Search Results - "Kimura, Fumiya"

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  1. 1

    Validation study on the practical accuracy of wood species identification via deep learning from visible microscopic images by Ma, T, Kimura, F, Tsuchikawa, S, Kojima, M, Inagaki, T

    Published in Bioresources (01-08-2024)
    “…This study aimed to validate the accuracy of identifying Japanese hardwood species from microscopic cross-sectional images using convolutional neural networks…”
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    Journal Article
  2. 2

    Tourist Participation in the Preservation of World Heritage – A Study at Bayon Temple in Cambodia by Kimura, Fumiya, Ito, Yutaka, Matsui, Toshiya, Shishido, Hidehiko, Kitahara, Itaru, Kawamura, Youhei, Morishima, Atsuyuki

    Published in Journal of cultural heritage (01-07-2021)
    “…•World Heritage Sites face problems such as an increase in maintenance cost, difficulty to check protection conditions, and destruction.•This paper proposes a…”
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    Journal Article
  3. 3

    Probing the solvation of the α-helix with extended amide III bands in Raman optical activity by Yamamoto, Shigeki, Kimura, Fumiya

    Published in Physical chemistry chemical physics : PCCP (02-02-2022)
    “…Experimental and theoretical Raman optical activity (ROA) study of α-helical peptides and proteins has suggested that the relative intensity of two extended…”
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    Journal Article
  4. 4

    High-pressure and high-temperature phase relations in the systems KAlSiO4-MgAl2O4 and CaAl2O4-MgAl2O4: Stability fields of NAL phases by Kimura, Fumiya, Kojitani, Hiroshi, Akaogi, Masaki

    “…Phase relations in the system KAlSiO4-MgAl2O4 were determined up to 28 GPa and 1500 °C. A hexagonal aluminous (NAL) phase is stable above 16 GPa with a narrow…”
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    Journal Article
  5. 5

    Validation Study on the Practical Accuracy of Wood Species Identification via Deep Learning from Visible Microscopic Images by Te Ma, Fumiya Kimura, Satoru Tsuchikawa, Miho Kojima, Tetsuya Inagaki

    Published in Bioresources (01-05-2024)
    “…This study aimed to validate the accuracy of identifying Japanese hardwood species from microscopic cross-sectional images using convolutional neural networks…”
    Get full text
    Journal Article