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.

Health dynamic measurement of tall building using wireless sensor network

Abstract: Health Monitoring is very important for large structures like suspension- and cable-stayed bridges, offshore platforms, tall buildings and so on. Due to recent developments in new sensor systems, wireless communication systems, Internet-based data sharing and monitoring, advanced technologies for structure health monitoring (SHM) have been caused much more attentions, in which the wireless sensor network is recently received special interests. Wireless sensor networks (WSNs) consist of large populations of wirelessly connected nodes, capable of computation, communication, and sensing. In this paper, a wireless sensor networks based health monitoring system for tall buildings has been explored integrated with wireless sensing communication, computation, data management and data remote access via Internet. Firstly, a laboratory prototype was designed and developed to demonstrate the feasibility and validity of the proposed system. Wireless sensor nodes were deployed on a test structure, the data being sensed by the sensor nodes in the network is eventually transmitted to a base station, where the information can be accessed. Through a Wireless Local Area Network (WLAN, IEEE802.11b), the simulated data was transferred among personal computers and wireless sensor nodes peripherals without cables. And then, a Wireless Sensor Network (WSN) includes eight sensor nodes and one base station was installed on Di Wang Tower to verify the performance of the present system in-depth. Finally, comparisons between WSN and cable-based monitoring analytical acceleration responses of field measurement have been performed. The proposed system is shown to be effective for structural health monitoring.

Monitoring and time-dependent analysis of vertical deformations of the tallest building in China

Abstract: Summary Ping-An Finance Center (PAFC) in Shenzhen, with a structural height of 597 m and a total height of 660 m, is currently the second tallest building in the world and the tallest in China. In this paper, based on the outputs of a structural health monitoring system installed in PAFC, a combined study of both on-site measurements and numerical analysis of the vertical deformations (axial shortenings) of the super tall building during its various construction stages is carried out. It is worth noting that a novel strategy to adjust the elevation of each floor of PAFC was implemented in the design and construction of the super tall building, in which the floor-to-floor height is modestly increased to compensate for the axial shortenings of gravity load bearing elements during the construction process and in-service stage. This design strategy is referred to as elevation reservation, and its effectiveness is verified through the field measurements of the vertical deformations in this study. A good agreement is found between the numerical results and the field measurements, which validates the finite element models of PAFC at different construction stages. The finite element models are further used to investigate the time-dependent effects due to the construction sequence and the shrinkage and creep of concrete on the vertical deformations. The numerical results show that the vertical deformations would be seriously underestimated without consideration of the time-dependent effects. The outcomes of this study would be of interest and practical use for engineers and researchers involved in the structural design, construction, and structural health monitoring of super tall buildings.

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)