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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 96 Documents
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Search results for , issue "Vol 11, No 3: June 2021" : 96 Documents clear
Partial discharges location in power transformers using piezoceramic sensors B. Danouj; S. A. Tahan; E. David; M. Lotfi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp1942-1950

Abstract

The detection and the spatial localization of partial discharges in high-voltage electrical machines are considered as an effective method in predictive maintenance that can provide valuable information on the health of the insulation system and allow to determine accurately the location of the risky insulation elements, which in turn will avoid any premature equipment’s deterioration by scheduling preventive maintenance action. After confirming in a previous published paper the efficiency of a new generation of piezoceramics sensors (high temperature ultrasonic transducers) to detect and characterize partial discharges, we are going to investigate, in this work, a second potential of this technology to locate the partial discharge sources by relying on its ability to detect acoustic signals emitted by partial discharge sources. We will present experimental results, demonstrating the effectiveness of these sensors to locate partial discharges sources and, we will also present an algorithm for calculating the partial discharge foci, based on the acoustic wave flight time.
Pain track analysis during gestation using machine learning techniques Praveen Naik; Vandana Reddy; Ramesha Shettigar
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2128-2133

Abstract

During the gestation period women experience Braxton Hicks which is called the false labor, contractions during the second trimester. These contractions are not in regular intervals and also they are often unnoticed. The real labour or the true labour contractions develop late in the third trimester of the gestation usually beyond 36th week (excluding pre-term birth). Some women often fail to identify these pains in the third trimester of the gestation where an efficient facial recognition algorithm along with the support vector machine (SVM) helps them to identify these pains and take optimum care of themselves. The authors in this paper convey a mechanism to identify the pains effectively by creating a database of images pertaining to the pregnant women, her emotional states through out the pregnancy. Using MATLAB the algorithm of decision tree is implemented and the values obtained from them help us analyze the pain type efficiently.
Multi-kernel CNN block-based detection for COVID-19 with imbalance dataset Muhammad Khaerul Naim Mursalim; Ade Kurniawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2467-2476

Abstract

COVID-19, which originated from Wuhan, rapidly spread throughout the world and became a public health crisis. Recognizing the positive cases at the earliest stage was crucial in order to restrain the spread of this virus and to perform medical treatment quickly for patients affected. However, the limited supply of RT-PCR as a diagnosis tool caused greatly delay in obtaining examination results of the suspected patients. Previous research stated that using radiologic images could be utilized to detect COVID-19 before the symptoms appeared. With the rapid development of Artificial intelligence in medical imaging in recent years, deep learning as the core of this technology could achieve human-level-performance in diagnostic accuracy. In this paper, deep learning was implemented to detect COVID-19 using a chest X-ray dataset. The proposed model employed a multi-kernel convolution neural network (CNN) block combined with pre-trained ResNet-34 to overcome an imbalanced dataset. The model block adopted different kernel sizes as follows 1x1, 3x3, 5x5, and 7x7. The findings show that the proposed model is capable of performing binary and three class classification tasks with an accuracy of 100% and 93.51% in the validation phase and 95% and 83% in the test phase, respectively.
A new Li-ion battery charger with charge mode selection based on 0.18 um CMOS for phone applications Fouad Farah; Mustapha El Alaoui; Abdellali Elboutahiri; Mounir Ouremchi; Karim El Khadiri; Ahmed Tahiri; Hassan Qjidaa
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp1994-2002

Abstract

A new architecture of Li-Ion battery charger with charge mode selection is presented in this work. To ensure high efficiency, good accuracy and complete protection mode, we propose an architecture based on variable current source, temperature detector and power control. To avoid the risk of damage, the Li- Ion batteries charging process must change between three modes of current (trickle current (TC), constant current (CC), and constant voltage (CV)) in order to charge the battery with degrading current. However, the interest of this study is to develop a fast battery charger with high accuracy that is able to switch between charging modes without reducing its power efficiency, and to guarantee a complete protection mode. The proposed charger circuit is designed to control the charging process in three modes using the charging mode selection. The obtained results show that the Li-ion batteries can be successfully charged in a short time without reducing their efficiency. The proposed charger is implemented in 180 nm CMOS technology with a maximum charging current equal to 1 A and a maximum battery voltage equal to 4.22 V, (with input range 2.7-4.5 V). The chip area is 1.5 mm2 and the power efficiency is 90.09 %.
myBas driving cycle for Kuala Terengganu city J. S. Norbakyah; M. I. Nordiyana; I. N. Anida; A. F. Ayob; A. R. Salisa
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2054-2061

Abstract

Driving cycles are series of data points that represent vehicle speed versus time sequenced profile developed for specific road, route, city or certain location. It is widely utilized in the application of vehicle manufacturers, environmentalists and traffic engineers. Since the vehicles are one of the higher air pollution sources, driving cycle is needed to evaluate the fuel consumption and exhaust emissions. The main objectives in this study are to develop and characterize the driving cycle for myBAS in Kuala Terengganu city using established k-means clustering method and to analyse the fuel consumption and emissions using advanced vehicle simulator (ADVISOR). Operation of myBAS offers 7 trunk routes and one feeder route. The research covered on two operation routes of myBAS which is Kuala Terengganu city-feeder and from Kuala Terengganu to Jeti Merang where the speed-time data is collected using on-board measurement method. In general, driving cycle is made up of a few micro-trips, defined as the trip made between two idling periods. These micro-trips cluster by using the k-means clustering method and matrix laboratory software (MATLAB) is used in developing myBAS driving cycle. Typically, developing the driving cycle based on the real-world in resulting improved the fuel economy and emissions of myBAS.
Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor Al Khowarizmi; Rahmad Syah; Mahyuddin K. M. Nasution; Marischa Elveny
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2696-2703

Abstract

Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.
An enhanced OFDM light weight physical layer encryption scheme Ahmed A. Alabdel Abass; Navya Prarthana Divvala
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2178-2190

Abstract

The broadcast nature of wireless networks makes them susceptible to attacks by eavesdroppers than wired networks. Any untrusted node can eavesdrop on the medium, listen to transmissions and obtain sensitive information within the wireless network. In this paper, we propose a new mechanism which combines the advantages of two techniques namely iJam and OFDM phase encryption. Our modified mechanism makes iJam more bandwidth efficient by using Alamouti scheme to take advantage of the repetition inherent in its implementation. The adversary model is extended to the active adversary case, which has not been done in the original work of iJam and OFDM phase encryption. We propose, through a max min optimization model, a framework that maximizes the secrecy rate by means of a friendly jammer. We formulate a Zero-Sum game that captures the strategic decision making between the transmitter receiver pair and the adversary. We apply the fictitious play (FP) algorithm to reach the Nash equilibria (NE) of the game. Our simulation results show a significant improvement in terms of the ability of the eavesdropper to benefit from the received information over the traditional schemes, i.e. iJam or OFDM phase encryption.
A new 4-D hyperchaotic hidden attractor system: Its dynamics, coexisting attractors, synchronization and microcontroller implementation Basil H. Jasim; Kadhim H. Hassan; Khulood Moosa Omran
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2068-2078

Abstract

In this paper, a simple 4-dimensional hyperchaotic system is introduced. The proposed system has no equilibria points, so it admits hidden attractor which is an interesting feature of chaotic systems. Another interesting feature of the proposed system is the coexisting of attractors where it shows periodic and chaotic coexisting attractors. After introducing the system, the system is analyzed dynamically using numerical and theoretical techniques. In this analysis, Lyapunov exponents and bifurcation diagrams have been used to investigate chaotic and hyperchaotic nature, the ranges of system parameters for different behaviors and the route for chaos and coexisting attractors regions. In the next part of our work, a synchronization control system for two identical systems is designed. The design procedure uses a combination of simple synergetic control with adaptive updating laws to identify the unknown parameters derived basing on Lyapunov theorem. Microcontroller (MCU) based hardware implementation system is proposed and tested by using MATLAB as a display side. As an application, the designed synchronization system is used as a secure analog communication system. The designed MCU system with MATLAB Simulation is used to validate the designed synchronization and secure communication systems and excellent results have been obtained.
Developing an automatic brachial artery segmentation and bloodstream analysis tool using possibilistic C-means clustering from color doppler ultrasound images Joonsung Park; Doo Heon Song; Kwang Baek Kim
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2653-2659

Abstract

Automatic segmentation of brachial artery and blood-flow dynamics are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose a software that is noise tolerant and fully automatic in segmentation of brachial artery from color Doppler ultrasound images. Possibilistic C-Means clustering algorithm is applied to make the automatic segmentation. We use HSV color model to enhance the contrast of bloodstream area in the input image. Our software also provides index of hemoglobin distribution with respect to the blood flow velocity for pathologists to proceed further analysis. In experiment, the proposed method successfully extracts the target area in 59 out of 60 cases (98.3%) with field expert’s verification.
Bleeding recognition technique in wireless capsule endoscopy images using fuzzy logic and principal component analysis A. Al Mamun; P. P. Em; T. Ghosh; M. M. Hossain; M. G. Hasan; M. G. Sadeque
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2688-2695

Abstract

Wireless capsule endoscopy is the most innovative technology to perceive the entire gastrointestinal (GI) tract in recent times. It can diagnose inner diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps. in a discretion way. It creates immense pressure and onus for clinicians to perceive a huge number of image frames, which is time-consuming and makes human oversight errors. Therefore a computer-automated system has been introduced for bleeding detection. A unique fuzzy logic technique is proposed to extract the specified bleeding and non-bleeding information from the image data. A particular quadratic support vector machine (QSVM) classifier is employed to classify the obtained statistical features from the bleeding and non-bleeding images incorporating principal component analysis (PCA). After extensive experiments on clinical data, 98% sensitivity, 98.4% accuracy, 98% specificity, 93% precision, 95.4% F1-score, and 99% negative predicted value have been achieved, which outperforms some of the states of art methods in this regard. It is optimistic that the proposed methodology would significantly contribute to bleeding detection techniques and diminish the additional onus of the physicians.

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