Qiusheng Li

Bio: Qiusheng Li is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Wind speed & Wind tunnel. The author has an hindex of 47, co-authored 429 publications receiving 8830 citations. Previous affiliations of Qiusheng Li include Chinese Ministry of Education & Guangzhou University.

Field measurements of amplitude-dependent damping in a 79-storey tall building and its efects on the structural dynamic responses

Abstract: This paper describes some results obtained from full-scale measurements of wind effects on a super-tall building, Di-Wang Tower, located in Shenzhen, China. This tall building has 79-storeys with a height of approximately 324 m. Field measurements including wind speed, wind direction and wind-induced acceleration responses have been made. The amplitude-dependent characteristics of damping are obtained by using the random decrement technique from the detailed analysis of the field acceleration measurements. The main objective of this paper is to present detailed investigations into the effects of nonlinear damping on the dynamic responses of the tall building subjected to various types of applied loads based on the measured amplitude-dependent damping characteristics. The predicted dynamic responses of the building obtained by using the measured damping characteristics were compared with those computed by using constant damping parameters assumed by the structural designers. It is concluded from the investigations that knowledge of actual damping characteristics are very important in the accurate prediction of the dynamic responses of a tall building when the major harmonic components of the applied loads overlap with the lowest natural frequencies of the building. The design damping level for tall building structures currently used by structural engineering practitioners appears to be high and not conservative. Copyright © 2002 John Wiley & Sons, Ltd.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Nonlinear Time Series Analysis.

Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)