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The purpose of this study is to observe epilepsy brain network evolution from network perspective and implement of epileptic disease prognosis. Local visibility graph method is on the basis of visibility graph method adding a sliding time window and building a number of sliding time window with the complex network topology. It is in order to observe the time dependence of the network. We divided the electrocorticogram(EEG) time series into three parts. They were the time series during normal period, pre-epilepsy period and seizures occur period. Then build three network topology graphs and observed its evolution process. The results show that the network module structure of the epileptic EEG from normal period to pre-epilepsy period then to seizures occur period disappeared. And it form the arc of the zonal distribution. These characteristics of complex networks provide new ideas for the prediction of epileptic disease.

In the pipeline leakage detection and location, what we need to know is whether the pipe is leaky and where the leakage point is. In addition, we are very concerned about the extent of leakage, false alarm rate and missing alarm rate. Both correlation analysis method and sequential probability ratio test method can be used in the pipeline leakage detection and location. Correlation analysis method can get the extent of leakage but it cannot limit false alarm rate and missing alarm rate. Sequential probability ratio test method can reflect the two rates, but it cannot get the extent of leakage. In this paper, the two method are fuzzily fused by means of fuzzy variable weight method, thus we can arrive at a decision about the extent and location of leakage. We verify this method in simulated pipeline in lab, and the results show that the fusion method has better effects than single method.