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Machine learning based quantity measurement method for grain silos
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DOI：10.1016/j.measurement.2019.107279.png)
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In this study, K-nearest neighbour (KNN) algorithm was used to determine grain quantity in the silo via thru the air radar. Thanks to the constituted stepped frequency continuous wave radar (SFCWR) system on a model silo, the back-scattering signal of different amount of grain for different stack structure were obtained. To create the training data, 5680 measurements were performed, and accuracy of the KNN was obtained with k-fold cross validation technique. Range profiles were obtained taking the inverse Fourier transform of SFCWR data and 8 features were extracted from range profiles. To found best feature combination on classification, 255 possible feature combinations were also constituted, then they were trained and tested. The best accuracy was achieved as 96,71% by using selected 5 features. These results show that, if the effective features are extracted from range profiles, the amount of the grain can be successfully determined using machine learning techniques. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Machine learning
KNN
Grain quantity measurement
Radar level measurement
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期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
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