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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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Study on the acceleration process of three-phase induction motors driving elevator loads Can, Do Van; Tri, Phan Gia
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp135-148

Abstract

Three-phase induction motor drive systems, especially in elevator applications and other precision motion systems, require optimized acceleration profiles to minimize vibrations and extend mechanical lifespan. Previous studies have primarily focused on fast speed response control but often overlooked the impact of jerk, which affects smoothness and operational safety. This paper proposes a combination of field-oriented control (FOC) and S-curve acceleration profiles to reduce jerk and improve motion quality. A dynamic model of the drive system is developed to simulate the acceleration process, demonstrating that the S-curve significantly reduces torque and current oscillations, thus enhancing stability. The S-curve trajectory generation algorithm is implemented and deployed on a field programmable gate array (FPGA) hardware platform. Experimental hardware results confirm that the generated speed control signals possess high resolution and fast response, making the method suitable for embedded control systems in elevator drives and other sensitive motion-control applications. This integrated approach not only addresses the limitations of previous methods but also provides a practical solution to improve comfort, safety, and durability in various electromechanical drive systems.
Cumulative aging effects of five-year intermittent exposure on flexible amorphous solar cells Salima, Djerroud; Amine, Boudghene Stambouli; Noureddine, Benabadji; Abdelghani, Lakhdari
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp65-75

Abstract

Amorphous silicon (a-Si) is rarely used for large scale photovoltaic energy production, it remains relevant in flexible electronic applications, where mechanical flexibility and lightweight design are prioritized, where exposure to sunlight is typically limited or irregular. This study conducts an experimental analysis of the long-term aging effects on the proprieties of an amorphous solar cells, under five years of intermittent outdoor climate conditions. Unlike conventional aging studies that focus on degradation over time, this research highlights the cumulative effects of environmental exposure, considering the discontinuous nature of exposure cycles and the non-linearity of degradation phenomena because of the abrupt transitions between outdoor exposure phases and indoor laboratory rest periods. The results show that nearly 50% of the panel’s performances is reduced, with the losses observed as follows: a substantial decline in the fill factor from 55.3% to 30%, a decrease in energy conversion efficiency from 11.36% to 5.5%. This accelerated deterioration mainly attributed to harsh environmental transitions caused by intermittent exposure, which amplify aging mechanism compared to continuous exposure. Beyond the experimental findings, the approach presented here, constitutes a meaningful scientific contribution. By introducing a realistic and underexplored aging scenario, it lays the groundwork for a new line of research.
A systematic review of software fault prediction techniques: models, classifiers, and data processing approaches Ramasamy, R. Kanesaraj; Rajendran, Venushini; Subramanian, Parameswaran
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp545-554

Abstract

Software fault prediction (SFP) plays a critical role in improving software reliability by enabling early detection and correction of defects. This paper presents a comprehensive review of 25 recent and significant studies on SFP techniques, focusing on data preprocessing strategies, classification algorithms, and their effectiveness across various datasets. The review categorizes the approaches into traditional statistical models, machine learning methods, deep learning architectures, and hybrid techniques. Notably, wrapper-based feature selection, neural network classifiers, and support vector machines (SVM) are identified as the most effective in achieving high accuracy, particularly when dealing with imbalanced or noisy datasets. The paper also highlights advanced approaches such as variational autoencoders (VAE), Bayesian classifiers, and fuzzy clustering for fault prediction. Comparative analysis is provided to assess performance metrics such as accuracy, F-measure, and area under the curve (AUC). The findings suggest that no single method fits all scenarios, but a combination of appropriate preprocessing and robust classification yields optimal results. This review provides valuable insights for researchers and practitioners aiming to enhance software quality through predictive analytics. Future work should explore ensemble learning and real-time SFP systems for broader applicability.
New control strategy for maximizing power extraction in the grid-connected CHP-PV-Wind hybrid system Maakoul, Othmane; Boulal, Abdellah
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp36-48

Abstract

This work represents a significant contribution to the advancement of modern electrical systems by combining advanced control strategies with robust protection solutions to address the challenges of the energy transition. It focuses on the integration of renewable energy within electrical grids, with particular attention to wind energy, cogeneration (CHP), and photovoltaic energy. The main contributions include the development of innovative methods to enhance system stability and improve energy quality. This is achieved notably through the use of advanced control algorithms, such as the synchronously rotating frame (SRF) transformation, applied to converters and voltage source converter-based high voltage direct current (VSC-HVDC) systems. These approaches enable precise voltage regulation, optimized power flow management, and significant reduction of harmonic distortion. The paper also explores novel techniques, such as control based on the ANFIS algorithm, to improve voltage regulation, current stability, and converter efficiency. Finally, an effective protection solution against voltage faults is proposed, ensuring the stable and reliable transfer of energy produced by offshore wind farms to onshore grids.
A novel stretched-compressed exponential low-pass filter and its application to electrocardiogram signal denoising de Fazio, Roberto; Al-Naami, Bassam; Rawash, Yahia; Al-Hinnawi, Abdel-Razzak; Al-Zaben, Awad; Visconti, Paolo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp230-245

Abstract

The study investigates a novel stretched-compressed exponential low-pass (SCELP) filter to denoise electrocardiogram (ECG) signals. As an extension of Gaussian filter and unlike other denoising filters, the SCELP filter utilizes the stretched-compressed exponential function (SCEF) in the convolution kernel, being the Gaussian function its particular case. A MATLAB implementation is provided with a single parameter (β), which allows to modify the filter strength, to increase the signal-to-noise ratio (SNR) and reduce the mean squared error (MSE). The SCELP filter’s advantages over traditional denoising filters (i.e., Gaussian, Mittag–Leffler, and Savitzky-Golay filters) were assessed on 100 ECG signals, 50 normal and 50 abnormal (affected by sleep apnea), provided by the PhysioNet dataset. The SCELP filter’s efficacy in rejecting noise was evaluated as the β parameter varies, quantifying the filters' performance in terms of mean SNR and MSE to determine the optimal β value. The obtained results showed that the SCELP filter's best performances are achieved for β equal to = 1.6 (i.e., 16.9508 dB and 13.7574 dB SNR values, and 0.01025 and 0.01178 MSE values for normal and abnormal ECGs, respectively). Furthermore, the SCELP filter was tested on ECG signals with added white noise; compared to Gaussian, Mittag–Leffler, and Savitzky-Golay filters, the SCELP filter yields better performance regarding SNR (16.495 and 14.940 dB) and MSE (0.0106 and 0.0114) values, for normal and abnormal ECGs, respectively, suggesting its applicability for ECG signals' denoising.
Generalization of reactive power definition for periodical waveforms Kosobudzki, Grzegorz; Ładniak, Leszek
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp102-110

Abstract

The article presents a selection of reactive power definitions, which are applicable for implementation in energy meters. For sinusoidal current and voltage waveforms, all provided dependencies yield equivalent reactive power values. However, in the presence of distorted current and voltage, the power values are determined by the applied method (algorithm). Standardization requirements for reactive energy meters stipulate metrological verification under sinusoidal conditions. The selection of an optimal reactive power definition remains a problematic and ongoing subject of debate within the field. The paper shows that a generalized unique definition of additive reactive power derives from the definition of active power. Unlike active power, reactive power must be independent of the conversion of electric energy into work and heat. This independence is achieved if one of the waveforms – the current in the scalar voltage and current product (specifying active power) – is replaced by a special orthogonal waveform. An orthogonal waveform can be derived through either differentiation or integration. Reactive power obtained by this method is an additive within the system. When differentiation is employed, the reactive power for a nonlinear resistive load with a unique, time-invariant current-voltage characteristic will be zero. Some other properties of reactive power defined in this way are presented. This method is straightforward to implement in reactive energy meters.
Image classification using two neural networks and activation functions: a case study on fish species Hutapea, Oppir; Gaol, Ford Lumban; Matsuo, Tokuro
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp383-394

Abstract

Lake Toba is utilized for aquaculture fishing as a clear example of how this technology can be applied. One of the species presents is the red devil fish (Amphilophus labiatus), which is known to have started appearing in the last 10 years. This species is known to be very aggressive and damage the ecosystem. When their populations go unchecked, red-devils can cause a decline in local fish populations, potentially destroying the balance of the food chain in those waters. This study used artificial neural network (ANN) and convolutional neural network (CNN) algorithms to successfully create two classification models for fish species from Lake Toba: red devil fish (Amphilophus labiatus), mujahir fish (Oreochromis mossambicus), sepat fish (Trichogaster trichopterus). The purpose of this model is to automatically identify fish species by using image-based classification techniques. According to the study's findings, both models performed exceptionally well and had a high degree of accuracy. This study addresses the lack of effective automated fish classification systems for ecosystems like Lake Toba, Indonesia, which are threatened by invasive species such as the red devil fish. By comparing CNN and ANN models with different activation functions and optimizers, we found that CNN with rectified linear unit (ReLU) activation and Adam optimizer provides the most accurate and stable results. The findings offer practical implications for fisheries management and biodiversity conservation.
Experimental comparison of air, oil, and liquid nitrogen cooling media on the efficiency of a single-phase transformer Nugraha, Heri; Imaduddin, Agung; Priandana, Eka Rakhman; Hermawan, Asep Dadan; Darsono, Nono; Pramono, Andika Widya; Syahid, Adi Noer; Palaloi, Sudirman; Herbirowo, Satrio; Hendrik, Hendrik
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp25-35

Abstract

Transformers are critical component in electric power system, where minimizing energy losses is essential for efficiency and reliability. While ideal transformers operate with zero losses, practical transformers dissipate energy through winding and core losses caused by resistive heating. This study investigates the impact of three cooling media with ambient air, mineral oil, and liquid nitrogen on the efficiency and thermal performance of a 1 kVA single phase copper wound transformer. The experiment applied a resistive load under each cooling condition, recording input and output parameters using a HIOKI power meter model PW3360. Thermal behavior was monitored using infrared thermography and thermocouples. Copper winding resistivity was evaluated using a four-point probe within a cryogenic magnet system. The results show that liquid nitrogen cooling significantly reduced copper resistivity due to low-temperature conditions, achieving a transformer efficiency of 89.9%. Oil cooling improved efficiency to 86.0%, compared to 80.7% with air cooling. Although liquid nitrogen provided the greatest efficiency enhancement, its practical use is limited due to handling complexity and cost. In contrast, oil cooling offers a more feasible and effective solution for improving transformer performance in real world applications. These finding provide valuable insight for optimizing transformer thermal management strategies in power systems.
FinFET technology: a comprehensive review on materials, structures, fabrication, and device performance Rahman, Yead; Al Islam, Md Faiaz; Islam, Nafiya; Hassan, Sunzid; Shuvo, Sabbir Alom; Nabi, Iftesam; Alam, Jarif Ul
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp89-101

Abstract

As semiconductor devices become smaller, FinFETs have replaced traditional planar MOSFETs. Planar devices face issues like weak electrostatic control and high leakage current at small sizes. FinFETs solve these problems with a three-dimensional structure and multigate design. This improves gate control and reduces short-channel effects. This paper explains FinFET design, materials, and fabrication methods. It highlights how fin geometry affects current flow and device performance. Gate-source voltage (VGS) and drain-source voltage (VDS) are important parameters. These control the device operation in the lin-ear, saturation, and pinch-off regions. Performance factors such as on/off current ratio (ION /IOFF), subthreshold swing (SS), and drain-induced barrier lowering (DIBL) show that FinFETs work well for low-power and high-speed uses. Achieving uniform doping below 5 nm remains difficult. Atomic layer deposition (ALD) helps improve doping control. In summary, FinFETs play a key role in modern semiconductor design by improving scalability and efficiency.
Microbubble size and rise velocity measurement in dissolved air flotation system Alegría, Jeimmy Adriana Muñoz; Lopera, Jesús Emilio Pinto; Muñoz-España, Elena; Flórez-Marulanda, Juan Fernando
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp174-186

Abstract

Water reuse and resource recovery are priority environmental goals under increasing water scarcity and climate stress. Dissolved air flotation (DAF) is widely applied in municipal, industrial, and decentralized treatment trains because fine microbubbles (MB) enhance solids removal efficiency. Accurate, low-cost characterization of MB size and rise velocity is therefore valuable for process monitoring and optimization. This study develops and validates a smartphone-based, computer-vision pipeline for laboratory-scale DAF systems. After camera calibration and lens un-distortion, each video sequence (235 frames per run) is processed through grayscale conversion, median, Gaussian, and local-Laplacian filtering, gamma correction, and Otsu thresholding, followed by morphological refinement. Circular Hough transform then identifies MB candidates, providing their diameters and centroid locations. These detections are then linked frame-to-frame using a distance-gated nearest-neighbor tracker with dynamic memory allocation to accommodate new MBs under turbulent, bubble-clustering conditions. Rise velocity is computed from interframe centroid displacement and frame interval. The system reliably tracked up to 32 microbubbles simultaneously per video. Across four operating pressure/airflow combinations, mean MB diameters ranged 95.47–216.42 µm and mean rise velocities 9.40×10³–2.76×10⁴ µm/s. The approach is low cost, computationally lightweight, and suitable for rapid MB characterization to support DAF monitoring, optimization, and research.

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