Top > JACIII > An Improved Tuning Control Algorithm Based on SVD for FID Sign ...

single-jc.php

JACIII Vol.21 No.1 pp. 133-138
doi: 10.20965/jaciii.2017.p0133
(2017)

Paper:

Views over last 60 days: 796

An Improved Tuning Control Algorithm Based on SVD for FID Signal

Huan Liu*,**,***, Hao Bin Dong*,***,†, Jian Ge*,***, Pei Pei Guo*, Bing Jie Bai**, and Cheng Zhang*

*Department of School of Automation, China University of Geosciences
Wuhan, Hubei 430074, China

**Department of Institute of Geophysics & Geomatics, China University of Geosciences
Wuhan, Hubei 430074, China

***Science and Technology on Near-Surface Detection Laboratory
Wuxi 214035, China

†Corresponding author

Received:
July 6, 2016
Accepted:
August 14, 2016
Published:
January 20, 2017
Creative Commons License
Keywords:
high precision, auto tuning, SVD, FID, proton precession magnetometer
Abstract
The free induction decay (FID) quality signal of a proton precession magnetometer is closely related to tuning precision. To solve the commonly used current tuning problem method, we propose improving control algorithm tuning based on singular value decomposition (SVD). The space matrix is constructed by acquiring an analog-to-digital converter (ADC) for untuned FID signals, then conducting SVD to eliminate noise and obtain a useful signal. The fast Fourier transform (FFT) is then applied to the denoised FID signal to extract the time-frequency feature. Based on theory analysis, simulation modeling and actual FID signal testing, results show that compared to general tuning methods such as peak detection and auto correlation, our proposed algorithm improves sensor tuning precision and shortens tuning process time to one second or less.
Cite this article as:
H. Liu, H. Dong, J. Ge, P. Guo, B. Bai, and C. Zhang, “An Improved Tuning Control Algorithm Based on SVD for FID Signal,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.1, pp. 133-138, 2017.
Data files:
References
  1. [1] V. Sapunov, E. Narkhov, A. Denisov et al., “A gradient tolerance measurement method of borehole ocerhauser magnetometer LOM-2 in laboratory condition,” Proc. of the 15th Int. Multidisciplinary Scientific GeoConf. SGEM, pp. 29-36, 2015.
  2. [2] A. Denisov, V. Sapunov, and B. Rubinstein, “Broadband mode in proton-precession magnetometers with signal processing regression methods,” Meas. Sci. Technol., Vol.25, No.5, pp. 55103-55108, 2014.
  3. [3] A. Zikmund, M. Janosek, M. Ulvr et al., “Precise Calibration Method for Triaxial Magnetometers No.Requiring Earth’s Field Compensation,” IEEE Trans. Instrum. Meas., Vol.64, No.5, pp. 1250-1255, 2015.
  4. [4] R. Winslow, C. Johnson, B. Anderson et al., “Observations of Mercury’s northern cusp region with MESSENGER’s Magnetometer,” Geophysical Research Letters, Vol.39, No.8, p. L08112, 2012.
  5. [5] N. Enkin, G. Liu, M. Gimenez-Lopez et al., “A high saturation factor in Overhauser DNP with nitroxide derivatives: the role of 14 N nuclear spin relaxation,” Physical Chemistry Chemical Physics, Vol.17, No.17, pp. 11144-11149, 2015.
  6. [6] S. Tenberg, R. McNeil, S. Rubbert et al., “Narrowing of the Overhauser field distribution by feedback-enhanced dynamic nuclear polarization,” Physical Review B, Vol.92, No.19, pp. 314-322, 2015.
  7. [7] Y. Seki, A. Kandori, T. Hashizume et al., “Microtesla NMR Measurement of Protons and Fluorine Nuclei Using a SQUID Gradiometer,” IEEE Trans. Applied Superconductivity, Vol.23, No.3, p. 1600904, 2013.
  8. [8] Y. Hatsukade, T. Abe, S. Tsunaki et al., “Application of ultra-low field HTS-SQUID NMR/MRI to contaminant detection in food,” IEEE Trans. Applied Superconductivity, Vol.23, No.3, p. 1602204, 2013.
  9. [9] H. B. Dong, H. Liu, J. Ge et al., “A high-Precision frequency measurement algorithm for FID signal of proton magnetometer,” IEEE Trans. Instrum. Meas., Vol.65, No.4, pp. 898-904, 2016.
  10. [10] R. Winck and W. Book, “The SVD System for First-Order Linear Systems,” IEEE Trans. Control Systems Technology, Vol.23, No.3, pp. 1213-1220, 2015.
  11. [11] D. Kalman, “A Singularly Valuable Decomposition: The SVD of a Matrix,” J. of College Mathematics, Vol.27, No.1, pp. 2-23, 1996.
  12. [12] H. Liu, H. B. Dong, J. Ge et al., “Research on a secondary tuning algorithm based on SVD & STFT for FID signal,” Meas. Sci. Technol., Vol.27, No.10, p. 105006, 2016.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024