To cope with the constraint problem of power consumption and transmission delay in the virtual backbone of wireless sensor network, a distributed connected dominating set （CDS） algorithm with （α,β）-constraints i...To cope with the constraint problem of power consumption and transmission delay in the virtual backbone of wireless sensor network, a distributed connected dominating set （CDS） algorithm with （α,β）-constraints is proposed. Based on the （α, β）-tree concept, a new connected dominating tree with bounded transmission delay problem（CDTT） is defined and a corresponding algorithm is designed to construct a CDT-tree which can trade off limited total power and bounded transmission delay from source to destination nodes. The CDT algorithm consists of two phases： The first phase constructs a maximum independent set（MIS）in a unit disk graph model. The second phase estimates the distance and calculates the transmission power to construct a spanning tree in an undirected graph with different weights for MST and SPF, respectively. The theoretical analysis and simulation results show that the CDT algorithm gives a correct solution to the CDTF problem and forms a virtual backbone with（ α,β）-constraints balancing the requirements of power consumption and transmission delay.展开更多
There has been a great deal of research on the driving force of aviation network.Moreover, the research methods and perspectives are also varied.This article mainly studies the driving force of aviation network,the in...There has been a great deal of research on the driving force of aviation network.Moreover, the research methods and perspectives are also varied.This article mainly studies the driving force of aviation network,the influencing factors of main driving force are analyzed.The driving force analysis of different airport and route is carried out respectively.The results can better reveal the connection mechanism of solid routes.Because the sample space of the hub airport and the trunk airport is too small,the conclusions are not sufficiently illustrative,therefore, this article studies only the branch airports.The conclusions are compared with the results of the comprehensive analysis,and we see if the conclusions are the same or similar.展开更多
A new class of support vector machine, nil-support vector machine, isdiscussed which can handle both classification and regression. We focus on nu-support vector machineregression and use it for phase space prediction...A new class of support vector machine, nil-support vector machine, isdiscussed which can handle both classification and regression. We focus on nu-support vector machineregression and use it for phase space prediction of chaotic time series. The effectiveness of themethod is demonstrated by applying it to the Henon map. This study also compares nu-support vectormachine with back propagation (BP) networks in order to better evaluate the performance of theproposed methods. The experimental results show that the nu-support vector machine regressionobtains lower root mean squared error than the BP networks and provides an accurate chaotic timeseries prediction. These results can be attributable to the fact that nu-support vector machineimplements the structural risk minimization principle and this leads to better generalization thanthe BP networks.展开更多
基金Major Program of the National Natural Science Foundation of China (No.70533050)High Technology Research Program ofJiangsu Province(No.BG2007012)+1 种基金China Postdoctoral Science Foundation(No.20070411065)Science Foundation of China University of Mining andTechnology(No.OC080303)
文摘To cope with the constraint problem of power consumption and transmission delay in the virtual backbone of wireless sensor network, a distributed connected dominating set （CDS） algorithm with （α,β）-constraints is proposed. Based on the （α, β）-tree concept, a new connected dominating tree with bounded transmission delay problem（CDTT） is defined and a corresponding algorithm is designed to construct a CDT-tree which can trade off limited total power and bounded transmission delay from source to destination nodes. The CDT algorithm consists of two phases： The first phase constructs a maximum independent set（MIS）in a unit disk graph model. The second phase estimates the distance and calculates the transmission power to construct a spanning tree in an undirected graph with different weights for MST and SPF, respectively. The theoretical analysis and simulation results show that the CDT algorithm gives a correct solution to the CDTF problem and forms a virtual backbone with（ α,β）-constraints balancing the requirements of power consumption and transmission delay.
文摘There has been a great deal of research on the driving force of aviation network.Moreover, the research methods and perspectives are also varied.This article mainly studies the driving force of aviation network,the influencing factors of main driving force are analyzed.The driving force analysis of different airport and route is carried out respectively.The results can better reveal the connection mechanism of solid routes.Because the sample space of the hub airport and the trunk airport is too small,the conclusions are not sufficiently illustrative,therefore, this article studies only the branch airports.The conclusions are compared with the results of the comprehensive analysis,and we see if the conclusions are the same or similar.
文摘A new class of support vector machine, nil-support vector machine, isdiscussed which can handle both classification and regression. We focus on nu-support vector machineregression and use it for phase space prediction of chaotic time series. The effectiveness of themethod is demonstrated by applying it to the Henon map. This study also compares nu-support vectormachine with back propagation (BP) networks in order to better evaluate the performance of theproposed methods. The experimental results show that the nu-support vector machine regressionobtains lower root mean squared error than the BP networks and provides an accurate chaotic timeseries prediction. These results can be attributable to the fact that nu-support vector machineimplements the structural risk minimization principle and this leads to better generalization thanthe BP networks.
