摩擦电效应
代表(政治)
人工智能
深度学习
计算机科学
材料科学
汽车工程
工程类
复合材料
政治
政治学
法学
作者
BaekGyu Kim,Jin Yeong Song,Do Young Kim,Min Woo Cho,Ji Gyo Park,Dongwhi Choi,Chengkuo Lee,Sang Min Park
出处
期刊:Small
[Wiley]
日期:2024-04-02
标识
DOI:10.1002/smll.202400484
摘要
Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire-road friction. The optimization of the process and structure of a laser-induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200-2000 rpm and contact fractions of line. Employing a hybrid model combining short-term Fourier transform with a convolution neural network-long short-term memory, the LIG-based triboelectric tire monitoring (LTTM) system decouples the driving information, such as traffic lines and road states, from varied environmental conditions of humidity (10%-90%) and temperatures (50-70 °C). The real-time line and road state recognition of the LTTM system is confirmed on a mobile platform across diverse environmental conditions, including fog, dampness, intense sunlight, and heat shimmer. This work provides an environmentally robust monitoring AIoT system by introducing a self-powered triboelectric sensor and hybrid deep learning for smart mobility.
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