Utilization of Machine Learning to Detect Sudden Water Leakage for Smart Water Meter

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Utilization of Machine Learning to Detect Sudden Water Leakage for Smart Water Meter

Popis výsledku v původním jazyce

This article deals with the use of machine learning to detect sudden water leakage. A smart water meter, which enables monitoring the water consumption of the observed object, is used as the source of input data. Based on these data and their analysis, a symbolic regression, which must know not only the input parameters but also the structure of the model, was finally used to build the model. After finding a suitable function and standard deviation from the model, it is possible to set the required sensitivity and thereby detect anomalous states of water consumption in monitored time windows. Since the smart water meter also has a ball valve, if a sudden water leakage is detected, the water meter can autonomously close the main supply and thus avoid extensive damage.

Název v anglickém jazyce

Utilization of Machine Learning to Detect Sudden Water Leakage for Smart Water Meter

Popis výsledku anglicky

This article deals with the use of machine learning to detect sudden water leakage. A smart water meter, which enables monitoring the water consumption of the observed object, is used as the source of input data. Based on these data and their analysis, a symbolic regression, which must know not only the input parameters but also the structure of the model, was finally used to build the model. After finding a suitable function and standard deviation from the model, it is possible to set the required sensitivity and thereby detect anomalous states of water consumption in monitored time windows. Since the smart water meter also has a ball valve, if a sudden water leakage is detected, the water meter can autonomously close the main supply and thus avoid extensive damage.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

29th International Conference Radioelektronika, RADIOELEKTRONIKA 2019

ISBN

978-1-5386-9323-0

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

340-344

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Pardubice

Datum konání akce

16. 4. 2019

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000492026100062