Suksangpanya, Nobphadon

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Suksangpanya, Nobphadon
Alternative Name
Suksangpanya, N.
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Email
nobphadon.su@kmitl.ac.th

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    Item type:Publication,
    Automatic para-rubber trees classification in Thailand from LANDSAT-8 imagery using DCED neural network
    (2021-06-02)
    Supunyachotsakul, C.
    ;
    Suksangpanya, N.
    Image classification has been one of the main processes performed on satellite imagery where there have been many studies attempt to developing an automatic approach since manual classification is known to be a time-consuming process. Automatic classifying specific types of vegetation in satellite imagery has been a challenging field of study. In this work, a neural network method, specifically deep convolutional encoder-decoder or known as DCED, is applied to the automatic classifying coverage area of Para-rubber trees in Thailand from LANDSAT-8 satellite imagery. The main procedure in this work comprises ground truth preparation, suitable model development with training and validation, and, finally, classification. As a result, the model developed in this work gives average accuracy of 86.9% in training datasets and 70.9% in validation datasets.
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    Crack twisting and toughening strategies in Bouligand architectures
    (2018-10-01)
    Suksangpanya, Nobphadon
    ;
    Yaraghi, Nicholas A.
    ;
    Pipes, R. Byron
    ;
    Kisailus, David
    ;
    Zavattieri, Pablo
    The Bouligand structure in some arthropods is a hierarchical composite comprised of a helicoidal arrangement of strong fibers in a weak matrix. In this study, we focus on the Bouligand structure present in the dactyl club of the smashing mantis shrimp due to its exceptional capability to withstand repetitive high-energy impact without catastrophic failure. We carry out a combined computational and experimental approach to investigate the high damage resistance of the Bouligand structure through a biomimetic composite material. This is studied by performing specific fracture experiments on the helicoidal composites specimens, where it was found that crack twisting, driven by the fiber architecture, is the main fracture mechanisms. This crack twisting mechanism competes with other alternative mechanisms such as crack branching and delamination, delaying catastrophic failure. The main mechanism of crack twisting is studied through specifically designed specimens in which the crack propagation path is controlled. Further quantification of the toughening mechanisms and crack growth rate is analyzed with analytical and finite element models. The biomimetic helicoidal composites are shown to have improved fracture resistance as the crack twists mainly driven by the increase in crack surface area and fracture mode mixity. Our analysis allowed us to study the effect of crack front shape, stress distribution and energy dissipation mechanisms.
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    Semi-Automatic Classification of Rotating Crops in Northern Thailand by Using Temporal LANDSAT Images
    (2022-01-01)
    Suksangpanya, N.
    ;
    Supunyachotsakul, C.
    This work focuses on rotating crops in the forest conservation areas in the northern region of Thailand which always cause false detection for forest encroachment and deforestation. Therefore, this work establishes a database of rotating crop areas in the northern region of Thailand and additionally develops a semi-automatic classification approach to help facilitate the classification process. LANDSAT images ranging from 1987 to 2018 are used as the input data for classifying the rotating crop areas. The semi-automatic classification approach is comprised of the automatic supervised classification and the manual classification by visual interpretation, respectively. The automatic and manual classification procedures are explained, and the results are verified by using ground truth locations distributed over the study region which gives 81.72% accuracy.