Multimodal Data-Driven Prediction of PEMFC Performance and Process Conditions Using Deep Learning
The proton-exchange membrane fuel cell (PEMFC) is one of the important technologies advancing sustainable energy. However, predicting its performance and optimizing processes is challenging due to the complexity of integrating various types of data with interdependent variables. This study introduce...
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| Main Authors: | Seoyoon Shin, Jiwon Kim, Seokhee Lee, Tae Ho Shin, Ga-Ae Ryu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10704654/ |
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