The quality and quantity of learning samples are very important for intelligent data classification systems. But there is no general good method for finding meaningful samples in data classification systems. For this reason, the concept of convex boundary of dataset was proposed, and a fast method of discovering meaningful sample set was given. Firstly, abnormal and incomplete samples in the learning sample set were cleaned by box-plot function. Secondly, the concept of data cone was proposed to divide the normalized learning samples into cones. Finally, each cone of sample subset was centralized, and based on convex boundary, samples with very small difference from convex boundary were extracted to form convex boundary sample set. In the experiments, 6 classical data classification algorithms, including Gaussian Naive Bayes (GNB), Classification And Regression Tree (CART), Linear Discriminant Analysis (LDA), Adaptive Boosting (AdaBoost), Random Forest (RF) and Logistic Regression (LR), were tested on 12 UCI datasets. The results show that convex boundary sample sets can significantly shorten the training time of each algorithm while maintaining the classification performance. In particular, for datasets with many noise data such as caesarian section, electrical grid, car evaluation datasets, convex boundary sample set can improve the classification performance. In order to better evaluate the efficiency of convex boundary sample set, the sample cleaning efficiency was defined as the quotient of sample size change rate and classification performance change rate. With this index, the significance of convex boundary samples was evaluated objectively. Cleaning efficiency greater than 1 proves that the method is effective. The higher the numerical value, the better the effect of using convex boundary samples as learning samples. For example, on the dataset of HTRU2, the cleaning efficiency of the proposed method for GNB algorithm is over 68, which proves the strong performance of this method.

Since the convergence speed of the Firefly Algorithm (FA) is slow, and the solution accuracy of the FA is low, an improved Firefly Algorithm with Uniform local search and Variable step size (UVFA) was proposed. Firstly, uniform local search was established by the uniform design theory to accelerate convergence and to enhance exploitation ability. Secondly, search step size was dynamically tuned by using the variable step size strategy to balance exploration and exploitation. Finally, uniform local search and variable step size were fused. The results of simulation tests on twelve benchmark functions show that the objective function mean of UVFA was significantly better than FA, WSSFA (Wise Step Strategy for Firefly Algorithm), VSSFA (Variable Step Size Firefly Algorithm) and Uniform local search Firefly Algorithm (UFA), and the time complexity was obviously reduced. UVFA is good at solving low dimensional and high dimensional problems, and has good robustness.

To effectively measure the strength of the directed relationship among the users in social network, based on the directed interaction frequency, a smooth model for computing the interaction strength of the user was proposed. Furthermore, user interaction strength was taken as dependent variable and user relationship strength was taken as latent variable, a latent regression model was constructed, and an Expectation-Maximization (EM) algorithm for parameter estimation of the latent regression model was given. Comprehensive experiments were conducted on two datasets extracted from Renren and Sina Weibo in the aspects of the best friends and the intensity ranking. On Renren dataset, the result of TOP-10 best friends chosen by the proposed model was compared with that of manual annotation, the mean of Normalized Discounted Cumulative Gain (NDCG) of the model was 69.48%, the average of Mean Average Precision (MAP) of the model was 66.3%, both of the parameters were significantly improved; on Sina Weibo dataset, the range of infection spread by nodes with higher relationship strength increased by 80% compared to the other nodes. The experimental results show that the proposed model can effectively measure user relationship strength.

In order to realize aircraft optimal target aiming in the situation of combat duel, a control method based on Gauss Pseudospectral Method (GPM) was proposed. Taking agility and multi-constraint into consideration, the dynamic equation of the aircraft was modeled, the two-stage target aiming condition expression was deduced, and the optimal index was designed. Afterwards, the aircraft optimal aiming control was described as the multi-stage optimal control problem with constraint and unknown final time. The GPM was used to equally convert the continuous optimal boundary value problem to a discrete Nonlinear Programming (NLP) problem and the initial solution was preprocessed through Genetic Algorithm (GA), then, the Sequential Quadratic Programming (SQP) algorithm was applied to solve it. The simulation results show that it can realize target aiming effectively and satisfy weapon launch condition.

Farmers manage the cropland in a decentralized manner at present in China, and usually fertilization could not be applied rationally and scientifically. This paper presented a variable fertilization system for dispersive farmer, which was designed on the basis of Geographic Information System (GIS). The system referred a relational database SQL Server 2008 as a built-in database, in order to store, inquire and update kinds of information, such as the variability of soil, the crop yields over the years, and the fertilization formulas. The statistical analysis of the soil attribute can be realized by spatial interpolation, the conversion from raster to vector, data fusion, and overlay analysis in the system. And a fertilization formula for a cropland can be generated automatically by invoking the yield model and the fertilization model. Furthermore, the fertilization formula can be delivered to SMC6480 to match the fertilizer scientifically. The experimental results indicate that the system is stable and reliable, and all the modules ran well.