基于自驱动传感器与深度学习结合的人工智能应用进展
Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning
AI总结：
本文针对物联网下智能传感器的供电难题，结合TENG自驱动传感器与机器学习技术，基于TENG智能声测系统评估其在泛在传感网中的应用可行性，推动智能传感网络发展。
摘要：
在物联网时代，如何开发出供电可持续、部署便捷、使用灵活的智能传感器系统，已成为亟待解决的难题。传统供电方式在使用中存在需频繁更换或充电等问题，限制了可穿戴设备的发展。本研究采用聚四氟乙烯（PTFE）和铝（Al）箔制备了接触-分离式摩擦纳米发电机（TENG），通过人体布设收集人体运动能量，并根据输出电信号的变化监测人体运动姿态。2012年，王中林院士团队发明了摩擦纳米发电机（TENG），其以麦克斯韦位移电流为驱动力，可将机械刺激直接转换为电信号，因此可作为自驱动传感器使用。基于TENG的传感器具备结构简单、瞬时功率密度高的优势，为构建智能传感器系统提供了重要途径。与此同时，机器学习作为一种成本低、开发周期短、数据处理能力与预测能力强的技术，对TENG产生的大量电信号的处理效果显著，与TENG传感器的结合将推动未来智能传感器网络的快速发展。因此，本文基于具备良好声音识别能力的TENG智能声音监测识别系统，旨在评估该声音感知模块架构在泛在传感器网络中的可行性。
英文摘要：
In the era of Internet of Things, how to develop a smart sensor system with sustainable power supply, easy deployment and flexible use has become a difficult problem to be solved. The traditional power supply has problems such as frequent replacement or charging when in use, which limits the development of wearable devices. The contact-to-separate friction nanogenerator (TENG) was prepared by using polychotomy thy lene (PTFE) and aluminum (AI) foils. Human motion energy was collected by human body arrangement, and human motion posture was monitored according to the changes of output electrical signals. In 2012, Academician Wang Zhong lin and his team invented the triboelectric nanogenerator (TENG), which uses Maxwell displacement current as a driving force to directly convert mechanical stimuli into electrical signals, so it can be used as a self-driven sensor. Teng-based sensors have the advantages of simple structure and high instantaneous power density, which provides an important means for building intelligent sensor systems. At the same time, machine learning, as a technology with low cost, short development cycle, strong data processing ability and prediction ability, has a significant effect on the processing of a large number of electrical signals generated by TENG, and the combination with TENG sensors will promote the rapid development of intelligent sensor networks in the future. Therefore, this paper is based on the intelligent sound monitoring and recognition system of TENG, which has good sound recognition capability, and aims to evaluate the feasibility of the sound perception module architecture in ubiquitous sensor networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
