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All Journal International Journal of Electrical and Computer Engineering
Masmoudi, Lhoussaine
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Computer Science & IT Electrical & Electronics Engineering

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An improved mining image segmentation with K-Means and morphology using drone dataset Haqiq, Nasreddine; Zaim, Mounia; Sbihi, Mohamed; El Alaoui, Mustapha; Masmoudi, Lhoussaine; Echarrafi, Hamza
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2655-2675

Abstract

The mining industry faces the challenge of incorporating advanced technology to explore new ways of increasing productivity and reducing costs. Our focus is on integrating drone technology to revolutionize mining tasks like inspection, mapping, and surveying. Drones offer a precision advantage over traditional satellite methods. To this end, we have created a dataset consisting of 373 aerial images captured by a DJI Phantom 4 drone, which depict a mining site in the Benslimane region of Western Morocco. These images, with a ground resolution of 2.5 cm per pixel, are the basis of our research. Our study aims to address the challenges posed by traditional mining techniques and to leverage technological innovations to improve segmentation and classification. The proposed approach includes new methodologies, particularly the combination of K-Means clustering and mathematical morphology, to overcome limitations and deliver better segmentation results. Our findings represent a significant step forward in advancing mining operations through the effective use of modern technologies.
Parameters estimation of BLDC motor based on physical approach and weighted recursive least square algorithm Majdoubi, Rania; Masmoudi, Lhoussaine; Bakhti, Mohammed; Elharif, Abderrahmane; Jabri, Bouazza
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp133-145

Abstract

Brushless DC motors (BLDCM) are widely used when high precision converters are required. Model based torque control schemes rely on a precise representation of their dynamics, which in turn expect reliable system parameters estimation. In this paper, we propose two procedures for BLDCM parameters identification used in an agriculture mobile robot’s wheel. The first one is based on the physical approach or equations using experimentation data to find the electrical and mechanical parameters of the BLDCM. The parameters are then used to elaborate the model of the motor established in Park’s reference frame. The second procedure is an online identification based on recursive least square algorithm. The procedure is implemented in a closed-loop scheme to guarantee the stability of the system, and it provide parameter matrices obtained by transforming electrical equations, established in Parks reference frame, and mechanical equation to discrete-time domain. From these matrices, and using well formulated intermediate variables, all desired parameters are deduced simultaneously. The identification procedures are being verified using simulation under Matlab-Simulink software.
Co-Authors BAKHTI, Mohammed Echarrafi, Hamza El Alaoui, Mustapha ELHARIF, Abderrahmane Haqiq, Nasreddine JABRI, Bouazza MAJDOUBI, Rania Sbihi, Mohamed Zaim, Mounia