论文标题
迈向深度工业转移学习：转移案例选择的聚类
Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection
论文作者
论文摘要
工业转移学习可以提高深度学习算法对异质和动态工业用例的适应性，而无需高度的手动努力。适当的转移选择可以大大改善转移结果。在本文中，提出了基于聚类的转移案例选择。为此，基于聚类算法的调查，为此而选择了桦木算法。从离散制造方案中的工业时间序列数据集进行了评估。结果强调了该方法的“适用性是由其结果”的可重复性和对（子）数据集的序列，大小和维度的实际冷漠的，要依次群集。
Industrial transfer learning increases the adaptability of deep learning algorithms towards heterogenous and dynamic industrial use cases without high manual efforts. The appropriate selection of what to transfer can vastly improve a transfer's results. In this paper, a transfer case selection based upon clustering is presented. Founded on a survey of clustering algorithms, the BIRCH algorithm is selected for this purpose. It is evaluated on an industrial time series dataset from a discrete manufacturing scenario. Results underline the approaches' applicability caused by its results' reproducibility and practical indifference to sequence, size and dimensionality of (sub-)datasets to be clustered sequentially.
