Iza Sazanita Isa

Weather Forecasting Using Photovoltaic System and Neural Network

This paper presents the applicability of Artificial Neural Network (ANN) for weather forecasting using a Photovoltaic system. The main objective is to predict daily weather conditions based on various measured parameters gained from the PV system. In this work, Multiple Multilayer Perceptron (MMLP) network with majority voting technique was used and trained using Levenberg Marquardt (LM) algorithm. Voting technique is widely used in many applications to solve real world problem. Different techniques of voting are used such as majority rules, decision making, consensus democracy, consensus government and supermajority. The way of the voting technique is different depending on the problem involved. Majority voting technique was applied in the study so that the performance of MMLP can be approved as compared to single MLP network. The proposed work has been used to classify four weather conditions; rain, cloudy, dry day and storm. The system can be used to represent a warning system for likely adverse conditions. Experimental results demonstrate that the applied technique gives better performance than the conventional ANN concept of choosing an MLP with least number of hidden neurons.

Improved Classification Performance for Multiple Multilayer Perceptron (MMLP) Network Using Voting Technique

This project investigates the capability of multiple multilayer perceptron (MMLP) system with majority voting technique. It is a system which consists of all the best-performed MLPs and a single final output from these MLPs is selected by the voting system. The MLP networks are trained using two types of learning algorithm, which are the Levenberg Marquardt and the Resilient Back Propagation algorithms. The performance of the MMLP networks are calculated based on the percentage of correct classificition. Data from three case studies, triangular waveform classification, breast cancer detection and transport classification, have been used to test the performance of the developed system. The results show that the MMLP system with voting technique has the capability of improving the classification correctness. The most well-known artificial neural network (ANN) architecture is a Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. By approaching these innovated MMLP system with automatic voting, the better classification result will be produced.

Comparative study of PID controlled modes on automatic water level measurement system

This paper presents the performances of Proportional (P), proportional-integral (PI) and proportional-integral-derivative (PID) modes controller to control an automatic water level control system. This project is developed to verify the performance of water level control system using PID control modes. The measurements of water level control system were collected from process plant which is located at Process Laboratory, UiTM Pulau Pinang. The measurements that have been carried out were analyzed based on three modes controlled that are Proportional, Proportional-Integral and Proportional-Integral-Derivative. The response of each control mode has been determined to identify the time constant, rise time, peak time and percentage overshoot. The evaluation of the output has been carried out and compared by software simulation using MATLAB toolbox. The results indicate that measured data and simulated data were showing a similarity in the responses and time constant.

Performance Comparison of Different Multilayer Perceptron Network Activation Functions in Automated Weather Classification

Multilayer perceptron network (MLP) has been recognized as a powerful tool for many applications including classification. Selection of the activation functions in the multilayer perceptron (MLP) network plays an essential role on the network performance. This paper presents a comparison study of two commonly used MLP activation function, sigmoid and hyperbolic tangent for weather classification. Meteorological data such as solar radiation, ambient temperature, current, surface temperature, voltage, wind direction and wind speed data are acquired from a photovoltaic (PV) system. Then, the meteorological data are input to the MLP network to classify the weather condition. In this study, weather conditions are classified into four types, rain, cloudy, dry day and storm. Levenberg-Marquardt algorithm is used to train the MLP network since it is the fastest training and ensure the best converges towards a minimum error. Experimental results show that hyperbolic tangent activation function is more efficient compared to sigmoid activation function. The MLP network using hyperbolic tangent function has achieved higher classification accuracy with less number of hidden nodes compared to sigmoid activation function.

New image enhancement technique for WMH segmentation of MRI FLAIR image

This paper proposes a new image enhancement technique known as Average Intensity Replacement based on Adaptive Histogram Equalization (AIR-AHE) for FLAIR image based on intensities and contrast mapping techniques. The proposed algorithm consists of partial contrast stretching, contrast limiting enhancement, window sliding neighborhood operation and new pixel centroid replacement. The fluid attenuated inversion recovery (FLAIR) sequences of MRI images which are used for segmentation have low contrast. Therefore, contrast stretching is used to improve the quality of the image. After improving the quality of image, the regions of high intensity are determined to represent potential WMH areas. The result shows that the image has a moderate enhancement on the WMH region which is significant to the image contrast enhancement. With complete brightness preservation, the proposed method gives a relatively natural brightness improvement on the WMH of the periventricular region.

Alpha and beta EEG brainwave signal classification technique: A conceptual study

This paper presents a conceptual of EEG analysis and classification of brainwaves signal for alpha and beta signals during Functional Electrical Stimulation, FES-assisted exercise. The characteristics of brainwave signals, data acquisition for electroencephalograph (EEG) signal and data session are identified. This paper also includes the criteria of the subject for both stroke patient and healthy person. The process of filtering the artifact and sampling the data were studied based on the established previous worked. In addition, a review on feature extraction for further classifying of brainwave signals stroke patients before and after performing FES-assisted exercised were also identified.

De-noising of noisy MRI brain image using the switching-based clustering algorithm

Magnetic Resonance Image is one of the technologies used for diagnosing brain cancer. Radiographers use the information obtained from MRI images to diagnose the disease and plan further treatment for the patient. MRI images are always corrupted with noise. Removing noise from images is crucial but it is not an easy task. Filtering algorithm is the most common method used to remove noise. A segmentation technique is normally used to process the image in order to detect the abnormality that has been observed, specifically in the brain. However, segmentation alone would be best to implement when the images are in good condition. In the case where the images are corrupted with noise, there are pre-processing steps that need to be implemented first before we can proceed to the next task. Therefore, in this project, we have proposed a simpler method that can de-noise and at the same time segment the image into several significant regions. The proposed method is called the switching-based clustering algorithm. The algorithm is implemented on the MRI brain images which are corrupted with a certain level of salt-and-pepper noise. During the segmentation process, the results show that the proposed algorithm has the ability to minimize the effect of noise without degrading the original images. The density of noise in the MRI images varies from 5% to 20%. The results are compared with the conventional clustering algorithm. Based on the experimental result obtained, the switching-based algorithm provides a better segmentation performance with fewer noise effects than the conventional clustering algorithm. Quantitative and qualitative analyses have shown positive results for the proposed switching-based clustering algorithm.

Modified hybrid median filter for removal of low density random-valued impulse noise in images

Random-valued impulse noise (RVIN) is a randomly distributed noise of two significant pixel values that degrades the quality of digital images during acquisition, processing, and storage. It is a variation of the salt-and-pepper or fixed-valued impulse noise (FIN) which instead of the black and white pixel, the noisy pixel value can be anywhere in the range of the grey level pixel. This paper introduces a new filter which is a modified hybrid median filter for removal of RVIN from digital images. The proposed filter is based on similar standard median filters and an improvement upon the hybrid median filter which make use of neighborhood pixels in reducing RVIN from the image. This filter operates using a window size of 3×3 and uses values in the window with a modified hybrid median algorithm to replace the targeted pixel during the filtering process. This technique has proven to be capable of overcoming the shortcomings of standard median filter as well as improve upon the hybrid median filter in restoring image details and in operating at higher noise density.

Assessing intensity of white matter hyperintensity and normal appearing white matter in healthy adults

There have been interest on white matter hyperintensity (WMH) and normal white matter (WM) changes reported but have not yet been fully characterized. Different image sequences of magnetic resonance imaging (MRI) scans may shows different gray scale intensity. However, it is difficult to differentiate the intensity of normal WM and WMH as their intensities are visually not much different. In this study, normal WM and WMH changes were investigated based on their intensity to determine the correlation of WMH types and severity in brain of healthy subjects. The assessment was performed by using fully automatic WMH detection and computing algorithms. The main brain regions were segregated into gray matter (GM), normal WM, cerebrospinal fluid (CSF) and non-brain tissue. From the results, it shows that there was significant difference seen between normal appearing WM and hyperintense WM in terms of their intensity levels. The study shows that the development of WMH is prevalent to the occasion of normal WM changes. This is shows that WMH intensity reflects the level of WMH classes and severity; however, further investigations are needed to improve their efficiency.

Comparisons of MLP transfer functions for different classification classes

This paper presents a comparison study of two different MLP transfer functions for three different classification cases of breast cancer, thyroid disease and weather classification. The transfer functions under investigation are sigmoid and hyperbolic tangent. In the study, MLP network was trained and tested to investigate the ability of the network to classify the breast cancer correctly between benign cell and malignant cell, classifying thyroid disease into normal, hyper or hypo thyroid and classifying weather conditions into four types; rain, cloudy, dry day and storm. Levenberg-Marquardt algorithm is adopted to train MLP network since it is the fastest training and ensure the best converges towards a minimum error. The performance of MLP networks was evaluated in terms of percentages for correct classification of the target outputs. Both functions are able to give accuracies up to 99% for classifying correctly. The hyperbolic tangent function had shown the capability of achieving the highest accuracy of an MLP performance with less number of hidden nodes.