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A Generalized Framework for Critical Heat Flux Detection Using Unsupervised Image-to-Image Translation

2022-12-18 15:26:08
Firas Al-Hindawi, Tejaswi Soorib, Han Hu, Md Siddiquee, Hyunsoo Yoon, Teresa Wu, Ying Sun

Abstract

This work proposes a framework developed to generalize Critical Heat Flux (CHF) detection classification models using an Unsupervised Image-to-Image (UI2I) translation model. The framework enables a typical classification model that was trained and tested on boiling images from domain A to predict boiling images coming from domain B that was never seen by the classification model. This is done by using the UI2I model to transform the domain B images to look like domain A images that the classification model is familiar with. Although CNN was used as the classification model and Fixed-Point GAN (FP-GAN) was used as the UI2I model, the framework is model agnostic. Meaning, that the framework can generalize any image classification model type, making it applicable to a variety of similar applications and not limited to the boiling crisis detection problem. It also means that the more the UI2I models advance, the better the performance of the framework.

Abstract (translated)

URL

https://arxiv.org/abs/2212.09107

PDF

https://arxiv.org/pdf/2212.09107.pdf


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