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Kuwahara, Yasutaka,Magatani, Yasuhiro,Yamashita, Hiromi Korean Society of Photoscience 2015 Rapid communication in photoscience Vol.4 No.1
Zeolite is an ideal host material for encapsulating nano-size metal catalyst species because of its defined microporous structure, prominent adsorption/condensation properties, high surface area, chemical/thermal stability, and transparency to light. In this study, $TiO_2$ photocatalyst was incorporated in highly hydrophobic Y zeolite and its photocatalytic activity was examined in the photocatalytic oxidation of olefins under UV-light irradiation using molecular oxygen as an oxygen source. $TiO_2$ nanoparticles incorporated in hydrophobic Y zeolite exhibited a markedly enhanced photocatalytic activity compared with bare $TiO_2$ owing to its excellent affinity toward organic moieties, which facilitates the mass transfer of organic substrates and allows them to efficiently access to the neighboring active $TiO_2$ surface.
REDUCING SUBSPACES FOR A CLASS OF TOEPLITZ OPERATORS ON WEIGHTED HARDY SPACES OVER BIDISK
Kuwahara, Shuhei Korean Mathematical Society 2017 대한수학회보 Vol.54 No.4
We consider weighted Hardy spaces on bidisk ${\mathbb{D}}^2$ which generalize the weighted Bergman spaces $A^2_{\alpha}({\mathbb{D}}^2)$. Let z, w be coordinate functions and $T_{{\bar{z}}^N}_w$ Toeplitz operator with symbol $_{{\bar{z}}^N}_w$. In this paper, we study the reducing subspaces of $T_{{\bar{z}}^N}_w$ on the weighted Hardy spaces.
Left Ventricular Image Processing and Displays of Cardiac Function
Kuwahara, Michiyoshi The Korea Society of Medical and Biological Engine 1985 의공학회지 Vol.6 No.1
Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It is know that conventional estimation techniques, such as least square estimates (LSE) or Gauasian maximum likelihood estimates (MLE-G) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.