Recurrent neural networks and exponential PAA for virtual marine sensors
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Virtual sensors are getting more and more important as replacement and quality control tool for expensive and fragile hardware sensors. We introduce a virtual sensor application with marine sensor data from two data sources. The virtual sensor models are built upon recurrent neural networks (RNNs). To take full advantage of past data, we employ the time dimensionality reduction method piecewise approximate aggregation (PAA). We present an extension of this method, called exponential PAA (ExPAA) that pulls finer details from recent values, but preserves less exact information about the past. Experimental results demonstrate that RNNs benefit from this extension and confirm the stability and usability of our virtual sensor models over a five-month period of multivariate marine time series data.
Original language | English |
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Title of host publication | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings |
Number of pages | 8 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Publication date | 30 Jun 2017 |
Pages | 4459-4466 |
Article number | 7966421 |
ISBN (Electronic) | 9781509061815 |
DOIs | |
Publication status | Published - 30 Jun 2017 |
Externally published | Yes |
Event | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States Duration: 14 May 2017 → 19 May 2017 |
Conference
Conference | 2017 International Joint Conference on Neural Networks, IJCNN 2017 |
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Land | United States |
By | Anchorage |
Periode | 14/05/2017 → 19/05/2017 |
Sponsor | Brain-Mind Institute (BMI), Budapest Semester in Cognitive Science (BSCS), Intel |
ID: 223196201