Phumpho, Suwilai

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Phumpho, Suwilai
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Email
suwilai.ph@kmitl.ac.th

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    Item type:Publication,
    Chaotic control for the reactor of a continuous microwave biomass carbonization process
    (2015-04-23)
    Payakkawan, Poomyos
    ;
    Jansri, Anurak
    ;
    Phumpho, Suwilai
    ;
    Tong-Aram, Direk
    ;
    Keattipun, Pithuk
    This paper presented a new heating technique using chaotic mode pattern generator to control a uniform- heating distribution of multi-feed microwave cavity in order to heat the reactor of a pilot-scale continuous microwave biomass carbonization process This technique not only suggest the potential of the proposed system which operates more smoothly but also reduce electric power consumptions of the continuous microwave biomass carbonization process.
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    Anti-copy of 2D Barcode Using Multi-encryption Technique
    (2014-01-01)
    Phumpho, Suwilai
    ;
    Payakkawan, Poomyos
    ;
    Jansri, Anurak
    ;
    Tongaram, Direk
    ;
    Promprayoon, Chirasak
    2D barcode can be found largely at product of department store. The general purpose of 2D barcode is to identify product. Process of 2D barcode is run in the assembly production line. Nowadays, the simple 2D barcode can be easily copied. Then, this paper proposes new technique to generate 2D barcode. The technique is multi encryption by chaotic equation that is very difficult to reproduce. In experimental design, 2D barcode can be used only one-time and can be recorded into decoding system to protect repeating. This paper has led to real benefit for Fresh Group company limited in Thailand.
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    Item type:Publication,
    Comparative Study of Machine Learning Models for Soil Fertilizer Classification in Precision Agriculture
    (2025-01-01)
    Archevapanich, Tuanjai
    ;
    Sirikham, Thanapat
    ;
    Chaowalittawin, Vasutorn
    ;
    Krungseanmuang, Woranidtha
    ;
    Sathaporn, Posathip
    This study explores the machine learning techniques compare for fertilizer classification based on soil nutrient dataset aligning with the goals of precision agriculture. Five models include Random Forest, Logistic Regression, SVM, XGBoost and Neural Network(ANN) were tested using precision, accuracy, F1-score, recall and confusion matrices. The highest F1-score is XGBoost model, while the best precision performance delivered by Random Forest model. Results emphasize the significance of model selection in handling imbalanced agricultural data. The approach supports data-driven decision-making for sustainable farming aligned with Thailand's 20-Year Agricultural Strategic Plan.