@inproceedings{147bf009ab9b49faa646791e1592fbfa,
title = "Recurrent neural networks and exponential PAA for virtual marine sensors",
abstract = "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.",
author = "Stefan Oehmcke and Oliver Zielinski and Oliver Kramer",
year = "2017",
month = jun,
day = "30",
doi = "10.1109/ijcnn.2017.7966421",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4459--4466",
booktitle = "2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings",
address = "United States",
note = "2017 International Joint Conference on Neural Networks, IJCNN 2017 ; Conference date: 14-05-2017 Through 19-05-2017",
}
