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Russian Journal of Nondestructive Testing

Erschienen in:

01.09.2020 | RADIO-WAVE TECHNIQUES

Detection of Hazardous Liquids Using Microwave Data and Well-Known Classification Algorithms

verfasst von: Ebru Efeoglu, Gurkan Tuna

Erschienen in: Russian Journal of Nondestructive Testing | Ausgabe 9/2020

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Abstract

The recent increase in terrorist attacks realized using liquid explosives has made it important to develop quick and reliable methods that can distinguish between nonhazardous liquids and other liquids that can be used in these explosives. Since the stability and sensitivity properties of microwave systems are high, microwave frequency band is preferred to differentiate hazardous liquids from non-hazardous liquids. In this study, a noncontact system based on electromagnetic response measurements of liquids in microwave frequency band is proposed to develop a classification approach that can be used in liquid scanners. Naive Bayes, linear discriminant analysis, qualitative data analysis, support vector machine, sequential minimal optimization, K-nearest neighbors classification algorithms are used to classify liquids and their classification performances are analyzed. The results of the set of classification experiments prove the success of the proposed measurement method. As the results prove, K-nearest neighbors is the most appropriate classification algorithm for hazardous liquid detection. Since it can be easily implemented and its detection process is fast, a classification system based on the proposed approach can be very useful in airports and shopping malls.

Vorheriger Artikel Estimating the Extent of Exfoliation of Dielectric and Magnetodielectric Coatings with Surface Microwaves

Nächster Artikel Hysteretic Characteristic Curve Monitoring and Finite Element Analysis in Nondestructive Testing of Fabricated Foundation Pit

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Titel: Detection of Hazardous Liquids Using Microwave Data and Well-Known Classification Algorithms
verfasst von: Ebru Efeoglu
Gurkan Tuna
Publikationsdatum: 01.09.2020
Verlag: Pleiades Publishing
Erschienen in: Russian Journal of Nondestructive Testing / Ausgabe 9/2020
Print ISSN: 1061-8309
Elektronische ISSN: 1608-3385
DOI: https://doi.org/10.1134/S106183092009003X

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