Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Optimized Cooperative Routing for Autonomous Vehicles(Institute of Electrical and Electronics Engineers Inc., 2025) Saydam, B.; Ayav, T.Current traffic control systems - comprising traffic lights, signs, and right-of-way rules - are often inadequate, leading to accidents, excessive fuel consumption, and unnecessary delays. Three key scenarios contribute to these inefficiencies. First, drivers may run red lights due to a lack of traffic signal timing information, leading to indecision when encountering a yellow light, a major cause of accidents. Second, abrupt speed changes in response to traffic signals force drivers to brake suddenly, increasing fuel consumption and travel time. For instance, a driver may accelerate at a green light only to encounter a red light shortly after, resulting in inefficient fuel use. Lastly, vehicles often remain stopped at red lights despite no cross-traffic, leading to wasted fuel and time.This study simulates these scenarios using the Eclipse SUMO tool, with results aligning with expected inefficiencies. The problem is mathematically modeled using Pyomo, and a centralized optimization approach is applied to enhance traffic synchronization and efficiency. By dynamically calculating vehicle velocities based on real-time traffic data, the study proposes an optimized, traffic light-free system. The results demonstrate improved fuel efficiency, reduced accidents, and minimized delays, highlighting the potential of centralized optimization in modern traffic management. © 2025 Elsevier B.V., All rights reserved.Article Citation - Scopus: 20Estrus Detection and Dairy Cow Identification With Cascade Deep Learning for Augmented Reality-Ready Livestock Farming(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Arıkan, İ.; Ayav, T.; Seçkin, A.Ç.; Soygazi, F.Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming. © 2023 by the authors.