Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Deep Learning-based Analysis of Meat Freshness Measurement

고기 신선도 측정 데이터의 딥러닝 기반 분석

  • Jang, Aera (Kangwon National University, College of Animal Life Science, Dept. of Applied Animal Science) ;
  • Kim, Hey-Jin (Kangwon National University, College of Animal Life Science, Dept. of Applied Animal Science) ;
  • Kim, Manbae (Kangwon National University, Dept. of Computer & Communications Engineering)
  • 장애라 (강원대학교 동물생명과학대학 동물응용과학과) ;
  • 김혜진 (강원대학교 동물생명과학대학 동물응용과학과) ;
  • 김만배 (강원대학교 컴퓨터정보통신공학과)
  • Received : 2020.02.14
  • Accepted : 2020.04.01
  • Published : 2020.05.30
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Abstract

The measurement of meat freshness at meat markets is important for the health of consumers. Currently a variety of sensors have been studied for the measurement of the meat freshness. Therefore, the analysis of sensor data is needed for the reduction of measurement errors. In this paper, we analyze the freshness measurement data of ten sensors based on deep learning. The measured data are composed of beef, pork and chicken, whose reliability and noise-robustness are examined by a deep neural network. Further, to search for multiple sensors better than a torrymeter, PCA (principle component analysis) is carried. Then, we validated that the performance of the three sensors outperforms the torrymeter in the experiment.

축산 판매장에서 판매하는 고기들의 신선도 측정은 소비자의 건강을 위해 필요한 기술이다. 신선도 측정을 목적으로 다양한 센서가 연구 개발되고 있다. 센서는 다양한 고기의 신선도 상태 때문에 측정 오류가 발생한다. 따라서 강인성을 가지는 센서를 검증한 후에, 사용하는 과정이 필요하다. 본 논문에서는 10개의 고기 신선도 측정 센서로 얻은 데이터의 분석을 통해서, 각 측정 센서의 성능을 심층신경망을 이용하여 조사한다. 고기 종류로는 소고기, 돼지고기, 닭고기를 대상으로 검증한다. 또한 토리미터보다 성능이 우수한 다중센서를 찾기 위해서 PCA를 이용하여 3개의 센서를 찾는다. 실험에서는 심층신경망으로 3개의 센서가 토리미터보다 우수함을 증명하였다.

Keywords

References

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