Browsing by Author "Zaoui, Sara"

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    BLDC motor controller : design, implementation and bech test
    (Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2019) Zaoui, Sara; Boudia, Dounia Zed; Ouadi, A. (Supervisor)
    Brushless DC (BLDC) motors are one of the most widely used electrical drive in industrial applications because of its performances; high efficiency, high torque than other motors. In this work, the permanent BLDC motor has been driven with the three arms inverter circuit (MOSFET) controlled using mainly rotor hall-effect position sensors. The analysis of the BLDC motor is simulated in open loop and closed loop design, including the loaded and loaded cases using MATLAB/ SIMULINK software. The conventional PI controller parameters are tuned using Ziegler-Nicholas method for the closed loop speed control of the BLDC motor. The dc voltage provided by the controller is used for adjusting the motor DC supply voltage by means of the duty cycle of the PWM circuit. This variable dc motor supply voltage causes the BLDC motor speed control. The Motor closed loop speed controller system is implemented using a PC based hardware and software interfaces. The hardware interface NI DAQ -6009 is used to read the motor actual speed and generating a variable dc voltage to control the PWM circuit. The software part which is a real-time LABVIEW program, aimed to implement the continuous PI controller and safety algorithms used for a correct system operation. For a deep study and tests of the behavior of the PI controller in loaded mode, a DC motor shaft is mechanically tied with the BLDC motor through a coupling mechanism. A PMBLDC motor rated at 24 Vdc, 1.56 A, 3000 rpm is used for the experimental tests. A Buhler PMDC motor is used rated at 24 Vdc, 2.5 A and 5000 rpm is used as load for the PM BLDC motor.
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    An enhanced evolutionary approach for solving the community detection problem
    (Taylor and Francis Online, 2021) Cheikh, Salmi; Bouchema, Sara; Zaoui, Sara
    Community detection concepts can be encountered in many disciplines such as sociology, biology, and computer science, etc. Nowadays, a huge amount of data is produced by digital social networks and needs to be processed. In fact, the analysis of this data makes it possible to extract new knowledge about groups of individuals, their communication modes, and orientations. This knowledge can be exploited in marketing, security, Web usage, and many other decisional purposes. Community detection problem (CDP) is NP-hard and many algorithms have been designed to solve it but not to a satisfactory level. In this paper, we propose a hybrid heuristic approach based on a combination of genetic algorithms and tabu search that does not need any prior knowledge about the number or the size of each community to tackle the CDP. The method is efficient because it uses an enhanced encoding, which excludes redundant chromosomes while performing genetic operations. This approach is evaluated on a wide range of real-world networks. The result of experiments shows that the proposed algorithm outperforms many other algorithms according to the modularity (Q) measure.
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    A Hybrid Heuristic Community Detection Approach
    (IEEE, 2020) Cheikh, Salmi; Bouchema, Sara; Zaoui, Sara
    Community detection is a very important concept in many disciplines such as sociology, biology and computer science, etc. Nowadays, a huge amount of data is produced by digital social networks. In fact, the analysis of this data make it possible to extract new knowledge about groups of individuals, their communication modes and orientations. This knowledge can be exploited in marketing, security, Web usage and many other decisional purposes. Community detection problem (CDP) is NP-hard and many algorithms have been designed to solve it but not to a satisfactory level. In this paper we propose a hybrid heuristic approach that does not need any prior knowledge about the number or the size of each community to tackle the CDP. This approach is evaluated on real world networks and the result of experiments show that the proposed algorithm outperforms many other algorithms according to the modularity (Q) measure