ST-HCSS：用于软测量的深度时空超图卷积神经网络
ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing
AI总结：
针对工业传感器时序数据的复杂高阶关联建模问题，提出ST-HCSS深度时空超图卷积软测量框架，在无先验结构时学习超图并融合门控时序与超图卷积，性能优于现有方法。
摘要：
高阶传感器网络允许超越简单成对图边的多节点连接，因此在表征现代工业环境中传感时间序列数据的非线性动力学方面更为准确。鉴于此，我们提出了一种用于软测量的深度时空超图卷积神经网络（ST-HCSS）。具体而言，我们提出的框架能够在缺乏先验结构知识的情况下，构建并利用高阶图（超图）来建模传感器节点之间复杂的多重交互。为捕捉传感器数据背后丰富的时空关系，所提出的ST-HCSS结合了堆叠的门控时间层和超图卷积层，以跨时间和节点有效地聚合与更新超图信息。我们的结果验证了ST-HCSS相较于现有最先进软测量器的优越性，并表明学习到的超图特征表示与传感器数据相关性良好吻合。代码可在https://github.com/htew0001/ST-HCSS.git获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
