| Autor: |
Sunaga, Kazuki, Kondo, Masaaki, Matsutani, Hiroki |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Micro (2023) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/MM.2023.3314711 |
| Popis: |
The accuracy of tinyML applications is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article introduces a neural network based on-device learning (ODL) approach to address this issue by retraining in deployed environments. Our approach relies on semi-supervised sequential training of multiple neural networks tailored for low-end edge devices. This article introduces its algorithm and implementation on wireless sensor nodes consisting of a Raspberry Pi Pico and low-power wireless module. Experiments using vibration patterns of rotating machines demonstrate that retraining by ODL improves anomaly detection accuracy compared with a prediction-only deep neural network in a noisy environment. The results also show that the ODL approach can save communication cost and energy consumption for battery-powered Internet of Things devices. |
| Databáze: |
arXiv |
| Externí odkaz: |
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