Small samples electromagnetic pulse classification&nbsp; using an optimal ensemble learning method

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (11) : 193-198.

Small samples electromagnetic pulse classification using an optimal ensemble learning method

WANG Xueqing1,2,3, LIU Xiaojun1,2, LIU Yan4, CHENG Lu1,2,3, XU Xin1,2, JI Yicai1,2, FANG Guangyou1,2

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Abstract

The classification of nuclear and lightning electromagnetic pulse is a key problem in nuclear identification system, in which the main challenge is that the level of class imbalance can be as huge as . For this purpose, we propose SMALLBAG, a novel ensemble learning method. First, we develop a preprocessing procedure to rebuild training set, in which data augmentation methods are used to generate more minority class samples and resampling scheme is proposed to approach class balance. Second, feature extraction is performed in time, frequency and wavelet domains, which are used to characterize the signal. Finally, the ensemble learning method is proposed to alleviate the influence of class imbalance and improve the performance of identification. Experimental results indicate that the simplicity of the proposed learning method ensures the identification accuracy and real-time requirements at the same time, i.e. 99.99% identification accuracy and 0.67 ms testing time per sample.

Key words

ensemble learning / class imbalance / small samples / nuclear electromagnetic pulse (NEMP) / lightning electromagnetic pulse (LEMP)

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WANG Xueqing1,2,3, LIU Xiaojun1,2, LIU Yan4, CHENG Lu1,2,3, XU Xin1,2, JI Yicai1,2, FANG Guangyou1,2. Small samples electromagnetic pulse classification using an optimal ensemble learning method[J]. Journal of Vibration and Shock, 2023, 42(11): 193-198

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