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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 5: October 2024" : 111 Documents clear
Model predictive control with finite constant set for five-level neutral-point clamped inverter fed interior permanent magnet synchronous motor drive of electric vehicle Cuong, Tran Hung; Anh, An Thi Hoai Thu
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.pp5038-5047

Abstract

This paper uses the five-level neutral-point clamped (NPC) inverter to feed an electric vehicle's traction motor-interior permanent magnet synchronous motor (IPMSM). The model predictive control method controls the energy conversion process according to the model with two prediction steps. The advantage of this method is its fast response, which increases the ability to operate the converter with good voltage quality. Model predictive control (MPC) control is a closed-loop strategy with much potential when integrating multiple control objectives; the calculation process is compact without complex modulation. Within the scope of this article, the MPC strategy will be implemented with two control goals for NPC, including output load current and capacitor voltage balance with low switching frequency. The simulation results on MATLAB/Simulink software were performed to verify the proposed algorithm's effectiveness in minimizing the grid current's harmonics and ensuring an uninterrupted power supply.
Model reference adaptive control of networked systems with state and input delays Wafi, Moh Kamalul; Indriawati, Katherin; Widjiantoro, Bambang L.
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.pp5055-5063

Abstract

Adaptive control strategies have been developed in response to more advanced complex systems and to deal with uncertain systems while maintaining the desired conditions. This paper addresses the networked unknown and unstable heterogeneous systems following a stable reference (leader), which is related to network synchronization. We deliver two different scenarios; each agent both fully communicates to the leader and shares communication among neighborhood agents and the leader. The communication among agents and the leader are weighted using Laplacian-like matrix and the model weight matrix in turn. Also, the state and input delays are induced to the systems to capture the real limited communication while the prediction of the reference signals and the augmented systems are proposed to deal with them. Moreover, the rigorous mathematical foundations of two adaptive laws, the stability analysis, the threshold of network, and the communication network are thoroughly presented. Also, the numerical illustrations of the two scenarios are given to show the effectiveness of the proposed method in the networked system. The results show that for both scenarios working on the required setting, the perfect tracking to the leader is guaranteed. Beyond that, the future research would implement the distributed adaptive control-oriented learning of networked system under some faults.
Deep neural networks for removing clouds and nebulae from satellite images Glazyrina, Natalya; Muratkhan, Raikhan; Eslyamov, Serik; Murzabekova, Gulden; Aziyeva, Nurgul; Rysbekkyzy, Bakhytgul; Orynbayeva, Ainur; Baktiyarova, Nazira
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.pp5390-5399

Abstract

This research paper delves into contemporary methodologies for eradicating clouds and nebulae from space images utilizing advanced deep learning technologies such as conditional generative adversarial networks (conditional GAN), cyclic generative adversarial networks (CycleGAN), and space-attention generative adversarial networks (space-attention GAN). Cloud cover presents a significant obstacle in remote sensing, impeding accurate data analysis across various domains including environmental monitoring and natural resource management. The proposed techniques offer novel solutions by leveraging spatial attention mechanisms to identify and subsequently eliminate clouds from images, thus uncovering previously concealed information and enhancing the quality of space data. The study emphasizes the necessity for further research aimed at refining cloud removal algorithms to accommodate diverse detection conditions and enhancing the overall efficiency of deep learning in satellite image processing. By highlighting potential benefits and advocating for ongoing exploration, the paper underscores the importance of advancing cloud removal techniques to improve data quality and unlock new applications in Earth remote sensing. In conclusion, the proposed approaches hold promise in addressing the persistent challenge of cloud cover in space imagery, paving the way for more accurate data analysis and future advancements in remote sensing technologies.
Forecasting creditworthiness in credit scoring using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Amirov, Azamat; Tassuov, Bolat; Makhatova, Valentina; Kaipova, Assemgul; Makhazhanova, Ulzhan; Ospanova, Tleugaisha
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.pp5534-5542

Abstract

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.
Methodology for the selection of an optimal optical sensor for a 6U CubeSat constellation Chirán-Alpala, William Efrén; Cárdenas-Espinosa, Lorena Paola; Garces-Gomez, Yeison Alberto
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.pp5297-5307

Abstract

The payload, in defining the central objective of a satellite mission, plays a critical role in determining the overall efficiency of the satellite. Consequently, the satellite's effectiveness is strongly influenced by both the payload itself and its configuration. Given the essential importance of choosing an optimal payload and aware of the direct impact it has on the success of a space mission, this article presents a methodology for selecting an optical sensor intended for the 6U CubeSat constellation of the FACSAT-3 mission and future space missions of the Colombian Aerospace Force (FAC). The methodology includes the definition of mission objectives, definition of key parameters, performance modeling, risk and reliability assessment, and other critical aspects that influence mission efficiency and success.
An enhanced Giza Pyramids construction for solving optimization problems Omar, Asmaa Hekal; Mostafa, Naglaa M.; Desuky, Abeer S.; Bakrawy, Lamiaa M. El
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.pp5672-5680

Abstract

Many real-world optimization problems can be solved by various algorithms that are not fast in convergence or gain enough accuracy. Meta-heuristic algorithms are used to solve optimization problems and have achieved their effectiveness in solving several real-world optimization problems. Meta-heuristic algorithms try to find the best solution out of all available solutions in the possible shortest time. A good meta-heuristic algorithm is characterized by its accuracy, convergence speed, and ability to solve high dimensions’ problems. Giza Pyramids construction (GPC) has recently been introduced as a physics-inspired optimization method. This paper suggests an enhanced Giza Pyramids construction (EGPC) by adding a new parameter based on the step length of each individual and iteratively revises the individual’ position. The EGPC algorithm is suggested for improving the GPC exploitation and exploration. Experiments were performed on 23 benchmark functions and four IEEE CEC 2019 benchmarks to test the performance of the proposed EGPC algorithm. The experimental results show the high competitiveness of the EGPC algorithm compared to the basic GPC algorithm and another four well known optimizers in terms of improved exploration, exploitation, convergence’ rate, and the avoidance of local optima.
Phishing detection using grey wolf and particle swarm optimizer Hamdan, Adel; Tahboush, Muhannad; Adawy, Mohammad; Alwada’n, Tariq; Ghwanmeh, Sameh; Husni, Moath
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.pp5961-5969

Abstract

Phishing could be considered a worldwide problem; undoubtedly, the number of illegal websites has increased quickly. Besides that, phishing is a security attack that has several purposes, such as personal information, credit card numbers, and other information. Phishing websites look like legitimate ones, which makes it difficult to differentiate between them. There are several techniques and methods for phishing detection. The authors present two machine-learning algorithms for phishing detection. Besides that, the algorithms employed are XGBoost and random forest. Also, this study uses particle swarm optimization (PSO) and grey wolf optimizer (GWO), which are considered metaheuristic algorithms. This research used the Mendeley dataset. Precision, recall, and accuracy are used as the evaluation criteria. Experiments are done with all features (111) and with features selected by PSO and GWO. Finally, experiments are done with the most common features selected by both PSO and GWO (PSO ∩ GWO). The result demonstrates that system performance is highly acceptable, with an F-measure of 91.4%.
Performance evaluation of a proposal for spectrum assignment based on combinative distance-based assessment multicriteria strategy Hernandez, Cesar; Giral, Diego; Vaca, Tania
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.pp5308-5318

Abstract

Cognitive radio networks offer an alternative to low spectral availability in some frequency bands due to their high demand for frequency channels. This article proposes to improve the spectral assignment based on the combinative distance-based assessment multicriteria algorithm. The metrics obtained are compared with a simple additive weighting algorithm and a RANDOM selection. To establish the algorithm 's performance, five quality-of-service metrics are used: number of handoffs, number of failed handoffs, average bandwidth, average throughput, and cumulative average delay. From the analysis of the results obtained, combinative distance-based assessment (CODAS) presented the best result for the cost metrics with the lowest levels, and for the benefit metrics, the highest levels were obtained.
Simulation of losses in a three-phase bank of transformers in the presence of harmonic distortion Grau-Merconchin, Frank; Hernández Areu, Orestes; Nuñez Alvarez, José Ricardo; Montenegro-Romero, Michael; Quintero-Ospino, Oscar
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.pp4827-4837

Abstract

The load losses of the transformers increase considerably with the presence of harmonic distortion in the currents. Its increase can be determined using an analytical method based on ANSI/IEEE Standard C57.110. On the other hand, when it comes to three-phase banks with transformers of different capacities, delta connections, or incomplete connections, the analytical method does not allow the losses to be accurately estimated, which is why digital simulation is necessary. This work presents an adjusted model to determine the load losses in a three-phase bank of three single-phase transformers with different connection schemes. The model allows for determining load and electrical losses and calculating total additional losses. It is also possible to decide on the load capacity of the bank's transformers, the power factor, and the efficiency with which they operate under these conditions.
The contribution of digitized electroencephalogram in the clinical and therapeutic monitoring of substance uses disorders Mengad, Aziz; Ertel, Merouane; Chakkouch, Meryem; Ouaamr, Ahmed; Elomari, Fatima
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.pp5172-5184

Abstract

The approaches used in clinical and therapeutic monitoring in addictology are multiple and generally based on subjective instruments such as interviews or observations. However, the lack of frankness can be an obstacle, as the patients monitored may not be entirely honest in their responses, and improvement in symptoms does not always mean continued abstinence. This article proposes a new objective method for monitoring the clinical and therapeutic evolution of addicted patients based on the study of electro-physiological changes collected by digitized electroencephalogram (EEG). The study is a case-control study of 30 hospitalized addicts who met the diagnostic and statistical manual of mental disorders (DSM-V) criteria for substance use disorders substance use disorders (SUD) and who underwent a standard digitized EEG at the end of their hospitalization. A control group of 30 healthy individuals was also included. This research shows a dominance of rapid-frequency beta 2 and hypovolted alpha 2 rhythms in cases with a clear sensitivity to activating maneuvers occasioned by hyperpnoea (HPN) and intermittent light stimulation (ISL) giving either a significant slowing of electrogenesis, bi-occipital entrainment, or an oculo-clonic response signifying a need for further care. However, the major challenge in understanding the EEG signal is that it is not always specific to SUD and suggests the need to consider the trans-diagnostic framework.

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