Machine learning algorithm for self-powered wireless humidity sensor system

Le résumé fourni par la source

Wireless self-powered sensors are necessary for long-term, large-scale, real-time environmental monitoring systems. Triboelectric nanogenerators can capture energy from the environment to power sensors. However, fewer works that directly transform energy into wireless sensing signals, and the majority of existing environmental sensors require energy storage devices to store energy. Direct-driven wireless self-powered sensing is further limited by low energy density in the environment. In this paper, we present a wireless humidity sensor system that is suitable for ambient energy harvesting. This method can be used in self-powered ambient energy harvesting devices to convert and modulate environmental energy into a sensing signal without any energy storage devices. The signal is processed by using a machine learning algorithm, and the humidity recognition accuracy can reach 98.7%.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.