MI 센서기반의 금속탐지를 위한 RNN 알고리즘 설계에 관한연구
A Study on the RNN Algorithm for Metal Detection Based on MI Sensor
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초록
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.
Abstract
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.
참고문헌 (15)
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