ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2025
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| author | Tew, Hwa Hui Ding, Fan Li, Gaoxuan Loo, Junn Yong Ting, Chee-Ming Ding, Ze Yang Tan, Chee Pin |
| author_facet | Tew, Hwa Hui Ding, Fan Li, Gaoxuan Loo, Junn Yong Ting, Chee-Ming Ding, Ze Yang Tan, Chee Pin |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02016 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing Tew, Hwa Hui Ding, Fan Li, Gaoxuan Loo, Junn Yong Ting, Chee-Ming Ding, Ze Yang Tan, Chee Pin Machine Learning Artificial Intelligence Signal Processing 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 |
| title | ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing |
| topic | Machine Learning Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2501.02016 |