MI 센서기반의 금속탐지용 뉴럴네트워크 성능비교에 관한연구

• 영문명: A Study on the Performance Comparison of Neural Network for Metal Detection Based on MI Sensor
• 발행기관: 한국산업기술융합학회(구. 산업기술교육훈련학회)
• 저자명: 하성재(Sungjae Ha) 이동우(Dongwoo Lee) 김회준(Hoijun Kim) 김응조(Eung-Jo Kim) 권순철(Soonchul Kwon) 이승현(Seunghyun Lee)
• 간행물 정보: 『산업기술연구논문지』산업기술연구논문지 제26권 2호, 113~124쪽, 전체 12쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2021.06.30

영문 초록

This paper is a study on the efficiency of the filtering method of signal processing and the metal detection method using deep learning for data obtained from multiple MI sensors. The MI sensor is a principle that detects changes in magnetic field and is a passive sensor that detects metal objects. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small, so there is a limit to the detectable distance. In order to effectively detect and analyze this, a method using deep learning was applied. In addition, the performance of the deep learning model was compared and analyzed using the filtering method of signal processing. In this paper, the detection performance of CNN and RNN networks was compared and analyzed from the data extracted from the self-impedance sensor. The RNN model showed higher performance than the CNN model. However, in the shallow stage, the CNN model showed higher performance than the RNN model.

목차

Ⅰ. 서 론

Ⅱ. 관련연구

Ⅲ. 네트워크

Ⅳ. 실험결과

Ⅴ. 결 론

키워드

합성곱신경망 딥러닝 전자기유도 자기임피던스센서 순환신경망 CNN Deep Learning Electromagnetic Induction MI sensor RNN