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This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI)
Badr Bououlid Idrissi
Moulay Ismaïl University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Performance of a vector control for DFIG driven by wind turbine: real time simulation using DS1104 controller board Sara Mensou; Ahmed Essadki; Issam Minka; Tamou Nasser; Badr Bououlid Idrissi; Lahssan Ben Tarla
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v10.i2.pp1003-1013

Abstract

In this research paper we investigate the modelling and control of a doubly fed induction generator (DFIG) driven in rotation by wind turbine, the control objectives is to optimize capture wind, extract the maximum of the power generated to the grid using MPPT algorithm (Maximum Power Point Tracking) and have a specified reactive power generated whatever wind speed variable, the indirect field oriented control IFOC with the PI correctors was used to achieve such as decoupled control. To validate the dynamique performance of our controller the whole system was simulated using dSPACE DS1104 Controller board Real Time Interface (RTI) which runs in Simulink/MATLAB software and ControlDesk 4.2 graphical interfaces.
Enhancing pedestrian detection in adverse conditions using YOLOv5s with adaptive weighted fusion efficient channel attention module Oumayma Rachidi; Badr Bououlid Idrissi; Chafik Ed-dahmani
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3299-3308

Abstract

Pedestrian detection constitutes a critical task within advanced driver assistance systems (ADAS), where reliable identification of pedestrians is essential for ensuring vehicular safety. Although deep learning has substantially improved detection performance, existing state-of-the-art models continue to exhibit notable degradation in adverse weather conditions and low-light. To mitigate these challenges, this study introduces an enhanced pedestrian detection framework based on you only look one version 5 (YOLOv5s), retrained on an augmented common object in context (COCO) dataset focused on the person class. Additionally, a novel, lightweight, and adaptive attention mechanism called: the weighted fusion efficient channel attention (WF-ECA) module is incorporated into the detection architecture. The WF-ECA module selectively focusses on important features without compromising computational efficiency or inference speed. Comparative experiments demonstrate a 5% increase in mean average precision (mAP) in comparison to the baseline model, thereby demonstrating the efficacy of the proposed attention module in improving detection robustness under challenging environmental conditions. These findings highlight the potential of attention-based mechanisms to enhance pedestrian detection performance in real-world ADAS applications.
Co-Authors Ahmed Essadki Chafik Ed-dahmani Issam Minka Lahssan Ben Tarla Oumayma Rachidi Sara Mensou Tamou Nasser