Time domain analysis is an essential implement to study the buffeting behavior of long-span bridges for it can consider the non-linear effect which is significant in long-span bridges. The prerequisite of time domain ...Time domain analysis is an essential implement to study the buffeting behavior of long-span bridges for it can consider the non-linear effect which is significant in long-span bridges. The prerequisite of time domain analysis is the accurate description of 3D turbulence winds. In this paper, some hypotheses for simplifying the 3D turbulence simulation of long-span cable-stayed bridges are conducted, considering the structural characteristics. The turbulence wind which is a 3D multivariate stochastic vector process is converted into four independent 1D univariate stochastic processes. Based on recorded wind data from structural health monitoring system (SHMS) of the Sutong Bridge, China, the measured spectra expressions are then presented using the nonlinear least-squares fitting method. Turbulence winds at the Sutong Bridge site are simulated based on the spectral representation method and the Fast Fourier transform (FFT) technique, and the relevant results derived from target spectra including measured spectra and recommended spectra are compared. The reliability and accuracy of the presented turbulence simulation method are validated through comparisons between simulated and target spectra (measured and recommended spectra). The obtained turbulence si-mulations can not only serve further analysis of the buffeting behavior of the Sutong Bridge, but references for structural anti-wind design in adjacent regions.展开更多
The continuous emerging of peer-to-peer(P2P) applications enriches resource sharing by networks, but it also brings about many challenges to network management. Therefore, P2 P applications monitoring, in particular,P...The continuous emerging of peer-to-peer(P2P) applications enriches resource sharing by networks, but it also brings about many challenges to network management. Therefore, P2 P applications monitoring, in particular,P2 P traffic classification, is becoming increasingly important. In this paper, we propose a novel approach for accurate P2 P traffic classification at a fine-grained level. Our approach relies only on counting some special flows that are appearing frequently and steadily in the traffic generated by specific P2 P applications. In contrast to existing methods, the main contribution of our approach can be summarized as the following two aspects. Firstly, it can achieve a high classification accuracy by exploiting only several generic properties of flows rather than complicated features and sophisticated techniques. Secondly, it can work well even if the classification target is running with other high bandwidth-consuming applications, outperforming most existing host-based approaches, which are incapable of dealing with this situation. We evaluated the performance of our approach on a real-world trace. Experimental results show that P2 P applications can be classified with a true positive rate higher than 97.22% and a false positive rate lower than 2.78%.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 50725828, 50908046, and 50978056)the Teaching & Scientific Research Fund for Excellent Young Teachers of Southeast University+2 种基金the Open Fund of Jiangsu Key Laboratory of Environmental Impact and Structural Safety in Engineeringthe Basic Scientific & Research Fund of Southeast University (No. Seucx-201106)the Priority Academic Program Development Foundation of Jiangsu Higher Education Institutions, China
文摘Time domain analysis is an essential implement to study the buffeting behavior of long-span bridges for it can consider the non-linear effect which is significant in long-span bridges. The prerequisite of time domain analysis is the accurate description of 3D turbulence winds. In this paper, some hypotheses for simplifying the 3D turbulence simulation of long-span cable-stayed bridges are conducted, considering the structural characteristics. The turbulence wind which is a 3D multivariate stochastic vector process is converted into four independent 1D univariate stochastic processes. Based on recorded wind data from structural health monitoring system (SHMS) of the Sutong Bridge, China, the measured spectra expressions are then presented using the nonlinear least-squares fitting method. Turbulence winds at the Sutong Bridge site are simulated based on the spectral representation method and the Fast Fourier transform (FFT) technique, and the relevant results derived from target spectra including measured spectra and recommended spectra are compared. The reliability and accuracy of the presented turbulence simulation method are validated through comparisons between simulated and target spectra (measured and recommended spectra). The obtained turbulence si-mulations can not only serve further analysis of the buffeting behavior of the Sutong Bridge, but references for structural anti-wind design in adjacent regions.
基金supported by the National Natural Science Foundation of China(Nos.61170286 and 61202486)
文摘The continuous emerging of peer-to-peer(P2P) applications enriches resource sharing by networks, but it also brings about many challenges to network management. Therefore, P2 P applications monitoring, in particular,P2 P traffic classification, is becoming increasingly important. In this paper, we propose a novel approach for accurate P2 P traffic classification at a fine-grained level. Our approach relies only on counting some special flows that are appearing frequently and steadily in the traffic generated by specific P2 P applications. In contrast to existing methods, the main contribution of our approach can be summarized as the following two aspects. Firstly, it can achieve a high classification accuracy by exploiting only several generic properties of flows rather than complicated features and sophisticated techniques. Secondly, it can work well even if the classification target is running with other high bandwidth-consuming applications, outperforming most existing host-based approaches, which are incapable of dealing with this situation. We evaluated the performance of our approach on a real-world trace. Experimental results show that P2 P applications can be classified with a true positive rate higher than 97.22% and a false positive rate lower than 2.78%.
