受bat365官网登录入口梅雪松教授、何正嘉教授邀请，黄鄂院士来我校访问，并受聘为西安交通大学名誉教授，访问期间黄鄂院士将为我校师生做学术报告，欢迎广大师生积极参加。
地 点：西安交大科学馆101报告厅
黄锷(Norden E. Huang)简历：
男，1937年12月13日出生于湖北，1956年毕业于省立新竹高中，1960年毕业于国立台湾大学土木系，1967年获得约翰霍普金斯大学流体力学博士学位。随后曾任华盛顿大学海洋地理系研究员、北卡罗来纳州立大学海洋地理学系副教授。1975年进入美国NASA，工作超过三十年，是NASA海洋科学首席科学家。1998年发明了著名的“希耳伯特-黄变换法”(HHT)，于2000年当选美国国家工程学院院士，2003年当选NASA年度发明家，2004年当选台湾中央研究院院士（第二十五届）。2002至2006年期间，连续四年荣获美国服务贡献奖。现为中国工程院外籍院士，任台湾国立中央大学国鼎讲座教授和数据分析方法研究中心主任。
时 间：2013年4月7日下午15:00;
演讲题目1.Recent Advances in HHT and its applications
摘要:For analyzing data from real physical world, we have to face the reality of nonstationarity and nonlinearity in the processes. Traditional analysis method based on rigorous mathematical rules cannot fully accommodate these conditions. The adaptive Hilbert-Huang Transform, consisted of Empirical Mode Decomposition (EMD) and the Hilbert Spectral Analysis (HSA) methods, introduced recently by Huang et al (1996, 1998, 1999 and 2003) is designed specifically for analyzing data from nonlinear and nonstationary processes. Since its introduction near twenty years ago, the EMD has attracted a wide range of applications, covering (among many others) biology, geophysics, ocean research, engineering, radar and medicine (see, for example, Huang and Attok-Okine, 2005; Huang and Shen, 2005; Huang and Wu, 2008). Many new advances are scattering in various papers that including the Ensemble Empirical Mode Decomposition (EEMD), instantaneous frequency computations, trend determination, Time-dependent Intrinsic Correlation (TIDC) and the extension of the time series analysis method to multivariate and multi-dimensional data. And the most exciting advances is in the establishment of theoretical foundations through basis pursuit by Hou (2011, 2012, 1nd 2013). It is hope that this review would summarize the recent advances. It is also hope that the review would not only facilitate future applications but also attract attention in the scientific communities to treat the adaptive method as a viable tool for research. Various examples of applications will be presented.
时 间：2013年4月8日下午15:00;
演讲题目2、A Plea for adaptive data analysis
摘要: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. That is an adaptive data analysis method, based on the Empirical Mode Decomposition and Hilbert Spectral Analysis. The result is present in a time-frequency-energy representation. In fact, we can only define true frequency with adaptive method, which would lead to quantify nonstationarity and nonlinearity. Examples from classic nonlinear system and recent climate will be used to illustrate the prowess of the new approach.
