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31,456 Article Results

Numerical computation for solving fuzzy differential equations

10.11591/ijeecs.v16.i2.pp1026-1033
Fuziyah Ishak , Najihah Chaini
Fuzzy differential equations (FDEs) play important roles in modeling dynamic systems in science, economics and engineering. The modeling roles are important because most problems in nature are indistinct and uncertain. Numerical methods are needed to solve FDEs since it is difficult to obtain exact solutions. Many approaches have been studied and explored by previous researchers to solve FDEs numerically. Most FDEs are solved by adapting numerical solutions of ordinary differential equations. In this study, we propose the extended Trapezoidal method to solve first order initial value problems of FDEs. The computed results are compared to that of Euler and Trapezoidal methods in terms of errors in order to test the accuracy and validity of the proposed method. The results shown that the extended Trapezoidal method is more accurate in terms of absolute error. Since the extended Trapezoidal method has shown to be an efficient method to solve FDEs, this brings an idea for future researchers to explore and improve the existing numerical methods for solving more general FDEs.
Volume: 16
Issue: 2
Page: 1026-1033
Publish at: 2019-11-01

An optimization of facial feature point detection program by using several types of convolutional neural network

10.11591/ijeecs.v16.i2.pp827-834
Shyota Shindo , Takaaki Goto , Tadaaki Kirishima , Kensei Tsuchida
Detection of facial feature points is an important technique used for biometric authentication and facial expression estimation. A facial feature point is a local point indicating both ends of the eye, holes of the nose, and end points of the mouth in the face image. Many researches on face feature point detection have been done so far, but the accuracy of facial organ point detection is improving by the approach usingConvolutional Neural Network (CNN). However, CNN not only takes time to learn but also the neural network becomes a complicated model, so it is necessary to improve learning time and detection accuracy. In this research, the improvement of the detection accuracy of the learning speed is improved by increasing the convolution layer.
Volume: 16
Issue: 2
Page: 827-834
Publish at: 2019-11-01

Coordinated and optimal voltage control for voltage regulation using firefly algorithm

10.11591/ijeecs.v16.i2.pp568-576
Muhamad Najib Kamarudin , Tengku Juhana Tengku Hashim
The operation and control of electricity in distribution networks has faced great challenges as a large number of distributed generations (DGs) are integrated. Connection of distributed generations (DGs) in the distribution system offers advantages in terms of reducing distribution and transmission costs as well as encouraging the use of renewable energy sources. The power flow in the distribution systems is no longer moving in a single direction and this resulted the system to become as active distribution networks (ADN). One of the main problems in ADN is the voltage regulation issue which is to maintain the voltage to be within its permissible limits. Several methods of voltage control methods are available and focus is given in finding the optimal voltage control using artificial intelligence techniques. This paper presents an optimal and coordinated voltage control method while minimizing losses and voltage deviation of the network. The optimal and coordinated voltage control scheme is implemented on an IEEE 13 bus distribution network for loss and voltage deviation minimization in the networks. Firefly Algorithm (FA) which is a known heuristic optimization technique for finding the optimal solution is used in this work. The results are compared with another optimization method known as Backtracking Search Algorithm (BSA) for identifying the best setting for solving the voltage regulation problem. In order to solve the multi-objective optimization issue, the MATPOWER load flow simulation is integrated in the MATLAB environment with the optimization algorithm.
Volume: 16
Issue: 2
Page: 568-576
Publish at: 2019-11-01

Computational study of flow around a NACA 0012 by using Roe FVM scheme and davis-yee TVD scheme

10.11591/ijeecs.v16.i2.pp1018-1025
Fatimah Yusop , Zamri Omar , Bambang Basuno , Nik Normunira Mat Hassan
Currently CFD had been considered as an important tool for solving engineering problems. The application of CFD had been used intensively in aircraft industries in design a new aircraft or in the effort of improvement on the exiting aircraft. In term of CFD computer code, the CFD code differs with any others may due to the difference in the numerical scheme have been used. Therefore, the present work presents the comparison result between two developed computer codes with ANSYS-FLUENT software to the case of transonic steady flow past through airfoil NACA 0012. The first computer code used a finite difference method with numerical scheme according to Davis-Yee TVD scheme. Meanwhile, the second computer code used a Roe’s cell centre finite volume scheme. The flow analysis is carried out at two Mach number, M (0.65 & 0.8). Each Mach number applied to two different angles of attacks (0° & 5°).  The flow domain discretized by use of C-topology with 193x63 grid points. The comparison in term of the pressure coefficient, along the airfoil surface are presented. From the result, indicated that developed computer code is able to capture the presence of shock wave in the flow field.
Volume: 16
Issue: 2
Page: 1018-1025
Publish at: 2019-11-01

Clonal evolutionary particle swarm optimization for congestion management and compensation scheme in power system

10.11591/ijeecs.v16.i2.pp591-598
N. Z. Mohd Ali , I. Musirin , H. Mohamad
This paper presents computational intelligence-based technique for congestion management and compensation scheme in power systems. Firstly, a new model termed as Integrated Multilayer Artificial Neural Networks (IMLANNs) is developed to predict congested line and voltage stability index separately. Consequently, a new optimization technique termed as Clonal Evolutionary Particle Swarm Optimization (CEPSO) was developed. CEPSO is initially used to optimize the location and sizing of FACTS devices for compensation scheme. In this study, Static VAR Compensator (SVC) and Thyristor Control Static Compensator (TCSC) are the two chosen Flexible AC Transmission System (FACTS) devices used in this compensation scheme. Comparative studies have been conducted between the proposed CEPSO and traditional Particle Swarm Optimization (PSO). Results obtained by the developed IMLANNs demonstrated high accuracy with respect to the targeted output. Consequently, the proposed CEPSO implemented for single objective in single unit of SVC and TCSC has resulted superior results as compared to the traditional PSO in terms of achieving loss reduction and voltage profile improvement.
Volume: 16
Issue: 2
Page: 591-598
Publish at: 2019-11-01

Analysis of landslide hazard mapping of penang island malaysia using bivariate statistical methods

10.11591/ijeecs.v16.i2.pp781-786
Ilyas A Huqqani , Lea Tien Tay , Junita Mohamad Saleh
Landslide is one of the disasters which cause property damages, infrastructure destruction, injury and death. This paper presents the analysis of landslide hazard mapping of Penang Island Malaysia using bivariate statistical methods. Bivariate statistical methods are simple approach which are capable to produce good results in short computational time. In this study, three bivariate statistical methods, i.e. Frequency Ratio (FR), Information Value (IV) and Modified Information Value (MIV) are used to generate the landslide hazard maps of Penang Island. These bivariate statistical methods are computed using MATLAB tool. Landslide hazard map is categorized into 4 levels of hazard. The accuracy of each method and effectiveness in predicating landslides are validated and determined by using Receiver of Characteristics curve. The accuracies of FR, IV and MIV methods are 79.58%, 79.14% and 79.37% respectively.
Volume: 16
Issue: 2
Page: 781-786
Publish at: 2019-11-01

Spectrum sensing in single channel and multi-channel cognitive radio networks

10.11591/ijeecs.v16.i2.pp812-817
Amira Osama Hashesh , Heba A.Tag El-Dien , Ahmad A.Aziz El-Banna , Adly Tag El-Din
Sensing the existence or absence of primary user is the major chore of cognitive radio networks. Nevertheless, Spectrum sensing is the core process of cognitive radio and with target to find idle channels.Various detection techniques exist, however, energy detection is considered as the most used detector because of its lower computational cost. In this paper, we proposed a study of throughput for a cognitive radio system. We had two scenarios, in the first scenario; a study of throughput against probability of false alarm was done; where, only one channel is sensed, to maximize the individual channel throughput. In the second scenario, multi-channel is sensed to maximize the overall system capacity. In addition, different number of channels is considered with different sensing times and at different throughput costs.The performance of the network has been investigated in terms of maximum throughput for optimal number of CR channels.      
Volume: 16
Issue: 2
Page: 812-817
Publish at: 2019-11-01

Heuristic based model for groceries shopping navigator

10.11591/ijeecs.v16.i2.pp932-940
Muhammad Wardi bin Peeyee , Shuzlina Abdul-Rahman , Nurzeatul Hamimah Abdul Hamid , Mohd Zaki Zakaria
This paper presents a heuristic based model for groceries shopping navigator that attempts to improve the navigation problem that usually face by customers while doing their shopping. A system known as Shopping Navigator or shortly SHoNa was developed to give the optimal sequence of shelves to be visited by the customer and the total estimated shopping time so that the user can plan their shopping task earlier. Genetic algorithm was employed and implemented in a web-based platform that is compatible with other devices such as smartphones and tablets. SHoNA can minimize the shopping time by identifying the most optimal order of shelves inside the supermarket that needs to be visited by the customer. A series of experimental was performed in producing the optimum model. Our findings showed that the combination of order one crossover and inverse mutation produced a better optimal performance, which is the minimum total amount of groceries shopping time.  SHoNA can be further enhanced with visualization features for a better shopping experience.
Volume: 16
Issue: 2
Page: 932-940
Publish at: 2019-11-01

Brute force algorithm implementation for traveljoy travelling recommendation system

10.11591/ijeecs.v16.i2.pp1042-1049
K.A.F.A. Samah , N. Sabri , R. Hamzah , R. Roslan , N.A. Mangshor , A.A.M. Asri
This paper presents the Brute Force algorithm implementation for TravelJoy Travelling Recommendation System.  Due to overwhelmed information in the internet, travelers faced difficulties in finding and comparing which places in Melaka that worth to visit. Melaka is a well-known place as one of the most popular tourist spots in Malaysia, famous with historical places. All the mentioned problems were time-consuming and required lots of efforts for manual comparison between places and planning the trip itinerary. An efficient application system is needed to assist travelers in planning their trip itinerary by providing details of interesting place in Melaka, budget estimating and recommendation of sequence places which to visit. The TravelJoy application applied Traveling Salesman Problem (TSP) concept using Brute Force algorithm in determining the least time duration for the selected places and adapting Expected Time Arrival (ETA). It was found through Brute Force algorithm adaptation; the recommendation system is reliable based on the functional and reliability testing with t-test result of 0.00067, indicates the system is accepted.
Volume: 16
Issue: 2
Page: 1042-1049
Publish at: 2019-11-01

Development of MIMO antenna with decoupling structure for ultra-wideband application

10.11591/ijeecs.v16.i2.pp818-826
Muhammad Aslam Hasin , M. T. Ali , Hamizan Yong , Bazilah Baharom , Hadi Jumaat
This paper presents the design of adouble-sided Multiple-Input Multiple-Output (MIMO) antenna with a decoupling structure for Ultra-WideBand (UWB) applications. The proposed antenna consists of four square radiating elements printed on FR-4 substrate with partial slotted ground. The substrate consists of two sides and each side are consisting of two slotted partial ground and two square radiator antennas. The elements of the front side are orthogonal to the elements of the back side because it was to increase the isolation of the antenna. The front and back sides of the substrate were also presented with the decoupling structure to avoid the mutual coupling of each antenna. The results of simulated and measured of the proposed UWB MIMO antenna are observed and analyzed. The objective of the UWB MIMO antenna was achieved that large bandwidth of return loss below than -10 dB from 3.3 GHz to 11GHz with an insertion loss lower than -20 dB within the required frequency band. The proposed MIMO antennas exhibits a nearly Omni-directional radiation pattern with average gain value 4.36 dBi.
Volume: 16
Issue: 2
Page: 818-826
Publish at: 2019-11-01

Hybrid MVDR-LMS beamforming for Massive MIMO

10.11591/ijeecs.v16.i2.pp715-723
Yasmine M. Tabra , Bayan Sabbar
With the high speed of communication in LTE-5G, fast beamforming techniques need to be adopted. The training time required to form and steer the main lobes toward 5G multiple users must be short. Least-Mean-Square (LMS) training time is not suitable to work with in LTE-5G, but it has a good performance in forming multiple beams to large number of users and producing nulls in the interference direction. In this paper, an optimized hybrid MVDR-LMS beamforming algorithm is proposed to reduce the time required to estimate the antenna’s weights. This optimization is made by the benefit of previously set weights calculated using MVDR algorithms. The performance of the proposed hybrid MVDR-LMS algorithm tested using MATLAB 2016a.
Volume: 16
Issue: 2
Page: 715-723
Publish at: 2019-11-01

OpenGL 3D crowd evacuation simulation at universiti tun hussein onn malaysia (UTHM) hostel

10.11591/ijeecs.v16.i2.pp1034-1041
Jamaludin, M.N , Mohamad, S. , Sunar, M.S. , Isa, K. , Hanifa, R.M. , Nasir, F.M. , Shah, S.M.
Crowd simulation is the process of simulating characterized agents or entities using computer application to analyse it in virtual scene or virtual environment. This paper investigates the best route path for agents to act in avoiding the fire hazards with different designated type of stairs in shop lots that were converted to hostel dormitory for students. 3D social force agent’s model and 3D fire hazards were designed in Microsoft Visual Studio C++ software and OpenGL library. A research was conducted using social force model behaviour and were taken by 10 and 15 agents to analyse the time taken to complete the evacuation process. The acceleration produced where it is related with route path taken by agents, interaction forces of agents and interaction forces of wall are the main research system to analyse agents’ behaviour during simulation. Different simulations have been used to determine the best and fastest route taken by agents. In summary, the lower the number of agents, the lower the time allocated by agents to complete the evacuation. Finally, less number of agents using the designated straight stairs gave a lower time to complete evacuation process and reached high level of security to avoid being exposed to fire hazards. 
Volume: 16
Issue: 2
Page: 1034-1041
Publish at: 2019-11-01

An exploratory study of industry 4.0 in Malaysia: a case study of higher education institution in Malaysia

10.11591/ijeecs.v16.i2.pp978-987
Mohd Arshad Mokhtar , Nurulhuda Noordin
Industry 4.0 is defining as a smart factory through the emerging technology of the virtual and physical world as a collective term of technologies and concepts of value chain organization. The lack of understanding about Industry 4.0 concepts in Malaysia, especially from Higher Education Institution perspective, becoming an issue in Malaysia. Furthermore, industries in Malaysia are still relying on external expertise to implement Industry 4.0 in their environment with the expectation of an increase in productivity, efficiency, quality and to move away from foreign workers. This research aims to describe the relationship between technologies within Industry 4.0, people and the environment from the perspective of the Higher Education Institution in Malaysia and identify the problem of applying Industry 4.0 at the Higher Education Institution. Qualitative interviews were conducted with participants from an academic area and Industry 4.0 expert who has experience in both industry and academia. The Work System Framework is adopted in this research as a theoretical lens to extract the relevant information and deepen the understanding of applying Industry 4.0 at the Higher Education Institution in Malaysia. Results of this research reveal the problems faced by the Higher Education Institution in Malaysia towards applying Industry 4.0 in teaching and learning based on the relationship of people, technology and the environment. The results may contribute knowledge to the universities in preparation for future-ready graduates or talents as human capital for Industry 4.0 in Malaysia.
Volume: 16
Issue: 2
Page: 978-987
Publish at: 2019-11-01

Visualization of job availability based on text analytics localization approach

10.11591/ijeecs.v16.i2.pp744-751
Nur Azmina Mohamad Zamani , Norhaslinda Kamaruddin , Abdul Wahab , Nur Shahana Saat
Rate of employment is a strong indicator of economic stability of a country. It relates to the number of volumes of produced products and services. If the unemployment rate is high, the amount of gross domestic product (GDP) of a country may be declined. One of the main factors that contributes to low rate of employment is the mismatch between job seeker and the requirement of the job applied.  This is due to the limited analysis performed on the relevant information on job advertisement; such as, skills, responsibilities of the job, location and expectation of the employers. The obscure job descriptions provided in the advertisement may result in application of unsuitable candidates that can cause rejection of the candidate and the potential employer may take a long time to filter and evaluate the applications. A system that is able to provide relevant information in a simple and catchy way is needed to simplify the task of job searching. In this paper we proposed a text analytics technique to extract users’ comments from social media such as Twitter and Facebook on job advertisement. The result is then displayed in a geotagged map that can reveal the density of job availability based on geographical location. The job seekers can easily observe and select their desired job location. The initial system shows potential of the inclusion of the proposed approach in job advertisement websites. In comparison to other job searching websites, this system can provide additional information on public view about the advertised job obtained from the social media text analytics. With this additional information, jobseekers have more confidence in job selection and allows employers to receive more suitable candidates for the available positions. It is hoped that the proposed system can tailor the job advertisements to the need of the jobseeker and making the job application more relevant hence reducing the potential employers’ processing time.
Volume: 16
Issue: 2
Page: 744-751
Publish at: 2019-11-01

Flower and leaf recognition for plant identification using convolutional neural network

10.11591/ijeecs.v16.i2.pp737-743
Nurul FatihahSahidan , Ahmad Khairi Juha , Norasiah Mohammad , Zaidah Ibrahim
This paper presents flower and leaf recognition for plant identification using Convolutional Neural Network (CNN). In this study, the performance of CNN for plant identification using images of the leaves, flowers and a combination of both are investigated.  Two publicly available datasets, namely Folio leaf dataset and Flower Recognition dataset, have been used for the training and testing purposes.  CNN has been proven to produce excellent results for object recognition but its performance can still be influenced by the type of images and the number of layers of the CNN architecture.   Experimental results indicate that the utilization of leaf images only arrive to the highest accuracy for plant identification compared to the images of flowers only or the combination of both, that are 98%, 85% and 74%, respectively.
Volume: 16
Issue: 2
Page: 737-743
Publish at: 2019-11-01
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