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.

Wind characteristics of a strong typhoon in marine surfaceboundary layer

Abstract: High-resolution wind data were acquired from a 100-m high offshore tower during the passage of Typhoon Hagupit in September, 2008. The meteorological tower was equipped with an ultrasonic anemometer and a number of cup anemometers at heights between 10 and 100 m. Wind characteristics of the strong typhoon, such as mean wind speed and wind direction, turbulence intensity, turbulence integral length scale, gust factor and power spectra of wind velocity, vertical profiles of mean wind speed were investigated in detail based on the wind data recorded during the strong typhoon. The measured results revealed that the wind characteristics in different stages during the typhoon varied remarkably. Through comparison with non-typhoon wind measurements, the phenomena of enhanced levels of turbulence intensity, gust factors, turbulence integral length scale and spectral magnitudes in typhoon boundary layer were observed. The monitored data and analysis results are expected to be useful for the wind-resistant design of offshore structures and buildings on seashores in typhoon-prone regions.

Evaluation of wind effects on a supertall building based on full-scale measurements

Abstract: This paper describes the results obtained from the full-scale measurements of wind effects on a 70-storey building in Hong Kong. The building which has a height of approximately 370 m is the second tallest structure in Hong Kong. The field data such as wind speed, wind direction and wind-induced acceleration responses have been measured since 1995 including the close passage of two typhoons; typhoon Sally and typhoon Kent. Detailed analysis of the field data is conducted. The full-scale measurements are compared with the wind tunnel results obtained in the Boundary Layer Wind Tunnel Laboratory at Western Ontario University. The amplitude-dependent characteristics of damping and natural frequency that were obtained by using the random decrement technique are investigated. Copyright © 2000 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)