TY - JOUR
T1 - Machine learning-assisted self-powered intelligent sensing systems based on triboelectricity
AU - Tian, Zhiyu
AU - Li, Jun
AU - Liu, Liqiang
AU - Wu, Han
AU - Hu, Xiaowei
AU - Xie, Mingjun
AU - Zhu, Yirui
AU - Chen, Xucong
AU - Ou-Yang, Wei
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/8
Y1 - 2023/8
N2 - The advancement of 5G and the Internet of Things (IoT) has ushered in an era of super-interconnected intelligence, which promises high-quality social development. Triboelectric-based sensing systems, combined with big data, artificial intelligence (AI), wireless communications, and cloud computing, among others, have gained considerable attention in self-powered sensing technologies. Machine learning (ML), as a critical component of AI, offers a practical strategy for efficiently processing multi-dimensional and multi-type data collected by triboelectric-based intelligent sensing systems (TISSs). In this review, a comprehensive and systematic summary of the latest advances in ML for TISSs from the perspective of technology implementation is innovatively presented. Then, we elaborate on the characteristics of common ML algorithms for TISSs and how ML broadens the applications of the triboelectric nanogenerator (TENG), which will provide valuable references for subsequent research. Finally, the limitations and prospects of ML for TENG are discussed for large-scale deployments, aiming to achieve higher-level intelligence in the future. These will provide significant insights to enable the advancement of self-powered sensing technology based on triboelectricity.
AB - The advancement of 5G and the Internet of Things (IoT) has ushered in an era of super-interconnected intelligence, which promises high-quality social development. Triboelectric-based sensing systems, combined with big data, artificial intelligence (AI), wireless communications, and cloud computing, among others, have gained considerable attention in self-powered sensing technologies. Machine learning (ML), as a critical component of AI, offers a practical strategy for efficiently processing multi-dimensional and multi-type data collected by triboelectric-based intelligent sensing systems (TISSs). In this review, a comprehensive and systematic summary of the latest advances in ML for TISSs from the perspective of technology implementation is innovatively presented. Then, we elaborate on the characteristics of common ML algorithms for TISSs and how ML broadens the applications of the triboelectric nanogenerator (TENG), which will provide valuable references for subsequent research. Finally, the limitations and prospects of ML for TENG are discussed for large-scale deployments, aiming to achieve higher-level intelligence in the future. These will provide significant insights to enable the advancement of self-powered sensing technology based on triboelectricity.
KW - Algorithm
KW - Artificial intelligence (AI)
KW - Machine learning (ML)
KW - Self-powered sensing
KW - Triboelectric nanogenerator (TENG)
UR - https://www.scopus.com/pages/publications/85161268526
U2 - 10.1016/j.nanoen.2023.108559
DO - 10.1016/j.nanoen.2023.108559
M3 - 文献综述
AN - SCOPUS:85161268526
SN - 2211-2855
VL - 113
JO - Nano Energy
JF - Nano Energy
M1 - 108559
ER -
