Machine Learning‐Enabled Tactile Sensor Design for Dynamic Touch Decoding (Adv. Sci. 32/2023)

Machine Learning‐Enabled Sensor Design In article number 2303949, Kaichen Xu, Geng Yang, and co‐workers propose a machine learning (ML)‐guided design of flexible tactile sensor system, enabling a high classification accuracy of tactile perception in six dynamic touch modalities. This ML‐guided perfo...

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Detalles Bibliográficos
Autores principales: Lu, Yuyao, Kong, Depeng, Yang, Geng, Wang, Ruohan, Pang, Gaoyang, Luo, Huayu, Yang, Huayong, Xu, Kaichen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Inside Back Cover
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646218/
http://dx.doi.org/10.1002/advs.202370224
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author Lu, Yuyao
Kong, Depeng
Yang, Geng
Wang, Ruohan
Pang, Gaoyang
Luo, Huayu
Yang, Huayong
Xu, Kaichen
author_facet Lu, Yuyao
Kong, Depeng
Yang, Geng
Wang, Ruohan
Pang, Gaoyang
Luo, Huayu
Yang, Huayong
Xu, Kaichen
author_sort Lu, Yuyao
collection PubMed
description Machine Learning‐Enabled Sensor Design In article number 2303949, Kaichen Xu, Geng Yang, and co‐workers propose a machine learning (ML)‐guided design of flexible tactile sensor system, enabling a high classification accuracy of tactile perception in six dynamic touch modalities. This ML‐guided performance optimization is realized by introducing a support vector machine‐based ML algorithm along with specific statistical criteria for fabrication parameters selection to excavate features deeply concealed in raw sensing data. [Image: see text]
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spelling pubmed-106462182023-11-14 Machine Learning‐Enabled Tactile Sensor Design for Dynamic Touch Decoding (Adv. Sci. 32/2023) Lu, Yuyao Kong, Depeng Yang, Geng Wang, Ruohan Pang, Gaoyang Luo, Huayu Yang, Huayong Xu, Kaichen Adv Sci (Weinh) Inside Back Cover Machine Learning‐Enabled Sensor Design In article number 2303949, Kaichen Xu, Geng Yang, and co‐workers propose a machine learning (ML)‐guided design of flexible tactile sensor system, enabling a high classification accuracy of tactile perception in six dynamic touch modalities. This ML‐guided performance optimization is realized by introducing a support vector machine‐based ML algorithm along with specific statistical criteria for fabrication parameters selection to excavate features deeply concealed in raw sensing data. [Image: see text] John Wiley and Sons Inc. 2023-11-14 /pmc/articles/PMC10646218/ http://dx.doi.org/10.1002/advs.202370224 Text en © 2023 Wiley‐VCH GmbH https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Inside Back Cover
Lu, Yuyao
Kong, Depeng
Yang, Geng
Wang, Ruohan
Pang, Gaoyang
Luo, Huayu
Yang, Huayong
Xu, Kaichen
Machine Learning‐Enabled Tactile Sensor Design for Dynamic Touch Decoding (Adv. Sci. 32/2023)
title Machine Learning‐Enabled Tactile Sensor Design for Dynamic Touch Decoding (Adv. Sci. 32/2023)
title_full Machine Learning‐Enabled Tactile Sensor Design for Dynamic Touch Decoding (Adv. Sci. 32/2023)
title_fullStr Machine Learning‐Enabled Tactile Sensor Design for Dynamic Touch Decoding (Adv. Sci. 32/2023)
title_full_unstemmed Machine Learning‐Enabled Tactile Sensor Design for Dynamic Touch Decoding (Adv. Sci. 32/2023)
title_short Machine Learning‐Enabled Tactile Sensor Design for Dynamic Touch Decoding (Adv. Sci. 32/2023)
title_sort machine learning‐enabled tactile sensor design for dynamic touch decoding (adv. sci. 32/2023)
topic Inside Back Cover
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646218/
http://dx.doi.org/10.1002/advs.202370224
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