MI 센서기반의 금속탐지용 뉴럴네트워크 성능비교에 관한연구
A Study on the Performance Comparison of Neural Network for Metal Detection Based on MI Sensor
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
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.
Abstract
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.
참고문헌 (24)
- 이동우, "얼굴 검출을 위한 Flood Fill 기반의 개선된 피부색 추출기법", 한국융합학회논문지, 10(6), pp.7~14 (2019), 한국융합학회[DOI]
- 김회준, "동영상에서 보행자 추적을 위한 변형된 HOG 특징 추출에 관한 연구", 한국융합학회논문지, 10(3), pp.39~47 (2019), 한국융합학회[DOI]
- Kim, H., "NRANet—Neg-Region Attention Network for Salient Object Detection with Gaze Tracking", Sensors, 21(5), pp.2021~
- 김동인, "얼굴 검출을 위한 피부색 추출 과정에서 피부색 손실 영역 개선에 관한 연구", 한국융합학회논문지, 10(5), pp.1~8 (2019), 한국융합학회[DOI]
- Kumar, S. D., "Prediction of depression from EEG signal using long short term memory(LSTM)", 3rd International Conference on Trends in Electronics and Informatics(ICOEI), pp.1248~1253 (2019)
- Liu, C., "Plant electrical signal prediction based on LSTM Neural Network", Chinese Automation Congress (CAC) (2019), IEEE
- Tozer, J. C., "Magnetic Impedance Tomography", Annals of the New York Academy of Sciences, 873(1), pp.353~359 (1999)
- Jankowski, J., "Hall sensors for extreme temperatures", Sensors, 11(1), pp.876~885 (2011)
- Freitas, P. P., "Magnetoresistive sensors", Journal of Physics : Condensed Matter, 19(16), pp.165221~ (2007)
- Li, B., "Flexible magnetoimpedance sensor", Journal of Magnetism and Magnetic Materials, 378, pp.499~505 (2015)
- Krizhevsky, A., "Advances in neural information processing systems", pp.1097~1105 (2012)
- LeCun, Y., "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86(11), pp.2278~2324 (1998)
- Basha, S. S., "Impact of fully connected layers on performance of convolutional neural networks for image classification", Neurocomputing, 378, pp.112~119 (2020)
- Zaremba, W., "Recurrent neural network regularization", arXiv preprint arXiv:1409. 2329, ~ (2014)
- Simonyan, K., "Very deep convolutional networks for large-scale image recognition", arXiv preprint arXiv :1409. 1556, ~ (2014)
- Szegedy, C., "Going deeper with convolutions", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1~9 (2015)
- Wang, F., "Residual attention network for image classification", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3156~3164 (2017)
- Deep neural networks for object detection, "Szegedy, C.", ~ (2013)
- Hochreiter, S., "Long short-term memory", Neural computation, 9(8), pp.1735~1780 (1997)
- Deep learning using rectified linear units (relu), "Agarap, A. F.", ~ (2018)
- Nwankpa, C., "Activation functions : Comparison of trends in practice and research for deep learning", arXiv preprint arXiv : 1811. 03378, ~ (2018)
- Zhao, H., "Loss functions for neural networks for image processing", ~ (2015)
- Katarzyna Janocha, "On Loss Functions for Deep Neural Networks in Classification", Schedae Informaticae, 1, pp.2016 (2017), ~[DOI]
- Kingma, D. P., "Adam: A method for stochastic optimization", arXiv preprint arXiv : 1412. 6980, ~ (2014)