Hierarchical neural network with efficient selection inference

Mi, Jian-Xun; Li, Nuo; Huang, Ke-Yang; Li, Weisheng; Zhou, Lifang

doi:10.1016/j.neunet.2023.02.015

Cited by 2 publications

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Machine Learning‐Enhanced Triboelectric Sensing Application

Shang,

Chen,

Cai

et al. 2024

Adv Materials Technologies

View full text Add to dashboard Cite

Triboelectric nanogenerator (TENG) has become a promising candidate for wearable energy harvesting and self‐powered sensing systems. However, processing large amounts of data imposes a computing power barrier for practical application. Machine learning‐assisted self‐powered sensors based on TENG have been widely used in data‐driven applications due to their excellent characteristics such as no additional power supply, high sensing accuracy, low cost, and good biocompatibility. This work comprehensively reviews the latest progress in machine learning (ML)‐assisted TENG‐based sensors. The future challenges and opportunities are discussed. First, the fundamental principles including the working mode of ML‐assisted TENG‐based sensor and common algorithms are systematically and comprehensively illustrated, which emphasizes the algorithm definition and principle. Subsequently, the progress of ML methods in the field of TENG‐based sensors is further reviewed, summarizing the advantages and disadvantages of various algorithms in practical examples, and providing guidance and suggestions on how to choose the appropriate methods. Finally, the prospects and challenges of ML‐assisted TENG‐based sensors is summarized. Directions and important insights for the future development of TENG and AI integration is provided.

show abstract