Recurrent neural networks and exponential PAA for virtual marine sensors
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Recurrent neural networks and exponential PAA for virtual marine sensors. / Oehmcke, Stefan; Zielinski, Oliver; Kramer, Oliver.
2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4459-4466 7966421.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Recurrent neural networks and exponential PAA for virtual marine sensors
AU - Oehmcke, Stefan
AU - Zielinski, Oliver
AU - Kramer, Oliver
PY - 2017/6/30
Y1 - 2017/6/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85031043285&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2017.7966421
DO - 10.1109/IJCNN.2017.7966421
M3 - Article in proceedings
AN - SCOPUS:85031043285
SP - 4459
EP - 4466
BT - 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Joint Conference on Neural Networks, IJCNN 2017
Y2 - 14 May 2017 through 19 May 2017
ER -
ID: 223196201