Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition
The effectiveness of the renowned empirical mode decomposition (EMD) is affected by the mode-mixing problem (MMP) if a signal contains intermittent modes. The ensemble EMD (EEMD) and several modified and extended algorithms solve this problem by adding random white noises. However, the necessary lar...
| Egile Nagusiak: | , , |
|---|---|
| Beste egile batzuk: | |
| Formatua: | Journal Article |
| Hizkuntza: | English |
| Argitaratua: |
2016
|
| Gaiak: | |
| Sarrera elektronikoa: | https://hdl.handle.net/10356/80310 http://hdl.handle.net/10220/40536 |
| _version_ | 1826130699346771968 |
|---|---|
| author | Wang, Chenxing Kemao, Qian Da, Feipeng |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Wang, Chenxing Kemao, Qian Da, Feipeng |
| author_sort | Wang, Chenxing |
| collection | NTU |
| description | The effectiveness of the renowned empirical mode decomposition (EMD) is affected by the mode-mixing problem (MMP) if a signal contains intermittent modes. The ensemble EMD (EEMD) and several modified and extended algorithms solve this problem by adding random white noises. However, the necessary large size of the ensemble and the inevitable manual intervention limits the application of EEMD. In this letter, a novel regenerated phase-shifted sinusoid-assisted EMD (RPSEMD) is proposed. Sinusoids with different scales are iteratively generated and added to cope with all possible MMPs in different intrinsic modes (IMs), where each sinusoid is designed adaptively and automatically. Furthermore, the sinusoids are shifted for better retaining the details of each IM and eliminating the added sinusoids. In the comparison experiments, the RPSEMD provides more reasonable results with less computation time. |
| first_indexed | 2024-10-01T08:00:31Z |
| format | Journal Article |
| id | ntu-10356/80310 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T08:00:31Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/803102020-05-28T07:18:47Z Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition Wang, Chenxing Kemao, Qian Da, Feipeng School of Computer Engineering Empirical mode decomposition Games Indexes Signal processing algorithms Time-frequency analysis White noise Technological innovation The effectiveness of the renowned empirical mode decomposition (EMD) is affected by the mode-mixing problem (MMP) if a signal contains intermittent modes. The ensemble EMD (EEMD) and several modified and extended algorithms solve this problem by adding random white noises. However, the necessary large size of the ensemble and the inevitable manual intervention limits the application of EEMD. In this letter, a novel regenerated phase-shifted sinusoid-assisted EMD (RPSEMD) is proposed. Sinusoids with different scales are iteratively generated and added to cope with all possible MMPs in different intrinsic modes (IMs), where each sinusoid is designed adaptively and automatically. Furthermore, the sinusoids are shifted for better retaining the details of each IM and eliminating the added sinusoids. In the comparison experiments, the RPSEMD provides more reasonable results with less computation time. Accepted Version 2016-05-13T04:08:15Z 2019-12-06T13:46:57Z 2016-05-13T04:08:15Z 2019-12-06T13:46:57Z 2016 2016 Journal Article Wang, C., Kemao, Q., & Da, F. (2016). Regenerated Phase-Shifted Sinusoid-Assisted Empirical Mode Decomposition. IEEE Signal Processing Letters, 23(4), 556-560. 1070-9908 https://hdl.handle.net/10356/80310 http://hdl.handle.net/10220/40536 10.1109/LSP.2016.2537376 191446 en IEEE Signal Processing Letters © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LSP.2016.2537376]. 5 p. application/pdf |
| spellingShingle | Empirical mode decomposition Games Indexes Signal processing algorithms Time-frequency analysis White noise Technological innovation Wang, Chenxing Kemao, Qian Da, Feipeng Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition |
| title | Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition |
| title_full | Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition |
| title_fullStr | Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition |
| title_full_unstemmed | Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition |
| title_short | Regenerated Phase-shifted Sinusoid-assisted Empirical Mode Decomposition |
| title_sort | regenerated phase shifted sinusoid assisted empirical mode decomposition |
| topic | Empirical mode decomposition Games Indexes Signal processing algorithms Time-frequency analysis White noise Technological innovation |
| url | https://hdl.handle.net/10356/80310 http://hdl.handle.net/10220/40536 |
| work_keys_str_mv | AT wangchenxing regeneratedphaseshiftedsinusoidassistedempiricalmodedecomposition AT kemaoqian regeneratedphaseshiftedsinusoidassistedempiricalmodedecomposition AT dafeipeng regeneratedphaseshiftedsinusoidassistedempiricalmodedecomposition |