论文标题
dannte：域移动下涡轮机传感器虚拟化的案例研究
DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift
论文作者
论文摘要
我们提出了一种对抗性学习方法来应对域适应性（DA）时间序列回归任务（Dannte）。该回归旨在构建安装在燃气轮机上的传感器的虚拟副本，以代替物理传感器，在某些情况下可能会缺少。我们的DA方法是搜索特征的域不变表示。学习者可以访问标有标记的源数据集和未标记的目标数据集（无监督的DA），并在这两者上都受过培训，从而利用了任务回归器和域分类器神经网络之间的Minmax游戏。两种模型共享相同的特征表示，由功能提取器学到。这项工作基于Ganin等人发布的结果。 ARXIV：1505.07818;确实，我们提出了适合时间序列应用程序的扩展名。与仅在源域上训练的基线模型相比，我们报告了回归性能的显着改善。
We propose an adversarial learning method to tackle a Domain Adaptation (DA) time series regression task (DANNTe). The regression aims at building a virtual copy of a sensor installed on a gas turbine, to be used in place of the physical sensor which can be missing in certain situations. Our DA approach is to search for a domain-invariant representation of the features. The learner has access to both a labelled source dataset and an unlabeled target dataset (unsupervised DA) and is trained on both, exploiting the minmax game between a task regressor and a domain classifier Neural Networks. Both models share the same feature representation, learnt by a feature extractor. This work is based on the results published by Ganin et al. arXiv:1505.07818; indeed, we present an extension suitable to time series applications. We report a significant improvement in regression performance, compared to the baseline model trained on the source domain only.
