Buranasiri, Prathan

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Buranasiri, Prathan
Alternative Name
Buranasiri, P.
Main Affiliation
Email
prathan.bu@kmitl.ac.th

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    Item type:Publication,
    Optimizing indoor digital holography for photovoltaic surface analysis: A novel approach for solar PV testing conditions
    (2025-01-01)
    Bako, Abdullahi
    ;
    Voochaiphum, Bunyarit
    ;
    Suchat, Suebtarkul
    ;
    Buranasiri, Prathan
    The sun is a clean, inexhaustible, free energy source, and it is environmentally safe. The rapid expansion of the solar photovoltaic (PV) industry demands innovative quality control techniques for defect detection and efficiency optimization of solar cells. Traditional methods such as electroluminescence, photoluminescence, and infrared thermography have limitations in detection resolution, real-time monitoring, and surface characterization. In this research, a novel approach digital holography-based non-contact method for detecting and analyzing defects in solar cells, such as micro-structural defects, surface roughness, dust, scratch, and cracks, was investigated. Laser module was used as our light source to optimize indoor test conditions. An integrated recognition system was developed to automate and improve accuracy using real hologram images. Reconstruction images and Fourier transforms were used to extract the phase and amplitude across the solar PV cell. This preserved the module's integrity and providing detailed defect information from the solar surface.
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    Item type:Publication,
    Digital holography with deep learning for algae identification and classification
    (2024-01-01)
    Ruttanasirawit, Chinnaphat
    ;
    Plaipichit, Suwan
    ;
    Thongsuwan, Setthanun
    ;
    Thonglim, Pachara
    ;
    Phunpruch, Saranya
    Recently, the characterization of marine objects, populations and biophysical interactions have become crucial within the research community. In this study, we leverage digital holographic imaging systems and deep learning networks to classify three distinct types of micro-algae: Chlamydomonas, Scenedesmus armatus, and Scenedesmus_sp-L. We employed reconstructed digital holographic images and deep learning to identify the results from both approaches. The integration of holographic imaging holds promises in replacing expensive characterization systems like AFM, x-ray diffraction, and Raman spectroscopy, offering a more costeffective solution. In our system, we utilize in-line microscopic digital holographic imaging to record and reconstruct images of the algae specimens. An essential advantage of holographic techniques is that they do not require intact samples of the specimens for effective object identification. To further enhance the process, we combined deep learning algorithms with holographic imaging, capitalizing on the advanced computers. This combination enables highly effective characterizing and classification of different types of algae. These innovative approaches pave the way for exciting advancement in marine research and monitoring.