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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Applied Power Engineering (IJAPE) International Journal of Evaluation and Research in Education (IJERE) Journal of Robotics and Control (JRC)
Talea, Mohamed
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Mechanical Engineering

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A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks Msala, Youssef; Hamed, Oussama; Talea, Mohamed; Aboulfatah, Mohamed
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.18650

Abstract

This paper presents an innovative task allocation method for multi-robot systems that aims to optimize task distribution while taking into account various performance metrics such as efficiency, speed, and cost. Contrary to conventional approaches, the proposed method takes a comprehensive approach to initialization by integrating the K-means clustering algorithm, the Hungarian method for solving the assignment problem, and a genetic algorithm specifically adapted for Open Loop Travel Sales Man Problem (OLTSP). This synergistic combination allows for a more robust initialization, effectively grouping similar tasks and robots, and laying a strong foundation for the subsequent optimization process. The suggested method is flexible enough to handle a variety of situations, including Multi-Robot System (MRS) with robots that have unique capabilities and tasks of varying difficulty. The method provides a more adaptable and flexible solution than traditional algorithms, which might not be able to adequately address these variations because of the heterogeneity of the robots and the complexity of the tasks. Additionally, ensuring optimal task allocation is a key component of the suggested method. The method efficiently determines the best task assignments for robots through the use of a systematic optimization approach, thereby reducing the overall cost and time needed to complete all tasks. This contrasts with some existing methods that might not ensure optimality or might have limitations in their ability to handle a variety of scenarios. Extensive simulation experiments and numerical evaluations are carried out to validate the method's efficiency. The extensive validation process verifies the suggested approach's dependability and efficiency, giving confidence in its practical applicability.
Innovative GMPPT searching algorithm and precise backstepping control for grid-connected PV system in challenging shading environments Bahri, Mohamed; Talea, Mohamed; Bahri, Hicham; Aboulfatah, Mohamed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1537-1546

Abstract

Photovoltaic (PV) systems encounters different problems of weather conditions that lowers their generated power. For this reason, maximum power point tracking (MPPT) have been designed to track the maximum power at all times and thus minimize these losses. However, under complexes partial shading condition (PSC) these losses are even higher. Classical MPPT algorithms fails to track the global MPP (GMPP) which further augment the power losses. Alternately, a grid connected topology of the PV system is chosen but needs a control method to phase the inverter current with the grid. This paper introduces a novel algorithm named power search algorithm (PSA) that memorizes the highest peak as it scans the PV curve then returns and locks it. Due to its simplicity, this proposed method is suitable for practical use and manages to track the GMPP with high efficiency of 99.5% and a mean response time of 0.04 s. Comparison was made with a gray wolf optimization (GWO) technique. Simulation was done in MATLAB/Simulink. Results shows that the proposed algorithm performed better than the GWO in all aspect of efficiency, tracking time and oscillations around GMPP. Also, a backstepping control was used to inject a good synchronized power to the grid.
Education and smart technologies: towards a new pedagogical paradigm Dehbi, Amine; Bakhouyi, Abdellah; Khaddar, Al Mahdi; Talea, Mohamed
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i1.30470

Abstract

Smart education, a new field of technology related to education, has emerged as a unique response to current educational challenges. This is becoming increasingly important for academic progress and aligns with the transformative impact of technology. This study addresses the transformative impact of smart technologies on education, focusing on the integration of the internet of things, big data, and artificial intelligence. Through a bibliometric and content analysis based on Scopus and Web of Science databases, we identify the most active researchers, leading universities, and the countries that contribute most significantly to the field of smart education. The findings reveal a significant increase in related publications, highlighting the growing importance of these technologies in enhancing teaching and learning experiences. The study shows the advantages and challenges of adopting such technologies, providing insights into their practical applications and the future direction of educational innovations. Integrating smart technologies in education is crucial for improving quality of life and academic outcomes, necessitating further research and development to fully realize their potential. This research contributes to the understanding of technological impacts on education and supports the development of strategies for their effective implementation.
Architecture of multi-agent systems for generative automatic matching among heterogeneous systems Batouta, Zouhair Ibn; Dehbi, Rachid; Talea, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2345-2355

Abstract

This paper presents the generative automatic matching (GAM) approach, implemented through a multi-agent system (MAS), to address the challenges of heterogeneity across meta-models. GAM integrates automatic meta-model matching with model generation, offering a comprehensive solution to complex systems involving diverse architectures. The key innovation lies in its ability to automate both the detection of correspondences and the transformation of models, improving the precision and recall of matching processes. The system's scalability and adaptability are enhanced by MAS, allowing for efficient management of diverse meta-models. The approach was evaluated through relational to big data UML meta-models (RBDU) case study. The results demonstrated high accuracy, with precision and recall metrics approaching 1, underscoring the robustness of GAM in managing heterogeneous systems. Compared to traditional methods, GAM offers significant advantages, including automated matching and generation, adaptability to various domains, and superior performance metrics. The study contributes to the field of model-driven engineering (MDE) by formalizing a method that effectively bridges the gap between heterogeneous meta-models. Future research will focus on refining matching heuristics, expanding case studies.
Integral backstepping control design for enhanced stability and dynamic performance of VSC-HVDC systems Lakhdairi, Chaimaa; Benaboud, Aziza; Bahri, Hicham; Talea, Mohamed
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp255-263

Abstract

The increasing demand for efficient and reliable high-voltage direct current (HVDC) transmission systems has underscored the necessity for advanced control strategies to augment system performance. This article presents the design and implementation of an integral backstepping control approach customized for voltage source converter (VSC)-based HVDC systems. The proposed methodology primarily concentrates on tackling the inherent nonlinearities, uncertainties, and disturbances that typically impede the stability and efficiency of VSC-HVDC systems. By incorporating integral action into the backstepping control framework, two key objectives are accomplished: i) precise regulation of the direct voltage at the rectifier station and accurate control of the active power at the inverter station, and ii) effective power factor correction (PFC) at both stations within the HVDC system. These objectives contribute to robust tracking performance, enhanced dynamic stability, and improved overall system efficiency. The theoretical design has been verified through extensive numerical simulations conducted in the MATLAB/Simulink environment, showcasing the efficacy of the proposed control strategy in ensuring stability and performance under varying conditions.
Predictive insights into student online learning adaptability: elevating e-learning landscape El Ghali, Mohamed; Atouf, Issam; El Guemmat, Kamal; Talea, Mohamed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp892-902

Abstract

In Morocco’s rapidly transforming educational landscape, this study delves into students’ adaptability to online learning environments by integrating sophisticated artificial intelligence (AI) algorithms and hyperparameter optimization techniques. This research uses the comprehensive “online learning adaptivity” dataset to identify pivotal factors influencing student flexibility and effectiveness in e-learning platforms. We applied various AI models, with a particular emphasis on the CatBoost classifier, which exhibited exceptional predictive performance, achieving an accuracy rate near 98%. This high precision in predicting student adaptiveness offers essential insights into tailoring digital education systems. The results underscore the significant potential of machine learning technologies to enhance educational methodologies by catering to the diverse needs of students. Such capabilities are instrumental for educators and policymakers dedicated to refining e-learning strategies that effectively accommodate individual learning styles, ultimately improving the broader educational outcomes in Moroccan tertiary education. These findings advocate for a more nuanced understanding of the interplay between student behavior and technological solutions, providing a roadmap for developing more responsive and effective educational platforms.
Co-Authors Aboulfatah, Mohamed ATOUF, ISSAM Bahri, Hicham Bahri, Mohamed Bakhouyi, Abdellah Batouta, Zouhair Ibn Benaboud, Aziza Dehbi, Amine Dehbi, Rachid El Ghali, Mohamed El Guemmat, Kamal Hamed, Oussama Khaddar, Al Mahdi Lakhdairi, Chaimaa Msala, Youssef