Hemp-Alumina Composite Radar Absorption Reflection Loss Classification
نویسندگان
چکیده
The Radar Absorption Material (RAM) method is a coating for reducing the energy of electromagnetic waves received by converting emitted radar into heat energy. Hemp has been studied to have strongest and most stable tensile characteristics 5.5 g/den higher resistance compared other natural fibers. Combining hemp with alumina powder (Al2O3) epoxy resin could provide stealth technology system that able absorb more optimally, considering light, anti-rust conductive properties. properties absorbent coatings can be predicted using machine learning. This study classifies reflection loss Hemp-Alumina Composite Random Forest, ANN, KNN, Logistic Regression, Decision Tree. These learning classifiers are generate predictions immediately learn critical spectral across wide range without influence data human bias. frequency 2-12 GHz was used measurements. composite result effective structure thickness 5mm, as RAM optimum absorption in S-Band frequencies -15,158 dB, C-Band -16,398 dB X-Band -23,135 dB. highest value found 5mm which equal -23.135 an bandwidth 1000 MHz efficiencyof 93.1%. From this result, it proven very on frequency. Based results experiments, Forest Classifier values accuracy (0.97) F1 score (0.98). 0.96 0.97, respectively, do not significantly differ from KNN.
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ژورنال
عنوان ژورنال: JOIV : International Journal on Informatics Visualization
سال: 2023
ISSN: ['2549-9610', '2549-9904']
DOI: https://doi.org/10.30630/joiv.7.2.1169