IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v481y2017icp1-10.html
   My bibliography  Save this article

A new approach to sparse decomposition of nonstationary signals with multiple scale structures using self-consistent nonlinear waves

Author

Listed:
  • Young, Hsu-Wen Vincent
  • Hsu, Ke-Hsin
  • Pham, Van-Truong
  • Tran, Thi-Thao
  • Lo, Men-Tzung

Abstract

A new method for signal decomposition is proposed and tested. Based on self-consistent nonlinear wave equations with self-sustaining physical mechanisms in mind, the new method is adaptive and particularly effective for dealing with synthetic signals consisting of components of multiple time scales. By formulating the method into an optimization problem and developing the corresponding algorithm and tool, we have proved its usefulness not only for analyzing simulated signals, but, more importantly, also for real clinical data.

Suggested Citation

  • Young, Hsu-Wen Vincent & Hsu, Ke-Hsin & Pham, Van-Truong & Tran, Thi-Thao & Lo, Men-Tzung, 2017. "A new approach to sparse decomposition of nonstationary signals with multiple scale structures using self-consistent nonlinear waves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 1-10.
    • Handle: RePEc:eee:phsmap:v:481:y:2017:i:c:p:1-10
    DOI: 10.1016/j.physa.2017.04.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117303096
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.04.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Yung-Hung & Young, Hsu-Wen Vincent & Lo, Men-Tzung, 2016. "The inner structure of empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1003-1017.
    2. Hu, Kun & Peng, C.K. & Huang, Norden E. & Wu, Zhaohua & Lipsitz, Lewis A. & Cavallerano, Jerry & Novak, Vera, 2008. "Altered phase interactions between spontaneous blood pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear assessment of cerebral autoregulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(10), pages 2279-2292.
    3. Wang, Yung-Hung & Yeh, Chien-Hung & Young, Hsu-Wen Vincent & Hu, Kun & Lo, Men-Tzung, 2014. "On the computational complexity of the empirical mode decomposition algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 159-167.
    4. Yeh, Chien-Hung & Lo, Men-Tzung & Hu, Kun, 2016. "Spurious cross-frequency amplitude–amplitude coupling in nonstationary, nonlinear signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 143-150.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yeh, Chien-Hung & Lo, Men-Tzung & Hu, Kun, 2016. "Spurious cross-frequency amplitude–amplitude coupling in nonstationary, nonlinear signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 143-150.
    2. Wang, Yung-Hung & Young, Hsu-Wen Vincent & Lo, Men-Tzung, 2016. "The inner structure of empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1003-1017.
    3. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
    4. Wei Sun & Ming Duan, 2019. "Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machin," Energies, MDPI, vol. 12(2), pages 1-27, January.
    5. Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
    6. Piersanti, Giovanni & Piersanti, Mirko & Cicone, Antonio & Canofari, Paolo & Di Domizio, Marco, 2020. "An inquiry into the structure and dynamics of crude oil price using the fast iterative filtering algorithm," Energy Economics, Elsevier, vol. 92(C).
    7. Liu, Hui & Tian, Hongqi & Liang, Xifeng & Li, Yanfei, 2015. "New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 83(C), pages 1066-1075.
    8. Dong, Shuoxuan & Zhou, Yang & Chen, Tianyi & Li, Shen & Gao, Qiantong & Ran, Bin, 2021. "An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    9. Wang, Haoyu & Di, Junpeng & Yang, Zhaojun & Han, Qing, 2020. "Assessment of mutual fund performance based on Ensemble Empirical Mode Decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    10. Xiao, Liye & Shao, Wei & Yu, Mengxia & Ma, Jing & Jin, Congjun, 2017. "Research and application of a combined model based on multi-objective optimization for electrical load forecasting," Energy, Elsevier, vol. 119(C), pages 1057-1074.
    11. Vincent Douchamps & Matteo Volo & Alessandro Torcini & Demian Battaglia & Romain Goutagny, 2024. "Gamma oscillatory complexity conveys behavioral information in hippocampal networks," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    12. Qiang Gao & He-Sheng Tang & Jia-Wei Xiang & Yongteng Zhong, 2018. "A multi-sensor fault detection strategy for axial piston pump using the Walsh transform method," International Journal of Distributed Sensor Networks, , vol. 14(4), pages 15501477187, April.
    13. Wei Sun & Mohan Liu & Yi Liang, 2015. "Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm," Energies, MDPI, vol. 8(7), pages 1-23, June.
    14. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
    15. Wei Jiang & Yanhe Xu & Yahui Shan & Han Liu, 2018. "Degradation Tendency Measurement of Aircraft Engines Based on FEEMD Permutation Entropy and Regularized Extreme Learning Machine Using Multi-Sensor Data," Energies, MDPI, vol. 11(12), pages 1-18, November.
    16. Eoghan T. Chelmiah & Violeta I. McLoone & Darren F. Kavanagh, 2023. "Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings," Energies, MDPI, vol. 16(14), pages 1-20, July.
    17. Wang, Yung-Hung & Yeh, Chien-Hung & Young, Hsu-Wen Vincent & Hu, Kun & Lo, Men-Tzung, 2014. "On the computational complexity of the empirical mode decomposition algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 159-167.
    18. Jujie Wang & Yanfeng Wang & Yaning Li, 2018. "A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction," Energies, MDPI, vol. 11(2), pages 1-33, February.
    19. Zhu, Ting & Wang, Wenbo & Yu, Min, 2023. "A novel hybrid scheme for remaining useful life prognostic based on secondary decomposition, BiGRU and error correction," Energy, Elsevier, vol. 276(C).
    20. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, vol. 9(8), pages 1-34, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:481:y:2017:i:c:p:1-10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.