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Träfflista för sökning "WFRF:(Huang Chiung Yi) "

Sökning: WFRF:(Huang Chiung Yi)

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  • Horng, Ray-Hua, et al. (författare)
  • Structure Effect on the Response of ZnGa2O4 Gas Sensor for Nitric Oxide Applications
  • 2022
  • Ingår i: Nanomaterials. - : MDPI. - 2079-4991. ; 12:21
  • Tidskriftsartikel (refereegranskat)abstract
    • We fabricated a gas sensor with a wide-bandgap ZnGa2O4 (ZGO) epilayer grown on a sapphire substrate by metalorganic chemical vapor deposition. The ZGO presented (111), (222) and (333) phases demonstrated by an X-ray diffraction system. The related material characteristics were also measured by scanning electron microscopy, transmission electron microscopy and X-ray photoelectron spectroscopy. This ZGO gas sensor was used to detect nitric oxide (NO) in the parts-per-billion range. In this study, the structure effect on the response of the NO gas sensor was studied by altering the sensor dimensions. Two approaches were adopted to prove the dimension effect on the sensing mechanism. In the first approach, the sensing area of the sensors was kept constant while both channel length (L) and width (W) were varied with designed dimensions (L x W) of 60 x 200, 80 x 150, and 120 x100 mu m(2). In the second, the dimensions of the sensing area were altered (60, 40, and 20 mu m) with W kept constant. The performance of the sensors was studied with varying gas concentrations in the range of 500 ppb similar to 10 ppm. The sensor with dimensions of 20 x 200 mu m(2) exhibited a high response of 11.647 in 10 ppm, and 1.05 in 10 ppb for NO gas. The sensor with a longer width and shorter channel length exhibited the best response. The sensing mechanism was provided to explain the above phenomena. Furthermore, the reaction between NO and the sensor surface was simulated by O exposure of the ZGO surface in air and calculated by first principles.
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  • Hsiung, Shih-Yi, et al. (författare)
  • Machine learning-based monosaccharide profiling for tissue-specific classification of Wolfiporia extensa samples
  • 2023
  • Ingår i: Carbohydrate Polymers. - : Elsevier. - 0144-8617 .- 1879-1344. ; 322
  • Tidskriftsartikel (refereegranskat)abstract
    • Machine learning (ML) has been used for many clinical decision-making processes and diagnostic procedures in bioinformatics applications. We examined eight algorithms, including linear discriminant analysis (LDA), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), Naïve Bayes classifier (NB), and artificial neural network (ANN) models, to evaluate their classification and prediction capabilities for four tissue types in Wolfiporia extensa using their monosaccharide composition profiles. All 8 ML-based models were assessed as exemplary models with AUC exceeding 0.8. Five models, namely LDA, KNN, RF, GBM, and ANN, performed excellently in the four-tissue-type classification (AUC > 0.9). Additionally, all eight models were evaluated as good predictive models with AUC value >0.8 in the three-tissue-type classification. Notably, all 8 ML-based methods outperformed the single linear discriminant analysis (LDA) plotting method. For large sample sizes, the ML-based methods perform better than traditional regression techniques and could potentially increase the accuracy in identifying tissue samples of W. extensa.
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  • Resultat 1-3 av 3

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