Data analysis is indispensable to every science and engineering endeavor, but it always plays the second fiddle to the subject area. The existing methods of data analysis either the probability theory or the spectral analysis are all developed by mathematicians or based on their rigorous rules. In pursue of the rigorous, we are forced to make idealized assumptions and live in a pseudo-real linear and stationary world. But the world we live in is neither stationary nor linear. For example, spectral analysis is synonymous with the Fourier based analysis. As Fourier spectrum can only give meaningful interpretation to linear and stationary process, its application to data from nonlinear and nonstationary processes is problematical. And probability distributions can only represent global properties, which imply homogeneity (or stationarity) in the population. As scientific research getting increasingly sophistic, the inadequacy is become glaringly obvious. The only alternative is to break away from these limitations; we should let data speak for themselves so that the results could reveal the full range of consequence of nonlinearity and nonstationarity. To do so, we need new paradigm of data analysis methodology without a priori basis to fully accommodating the variations of the underlying driving mechanisms.
Inaugurated on 18 December 2006, RCADA is established specifically to develop adaptive data analysis techniques. Currently, we do have an adaptive data analysis, the Hilbert-Huang Transform (HHT); it is a very versatile method in analysis complicate data from nonlinear and nonstationary processes that include all the natural and man-made systems. When the method was first invented by Dr. Norden E. Huang, NASA selected it one of the most prestigious Space Act Award, and lauded it as follows: ‘[HHT] is one of the most important discoveries in the field of applied mathematics in NASA history.’ One of the primary goals of the RCADA is to develop the theory behind HHT method, and explore its applications. To document the advances of research in this particular area, we will initiate a new Journal, ‘Advances in Adaptive data Analysis: Theory and Applications’, edited by the staff of RCADA and others to be inaugurated in the middle of 2007.
At the present, this simple adaptive method has been used in the following area:
Non-destructive Evaluation for Structural Health Monitoring
•(DOT, NSWC, and DFRC/NASA, KSC/NASA Shuttle, NCU)
Vibration, speech, and acoustic signal analyses
•(FBI, MIT, and DARPA)
•(Harvard, UCSD, Johns Hopkins, and Southampton , UK )
Global Primary Productivity Evolution map from LandSat data
•(NASA Goddard, NOAA)
Cosmological Gravity Wave
Financial market data analysis
•( National Central University )