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    Optimization of Compact Microstrip Monopole Antenna Parameters with NS-RFO and Artificial Intelligence in Antenna Design
    (Institute of Electrical and Electronics Engineers Inc., 2024) Pak, S.; Güneser, M.T.; Seker, C.
    Developments in artificial intelligence directly affect various fields of science. This is especially important in the solution of higher-order models and systems that do not have an exact equation. Due to the difficulties in modeling electromagnetic systems, artificial intelligence-based studies have a special importance in this field. In this study, the multidimensional optimization of a compact microstrip monopole antenna that can be used in next generation communication applications is studied with a new artificial intelligence algorithm, the red fox algorithm. Thus, a compact microstrip antenna with broadband (8.75 GHz - 11.25 GHz) radiation at 10 GHz resonant frequency is designed for X-Band applications. © 2024 IEEE.
  • Küçük Resim Yok
    Öğe
    Parameter estimation of the wind speed distribution model by dragonfly algorithm
    (Gazi Universitesi, 2023) Köse, B.; Aygün, H.; Pak, S.
    In order to meet the increasing energy demand and to solve environmental problems, the interest in renewable energy sources continues with technology development studies and economic investments. Various methods are used to determine and estimate sustainable and renewable energy sources. Probability distribution functions are used in wind characterization and potential calculation of wind energy. In this study, the Dragonfly Algorithm (DA) is proposed to estimate the Weibull probability distribution function (Wpdf) parameters used in wind speed modeling and the two-component mixed Weibull distribution parameters used in modeling non-single peak wind speed data. The performance of the proposed method has been evaluated by comparing not only the classical methods which are the moment method (MM) and the least squares method (LSM) but also metaheuristic optimization algorithms which are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Determination coefficient (R2) and root mean square error (RMSE) were used to evaluate the performance of these parameter estimation methods. Data obtained from 6 measurement stations were used in the study. According to the performance criteria, the two-component Weibull distribution was found to be more effective at all stations compared to the Weibull distribution model. It has been concluded that the proposed DA algorithm can be used effectively for parameter estimation in wind speed modeling. © 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.