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About this book

This book presents the proceedings of the 5th International Conference on Electrical, Control & Computer Engineering 2019, held in Kuantan, Pahang, Malaysia, on 29th July 2019. Consisting of two parts, it covers the conferences’ main foci: Part 1 discusses instrumentation, robotics and control, while Part 2 addresses electrical power systems. The book appeals to professionals, scientists and researchers with experience in industry.The conference provided a platform for professionals, scientists and researchers with experience in industry.

Table of Contents

Frontmatter

Instrumentation, Control and Artificial Systems

Frontmatter

Position Control of Pneumatic Actuator Using Cascade Fuzzy Self-adaptive PID

Pneumatic systems are widely used in the industrial automation with its advantages in high power ratio, low cost and cleanliness fluid medium. However, the complex nonlinearities of pneumatics system make this system having difficulty to perform precise motion control especially in providing precise steady state tracking error on rod piston and stable pressure control. To overcome this issue, a cascade control technique named Fuzzy Self-Adaptive PID (CFSAPID) control is proposed. The adaptive tuning by Fuzzy Logic Controller (FLC) is designed as tuner for PID controller. The proposed CFSAPID is simulated and verified on single-piston double acting valve pneumatic system model plant, and compared with single FSAPID controller. Five parameters are focused for analysis including piston rise time, piston settling time, piston velocity, pressure on piston chambers and force friction. The capability of proposed CFSAPID has been successfully verified by simulation studies.

Mohd Iskandar Putra Azahar, Addie Irawan, Raja Mohd Taufika, Mohd Helmi Suid

Effect of Excitation Frequency on Magnetic Response Induced by Front- and Back-Side Slits Measured by a Differential AMR Sensor Probe

Defects in steel structures are one of the major problems that may affect the functionality of the structure. Thus, the detection of the defects is fairly crucial to prevent any unwanted accident from occurring. Nondestructive Testing (NDT) is a group of methods that is widely used to detect those defects, especially cracks. This paper will be focusing on the detection of cracks (artificial slits) by using the Magnetic Flux Leakage (MFL) technique in the magnetic method of NDT. A non-saturated differential MFL probe consists of two AMR sensors has been fabricated for the detection of front as well as backside slits. A measurement system which incorporates the developed probe attached on an XY-stage, a set/reset circuit, an amplifier circuit, a DAQ card, and PC is constructed where an XY-stage controller and a digital lock-in amplifier are developed with the implementation of LabVIEW. Then, the developed MFL probe’s performance is evaluated by running several line scan measurements on a 2-mm galvanized steel plate sample engraved with artificial slits with depths that varies from 1.0 to 1.6 mm with variable excitation frequencies. The results show promising output where the slits could be successfully detected and its position could be further estimated. Furthermore, the correlation between the slit depth and difference (delta value) between the signal peaks and troughs could also be founded. Consequently, the optimum excitation frequency can be determined by plotting a graph of the slope of trend line of the delta values versus the frequency.

M. A. H. P. Zaini, M. M. Saari, N. A. Nadzri, A. M. Halil, A. J. S. Hanifah, M. Ishak

Model-Free PID Controller Based on Grey Wolf Optimizer for Hovering Autonomous Underwater Vehicle Depth Control

Traditionally, the wearisome effort is required to tune the PID parameters and always resulting in erroneous system behavior. The objective of the present work paper is to develop a tuning method for model-free PID controller parameters by using Grey Wolf Optimizer (GWO) to control the depth of Hovering Autonomous Underwater Vehicle (HAUV). The speed of HAUV thrusters is controlled by a PID controller where the tuning for three PID parameters is done by using GWO algorithms. Sum Square Error (SSE), percentage overshoot and settling time of the depth response are chosen as the fitness functions. The differential equation of the HAUV system in heave direction is considered with the aim to confirm the design of PID controller. The proposed approach is compared with Sine Cosine Algorithm (SCA). The time response specifications of input tracking of HAUV with the presences of external disturbances, model nonlinearities, buoyancy force, hydrodynamic drag force and added mass on the HAUV system are considered as a control scheme performance while the convergence curve of the fitness function indicates the performance of optimization algorithm. Finally, the suggested tuning method promises a fast depth tracking capability as shown in simulation results.

Mohd Zaidi Mohd Tumari, Amar Faiz Zainal Abidin, Ahmad Anas Yusof, Mohd Shahrieel Mohd Aras, Nik Mohd Zaitul Akmal Mustapha, Mohd Ashraf Ahmad

Experimental Study of Optimization of Electrode Dimension for Non-invasive Electrical Resistance Tomography Application

Electrical resistance tomography is used to reconstruct the image of the objects within the medium of interest based on electrical conductivity distribution. Besides, the ordinary technique of ERT applied the invasive technique and causing corrosion to the electrodes because of the contact between the electrode and the conductive liquid. Therefore, the ERT system proposed in this work is to investigate the optimize dimension of the electrode in ERT using an experimental approach for non-invasive measurement. In this project, four electrodes are used as transmitter and receiver. All the electrodes are arranged side by side around the pipe. In this process, only one electrode is used as a transmitter and the rest as a receiving sensor. When water is inserted in the pipe, the output of the voltage will be compressed and recorded. Nine different dimensions are investigated and it produces a different voltage output. Therefore, the appropriate electrode dimension must be determined as it also affects the conductivity of the conducting medium. In addition, the appropriate electrode dimension which is 35.34 mm (width) × 250 mm (height) was chosen as the optimize dimension from the experiment to improve the performance of the existing system.

Yasmin Abdul Wahab, Mahanum Muhamad Sakri, Mohd Anwar Zawawi, Muhammad Sharfi Najib, Normaniha Abd Ghani

A Fictitious Reference Iterative Tuning Method for Buck Converter-Powered DC Motor Control System

This paper presents a model-free optimization algorithm for a PID controller based on Fictitious Reference Iterative Tuning and Simulated Kalman Filter. The modeling of a buck converted-powered DC motor system is first provided to form the basis of data collection and fictitious reference signal derivation. The supplied model is however not a necessity in the scope of this work but is provided for the purpose of performance comparison. A cost function is formulated based on the minimization of error between the output response of the desired model with the output response of the closed-loop system. Simulation analyses using Matlab Software have been conducted for results validation and verification. Furthermore, a performance comparison between the proposed method and a model-based controller design has been carried out. From the numerical example, it shows that the system with the tuned PID controller exhibited a better angular velocity trajectory tracking compared to the system with the state feedback controller with integral gain.

Mohd Syakirin Ramli, Seet Meng Sian, Mohd Naharudin Salim, Hamzah Ahmad

Depth Evaluation of Slits on Galvanized Steel Plate Using a Low Frequency Eddy Current Probe

This study performs an analysis of a small eddy current probe configuration based on differential anisotropic magnetoresistance (AMR) sensors for characterization of small surface defects in galvanized steel plates. Owing to the advantage of the AMR sensor, the system of eddy current testing (ECT) with the AMR sensor has a huge benefit to detect sub-millimeter defects in steel structures. In this study, an ECT probe is developed by using AMR sensors to perform crack detection in 2-mm galvanized steel plates with regards to the depth of artificial slits where the ECT probe is scanned above the slits’ area. The signal that is detected by a lock-in amplifier is investigated with different frequencies of an excitation field. The line-scanned of signal intensity shows a clear intensity change at the crack area. This signal depends on the depth and frequencies. Finally, a correlation between depth and detected signals is clarified with respect to different frequencies.

N. A. Nadzri, M. M. Saari, M. A. H. P. Zaini, A. M. Halil, A. J. S. Hanifah, M. Ishak

Sensitivity Maps Preparation for Electrical Capacitance Tomography Using Finite Element Approach

Electrical Capacitance Tomography is part of Electrical Tomography which uses the concept of electric field distribution and it is widely used due to its advantages such as non-invasive, low-cost, high acquisition speed and relatively easy computation. The ECT system involves two computational problems in its mechanism which Forward Problem and Inverse Problem. The forward problem involves the computation of the potentials done at the voltage pick-up electrodes for a given set of current-carrying electrodes. This allows calculation for the distribution of the electrical voltage when the given with condition of known sensor structure and given permittivity distribution. The Forward Problem in this study refers to the sensitivity map which is later used for image reconstruction in the Inverse Problem image. This study explores sensitivity map generation and preparation which can be accomplished using the numerical method, for example, the Finite Element Method. Based on the simulated result, the sensitivity map for each projection shows different strength depending on the position and distance between the electrode pair.

Wan A. N. Ropandi, N. A. Zulkiflli, J. Pusppanathan, F. A. Phang, N. D. Nawi, M. E. Johana, N. H. A. Ngadiman

Infrared Thermal Sensor for a Low Cost and Non-invasive Detection of Skin Cancer

Skin cancer is in a rising trend over the years. Though there are of many conventional approaches for skin cancer diagnosis, there is still a massive demand for the device with features of low cost, compact, portable, less diagnosis time, comfortable (no biopsy), high sensitivity and accuracy. The proposed system is the implementation of infrared (IR) thermal sensor in a non-contact manner which detects the temperature of the epidermal layer of skin, where the temperature of the skin varies for the subjects if they have cancer. The device receives the signal from the sensor unit, and it is further processed to detect the various level of skin cancer. The system process optimization was performed, and optimization factors were reported based on the sensor operating distance to detect the values efficiently. From the analysis, it was observed that there’s a 2.4 °C temperature difference for the thermometer and infrared thermal sensor reading. Also, the thermometer reading was greater by 2.4 °C comparing to the sensor values. This is attributed to the emissivity nature of the heated objects to the ambiance. This system can also be used as a wearable device by alerting the subject of their condition. This system provides better monitoring with high accuracy through non-invasive technique and early detection can be made to prevent cancer deaths.

A. Noora Safrin, B. Pooja, K. Hema, P. Padmapriya, Vigneswaran Narayanamurthy, Fahmi Samsuri

T-Way Strategy for Sequence Input Interaction Test Case Generation Adopting Fish Swarm Algorithm

In Combinatorial Input Interaction (CII) based system, the increasing number of input event causes the increasing number of test cases. Since twenty years many useful T-way strategies have been developed to reduce test case size. In order to reduce test cases several T-way sequence input interaction strategies are explored, such as, Bee Algorithm(BA), Kuhn encoding (K), ASP with Clasp, CP with Sugar, Erdem (ER) exact encoding, Tarui (TA) Method, U, UR, D and DR, Brain (BR). However, none of them claim that for all test configuration the produced test cases are best. The reason is that the T-way sequence input interaction is NP-Hard problem. In this research, Fish Swarm algorithm is proposed to adapt with T-way sequence input interaction test strategy. The proposed system is compared with the other renowned search-based T-way strategies. The result shows that the proposed system is able to generate feasible and optimal results.

Mostafijur Rahman, Dalia Sultana, Sabira Khatun, Mohd Falfazli Mat Jusof, Syamimi Mardiah Shaharum, Nurhafizah Abu Talip Yusof, Khandker M. Qaiduzzaman, Md Hasibul Hasan, Md Mushfiqur Rahman, Md Anwar Hossen, Afsana Begum

Development of AC and DC Drive Coils for a Small Volume Magnetic Particle Imaging System

Recent development in a new imaging modality called Magnetic Particle Imaging (MPI) technique has attracted much interests from researchers where it is expected to provide a higher spatial and temporal resolutions of images. The MPI technique works by utilizing an AC field to modulate the magnetic response from magnetic nanoparticles and a gradient DC field to localize the magnetic nanoparticles, where the characteristics of AC and DC fields affect the performance of MPI technique. The purpose of this study is to develop compact DC and AC drive coils as a preliminary step towards implementation in a small volume MPI system. The AC drive coil is designed based on a Helmholtz-coil configuration and resonated at a frequency to lower its circuit impedance. The gradient DC field is realized by combination of permanent magnets and a DC coil to shift a Flux Free Line (FFL) vertically. A 3rd-order Butterworth low-pass filter is implemented in the DC drive coil circuit to protect its DC current source from high-frequency field induction. The AC drive coil is able to be resonated at the designed frequency of 8 kHz and fairly good horizontal and vertical gradient DC fields are obtained. The DC drive coil is able to shift the FFL vertically at 0.33 mm/A and further improvement can be expected in the coil design for future implementation in the small volume MPI system.

Mohd Mawardi Saari, Ahmad Zahir Irsyad Razak, Mohd Aufa Hadi Putera Zain, Nurul A’in Nadzri, Mohd Razali Daud, Hamzah Ahmad

A Diversity-Based Adaptive Synchronous-Asynchronous Switching Simulated Kalman Filter Optimizer

The original Simulated Kalman Filter (SKF) is an optimizer that employs synchronous update mechanism. The agents in SKF update their solutions after all fitness calculations, prediction process, and measurement process are completed. An alternative to synchronous update is asynchronous update. In asynchronous update, only one agent does fitness calculation, prediction, measurement, and estimation processes at one time. Recent study found that the original SKF is subjected to premature convergence. Thus, synchronous and asynchronous mechanisms are combined in SKF to address the premature convergence problem in SKF. At first, the SKF starts with synchronous update. If no improved solution is found, the SKF changes its update mechanism. The decision to switch from synchronous to asynchronous or vice versa is made based on the information of the population. In this paper, population’s diversity is used as switching indicator. Using the CEC2014 benchmark test suite, experimental results indicate that the proposed diversity-based adaptive switching synchronous-asynchronous SKF outperforms the original SKF significantly.

Nor Azlina Ab. Aziz, Nor Hidayati Abdul Aziz, Badaruddin Muhammad, Zuwairie Ibrahim, Marizan Mubin, Norrima Mokhtar, Mohd Saberi Mohamad

Combinatorial Test Suite Generation Strategy Using Enhanced Sine Cosine Algorithm

Owing to its simplicity and having no control parameters, the Sine Cosine Algorithm (SCA) has attracted much attention among researchers. Although useful, the SCA algorithm adopts a linear magnitude update to determine its sine or cosine position updates. In the actual searching process, the magnitude update is rarely linear. In fact, the magnitude update is also non-exponential and is highly dependent on the problem domain and its search topology. For this reason, our work proposes a combination of linear and exponential magnitude update for the search displacement. In doing so, we adopt the combinatorial testing problem as our case study. Combinatorial testing strategies generate test data which cover all required interactions among parameter values of a system-under-test in order to explore interaction faults. Our evaluation gives promising results on the improved performance over the original SCA algorithm. As far as test data generation time is concerned, the enhanced SCA outperformed all its counterparts, whereas its results in terms of test suite sizes are comparable to other parameter free meta-heuristic algorithms.

Kamal Z. Zamli, Fakhrud Din, Abdullah B. Nasser, AbdulRahman Alsewari

Classification of Lubricant Oil Geometrical Odor-Profile Using Cased-Based Reasoning

The lubricant oil is one of the petroleum refinery product. The lubricant oil usage is very important in order to make sure the operation of vehicle engine at the highest performance. In determining the lubricant oil adulteration level, there were so many methods of classification using various instruments such as ICP-MS, AAS and Dielectric Spectroscopy. E-nose is one of the significant instrument using odor approach to classify the odor of the sample. The purpose of this study is to classify the lubricant oil degradation level based on odor-pattern that extracted from the odor data that collected using electronic nose. The lubricant oil sample consists of 4 levels of lubricant oil adulteration level which are virgin lube oil, 3000, 7000 and 10,000 km lubricant oil sample. Pre-processing technique was applied by implementing normalization formulation in order to standardize the odor raw data. Normalized data very beneficial in features extraction process, so that the significant odor-patterns can be established. In this study, geometry average calculation method was applied in order to establish the odor-profile for lubricant oil sample. The odor-pattern then were classified using case-based reasoning classifier. Based on the classification results, it shows that the accuracy of the classification is 100% correct classification.

Suhaimi Mohd Daud, Muhammad Sharfi Najib, Nurdiyana Zahed, Muhammad Faruqi Zahari, Nur Farina Hamidon Majid, Suziyanti Zaib, Mujahid Mohamad, Addie Irawan, Hadi Manap

Optimization of Quaternion Based on Hybrid PID and Control

The aim of this article is to present an optimization of full non-linear quaternion based on hybrid control scheme using Genetic Algorithm (GA). A comprehensive objective is used to find novel solutions to design hybrid controller based on PID and $$\varvec{ P}_{\varvec{\omega}}$$ control so that the performance and functionality system and may be compromised. The proposed hybrid control algorithm and quadrotor attitude model have been implemented in the fully quaternion space without any conversion and calculations in the Euler’s angles. In this paper, the optimized quaternion with fitness function composed of $$\varvec{ K}_{\varvec{P}}$$, $$\varvec{ K}_{\varvec{I}}$$, $$\varvec{ K}_{\varvec{D}}$$, and $$\varvec{ P}_{\varvec{\omega}}$$ are proposed, and the output effective waveform is shown by simulations using MATLAB.

Balya Darohini, M. F. Abas, N. Md. Saad, Dwi Pebrianti, H. Ahmad, M. H. Ariff, M. R. Arshad

Elimination-Dispersal Sine Cosine Algorithm for a Dynamic Modelling of a Twin Rotor System

This paper presents an improved version of Sine Cosine Algorithm (SCA). The original SCA is a simple algorithm and it offers a good accuracy. However, for some problems and fitness landscapes, the accuracy achievement of the algorithm is not at optimal. Search agents of the algorithm stuck at the local optima. The proposed new algorithm which is called an Elimination-Dispersal Sine-Cosine Algorithm adopts Elimination-Dispersal (ED) strategy from Bacterial Foraging Algorithm. The ED helps search agents to solve the local optima problem. At the same time, an elitism approach is applied in the proposed algorithm. The elitism ensures some agents continue the next search operation from the currently best found solution. The proposed algorithm is tested on CEC2014 benchmark functions that have various fitness landscapes and properties. The accuracy performance is compared with the original SCA and analyzed. It also is applied to acquire and optimize a dynamic model for a Twin Rotor System (TRS). Result of the modelling shows that the proposed algorithm achieves a better accuracy and thus present less modelling error and better dynamic response for the TRS.

Shuhairie Mohammad, Mohd Falfazli Mat Jusof, Nurul Amira Mhd Rizal, Ahmad Azwan Abd Razak, Ahmad Nor Kasruddin Nasir, Raja Mohd Taufika Raja Ismail, Mohd Ashraf Ahmad

The Investigation of Meat Classification Based on Significant Authentication Features Using Odor-Profile Intelligent Signal Processing Approach

Meat is the flesh or another edible part of an animal and includes uncooked meat prepared or otherwise but does not include meat products. Meat is the most valuable livestock product and for many people serves as their first-choice source of animal protein. Fraud meat products are causing annoyance to consumer’s, especially Muslim users. There are many cases that have been brought to the public attention regarding fraud on meat products such as incidences of meat that is labeled, certified or sold as halal may not be so. This project sets out to identify two types of different meat which is beef meat and pork meat. Therefore, the significant authentication features using odor-profile intelligent signal processing approach which is Electronic Nose (E-nose) was used to measure odor-profile from meat. E-nose is one of the chemical-based sensor arrays instruments which have a capability to measure odor-profile based sample data. The data measurement of odor-profile for different meat samples was collected based on the designated experimental procedure. Then, the normalized and their unique features were extracted using statistical tools for feature extraction. The input of features will be inserting into Case-Based Reasoning (CBR) library and intelligently classified using CBR method and will be validated based specific performance measure. From the CBR performance measures result, it is observed that the classification of CBR is 100%.

Nur Farina Hamidon Majid, Muhammad Sharfi Najib, Suhaimi Mohd Daud, Nurdiyana Zahed, Muhamad Faruqi Zahari, Suziyanti Zaib, Mujahid Mohamad, Tuan Sidek Tuan Muda, Hadi Manap

The Study of Raw Water Based on Quality Parameter Using Smell-Print Sensing Device

Water is a renewable natural resource and comprises about 70% of earth whilst the balance is land. Cleanliness and purity of drinking water is important for human health worldwide, thus it is important to know the water body source content so that consumption of it does not give any risk to human body’s health. This study focuses on establishing a case library profile and classification of water based on recommended by Ministry of Health (MOH). This study water quality parameters such as iron (Fe) and pH is obtained using Electronic nose (E-nose). E-nose is an instrument that mimics human nose that has the ability to sniff in advance for volatile odor. However, colourless and odourless chemical usually undetectable by normal eyes or noses. Case Based Reasoning (CBR) is used in performing the intelligent classification that involved CBR computation, voting and performance measure. The similarity result shows that the technique accomplished to classify with 97.5% accuracy, 88.0% specificity and 92.2% accuracy.

Suziyanti Zaib, Muhammad Sharfi Najib, Suhaimi Mohd Daud, Nurdiyana Zahed, Muhamad Faruqi Zahari, Nur Farina Hamidon Majid, Mujahid Mohamad, Hadi Manap

Camera Orientation Determination Based on Copper Wire Spool Shape

A simple and inexpensive system but effective in performing required tasks is the most preferable in industry. In this study, a vision system is developed to solve the peg-in-hole problem of a robot-like forklift to pick up copper wire spool arranged side by side on a rack, without using any sensors, except a low-cost camera. Inspired by how human perceive an object orientation based on its shape, an algorithm is developed to determine robot orientation based on the shape of a copper wire spool relative to camera position and yaw angle. The center point of the spool (CPS) should be on the center line of camera FOV (CFOV) if the camera is perpendicular or 0° parallel to the spool. Thus, the coordinate of the CPS and the CFOV is same. Instead, when the camera is seeing the spool from the angle less or bigger than 0°, the CPS and CFOV will be different, and the difference shows the position and the yaw angle of the camera relative to the spool. A copper wire spool has three circles; the outer circle, the tapper part around its center hole and the center hole itself. The proposed system uses Circular Hough Transform (CHT), filtering, binary, morphology and Sobel edge detection of the sampled images from real-time video recording to determine the orientation of the camera related to the copper wire spool shape, in which the center coordinate of the three circles was determined. Results from the experiments that had been done show that the system is able to determine the orientation of the camera related to the spool.

Farah Adiba Azman, Mohd Razali Daud, Amir Izzani Mohamed, Addie Irawan, R. M. Taufika R. Ismail, Mohd Mawardi Saari

A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem

To date, there are much increasing trends on adopting parameter free meta-heuristic algorithms for solving general optimization problems. With parameter free algorithms, there are no parameter controls for tuning. As such, the adoption of parameter free meta-heuristic algorithms is often straightforward. On the negative note, exploration (i.e. roaming the search space thoroughly) and exploitation (i.e. manipulating the current known best neighbor) are pre-set. As the search spaces are problem dependent, any pre-set exploration and exploitation can lead to entrapment in local optima. In this paper, we investigate the use of Lévy flight to enhance the exploration of a parameter free meta-heuristic algorithm, called Modified Symbiotic Organism Search Algorithm (MSOS), via its population initialization. Our experimentations involving the software module clustering problems have been encouraging, as MSOS gives competitive results against existing selected parameter free meta-heuristic algorithms. For all the given module clustering problems, MSOS generates overall best mean results.

Nurul Asyikin Zainal, Kamal Z. Zamli, Fakhrud Din

Classification of Agarwood Types (Malaccensis and Crassna) Between Oil and Smoke Using E-Nose with CBR Classifier

The issue of quality of agarwood quality among sellers and buyers is still ongoing due to manual olfactory methods. This study purpose classification of Malaccensis and Crassna agarwood in oil and smoke by electronic nose using Case-based Reasoning classifier. The CBR performance measurement shows that classification of agarwood Malaccensis and Crassna for both oil and smoke using CBR technique can achieve 100% classification success.

Mujahid Mohamad, Muhammad Sharfi Najib, Suhaimi Mohd Daud, Nurdiyana Zahed, Muhamad Faruqi Zahari, Nur Farina Hamidon Majid, Suziyanti Zaib, Hadi Manap

Applied Electronics and Computer Engineering

Frontmatter

SCAR-CNN: Secondary-Classification-After-Refinement Convolutional Neural Network for Fine-Grained Categorization

The majority of existing approaches for fine-grained image recognition that work on attention-based learning, have their respective Top-K prediction accuracy better than Top-1 prediction. It is to say, there is a significant number of correct class falls in the range of Top-K predictions where K = 2, 3, 4, 5. This is the indirect indication for researchers not to neglect the need to explore the possibility of getting better prediction based on the discriminative feature of Top-K classes. This paper presents Secondary-Classification-After-Refinement Convolutional Neural Network (SCAR-CNN) which have an adaptive secondary classification model built on top of primary classification Top-K classes. Our focus is also on how to maximize the effect of removing unwanted classes in secondary classification, by performing image-enhancement on the input image of primary classification. Experiments show that these approaches achieve 86.9% of total accuracy as compared to the current state-of-the-art 86.5%.

Bernard Jun Kai Cheah, Abduljalil Radman, Shahrel Azmin Suandi

Forecasting Road Deaths in Malaysia Using Support Vector Machine

An average of 6,350 people died every year in Malaysia due to road traffic accidents. A published data of Malaysian road deaths in 20 years since 1997 reveals that the number of fatalities has not really declined with a difference of less than 10% from one year to the next. Forecasting the number of fatalities is beneficial in planning a countermeasure to bring down the death toll. A predictive model of Malaysian road death has been developed using a time-series model known as autoregressive integrated moving average (ARIMA). The model was used in the previous Road Safety Plan of Malaysia to set a target death toll to be reduced in 2020, albeit being inaccurate. This study proposes a new approach in forecasting the road deaths, by means of a machine learning algorithm known as Support Vector Machine. The length of various types of road, number of registered vehicles and population were among the eight features used to develop the model. Comparison between the actual road deaths and the prediction demonstrates a good agreement, with a mean absolute percentage error of 2% and an R-squared value of 85%. The Linear kernel-based Support Vector Machine was found to be able to predict the road deaths in Malaysia with reasonable accuracy. The developed model could be used by relevant stakeholders in devising appropriate policies and regulations to reduce road fatalities in Malaysia.

Nurul Qastalani Radzuan, Mohd Hasnun Arif Hassan, Anwar P. P. Abdul Majeed, Khairil Anwar Abu Kassim, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Nur Aqilah Othman

Investigation of Dimensionality Reduction on Numerical Attribute Features in a Finger Vein Identification System

With the large number of people travelling internationally, there is an increasing demand to be able to deal with security clearance rapidly and with a minimum of inconvenience. Using finger vein biometric traits fulfils these requirements. In previously-reported work, the data obtained from finger veins underwent dimensionality reduction using principal components analysis (PCA) followed by linear discriminant analysis (LDA) and this was shown to improve the identification rate compared to the more commonly applied Discrete Wavelet Transform (DWT). Although PCA was found to be effective at reducing the noise residing in the discarded dimension, this work demonstrates that the corresponding eigenvalue may in fact also contain useful local information that is important in identification and so should be retained. To overcome this problem, this paper proposes the use of feature extraction using DWT and local binary patterns (LBPs) to generate the feature vectors, before they undergo dimensionality reduction using PCA. Support Vector Machines (SVMs) are used for classification. The performance of the proposed method was compared with previous work, with the identification rate of the proposed method offering the best accuracy of 95.8%.

Ei Wei Ting, M. Z. Ibrahim, D. J. Mulvaney, W. N. A. W. Samsudin, S. Khatun

Intelligent Gender Recognition System for Classification of Gender in Malaysian Demographic

Identification of a person gender as a man or woman based on the past experiences through features of face such as eyes, mouth, cheek can be obtained through an intelligent gender recognition system. Detection of a person’s gender can be difficult but important for security purposes, especially where safety issues concerning woman in public amenities. The objectives of this research are to identify the techniques for classifying features from man and woman facial images, through which embed as a system and validify using photos within Malaysian demographic. This research is focused on utilizing facial features for gender classification in real time, emphasizing on deep learning-based gender recognition and HAAR Cascade classifier using pre-trained caffe model in OpenCV library. Results show that under Malaysian demographic, probability of 86% accuracy of gender recognition were obtained.

Yap Su Chi, Syafiq Fauzi Kamarulzaman

A Novel Approach Towards Tamper Detection of Digital Holy Quran Generation

Quran phrases are found in many Arabic websites. Lamentably, many mistakes and typos appear on most of the websites embedded with Quran texts. Therefore, it becomes very difficult to recognize the legal document of the religious book, whether the online document is tampered or not. Hence, verifying the Quran expression has become a crucial issue for most of the online users who read the digital copy. We propose a novel approach for the tamper detection of a digital document of the Holy Quran. We have implemented a desktop application, having modified UI that utilizes Jaro-Winkler distance and Difflib function as String Edit distance algorithm to highlight the words in the Holy Quran for the verification purpose. A reliable and trustworthy Quran database was taken for testing. The results obtained from the application show higher performance. The system achieved the detection accuracy of 95.9% and 95% by Jaro-Winkler and Difflib, respectively along with the precision of 93.29% and 96% in the case of diacritics. Additionally, F-score is 93.22% and 96.41% obtained by Jaro-Winkler and Difflib, respectively in the case of no diacritics.

Md. Milon Islam, Muhammad Nomani Kabir, Muhammad Sheikh Sadi, Md. Istiak Morsalin, Ahsanul Haque, Jing Wang

A Comparative Study of AFM-Assisted Direct and Least-Square Attitude Determination Algorithm

Based on GNSS (Global Navigation Satellite System) technology, the importance of vehicle attitude calculation has become more and more prominent in military and civilian fields. In this paper, an attitude determination algorithm assisted by ambiguity function method (AFM) is proposed. Due to the characteristics of the AFM algorithm is insensitive to cycle slip and independent of initial ambiguity, and considering the large amount of computation and the long computation time, it is used as an auxiliary means for initial attitude search and error correction in the search process, and C-LAMBDA algorithm is used to complete the ambiguity resolution. The attitude angle is calculated by direct and least square method, and the accuracy of the attitude angle based on the AFM-assisted method is compared. Through the static experiment of dual antenna direction finding and three antenna attitudes finding, the accuracy of direction and attitude angle is analyzed. It is concluded that the attitude calculation accuracy based on AFM-assisted least square method is usually higher.

Suqing Yan, Yue Wu, Yuanfa Ji, Kamarul Hawari Ghazali, Xiyan Sun

Design and Development of Wearable Human Activity Recognition for Healthcare Monitoring

This research deals with development of a wearable sensoring system for human activity recognition focusing on hand and leg assessments. The research attempts to sufficiently recognize the motion to provide physiotherapist about the patient condition in the remote area. The system is designed by applying Arduino as the main controller with the help of accelerometer to identify human movements and then classifying them into soft, medium and hard motions categories. From the research, data acquired from the assessment is then imported into Microsoft Excel by using Guino software to describe the human motions. The accelerometer sensors are placed as follows; the on the right hand for three positions which are on the wrist, on the elbow, and on the shoulder. Meanwhile on right leg for three position which is in tight, calf and ankle. Experimental results show that the proposed system is capable to provide reliable information to both patient and physiotherapist about the motions. The recognition for the activity is based on physiotherapist consultation which provides early descriptions of human various activities using hands and legs. The proposed system can be applied for rehabilitation and monitoring system to realize a home-based smart monitoring and assessment system.

Hamzah Ahmad, Nurul Syafiqah Mohd, Nur Aqilah Othman, Mohd Mawardi Saari, Mohd Syakirin Ramli

Region of Interest Extraction of Finger-Vein Image Using Watershed Segmentation with Distance Transform

Finger Vein Recognition System (FVRS) is a biometric technology that identifies or verifies an individual based on unique vein patterns. Region of interest (ROI) extraction is one of the essential steps in FVRS. Current ROI extraction methods cannot extract an accurate ROI for a finger vein image with non-uniform background illumination. In this paper, we propose a new ROI extraction method that is immune to non-uniform background illumination. To detect the edge of the finger for the ROI extraction, we utilise watershed segmentation with distance transform and Canny edge detector. Experimental results show that the proposed ROI extraction method can extract consistent ROI from a finger vein image with non-uniform background illumination.

Lim Yuan Zhang, Bakhtiar Affendi Rosdi

The Classification of Skateboarding Trick Manoeuvres Through the Integration of Image Processing Techniques and Machine Learning

More often than not, the evaluation of skateboarding tricks executions is assessed intuitively according to the judges’ observation and hence are susceptible to biasness if not inaccurate judgement. Hence, it is crucial to underline the benchmark for analyzing the rate of successful execution of skateboarding trick for high level tournaments. The common tricks in skateboarding such as Kickflip, Ollie, Nollie, Pop Shove-it and Frontside 180 are investigated in this study via the synthetization of image processing and machine learning classifiers. The subject used for accomplishing the tricks is a male amateur skateboarder at the age of 23 years old with ±5.0 years’ experience using ORY skateboard. Each trick is collected upon five successful landings and the camera is placed 1.26 m from the subject on a flat cemented ground. The features extracted from each trick were engineered using Inception-V3 image embedder. Several classification models were evaluated, namely, Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Logistic Regression (LR), Random Forest (RF) and Naïve Bayes (NB) on their ability in classifying the tricks based on the engineered features. It was observed from the preliminary investigation that the SVM model attained the highest classification accuracy with a value of 99.5% followed by LR, k-NN, RF, and NB with 98.6%, 95.8%, 82.4% and 78.7%, respectively. It could be inferred that the method proposed decisively provide the classification of skateboarding tricks efficiently and would certainly provide a more objective based judgment in awarding the score of the tricks.

Muhammad Nur Aiman Shapiee, Muhammad Ar Rahim Ibrahim, Mohd Azraai Mohd Razman, Muhammad Amirul Abdullah, Rabiu Muazu Musa, Mohd Hasnun Arif Hassan, Anwar P. P. Abdul Majeed

Review and Analysis of Risk Factor of Maternal Health in Remote Area Using the Internet of Things (IoT)

IoT is the greatest ingenious innovation in the modern era, which can exploit also in mission-critical like the healthcare industry. This paper demonstrates effective monitoring of pregnant women mostly in a rural area of a developing country, with the help of wearable sensing enabled technology, which also notifies the pregnant women and her family about the health conditions. There are many researchers have been researched to reduce the maternal and fetal mortality but the mortality rate is not reducing, where it should be in zero tolerance. This research intended to use machine learning algorithms for discovering the risk level on the basis of risk factors in pregnancy. In this research, an existing dataset (Pima-Indian-diabetes dataset) has been used for the analysis of risk factor and comparison of some machine learning algorithm shows that Logistic Model Tree (LMT) gives the highest accuracy in case of classification and prediction of the risk level. Regardless, few selected pregnant women’s data has been collected (through IoT enabled devices) and the same process also applied for this dataset also by using LMT. Comparison results show that the prediction of risks is the same for the existing and real dataset.

Marzia Ahmed, Mohammod Abul Kashem, Mostafijur Rahman, Sabira Khatun

Recent Trends and Open Challenges in EEG Based Brain-Computer Interface Systems

Recent advances in computer hardware and signal processing have made possible the use of electroencephalogram (EEG) for communication between human brain and computers and this technology is known as brain-computer interface (BCI). Locked-in patients have now a way to communicate with the outside world using BCI technology. Nowadays, BCIs are getting popularity among the researchers to control devices using brainwaves especially in providing good assistance to disabled people. Impressive development and integration of both hardware and software in BCI have been carried out in the last two decades. However, some open challenges and limitations have also been exposed in the previous researches. In this paper, we have tried to mention some critical issues of EEG based BCI system including EEG modalities, EEG acquisition, signal processing algorithm and performance evaluation. These issues need to be solved to develop error-free BCI system. In addition, possible solutions and future directions have also been discussed.

Mamunur Rashid, Norizam Sulaiman, Mahfuzah Mustafa, Sabira Khatun, Bifta Sama Bari, Md Jahid Hasan

Early Rubeosis Iridis Detection Using Feature Extraction Process

Iris image analysis studies the relationship between human health and changes in the anatomy of the iris. One of the changes related to the anatomy of the iris is diabetes. This illness can be determined from the iris of human eyes because it affects the eyes. Latest advanced technologies are introduced in the image processing that helps automate the detection of diabetes based on the analysis of iris feature extractions. Various features are detected on iris such as texture, colour, histogram and shape. In this paper, the dataset of iris image from Warsaw Biobase are used to detect and recognise the rubeosis iridis by extracting their details using image processing methods. The results obtained from the experiment show that the normal and abnormal iris image can be classified using original and small size of iris image. Through this experiment, it was discovered that images for abnormal original are greater than 1,200,000 pixels while for small size are less than 35,000 pixels. On the contrary, normal original size are less than 1,200,000 pixels and for small are less than 25,000 pixel. By considering these results, the proposed method can be extended to the iris monitoring system.

Rohana Abdul Karim, Nur Amira Adila Abd Mobin, Nurul Wahidah Arshad, Nor Farizan Zakaria, M. Zabri Abu Bakar

Multi-hop File Transfer in WiFi Direct Based Cognitive Radio Network for Cloud Back-Up

In this chapter, an application for Android WiFi Direct multi-hop communications with log-file generation and cloud-based back-up have been proposed. WiFi Direct technology is used to peer-to-peer files transfer between neighboring devices without going through any access point. Distributed file systems for the cloud is a system that enables users to have access to the same data or file remotely (any-time any-where). The proposed custom WiFi Direct based Cognitive Radio (CR) application is able to create an ad-hoc network for multi-hop file transfer wirelessly using WiFi between two or more devices. Besides, to customize the channel according to the user demand, CR technique is used. An application (App) is developed and used in mobile devices (smart phones, note book, etc.) in a testbed to verify the system performances. This App detects and saves all the network activities information (in terms of log file) to keep track of the user activity and connection details in the network. The generated log files are stored in the cloud for further processing and security purpose. The performance of WiFi Direct based CR discovery service, channel detection, log file generation, multi-hop communication and WiFi Direct applications were successfully tested intensively with ~93% efficiency. Based on experimental data, an empirical model for multi-hop communication is proposed and validated. This shows, multi-hop file transfer and cloud back-up of log-files are possible through neighbor nodes with WiFi direct connection for at least one node in a network. This can be helpful for data safety, recovery and connection status monitoring/analysis for possible intrusion detection.

N. J. Shoumy, D. M. Rahaman, S. Khatun, W. N. Azhani, M. H. Ariff, M. N. Morshed, M. Islam, S. N. A. Manap, M. F. M. Jusof

The Multifocus Images Fusion Based on a Generative Gradient Map

The limitation of camera lens is inability to make focus region for whole scene in one shot image. The camera creates one focus object for one image. It is needed several images to get many focus objects of the scene. It makes difficult to read many focus objects from several images. Multifocus image fusion is a process of combining many focus objects from several images into one image. This operation gives easier way to read focus information from many images clearer. It commonly needed in medical examination, robotics and bioinformatics fields. The clearness information enables machine, computer and human understand the image better and prevents any mistake. In an image, the clear object is only located in focus region. In order to generate all objects in focus region, the multi focus images will be fused into fused image. The methods generally use complicated mathematic equation and hard algorithm. In addition to handle the problem, we design a simple way and have accurate output. Our method is the multifocus image fusion based on generative gradient map. By generative gradient map, it quickly determines the initial prediction of focus region precisely. The Generative gradient map is the external information, generated from gradient of blurred random number image. This procedure substitutes complicated mathematical equations or hard algorithm sequence implementation. Finally, our algorithm able to produces a fused image with high quality. The assessment of our method is according to Mutual Information and Structure Similarity parameter.

Ismail, Kamarul Hawari Bin Ghazali

A Comparative Analysis of Four Classification Algorithms for University Students Performance Detection

The student’s performance plays an important role in producing the best quality graduate who will responsible for the country’s economic growth and social development. The labor market also concerns with student’s performance because the fresh graduate students are considered as an employee depends on their academic performance. So, identification of the reason behind student’s performance variation provides valuable information for planning education and policies. Many researchers try to find out the reason with different types of data mining approaches in different countries. However, none of them worked with Bangladeshi students. This paper proposed a model for identifying the key factors of variation Bangladeshi students’ academic performance and predicts their results. This paper proposes a model which able to identify the students who need special attention. Different types of feature selection methods were used such as Co-relation, Chi-Square and Euclidean distance to select valuable features and feature selections result through decision tree, Naive Bayes, K-nearest neighbor and Artificial Neural Network classifiers algorithm were compared. The performance analysis is done by using student SGPA and review on given facilities from a university. From the performance analysis result it is found that, decreasing number of classes in dataset, the Artificial Neural Network (ANN) (93.70%) performs better than Decision Tree (DT) (92.18%), K-Nearest Neighbors (KNN) (77.74%) and Naïve Bayes (NB) (68.33%). However, an increasing number of classes in dataset the DT perform better than ANN, KNN, NB.

Dipta Das, Asif Khan Shakir, Md. Shah Golam Rabbani, Mostafijur Rahman, Syamimi Mardiah Shaharum, Sabira Khatun, Norasyikin Binti Fadilah, Khandker M. Qaiduzzaman, Md. Shariful Islam, Md. Shohel Arman

Open-Set Face Recognition in Video Surveillance: A Survey

Face recognition has received a substantial attention by the vision community over the past few decades. Most of the proposed frameworks have adopted the closed-set form of face recognition. However, when a novel unregistered face is presented to the system, the result will be misclassification. A more general and challenging open-set face recognition scheme is highly desirable due to its ability in dealing with the unknown persons which are not enrolled before. We observed that there is a shortage in survey papers that explore the research endeavors in open-set face recognition. In this paper, we present a literature survey of the open-set face recognition approaches that have been introduced for real-world scenarios focusing on video surveillance applications. Moreover, we discuss the current difficulties and suggest the promising directions for future research. The paper also describes the evaluation metrics and available benchmarking face video surveillance databases.

Wasseem N. Ibrahem Al-Obaydy, Shahrel Azmin Suandi

Hardware Development of Auto Focus Microscope

The scientific instrument technology has growth faster than we all could imagine, there are many research team keeping their momentum in creating new innovation in scientific instrumentation technologies. The optical microscopes are still being used widely in the scientific research especially by researcher and medical practitioners. Manually deal with the microscope could make the user spend so much time to obtain the result of cleared image. It could cost hours to obtain the desire result. From this problem, this study proposes the development of hardware system for auto focused of an optical microscope. The proposed system consists of two stepper motors that will move the fine focus knob and the course focus knob on a microscope. The timing belts are being used as mounting between the stepper motor and the fine/course focus knob. The motor will move step by step in same degree given from the command of a program. The motor is able to be controlled and it moves slowly to perform an auto focus task. Additionally, it is able to move in a small angle to find the proper exposure of the images scan. The hardware implementation of auto focus on the optical microscope has been tested and it worked perfectly. The result presented in this study shows that the proposed system is able to do auto focus in precise step which is $$5^\circ$$ step.

Dwi Pebrianti, Rosyati Hamid, Faradila Naim, Mohd Falfazli Mat Jusof, Nurul Wahidah Arshad, Luhur Bayuaji

Overview on Fingerprinting Authentication Technology

This paper addresses the characteristics, technology, and possible future of fingerprints authentication method. Fingerprint physiology makes it an ideal for biometrics authentication, primarily the tiny details located on its surface called minutiae. Fingerprint scanning systems are designed to detect minutiae. Images of detected minutiae are processed through matching algorithms in order to verify a query fingerprint that is identical to a stored fingerprint. However, fingerprint authentication based on minutiae can be easily bypassed and the need for a more secure method is required. With respect to the issue, this work explores the possibility of detecting the thickness of the skin layer within a fingerprint as a method of biometrics authentication. Current thickness measuring methods that are non-invasive for that task are identified as Laser Scanning Microscopy (LSM), Optical Coherence Tomography (OCT) and Near Infrared Spectroscopy (NIR). Of the three listed, only OCT and NIR methodology seems viable for simple yet reliable use and can become as promising methods for authentication based on skin layer thickness.

N. Sulaiman, Q. A. Tajul Ariffin

Bandwidth and Gain Enhancement of a Modified Ultra-wideband (UWB) Micro-strip Patch Antenna Using a Reflecting Layer

A novel technique to enhance bandwidth and gain of an Ultra-Wideband (UWB) antenna using a reflecting layer is presented in this chapter. A Microstrip Patch Antenna (MPA) with T-shaped patch and partially grounded plane is used in this design where a T-slot is inserted into the patch. The proposed compact-size antenna is designed and simulated using Computer Simulation Technology (CST) Microwave Studio software by considering flame retardant 4 (FR-4) as substrate with a relative permittivity of 4.3 and a thickness 1.6 mm. The antenna efficiency includes, a wide impedance bandwidth of 9.31 GHz ranging from 3.19 to 12.5 GHz, for voltage standing wave ratio, VSWR < 2; 5.74 dB gain; and 6.87 dBi directivity. In comparison with the MPA without the reflecting layer, the bandwidth, gain and directivity of the proposed antenna (with reflector) is increased by 123%, 3.64 dB, and 3.44 dBi respectively. Thus, the proposed antenna can cover a wider range than the UWB range (3.1–10.6 GHz) and can be suitable for the use of various bio-medical applications.

Bifta Sama Bari, Sabira Khatun, Kamarul Hawari Ghazali, Md. Moslemuddin Fakir, Mohd Hisyam Mohd Ariff, Mohd Faizal Jamlos, Mamunur Rashid, Minarul Islam, Mohd Zamri Ibrahim, Mohd Falfazli Mat Jusof

Oil Palm Tree Detection and Counting in Aerial Images Based on Faster R-CNN

Malaysian oil palm industry has been a great contributor to the country’s creation of job opportunity, foreign exchange earnings and GDP. Information about the amount and the distribution of oil palm trees in a plantation are important for sustainable management. In this paper, we propose an oil palm tree detection and counting method based on the Faster Regions with Convolutional Neural Network algorithm (Faster R-CNN). Experiment on the oil palm tree images collected by a drone shows that the proposed method can effectively detect the oil palm trees and counting its number when the age of the trees in a plantation is different from 2 years old to 8 years old. The proposed approach can be used to predict the scale of the plantation and meets the requirements of real-time detection.

Xinni Liu, Kamarul Hawari Ghazali, Fengrong Han, Izzeldin Ibrahim Mohamed, Yue Zhao, Yuanfa Ji

EEG Pattern of Cognitive Activities for Non Dyslexia (Engineering Student) due to Different Gender

The purpose of this study is to identify the electroencephalogram (EEG) pattern of male and female engineering student during the cognitive activity. EEG is a method to monitoring electrical activity in the brain and has four main brainwave signal Delta Wave, Theta Wave, Alpha Wave and Beta Wave. Delta wave is a slow wave generated in deepest meditation, Theta Wave usually occurs in sleep, Alpha Wave dominant in calming, relaxing condition and Beta Wave dominant in wakeful condition. The raw data collected analysis using SPSS and Microsoft Excel to analysis the accuracy and the brainwave pattern between male and female. The average, standard derivation, correlation and Q-Q Plot are used to identify the EEG pattern between male and female during cognitive activity. Cognitive is one of the bloom taxonomy formulate for education activities. The process involves in decision making, understanding of information, attitudes and solving. Subjects are given a set of question to answer. A total of 24 students, 12 males and 12 female involve recording their EEG signal while answering the cognitive question by wearing the Emotive Insight device. All subjects are from UTHM engineering students. Data collected are focused in Alpha Wave and Beta wave which exist in when someone is in awaken condition. The difference between male and female brainwave during the cognitive activity can be observed from the analysis and discussion of the result. For future recommendation for this research is the number of subject can be increased to get more accurate data.

E. M. N. E. M. Nasir, N. A. Bahali, N. Fuad, M. E. Marwan, J. A. Bakar, Danial Md Nor

Intelligent Autism Screening Using Fuzzy Agent

In the diagnosis of diseases, either physical or psychological, there are situations causing reaching for second independent opinion very hard. This is especially true in the diagnosis of Autism due to the complex process of diagnosis. Apart from the complex process, the challenges include cost and the availability of experts. This, however, does not change the fact that having regular independent second opinions is crucial. Hence, this study proposes an intelligent autism screening model using fuzzy agent, to assist the expert and non-expert in making the diagnosis. In this study, the fuzzy inputs are assigned based on five categories, which are Communication, Gross Motor, Fine Motor, Problem Solving, and Personal Social, and is specifically for three-year-old children only. The proposed model will be able to produce output in the form of sequences based on lowest to highest mark of the scores for each category. This output will then relate to the suggestion of activities to autistic children by priority (based on the scores obtained).

Nurul Najihah Che Razali, Ngahzaifa Ab. Ghani, Syifak Izhar Hisham

Ultra Wide Band (UWB) Based Early Breast Cancer Detection Using Artificial Intelligence

Breast cancer is a silent killer malady among women community all over the world. The death rate is increased as it has no syndrome at an early stage. There is no remedy; hence, detection at the early stage is crucial. Usually, women do not go to clinic/hospital for regular breast health checkup unless they are sick. This is due to the long queue and waiting time in the hospital, high cost, people’s busy schedule, and so on. Recently, several research works have been done on early breast cancer detection using Ultra Wide Band (UWB) technology because of its non-invasive and health-friendly nature. Each proposed UWB system has its limitation including system complexity, expensive, expert operable in the clinic. To overcome these problems, a system is required which should be simple, cost-effective and user-friendly. This chapter presents the development of a user friendly and affordable UWB system for early breast cancer detection utilizing Artificial Neural Network (ANN). A feed-forward back propagation Neural Network (NN) with ‘feedforwardnet’ function is utilized to detect the cancer existence, size as well as the location in 3-dimension (3D). The hardware incorporates UWB transceiver and a pair of pyramidal shaped patch antenna to transmit and receive the UWB signals. The extracted features from the received signals were fed into the NN module to train, validate, and test. The average system’s performance efficiency in terms of tumor/cancer existence, size and location is approximately 100%, 92.43%, and 91.31% respectively. Here, in our system, use of ‘feedforwardnet’ function; detection-combination of tumor/cancer existence, size and location in 3D along with improved performance is a new addition compared to other related researches and/or existing systems. This may become a promising user-friendly system in the near future for early breast cancer detection in a domestic environment with low cost and to save precious human life.

Bifta Sama Bari, Sabira Khatun, Kamarul Hawari Ghazali, Md. Moslemuddin Fakir, Wan Nur Azhani W. Samsudin, Mohd Falfazli Mat Jusof, Mamunur Rashid, Minarul Islam, Mohd Zamri Ibrahim

Design and Analysis of Circular Shaped Patch Antenna with Slot for UHF RFID Reader

This paper presents an analysis of microstrip circular shaped antenna with slot for ultra-high frequency (UHF) portable radio frequency identification (RFID) reader applications. The fabricated antenna is designed to work with UHF RFID system in Malaysia with frequency allocated from 919 to 923 MHz. The antenna design was made with circular patch and rectangular slot that has the dimension of 122 mm × 122 mm. Moreover, the FR-4 material used in this project has thickness of 1.6 mm with dielectric constant of 4.7 and loss tangent of 0.019. Thus, it is easily connected to the portable RFID reader module together with the antenna characteristics of easy fabrication, low profile and simple structure. From the results, the antenna has the reflection coefficient (S11) less than −10 dB along the bandwidth of 3.6% (903–936 MHz) for operating frequency at 921 MHz.

Mohd Hisyam Mohd Ariff, Muhammad Solihin Zakaria, Rahimah Jusoh, Sabira Khatun, Mohammad Fadhil Abas, Mohd Zamri Ibrahim

Analysis of EEG Features for Brain Computer Interface Application

Electroencephalography (EEG) based assistive devices are the great support to the paralyzed patients to be in contact with their surroundings. These devices use Brain-Computer Interface (BCI) technology which is presently getting more attention by the related research community. In this paper, EEG features from multiple cognitive states have been explored for BCI applications. Here, Power Spectral Density (PSD), log Energy Entropy (logEE) and Spectral Centroid (SC) have been investigated as EEG feature. The EEG data have been captured from three different cognitive exercises; (i) solving math problem, (ii) playing game and (iii) do nothing (relax). The average PSD, average logEE and average SC of EEG Alpha and Beta band for three mental exercises are calculated in order to determine the best features that can be used for BCI application. The results of the research show that the EEG features when considering PSD, logEE and SC can be used to indicate the change in cognitive states after exposing the human to several cognitive exercises.

Mamunur Rashid, Norizam Sulaiman, Mahfuzah Mustafa, Mohd Shawal Jadin, Muhd Sharfi Najib, Bifta Sama Bari, Sabira Khatun

Hybrid Sampling and Random Forest Based Machine Learning Approach for Software Defect Prediction

The software has turn into an imperious part of human’s life. In the recent computing era, many large-scale complex network systems and millions of modern technological devices produce a huge amount of data every second. Among these data, the amount of imbalanced data is relatively excessive. The machine learning model is miss leaded by these imbalanced data. Software Defect Prediction (SDP) is a standout amongst the most helping exercises during the testing phase. The estimated cost of finding and fixing defects is approximately billions of pounds per year. To reduce this problem, software defect prediction has come forth but need fine tuning to have expected efficiency. In this chapter, we have proposed a new model based on machine learning approach to predict software defect and identify the key factors that may help the software engineer to identify the most defect-prone part of the system. The proposed model works as follows. First, need to remove highly correlated features and turn all the feature in the same scale using the scaling feature approach. Second, we have used Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic (ADASYN) and Hybrid sampling method to balance highly imbalanced datasets. Third, Random Forest Importance and Chi-square algorithms are chosen to find out the factors which have high effect on software defect. Cross validation is used to remove overriding problem. Scikit-learn library is used for machine learning algorithms. Pandas library is used for data processing. Matplotlib, and PyPlot are used for graph and data visualization respectively. The hybrid sampling method and Random Forest (RF) algorithms achieved the highest prediction accuracy about 93.26% by showing its superiority.

Md Anwar Hossen, Md. Shariful Islam, Nurhafizah Abu Talip Yusof, Md. Sakib Rahman, Fatema Siddika, Mostafijur Rahman, Sabira Khatun, Mohamad Shaiful Abdul Karim, S. M. Hasan Mahmud

kNN and SVM Classification for EEG: A Review

This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances.

M. N. A. H. Sha’abani, N. Fuad, Norezmi Jamal, M. F. Ismail

Flexible Graphene-Silver Nanowires Polydimethylsiloxane (PDMS) Directional Coupler

In this research paper, graphene-silver nanowires are demonstrated as the transmission line of directional coupler fabricated on an elastomeric substrate Polydimethylsiloxane, PDMS at 2.4 GHz for wireless wearable application. In the experimental process, highly conductive of 0.6 mg/ml silver nanowires (AgNWs) is embedded in the graphene dispersion and spin coated onto flexible PDMS elastomer. The proposed directional coupler provides excellent return loss lower than −10 dB, good mutual coupling of −3 ± 1 dB and comparable phase difference between output ports between S21 and S31 with value of 90° ± 1°. Comparison of return loss and phase difference between output port performances was performed in three different angles of bending, 0,° 90° and 180°. The simulation and measurement outcomes show promising results, indicate that graphene-silver nanowires on PDMS elastomer as a good material for flexible devices and able to withstand mechanical strains without degrading the performance of the directional coupler. The compact sizes and unique properties of the coupler can be realized for wireless wearable electronics applications.

Nor Nadiah Aliff, Noorlindawaty Md Jizat, Nazihah Ahmad, Mukter Uz-Zaman

Investigating the Possibility of Brain Actuated Mobile Robot Through Single-Channel EEG Headset

Brain-computer interface (BCI) is a fast-growing technology involving hardware and software communication systems that allow controlling external assistive devices through Electroencephalogram (EEG). The primary goal of BCI technology is to ensure a potential communication pathway for patients with severe neurologic disabilities. A variety of BCI applications have been presented in the last few decades which indicate that the interest in this field has dramatically increased. In this paper, the possibility of a brain-actuated mobile robot using single-channel EEG headset has been investigated. EEG data has been collected from Neurosky Mindwave EEG headset which consists of a single electrode. EEG feature in terms of power spectral density (PSD) has been extracted and classified this feature using the support vector machine (SVM). Then the classified signal has been translated into three devices command to control the mobile robot. This mobile robot can be driven in three directions namely forward, right and left direction. Data collection from EEG headset and sending commands to a mobile robot, the entire process has been done wirelessly.

Mamunur Rashid, Norizam Sulaiman, Mahfuzah Mustafa, Sabira Khatun, Bifta Sama Bari, Md Jahid Hasan, Nawfan M. M. A. Al-Fakih

Campus Hybrid Intrusion Detection System Using SNORT and C4.5 Algorithm

The rapid development of the internet greatly helps human work. However, the number of information system security incidents has risen sharply, so that in fact the sides of human life are threatened. Detection techniques against attacks on computer networks must be continuously developed so that integrity, availability, and confidentiality on a computer network become more secure. In general, intrusion detection systems currently use two detection methods, namely anomaly detection, and misuse detection, which both have their own deficiencies. In this paper, the authors built a Hybrid Intrusion Detecting System combines anomaly detection system with the misuse detection system. Snort is used as the basis of misused detection module and Algorithm C4.5 detector is used to construct an anomaly detection module. This system works by creating alerts built from an engine that reads the parameters in the attacker’s IP address. Webmin is used to simplify rule management. Whereas for analyzing logs (attack history), an ACID (Analysis Console for Intrusion Databases) is used. Attack and detection testing are carried out in the campus network of Institut Bisnis dan Informatika Stikom Surabaya. The system implementation uses a PC Router with the Ubuntu 18.04 Linux as the operating system. As a result of implementing this system: the signature of attacks as misuses detection module uses to detection the known attacks; unknown attacks can be detected by the anomaly detection module; signature of attacks that are detected by Anomaly Detection System module extracted by signature generation module, and maps the signatures into snort rules.

Slamet, Izzeldin I. Mohamed, Fahmi Samsuri

Image Segmentation of Women’s Salivary Ferning Patterns Using Harmony Frangi Filter

Medical research proves that entering the fertile period, especially during ovulation, all-female body fluids contain ferning patterns in the form of crystallization of salt shaped like a fern tree. Until now, not many research topics have been carried out related to the segmentation process in the salivary ferning pattern, this is due to several problems including first, the unavailability of a database of image salivary ferning pattern online. Second, the salivary ferning pattern has several hidden layers and uneven intensity. The purpose of this study was to detect and determine the line shape of the salivary ferning crystal pattern using the Harmony Frangi Filter method based on the Hessian matrix operation. The results of the segmentation process from this study are a crucial basis in determining the level of accuracy and precision at the next stage of research, namely: the prediction process of a woman’s ovulation in each menstrual cycle. The measurement of segmentation results has an average value of MSE 2.25, PSNR 44.86 dB, FSIM 0.954, accuracy 99.88%, sensitivity 99.98% and specificity 99.88%.

Heri Pratikno, Mohd Zamri Ibrahim

Autonomous Self-exam Monitoring for Early Diabetes Detection

Diabetes can be prevented by early detection. In Malaysia, new case of diabetes is increasing year by year. Insufficient number of physicians tasked to treat a large number of patients will increase their burdens and also make them more stressed. An autonomous self-exam monitoring is developed in order to assist the physicians in identifying diabetes at the early stage. Iris image is used to recognise the early detection of diabetes. Based on iridology theory, the image is evaluated by detecting the presence of broken tissues and change in colour pattern. It can be integrated with computer vision for an accurate identification of abnormality in iris image. This paper focuses on developing an iris imaging system that extracts the presence of orange pigmentation which is the sign of diabetes. This project comprises of three stages which are pre-processing, processing and post processing stage. The designed tool convert an iris image into new picture using image processing algorithms and analyses some changes in colour pattern and lastly diagnose whether it is diabetic or non-diabetic iris. The experimented images in this project are the iris image that was taken from public database UBIRIS.v1. At the end of this project, we discovered whether this system can detect the presence of broken tissues and change in colour pattern of iris or not. The final result shows the accuracy of 80% for detecting the orange pigmentation as the sign for early diabetes detection.

Rohana Abdul Karim, Nur Alia Fatiha Azhar, Nurul Wahidah Arshad, Nor Farizan Zakaria, M. Zabri Abu Bakar

Quantitative Assessment of Remote Code Execution Vulnerability in Web Apps

With the exponentially increasing use of online tools, applications that are being made for day to day purpose by small and large industries, the threat of exploitation is also increasing. Remote Code Execution (RCE) is one of the topmost critical and serious web applications vulnerability of this era and one of the major concerns among cyber threats, which can exploit web servers through their functionalities and using their scripts/files. RCE is an application layer vulnerability caused by careless coding practice which leads to a huge security breach that may bring unwanted resource loss or damages. An attacker may execute malicious code and take complete control of the targeted system with the privileges of an authentic user with this vulnerability. Attackers can attempt to advance their privileges after gaining access to the system. Remote Code Execution can lead to a full compromise of the vulnerable web application as well as the web server. This chapter highlights the concern and risk needed to put under consideration caused by RCE vulnerability of a system. Moreover, this study and its findings will help application developers and its stakeholders to understand the risk of data compromise and unauthorized access to the system. An exploitation algorithm is proposed to identify RCE vulnerability in web application. Then based on it, around 1011 web applications were taken under consideration and experiments were conducted by following manual double blinded penetration testing strategy. The experiments show that more than 12% of web application were found vulnerable to RCE. This study also explicitly listed the critical factors of Remote Code Execution vulnerability and improper input handling. The experimental results are promising to motivate developers to focus on security enhancement through proper and safe input handling.

Md Maruf Hassan, Umam Mustain, Sabira Khatun, Mohamad Shaiful Abdul Karim, Nazia Nishat, Mostafijur Rahman

Sustainable Energy and Power Engineering

Frontmatter

A Salp Swarm Algorithm to Improve Power Production of Wind Plant

Currently, the main problem of wind plant power production is definitely the control system of a wind generator that is not able to cope with the impact of turbulence and thus weakens complete power output. In this paper a Salp Swarm Algorithm (SSA) is proposed as a data-driven method to improve the controller variable and thus optimize the complete power production of the wind plant. The SSA is among of the meta-heuristic technique and imitates the salps chain’s swarm movement depending on the food placement. The model used in this study originates from Denmark’s actual Horns Rev wind plant. The analysis result demonstrates the SSA generates significantly better total wind power production as opposed to the Spiral Dynamic Algorithm (SDA) and the Particle Swarm Optimization (PSO) technique.

Ahmad Zairi Mohd-Zain, Mohd Ashraf Ahmad

Improvement of Performance and Response Time of Cascaded Five-Level VSC STATCOM Using ANN Controller and SVPWM During Period of Voltage Sag

Power system is an extremely nonlinear system with a number of interconnected loads. When the system is subjected to the faults, the stability of the system will be disturbed. The major problem dealt here is voltage sag. A static synchronous compensator (STATCOM) is one of the FACTS devices which can inject proper reactive current at the point of common coupling (PCC) to compensate voltage sag. A non-linear controller like artificial neural network (ANN) is used with the FACTS devices for better performance. This paper introduces the design of a cascaded 5-level voltage source converter (VSC) STATCOM based on the ANN controller and space vector PWM (SVPWM) technique to nullify the impacts of voltage sag. ANN and SVPWM were employed to enhance the performance and response time (RT) of STATCOM with regard to correction of voltage magnitude and power factor (PF) amplitude during voltage sag period. The performance of STATCOM was analyzed using MATLAB in IEEE 3-bus system with two different types of faults, which are single line to ground (SLG) fault and line to line (LL) fault (both creates voltage sag). The simulation result showed that the ANN-based STATCOM control circuit performed efficiently compared to the PI controller. The ANN controller was able to recover voltage magnitude very quickly (during 0.02 s) with unity.

Mohamad M. Almelian, Izzeldin I. Mohd, Abu Zaharin Ahmad, Mohamed A. Omran, Muhamad Z. Sujod, N. M. Elasager, Mohamed Salem

Development of Maximum Power Point Tracking for Doubly-Fed Induction Generators in Wind Energy Conversion Systems

Maximum power point tracking (MPPT) control is one of the essential requirements in harnessing wind power of wind energy conversion systems (WECS). The more precise the maximum power point (MPP) is determined, the more optimal the WECS is operated. Amongst MPPT algorithms, a hill-climb search (HCS) algorithm is preferred because of its simplicity however it also has few drawbacks such as the difficulty of selecting an appropriate step size, the premature convergence phenomenon and the speed-efficiency trade-off. A cuckoo search (CS) algorithm is proposed in this paper for finding out a MPP of a WECS driven by a doubly-fed induction generator (DFIG) under various wind speeds which mostly overcomes the above disadvantages. Then, the DFIG-WECS is controlled to track the MPP. This ensures that the DFIG-WECS is always operated at MPPs regardless of various wind speeds. It is realized that the proposed CS algorithm is also a population-based algorithm inspired from the breeding behavior of cuckoos but it is quite simple, powerful and especially requires less parameters to define optimal solutions than a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. Though numerical results, the CS algorithm shows its effectiveness in finding out MPPs in the DFIG-WECS. Furthermore, the obtained results using the CS algorithm are also compared with those using the HCS and PSO algorithms. The comparison demonstrates that the convergence value and speed of the CS algorithm are always better than those of the HCS and PSO algorithms in the MPPT application in the DFIG-WECS.

Duy C. Huynh, Khai H. Nguyen, Matthew W. Dunnigan

Development of PV Module Power Degradation Analyzer

The aim of this paper is to design and develop a system that can analyze the performance degradation of a PV module based on real operating condition (ROC). In this research, the system will capture voltage and current under the real operating condition in order to get the Standard Test Condition (STC) parameters. Then this system will calculate the power degradation of the tested PV module. The experiment showed that the proposed system can estimate the PV module performance degradation by up to 90% accuracy. The system has been designed so that it can be a portable device which can be easily taken everywhere at any time.

Mohd Shawal Jadin, Muhammad Aiman Ibrahim, Norizam Sulaiman

Direct Power Control Method of Maximum Power Point Tracking (MPPT) Algorithm for Pico-Hydrokinetic River Energy Conversion System

In this paper, a design of maximum power point tracking (MPPT) algorithm for the pico-hydrokinetic system in river application has been proposed. The design topology consists of the permanent magnet synchronous generator (PMSG), a three-phase bridge rectifier and a DC boost converter. The proposed MPPT algorithm is a combination of modified hill-climbing search algorithm (MHCS) with the current PI-controller. The MPPT concept is based on measuring the rectifier output voltage and current respectively to produce the reference current (IMPP). The PI-controller has been used to tune the error signal between IMPP and actual inductance current (Idc) to provide the duty-cycle of the boost converter. A comparison is performed between the fixed step HCS and the proposed MPPT to investigate the performance of the algorithm. The results show the proposed algorithm able to harness the maximum power with 96.32% efficiency.

W. I. Ibrahim, M. R. Mohamed, R. M. T. R. Ismail

Load Estimation of Single-Phase Diode Bridge Rectifier Using Kalman Filter

These days most electronic loads are nonlinear. Electronic equipment such as audio devices, personal computers and electronic ballasts for discharge lamps are the example of nonlinear loads. These electronic loads are works in DC voltage. As the energy distribution system is performed in AC voltage, the AC voltage need to change into DC voltage. The single-phase rectifier performed the conversion of AC voltage to DC voltage in low power applications. The main drawback of these rectifiers is that they generate significant harmonic distortion. Components aging, power system efficiency lessen and excessive heat of equipments are the effects of harmonics in power system. Thus, the self-resonance, non-dielectric and hysteresis existed in the power system affected system designer to choose the passive components for the simulation. This paper portrays the study and development of an estimation method for the values of the electrical parts in the majority of the electronic equipment accessible in the market. It is conceivable to identify the values of equivalent capacitance, resistance and inductance that associated with the rectifier through this method. Simulation results validate the better accuracy of the proposed method when contrasted to the measurement-based method. The proposed method using Kalman filter to this rectifier topology enabled the expansion for future works to think about their harmonic effect on the power quality (PQ) of power distribution systems.

Nor Syuhaida Othman, Hamzah Ahmad

A Study on Residual Current Device Nuisance Tripping Due to Grounding Resistance Value

Protection against leakage current is vital in an electrical system to protect humans and equipment from electric shocks and fire risk. Residual Current Device (RCD) is a protective device used for protection against small leakage current. This device is designed to disconnect the circuit whenever a fault is occurred, by measuring the different value of current between phase and neutral. However, inappropriate tripping also known as nuisance tripping is influenced by the improper grounding system, high frequency from power supplied and presence of harmonics. The aim of this paper is to investigate the residual operating current and operating time of RCD behavior towards the poor grounding resistance value. Besides that, the effect of different type of load were considered in the experiments. The sensitivity of RCD sample used in this research is 30 mA type-AC. RCD operating current and voltage are evaluated and compared with the requirement by international standard The results show grounding system with high resistance value affects the operating time of RCD, thus lead to nuisance tripping.

Izzatul Liyana, Farhan Bin Hanaffi, Mohd Hendra Bin Hairi

DC-Link Protection for Grid-Connected Photovoltaic System: A Review

As the economic growth and population increase, the demand for energy supply has also increased. The disadvantages that power generation based on non-renewable energy sources bring to the environment has stimulate the idea of generating clean and sustainable power in a huge amount from renewable energy sources like solar and wind energies. In recent years, photovoltaic (PV) systems are mostly used due to its light and easy-installable characteristics. It has two approaches which are stand-alone PV system and grid-connected PV system (GCPV). Although it is said to be the most promising renewable energy, it could not avoid disturbance. In GCPV, faults could occur on the grid side, leading to overshoot voltage in DC-link and overshoot grid current. These situations could stress electrical components and decrease power quality of the system. Therefore, many protection schemes have been introduced to overcome this matter. A brief discussion on the growth of GCPV technology together with the impacts of grid faults on it were presented in this paper. Then, several conventional protection schemes implemented in GCPV were also reviewed. In the end, a new protection scheme namely zero state protection scheme that has the same function to limit the overshoot DC-link voltage was proposed.

Wan Nur Huda Aqilah Alias, Muhamad Zahim Sujod, Nor Azwan Mohamed Kamari

An Improved Efficiency of Solar Photo Voltaic System Applications by Using DC-DC Zeta Converter

This study investigates on how a DC-DC Zeta converter act as intermediate among SPV and VSI, in which it drags the maximum power from the solar photovoltaic (SPV) system and to drive a BLDC motor connected to a water pumping system application. Here INC-MPPT (Incremental Conductance Maximum Power Point Tracking) method is utilized smartly to control the zeta converter in order to drive brushless DC (BLDC) motor smoothly. Soft starting current prevents the influence of peak starting current on the BLDC motor windings. The fundamental frequency of Electronic computational from the BLDC motor is used to avoid the voltage source inverter losses. The proposed converter is also suitable to increase the voltage of DC link connected to the VSI. The major benefit of this configuration is designed and modelled in such a way that even under dynamic conditions, the performance of a solar photovoltaic application is not affected. The suggested system is developed by using MATLAB/Simulink software.

A. S. Veerendra, M. R. Mohamed, M. H. Sulaiman, K. Peddakapu

Hydrophobic Sol-Gel Based Self-cleaning Coating for Photovoltaic Panels

Maintaining photovoltaic performance from soiling issues using manual cleaning is costly and tedious which has been a major concern in deploying this technology. Therefore, a soiling mitigation technique with self-cleaning properties such as hydrophobic coating is effective to minimize performance degradation of photovoltaic panels using sol-gel as a low-cost and scalable fabrication method. This study proposes the development and application of hydrophobic sol-gel based coating in the photovoltaic system. The aims include synthesizing a hydrophobic sol-gel based self-cleaning coating for solar panel and characterizing the hydrophobic sol-gel based self-cleaning coating. A solution is prepared using sol-gel process comprises of three different materials including vinyltriethoxysilane (VTES), tetraethoxysilane (TEOS) and tetrabutoxytitanate (TTBU) called VTT (VTES-TEOS-TTBU) sol as the organic-inorganic hybrid sol. Then, this sol is applied onto glass substrates using spin-coating method for laboratory-scale working samples. Coated samples have undergone characterizations including water contact angle measurement to obtain hydrophobic properties and surface morphology observation using microscope. The resultant VTT sol samples proven to exhibit self-cleaning ability with contact angle of 99.58° when undergo 150 °C post-bake process. The switchability of sol (hydrophilic-hydrophobic) was achieved and better transparency was observed with transmittance of 90.73% when the samples undergoing different thermal treatment during pre-bake and post-bake processes.

Siti Nur Nashya Azlika Hamidon, Amirjan Nawabjan, Ahmad Sharmi Abdullah, Siti Maherah Hussin

Effect of Graphene Oxide Nanoparticles on Thermal Properties of Paraffin Wax

Whereas previous studies analyzed thermal properties of pure paraffin, this paper analyzed thermal properties of paraffin added with Graphene Oxide (GO) nanoparticles experimentally. The tested samples are paraffin wax and GO added at various percentages of weight, 1 wt%, 3 wt%, 5 wt% which typically used for photovoltaic panel cooling. The objective is to explore the effect of various weight percentages of GO nanoparticles addition on the thermal properties of the paraffin. All the thermal properties were measured by using thermographic camera, and Differential Scanning Calorimetry (DSC). DSC showed that melting and solidification temperature for paraffin/5 wt% GO has highest reduction which is at 45.91 ℃ and 41.85 ℃, followed by paraffin/3 wt% GO with 46.15 ℃ and 42.02 ℃, and then paraffin/1 wt% GO with 46.25 ℃ and 42.02 ℃, when compared to 63 ℃ and 59.5 ℃ for pure paraffin. Thermographic camera recorded the melting temperature history of all samples for 600 s. From the measurement, it is revealed that paraffin/5 wt% GO has largest heat transfer rate. This is shown by the bigger average temperature gradient of paraffin/5 wt% GO which is at 2.93 followed by paraffin/3 wt% GO at 2.69, paraffin/1 wt% GO at 2.52 and paraffin at 2.03. DSC also revealed that paraffin/5 wt% GO has highest improvement in latent heat which is 163.99 kJ/kg, followed by paraffin/1 wt% GO, paraffin/3 wt% GO and pure paraffin each at 155.85 kJ/kg, 155.0813 kJ/kg and 102 kJ/kg. Paraffin/5 wt% GO also can be seen to have the largest amount of heat stored with 0.62 kJ, followed by paraffin/3 wt% GO, paraffin/1 wt% GO and lastly pure paraffin with 0.44, 0.4 and 0.33 kJ respectively. The results indicate that the rise of GO nanoparticles percentages weight added results in better thermal properties of paraffin. With better charging and discharging rate, highest latent heat, largest amount of heat can be stored, paraffin/5 wt% GO is the most favorable to be used as a photovoltaic panel coolant.

Nurul Humaira Muhd Zaimi, Amirjan Nawabjan, Shaharin Fadzli Abdul Rahman, Siti Maherah Hussin

Reliability Performance of Low Voltage (LV) Network Configuration

Networks are typically modelled in single phase diagram especially for medium voltage (MV) and high voltage (HV) networks. For low voltage (LV) networks, it is not suitable to model it in a single phase diagram. The reliability performance of LV network may be overestimated or underestimated if the network is modelled in a single phase diagram. Analytical technique is used to quantify the performance of LV network in single and three phase network diagrams. Three phase LV network diagram illustrates the true reliability performance compared to single phase LV network diagram in term of the best, median and worst location of customers. Accurate network configuration may benefit in minimizing energy core losses and reducing paying penalty to the customer by distribution network operators (DNOs).

Mohd Ikhwan Muhammad Ridzuan, Muhammad Adib Zufar Rusli, Norhafidzah Mohd Saad

Detailed Non-Linear Constrained Multi-Objective Optimal Operation of Power Systems Including Renewable Energy Sources

A modified cuckoo search (MCS) algorithm is proposed in this paper for an optimal operation problem of power systems with renewable energy sources including solar and wind energy sources. A non-linear constrained multi-objective optimal operation problem is formulated and detailed for an integrated power system in order to make it more realistic. Furthermore, the cuckoo search (CS) algorithm is modified to increase the convergence rate that is called the MCS algorithm. This variant mentions the step size of the Lévy flight. The modified IEEE 10-generator power system with integrated solar and wind power sources is considered in this paper. The numerical results on the above power system confirm the effectiveness of the proposal for the optimal operation of the integrated power system. A comparison with the CS algorithm and variants of the particle swarm optimization (PSO) algorithm indicates the superiority of the MCS algorithm for resolving complicated optimal operation problems of integrated power systems.

Duy C. Huynh, Hong V. Nguyen, Matthew W. Dunnigan

Voltage Sag Immunity Testing for AC Contactors in Industrial Environment

The voltage sag is one of the prominent power quality issues faced by industrial consumers in Malaysia. Frequent voltage sag incidences have caused sensitive equipment to trip causing significant production losses. One of the identified weak links is the AC contactor. In addition, there are many ageing contactors which are still in service in the industry due to their robust design and long lifetime. This paper aims to study the immunity of voltage sag of AC contactors which are installed in a petrochemical plant. New and ageing contactors have been chosen to undergo practical testing. Well-defined test procedures are carried out based on the IEC 61000-4-11 standard. The AC contactors were exposed to rectangular voltage sag with variations in magnitude, duration and point on wave. The results are compared to IEC 61000-4-37 voltage tolerance curve. New contactor’s voltage tolerance curve exhibits nearly consistent response and a slight deviation between different points on wave. It also recorded minimum sensitivity at voltage sag magnitude of 25%. On the other hand, ageing contactor exhibits mixed conformity to the IEC curve, with minimum sensitivity at 55% of voltage sag magnitude. To summarise, the ageing contactor is more sensitive to voltage sag magnitude and point on wave has little influence to the voltage tolerance curve of the new contactors.

Hazri Dahalan Razip, Abu Zaharin Ahmad

Vertical Axis Wind Turbines: An Overview

In recent decades, wind energy becoming one of the most important types of renewable energy in electrical power production. It has been recognized as an encouraging renewable choice and one of the cleanest way to generate electricity. This paper provides brief ideas of a few types of vertical axis wind turbine (VAWT) utilized in the electrical power generation system. The growth and implementations of wind energy harnessing, wind turbine behaviors, related findings and the future trends of VAWTs were analyzed. The existence of some energy issues such as global warming and the diminishing of fossil fuels throughout the world nowadays need to be concerned and it was perceived that VAWT plays an important role in handling these current energy issues. VAWT seems to be more advantageous compared to HAWT in term of cost basis and simple design, but lags in performance efficiency. However, VAWT demonstrates better execution in complex wind condition with small wind access, which discussed throughout this paper. Currently, a lot of researches about the enhancement and augmentation of VAWT to increase the power production efficiency are ongoing. From the literature, the maximum VAWT’s efficiency reached only about 40–50% which is still below the theoretical efficiency of the wind turbine. This shows the potential for further improvement in VAWTs to enhance the performance of wind turbine efficiencies. In summary, it can be concluded that further studies are critically needed to establish a greater acceptance of VAWTs as a feasible, reliable and reasonable power generation system especially for the low wind speed countries like Malaysia.

A. Yusof, M. R. Mohamed

Hyperheuristics Trajectory Based Optimization for Energy Management Strategy (EMS) of Split Plug-In Hybrid Electric Vehicle

To date, with the advancement in energy-related technology, the regulations prior to the environment and energy emission are also strictly increasing due to the impact of global warming. Hence, many electric vehicles are affected since one of their principles is closely related to the issue of short-range storage capacity and long charging time which are not in favor to most of the automotive customers. Thus, it embarks many kinds of research to establish development of an efficient energy management storage (EMS) to fulfill the objectives at the same time keeping the vehicle performance at its convincing standard. This work presents the Hyperheuristics Trajectory Based Optimization for Energy Management Storage (EMS) of The Split Plug-in Hybrid Electric Vehicle. The Split Plug-in Hybrid Electric Vehicle is discussed in different and recent perspective by zooming into other aspects of EMS point of view. A comprehensive discussion is elaborated by comparing various strength and weaknesses of this research concept. The performance results from this improved approach are compared with the conventional HEVs. The analytical modeling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with some powertrain component and system modeling application examples.

Muhammad Ikram Mohd Rashid, Ahmad Amir Solihin Mohd Apandi, Hamdan Daniyal, Mohd Ashraf Ahmad

Utilization of Filter Harmonic Current Based on Shunt HPF Within the Acceptable IEEE-519 Standard

Harmonic-related problems such as communication noise, malfunctioning of the solid-state control circuit, etc., are often encountered in industrial plants that have a significant amount of rectification. Different techniques to eliminate harmonics current from power systems to the ground have been proposed and one of them is shunt HPF which is an effective and widely-used method for power quality improvement. This paper presents the idea of reusing the HPF harmonic current created based on STF–SRF theory to feed AC load within the limits of IEEE-519 (Less 5%). The circuit has been simulated in the MATLAB-Simulink and tested under distorted source voltage with varying loads. The outcomes of the simulations showed the THD at PCC to be less than 5% even when the HPF current was connected to the system (increasing the source current).

Mohamed A. Omran, Izzeldin I. Mohd, Abu Zaharin Ahmad, Mohamad M. Almelian, Fahmi Samsuri, Muhamad Z. Sujod, Walid K. A. Hasan, Mohamed Salem

Vehicle-to-Grid as Frequency Regulator in a Micro Grid System

This paper evaluated the capability of the Vehicle-to-Grid (V2G) to provide frequency regulation in a micro grid system. To begin with, the impact of sudden increase of residential peak load due to the Electric Vehicle charging load is investigated. Then the Plug-in Hybrid Electric Vehicle Charging Load Profile (PHEVCLP) is generated based on real the data taken from National Household Travel Survey 2017. In this project, the model of V2G in a micro grid system is developed and analyzed using MATLAB software. The results show that the integration of PHEV on a micro grid has an impact on peak load and system frequency. Moreover, the rise of the total number of PHEVs penetration has a significant impact on system frequency. The percentage of improvement in system frequency as V2G system implemented increased as the charger power level increased. It can be concluded that V2G on a micro grid improved and regulated system frequency.

Mohd Redzuan Ahmad, Laylatun Qadrina Amrizal

Development of PV Module Hotspot Detector

The aim of this paper is to develop an analyzer for detecting and evaluating hotspot in problem in a photovoltaic (PV) module. There are many causes that can lead to the hotspot in PV modules such as shading effect, impurities present on the module surface and many more. The proposed system will capture the thermal images of the PV module and analyze each region in the image. The system will detect the hotspot regions and evaluate the severity level of the hotspots. Though the experiment, the proposed system could produce a reliable result compared to the conventional approach.

Mohd Shawal Jadin, Kamil Ashman Bin Zamridin, Ahmad Syahiman Mohd Shah

Comparative Analysis for LED Driver with Analog and Digital Controllers

The trend of utilizing light emitting diodes (LEDs) in some applications has attracted the attention of many researchers, to study its applications. This article investigates the performance analysis of the dc–dc converter systems based on analog and digital controllers for a low voltage dc–dc buck converter, to drive strings of LEDs at different conditions, to judge system’s robust performances. This converter comprises of a single controller, working with a voltage control feedback system, in a continuous conduction mode. The analog and digital type-3 controllers are designed for the said system while using standard frequency response techniques. Simulations are shown to validate the design and the response of these controllers under various dynamic load conditions.

Shaheer Shaida Durrani, Abu Zaharin, Bakri Hassan, Ruhaizad Bin Ishak

Characterization of Positive Porous Electrode Felt for Organic Redox Flow Battery Application

The newly emerging organic redox flow battery (RFB) as one of the most promising technology for energy storage system due to their flexible molecule modification. Nevertheless, the study on treated electrode in electrocatalytic activity for organic chemistry is limited. Most of the conventional studies reported a single treatment for carbon porous electrode and mostly focus on vanadium electrochemistry. To investigate the effect of sulphonation and oxidation of carbon felt in organic active material, two-stage surface treatment involving acid with thermal treatment was introduced in this study. The electrochemical investigation of acid treated felt and pristine felt were performed using cyclic voltammetry (CV) for selected positive electrolyte benze-1,4-diol in supporting acid—sulfuric acid. The results disclosed the potential of acid treated felt with good reversibility in cyclic voltammetry analysis with increase anodic peak potential.

A. C. Khor, K. F. Chong, M. R. Mohamed
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