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Identification of Unknown Electromagnetic Interference Sources Based on Siamese-CNN

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Abstract

The prerequisite for promptly locating electromagnetic interference sources (EMIS) is the identification of EMIS. This research provides a new method for EMIS identification based on Siamese-CNN. A new convolutional neural network (CNN) structure is developed to extract the features of the EMIS. The symmetrical Siamese is adopted to enhance the number of training samples. The similarity metric of Siamese and the CNN-based subnetwork are merged in order to increase the similarity of samples from the same class and the differences between samples from different classes. A new loss function based on contrastive loss and cross-entropy loss is proposed to increase classification accuracy and discover unknown EMIS. The spectrums of EMIS are used as experimental datasets. The results show that the proposed method based on Siamese-CNN is resilient and has good generalization for training sets of various sizes. The identification accuracy for known EMIS can reach 100%, and the identification accuracy for unknown EMIS is more than 90%.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work is supported by National Key R&D Program of China (No. 2018YFC0809500).

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Authors and Affiliations

  1. BaiLie School of Petroleum Engineering, Lanzhou City University, Lanzhou, 730070, China

    Ying-Chun Xiao

  2. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 611756, China

    Ying-Chun Xiao, Feng Zhu, Shengxian Zhuang & Yang Yang

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  1. Ying-Chun Xiao
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  2. Feng Zhu
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  3. Shengxian Zhuang
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  4. Yang Yang
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Correspondence to Ying-Chun Xiao.

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Xiao, YC., Zhu, F., Zhuang, S. et al. Identification of Unknown Electromagnetic Interference Sources Based on Siamese-CNN. J Electron Test 39, 597–609 (2023). https://doi.org/10.1007/s10836-023-06082-7

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