| Issue |
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 7 | |
| Section | Computer Science and System Design, Application | |
| DOI | https://doi.org/10.1051/itmconf/20224701004 | |
| Published online | 23 June 2022 | |
Bert-GCN: multi-sensors network prediction
China Ship Research and Development Academy, 100101 Beijing, China
* Corresponding author: 15248156291@qq.com
With the application of neural network technologies such as GCN and GRU in sensor networks, the accuracy and robustness of multi-sensor prediction have been greatly improved. GCN effectively uses the spatial characteristics of the sensor network, and GRU effectively uses the temporal characteristics of the sensor network, so the PROPOSED T-GCN model has achieved excellent results. However, there are still shortcomings: i) The prediction is only for a single sensor feature, and multiple features cannot be trained at the same time. ii) Only the connections between sensors are considered, while the connections between multiple features of sensors are ignored. iii) Modeling for multiple features leads to the deepening of the model from 2d to 3D, resulting in slow model training and poor learning effect. To solve the above problems, this paper proposed the Bert-GCN model. Bert pre-training was added on the basis of the original GCN-GRU model to effectively improve the learning effect of multiple features of a single sensor.
Key words: Multi-sonsor network / Graph convolutional network (GCN) / Gated recurrent unit (GRU) / Bert / Transformer
© The Authors, published by EDP Sciences, 2022
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