Use of Hybrid Clustering and Scattering Parameters for Liquid Classification
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Istanbul Univ-Cerrahpasa
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
With the advancement of technology, the use of machine learning techniques has increased. The need for the prevention of terrorist attacks has brought upon the use of machine learning techniques to explosive detection. Flammable liquids such as alcohol are easily available and widely used in various terrorist attacks. In this study, a new microwave measurement system is developed and a hybrid clustering approach is proposed to classify liquids. With the proposed measurement system, the reflection coefficient (S-11 parameter) of liquids in bottles is measured at room temperature and these measurements are used as inputs by the proposed clustering algorithm. The results obtained using the proposed clustering algorithm are compared with the results obtained using a set of well-known clustering algorithms, that is, K-means, hierarchical clustering, farthest first, and fuzzy C-means, in order to make a fair comparison. The results show that the proposed clustering algorithm provides 100% accuracy and is superior to the well-known algorithms used in this study. The results will enable us to manufacture a low-cost liquid scanner for railway stations and shopping malls as well as small airports. The proposed liquid scanner's design was completed, and the manufacturing phase has been started.
Açıklama
Anahtar Kelimeler
Accuracy, Explosive Liquids, Flammable Liquids, Hybrid Clustering Algorithm, Liquid Classification, Gasoline, Spectroscopy
Kaynak
Electrica
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
22
Sayı
2
