Prof. Youssef Zaatar Profile

Prof. Youssef Zaatar

at Lebanese Univ

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Publications (2)

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Proceedings Article | 12 April 2021 Presentation + Paper

Distributed dynamical temperature measurement of the rotor of small rotating machines using Fiber Bragg Gratings (FBGs) sensors

Rita Abboud, Hani Al Hajjar, Alejandro Ospina, Jad Abou Chaaya, Youssef Zaatar, Frédéric Lamarque

Proceedings Volume 11739, 1173908 (2021) https://doi.org/10.1117/12.2585572

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This article presents a contactless measurement technique of the rotor temperature of small rotating machines using Near-Infrared Fiber Bragg Gratings (FBGs) sensors. This principle allows localizing heat spots in the rotor of electrical machines. The temperature information can be used to protect the machine by stopping its operation due to a heat spot. The concept is to measure the wavelength shift due to temperature changes for several FBGs integrated into a rotor. First, the temperature response of the FBG is simulated using Matlab. Then, a test bench is designed including a geometrically small electrical motor, a mechanical coupler, two bearings and a 3D printed cylinder. It has a rotational speed equivalent to a real electrical machine. The measurement principle uses a super-luminescent diode (815-855 nm) which is continuously coupled into an FBG embedded onto the rotor using suitable optics. The heating system is calibrated using a T-type thermocouple (class A: +/- 1 °C). Then, the Fiber Bragg Grating is heated while rotating the cylinder. The reflected signals are detected by a spectrometer. Finally, wavelength shifts due to temperature variations (10°C steps from 20 °C up to 70 °C) are experimentally measured up to 754 RPM. A temperature sensibility of 4.7 pm/°C is experimentally reached. As future work, the system with several gratings will be integrated into a small power rotating machine (kW) suitable for automotive applications. Reflected signals that correspond to temperature variations will be detected while rotating the FBGs to measure high temperatures ~ 150 °C for 1500 RPM.

Proceedings Article | 22 April 2020 Presentation + Paper

Evolutionary clustering for dynamic partitioning of transportation network

P. Al Alam, D. Hamad, J. Constantin, I. Constantin, Y. Zaatar

Proceedings Volume 11400, 1140005 (2020) https://doi.org/10.1117/12.2557588

KEYWORDS: Roads, Evolutionary algorithms, Detection and tracking algorithms, Algorithm development, Distance measurement, Quality measurement, Matrices

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The problem of clustering transportation networks has been studied in the static framework by considering traffic conditions at a given time. Nevertheless, it is important to underline that traffic is a strongly time-variant process and it needs to be studied in the spatio-temporal dimension. Considering the fact that the congestion is spatially correlated in adjacent roads and it propagates with different speeds, partitioning a transport network into homogeneous trajectories that evolve over time can be extremely useful in order to design a real-time traffic control schema which alleviate or postpone congestion. The paper proposes an evolutionary spectral clustering approach to partition a graph transport network into connected homogeneous trajectories that evolve over time. In order to choose the number of clusters automatically, we use the density peaks algorithm which is based on the idea that cluster centers are characterized by a higher density than their neighbors and by relatively large distance from trajectories with higher densities. The clusters are recognized and the outliers are excluded from the analysis. This method is proved to be efficient regardless the shape and the dimension of the data set. We perform experiments on real road speeds for Amsterdam city traffic network, our results show that the proposed evolutionary spectral clustering algorithm outperforms the static clustering algorithms in its efficiency and robustness.

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