标题： ST-HCSS：用于软传感的深度时空超图卷积神经网络 显示英文标题

标题： ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing

摘要： 高阶传感器网络通过允许多节点连接，超越了简单的成对图边，在现代工业环境中更准确地表征传感时间序列数据的非线性动态。 鉴于此，我们提出了一种用于软传感的深度时空超图卷积神经网络（ST-HCSS）。 特别是，我们提出的框架能够在没有先验结构知识的情况下构建和利用高阶图（超图）来建模传感器节点之间的复杂多交互作用。 为了捕捉传感器数据背后的丰富时空关系，我们提出的ST-HCSS结合了堆叠的门控时间和超图卷积层，以有效聚合和更新随时间和节点变化的超图信息。 我们的结果验证了ST-HCSS相比现有最先进的软传感器的优势，并表明学习到的超图特征表示与传感器数据相关性很好地对齐。 代码可在 https://github.com/htew0001/ST-HCSS.git 获取 显示英文摘要

摘要： Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections beyond simple pairwise graph edges. In light of this, we propose a deep spatio-temporal hypergraph convolutional neural network for soft sensing (ST-HCSS). In particular, our proposed framework is able to construct and leverage a higher-order graph (hypergraph) to model the complex multi-interactions between sensor nodes in the absence of prior structural knowledge. To capture rich spatio-temporal relationships underlying sensor data, our proposed ST-HCSS incorporates stacked gated temporal and hypergraph convolution layers to effectively aggregate and update hypergraph information across time and nodes. Our results validate the superiority of ST-HCSS compared to existing state-of-the-art soft sensors, and demonstrates that the learned hypergraph feature representations aligns well with the sensor data correlations. The code is available at https://github.com/htew0001/ST-HCSS.git