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عنوان انگلیسی مقاله:
Enhanced surface roughness discrimination with optimized features from bio-inspired tactile sensor
ترجمه فارسی عنوان مقاله:
بالا بردن سطح تبعیض خشونت با ویژگی های بهینه از سنسور لمسی الهام گرفته از زیست شناسی
Sciencedirect - Elsevier - Sensors & Actuators: A. Physical, 264 (2017) 133-140. doi:10.1016/j.sna.2017.07.054
Longhui Qin, Zhengkun Yi, Yilei Zhang
Article history:Received 21 March 2017Received in revised form 11 July 2017 Accepted 27 July 2017Available online 29 July 2017Keywords:Surface roughness discrimination Tactile sensorDiscrete wavelet transform Sequential forward selection Extreme learning machine Feature selectionThe ability of surface roughness discrimination is of great signiﬁcance in the development of biomimetic robots and prosthetic limb as well as the research on humanoid tactile sensing. In our previous research, a bio-inspired tactile sensor was developed to discriminate different surface roughness. But its discrim- ination accuracy decreased greatly as the roughness became smaller. Furthermore, the discrimination accuracy declined when signals from an extra PVDF ﬁlm parallel to the sliding direction were included. In this paper, enhanced surface roughness discrimination using the same bio-inspired tactile sensor was achieved, including: 1) discrete wavelet transform was applied at ﬁrst to decompose the original sensing data into different scale and frequency components; 2) the most discriminative features were selected from as many as 80 features deﬁned both in time and frequency domains based on components via the algorithm of sequential forward selection; 3) instead of kNN or SVM, extreme learning machine (ELM) was applied to discriminate surface roughness. It was found that the extra PVDF ﬁlm parallel to the sliding direction could provide the feature that affects the discrimination accuracy the most signiﬁcantly if its signals were decomposed and the feature was selected from the high order component. Furthermore, the neural network based ELM was shown to have better discrimination accuracy than kNN or SVM using the same dataset and features. With ELM, the discrimination accuracy was improved from 82.6% to 97.88%. The result indicates the importance of signal decomposition and feature selection for surface roughness discrimination based on the bio-inspired tactile sensor. The proposed framework could also be applicable to other researches in sensor development and signal processing.© 2017 Published by Elsevier B.V.
Keywords:Surface roughness discrimination | Tactile sensor | Discrete wavelet transform | Sequential forward selection | Extreme learning machine | Feature selection