SavedAdd your journal

Journals / Electrica / 2022 / Cilt: 22 - Sayı: 2

Use of Hybrid Clustering and Scattering Parameters for Liquid Classification

Gürkan TUNA
Ebru EFEOĞLU
Journal
Electrica
Pages
237–243
Publication Date
2022-10-12
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that the proposed clustering algorithm provides 100% accuracy and is superior to the well-known algorithms used in this study.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the advancement of technology, the use of machine learning techniques has increased.
📊
Key Findings & Data:With the proposed measurement system, the reflection coefficient (S11 parameter) of liquids in bottles is measured at room temperature and these measurements are used as inputs by the proposed clustering algorithm.
💡
Conclusion & Impact:The results show that the proposed clustering algorithm provides 100% accuracy and is superior to the well-known algorithms used in this study.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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 (S11 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.

🔖

Cite This Article

TUNA, G., EFEOĞLU, E. (2022). Use of Hybrid Clustering and Scattering Parameters for Liquid Classification. Electrica, Cilt: 22 - Sayı: 2, 237–243. https://ajindex.com/articles/499907

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. E. Davicioni, and Z. Haddad, “Thyroid cancer diagnostics,” Google Patents, 2018
  2. K. Wang, X. Qi, H. Liu, and J. Song, “Deep belief network based k-means cluster approach for short-term wind power forecasting,” Energy, vol. 165, pp. 840–852, 2018
  3. W. Zhang, Z. Lian, and C. Huang, “Evaluation of unsupervised clustering methods on hyperspectral image data sets,” in IEEE International Conference on Progress in Informatics and Computing (PIC), 2018, pp. 117–121
  4. S. N. H. Sheikh Abdullah et al., “Round randomized learning vector quantization for brain tumor imaging,” Comp. Math. Methods Med., vol. 2016, 8603609, 2016
  5. B. R. Shivakumar, and S. V. Rajashekararadhya, “Classification of Landsat 8 imagery using Kohonen’s self organizing maps and learning vector quantization,” in Adv. Commun. Signal Process. VLSI Embedded Syst. Berlin: Springer, 2020, pp. 445–462
  6. J. Lee et al., “Classification of diesel and gasoline dual-fuel combustion modes by the analysis of heat release rate shapes in a compression ignition engine,” Fuel, vol. 209, pp. 587–597, 2017
  7. I. Barnett, and M. Zhang, “Discrimination of brands of gasoline by using DART-MS and chemometrics,” Forensic Chem., vol. 10, pp. 58–66, 2018
  8. V. D. Baquiran, J. A. M. Bate, M. J. B. Sembrano, J. F. Villaverde, and G. V. Magwili, “Determination of methanol, ethanol, and ethylene glycol content in coconut wine using PCA and SIMCA,” in Proc. 2020 12th Int. Conf. Comput. Automat. Eng., 2020, pp. 150–154
  9. J. Wang, M. Chang, W. He, X. Lu, S. Fei, and G. Lu, “Optimization of electronic nose sensor array for tea aroma detecting based on correlation coefficient and cluster analysis,” Sens. Actuators B, p. 128068, 2020
  10. J. A. Ardila, F. L. F. Soares, M. A. dos Santos Farias, and R. L. Carneiro, “Characterization of gasoline by Raman spectroscopy with chemometric analysis,” Anal. Lett., vol. 50, no. 7, pp. 1126–1138, 2017
  11. J. Dadson, S. Pandam, and N. Asiedu, “Modeling the characteristics and quantification of adulterants in gasoline using FTIR spectroscopy and chemometric calibrations,” Cogent Chem., vol. 4, no. 1, p. 1482637, 2018
  12. I. M. A. G. Wirasuta et al., “A rapid method for screening and determination test of methanol content in ethanol-based products using portable Raman spectroscopy,” Forensic Chem., vol. 16, p. 100190, 2019
  13. F. Mabood et al., “Detection and estimation of Super premium 95 gasoline adulteration with Premium 91 gasoline using new NIR spectroscopy combi