Yuangyai, Chumpol

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Preferred name
Yuangyai, Chumpol
Alternative Name
Yuangyai, Čhumpol
Yuangyai, C.
Main Affiliation
Email
chumpol.yu@kmitl.ac.th

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    Item type:Publication,
    Multiple Performance Optimization for Microstrip Patch Antenna Improvement
    (2023-05-01)
    Chen, Ja Hao
    ;
    Cheng, Chen Yang
    ;
    Chien, Chuan Min
    ;
    Yuangyai, Chumpol
    ;
    Chen, Ting Hua
    As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.
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    Item type:Publication,
    Innovative Anomaly Detection in PCB Hot-Air Ovens Using Adaptive Temporal Feature Mapping
    (2025-10-01)
    Cheng, Chen Yang
    ;
    Chien, Chuan Min
    ;
    Chen, Tzu Li
    ;
    Yuangyai, Chumpol
    ;
    Kong, Pei Ling
    As automated equipment in PCB manufacturing becomes increasingly reliant on precision hot-air ovens, ensuring operational stability and reducing downtime have become critical challenges. Existing anomaly detection methods, such as Support Vector Machines (SVMs), Deep Neural Networks (DNNs), and Long Short-Term Memory (LSTM) Networks, struggle with high-dimensional dynamic data, leading to inefficiencies and overfitting. To address these issues, this study proposes an innovative anomaly detection system specifically designed for fault diagnosis in PCB hot-air ovens. The motivation is to improve accuracy and efficiency while adapting to dynamic changes in the manufacturing environment. The core innovation lies in the introduction of the Adaptive Temporal Feature Map (ATFM), which dynamically extracts and adjusts key temporal features in real time. By combining ATFM with Bi-Directional Dimensionality Reduction (BDDR) and eXtreme Gradient Boosting (XGBoost), the system effectively handles high-dimensional data and adapts its parameters based on evolving data patterns, significantly enhancing fault detection accuracy and efficiency. The experimental results show a fault prediction accuracy of 99.33%, greatly reducing machine downtime and product defects compared to traditional methods.