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학술대회 Object Classification of UWB Responses Using ST-CNN
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저자
고석갑, 이병탁
발행일
201610
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2016, pp.794-796
DOI
https://dx.doi.org/10.1109/ICTC.2016.7763299
협약과제
16ZI2200, 지역광부품 고도화를 위한 광융합 기술개발, 이병탁
초록
UWB response includes unique characteristics of reflecting objects. Because the response is the combination of many distortion, resonance, and multi-paths, the object classification of UWB response is difficult. In this paper, we propose an object classification method using S-Transform and convolution neural network. S-Transform converts time series data of UWB response to frequency-Time domain which convolutional neural network can learn and classify.
키워드
classification, CNN, Deep Learning, Machine Learning, S-Transform, UWB
KSP 제안 키워드
Classification method, Convolution neural network(CNN), Object classification, S-transform, Time series data, deep learning(DL), machine Learning, time-domain, unique characteristics