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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 113 Documents
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Search results for , issue "Vol 13, No 3: June 2023" : 113 Documents clear
Software calibration for AK8963 magnetometer based on optimal ellipsoidal fitting Aziz El fatimi; Adnane Addaim; Zouhair Guennoun
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2743-2751

Abstract

With the rapid development of mechatronics, systems in package (SiP), in particular the MPU-9250 inertial measurement Unit 9DOF (MPU-6050 6DOF and AK8963 3DOF), are becoming ubiquitous in applications for autonomous navigation purposes. Nevertheless, they suffer from some accuracy problems related to axis misalignment, disturbances, and deviation over time that make them unable to work autonomously for a long time. This paper will present a simple and practical calibration method using a least-squares based ellipsoid fitting method to calibrate and compensate for the error interference of the AK8963 sensor. Towards the end of this paper, a comparison between before and after the calibration is presented to study the software compensation effect and the stability of the magnetic sensor under study.
Optimal protective relaying scheme of distributed generation connected distribution network using particle swarm optimization-gravitational search algorithm technique Arathi Pothakanahalli Bheemasenarao; Shankaralingappa C Byalihal
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2568-2578

Abstract

This paper develops particle swarm optimization integrated with gravitational search algorithm (PSO-GSA) to coordinate the relays in a distribution system with distributed generation (DG) connectivity. This algorithm combines PSO and GSA to improve the performance of the relay protection system. To prevent relay malfunctions following DG penetration, a suitable primary and backup relay is chosen. The PSO-GSA is coded using MATLAB software and tested on an IEEE 4-bus system simulated in Simulink. Results indicate that, when compared to using regular PSO and GSA procedures individually, the PSO-GSA technique reduces the operating time of the relay significantly.
Dysgraphia detection based on convolutional neural networks and child-robot interaction Soukaina Gouraguine; Mustapha Riad; Mohammed Qbadou; Khalifa Mansouri
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2999-3009

Abstract

Dysgraphia is a disorder of expression with the writing of letters, words, and numbers. Dysgraphia is one of the learning disabilities attributed to the educational sector, which has a strong impact on the academic, motor, and emotional aspects of the individual. The purpose of this study is to identify dysgraphia in children by creating an engaging robot-mediated activity, to collect a new dataset of Latin digits written exclusively by children aged 6 to 12 years. An interactive scenario that explains and demonstrates the steps involved in handwriting digits is created using the verbal and non-verbal behaviors of the social humanoid robot Nao. Therefore, we have collected a dataset that contains 11,347 characters written by 174 participants with and without dysgraphia. And through the advent of deep learning technologies and their success in various fields, we have developed an approach based on these methods. The proposed approach was tested on the generated database. We performed a classification with a convolutional neural network (CNN) to identify dysgraphia in children. The results show that the performance of our model is promising, reaching an accuracy of 91%.

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