Kyoto University · 공학
Tomoki Uchiyama 교수의 연구실은 주로 전자기기 및 에너지 변환 소재의 표면 및 계면 반응 메커니즘을 해석하고자 하며, 특히 이온막과 촉매의 상호작용을 전기화학적·구조적 분석을 통해 규명합니다. 고성능 연료전지 촉매의 활성도 저하 원인을 분석하고, 나프론과 같은 이온막의 특수 흡착 효과가 반응성에 미치는 영향을 정량적으로 평가합니다. 또한, 영상 분석 및 인공지능 기반의 3D 시공간 데이터 해석 기법을 활용해 딥러닝 모델의 의사결정 과정을 시각화하는 연구도 진행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
