J
Junliang Liu
Researcher at National University of Defense Technology
Publications - 13
Citations - 442
Junliang Liu is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Engineering & Nutation. The author has an hindex of 4, co-authored 10 publications receiving 255 citations.
Papers
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Journal ArticleDOI
Convolutional neural networks for time series classification
TL;DR: A novel convolutional neural network framework is proposed for time series classification that can discover and extract the suitable internal structure to generate deep features of the input time series automatically by using convolution and pooling operations.
Journal ArticleDOI
Micromotion dynamics and geometrical shape parameters estimation of exoatmospheric infrared targets
TL;DR: In this paper, the authors explored a way of jointly estimating micromotion dynamics and geometrical shape parameters from the IR signals of targets in remote detection distance, and they found that the dynamic properties of the target would induce a periodic fluctuating variation on the IR irradiance intensity signature.
Proceedings ArticleDOI
Waveforms classification based on convolutional neural networks
TL;DR: Experimental results show that CNN can obtain state of the art performance for waveforms classification in terms of classification accuracy and noise tolerance.
Journal ArticleDOI
Calibration Experiments of CFOSAT Wavelength in the Southern South China Sea by Artificial Neural Networks
TL;DR: In this paper , the authors analyzed the applicability of calibrating the dominant wavelength by using artificial neural networks and mean impact value analysis based on two sets of buoy data with a 2-year observation period and contemporaneous ERA5 reanalysis data.
Journal ArticleDOI
Ballistic targets micro-motion and geometrical shape parameters estimation from sparse decomposition representation of infrared signatures
TL;DR: Experimental results demonstrate that the micro-motion and geometrical shape parameters can be effectively estimated using the proposed method, when the noise of the IR signature is in an acceptable level, for example, SNR>0 dB.