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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 116 Documents
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Search results for , issue "Vol 10, No 6: December 2020" : 116 Documents clear
Evaluation of lightweight battery management system with field test of electric bus in campus transit system Watcharin Srirattanawichaikul; Paramet Wirasanti
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6202-6213

Abstract

A battery management system is a crucial part of a battery-powered electric vehicle, which functions as a monitoring system, state estimation, and protection for the vehicle. Among these functions, the state estimation, i.e., state of charge and remaining battery life estimation, is widely researched in order to find an accuracy estimation methodology. Most of the recent researches are based on the study of the battery cell level and the complex algorithm. In practice, there is a statement that the method should be simple and robust. Therefore, this research work is focused on the study of lightweight methodology for state estimation based on the battery pack. The discrete Coulomb counting method and the data-driven approach, based on the Palmgren-Miner method, are proposed for the estimation of the state of charge and remaining battery life, respectively. The proposed methods are evaluated through a battery-powered electric bus under real scenario-based circumstances in the campus transit system. In addition, the battery life-cycle cost analysis is also investigated. The tested bus has currently been in operation in the transit system for more than one year.
Survey on Deep Learning applied to predictive maintenance Youssef Maher; Boujemaa Danouj
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (559.99 KB) | DOI: 10.11591/ijece.v10i6.pp5592-5598

Abstract

Prognosis Health Monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0.
Design and implementation of a java based virtual laboratory for data communication simulation Obinna Okoyeigbo; Edevbie Agboje; Evioghene Omuabor; Uyi Aiyudubie Samson; Abidemi Orimogunje
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (639.042 KB) | DOI: 10.11591/ijece.v10i6.pp5883-5890

Abstract

Students in this modern age find engineering courses taught in the university very abstract and difficult, and cannot relate theoretical calculations to real life scenarios. They consequently lose interest in their coursework and perform poorly in their grades. Simulation of classroom concepts with simulation software like MATLAB, were developed to facilitate learning experience. This paper involves the development of a virtual laboratory simulation package for teaching data communication concepts such as coding schemes, modulation and filtering. Unlike other simulation packages, no prior knowledge of computer programming is required for students to grasp these concepts.
Design and implement a smart system to detect intruders and firing using IOT Hussam Jawad Kadhim; Mohammed Jabbar MohammedAmeen
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1990.607 KB) | DOI: 10.11591/ijece.v10i6.pp5932-5939

Abstract

The security system is essential for occupants' convenience and protection from intruders and fire. Theft and fire are the most important requirement for the security system. The advancement of wireless sensor networks using IOTs increased the features in a security system and play an important role in daily life. In this paper, the proposed system is divided into two units. The first one about security which use to take snapshots by a camera whenever there is fire or intruders in the security zone and mail it to the owner every three seconds by using Arduino configured with MATLAB program. MATLAB program plays the main role to coordinate between sensors and to turn on/off the cameras. The second unit is about controlling the appliances and also the main door by using AVR microcontroller configured by CVAVR software that connected with Bluetooth sensor and controlled by a smartphone by using the implementation software built-up in the smartphone. To arrival of the control unit, the user should send code from the software implementation to the framework that use to turn on /off the devices or open/close the door. This proposed system is designed and implemented in details in this paper.
Numerical algorithm for solving second order nonlinear fuzzy initial value problems A. F. Jameel; N. R. Anakira; A. H. Shather; Azizan Saaban; A. K. Alomari
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6497-6506

Abstract

The purpose of this analysis would be to provide a computational technique for the numerical solution of second-order nonlinear fuzzy initial value (FIVPs). The idea is based on the reformulation of the fifth order Runge Kutta with six stages (RK56) from crisp domain to the fuzzy domain by using the definitions and properties of fuzzy set theory to be suitable to solve second order nonlinear FIVP numerically. It is shown that the second order nonlinear FIVP can be solved by RK56 by reducing the original nonlinear equation intoa system of couple first order nonlinear FIVP. The findings indicate that the technique is very efficient and simple to implement and satisfy the Fuzzy solution properties. The method’s potential is demonstrated by solving nonlinear second-order FIVP.
Visual control system for grip of glasses oriented to assistance robotics Robinson Jimenez-Moreno; Astrid Rubiano; Jose L. Ramirez
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6330-6339

Abstract

Assistance robotics is presented as a means of improving the quality of life of people with disabilities, an application case is presented in assisted feeding. This paper presents the development of a system based on artificial intelligence techniques, for the grip of a glass, so that it does not slip during its manipulation by means of a robotic arm, as the liquid level varies. A faster R-CNN is used for the detection of the glass and the arm's gripper, and from the data obtained by the network, the mass of the beverage is estimated, and a delta of distance between the gripper and the liquid. These estimated values are used as inputs for a fuzzy system which has as output the torque that the motor that drives the gripper must exert. It was possible to obtain a 97.3% accuracy in the detection of the elements of interest in the environment with the faster R-CNN, and a 76% performance in the grips of the glass through the fuzzy algorithm.
Spectrum sharing in cognitive radio networks Julian Martinez; Cesar Hernandez; Luis Pedraza
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6472-6483

Abstract

Cognitive radio networks are the next step to tackle scarcity in wireless networks given the increasing demand of radioelectric spectrum where the proposed solution is to share said resource to improve this situation. In the present article, a review of the current state of spectrum sharing in cognitive radio networks. To achieve this purpose, the articles published over the last 4 years on the matter were reviewed including topics such as mobile networks and TV. Some studies and simulations proposed to share the spectrum is shown. The current state of the studies reveals that there has been significant progress in this research area yet it is necessary to continue similar studies and set in motion different schemes.
Multi-objective Pareto front and particle swarm optimization algorithms for power dissipation reduction in microprocessors Diary R. Sulaiman
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i6.pp6549-6557

Abstract

The progress of microelectronics making possible higher integration densities, and a considerable development of on-board systems are currently undergoing, this growth comes up against a limiting factor of power dissipation. Higher power dissipation will cause an immediate spread of generated heat which causes thermal problems. Consequently, the system's total consumed energy will increase as the system temperature increase. High temperatures in microprocessors and large thermal energy of computer systems produce huge problems of system confidence, performance, and cooling expenses. Power consumed by processors are mainly due to the increase in number of cores and the clock frequency, which is dissipated in the form of heat and causes thermal challenges for chip designers. As the microprocessor’s performance has increased remarkably in Nano-meter technology, power dissipation is becoming non-negligible. To solve this problem, this article addresses power dissipation reduction issues for high performance processors using multi-objective Pareto front (PF), and particle swarm optimization (PSO) algorithms to achieve power dissipation as a prior computation that reduces the real delay of a target microprocessor unit. Simulation is verified the conceptual fundamentals and optimization of joint body and supply voltages (Vth-VDD) which showing satisfactory findings.
LMI based antiswing adaptive controller for uncertain overhead cranes Nga Thi-Thuy Vu
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (561.532 KB) | DOI: 10.11591/ijece.v10i6.pp5793-5801

Abstract

This paper proposes an adaptive anti-sway controller for uncertain overhead cranes. The state-space model of the 2D overhead crane with the system parameter uncertainties is shown firstly. Next, the adaptive controller which can adapt with the system uncertainties and input disturbances is established. The proposed controller has ability to move the trolley to the destination in short time and with small oscillation of the load despite the effect of the uncertainties and disturbances. Moreover, the controller has simple structure so it is easy to execute. Also, the stability of the closed-loop system is analytically proven. The proposed algorithm is verified by using Matlab/Simulink simulation tool. The simulation results show that the presented controller gives better performances (i.e., fast transient response, position tracking, and low swing angle) than the state feedback controller when there exist system parameter variations as well as input disturbances.
Automatic recognition of the digital modulation types using the artificial neural networks Saad S. Hreshee
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1201.85 KB) | DOI: 10.11591/ijece.v10i6.pp5871-5882

Abstract

As digital communication technologies continue to grow and evolve, applications for this steady development are also growing. This growth has generated a growing need to look for automated methods for recognizing and classifying the digital modulation type used in the communication system, which has an important effect on many civil and military applications. This paper suggests a recognizing system capable of classifying multiple and different types of digital modulation methods (64QAM, 2PSK, 4PSK, 8PSK, 4ASK, 2FSK, 4FSK, 8FSK). This paper focuses on trying to recognize the type of digital modulation using the artificial neural network (ANN) with its complex algorithm to boost the performance and increase the noise immunity of the system. This system succeeded in recognizing all the digital modulation types under the current study without any prior information. The proposed system used 8 signal features that were used to classify these 8 modulation methods. The system succeeded in achieving a recognition ratio of at least 68% for experimental signals on a signal to noise ratio (SNR = 5dB) and 89.1% for experimental signals at (SNR = 10dB) and 91% for experimental signals at (SNR = 15dB) for a channel with Additive White Gaussian Noise (AWGN).

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