WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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  • Article
    Comprehensive Analysis and Machine Learning-Based Solutions for Drift Behavior in Ambient Atomic Force Microscope Conditions
    (Pergamon-Elsevier Science Ltd, 2025) Deveci, D. Gemici; Barandir, T. Karakoyun; Unverdi, O.; Celebi, C.; Temur, L. O.; Atilla, D. C.
    This study outlines the effectiveness of combining numerical methods, Computer Vision (CV) and Machine Learning (ML) approaches to analyze and predict drift behavior in high-resolution Atomic Force Microscope (AFM) scanning procedures. Using Long Short-Term Memory (LSTM) models for time series analysis and the Light Gradient Boosting Machine (LightGBM) algorithm for predictive modeling, significant progress was achieved in understanding the dynamic and variable nature of drift and mitigating its impact on scanning. The models demonstrated a robust predictive capability, achieving approximately 94% accuracy in drift predictions. The study emphasizes the nonstationary characteristics of drift and demonstrates how the selection of features directly related to the target variable enhances the efficiency of the model and enables adaptive real-time correction. These findings confirm the predictive strength of the models and highlight the potential for integrating ML predictions with real-time feedback mechanisms to improve the resolution and stability of AFM imaging in both scientific and industrial applications.
  • Editorial
    Comments on “Relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems”
    (Ieee-inst Electrical Electronics Engineers inc, 2025) Sahan, Gokhan; Trenn, Stephan
    This study addresses the deficiencies in the assumptions of the results in (Chen and Yang, 2017) due to the lack of uniformity. We first show the missing hypothesis by presenting a counterexample. Then, we prove why they are wrong in that form and show the errors in the proof of the main result of (Chen and Yang, 2017). Next, we compare the assumptions and related results of (Chen and Yang, 2017) with similar works in the literature. Lastly, we give suggestions to complement the shortcomings of the hypotheses and thus correct them.