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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 111 Documents
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Search results for , issue "Vol 14, No 6: December 2024" : 111 Documents clear
Investigation of duty cycle controlled inductive wireless power transfer converter using series-series compensation for electric vehicle application Bhukya, Bhavsingh; Gotluru, Suresh Babu; Bhukya, Mangu; Bhukya, Ravi Kumar; Dongari, Vaani
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6214-6224

Abstract

This paper presents series-series (SS) compensation topologies that include both primary side duty cycle control (PSDCC) and secondary side duty cycle control (SSDCC) methods. The main challenge for noncontact charging (NCC) for electric vehicles (EVs) batteries, the power transfer capability and efficiency in primary side proved to be unproductive. The investigation considers the primary side control duty cycle control (transmitter and receiver) and the secondary side duty cycle control (transmitter and receiver) in terms of compensation capacitor voltage, coil voltage, load side voltage, current, and power. By adjusting the duty cycle within the range of 0.1 to 0.5, it is possible to control power without significantly decreasing the system's efficiency, by using the SSDCC method. The evaluated parameters, including 1.5 kW output power, 85 kHz resonance frequency, and 120 mm ground clearance, are suitable for three-wheeler auto rickshaws. These findings are verified through MATLAB/Simulink software and compared with experimental results.
Processing of real-time surface electromyography signals during knee movements of rehabilitation participants Sengchuai, Kiattisak; Sittiruk, Thantip; Jindapetch, Nattha; Phukpattaranont, Pornchai; Booranawong, Apidet
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6526-6537

Abstract

In this work, we present a knee rehabilitation system focusing on the processing of surface electromyography (sEMG) signals measured from the vastus lateralis (VL) and vastus medialis (VM) muscles of rehabilitation participants. A two-channel electromyography (EMG) device and the NI-myRIO embedded device are used to collect real-time sEMG signals in accordance with pre-designed rehabilitation programs. The novelty and contribution of this work is that we develop an sEMG processing function where real-time sEMG data are automatically processed and sEMG results of both VL and VM in terms of root mean square value (RMS), different RMS levels of VL and VM, and maximum RMS for each round of knee movements are provided. The results here indicate how well the rehabilitation users can move their knees during rehabilitation, referring to knee and muscle performances. Experimental results from healthy participants show that we can automatically and efficiently collect and monitor rehabilitation results, allowing rehabilitation participants to know how their knees performed during testing and medical experts to evaluate and design treatment.
Anomaly detection system based on deep learning for cyber physical systems on sensory and network datasets Almendli, Muhammed; Mohasefi, Jamshid Bagherzadeh
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6827-6837

Abstract

Cyber-physical systems (CPSs), a type of computing system integrated with physical devices, are widely used in many areas such as manufacturing, traffic control, and energy. The integration of CPS and networks has expanded the range of cyber threats. Intrusion detection systems (IDSs), use signature based and machine learning based techniques to protect networks, against threats in CPSs. Water purifying plants are among the important CPSs. In this context some research uses a dataset obtained from secure water treatment (SWaT) an operational water treatment testbed. These works usually focus solely on sensory dataset and omit the analysis of network dataset, or they focus on network information and omit sensory data. In this paper we work on both datasets. We have created IDSs using five traditional machine learning techniques, decision tree, support vector machine (SVM), random forest, naïve Bayes, and artificial neural network along with two deep methods, deep neural network, and convolutional neural network. We experimented with IDSs, on three different datasets obtained from SWaT, including network data, sensory data, and Modbus data. The accuracies of proposed methods show higher values on all datasets especially on sensory (99.9%) and Modbus data (95%) and superiority of random forest and deep learning methods compared to others.
Wheelchair safety system using fuzzy logic controller to avoid obstruction Yulianto, Endro; Salwa, Umaimah Mitsalia Ummi; Triwiyanto, Triwiyanto; Indarto, Tri Bowo
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7001-7012

Abstract

A wheelchair is the primary means of mobility for individuals unable to walk. This study aimed to develop a safety system for electric wheelchairs to help people with tetraplegia avoid obstructions. The main contribution of this study is the implementation of a sensor with a wider reflection angle and the adjustment of the wheelchair's speed based on the distance to the obstruction, eliminating the need for manual speed selection. The safety system utilizes LV-MaxSonarEZ1 ultrasonic sensors, which function as reflectance distance readers placed on the front, rear, right, and left sides of the wheelchair. The output from the sensors is input into an Arduino, which functions as the controller. The safety system employs adaptive speed control based on distance through a fuzzy logic controller. The wheelchair was tested with obstruction distances of 1, 1.8, 3, and 10 m. The wheelchair could stop at a distance of 34.06 cm for forward movement and 45.16 cm for reverse movement. The results of this study successfully demonstrate the creation of a safety system on a wheelchair using ultrasonic sensors to avoid obstructions and detect areas, with more adaptive speed control based on distance through a fuzzy logic controller.
Enhancing sentiment analysis in Kannada texts by feature selection Eshwarappa, Sunil Mugalihalli; Shivasubramanyan, Vinay
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6572-6582

Abstract

In recent years, there has been a noticeable surge in research activities focused on sentiment analysis within the Kannada language domain. The existing research highlights a lack of labelled datasets and limited exploration in feature selection for Kannada sentiment analysis, hindering accurate sentiment classification. To address this gap, the study aims to introduce a novel Kannada dataset and develop an effective classifier for improved sentiment analysis in Kannada texts. The study presents a new Kannada dataset from SemEval 2014 Task4 using Google Translate. It then introduces a modified bidirectional encoder representation from transformers BERT for Kannada dataset called as Kannada-BERT (K-BERT). Further, a probability-clustering (PC) approach is presented to extract the topics and its related aspects. Both the K-BERT classifier and PC approach were merged to attain a K-BERT-PC classifier, integrating a modified BERT model and probability clustering approach for achieving better results. Experimental results demonstrate that K-BERT-PC achieves superior performance in polarity and sentiment analysis accuracy, with an impressive accuracy rate of 91%, surpassing existing classifiers. This work contributes by providing a solution to the scarcity of labelled datasets for Kannada sentiment analysis and introduces an effective classifier, K-BERT-PC, for enhanced sentiment analysis outcomes in Kannada texts.
PdM-FSA: predictive maintenance framework with fault severity awareness in Industry 4.0 using machine learning Moulla, Donatien Koulla; Mnkandla, Ernest; Aboubakar, Moussa; Abba Ari, Ado Adamou; Abran, Alain
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7211-7223

Abstract

Predictive maintenance contributes to Industry 4.0, as it enables a decrease in maintenance costs and downtime while aiming to increase production and return on investment. Despite the increasing utilization of machine learning techniques in predictive maintenance in industrial systems over the past few years, several challenges remain to be addressed in the implementation of ML, including the quality of the data collected, resource constraints, and equipment heterogeneity. This study proposes an adaptive framework for predictive maintenance in the context of Industry 4.0, specifically in internet of things (IoT) systems, using machine learning (ML) models. In particular, this study introduces PdM-FSA, a new framework based on an ensemble classifier that takes advantage of four widely adopted ML models in the predictive maintenance literature: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The performance evaluation results showed that the PdM-FSA framework can perform well for predictive maintenance according to the severity of equipment malfunctions in a smart factory. The results of this study provide significant knowledge to researchers and practitioners on predictive maintenance in the context of Industry 4.0. and enables the optimization of processes and improves productivity.
Edge internet of things based smart home passwordless authentication Helal, Maha; Aldawsari, Abdullah; Al-Akhras, Mousa; Shawar, Bayan Abu; Omar, Hani
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7186-7197

Abstract

The internet of things (IoT) has transformed the way appliances and devices are connected and especially in the case of smart homes, in which smart devices can communicate through networks to improve everyday activities. However, it might be difficult to provide a high level of security for the data produced by these devices. Current security mechanisms might not always function adequately in all circumstances, especially when the number of devices increases. This research proposes an edge IoT-based smart home authentication scheme that adopts IPv6. For devices that use a smartphone application, it also offers a passwordless user authentication approach through the use of the smartphone ID and biometrics. The proposed authentication scheme was simulated to verify its ease of use and security. Security and cost analysis was also performed by reviewing and comparing the proposed scheme with previous research on IoT authentication systems. This research finds that the proposed authentication scheme is efficient at shielding home IoT networks from possible attacks, as well as maintaining a high level of usability.
A novel approach for imbalanced instance handling toward better preterm birth classification Deshpande, Himani; Ragha, Leena
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6129-6139

Abstract

Preterm birth (PTB) is a major cause of child and mother mortality, a PTB classification model can assist in assessing the health condition ahead of time and help avoid complications during childbirth. Mother’s significant feature (MSF) dataset created for this study has features derived from mother’s physical, lifestyle, social and stress attributes. MSF dataset consists of 119 features of 1,000 mothers with 172 preterm and 828 full-term deliveries, resulting in issues of dataset imbalance namely class inseparability and classification bias. To overcome the imbalance issue, a novel algorithm named majority penalizing minority upsampling (MPMU) is proposed. MPMU forms clusters looking into the degree of dataset imbalance, it analyses the composition of each cluster individually and computes the varied penalty for majority class instances. It further balances dataset composition by oversampling minority class instances. MPMU processed dataset is further used to train the proposed 6L-ANN network which finds the probability of occurrence of PTB. The proposed model has shown efficient results on MSF sub-datasets with precision values ranging from 0.90 to 0.97, area under the curve (AUC) between 0.86 to 0.99, and prediction accuracy ranging from 93.04% to 99.47%. Experiment results show that a mother’s lifestyle and stress features have a strong influence on the childbirth outcome.
Research trends about Visual Basic as a programming language in the learning process: a bibliometric analysis Nurjaman, Adi; Juandi, Dadang; Supriyadi, Edi; Hidayat, Wahyu; Darhim, Darhim
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6498-6507

Abstract

This study employs bibliometric analysis to systematically explore the Visual Basic (VB) education research landscape, identifying significant trends, influential authors, and future research directions. Utilizing data from Scopus-indexed journals, we examined 529 papers published between 1994 and October 2023, identified through the keywords "visual basic," "visualbasic," "teaching," and "learning." These papers were analyzed using Biblioshiny to generate a bibliometric map, following four steps: data harvesting, data screening, data visualization, and data analysis. Our research reveals critical VB programming trends from 1994 to 2023, with academic output peaking in 2010 and declining since 2007. Ongoing interest is noted due to legacy system applications. Global publication reach facilitates cross-border information exchange, and top affiliations and authors underscore extensive and influential participation in this field. The research emphasizes incorporating fundamental and advanced themes in educational curricula. It suggests future research focusing on new programming paradigms, longitudinal studies, and VB relevance to technological advancements and industrial needs. Enhanced collaboration, interdisciplinary research, and attention to global trends are essential for maintaining the relevance of VB programming education, optimizing legacy systems, improving educational practices, and preparing students for modern programming environments.
Internet of things important roles in hybrid photovoltaic and energy storage system: a review Ahmed, Habiba; Barbulescu, Eva-Denisa; Nassereddine, Mohamad; Al Khatib, Obada
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6182-6194

Abstract

Renewable energy systems have become integral components of the electrical grid, offering environmental benefits and cost-effective power generation. Technological advancements have introduced internet of things (IoT) devices with robust data collection and execution capabilities. Solar photovoltaic systems, reliant on unpredictable solar radiation, require hybrid systems incorporating various renewable energy sources and energy storage to ensure system stability. Successful operation necessitates data gathering through IoT devices, which have played a crucial role in enhancing system sustainability. This paper provides a comprehensive review of the role of IoT in photovoltaic systems and energy storage, highlighting its significant contributions to system efficiency, fault detection, output prediction, system stability, and load management. The study underscores the critical dependence of advancements in the renewable energy sector on IoT systems.

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