본 논문에서는 실화재와 비화재보를 분류하기 위한 경량형 XGBoost 모델의 배포와 장치에서의 생성을 다룬다. 실제 화재 연기와 비화재보 데이터를 이용하여 모델을 학습하고, 다양한 파라미터 조합을 통해 성능을 평가하였다. 생성된 모델의 파라미터를 추출하여 자원 제약이 있는 MCU 에서 재구성하고, 분류 모델 패키지와의 성능 차이를 비교하였다. 실험 결과, 경량화된 모델이 제한된 자원 환경에서도 유사한 성능을 유지할 수 있음을 확인하였다. BLE 통신을 통해 모델 파라미터를 전송하고, 최적화된 메모리 사용을 통해 MCU 에서의 효과적인 예측이 가능함을 검증하였다.
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