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MI 센서기반의 금속탐지용 뉴럴네트워크 성능비교에 관한연구
하성재(Sungjae Ha),이동우(Dongwoo Lee),김회준(Hoijun Kim),김응조(Eung-Jo Kim),권순철(Soonchul Kwon),이승현(Seunghyun Lee) 산업기술교육훈련학회 2021 산업기술연구논문지 (JITR) Vol.26 No.2
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.
MI 센서기반의 금속탐지를 위한 RNN 알고리즘 설계에 관한연구
하성재(Sungjae Ha),이동우(Dongwoo Lee),김회준(Hoijun Kim),김응조(Eung-Jo Kim),권순철(Soonchul Kwon),이승현(Seunghyun Lee) 산업기술교육훈련학회 2021 산업기술연구논문지 (JITR) Vol.26 No.2
This paper is a study of metal detection methods using multiple MI sensors using deep learning. The MI sensor is a sensor that measures the impedance change of an atypical wire by the electrical conduction and transmittance of a detector. Simple principles, small power consumption, and compact manufacturing are possible. This is a field-available sensor that requires limited weight and power, such as a mobile environment. However, sensors are generally weak in strength due to the nature of sensors that detect changes in magnetic fields. This paper compares and analyzes the detection performance with the depth of each network for location detection via RNN on 16-channel raw data extracted from sensors in a self-impedance manner.