The methods of Spatial Data Mining and Knowledge Discovery (SDMKD) are introduced into the paper on spatial allocation and internal structure of single settlement site of prehistoric settlement archaeology. The spatial database is designed, the Jiangzhai relic map is pretreated by drawing the area and the azimuth from relic such as houses, hearths, pits, urn tombs and pit tombs in the habitation area map in first culture period of Jiangzhai site. With the decision tree classification C4.5 algorithm, this paper makes spatial classification and spatial partition to the relic, draws the rules of classification, and realizes the rapid quantitive analysis for internal structure of single site of settlement archaeology. A simple analysis of clustering algorithm is made. From a different view, paper analyses the distribution rules of each house group and the internal structure of Jiangzhai site. It draws the spatial clustering rules of each type of house group by virtue of k-means clustering algorithm.
The fault diagnosis of electrical control system of certain type mine sweeping vehicle is difficult due to its complex structure and advanced technique. So in the multi-sensor failure diagnosis process, as a result of various reasons, such as the existence of measurement noise, diagnosis knowledge incomplete and so on, it makes the fault diagnosis uncertainty and affects the reliability and the accuracy of the diagnosis result. This article according to the analysis of electrical control system's fault characteristic of the mine sweeping plough’s, proposes a technique based on data fusion fault diagnosis method. The diagnosis process is divided into the sub system and the system-level, the subsystem uses the BP neural network to classify the fault mode, the system-level uses the D-S evidence theory carries on the comprehensive decision judgment for the whole system's fault. Application shows if some sub-neural network diagnosis has error, using D-S evidence theory fusion can effectively improve the accuracy of diagnosis.
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