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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Ed-dahmani, Chafik
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Control & Systems Engineering Electrical & Electronics Engineering

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Experimental validation of the design and control of a compressed air energy storage system Rais, Ilham; Ed-dahmani, Chafik; Benami, Abdellah
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2214-2221

Abstract

In this paper, we introduce a comprehensive design and control strategy for an energy storage system based on compressed air to enhance both electrical energy quality and operational flexibility. The formulation of this control structure involved extensive calculations and computer simulations, which now require experimental validation. We describe the specifically designed test benches for this purpose and present an analysis of the experimental results. The paper begins with a brief overview of the didactic bench used to test the pure pneumatic conversion system, followed by the presentation and discussion of the initial practical results of the maximum energy point tracking (MEPT) strategy derived from this bench.
A stereo-vision system for real-time person detection in ADAS applications using a fine-tuned version of YOLOv5 Rachidi, Oumayma; Ed-Dahmani, Chafik; Bououlid Idrissi, Badr
Bulletin of Electrical Engineering and Informatics 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/eei.v14i1.8417

Abstract

Pedestrian detection holds significant importance in advanced driver assistance systems (ADAS) applications, and presents a challenging task in this field. While the advent of deep learning has facilitated the introduction of various pedestrian detectors characterized by accuracy and low inference speed, there persists a need for further improvements. Notably, ADAS requires accurate detection of pedestrians in various environmental conditions that can adversely impact the model’s performance, such as poor lighting, and bad weather. Furthermore, an imperative requirement involves the incorporation of distance estimation in conjunction with pedestrian detection, with an extension of detection capabilities to encompass cyclists and riders, who are equally crucial for ensuring road safety. Therefore, this paper introduces a stereovision system designed for the detection of pedestrians, cyclists, and riders. The initial phase, involves improving the performance of you only look once version 5 (YOLOv5s) through a fine-tuning process with a custom dataset integrating augmentation techniques to common objects in context (COCO) dataset. The detector is trained using Google Colab, and tested in real-time with a Raspberry Pi 4 model B, 8 G RAM. A comparative analysis is conducted between the YOLOv5s and the fine-tuned model to prove the accuracy of our approach. The results showcase a high performance of the detector reaching an accuracy exceeding 79%.
Co-Authors Benami, Abdellah Bououlid Idrissi, Badr Rachidi, Oumayma Rais, Ilham