A framework for generalizing critical heat flux detection models using unsupervised image-to-image translation
A framework for generalizing critical heat flux detection models using unsupervised image-to-image translation
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DOI：
10.1016/j.eswa.2023.120265
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发表时间：
2023-05-11
影响因子：
8.5
通讯作者：
Sun, Ying
中科院分区：
文献类型：
--
作者：
Al-Hindawi, Firas;Soori, Tejaswi;Sun, Ying
临界热通量 (CHF) 的检测在热沸腾应用中至关重要，因为如果不这样做可能会导致温度快速上升，从而导致设备故障。许多机器学习模型都可以检测 CHF，但在对不同领域的数据进行测试时，它们的性能会显着降低。为了处理来自新领域的数据集，需要从头开始训练模型。此外，数据集需要由领域专家进行注释。为了解决这个问题，我们提出了一个新的框架，以无监督的方式支持经过训练的 CHF 检测模型的通用性和适应性。此方法使用无监督图像到图像 (UI2I) 转换模型来转换目标数据集中的图像，使其看起来像是从先前训练模型的同一域中获得的。与处理域转移的其他框架不同，我们的框架不需要对训练后的分类模型进行重新训练或微调，也不需要在分类模型或 UI2I 模型的训练过程中合成数据集。该框架在来自不同领域的三个沸腾数据集上进行了测试，我们表明在一个数据集上训练的 CHF 检测模型能够以高精度泛化到其他两个以前未见过的数据集。总体而言，该框架使 CHF 检测模型能够适应不同领域生成的数据，而无需额外的注释工作或模型的重新训练。
The detection of critical heat flux (CHF) is crucial in heat boiling applications as failure to do so can cause rapid temperature ramp leading to device failures. Many machine learning models exist to detect CHF, but their performance reduces significantly when tested on data from different domains. To deal with datasets from new domains a model needs to be trained from scratch. Moreover, the dataset needs to be annotated by a domain expert. To address this issue, we propose a new framework to support the generalizability and adaptability of trained CHF detection models in an unsupervised manner. This approach uses an unsupervised Image-to-Image (UI2I) translation model to transform images in the target dataset to look like they were obtained from the same domain the model previously trained on. Unlike other frameworks dealing with domain shift, our framework does not require retraining or fine-tuning of the trained classification model nor does it require synthesized datasets in the training process of either the classification model or the UI2I model. The framework was tested on three boiling datasets from different domains, and we show that the CHF detection model trained on one dataset was able to generalize to the other two previously unseen datasets with high accuracy. Overall, the framework enables CHF detection models to adapt to data generated from different domains without requiring additional annotation effort or retraining of the model.