Title
The EU-SENSE System for Chemical Hazards Detection, Identification, and Monitoring
Authors
[ 1 ] Instytut Zarządzania, Wydział Zarządzania i Dowodzenia, Akademia Sztuki Wojennej | [ 2 ] Uniwersytet Warszawski | [ 3 ] ITTI | [ 4 ] Technisch-Mathematische Studiengesellschaft mbH (tms) | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2021
Published in
Article type
scientific article
Publication language
english
Keywords
EN
- Chemical detection
- Chemical identification
- Chemical monitoring
- Chemical reconnaissance
- Machine learning
- Modelling algorithms
PL
- Sztuczna inteligencja
- Rozpoznanie skażeń i zakażeń
- Obrona przeciwchemiczna
- Uczenie się maszyn
- Algorytmy
Abstract
EN Chemical reconnaissance, defined as hazards detection, identification, and monitoring, requires tools and solutions which provide reliable and precise data. In this field, the advances of artificial intelligence can be applied. This article aims to propose a novel approach for developing a chemical reconnaissance system that relies on machine learning, modelling algorithms, as well as the contaminant dispersion model to combine signals from different sensors and reduce false alarm rates. A case study of the European Union Horizon 2020 project–EU-SENSE is used and the main features of the system are analysed: heterogeneous sensor nodes components, chemical agents to be detected, and system architecture design. Through the proposed approach, chemical reconnaissance capabilities are improved, resulting in more effective crisis management. The idea for the system design can be used and developed in other areas, namely, in biological or radiological threat reconnaissance.
Date of online publication
03.11.2021
Pages (from - to)
1 - 12
License type
CC BY (attribution alone)
Open Access Mode
open journal
Open Access Text Version
final published version
Release date
03.11.2021
Date of Open Access to the publication
at the time of publication
Ministry points / journal
100
Ministry points / journal in years 2017-2021
100
Impact Factor
2,838
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