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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]

Mobile cloud computing can use the computation offloading to improve the energy efficiency of mobile devices and the execution delay of applications. However, in the face of the multiple service selection from cloud, the computation offloading d ecision is a NP problem. In order to address this problem, this paper designed a genetic algorithm to find the best application p artitioning decision solution for computation offloading. In genetic population initialization, this algorithm combined of predefined and random chromosomes to initialize the population, which could reduce the generation of the inefficient chromosomes . At the same time, the algorithm designed a specific fitness function based on Hamming distance function for the predefined reserved population, which could better measure the difference between chromosomes. The population crossover used respectively the inbreeding and c rossbreeding to enrich individual species. The algorithm used the modified genetic operations to reduce the ineffective solutions and obtain the best feas ible solution in a reasonable time. It evaluated the efficiency of the proposed algorithm using graphs of real mobile applications in simulation experime nts. The evaluated conclusions denote that this designed algorithm has a better performance than the comparision algorithms on the application execution energy, the execution time and the overall weight cost. [ABSTRACT FROM AUTHOR]

Most existing Web image retrieval approaches only consider visual features. They ignore the valuable semantics involved in the associated texts,and fail to take advantages of text. This paper proposed a new unsupervised visual hashing approach called STDVH. Firstly, it extracted the semantic information of the training text by spectral clustering. Then, it constructed a deep convolutional neural network to transfer the text semantic information into the learning of the image hash code. At last, it trained the image hash codes and hash functions in a unified framework, and completed the effective retrieval of largescale image data in low-dimensional Hamming space. Experiments on two publicly available image datasets Wiki and MIR Flickr indicate that the proposed approach can achieve superior performance over other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

This paper researched image matching issue and proposed a feature point matching algorithm to improve the BRIEF algorithm. The proposed algorithm generated the feature point description operator according to the difference signs and the difference magnitudes relation between the random point and the feature point. BRIEF was sensitive to noise because the small difference magnitude was more susceptible to the noise. To solve this problem, this paper determined a small pixel difference threshold by the neighborhood mean value of the BRIEF. Comparing the Hamming distance between descriptions realized the feature points matching. Compared with BRIEF and ORB algorithm, the experiments prove that the operator has higher discriminant, simple calculation and good noise suppression performance. And the matching accuracy is higher. [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]

In the extraction process of LBP features, most consumption of time and memory were paid for clustering. In order to address these problems, an improved local binary algorithm for image categorization was proposed. The algorithm replaced decimal system encoding LBP with binary descriptor. Meanwhile, Hamming distance was employed rather than Euclidean metric for features clustering. The multi-scale LBP features was flued for a new local binary descriptor. The result of the experiment on the PASCAL VOC 2007 dataset showed that the adopted local binary descriptor was better than the classical LBP, specifically for time consumption. [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]

In order to improve the accuracy of stereo matching based on Census transform, this paper proposed an improved Census transform. Firstly, it designed a transform template whose coordinated follow Gaussian distribution as a guidance of Census transform; secondly, it used the Hamming distance as the matching cost for computing initial disparity, meanwhile, accelerated the speed of Hamming distance computing by employing SSE4. 2 instructions; lastly, it applied a non-local cost aggregation method to refine the disparity. Several experimental results demonstrate that this method is more accuracy and efficiency. [ABSTRACT FROM AUTHOR]