WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14517/18

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Browsing WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection by Author "Alp, Sina"

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    Article
    An investigation into vibration analysis for detecting faults in vehicle steering outer tie-rod
    (Imeko - int Measurement Confederation, 2024) Alaraji, Yousif; Alp, Sina
    This study presents a novel fault detection method in car gear steering systems, employing MSC Adams and MATLAB simulations to analyze angular acceleration from the outer tie rod. The approach closely mimics real accelerometer data to differentiate between normal and faulty conditions, including wear and obstacle navigation. Emphasis is on noise robustness, utilizing advanced noise injection and denoising techniques. The efficacy of wavelet scattering, discrete wavelet transform (DWT) methods, and classifiers like Support Vector Machines (SVM) and Neural Networks (NN) is extensively evaluated. Among fifteen fault detection methods, the combination of wavelet scattering with Long Short-Term Memory (LSTM) Neural Networks, optimized with Adam tuning, is notably stable across four scenarios. The research highlights the importance of precise feature selection, employing techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Recursive Feature Elimination (RFE). This research significantly advances the reliability of autonomous driving systems and provides essential insights into fault detection in gear steering systems.
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    Book Part
    Micromobility and Artificial Intelligence
    (Springer International Publishing AG, 2025) Alp, Sina
    The intersection of micromobility and artificial intelligence (AI) is transforming urban transportation, offering sustainable and efficient alternatives to traditional vehicles. Micromobility, encompassing devices like electric scooters and bicycles, provides a flexible means of navigating urban environments, contributing to reduced traffic congestion and lower carbon emissions. AI enhances these solutions by introducing advanced navigation systems, automated safety measures, and adaptive technologies that personalize the user experience. AI-driven navigation systems leverage real-time data and machine learning algorithms to optimize routes, avoid traffic, and ensure safety. Safety is further improved through AI-powered obstacle detection and collision avoidance systems, which use sensors and real-time data processing to prevent accidents. The integration of AI also supports the maintenance of micromobility devices through predictive diagnostics, extending their lifespan and reliability. Moreover, AI-driven micromobility contributes to the development of smart cities by providing valuable data for urban planners, optimizing infrastructure, and improving traffic management. These advancements not only enhance the quality of life for urban residents but also promote social and economic benefits, such as reducing social inequalities and supporting economic growth. However, challenges such as data reliability, ethical concerns, and regulatory hurdles must be addressed to fully realize the potential of AI-enhanced micromobility in creating more connected and accessible urban environments.