黃鍔Norden Huang E. 院士



  • 海洋科學
  • 數學
  • 土木工程(結構、材料、營建)


  • 1967年約翰霍普金斯大學流體力學博士學位
  • 1975年進入美國NASA工作
  • 1990年起擔任《自然海洋地理學期刊》助理主編
  • 2000年當選美國國家工程學院院士
  • 2003年當選NASA年度發明家
  • 2004年當選台灣中央研究院院士(第二十五屆)
  • 2006年國立中央大學榮聘講座教授


  • NASA Medal for Exceptional Technology Achievement, 2005
  • 院士,中央研究院(ademician, Academia Sinica) , 2004
  • Presidential Rank Meritorious Award, 2004
  • NASA Inventor of the Year, 2003
  • NASA Goddard Space Flight Center, James Kerley Award, 2003
  • NASA Exceptional Space Act Award, 2003
  • NASA Exceptional Space Act Award, 2002
  • R&D 100 Award, 2001
  • Outstanding Alumnus, Civil Engineering Department, National Taiwan University, 2001.
  • Federal Laboratory Consortium Technology Development Award, 2001
  • Member, National Academy of Engineering, 2000
  • Federal Government Technology Leadership Award, 1999
  • NASA Exceptional Space Act Award, 1998
  • NASA Laboratory for Hydrospheric Process, Best Publication Award, 1997
  • NASA Medal for Meritorious Service, 1985
  • Whitehead Fellowship, The Johns Hopkins University, 1962 1967




Data analysis is indispensable to every science and engineering endeavor. The existing methods of data analysis, either the probability theory or the spectral analysis, are all developed by mathematicians or based on their rigorous mathematical rules. As a result, the bases are all a priori determined. And to conform to the rigorous mathematical rules, we are forced to live in a pseudo-real world in which all processes are idealized to conform to mathematician‘s conditions and requirements. There is always a conflict between reality and the ideal world, which was summarized succinctly by Einstein: ‘ As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.‘ As research becomes increasingly sophistic, the inadequacies of the traditional data analysis methods have become glaringly clear. For scientific research, the a priori basis approach would never work well. To accommodate the needs of scientific, rather than mathematical, data analysis, we have to face the reality of nonstationarity and nonlinearity in the data. The only viable way is to break away from the traditional limitations and establish a new paradigm in adaptive data analysis method.

The students should have a priori knowledge of Calculus and Fourier analysis.


In this course, we will discuss the following topics:

(01) Adaptive decomposition method: the Empirical Mode Decomposition

Spline functions

Stoppage criteria

End effects

Ensemble EMD

(02) The Instantaneous frequency

Instantaneous frequency for nonlinear systems

The Teager Energy operator

Generalized zero-crossing

Hilbert Transform


(03) The time-frequency spectral analysis


Wavelet analysis

Hilbert spectral analysis

(04) Generalization of the adaptive method to higher dimensional spaces

Bi-dimensional EMD

Multi-dimensional EEMD

(05) Applications with examples drawing from science and engineering problems