Hilbert-Huang transform and its applications /:
This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and vari...
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| Weitere Verfasser: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New Jersey :
World Scientific,
[2014]
|
| Ausgabe: | 2nd edition. |
| Schriftenreihe: | Interdisciplinary mathematical sciences ;
16. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges. The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD. The book also provides a platform for researchers to develop the HHT method further and to identify more applications. |
| Beschreibung: | 1 online resource (xii, 386 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9789814508247 9814508241 |
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| 245 | 0 | 0 | |a Hilbert-Huang transform and its applications / |c editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. |
| 250 | |a 2nd edition. | ||
| 264 | 1 | |a New Jersey : |b World Scientific, |c [2014] | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (xii, 386 pages) : |b illustrations | ||
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| 490 | 1 | |a Interdisciplinary Mathematical Sciences ; |v v. 16 | |
| 520 | |a This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges. The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD. The book also provides a platform for researchers to develop the HHT method further and to identify more applications. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. HHT-based bridge structural health-monitoring method. 15.1. Introduction. 15.2. A review of the present state-of-the-art methods. 15.3. The Hilbert-Huang transform. 15.4. Damage-detection criteria. 15.5. Case study of damage detection. 15.6. Conclusions -- ch. 16. Applications of HHT in image analysis. 16.1. Introduction. 16.2. Overview. 16.3. The analysis of digital slope images. 16.4. Summary. | |
| 588 | 0 | |a Print version record. | |
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| 650 | 6 | |a Transformation de Hilbert-Huang. | |
| 650 | 6 | |a Décomposition (Mathématiques) | |
| 650 | 7 | |a MATHEMATICS |x Calculus. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Mathematical Analysis. |2 bisacsh | |
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| 650 | 7 | |a Hilbert-Huang transform |2 fast | |
| 700 | 1 | |a Huang, Norden E. |q (Norden Eh), |d 1937- |e editor. |1 https://id.oclc.org/worldcat/entity/E39PBJcBVKKGb7FxQftTR9yWXd |0 http://id.loc.gov/authorities/names/n78095666 | |
| 700 | 1 | |a Shen, Samuel S., |e editor. |0 http://id.loc.gov/authorities/names/n93027986 | |
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| contents | Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. HHT-based bridge structural health-monitoring method. 15.1. Introduction. 15.2. A review of the present state-of-the-art methods. 15.3. The Hilbert-Huang transform. 15.4. Damage-detection criteria. 15.5. Case study of damage detection. 15.6. Conclusions -- ch. 16. Applications of HHT in image analysis. 16.1. Introduction. 16.2. Overview. 16.3. The analysis of digital slope images. 16.4. Summary. |
| ctrlnum | (OCoLC)881416737 |
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It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges. The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD. The book also provides a platform for researchers to develop the HHT method further and to identify more applications.</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. HHT-based bridge structural health-monitoring method. 15.1. Introduction. 15.2. A review of the present state-of-the-art methods. 15.3. The Hilbert-Huang transform. 15.4. Damage-detection criteria. 15.5. Case study of damage detection. 15.6. Conclusions -- ch. 16. Applications of HHT in image analysis. 16.1. Introduction. 16.2. Overview. 16.3. The analysis of digital slope images. 16.4. 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| id | ZDB-4-EBA-ocn881416737 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:26:01Z |
| institution | BVB |
| isbn | 9789814508247 9814508241 |
| language | English |
| oclc_num | 881416737 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xii, 386 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | World Scientific, |
| record_format | marc |
| series | Interdisciplinary mathematical sciences ; |
| series2 | Interdisciplinary Mathematical Sciences ; |
| spelling | Hilbert-Huang transform and its applications / editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. 2nd edition. New Jersey : World Scientific, [2014] ©2014 1 online resource (xii, 386 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Interdisciplinary Mathematical Sciences ; v. 16 This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges. The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD. The book also provides a platform for researchers to develop the HHT method further and to identify more applications. Includes bibliographical references and index. Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. HHT-based bridge structural health-monitoring method. 15.1. Introduction. 15.2. A review of the present state-of-the-art methods. 15.3. The Hilbert-Huang transform. 15.4. Damage-detection criteria. 15.5. Case study of damage detection. 15.6. Conclusions -- ch. 16. Applications of HHT in image analysis. 16.1. Introduction. 16.2. Overview. 16.3. The analysis of digital slope images. 16.4. Summary. Print version record. Hilbert-Huang transform. http://id.loc.gov/authorities/subjects/sh2005004631 Decomposition (Mathematics) http://id.loc.gov/authorities/subjects/sh85036222 Transformation de Hilbert-Huang. Décomposition (Mathématiques) MATHEMATICS Calculus. bisacsh MATHEMATICS Mathematical Analysis. bisacsh Decomposition (Mathematics) fast Hilbert-Huang transform fast Huang, Norden E. (Norden Eh), 1937- editor. https://id.oclc.org/worldcat/entity/E39PBJcBVKKGb7FxQftTR9yWXd http://id.loc.gov/authorities/names/n78095666 Shen, Samuel S., editor. http://id.loc.gov/authorities/names/n93027986 has work: Hilbert-Huang transform and its applications (Text) https://id.oclc.org/worldcat/entity/E39PCGP88cJMhgDkpKJvfmFfG3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Hilbert-Huang transform and its applications. 2nd edition. [Hackensack] New Jersey : World Scientific, [2014] 9789814508230 (DLC) 2014009123 (OCoLC)874119654 Interdisciplinary mathematical sciences ; 16. http://id.loc.gov/authorities/names/n2004018211 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=787894 Volltext |
| spellingShingle | Hilbert-Huang transform and its applications / Interdisciplinary mathematical sciences ; Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. HHT-based bridge structural health-monitoring method. 15.1. Introduction. 15.2. A review of the present state-of-the-art methods. 15.3. The Hilbert-Huang transform. 15.4. Damage-detection criteria. 15.5. Case study of damage detection. 15.6. Conclusions -- ch. 16. Applications of HHT in image analysis. 16.1. Introduction. 16.2. Overview. 16.3. The analysis of digital slope images. 16.4. Summary. Hilbert-Huang transform. http://id.loc.gov/authorities/subjects/sh2005004631 Decomposition (Mathematics) http://id.loc.gov/authorities/subjects/sh85036222 Transformation de Hilbert-Huang. Décomposition (Mathématiques) MATHEMATICS Calculus. bisacsh MATHEMATICS Mathematical Analysis. bisacsh Decomposition (Mathematics) fast Hilbert-Huang transform fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2005004631 http://id.loc.gov/authorities/subjects/sh85036222 |
| title | Hilbert-Huang transform and its applications / |
| title_auth | Hilbert-Huang transform and its applications / |
| title_exact_search | Hilbert-Huang transform and its applications / |
| title_full | Hilbert-Huang transform and its applications / editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. |
| title_fullStr | Hilbert-Huang transform and its applications / editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. |
| title_full_unstemmed | Hilbert-Huang transform and its applications / editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. |
| title_short | Hilbert-Huang transform and its applications / |
| title_sort | hilbert huang transform and its applications |
| topic | Hilbert-Huang transform. http://id.loc.gov/authorities/subjects/sh2005004631 Decomposition (Mathematics) http://id.loc.gov/authorities/subjects/sh85036222 Transformation de Hilbert-Huang. Décomposition (Mathématiques) MATHEMATICS Calculus. bisacsh MATHEMATICS Mathematical Analysis. bisacsh Decomposition (Mathematics) fast Hilbert-Huang transform fast |
| topic_facet | Hilbert-Huang transform. Decomposition (Mathematics) Transformation de Hilbert-Huang. Décomposition (Mathématiques) MATHEMATICS Calculus. MATHEMATICS Mathematical Analysis. Hilbert-Huang transform |
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