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논문 기본 정보

자료유형
학술저널
저자정보
Jeehyun Lee (Sogang University) Jihoon Kwon (Seoul National University) Jin-Ho Bae (Jeju National University) Chong Hyun Lee (Jeju National University)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.6 No.1
발행연도
2017.2
수록면
10 - 17 (8page)

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초록· 키워드

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We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and 16<SUP>th</SUP>-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a twolayer convolutional neural network (CNN). The proposed CNN is composed of six (6x6) convolution filters at the first and second layers, with (5x5) max pooling for the first layer and (2x2) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the 16<SUP>th</SUP>-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultrawideband (UWB) sensor.

목차

Abstract
1. Introduction
2. Data Measurement
3. The Proposed Scheme
4. Performance Evaluation
5. Conclusion
References

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UCI(KEPA) : I410-ECN-0101-2017-569-002238372