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
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.
Improving multi-class classification: scaled extensions of harmonic mean-based adaptive k-nearest neighbors
(Springer, 2025) Acikkar, Mustafa; Tokgoz, Selcuk
This paper proposes a novel extension of the harmonic mean-based adaptive k-nearest neighbors (HMAKNN) algorithm, called scaled HMAKNN (SHMAKNN), which builds on HMAKNN's strengths to achieve improved multi-class classification accuracy. HMAKNN uses a modified voting mechanism based on the harmonic mean and adaptive k-value selection to address issues like the sensitivity to k-value selection and the limitations of majority voting. SHMAKNN further improves the decision process by adjusting the components of the harmonic mean, focusing on voting values and the average distances of each class label. Additionally, SHMAKNN applies a re-scaling process to adjust the distances of the nearest neighbors within a specific range, enhancing the consistency of distances at different scales. These improvements help align the elements of the harmonic mean more effectively, leading to a balanced and less biased classification process. The study utilized 26 benchmark datasets, carefully curated to ensure accuracy and consistency, selected from diverse domains to evaluate the proposed method on real-world problems. These datasets were chosen to represent challenges like noise, imbalance, and sparsity, ensuring robustness in handling common data complexities. Additionally, small to medium-sized datasets were used to reduce computational burden and allow for efficient evaluation. The evaluation results show that the proposed SHMAKNN models outperform existing methods in both accuracy and F1-score for datasets with four or more classes. Specifically, SHMAKNN achieved the highest average accuracy and F1-score (86.36% and 86.16%) compared to HMAKNN (86.10% and 85.74%) and traditional k-nearest neighbors (84.87% and 84.69%). The performance improvements were validated using Friedman's test at a significance level of 0.05, confirming their statistical significance of the results. Consequently, the findings indicate that the proposed algorithm exhibits remarkable performance, thereby confirming its reliability and validity in the context of real-world applications, particularly those involving multiple classes.
A novel repair method for the lifespan and performance improvement of a shell-and-tube heat exchanger: A thermo-mechanical approach
(Pergamon-Elsevier Science Ltd, 2025) Delibas, Hulusi; Yilmaz, Ibrahim Halil
Heat exchangers play a critical role in the functioning of many engineering systems. Shell-and-tube heat exchangers (STHEs) are more traditional and widely used devices due to their efficiency, versatility, and ability to handle a range of flow conditions and fluid types. STHEs experience a number of problems over time, including corrosion, mechanical wear, or leaking, and thus need repairs to keep operating. This study has introduced a novel repair approach for extending the lifespan of damaged STHE tubes by fitting new tubes. An original thermo-mechanical model, including the analyses of the STHE, thermal contact resistance between the fitted tubes, and mechanical design of the built structures, is proposed for the problem solution, and all governing equations are simultaneously solved in Engineering Equation Solver (EES). All submodels are validated with analytical or experimental data, and good agreements are obtained. The most significant design parameters and their effects on the thermal and mechanical performances of an STHE are parametrically investigated. Results reveal that increasing the contact surface slope over 10 degrees but lowering the effective surface roughness below 3 mu m provides an advantage for keeping the heat load of the STHE high. Among the interference fits, the locational interference fit is the most advantageous in terms of thermal and mechanical performances relative to other fit conditions. Both increasing operating pressure and tube diameter are two key pillars that can allow for a safety factor > 1.5. Fitting tube materials are parametrically independent and applicable to any STHE tube diameter as the yield strength > 300 MPa. Even if all tubes are press-fitted, the maximum heat load drop in the current repair method corresponds to 4.23 % which is lower than the tolerable value i.e., <10 % of the initially planned heat load.
K-Salp Swarm Anomaly Detection (K-SAD): A novel clustering and threshold-based approach for cybersecurity applications
(Elsevier Advanced Technology, 2025) Kilic, Vahide Nida; Essiz, Esra Sarac
Anomaly detection is a critical task in various domains, particularly in cybersecurity, where ensuring data integrity and security is paramount. In this study, we propose a novel approach to anomaly detection utilizing both the K-medoid and Salp Swarm Algorithms. Our methodology involves clustering the data using K-medoid and determining thresholds with an improved Salp Swarm Algorithm, enabling the identification of outliers within datasets. We conducted experiments on real-world datasets to evaluate the effectiveness of our approach. Significantly, proposed method surpassed alternative methods in performance across 5 of the 10 datasets, thereby showcasing its superior efficacy. For example, It demonstrated superior performance compared to alternative methods, achieving an AUC value of 0.8651 on the Thyroid dataset. Additionally, our approach yielded outcomes falling within the average spectrum across 3 datasets. These observations underscore the effectiveness of our proposed method in factifying anomaly detection methods and factifying cybersecurity protocols.
