T
Tao Yang
Researcher at University of Science and Technology Beijing
Publications - 30
Citations - 440
Tao Yang is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Corrosion & Multiple kernel learning. The author has an hindex of 7, co-authored 29 publications receiving 149 citations.
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Journal ArticleDOI
Piezoelectric Nanogenerator Based on In Situ Growth All-Inorganic CsPbBr3 Perovskite Nanocrystals in PVDF Fibers with Long-Term Stability
Chen Huiying,Linlin Zhou,Zhi Fang,Shuize Wang,Tao Yang,Laipan Zhu,Xinmei Hou,Hailong Wang,Zhong Lin Wang +8 more
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Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning
Zibo Pei,Dawei Zhang,Yuanjie Zhi,Tao Yang,Lulu Jin,Dongmei Fu,Xuequn Cheng,Herman Terryn,Herman Terryn,Johannes M. C. Mol,Xiaogang Li +10 more
TL;DR: In this article, the atmospheric corrosion of carbon steel was monitored by a Fe/Cu type galvanic corrosion sensor for 34 days using a random forest (RF)-based machine learning approach, which demonstrated higher accuracy than artificial neural network (ANN) and support vector regression (SVR) models in predicting instantaneous atmospheric corrosion.
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Prediction and knowledge mining of outdoor atmospheric corrosion rates of low alloy steels based on the random forests approach
TL;DR: Wang et al. as mentioned in this paper developed an approach to forecast the outdoor atmospheric corrosion rate of low alloy steels and do corrosion-knowledge mining by using a Random Forests algorithm as a mining tool.
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The oxidation and thermal stability of two-dimensional transition metal carbides and/or carbonitrides (MXenes) and the improvement based on their surface state
TL;DR: In this article, the authors focus on the current research on the stability of two-dimensional transition metal carbides and/or carbonitrides labeled MXenes including oxidation and thermal stability under various conditions.
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Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model
Yuanjie Zhi,Yuanjie Zhi,Zhihui Jin,Zhihui Jin,Lin Lu,Tao Yang,Deyun Zhou,Zibo Pei,Dequan Wu,Dongmei Fu,Dawei Zhang,Xiaogang Li +11 more
TL;DR: In this paper, a support vector regression (SVR) model was used for atmospheric corrosion prediction based on the corrosion rates of carbon steel and 12 environmental factors from long-term exposure tests.