetation by measuring its health and classifying them according to its severity while considering the rainfall reduction at the specific time and location. The results obtained from this study shows that the central and north west of Kedah was vulnerable to the occurrence of drought. Kedah was more impacted by the dry event during the northeast monsoon. This study is significant as a fundamental input for further research and as an alternative approach by the application of space technology