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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.


Papers
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Journal ArticleDOI
TL;DR: Three numerical examples involving both structural and non-structural problems illustrate the application and effectiveness of the methods developed, which indicate that the proposed methods can provide accurate and computationally efficient estimates of probability of failure.

104 citations

Journal ArticleDOI
TL;DR: In this paper, a detailed analysis of the field data was conducted to investigate the typhoon effects on the super tall building and the structural responses of the building were presented and discussed in detail.

104 citations

Journal ArticleDOI
TL;DR: In this paper, the Moho is delineated as a relatively flat boundary between a strongly reflective lower crust and a transparent mantle at a depth of ~40-45 km (~ 14.5 s two-way travel time), which is in agreement with the refraction data recorded along the same profile.

97 citations

Journal ArticleDOI
TL;DR: This paper presents an eigensensitivity-based finite element (FE) model updating procedure for FE model updating of an existing 310 m tall Nanjing TV Tower based on ambient vibration measurements and finds that the FE model update procedure based on the weighted least squares method with groups of updating parameters is the most effective approach.

95 citations

Journal ArticleDOI
TL;DR: In this article, the authors presented some selected results obtained from the full-scale measurements of wind effects on the Jin Mao Building during the passage of Typhoon Rananim in August, 2004.

94 citations


Cited by
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Journal ArticleDOI

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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

Book ChapterDOI
11 Dec 2012

1,704 citations

Journal ArticleDOI

1,604 citations

01 Mar 1995
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.
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)

1,545 citations