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Bao et al. Complex Eng Syst 2022;2:16 I http://dx.doi.org/10.20517/ces.2022.30 Page 9 of 10

4. CONCLUSIONS
In this paper, a generative adversarial network-based pulsar positive sample generation method is proposed
for high-quality sample generation in light of the sample imbalance problem in pulsar candidate identification
tasks. Training is performed on a dataset containing only positive samples, and the converged model is used
to generate a series of high-quality samples to expand the dataset. A residual network-based pulsar candidate
identification method is proposed, and it has a better fitting ability compared to shallow neural network mod-
els. Comparison experiments have been conducted with recent pulsar identification methods on the HTRU
dataset [26] , and the experimental results demonstrated that the proposed method achieved optimal results on
the dataset compared to the CNN method.

DECLARATIONS

Authors’ contributions
Made significant contributions to the conception and experiments: Yin Q, Liu G, Zhe X
Made significant contributions to the writing: Bao Z, Li Y
Made substantial contributions to the revision and translation: Xie Y, Xu Y, Zhang Z

Availability of data and materials
The data underlying this article are available at http://astronomy.swin.edu.au/~vmorello.

Financial support and sponsorship
TheresearchworkdescribedinthispaperwassupportedbytheJointResearchFundinAstronomy(U2031136)
under cooperative agreement between the NSFC and CAS and the National Key Research and Development
Program of China (No. 2018AAA0100203).

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2022.

REFERENCES
1. Personalname=Teoh A. The ATNF Pulsar Database. Available from http://astronomy.swin.edu.au/~vmorello/
2. Keith MJ, Jameson A, Van Straten W, et al. The High Time Resolution Universe Pulsar Survey - I. System configuration and initial
discoveries. Monthly Notices of the Royal Astronomical Society 2010;409:619-27. DOI
3. Eatough RP, Molkenthin N, Kramer M, et al. Selection of radio pulsar candidates using artificial neural networks. Monthly Notices of the
Royal Astronomical Society 2010;407:2443–50. DOI
4. Shamsolmoali P, Chanussot J, Zareapoor M, Zhou H, Yang J. Multipatch feature pyramid network for weakly supervised object detection
in optical remote sensing images. IEEE Trans Geosci Remote Sensing 2022;60:1-13. DOI
5. Bates S, Bailes M, Barsdell B, et al. The high time resolution universe pulsar survey—VI. An artificial neural network and timing of 75
pulsars. Monthly Notices of the Royal Astronomical Society 2012;427:1052–65. DOI
6. Morello V, Barr E, Bailes M, et al. SPINN: a straightforward machine learning solution to the pulsar candidate selection problem. Monthly
Notices of the Royal Astronomical Society 2014;443:1651–62. DOI
7. Zhu W, Berndsen A, Madsen E, et al. Searching for pulsars using image pattern recognition. ApJ 2014;781:117. DOI