Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Beibei Xie1, Deming Kong This email address is being protected from spambots. You need JavaScript enabled to view it.2, Weihang Kong1 and Jiliang Chen1

1School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R. China
2School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R. China


 

Received: March 22, 2018
Accepted: September 11, 2018
Publication Date: March 1, 2019

Download Citation: ||https://doi.org/10.6180/jase.201903_22(1).0002  

ABSTRACT


Anovel noise reduction method based on variational mode decomposition (VMD) and singular value decomposition (SVD) is proposed to retrieve measurement information from the recovered Manchester coding signals. Firstly, the recovered Manchester coding signals are decomposed into the intrinsic mode functions (IMFs) by VMD. The time-frequency matrix constructed by the IMFs is further decomposed by SVD. Subsequently, the singular values, which are corresponding to the noise component in the signals, are removed with the aid of an appropriate threshold. Finally, the signal is reconstructed from the remained singular values by singular value inverse transform. The feasibility and effectiveness of the novel noise reduction method are verified by simulation signals and real signals. And the complexity of the proposed VMD-SVD method is evaluated. The experiment results show that the proposed method is more effective in the noise reduction for the recovered Manchester coding signals by comparing with the existing methods.


Keywords: Noise Reduction, Remote Transmission, Recovered Manchester Coding Signals, Variational Mode Decomposition, Singular Value Decomposition


REFERENCES


  1. [1] Xie, B. B., L. F. Kong, D. M. Kong, et al. (2017) Design and numerical simulation on an auto-cumulative flowmeter in horizontal oil-water two-phase flow, Review of Scientific Instruments 88(11), 115003. doi: 10. 1063/1.4995326
  2. [2] Xu, L., J. Chen, Z. Cao, X. Liu, and J. Hu (2014) Manchester code telemetry system for well logging using quasi-parallel inductive-capacitive resonance, Review of Scientific Instruments 85(7), 074704.doi: 10. 1063/1.4889888
  3. [3] Xu, L., J. Chen, Z. Cao, Z. Wen, R. Xie, X. Liu, and J. Hu (2016) Identification of oil-water flow patterns in a vertical well using a dual-ring conductance probe array, IEEE Transactions on Instrumentation & Measurement 65(5), 1249 1258. doi: 10.1109/TIM. 2016. 2537498
  4. [4] Sun, B., Y. L. Zhou, Y. B. Guan, and W. P. Hong (2006) Confirmation of threshold rule in signal denosing of gas-liquid two-phase flow differential pressure fluctuation using wavelet transform, Signal Processing 22(1), 96 99. doi: 10. 3969/j.issn.1003-0530. 2006.01.023
  5. [5] Wotunde, A. A., and R. N. Horne (2012) An improved adjoint sensitivity computation for multiphase flow using wavelets, Spe Journal 17(2), 402 417. doi: 10.2118/133866-PA
  6. [6] Shiu, M. C., L. Y. Wei, J. W. Liu, et al. (2017) Ahybrid one-step-ahead Time series model based on GA-SVR and EMD for Forecasting electricity loads, Journal of Applied Science and Engineering 20(4), 467 476. doi: 10.6180/jase.2017.20.4.08
  7. [7] Dragomiretskiy, K., and D. Zosso (2014) Variational mode decomposition, IEEE Transactions on Signal Processing 62(3), 531 544. doi: 10.1109/TSP.2013. 2288675
  8. [8] Kyrchei, I. (2017) Weighted singular value decomposition and determinantal representations of the quaternion weighted moore-penrose inverse, Applied Mathematics & Computation 309-C, 1 16. doi: 10.1016/ j.amc.2017.03.048
  9. [9] Xie, B., D. Kong, L. Kong, et al. (2018) Analysis of vertical upward oil-gas-water three-phase flow based on multi-scale time irreversibility, Flow Measurement and Instrumentation 62, 9 18. doi: 10.1016/j. flowmeasinst.2018.03.003
  10. [10] He, B., and Y. Bai (2016) Signal-noise separation of sensor signal based on variational mode decomposition, IEEE International Conference on Communication Software and Networks, 132 138. doi: 10.1109/ ICCSN.2016.7586634
  11. [11] Wu, H., F. Zhou, and Y. Wu (2001) Intelligent identification system of flow regime of oil-gas-water multiphase flow, International Journal of Multiphase Flow 27(3),459 475.doi:10.1016/S0301-9322(00)00022-7
  12. [12] Nakayama, H., and T. Tsuda (2016) Efficient two-step middle-level part feature extraction for fine-grained visual categorization, IEICE Transactions on Information and Systems E99-D(6), 1626 1634. doi: 10. 1587/transinf.2015EDP7358