P2P traffic has always been a dominant portion of Internet traffic since its emergence in the late 1990s. The method used to accurately classify P2P traffic remains a key problem for Internet Service Producers （ISPs...P2P traffic has always been a dominant portion of Internet traffic since its emergence in the late 1990s. The method used to accurately classify P2P traffic remains a key problem for Internet Service Producers （ISPs） and network managers. This paper proposes a novel approach to the accurate classification of P2P traffic at a fine-grained level, which depends solely on the number of special flows during small time intervals. These special flows, named Clustering Flows （CFs）, are de- fined as the most frequent and steady flows generated by P2P applications. Hence we are able to classify P2P applications by detecting tlle appearance of corresponding CFs. Com- pared to existing approaches, our classifier can realise high classification accuracy by ex- ploiting only several generic properties of flows, instead of extracting sophisticated fea- tures from host behaviours or transport layer data. We validate our framework on a large set of P2P traffic traces using a Support Vector Machine （SVM）. Experimental results show that our approach correctly classifies P2P ap- plications with an average true positive rate of above 98% and a negligible false positive rate of about 0.01%.展开更多
.GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN alg....GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN algorithm. The algorithm firstly get the network center nodes set, then use the shortest paths between center nodes and other nodes to calculate the edge betweenness, and then use incremental module degree as the algorithm terminates standard. Experiments show that, the new algorithm not only ensures accuracy of network community division, but also greatly reduced the time complexity, and improves the efficiency of community division.展开更多
In this paper, the nonconforming mortar finite element with a class of meshes is studied without considering the global regularity condition or quasi-uniformly assumption. Meanwhile, the superclose result coincides wi...In this paper, the nonconforming mortar finite element with a class of meshes is studied without considering the global regularity condition or quasi-uniformly assumption. Meanwhile, the superclose result coincides with conventional methods is obtained by means of integral identities techniques.展开更多
In the smoothed particle hydrodynamics (SPH) method, a meshless interpolation scheme is needed for the unknown function in order to discretize the governing equation.A particle approximation method has so far been use...In the smoothed particle hydrodynamics (SPH) method, a meshless interpolation scheme is needed for the unknown function in order to discretize the governing equation.A particle approximation method has so far been used for this purpose.Traditional particle interpolation (TPI) is simple and easy to do, but its low accuracy has become an obstacle to its wider application.This can be seen in the cases of particle disorder arrangements and derivative calculations.There are many different methods to improve accuracy, with the moving least square (MLS) method one of the most important meshless interpolation methods.Unfortunately, it requires complex matrix computing and so is quite time-consuming.The authors developed a simpler scheme, called higher-order particle interpolation (HPI).This scheme can get more accurate derivatives than the MLS method, and its function value and derivatives can be obtained simultaneously.Although this scheme was developed for the SPH method, it has been found useful for other meshless methods.展开更多
The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted pa...The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.展开更多
基金supported by the National Natural Science Foundation of China under Grants No.61170286,No.61202486
文摘P2P traffic has always been a dominant portion of Internet traffic since its emergence in the late 1990s. The method used to accurately classify P2P traffic remains a key problem for Internet Service Producers （ISPs） and network managers. This paper proposes a novel approach to the accurate classification of P2P traffic at a fine-grained level, which depends solely on the number of special flows during small time intervals. These special flows, named Clustering Flows （CFs）, are de- fined as the most frequent and steady flows generated by P2P applications. Hence we are able to classify P2P applications by detecting tlle appearance of corresponding CFs. Com- pared to existing approaches, our classifier can realise high classification accuracy by ex- ploiting only several generic properties of flows, instead of extracting sophisticated fea- tures from host behaviours or transport layer data. We validate our framework on a large set of P2P traffic traces using a Support Vector Machine （SVM）. Experimental results show that our approach correctly classifies P2P ap- plications with an average true positive rate of above 98% and a negligible false positive rate of about 0.01%.
文摘.GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN algorithm. The algorithm firstly get the network center nodes set, then use the shortest paths between center nodes and other nodes to calculate the edge betweenness, and then use incremental module degree as the algorithm terminates standard. Experiments show that, the new algorithm not only ensures accuracy of network community division, but also greatly reduced the time complexity, and improves the efficiency of community division.
基金Foundation item: Supported by the NSF of China(10371113)Supported by the Foundation of Overseas Scholar of China(2001(119))Supported by the project of Creative Engineering of Province of China(2002(219))
文摘In this paper, the nonconforming mortar finite element with a class of meshes is studied without considering the global regularity condition or quasi-uniformly assumption. Meanwhile, the superclose result coincides with conventional methods is obtained by means of integral identities techniques.
基金Supported by the National Natural Science Foundation of China under Grant No.10572041,50779008Doctoral Fund of Ministry of Education of China under Grant No.20060217009
文摘In the smoothed particle hydrodynamics (SPH) method, a meshless interpolation scheme is needed for the unknown function in order to discretize the governing equation.A particle approximation method has so far been used for this purpose.Traditional particle interpolation (TPI) is simple and easy to do, but its low accuracy has become an obstacle to its wider application.This can be seen in the cases of particle disorder arrangements and derivative calculations.There are many different methods to improve accuracy, with the moving least square (MLS) method one of the most important meshless interpolation methods.Unfortunately, it requires complex matrix computing and so is quite time-consuming.The authors developed a simpler scheme, called higher-order particle interpolation (HPI).This scheme can get more accurate derivatives than the MLS method, and its function value and derivatives can be obtained simultaneously.Although this scheme was developed for the SPH method, it has been found useful for other meshless methods.
基金financially supported by the Important National Science and Technology Specific Project of China (Grant No. 2016ZX05047-002)
文摘The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.
