[1] Likar A, Vidmar T. A peak-search method based on spectrum convolution[J]. Journal of Physics D: Applied Physics, 36, 1903-1909(2003).

[2] Li Xiaozhe, Zhang Qingxian, Tan Heyi et al. Fast nuclide identification based on a sequential Bayesian method[J]. Nuclear Science and Techniques, 32, 143(2021).

[3] Ling Yongsheng, Huang Tian, Yue Qi et al. Improving the estimation accuracy of multi-nuclide source term estimation method for severe nuclear accidents using temporal convolutional network optimized by Bayesian optimization and hyperband[J]. Journal of Environmental Radioactivity, 242, 106787(2022).

[4] Zhang Jiangmei, Ren Junsong, Li Peipei. Complex radioactive nuclide identification method based on support vector machine[J]. Nuclear Electronics & Detection Technology, 36, 856-861(2016).

[5] El_Tokhy M S. Rapid and robust radioisotopes identification algorithms of X-Ray and gamma spectra[J]. Measurement, 168, 108456(2021).

[6] Wen Siying, Wang Bairong, Xiao Gang. The study on nuclide identification algorithm based on sequential Bayesian analysis[J]. Nuclear Electronics & Detection Technology, 36, 179-183(2016).

[7] Qi Sheng, Zhao Wei, Chen Ye et al. Comparison of machine learning approaches for radioisotope identification using NaI (TI) gamma-ray spectrum[J]. Applied Radiation and Isotopes, 186, 110212(2022).

[8] Hu Haohang, Zhang Jiangmei, Wang Kunpeng. Application of convolutional neural networks in identification of complex nuclides[J]. Transducer and Microsystem Technologies, 38, 154-156,160(2019).

[9] Wang Yao, Liu Zhiming, Wan Yaping. Energy spectrum nuclide recognition method based on long short-term memory neural network[J]. High Power Laser and Particle Beams, 32, 106001(2020).

[10] Turner A N, Wheldon C, Wheldon T K et al. Convolutional neural networks for challenges in automated nuclide identification[J]. Sensors, 21, 5238(2021).

[11] Zhao Wei, Shi Rui, Tuo Xianguo et al. Novel radionuclides identification method based on Hilbert–Huang Transform and Convolutional Neural Network with gamma-ray pulse signal[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1051, 168232(2023).

[12] Zhang Junyang, Wang Huili, Guo Yang. Review of deep learning[J]. Application Research of Computers, 35, 1921-1928,1936(2018).

[13] Chen Chen, Chai Zhilei, Xia Jun. Design and implementation of YOLOv2 accelerator based on Zynq7000 FPGA heterogeneous platform[J]. Journal of Frontiers of Computer Science and Technology, 13, 1677-1693(2019).

[14] He Dazhong, He Junhua, Liu Jun et al. An FPGA-based LSTM acceleration engine for deep learning frameworks[J]. Electronics, 10, 681(2021).

[15] Pacini T, Rapuano E, Fanucci L. FPG-AI: a technology-independent framework for the automation of CNN deployment on FPGAs[J]. IEEE Access, 11, 32759-32775(2023).

[16] Wang Bo, Shi Rui, Liu Minjun. Hardware acceleration method of convolutional neural network nuclide identification algorithm based on FPGA[J]. Nuclear Electronics & Detection Technology, 44, 334-343(2024).

[17] [17] Chen Liang. Research on the nuclide identification algithm digital spectra acquisition system[D]. Beijing: Tsinghua University, 2009

[18] Wang Jun, Feng Suncheng, Cheng Yong. Survey of research on lightweight neural network structures for deep learning[J]. Computer Engineering, 47, 1-13(2021).

[19] [19] Howard A G, Zhu Menglong, Chen Bo, et al. Mobiles: efficient convolutional neural wks f mobile vision applications[DBOL]. arXiv preprint arXiv: 1704.04861, 2017.

[20] [20] Sler M, Howard A, Zhu Menglong, et al. MobileV2: inverted residuals linear bottlenecks[C]Proceedings of 2018 IEEECVF Conference on Computer Vision Pattern Recognition. 2018: 45104520.