Abstract
Dwarf nova is a specific type of erupting cataclysmic viable star. Finding more Dwarf novae is significant in studying the theory about transferring matter to an accretion. To extract spectral features, convolution operation is an effective means which can improve the accuracy of spectral recognition. One dimensional convolutional neural network with four hidden layers is designed in this paper. Its feature detection layer implicitly learns the spectral features through training data. It reduces the complexity of the network through weight sharing so as to avoid the explicit extraction of spectral features. Convolution kernel with stable distribution is fitted in the form of discriminant learning from a mass of mixed spectra. The strategy can effectively reduce the complexity of data reconstruction in the process of feature extraction and classification. The experimental results indicate that the proposed technique achieves better accuracy and reliability.
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Acknowledgments
This paper is partially supported by Shandong Provincial Natural Science Foundation, China (ZR2017MA046) and the National Science Foundation of China (11473019). The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Zhao, Y. (2020). Feature Extraction of Dwarf Nova with Convolution Operation. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_18
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DOI: https://doi.org/10.1007/978-3-030-25128-4_18
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