复杂网络中,评估节点的重要性对于研究网络结构和传播过程有着重要意义.通过节点的位置,K-shell分解算法能够很好地识别关键节点,但是这种算法导致很多节点具有相同的K-shell(Ks)值.同时,现有的算法大都只考虑局部指标或者全局指标,导...复杂网络中,评估节点的重要性对于研究网络结构和传播过程有着重要意义.通过节点的位置,K-shell分解算法能够很好地识别关键节点,但是这种算法导致很多节点具有相同的K-shell(Ks)值.同时,现有的算法大都只考虑局部指标或者全局指标,导致评判节点重要性的因素单一.为了更好地识别关键节点,提出了EKSDN(Extended K-shell and Degree of Neighbors)算法,该算法综合考虑了节点的全局指标加权核值以及节点的局部指标度数.与SIR(Susceptible-Infectious-Recovered)模型在真实复杂网络中模拟结果相比,EKSDN算法能够更好地识别关键节点.展开更多
In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification probl...In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.展开更多
文摘复杂网络中,评估节点的重要性对于研究网络结构和传播过程有着重要意义.通过节点的位置,K-shell分解算法能够很好地识别关键节点,但是这种算法导致很多节点具有相同的K-shell(Ks)值.同时,现有的算法大都只考虑局部指标或者全局指标,导致评判节点重要性的因素单一.为了更好地识别关键节点,提出了EKSDN(Extended K-shell and Degree of Neighbors)算法,该算法综合考虑了节点的全局指标加权核值以及节点的局部指标度数.与SIR(Susceptible-Infectious-Recovered)模型在真实复杂网络中模拟结果相比,EKSDN算法能够更好地识别关键节点.
基金Supported in part by Science & Technology Department of Shanghai (05dz15004)
文摘In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.