Yazar "Cho, Yongwon" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    A hybrid CNN-ViT based framework for automatic traffic actions detection in smart cities
    (Public Library Science, 2026) Karaduman, Mucahit; Han, Neunggyu; Karaduman, Gulsah; Yildirim, Muhammed; Cho, Yongwon; Nam, Yunyoung
    It is crucial to automatically detect traffic accidents and hazardous situations in a timely and accurate manner. In this way, both individual security will be ensured and significant contributions will be made to economic efficiency and sustainable urban life. Millions of people die in traffic accidents every year. This situation also places an additional burden on health systems and will lead to many undesirable consequences. Early detection of events such as traffic density, accidents, and road closures accelerates emergency response processes, regulates traffic flow, and prevents secondary accidents. Therefore, artificial intelligence-supported automatic systems stand out as a key component of smart cities. This study aims to detect traffic accidents and traffic situations automatically. For this purpose, feature extraction was performed with five Convolutional Neural Network (CNN) and five Vision Transformer (ViT) based models. Then, the features obtained from these models were evaluated in different classifiers. The ViT model and the CNN model, which yielded the most successful results, served as the base for the proposed model. The features obtained from the best ViT model and CNN model were combined to bring together different features of the same image. Then, these features were classified into eight different categories using various classifiers. It was observed that the proposed model produced more successful results than the ten models whose preliminary results were obtained in the study. The accuracy value of the proposed model was 96.88%. This value is promising for future studies and plays a strategic role in terms of sustainability and enhancing the quality of life in smart cities.
  • Küçük Resim Yok
    Öğe
    Optimal control strategy to charging and discharging techniques for electric vehicle battery pack optimization based on genetic algorithm and machine learning
    (Elsevier, 2026) Yalcin, Sercan; Yildirim, Muhammed; Khan, Muhammad Attique; Li, Yang; Alabdullah, Bayan; Cho, Yongwon; Nam, Yunyoung
    This paper investigates optimal control strategies for charging and discharging battery packs, aiming to maximize lifespan and performance. The focus is on developing efficient techniques based on Genetic Algorithms (GAs) and machine learning (ML) to optimize battery pack operation. This study uses a proposed GA as a global search engine for optimal control parameters, while integrating Support Vector Machine (SVM) to improve the prediction accuracy of the battery state affected by these parameters. Furthermore, the Deep Reinforcement Learning (DRL) agent is trained in a physics-based simulation environment, such as PyBaMM, directly learning physics-informed, dynamic charging current profiles, unlike traditional DRL studies.The research explores various control parameters, including charging/discharging rates, current profiles, and temperature management, to minimize degradation and maximize energy efficiency. This approach effectively searches the vast solution space to identify optimal control strategies that balance immediate energy demands with long-term battery health. Simulation results demonstrate the effectiveness of the proposed GA-based optimization framework in achieving significant improvements in battery pack lifespan, energy efficiency, and overall performance compared to conventional control methods.