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Aiming at the uncertainty in the process of landslide hazard evaluation along the railway, game theory and cloud model evaluation methods were introduced, and Hamming distance was used to verify the similarity comparison results to evaluate the landslide hazard along the railway. Firstly, 12 influencing factors such as rainfall, stratigraphic lithology and land use type were selected to establish the risk evaluation index system. Secondly, the improved CRITIC method, coefficient of variation method, G1 method and order diagram method were adopted to determine the basic weight. Then the game theory is introduced to calculate the optimal weight, and the comprehensive evaluation cloud is determined according to the cloud model and the optimal weight of each evaluation index. Finally, the Hamming distance is used to verify the similarity comparison results of the comprehensive evaluation cloud and the evaluation standard cloud, and the landslide risk grade is determined according to the maximum similarity principle, and the landslide risk distribution map of the study area is generated based on ArcGIS. The results show that the landslide hazard grade is level four, which is basically consistent with the actual situation. The landslide hazard evaluation model based on game theory and cloud model can accurately reflect the grade of landslide. The cloud model is used to realize the conversion of qualitative and quantitative problems, which makes the landslide hazard level more intuitive and has certain reference value for landslide hazard risk management. [ABSTRACT FROM AUTHOR]

The local binary descriptors in AKAZE algorithm are not sensitive to image scale change and fuzzy change, which leads to uneven feature point extraction and low accuracy of feature matching. An improved feature matching algorithm combining AKAZE and MAGSAC was thus proposed. In this algorithm, the FREAK descriptor was used to describe the feature points, and the main direction of the feature points was determined by calculating the gradient of the sample points. The MAGSAC method was used to eliminate the wrong match points. The experimental results showed that the matching accuracy of the improved algorithm was 6. 98% higher than that of the traditional AKAZE algorithm when the image changed in scale and rotation, and the average time of feature point extraction was 0. 084 ms less than that of the traditional AKAZE algorithm when the image changed in scale; the matching accuracy was 8. 57% higher than that of the traditional AKAZE algorithm when the image changed in blur, and the average time of feature point extraction was 0. 05 ms less than that of the traditional AKAZE algorithm. [ABSTRACT FROM AUTHOR]

In order to solve the flexible flow shop scheduling problem,this paper proposed the SEBA . The existing BA can not solve the discrete problem because it is easily trapped in local extremum and has low accuracy of the optimization results. SEBA adopt the ROV coding method, which made the algorithm suitable for solving discrete FFSP problems; This paper designed the set of the elite individuals based on hamming distance,which have higher fitness and lower similarities.It could also take turns to lead the population evolution, enhance the vitality of population evolution and avoid optimization process trap in local extremum. It designed an adaptive position update method to improve the accuracy of algorithm. Finally, It measured the SEBA by the datas from different scale scheduling benchmark problems with comparison of several algorithms. Simulation results show that SEBA is efficient for solving FFSP. [ABSTRACT FROM AUTHOR]

Aimed at the limitation of traditional string similarity complex algorithm, this paper proposed an algorithm based on vector space model to calculate string similarity, which fused both character adjacent position relation and word order information. This method described adjacent degree through computing Hamming distance of vector in VSM. Then it figured out word order similarity based on vector Manhattan distance. Finally, the algorithm presented quantitative description to string similarity fusing the word order and character adjacent degree. Compared with the traditional method, the proposed algorithm decreases the time complexity to O(n log(n)). Experimental results show that the method improves precision rate and gets more reasonable data result. [ABSTRACT FROM AUTHOR]