Recurrent neural networks and exponential PAA for virtual marine sensors
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Titel | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings |
Antal sider | 8 |
Forlag | Institute of Electrical and Electronics Engineers Inc. |
Publikationsdato | 30 jun. 2017 |
Sider | 4459-4466 |
Artikelnummer | 7966421 |
ISBN (Elektronisk) | 9781509061815 |
DOI | |
Status | Udgivet - 30 jun. 2017 |
Eksternt udgivet | Ja |
Begivenhed | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, USA Varighed: 14 maj 2017 → 19 maj 2017 |
Konference
Konference | 2017 International Joint Conference on Neural Networks, IJCNN 2017 |
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Land | USA |
By | Anchorage |
Periode | 14/05/2017 → 19/05/2017 |
Sponsor | Brain-Mind Institute (BMI), Budapest Semester in Cognitive Science (BSCS), Intel |
ID: 223196201