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Author: search.filters.author.Pavitpok, Siwagorn

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    Skip-Feedback Neural Network With Unsupervised Feature Learning for Railway Crack Distance Estimation
    (2026-01-01)
    Pavitpok, Siwagorn
    ;
    Phasukkit, Pattarapong
    Accurate estimation of crack distance is critical for railway safety and structural health monitoring. This paper proposes a Skip-Feedback Neural Network (SFNN) for single-sensor acoustic emission (AE)–based crack distance estimation in railway rails. Unlike conventional multi-sensor or high-cost inspection systems, the proposed framework employs a single AE sensor together with entropy-based signal representations to capture distance-dependent crack characteristics under noisy operating conditions. The SFNN integrates learnable skip and feedback pathways to enhance feature reuse, stabilize gradient propagation, and improve robustness against signal attenuation. Experimental evaluation was performed using controlled pencil-lead break simulations at multiple crack-to-sensor distances, as well as field measurements at real crack locations validated by phased-array ultrasonic testing. Results demonstrate that the proposed SFNN consistently outperforms baseline neural-network models across different distance ranges. The proposed framework offers a cost-efficient, noise-resilient, and scalable solution for AE-based railway crack monitoring and future structural health-monitoring applications.
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    AI-Enhanced Driver Fatigue Detection Through Multi-Parameter Analysis: Integration of Radar-Based Heart Rate Monitoring and Deep Learning Techniques
    (2025-01-01)
    Pairoch, Sathit
    ;
    Pavitpok, Siwagorn
    ;
    Wanluk, Nutthanan
    ;
    Phasukkit, Pattarapong
    This research presents an innovative transportation safety system integrating millimeter-wave radar sensing with artificial intelligence. Our approach employs the MR60BHA1 radar sensor mounted behind the driver's seat to non-intrusively monitor vital signs, combined with deep learning algorithms that analyze correlations between heart rate variability, respiratory patterns, body movements, and environmental factors. Trained on Thailand's first comprehensive driver fatigue database, our custom neural network architecture achieved 97.5% detection accuracy, significantly outperforming traditional single parameter monitoring approaches. The research contributes three key innovations: (1) a novel deep learning framework optimized for Thai driving conditions that correlates multiple radar-derived physiological parameters; (2) an adaptive feature extraction algorithm that identifies and weights the most significant fatigue indicators based on individual characteristics and local contexts; and (3) a comprehensive multiparameter database incorporating synchronized measurements calibrated for various Thai driving environments. This culturally adaptive monitoring technology establishes a new paradigm for intelligent transportation systems particularly suited for tropical regions.
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    Vertical speed prediction for the efficient landing of aircraft using GRU
    (2020-11-04)
    Pavitpok, Siwagorn
    ;
    Phasukkit, Pattarapong
    ;
    Pradabpet, Chusit
    Air traffic management is currently the process of managing air traffic demand. For support the capacity to handle the amount of air traffic. Whether it is in the airspace sector or the airport area. There are many factors such as the ability of the air traffic providers, physical characteristics of airspace and airport terrain. The main goal of air traffic management is to manage air traffic control. Streamlined and demand-capacity balancing are safe and effective. Whether it is air traffic service providers or passengers. In this research, the experiment was carried out on specific aircraft model A-320. Traveling from Bangkok Suvarnabhumi Airport to Phuket International Airport. In which the landing airport has island terrain. Therefore, requires high security due to physical characteristics in the airspace which has many factors such as wind and wind direction and to provide safety and effective landing. Therefore, take the factors affecting the landing to predict the vertical speed. The epoch =2000, learning rate =0.01, accuracy more than 90% for training set and 90% for test set.