Implementing Ensemble Learning to Predict Temperature Variables of Power Electronics Devices-Reference-Cited by-同舟云学术

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Implementing Ensemble Learning to Predict Temperature Variables of Power Electronics Devices

Published:2022-08-10 Issue: Volume: Page:
ISSN:
Container-title:2022 23rd International Conference on Electronic Packaging Technology (ICEPT)
language:
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Author:

Dai Weijing¹,Tang Qi²,Wang Zhiqi²,Cong Jie³,Xue Ke³,Yang Shangbing⁴,Lu Dong¹

Affiliation:

1. Guangzhou HKUST Fok Ying Tung Research Institute,Center for Engineering Materials and Reliability,Guangzhou,China

2. Monolithic Power System,Chengdu,China

3. The Hong Kong University of Science and Technology (Guangzhou),Guangzhou,China

4. Fenghua Research Institute (Guangzhou) Co., Ltd,Guangzhou,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9872527/9872536/09873202.pdf?arnumber=9873202

Reference14 articles.

1. Gallium Nitride Power Electronic Devices Modeling Using Machine Learning

2. Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics

3. A survey on ensemble learning

4. Generating highly accurate prediction hypotheses through collaborative ensemble learning

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