Sayrafian, K.
, Roudneshin, M.
and Aghdam, A.
(2019),
Output Power Maximization of a Microgenerator Using Machine Learning Approach, The 7th Annual IEEE International Conference on Wireless for Space and Extreme Environments, Ottawa, -1, [online], https://doi.org/10.1109/WiSEE.2019.8920332
(Accessed December 13, 2024)
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Output Power Maximization of a Microgenerator Using Machine Learning Approach
Published
Author(s)
, Masoud Roudneshin, Amir G. Aghdam
Abstract
Wearable medical sensors are one of the key components of remote health monitoring systems which allow patients to stay under continuous medical supervision away from the hospital environment. These sensors are typically powered by small batteries which allow the device to operate for a limited time. Any disruption in the battery power could lead to temporary loss of vital data. Kinetic-based micro-energy-harvesting is a technology that could prolong the battery life time or equivalently reduce the frequency of recharge or battery replacement. Focusing on a Coulomb-Force Parametric Generator (CFPG) micro harvesting architecture, several machine learning approaches are presented in this paper to optimally tune the electrostatic force parameter; and therefore, maximize the generated power.Conference Dates
October 16-18, 2019
Conference Location
Ottawa
Conference Title
The 7th Annual IEEE International Conference on Wireless for Space and Extreme Environments
Pub Type
Conferences
Download Paper
Keywords
energy harvesting, wearable sensors, microgenerator, CFPG
Citation
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Created December 5, 2019, Updated May 13, 2020