Aiming at the problem that the existing ensemble classification methods have poor learning ability for unbalanced time series data, the idea of optimizing component algorithm performance and integration strategy was adopted, and based on the heterogeneous ensemble method Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE), an ensemble classification algorithm IMHIVE-COTE (Imbalanced Hierarchical Vote Collective of Transformation-based Ensembles) for unbalanced time series was proposed. The algorithm mainly contains two improvements:first, a new unbalanced classification component SBST-HESCA (SMOM ( K-NN-based Synthetic Minority Oversampling algorithm for Multiclass imbalance problems) & Boosting into ST-HESCA (Shapelet Transformation-Heterogeneous Ensembles of Standard Classification Algorithm) algorithm) was added, the idea of boosting combined with resampling was introduced, and the sample weights were updated through cross-validation prediction results, so as to make the re-sampling process of the dataset more conducive to improving the classification quality of minority samples; second, the HIVE-COTE calculation framework was improved by combining the SBST-HESCA component, and the weight of the component algorithm was optimized, so that the unbalanced time series classification algorithm had higher voting weight to the classification result, as a result, the overall classification quality of the ensemble algorithm was further improved. The experimental part verified and analyzed the performance of IMHIVE-COTE:compared with the comparison methods, IMHIVE-COTE had the highest overall classification evaluation, and the best, the best and third overall classification evaluation on three unbalanced classification indexes. It is proved that IMHIVE-COTE's ability to solve the problem of unbalanced time series classification is significantly better.

The effective planning and scheduling of the rescue operation plays an important role in saving lives and reducing property losses. Relying on mathematical modeling methods and computer simulation technology to assist decision-makers to complete the vehicle scheduling for emergency material distribution has become a consensus in the academic world. Focused on two key issues, i.e., model and optimization, the recent research status of the vehicle scheduling problem for emergency material distribution were analyzed. The main optimization objects and influence factors in the model of vehicle scheduling problem for emergency material distribution were reduced. The application effects of different optimization algorithms were compared and analyzed. And the existing problems of current research were brought forward. Finally, the future research trends of the vehicle scheduling for emergency material distribution were discussed.

On account of the features that the information in microblogging is enormous and the microbloggers' interests change over time, a personalized microblogging recommendation model based on Weighted Dynamic Degree of Interest (WDDI) was proposed. WDDI model considered the microblogging retweet features and the time factor of tweets, studied the tweets of microbloggers by exploiting the microblog topic model Retweet-Latent Dirichlet Allocation (RT-LDA) and built the individual dynamic interest model. Then WDDI got user's group dynamic interest by the similarity and the interacted frequency between users and their followee. Combining the user's individual interest and the group interest, the weighted dynamic degree of interest model was built. By ranking the new tweets that the user received in descending order by the degree of interest, the dynamic personalized microblogging recommendation was achieved. The experimental results show that WDDI is able to reflect the users' dynamic interest more precisely than the traditional models.

The high speed of the information propagation exacerbates the diffusion of rumors or other network pollutions in the microblogging. As the size of microbloggers and information of sub-networks in microblogging is enormous, the study of the propagation mechanism of microblogging pollution and pollution detection becomes very significant. According to the rumor spreading model for the microblogging established on the basis of influence of users, in this paper, ant colony algorithm was used to search for the rumor spreading route. Based on the data obtained from Twitter and Sina microblogging, the feasibility of the model was verified by comparison and analysis. The results show that: with the search of the affected individual, this model narrows down the pollution detection range, and improves the efficiency and accuracy of pollution management in microblogging.

According to the different features between plant trunks and leaves, fractal method was adopted to draw the branches during simulation process. Through controlling functions to adjust parameters, such as branching growth direction, length, and radius, all kinds of branch structures were created to change the original fractal method in generating results of too regular characteristics. Image method was adopted to draw blades and Alpha test technology was utilized to filter the background of the leaf picture and retain the complex edge and color information. Through the rotating and scaling method, various lifelike leaves were generated. This image-based method was simpler and faster than the graph method. Besides, the process of plant flickering in the wind was approximately simulated according to the branches of different deformation under the influence of wind from morphology point of view.

The static resource allocation algorithm based on the theory of effective bandwidth was introduced firstly. When the real behavior of Internet traffic is taken into account, this algorithm is inefficient. So here a dynamic resource management algorithm based on Internet traffic prediction was proposed to take the place of it. This algorithm was applied to a Differentiated Service network, and implemented on the boundary node. The basic idea under this algorithm was to allocate resources (bandwidth/buffer size) between different kinds of flows dynamically, according to the result of prediction. At last, ns-2 was used to run the simulation and find out the lost packets rate and output link utilization of this algorithm, which were superior to those of the static resource allocation algorithm.