Reliability Estimation of Critical Infrastructure Segment Backed up by the Circular Data Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG42__%2F25%3A00560218" target="_blank" >RIV/60162694:G42__/25:00560218 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60162694:G43__/25:00560218

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10171240" target="_blank" >http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10171240</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICMT58149.2023.10171311" target="_blank" >10.1109/ICMT58149.2023.10171311</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reliability Estimation of Critical Infrastructure Segment Backed up by the Circular Data Analysis

Popis výsledku v původním jazyce

In this paper, we present a novel statistical approach to assess and model data of water distribution network (WDN) failures which contain only few pieces of information, namely the number of failures in a month. The applied statistical method is known as the circular (directional) statistics. It concerns with angular/cyclical data in degrees or radians. The sample space is typically a circle or a sphere and due to the nature of the circular data, they cannot be analysed with commonly used statistical techniques. Circular data approaches can be adapted to analyse time-of-year data and year cycles. Using the methods of descriptive and inferential statistics for circular data, we show that the WDN failure data show a deviation from the uniform model and cannot be modelled by the parametric models. Therefore, we apply the nonparametric circular kernel density estimates to assess and model the data and predict the expected numbers of failures in the respective months of a year.

Název v anglickém jazyce

Reliability Estimation of Critical Infrastructure Segment Backed up by the Circular Data Analysis

Popis výsledku anglicky

In this paper, we present a novel statistical approach to assess and model data of water distribution network (WDN) failures which contain only few pieces of information, namely the number of failures in a month. The applied statistical method is known as the circular (directional) statistics. It concerns with angular/cyclical data in degrees or radians. The sample space is typically a circle or a sphere and due to the nature of the circular data, they cannot be analysed with commonly used statistical techniques. Circular data approaches can be adapted to analyse time-of-year data and year cycles. Using the methods of descriptive and inferential statistics for circular data, we show that the WDN failure data show a deviation from the uniform model and cannot be modelled by the parametric models. Therefore, we apply the nonparametric circular kernel density estimates to assess and model the data and predict the expected numbers of failures in the respective months of a year.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 9th International Conference on Military Technologies, ICMT 2023 - Proceedings

ISBN

979-8-3503-2568-3

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

—

Místo konání akce

Brno

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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