In Vehicular Cyber-Physical Systems (VCPS), the collected sensor data are always uncertain and conflicting. Dempster-Shafer (DS) evidence theory can effectively deal with uncertain information, but the Dempster's rule may produce counter-intuitive results when the information is conflicting. This paper proposed an improved approach for combining conflicting evidence with different weighting factors based on a novel dissimilarity measure. Firstly, a new dissimilarity measure mixing fuzzy nearness with the introduced correlation coefficient is proposed to characterize the divergence degree between two basic probability assignments (BPAs). Then, the weighting factors are developed by using the proposed dissimilarity measure. Finally, the Dempster's rule is chose to combine the revised sources. Simulation experiment shows that the improved method can effectively solve the problem of sensor data fusion in VCPS with better convergence performance.
Hand gesture of rotation, scaling and translation is the key problem of gesture recognition. This paper proposes a gesture recognition algorithm based on Hausdorff-like distance template matching of gesture main direction. Firstly, we segment hand gesture from video stream. Secondly, we calculate the main direction of gesture in the image, and build a 2D rectangular coordinate system. Then, we clockwise divide the gesture into eight sub-image area along the main direction of gesture and calculate the coordinates of target pixel points in each sub-image area in the 2D rectangular coordinate system. Finally, the algorithm of Hausdorff-like distance template matching is used to recognize the final gesture. Experimental results show that this algorithm can achieve real-time correct recognition of gestures in relatively stable light conditions. The overall recognition rate can reach 95%.
Traditional R-Tree organization methods usually choose a bottom-up method, while a small number of top-down algorithms cannot give an optimal performance due to the limitations of the division mode. This paper proposed a new R-tree organization method based on top-down recursive clustering against the deficiencies of the traditional bottom-up divided structure node. This method divided our spatial data into several classes by a K-means clustering method and uses the method of STLT to build the R-tree top-down, instead of adjusting each class. Apply this method to all the classes by recursions to construct the whole tree. The experiment shows that the new algorithm gives a better performance in query efficiency than Hilbert and STR, but has a bad construction efficiency to be improved.
In this paper, the author make the grey forecast united with the fuzzy synthetic assessment to determine the regional classification problems for regulating and planning groundwater. In this case, the following models are adopted to forecast the water quality system: GM(1,1) model groups
,GM(1,l) residual difference model,GM(1,N)
,GM(1,1) Weighted model.Keywords-groundwater quality; GM (1,1) model groups; GM (1,l) residual difference model; fuzzy synthetic assessment.
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