京都大学 · 工学
Uchiyama教授の研究室では、画像認識と材料科学の分野で、特に3次元画像処理とナノ材料の電気化学的特性に注力した研究を行っています。3次元CNNの意思決定プロセスを可視化するための新規なオクルージョン感度解析手法の開発や、燃料電池触媒の反応機構をXASや電化学測定で解明する研究が進められています。特に、色空間のクラスタリングによる画像セグメンテーションや、イオンタイマーの特定吸着効果が触媒活性に与える影響の定量的評価が特徴です。
Figures are computed from collected data and may differ slightly.
Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sum-of-squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color c
The influence of specific adsorption of the sulfo group in the perfluorosulfonic acid ionomer, Nafion, on the oxygen reduction reaction (ORR) of a carbon-supported Pt/C catalyst using a thin-film rotating disk electrode was investigated. The relationship between the catalyst activity and coating of the Pt/C catalyst with Nafion was quantitatively evaluated through electrochemical measurements, operando X-ray absorption spectroscopy (XAS), and CO stripping voltammetry. Activity of the Pt/C cataly
This paper proposes a method for visually explaining the decision-making process of 3D convolutional neural networks (CNN) with a temporal extension of occlusion sensitivity analysis. The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D temporalspatial data space and then measure the change degree in the output score. The occluded volume data that produces a larger change degree is regarded as a more critical element for classification. However, while the occlusi
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