Sampling control in environmental monitoring systems using recurrent neural networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F16%3A39902123" target="_blank" >RIV/00216275:25530/16:39902123 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/document/7726859/" target="_blank" >http://ieeexplore.ieee.org/document/7726859/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CCECE.2016.7726859" target="_blank" >10.1109/CCECE.2016.7726859</a>
Alternative languages
Result language
angličtina
Original language name
Sampling control in environmental monitoring systems using recurrent neural networks
Original language description
Minimization of energy consumption of environmental monitoring systems is important to ensure their extended operational lifetime and low maintenance costs. One possible way to conserve energy is the use of low-frequency analog-to-digital conversion devices and associated data sampling techniques. In this paper, a new approach to lowering sampling frequency using model-based data imputation is proposed and discussed. Recorded data is used to develop models based on time-delay recurrent neural network. While sampling at low frequencies, the models are used to predict future and missing values. The models are updated when predicted values differ significantly from the actual measurements. The proposed approach is demonstrated using actual measurements sampled at different frequencies. The results are thoroughly analyzed from the perspective of approximation error and energy savings.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)
ISBN
978-1-4673-8721-7
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
101-110
Publisher name
IEEE (Institute of Electrical and Electronics Engineers)
Place of publication
New York
Event location
Vancouver
Event date
May 15, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000390779200262