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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 6,301 Documents
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Design and implementation of a low-cost circuit for medium-speed flash analog to digital conversions Hussain Hassan, Nashaat M.; Esmaeel Salama, Mohamed Adel; Hussein, Aziza I.; Mabrook, Mohamed Mourad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2361-2368

Abstract

Despite the considerable advancements in analog-to-digital conversion (ADC) circuits, many papers neglect several crucial considerations: Firstly, it does not ensure that ADCs work well in the software or hardware. Secondly, it is not certain that ADCs have a wide range of amplitude responses for the input voltages to be convenient in many applications, especially in electronics, communications, computer vision, CubeSat circuits, and subsystems. Finally, many of these ADCs need to look at the suitability of the proposed circuit to the most extensive range of frequencies. In this paper, a design of a low-cost circuit is proposed for medium-speed flash ADCs. The proposed circuit is simulated based on a set of electronic components with specific values to achieve high stability operation for a wide range of frequencies and voltages, whether in software or hardware. This circuit is practically implemented and experimentally tested. The proposed design aims to achieve high efficiency in the sampling process over a range of amplitudes from 10 mV to 10 V. The proposed circuit operates at a bandwidth of frequencies from 0 Hz to greater than 10 kHz in the simulation and hardware implementation.
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.
Design of a prototype for sending fire notifications in homes using fuzzy logic and internet of things Huaman Castañeda, Johan; Tamara Perez, Pablo Cesar; Paiva-Peredo, Ernesto; Zarate-Segura, Guillermo
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp248-257

Abstract

This paper highlights the need to address fire monitoring in densely populated urban areas using innovative technology, in particular, the internet of things (IoT). The proposed methodology combines data collection through sensors with instant notifications via text messages and images through the user’s email. This strategy allows a fast and efficient response, with message delivery times varying from 1 to 4 seconds on Internet connections. It was observed that the time to send notifications on 3G networks is three times longer compared to Wi-Fi networks, and in some 3G tests, the connection was interrupted. Therefore, the use of Wi-Fi is recommended to avoid significant delays and possible bandwidth issues. The implementation of fuzzy logic in the ESP32 microcontroller facilitates the identification of critical parameters to classify notifications of possible fires and the sending of evidence through images via email. This approach successfully validated the results of the algorithm by providing end users with detailed emails containing information on temperature, humidity, gas presence and a corresponding image as evidence. Taken together, these findings support the effectiveness and potential of this innovative solution for fire monitoring and prevention in densely populated urban areas.
Fuzzy integral tracking control of an activated sludge process Bekaik, Mounir; Bouras, Hichem; Hamana, Ahmed Sami
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5083-5093

Abstract

This paper addresses the issue of tracking the output of an activated sludge process using fuzzy integral control. First, the dynamics of the nonlinear process are modeled with a dynamic state space fuzzy model integrating the effect of external disturbances, and then an additional integral state of the output tracking error is introduced to obtain an augmented Takagi-Sugeno (TS) fuzzy model. The TS fuzzy model is able to describe the dynamics of complex nonlinear systems with an excellent degree of accuracy. It is formulated by fuzzy if-then rules which can give local linear representation of the overall nonlinear system. Second, the design of the fuzzy integral control is performed, in which the state feedback gains are obtained by solving linear matrix inequalities (LMI). The objective is to ensure trajectory tracking of an activated sludge process (ASP) by controlling two key variables: the substrate concentration and the level of dissolved oxygen. To assess the performance of the proposed control strategy, a comparative analysis is carried out with a gain scheduling PI (GS-PI) controller. Simulation results are provided to illustrate the effectiveness of the proposed approach. Where, the fuzzy integral control reduces the high energy consumption in water treatment plants.
A rule-based machine learning model for financial fraud detection Islam, Saiful; Haque, Md. Mokammel; Rezaul Karim, Abu Naser Mohammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp759-771

Abstract

Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs Fauzan, Hermawan; Sarno, Riyanarto; Saikhu, Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4214-4227

Abstract

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.
Advanced control scheme of doubly fed induction generator for wind turbine using second sliding mode control Bekouche, Hafida; Chaker, Abdelkader
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2562-2570

Abstract

This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator(DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC).Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Deep learning and quantization for accurate and efficient multi-target radar inference of moving targets Ernest Mashanda, Nyasha; Watson, Neil; Berndt, Robert; Abdul Gaffar, Mohammed Yunus
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3187-3196

Abstract

Real-time, radar-based human activity and target classification is useful for wide-area ground surveillance. However, the feasibility of deploying deep learning (DL) models in radar-based systems with limited computational resources remains unexplored. This paper investigated the effect of quantization on model throughput and accuracy for deployment in radar systems. A seven-layer residual network was proposed to classify ground-moving targets and achieved a test accuracy of 87.72%. The model was then quantized to 16-bit and 8-bit precision, resulting in a 3.8 times speedup in inference throughput, with less than a 0.4% drop in test and validation accuracy. The results showed that quantization can improve inference throughput with a negligible decrease in target classification accuracy. The increase in throughput and reduction in computational expense that comes with quantization promotes the feasibility of the deployment of DL models in systems with limited computational resources. The findings of this paper hold significant promise for the successful use of quantized models in modern radar systems, while adhering to stringent size, weight and power consumption constraints.
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.
Field oriented control driver development based on BTS7960 for physiotherapy robot implementation Halisyah, Andi Nur; Adiputra, Dimas; Al Farouq, Ardiansyah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1486-1495

Abstract

In conjunction with sustainable development goal 3 (SDG 3), it is important to develop a national electrical component for physiotherapy robot development. This study aimed to develop an open-loop field-oriented control (FOC) driver utilizing BTS7960. The driver utilized three BTS7960s that produce sinewave with variable angular frequency (ω). The research then compared the open-loop FOC driver with electronics speed controller (ESC) performance to drive a brushless DC (BLDC) motor with an initial rotation per minute (RPM) of 400, 500, and 600. The main observation was RPM reduction when the BLDC motor was subjected to loads of 20, 35, 50, 65, and 80 gr. The result showed that the open-loop FOC driver performed better, especially on an 80 gr load. For an initial RPM of 600, the RPM reduced to 100 when controlled with an open-loop FOC driver, but lesser when controlled using ESC. The open-loop FOC driver produces higher torque on the BLDC motor so it could rotate with less reduction compared to ESC, which is evident. The open-loop FOC driver can be easily developed using BTS7960 with a settling time of 4 seconds. However future studies should still consider close-loop FOC drivers to achieve higher torque performance and faster transient response for physiotherapy robot applications.

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