Unordered tree is widely used for semi-structured data modeling, frequent subtrees mining on it has benefit for finding hidden knowledge. The traditional methods of mining frequent subtrees often output large-scale frequent subtrees with redundant information, such an output will reduce the efficiency of subsequent operations. In view of the shortcomings of traditional methods, the Mining CoveRage Pattern (MCRP) algorithm was proposed for mining coverage patterns. Firstly, a tree coding rule according to the tree width and the number of children was presented. Then, all candidate subtrees were generated by edge extension based on the maximum prefix coding sequence. Finally, a set of coverage patterns was output on the basis of frequent subtrees and δ'-coverage concept. Compared with the traditional algorithms for mining frequent closed tree patterns and maximal frequent tree patterns, the proposed algorithm can output fewer frequent subtrees in the case of preserving all the frequent subtrees, and the processing efficiency is increased by 15% to 25%.The experimental results show that the algorithm can effectively reduce the size and redundant information of the output frequent subtrees, and it has high feasibility in practical operation.

For automatic detection of tire crown cord overlap defect, a detection method based on the crown X ray image was presented. Firstly, the gray cumulative projection curves that X-ray image was projected along different angles were obtained. Secondly, the local peak energy distribution of curves were calculated and the energy feature vector was constructed by the n largest peak energy values. Thirdly, the tire crown crack image was recognized by the maximum projection curve which could be distinguished through the energy feature vector by Support Vector Machine (SVM). Lastly, using the position inverse calculation, the tire crown crack was located. The experimental results demonstrate that the proposed approach was effective to detect the defects of tire crown which caused by tire cord overlap. The highest rate of correct detection can reach 97.7% in the 1000 crown images collected by the process of production.

Since the data gathered in Wireless Sensor Network (WSN) are inaccurate and unreliable, a flexible space model based on the spatial correlation of sensor data was defined, and an adaptive neighbor-space approach for data cleansing (ANSA) was proposed. The approach adjusted neighbor-space dynamically according to sensor data fluctuation and calculated the weighted average of neighbors' measurements to clean local raw data. The experimental results show that, the sensor data error after cleansing by the proposed approach is less than 0.5, and compared to the classic Weighted Moving Average (WMA), it is more accurate and the energy consumption is reduced by about 36%.