디지털산업정보학회논문지 (Journal of Korea Society of Digital Industry and Information Management)

  • 제17권3호
  • /
  • Pages.9-15
  • /
  • 2021
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  • 1738-6667(pISSN)
  • /
  • 2713-9018(eISSN)
디지털산업정보학회 (Korea Society of Digital Industry and Information Management)
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Edge Impulse 기계 학습 기반의 임베디드 시스템 설계

Edge Impulse Machine Learning for Embedded System Design

  • 홍선학 (서일대학교 컴퓨터전자공학과)
  • 투고 : 2021.07.27
  • 심사 : 2021.09.10
  • 발행 : 2021.09.30
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초록

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

키워드

과제정보

본 연구는 2021년 서일대학교 교내학술연구비 지원으로 수행되었음.

참고문헌

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