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Hilbert-Huang transform and its applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hilbert-Huang transform and its applications/ editors, Norden E. Huang, Samuel S.P. Shen.
其他作者:	Huang, Norden E.
出版者:	New Jersey :World Scientific, : 2014.,
面頁冊數:	1 online resource (xii, 386 p.) :ill.
內容註:	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.
標題:	Hilbert-Huang transform. -
電子資源:	http://www.worldscientific.com/worldscibooks/10.1142/8804#t=toc
ISBN:	9789814508247

Hilbert-Huang transform and its applications
Hilbert-Huang transform and its applications[electronic resource] /editors, Norden E. Huang, Samuel S.P. Shen. - 2nd ed. - New Jersey :World Scientific,2014. - 1 online resource (xii, 386 p.) :ill. - Interdisciplinary Mathematical Sciences ;v.16. - Interdisciplinary mathematical sciences ;vol. 8..

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.

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.

ISBN: 9789814508247Subjects--Topical Terms:

812395
Hilbert-Huang transform.

LC Class. No.: QA432 / .H55 2014eb

Dewey Class. No.: 515.723

Hilbert-Huang transform and its applications
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245 0 0 $a Hilbert-Huang transform and its applications $h [electronic resource] / $c editors, Norden E. Huang, Samuel S.P. Shen.
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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.
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.
588 $a Description based on print version record.
650 0 $a Hilbert-Huang transform. $3 812395
650 0 $a Decomposition (Mathematics) $3 700366
700 1 $a Huang, Norden E. $q (Norden Eh), $d 1937- $3 2070361
700 1 $a Shen, Samuel S. $3 2070362
830 0 $a Interdisciplinary mathematical sciences ; $v vol. 8. $3 1324964
856 4 0 $u http://www.worldscientific.com/worldscibooks/10.1142/8804#t=toc