[论文解读] Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning

Weixiang Wan, Wenjian Sun+4 more|arXiv (Cornell University)|Jan 30, 2024

Advanced Sensor and Control Systems被引用 16

一句话总结

论文评估使用基于摩擦电纳米发电机（TENG）的自驱动传感器进行智能声音监测与识别，并评估在无处不在传感器网络中的声音感知模块的可行性。

ABSTRACT

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.

研究动机与目标

• 推动具备可持续能源与易部署的智能传感器系统的发展。
• 探索以 triboelectric nanogenerators (TENG) 作为自驱动传感器用于可穿戴与环境感知。
• 评估在无处不在传感器网络中声音感知模块架构的可行性。

提出的方法

• 讨论 triboelectric nanogenerator (TENG) 作为 Maxwell 位移电流驱动的直接电信号来源。
• 突出基于 TENG 的传感器的优点，如结构简单和瞬态功率密度高。
• 将机器学习技术整合以处理来自 TENG 传感器的电信号以进行声音识别。
• 聚焦于利用 TENG 传感器的智能声音监测与识别系统。

实验结果

研究问题

• RQ1 基于 TENG 的自驱动传感器是否能够可靠支持无处不在传感器网络中的声音感知任务？
• RQ2 哪些体系架构考虑因素能够实现 TENG 传感器与深度学习在声音识别中的有效集成？
• RQ3 在智能系统中的可穿戴与环境感知中，TENG 传感器的优点与局限性是什么？

主要发现

• TENG 传感器具有结构简单和瞬态功率密度高，能够实现自供电感知。
• 机器学习可以处理 TENG 传感器产生的电信号以完成声音识别任务。
• 本文强调在无处不在传感器网络中部署基于 TENG 的声音感知的可行性与体系结构考虑。
• 基于 TENG 的智能声音监控与识别系统展示了快速发展智能传感器网络的潜力。