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Application of a ring coupled double-Duffing oscillator to a scheme for identifying the coulter signal with a low SNR

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Języki publikacji
EN
Abstrakty
EN
In order to use chaotic oscillators to identify Coulter signals with a low SNR (SNR⩽0), a Gaussian pulse signal is used to simulate the Coulter signal, and we study the continuous synchronous mutation (CSM) phenomenon of a chaotic ring coupled double-Duffing (RCDD) oscillator to identify the signals. The maximum difference between the two state variables in the oscillator can be used to determine the anti-noise ability of the oscillator and construct a function to identify pulse amplitudes. A Simulink model is constructed to verify that the proposed method can be used to identify pulse amplitudes with a low SNR, which provides an approach for developing a technology of measuring Coulter signals with the low SNR.
Rocznik
Strony
379--393
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
  • Center of Materials Science and Optoelectronics Engineering, College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
autor
  • Center of Materials Science and Optoelectronics Engineering, College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
  • Center of Materials Science and Optoelectronics Engineering, College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, China
Bibliografia
  • 1. Agrawal M., Prasad A., Ramaswamy R., 2010, Quasiperiodic forcing of coupled chaotic systems, Physical Review E, 81, 2, 026202.
  • 2. Anishchenko V., Nikolaev S., Kurths J., 2008, Bifurcational mechanisms of synchronization of a resonant limit cycle on a two-dimensional torus, Chaos: An Interdisciplinary Journal of Nonlinear Science, 18, 3, 037123.
  • 3. Baibolatov Y., Rosenblum M., Zhanabaev Z.Z., Kyzgarina M., Pikovsky A., 2009, Periodically forced ensemble of nonlinearly coupled oscillators: From partial to full synchrony, Physical Review E, 80, 4, 046211.
  • 4. Birx D.L., Pipenberg S.J., 1992, Chaotic oscillators and complex mapping feed forward networks (CMFFNS) for signal detection in noisy environments, Proceedings of the IJCNN International Joint Conference on Neural Networks.
  • 5. Ciszak M., Montina A., Arecchi T., 2009, Control of transient synchronization with external stimuli, Chaos: An Interdisciplinary Journal of Nonlinear Science, 19, 1, 015104.
  • 6. Coulter W.H., 1953, Means for Counting Particles Suspended in a Fluid, U.S. Patent 2,656,508.
  • 7. Doutre D., 1984, The development and application of a rapid method of evaluating molten metal cleanliness, Ph.D.Thesis, McGill University, Montreal.
  • 8. Duane G.S., Grabow C., Selten F., Ghil M., 2017, Introduction to focus issue: Synchronization in large networks and continuous mediadata, models, and supermodels, Chaos: An Interdisciplinary Journal of Nonlinear Science, 27, 126601.
  • 9. Feng J., Zhang Q., Wang W., 2012, Chaos of several typical asymmetric systems, Chaos, Solitons and Fractals, 45, 7, 950-958.
  • 10. Fu G.-Y., Li Z.-S., 2010, Adaptive synchronization of a hyperchaotic Lü system based on extended passive control, Chinese Physics B, 19, 6, 060505.
  • 11. Goldobin D.S., Pikovsky A., 2005, Synchronization and desynchronization of self-sustained oscillators by common noise, Physical Review E, 71, 4, 045201.
  • 12. Guthrie R.I.L., Li M., 2001, In situ detection of inclusions in liquid metals: Part II. Metallurgical applications of LiMCA systems. Metallurgical and Materials Transactions B, 32, 6, 1081-1093.
  • 13. Huang D., Yang J., Zhou D., Sanjuán M.A.F., Liu H., 2019, Recovering an unknown signal completely submerged in strong noise by a new stochastic resonance method, Communications in Nonlinear Science and Numerical Simulation, 66, 156-166.
  • 14. Jagtiani A., Sawant R., Carletta J., Zhe J., 2008, Wavelet transform-based methods for denoising of Coulter counter signals, Measurement Science and Technology, 19, 6, 065102.
  • 15. Li H., Tian R., Xue Q., Zhang Y., Zhang X., 2022, Improved variable scale-convex-peak method for weak signal detection, Chaos Solitons and Fractals, 156, 111852.
  • 16. Li J., Zhang L., Wang D., 2014, Unique normal form of a class of 3 dimensional vector fields with symmetries, Journal of Differential Equations, 257, 7, 2341-2359.
  • 16. Ott E., Platig J.H., Antonsen T.M., Girvan M., 2008, Echo phenomena in large systems of coupled oscillators, Chaos: An Interdisciplinary Journal of Nonlinear Science, 18, 3, 037115.
  • 17. Pecora L.M., Carroll T.L., 1990, Synchronization in chaotic systems, Physical Review Letters, 64, 8, 821-824.
  • 18. Woafo P., Kraenkel R.A., 2002, Synchronization: Stability and duration time, Physical Review E, 65, 3, 036225.
  • 19. Wu Y., Zhang S., Sun J., Rolfe P., 2011a, Abrupt change of synchronization of ring coupled Duffing oscillator, Acta Physica Sinica – Chinese Edition, 60, 2.
  • 20. Wu Y., Zhang S., Sun J., Rolfe P., Li Z., 2011b, Transient synchronization mutation of ring coupled Duffing oscillators driven by pulse signal, Acta Physica Sinica – Chinese Edition, 60, 10, 100509-100501.
  • 21. Ye Y., Yue L., Mandic D., Bao-Jun Y., 2009, Regular nonlinear response of the driven Duffing oscillator to chaotic time series, Chinese Physics B, 18, 3, 958-968.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9fe0b302-b3a5-4fab-83cd-0415f483b4b0
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