Recurrent neural networks and exponential PAA for virtual marine sensors

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

T3 - Proceedings of the International Joint Conference on Neural Networks

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 -