• Laser Journal
  • Vol. 45, Issue 8, 120 (2024)
SUN Jiabao1, QIU Yijian2,3,*, and QIN Kun4
Author Affiliations
  • 1Yuan Pei College, Shaoxing University, Shaoxing Zhejiang 312000, China
  • 2School of Mechanical and Electrical Engineering, Nanchang University, Nanchang 330031, China
  • 3The Institute of Applied Physics, Jiangxi Academy of Sciences, Nanchang 330029, China
  • 4College of Industrial Design, Hubei University of Technology, Wuhan 430068, China
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    DOI: 10.14016/j.cnki.jgzz.2024.08.120 Cite this Article
    SUN Jiabao, QIU Yijian, QIN Kun. Research on continuous interpolation of traffic data in optical communication networks based on improved transfer learning[J]. Laser Journal, 2024, 45(8): 120 Copy Citation Text show less

    Abstract

    The traffic data in optical communication networks has the characteristics of large-scale and high dimensionality, and the inconsistency of data dimensions amplifies the differences between the data, resulting in unsatisfactory interpolation effects. Therefore, a continuous interpolation method for optical communication network traffic data based on improved transfer learning is proposed. The Box-Cox transformation method is used to standardize the traffic data and unify the data scales and dimensions. The convolutional neural network is improved using deep learning theory and VNet technology. By updating the network parameters, the continuous interpolation results are matched with the ideal data, obtaining the continuous interpolation results of the traffic data. Experimental results show that the signal-to-noise ratio of the proposed method is always higher than 27.83 dB, and the frequency-waveform distribution graph is most similar to the ideal data, with a coefficient of determination above 0.8, which can obtain high-quality interpolation results.
    SUN Jiabao, QIU Yijian, QIN Kun. Research on continuous interpolation of traffic data in optical communication networks based on improved transfer learning[J]. Laser Journal, 2024, 45(8): 120
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