Sampling control in environmental monitoring systems using recurrent neural networks

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Sampling control in environmental monitoring systems using recurrent neural networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Sampling control in environmental monitoring systems using recurrent neural networks

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

ISBN

978-1-4673-8721-7

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

101-110

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Vancouver

Datum konání akce

15. 5. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000390779200262