Capacities of Shallow Neural Network to Indicate and Clasify Airport Surface Distresses

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00565948" target="_blank" >RIV/60162694:G43__/26:00565948 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11061248" target="_blank" >http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11061248</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICMT65201.2025.11061311" target="_blank" >10.1109/ICMT65201.2025.11061311</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Capacities of Shallow Neural Network to Indicate and Clasify Airport Surface Distresses

Popis výsledku v původním jazyce

Enhancing air traffic safety at airports is crucial for reducing incidents and aircraft accidents. Incidents linked to foreign object debris are related to the emergence and progression of pavement distress. To address efficient maintenance and repairs, a pavement management system is in place at airports. This system involves visual inspections of pavements, which we recommended integrating with cutting-edge smartphone technologies mounted in a maintenance vehicle. We developed an application that utilizes data from smartphone accelerometers to assess the potential of a shallow neural network to inspect airport pavement distresses. The straightforward design of this specific neural network offers benefits such as considerable accuracy and rapid training, while not demanding significant computational resources. Conversely, data processing was reasonably complex and required substantial effort.

Název v anglickém jazyce

Capacities of Shallow Neural Network to Indicate and Clasify Airport Surface Distresses

Popis výsledku anglicky

Enhancing air traffic safety at airports is crucial for reducing incidents and aircraft accidents. Incidents linked to foreign object debris are related to the emergence and progression of pavement distress. To address efficient maintenance and repairs, a pavement management system is in place at airports. This system involves visual inspections of pavements, which we recommended integrating with cutting-edge smartphone technologies mounted in a maintenance vehicle. We developed an application that utilizes data from smartphone accelerometers to assess the potential of a shallow neural network to inspect airport pavement distresses. The straightforward design of this specific neural network offers benefits such as considerable accuracy and rapid training, while not demanding significant computational resources. Conversely, data processing was reasonably complex and required substantial effort.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

21100 - Other engineering and technologies

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

2025 10th International Conference on Military Technologies, ICMT 2025 - Proceedings

ISBN

—

ISSN

—

e-ISSN

2996-4474

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

—

Místo konání akce

Brno, Czech Republic

Datum konání akce

27. 5. 2025

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

001545807300053