07. Rektörlüğe Bağlı Birimler

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    Citation - WoS: 4
    Citation - Scopus: 4
    A Novel Adaptive Traffic Signal Control Based on Cloud/Fog Computing
    (Springer, 2022) Çeltek, Seyit Alperen; Durdu, Akif
    This paper proposes the Internet of Things-based real-time adaptive traffic signal control strategy. The proposed model consists of three-layer; edge computing layer, fog computing layer, and cloud computing layer. The edge computing layer provides real-time and local optimization. The middle layer, which is the fog computing layer, performs a real-time and global optimization process. The cloud computing layer, which is the top layer, acts as a control center and optimizes the parameters of the fog layer and the edge layer. The proposed strategy uses the Deep Q-Learning algorithm for the optimization process in all three layers. This study employs the SUMO traffic simulator for performance evaluation. These results are compared with the results of adaptive traffic control methods. The output of this study shows that the proposed model can reduce waiting times and travel times while increasing travel speed.
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    Sociophysics of Income Distributions Modeled by Deformed Fermi-Dirac Distributions
    (TAYLOR & FRANCIS INC, 2023) Dil, Emre; Dil, Elif
    In order to model the income data, the physical distributions of Fermi-Dirac and Bose-Einstein families have already been proposed in the literature. In this study, we generalize Fermi-Dirac distribution by using a q,p-deformed version of Fermi-Dirac distribution which provides the advantage of working with flexible free q, p deformation parameters as the regression parameters for modeling the income data. We analyze the accuracy of the generalized version, q,p-deformed Fermi-Dirac distribution, on describing the data of income share held by quintiles for countries, and household income for the states of U.S.A. in 2018. We also use chi(2) minimization routine for modeling the data which leads to the best fit parameters for the deformation parameters q and p. Subsequently, we plot the fitted q, p-deformed Fermi-Dirac distribution as income distribution with the obtained deformation parameters, then find the statistical confidence values r(2) from the fitted curve. We figure out that our model properly describes the income data for the systems experiencing a high level of income inequality, and also r(2) values are correlated with the Gini index for those of considered systems.