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30,609 Article Results

Evaluating user experience of a mobile website and redesigning its user interface using goal-directed design method

10.11591/ijict.v15i2.pp634-643
Aang Subiyakto , Muhammad R. Alghifari , Nuryasin N. , Muhammad Q. Huda , Nashrul Hakiem , Viva Arifin , Dwi Yuniarto , Hadi Rahman , Thosporn Sangsawang , Naeem Atanda Balogun
This study evaluated the usability of the user interface (UI) of a mobile website using its user experience (UX) perspectives. The website serves as an information portal intended for access via smartphones and other handheld devices. The objective of the study was to assess the usability of its current interface, redesign it using the goal-directed design (GDD) method, and compare the usability performance before and after the redesign. The study was conducted in five main steps using the cognitive walkthrough, think-aloud, post-study system usability questionnaire (PSSUQ), and interview techniques with five representative participants and 50 respondents. The most important findings of the study were that the redesigned mobile website showed improved usability of the website, as indicated by increased effectiveness and efficiency values, enhanced PSSUQ satisfaction scores, and more positive user feedback.
Volume: 15
Issue: 2
Page: 634-643
Publish at: 2026-06-01

ChatGPT in the university classroom: perceptions, perceived usefulness and use intentions among undergraduate students

10.11591/ijere.v15i3.38709
Vidnay Noel Valero-Ancco , Yolanda Lujano-Ortega
The objectives of the present study are to analyze university students’ perceptions of and attitudes toward ChatGPT as a support tool for learning, as well as the user profiles derived from their technological acceptance, were analyzed. A quantitative, nonexperimental and cross-sectional design was applied to a non-probabilistic convenience sample of 438 undergraduate students, and a validated questionnaire composed of three dimensions compatibility with learning styles, ease of use and perceived usefulness, and continued use intentions was used. The data were analyzed with hierarchical cluster analysis (Ward method). The results reveal three groups of users: i) enthusiasts, with highly favorable perceptions and high use intentions; ii) moderate users, who exhibit partial acceptance; and iii) critical or disconnected users, who have a negative view of the tool. Taken together, the findings confirm that perceived usefulness, ease of use and trust are determining factors in the intention to use ChatGPT. This study provides empirical evidence on the diversity of attitudes toward generative artificial intelligence (GenAI) in university contexts and highlights the need to promote institutional strategies of critical digital literacy and teacher training for its ethical and pedagogical integration.
Volume: 15
Issue: 3
Page: 2073-2081
Publish at: 2026-06-01

Real-time implementation and comparative analysis of fault-tolerant control strategies for induction motor drives

10.11591/ijpeds.v17.i2.pp894-907
Asmaa Hammou , Mokhtar Bendjebbar , Mohammed Benslimane
For nearly five decades, the induction motor has been the most widely used electrical machine in industry due to its robustness, simplicity, and low cost, supported by advances in power electronics enabling effective performance control. While DC motors were previously favored for their ease of speed and torque regulation, induction motors have gained prominence because they do not require brushes and involve fewer wear-prone components, resulting in reduced maintenance and improved reliability. Consequently, they are widely employed in industrial applications and emerging fields such as electric and hybrid vehicles. This study presents a comparative analysis of two fault-tolerant control (FTC) strategies: field-oriented control (FOC) and direct torque control (DTC). The evaluation focuses on sensitivity to parameter variations, dynamic performance, and steady-state behavior. Both strategies, classified under vector control techniques, are implemented in real time using a dSPACE platform to control an induction motor under an open-circuit fault in a two-level inverter. Results demonstrate that the DTC-based FTC approach offers superior robustness and stability compared to the IFOC-based method, particularly under fault conditions, load disturbances, and speed variations.
Volume: 17
Issue: 2
Page: 894-907
Publish at: 2026-06-01

Adaptive control of the virtual synchronous generator by deep neural networks for a wind high power conversion chain

10.11591/ijpeds.v17.i2.pp1440-1450
Wijdane El Maataoui , Abdelouahed Abounada
The virtual synchronous generator (VSG) is commonly used to reproduce the inertial response of conventional synchronous machines. However, the VSG control architecture relies on controller chains, benchmark transformations, and parameter settings, including virtual inertia and damping, which limit its flexibility in highly dynamic environments. This paper proposes an innovative end-to-end control approach based on a neural network to fully replace the classical VSG control structure. The neural network developed is trained to directly generate inverter control signals from real-time electrical measurements, including voltages and currents, as well as active and reactive power. A dataset is generated from a detailed VSG model under different operating conditions, and then a multilayer neural network is trained using supervised learning with MATLAB. The resulting model is then integrated into a complete wind energy conversion chain simulated in Simulink. The simulation results demonstrate that control based on artificial neural networks ensures better frequency and voltage stability, more accurate tracking of the active power injected, and a significant improvement in power quality, with total harmonic distortion (THD) reduced to 0.04%, compared to 0.51% for conventional VSG control. These results confirm the potential of artificial intelligence-based approaches for the intelligent control of renewable energy systems.
Volume: 17
Issue: 2
Page: 1440-1450
Publish at: 2026-06-01

High step-up interleaved multilevel hybrid boost converter with switched-capacitor multiplier

10.11591/ijpeds.v17.i2.pp1118-1129
Andi M. Nur Putra , Adrianti Adrianti , Muhammad Imran Hamid
The global integration of renewable energy sources like photovoltaics requires efficient high-step-up DC-DC converters. Conventional boost converters exhibit inherent limitations in achieving high voltage gain efficiently, particularly under high duty cycle operation, where switching losses, device stress, and output voltage ripple become significant. This paper proposes a novel hybrid DC-DC converter that integrates a four-phase interleaved input stage with a five-level switched-capacitor (SC) multiplier network. The proposed topology introduces a modular and structurally decoupled architecture, in which current conditioning and voltage boosting functions are independently realized. This enables scalable voltage gain through modular expansion without requiring extreme duty cycles or additional magnetic components. The interleaved stage reduces input current ripple and improves current sharing, while the multilevel SC network provides a high voltage conversion ratio and balanced voltage stress across components. Comprehensive simulations using PSIM software validate the converter's performance. With a 25 V input, the proposed converter achieves an output voltage of approximately 250 V (gain of 10), a high efficiency of 95.2%, output voltage ripple below 2%, and balanced capacitor voltages. The results confirm that the proposed converter offers an efficient, scalable, and high-performance solution for high step-up applications.
Volume: 17
Issue: 2
Page: 1118-1129
Publish at: 2026-06-01

Hybrid control strategy for trajectory tracking and obstacle avoidance in differential wheeled robots: integrating PSO-NMPC, GA, and fuzzy logic

10.11591/ijpeds.v17.i2.pp1008-1024
Abdennour Zeghida , Lotfi Farah , Halim Merabti , Abdelfateh Kerrouche
Mobile robots frequently encounter challenges in maintaining accurate trajectory tracking and effective obstacle avoidance in dynamic and uncertain environments. Traditional control methods, such as proportional integral derivative (PID) and standard MPC, often fail to provide the necessary adaptability and robustness for complex navigation tasks. To overcome these limitations, this study proposes a hybrid control framework for differential-drive wheeled robots that integrates particle swarm optimization–based nonlinear model predictive control (PSO-NMPC), adaptive neuro-fuzzy inference system (ANFIS) optimized by PSO, and genetic algorithm (GA) tuning. The PSO-NMPC computes optimal control inputs in real time while satisfying system constraints to ensure precise trajectory tracking, achieving an average RMSE of 0.0941 m (RMSEx = 0.0884 m, RMSEy = 0.0812 m). The ANFIS-PSO controller manages nonlinearities and environmental uncertainties for reliable obstacle avoidance, with an overall RMSE of 0.1084 m (RMSEx = 0.0761 m, RMSEy = 0.0772 m). The GA further optimizes key parameters and trajectories, ensuring global path refinement and robust obstacle clearance, achieving an overall RMSE of 0.1094 m (RMSEx = 0.1059 m, RMSEy = 0.0274 m). Simulation results in Matlab2024b confirm that the proposed hybrid framework provides precise trajectory tracking, smooth control, and robust obstacle avoidance, making it a promising solution for autonomous mobile robots operating in dynamic and uncertain environments.
Volume: 17
Issue: 2
Page: 1008-1024
Publish at: 2026-06-01

Multi-objective energy management optimization in electric vehicles using fuzzy logic and particle swarm optimization

10.11591/ijpeds.v17.i2.pp1025-1035
V. Lakshmi Devi , Damodhar Reddy , Srikanth Velpula , K. Kumar , Basi Reddy Avula
This paper proposes a hybrid energy management system (EMS) for electric vehicles by integrating fuzzy logic control (FLC) with particle swarm optimization (PSO) to improve power-split decision-making under dynamic driving conditions. The FLC is designed using state of charge (SoC) and vehicle speed as input variables and power split as the output. A set of fuzzy rules defines the EMS behavior, while PSO is employed to fine-tune decisions by maximizing an efficiency objective function defined as the closeness of the power split to an ideal reference. The simulation is implemented in Python using Colab-compatible packages such as scikit-fuzzy, DEAP, and matplotlib, ensuring accessibility and reproducibility. A test grid covering 10 SoC levels (10-100%) and 10 speed levels (10-120 km/h) is used to evaluate the system. Visualization tools, including heatmaps, 3D surface plots, and contour plots, are employed to represent the EMS behavior. The PSO-enhanced system achieved a maximum efficiency of 98.2% at an optimized SoC of 61.7% and a speed of 53.6 km/h, outperforming standalone fuzzy logic control. Tabulated results and statistical summaries validate the effectiveness of the proposed system.
Volume: 17
Issue: 2
Page: 1025-1035
Publish at: 2026-06-01

Permanent magnet generator for small and medium-scale hydropower: a systematic review

10.11591/ijpeds.v17.i2.pp1462-1474
Ngatono Ngatono , Raja Nor Firdaus Kashfi Raja Othman , M. Nazri Othman , Mohd Zulkifli Ab Rahman
Renewable energy, particularly hydropower, is a key focus in reducing reliance on fossil fuels and mitigating environmental impacts. Permanent magnet generator (PMG) has emerged as a highly efficient option for converting hydro-energy into electricity, offering advantages such as high efficiency, compact design, and minimal maintenance. This review explores the latest developments in PMG technology, particularly for small and medium-scale hydropower applications. A systematic review method was used to analyse 617 papers and narrow them down to 20 relevant studies. Key findings highlight advancements in PMG design, including modular stators, counter-rotating turbines, and cordless designs that enhance efficiency and adaptability in low-speed environments. However, significant challenges remain, including the high cost of magnetic materials like Neodymium Iron Boron (NdFeB), thermal stability issues, and more robust control systems to manage variable water flow conditions. The review concludes that while PMG holds great potential for hydropower applications, Further research is needed to optimize material usage, improve design, and reduce costs. Future work should focus on developing new magnetic materials and innovative rotor designs to ensure PMG can provide a scalable and sustainable solution for global energy needs.
Volume: 17
Issue: 2
Page: 1462-1474
Publish at: 2026-06-01

Proximal policy optimization-based type II PPC for EV fast charging

10.11591/ijpeds.v17.i2.pp835-848
Franco Aldrin Joseph Menezes , Gopala Reddy Krishnappa
In recent years, efficient and fast charging is critical for accelerating the adoption of electric vehicle (EV). However, traditional fully rated converters process the total power flow to the battery, but leading to excessive thermal stress, high energy losses, and quick battery degradation. Similarly, existing partial power converter (PPC) designs like type I and type II PPC, improve efficiency by processing only a fraction of the total power; however, they still face challenges such as additional isolation requirements, limited step-down performance, and lack of advanced control for fluctuating state of charge (SoC) conditions. To overcome these challenges, this research proposes a proximal policy optimization (PPO)-enhanced type II PPC for fast EV charging. Initially, the power is routed through a low-frequency (LF) isolation transformer and filtered to mitigate high-frequency noise. A portion of the power is partially processed through a SiC MOSFET-based phase-shifted full-bridge converter, while the remaining power bypasses directly to the battery. The PPO controller efficiently adjusts the phase shift angle in real time, optimizing switching cycles to reduce switching and thermal losses. The proposed PPO-type II PPC achieved better results in terms of peak efficiency (99.36%) and partial power handling (12.21%) when compared to existing type II PPC designs.
Volume: 17
Issue: 2
Page: 835-848
Publish at: 2026-06-01

Stability analysis of photovoltaic grid-connected power systems employing virtual synchronous generator control

10.11591/ijpeds.v17.i2.pp1451-1461
Abdallah El Ghaly , Abdullah Hamdan , Mohamad Tarnini
The rapid integration of photovoltaic (PV) systems into power networks poses significant challenges to grid stability, including reduced inertia, voltage fluctuations, and limited fault ride-through (FRT) capabilities. This study presents a comparative analysis of two inverter control strategies: the synchronous reference frame (SRF) controller and the virtual synchronous generator (VSG) controller. A high-fidelity MATLAB/Simulink model was developed, incorporating the effects of irradiance and temperature, maximum power point tracking (MPPT), and battery energy storage system (BESS) interaction. Standardized fault scenarios were applied at PV penetration levels ranging from 30% to 150% in accordance with IEEE-1547, IEEE-519, and IEC 61727 requirements. The results show that SRF control achieves superior harmonic suppression, with a total harmonic distortion (THD) consistently below 0.5%, confirming its suitability for strong grids prioritizing power quality. However, its stability deteriorated at higher penetration levels, with the voltage overshoot reaching approximately 16% and recovery times exceeding 3 s. In contrast, the VSG control demonstrates enhanced transient stability and effective FRT performance, with the overshoot limited to ≤5% and recovery achieved within 0.8 s across all operating conditions. The main contribution of this study lies in the direct benchmarking of the SRF and VSG control strategies under identical operating conditions using a unified evaluation framework, including an extended analysis beyond 100% PV penetration. The findings highlight a fundamental trade-off between harmonic performance and transient stability and provide practical guidance for selecting appropriate inverter control strategies for renewable-dominated power systems.
Volume: 17
Issue: 2
Page: 1451-1461
Publish at: 2026-06-01

Improved control strategy for harmonic current mitigation in DFIG-based wind turbines supplying linear and nonlinear loads

10.11591/ijpeds.v17.i2.pp933-945
Hind Elaimani , Noureddine Elmouhi
Improving power quality is a major challenge in grid-connected wind energy systems, especially under mixed linear and nonlinear load conditions. This paper proposes an enhanced control strategy for harmonic current mitigation in a doubly fed induction generator (DFIG)-based wind turbine. The proposed approach integrates flux-oriented vector control with an active harmonic compensation algorithm implemented through the rotor-side converter (RSC). Unlike conventional methods that target only specific harmonic orders, the proposed strategy mitigates all current harmonics at the point of common coupling (PCC). Simulation studies conducted under various load conditions demonstrate that the method significantly reduces the total harmonic distortion (THD) and ensures near-sinusoidal stator currents. The results confirm the effectiveness and robustness of the proposed control approach in improving the power quality of DFIG-based wind energy conversion systems.
Volume: 17
Issue: 2
Page: 933-945
Publish at: 2026-06-01

Robust power optimization strategy for wind-driven induction machines using type-2 and type-1 fuzzy logic controllers

10.11591/ijpeds.v17.i2.pp1313-1325
Driss Belkhiri , Boujemaa Nassiri , Mohamed Ajaamoum
This paper proposes a reliable power optimization strategy that maximizes the harvested power of induction machines driven by wind, taking into account variable wind turbulence and uncertain machine parameters. This work explores the challenging task of designing type-2 fuzzy logic (T2FL) and conventional type-1 fuzzy logic (T1FL) controllers for wind energy conversion systems that exhibit multiple non-linearities. T2FL controllers are proficient in tackling uncertainties and offer quicker and more precise decision-making capabilities. The proposed approach is beneficial as it is independent of accurate wind turbine parameters, wind speed data, or additional sensors. Rather, it utilizes the mechanical rotor speed and the wind turbine power as input, which corresponds to maximum power point tracking (MPPT) through the management of the rotor speed via the machine-side converter. Real data validates the scheme against classical controllers, and via a set of simulations and statistical analyses, performance metrics like steady-state error, overshoot, tracking speed, and efficiency are widely assessed. The results show that the proposed scheme, which is independent of a dedicated wind speed sensor, demonstrates superior tracking performance, lower tracking errors, such as lower RMSE/MAE, and higher energy yield, although the wind speed and the system parameters change rapidly. Overall, this design provides more robust performance to random wind speed variations, increases operational efficiency and wind turbines' service life, and is low in adding mass and cost.
Volume: 17
Issue: 2
Page: 1313-1325
Publish at: 2026-06-01

Student perspectives on designing generative AI-based learning companions for higher education in Oman

10.11591/ijere.v15i3.38991
Saleem Raja Abdul Samad , Pradeepa Ganesan , Shanmuga Pria , Madhubala Radhakrishnan , Khadija Ahmed Al Isaei
Artificial intelligence (AI) has rapidly permeated nearly every sector, from healthcare and finance to manufacturing, transportation, and communication. The emergence of generative AI (GenAI) applications, including intelligent assistants, recommendation engines, and predictive analytics, has further accelerated this transformation. Within education, these advances are reshaping teaching and learning, moving away from traditional instructor-centered models toward learner-centered ecosystems that emphasize adaptability, inclusivity, and self-directed growth. Despite the growing interest in AI-supported learning, existing literature reveals several important gaps that limit the effective integration of AI in educational contexts especially, students’ learning behaviors, and preferred learning resources, have received limited attention. This study explores student’s perceptions of AI-based educational tools to inform the design of an effective AI assistive learning companion for higher education. A structured survey was administered to 135 students across IT and business programs, examining demographics, language skills, learning habits, AI tool exposure, perceptions, concerns, and self-assessed learning confidence. The result of study highlights the need for AI learning companions that are adaptive, language-aware, discipline-specific, and ethically responsible, providing personalized scaffolding, practical skill reinforcement, and support for self-directed learning. These insights inform the design of AI tools that enhance learning confidence, inclusivity, and effectiveness in higher education.
Volume: 15
Issue: 3
Page: 2366-2378
Publish at: 2026-06-01

Modeling academic leadership in secondary schools: evidence from northeastern Thailand

10.11591/ijere.v15i3.39185
Dusadee Butburee , Nawee Udorn , Paitoon Puangyod
This study aimed to develop and empirically test a structural equation model of academic leadership among secondary school administrators in northeastern Thailand. Data were collected from 480 administrators using a structured questionnaire and analyzed through confirmatory factor analysis (CFA) and structural equation modeling (SEM). The results indicate that leadership personality and organizational context significantly influence curriculum leadership and innovation culture, which subsequently shape academic leadership outcomes. The proposed model explains 73.8% of the variance in academic leadership, demonstrating strong explanatory power. These findings contribute to the literature by providing an integrated structural framework that highlights the interplay between leadership capacity and contextual support in enhancing instructional quality and school effectiveness. However, the findings should be interpreted with caution due to the cross-sectional design and reliance on self-reported data.
Volume: 15
Issue: 3
Page: 2001-2010
Publish at: 2026-06-01

Measurement of the ethical organizational climate in higher education institutions

10.11591/ijere.v15i3.34851
Maria Camila Bermeo-Giraldo , Alejandro Valencia-Arias , Iván Alonso Montoya Restrepo , Luz Alexandra Montoya Restrepo , Jackeline Valencia Arias
Assessing the ethical climate in higher education institutions (HEIs) is essential, given their role in training future professionals and promoting social responsibility, academic integrity, and transparency. This study used a quantitative, cross-sectional approach to assess the ethical climate perceived by 150 students at a private university in Medellín, Colombia. Based on Victor and Cullen’s six-dimensional model, and using exploratory factor analysis and Cramer’s V statistic, significant associations were identified. In particular, positive relationships were found between rule-based and law/code-based ethical climates and perceptions of institutional efficiency. Likewise, a relationship was found between the dimensions of independence and care, such that students who act based on their own moral judgment appreciate supportive institutional environments. In addition, 65% of students value autonomy (independence), 43.2% prioritize compliance with legal norms (laws and codes), and 33.33% emphasize following strict rules (rules). These findings highlight the importance of both personal ethical values and external regulatory frameworks in the formation of ethical perceptions. Similarly, the results contribute practical guidance for HEIs managers to strengthen ethical leadership and institutional policies, promoting a more transparent and efficient academic environment. It is recommended that HEIs implement policies that strengthen ethical leadership and promote an organizational climate that favors ethical decisions and compliance with standards.
Volume: 15
Issue: 3
Page: 1876-1893
Publish at: 2026-06-01
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