Journal
ACTA ACUSTICA UNITED WITH ACUSTICA
Volume 102, Issue 6, Pages 1108-1117Publisher
S HIRZEL VERLAG
DOI: 10.3813/AAA.919022
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Funding
- National Natural Science Foundation of China [11172342, 11372167]
- Science Research and Development Program of Shaanxi Province of China [2016NY-176]
- Fundamental Research Funds for the Central Universities [GK201405007]
- Interdisciplinary Incubation Project of Learning Science of Shaanxi Normal University
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Internally feeding insects inside wheat kernels cause significant, but unseen economic damage to stored grain. In this paper, a new scheme based on ensemble empirical mode decomposition (EEMD) using impact acoustics is proposed for detection of insect-damaged wheat kernels, based on its capability to process non-stationary signals and its suppression of mode mixing. The intrinsic mode function (IMF) kurtosis, IMF form factors, IMF third-order Renyi entropies, and the mean of the degree of stationarity were extracted as discriminant features used as the inputs to a support vector machine (SVM) for non-linear classification. In these experiments, 98.7% of undamaged wheat kernels and 93.3% of insect-damaged ones were correctly detected, which indicated the effectiveness of the proposed method for categorizing undamaged wheat kernels from insect-damaged wheat kernels (IDK).
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