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- W3145104029 abstract "Under the background of the booming development of big data and the robot industry, the existing navigation technology has a certain delay in the identification of robot drive form and the unstructured surface physical environment. In this paper, by processing the statistical data of the robot foot sensor, the hybrid features of the robot in different environments are constructed in the two dimensions of time domain and frequency domain (fast Fourier transform and continuous wavelet transform). Finally, by combining decision tree and random forest algorithm, the mixed features are fully learned, and the high-precision classification results are obtained." @default.
- W3145104029 created "2021-04-13" @default.
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- W3145104029 date "2021-03-26" @default.
- W3145104029 modified "2023-10-16" @default.
- W3145104029 title "Robot Perceptual Classification Method Based on Mixed Features of Decision Tree and Random Forest" @default.
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- W3145104029 doi "https://doi.org/10.1109/icbaie52039.2021.9389973" @default.
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