For the actual background of the shortage of self-owned vehicles of the transporters in the early stage of emergency, the combinatorial optimization problem of hybrid vehicle paths with transportation mode of joint distribution of self-owned vehicles and vehicles rented by third-party was studied. Firstly, with the different interests between demand points and transporters considered, a multi-objective hybrid vehicle routing optimization model with soft time windows was established with the goal of maximizing system satisfaction and minimizing system delivery time and total cost. Secondly, the shortcomings of NSGA-Ⅱ algorithm in solving this kind of problems such as poor convergence and uneven distribution of Pareto frontiers were considered, the heuristic strategy and pheromone positive feedback mechanism of ant colony algorithm were used to generate offspring population, non-dominated sorting strategy model was used to guide the multi-objective optimization process, and the variable neighborhood descent search was introduced to expand the search space. A multi-objective non-dominated sorting ant colony algorithm was proposed to break through the bottleneck of the original algorithm. The example shows that the proposed model can provide reference for decision makers to choose reasonable paths according to different optimization objectives in different situations, and the proposed algorithm shows better performance in solving different scale problems and different distribution type problems.
Since the cost of mobile crowdsensing in full coverage of area is excessively high, a Compressive Sensing-based mobile crowdsensing Task Distribution (CS-TD) mechanism was proposed. Firstly, an overall cost model of perceived task was proposed. In this model, the number of nodes participating in a perceived task, the number of nodes perceived and data uploaded were comprehensively considered. Then based on cost model, the daily movement trajectory of sensory node was analyzed, by combining with the compressed sensing data acquisition technology, a compressed sensing sampling method based on perceived node trajectory was proposed. Secondly, the optimal node set was selected by the Region Covers Least Nodes (RCLN) algorithm, the tasks were assigned to the nodes, and then the compressed sensing technology was used to recover node data. Finally, the trustworthiness of perceived node was evaluated in iteration of multiple perceived tasks to ensure the optimality of task plan. The CS-TD distribution model was tested several times. Compared with the existing CrowdTasker algorithm, the average cost of CS-TD algorithm is reduced by more than 30%. CS-TD model can effectively reduce consumption of sensing node and reduce overall perceived cost in full coverage sensing task.
The current method of image classification which uses the Speed Up Robust Feature (SURF) is low in efficiency and accuracy. To overcome these shortages, this paper proposed an approach for image classification which uses the statistical features of the SURF set. This approach took all dimensions and scale information of the SURF as independent random variables, and split the data with the sign of Laplace response. Firstly, the SURF vector set of the image was got. Then the feature vector was constructed with the first absolute order central moments and weighted first absolute order central moments of each dimision. Finally, the Support Vector Machine (SVM) accomplished the image classification process with this vector. The experimental results show that the precision of this approach is better than that of the methods of SURF histogram and 3-channel-Gabor texture features by increases of 17.6% and 5.4% respectively. By combining this approach with the HSV histogram, a high-level feature fusion method was got, and good classification performance was obtained. Compared with the fused method of the SURF histogram and HSV histogram, the fused method of 3-channel-Gabor texture features and HSV histogram, and the multiple-instance-learning method based on the model of Bag of Visual Word (BoVW), the fused method of this approach and HSV histogram has better precision with the increases of 5.2%, 6.8% and 3.2% respectively.