Research and Application of Fire Forecasting Model for Electric Transmission Lines Incorporating Meteorological Data and Human Activities

Recently, there is a rise in frequency of fires which pose a serious threat to the safety operation of electric transmission lines. Several ultrahigh voltage (UHV) electric transmission lines, including Fufeng line, Jinsu line, Longzheng line, and Changnan line, showed many times tripping or bipolar latching caused by fire disasters. Fire disasters have tended to be the biggest threat to the safety operation of electric transmission lines and even can cause power grid collapse in some severe situations. Researchers have made much research on fires forecasting. However, these studies are mainly concentrated on predicting fires based on measured or forecasting meteorological data and do not take into account the effect of human activities. In fact, fire disasters have a very close relationship with human activities. In our research, a fire prediction model is proposed incorporating meteorological data as well as human activities. And this model is applied in Hunan province and Anhui province, which seriously suffer from fire disasters. The results show that the model has good prediction precision and can be a powerful tool for practical application

A Fast Clustering Algorithm based on pruning unnecessary distance computations in DBSCAN for High-Dimensional Data

Clustering is an important technique to deal with large scale data which are explosively created in internet. Most data are high-dimensional with a lot of noise, which brings great challenges to retrieval, classification and understanding. No current existing approach is “optimal” for large scale data. For example, DBSCAN requires O(n2) time, Fast-DBSCAN only works well in 2 dimensions, and ρ-Approximate DBSCAN runs in O(n) expected time which needs dimension D to be a relative small constant for the linear running time to hold. However, we prove theoretically and experimentally that ρ-Approximate DBSCAN degenerates to an O(n2) algorithm in very high dimension such that 2D >  > n. In this paper, we propose a novel local neighborhood searching technique, and apply it to improve DBSCAN, named as NQ-DBSCAN, such that a large number of unnecessary distance computations can be effectively reduced. Theoretical analysis and experimental results show that NQ-DBSCAN averagely runs in O(n*log(n)) with the help of indexing technique, and the best case is O(n) if proper parameters are used, which makes it suitable for many realtime data