ATÜ Kurumsal Akademik Arşivi
DSpace@ATÜ, Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Cebeli bereket vilayetine ait coğrafi,iktisadi, içtimai ,tarihi, idari malumat-ı ihtiva eden bir takaddüme
(Adana Türk Sözü Matbaası, 1925)
Adanayı iyini
(1919) H., Sarkizyan
ANFIS-SA-based design of a hybrid reconfigurable antenna for L-Band, C-band, 5G and ISM band applications
(Pergamon-Elsevier Science Ltd, 2025) Gencoglan, Duygu Nazan
This study presents a novel hybrid reconfigurable antenna design optimized using an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with a Simulated Annealing (SA) algorithm for L-band, C-band, 5G, and ISM applications. The antenna is fabricated on an FR-4 substrate with dimensions of 17 x 28 x 1.6 mm3, and two PIN diodes are employed to achieve frequency and radiation pattern reconfigurability. In the ON-ON state, the antenna operates in dual bands, covering 1.33-1.38 GHz (L-band) and 3.57-3.95 GHz (C-band). For the OFF-ON state, it operates from 3.56 to 3.95 GHz (C-band, 5G). In the ON-OFF state, it covers 1.50-1.54 GHz (L-band) and 5.66-5.90 GHz (ISM band), while in the OFF-OFF state, it operates from 5.49 to 5.82 GHz (ISM band). The antenna exhibits common bands at 3.8 GHz (C-band) and 5.8 GHz (ISM) across different states, facilitating pattern reconfigurability. ANFIS-SA is applied to optimize the switch locations, significantly improving resonance frequency and S11 performance. The antenna supports beam steering between 0 degrees and 180 degrees, enhancing adaptive coverage for modern applications such as Wi-Fi, Vehicle-to-Vehicle (V2 V), and Vehicle-to-Infrastructure (V2I) communication. This study addresses a critical gap by combining hybrid optimization techniques to improve frequency agility and radiation pattern control for next-generation wireless systems.
A comprehensive benchmark of machine learning-based algorithms for medium-term electric vehicle charging demand prediction
(Springer, 2025) Tolun, Omer Can; Zor, Kasim; Tutsoy, Onder
The current difficulties faced by evolutionary smart grids, as well as the widespread electric vehicles (EVs) into the modernised electric power system, highlight the crucial balance between electricity generation and consumption. Focusing on renewable energy sources instead of fossil fuels can provide an enduring environment for future generations by mitigating the impacts of global warming. At this time, the popularity of EVs has been ascending day by day due to the fact that they have several advantages such as being environmentally friendly and having better mileage performance in city driving over conventional vehicles. Despite the merits of the EVs, there are also a few disadvantages consisting of the integration of the EVs into the existing infrastructure and their expensiveness by means of initial investment cost. In addition to those, machine learning (ML)-based techniques are usually employed in the EVs for battery management systems, drive performance, and passenger safety. This paper aims to implement an EV monthly charging demand prediction by using a novel technique based on an ensemble of Pearson correlation (PC) and analysis of variance (ANOVA) along with statistical and ML-based algorithms including seasonal auto-regressive integrated moving average with exogenous variables (SARIMAX), convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) decision trees, gated recurrent unit (GRU) networks, long short-term memory (LSTM) networks, bidirectional LSTM (Bi-LSTM) and GRU (Bi-GRU) networks for the Eastern Mediterranean Region of T & uuml;rkiye. The performance and error metrics, including determination coefficient (R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} ), mean absolute percentage error (MAPE), mean absolute error (MAE), and mean absolute scaled error (MASE), are evaluated in a benchmarking manner. According to the obtained results, in Scenario 1, a hybrid of PC and XGBoost decision trees model achieved an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} of 96.21%, MAPE of 5.52%, MAE of 6.5, and MASE of 0.195 with a training time of 2.08 s and a testing time of 0.016 s. In Scenario 2, a combination of ANOVA and XGBoost decision trees model demonstrated an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} of 96.83%, a MAPE of 5.29%, a MAE of 6.0, and a MASE of 0.180 with a training time of 1.62 s and a testing time of 0.012 s. These findings highlight the superior accuracy and computational efficiency of the XGBoost models for both scenarios compared to others and reveal XGBoost's suitability for EV charging demand prediction.
