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Author: search.filters.author.Phumpho, SuwilaiStart date: 2020

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    Innovative educational technology for visually impaired learners using a 3D-Printed foot reflexology robot
    (2026-06-01)
    Nabudda, Kriengkrai
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    Nabudda, Kanokpit
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    Kitprathaung, Nustha
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    Phumpho, Suwilai
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    Poungthong, Pongthep
    This study presents the design, development, and validation of a foot reflexology teaching robot that integrates multidisciplinary design, rapid prototyping, and user-centred assistive educational strategies to support tactile learning for visually impaired individuals. A digital foot model, derived from anthropometric data of an average Thai female, was refined to achieve a balance between anatomical accuracy and manufacturability. The structural design featured modular internal compartments, precision openings, and detachable covers to support maintenance without compromising anatomical realism. Fabrication using PLA-based fused deposition modelling (FDM) produced a lightweight, durable, and cost-effective prototype incorporating interactive tactile sensing and modular electrical components. A Raspberry Pi platform programmed in Python enabled audio-based instructional feedback triggered by tactile interaction, creating a multisensory educational device for experiential reflexology training. Structured evaluation sessions with 29 visually impaired learners confirmed its effectiveness in facilitating hands-on engagement, multisensory learning, and usability, with high satisfaction across safety and practicality. Overall, this work establishes a replicable framework for developing accessible and inclusive assistive educational technologies.• Integrated multidisciplinary design and user-centred development approach.• Applied rapid prototyping to translate digital anatomical models into functional physical components.• Conducted user-based evaluation to assess educational effectiveness and accessibility.
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    Fuzzy Analytical Hierarchy Process-Based Multi-Criteria Decision Framework for Risk-Informed Maintenance Prioritization of Distribution Transformers
    (2026-01-01)
    Rodkumnerd, Pannathon
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    Pothinun, Thunpisit
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    Phumpho, Suwilai
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    Watson, Neville
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    Siritaratiwat, Apirat
    Effective asset management is crucial for improving the reliability, resilience, and cost efficiency of distribution networks throughout the asset life cycle. Distribution transformers are among the most critical components, as their failures can cause extensive service interruptions and substantial economic impacts. Therefore, robust and transparent maintenance prioritization strategies are essential, particularly for utilities managing several transformers. Traditional time-based maintenance, while simple to implement, often results in inefficient resource allocation. Condition-based maintenance provides a more effective alternative; however, its performance depends strongly on the reliability of indicator selection and weighting. This study proposes a systematic weighting framework for distribution transformer maintenance prioritization using a multi-criteria decision-making (MCDM) approach. Each transformer is evaluated across two dimensions, including health condition and operational impact, based on indicators identified from the literature and expert judgment. To address uncertainty and judgmental inconsistency, particularly when the consistency ratio (CR) exceeds the conventional threshold of 0.10, the Fuzzy Analytic Hierarchy Process (FAHP) is employed. Seven condition parameters characterize transformer health, while impact is quantified using five indicators reflecting failure consequences. The proposed framework offers a transparent, repeatable, and defensible decision-support tool, enabling utilities to prioritize maintenance actions, optimize resource allocation, and mitigate operational risks in distribution networks.
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    Comparative Study of Machine Learning Models for Soil Fertilizer Classification in Precision Agriculture
    (2025-01-01)
    Archevapanich, Tuanjai
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    Sirikham, Thanapat
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    Chaowalittawin, Vasutorn
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    Krungseanmuang, Woranidtha
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    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.