Existing tree decoder is only suitable for solving single variable problems， but has no good effect of solving multivariate problems. At the same time， most mathematical solvers select truth expression wrongly， which leads to learning deviation occurred in training. Aiming at the above problems， a Graph to Equation Tree （GET） model based on expression level-by-level aggregation and dynamic selection was proposed. Firstly， text semantics was learned through the graph encoder. Then， subexpressions were obtained by aggregating quantities and unknown variables iteratively from bottom of the equation tree layer by layer. Finally， combined with the longest prefix of output expression， truth expression was selected dynamically to minimize the deviation. Experimental results show that the precision of proposed model reaches 83.10% on Math23K dataset， which is 5.70 percentage points higher than that of Graph to Tree （Graph2Tree） model. Therefore， the proposed model can be applied to solution of complex multivariate mathematical problems， and can reduce influence of learning deviation on experimental results.

Feature selection can improve the performance of data classification effectively. In order to further improve the solving ability of Ant Colony Optimization (ACO) on feature selection, a hybrid Ant colony optimization with Brain storm Optimization (ABO) algorithm was proposed. In the algorithm, the information communication archive was used to maintain the historical better solutions, and a longest time first method based on relaxation factor was adopted to update archive dynamically. When the global optimal solution of ACO was not updated for several times, a route-idea transformation operator based on Fuch chaotic map was used to transform the route solutions in the archive to the idea solutions. With the obtained solutions as initial population, the Brain Storm Optimization (BSO) was adopted to search for better solutions in wider space. On six typical binary datasets, experiments were conducted to analyze the sensibility of parameters of the proposed algorithm, and the algorithm was compared to three typical evolutionary algorithms:Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm, Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) and Genetic Algorithm (GA). Experimental results show that compared with the comparison algorithms, the proposed algorithm can improve the classification accuracy by at least 2.88% to 5.35%, and the F1-measure by at least 0.02 to 0.05, which verify the effectiveness and superiority of the proposed algorithm.

Concerning the contradiction of accuracy and real-time in image fire detection, a fire image features selection and recognition algorithm based on rough set was proposed. Firstly, through in-depth study on the flame image features, the top edge of flame driven by the combustion energy is very irregular, and obvious vibration phenomenon occurres. But the lower edge is the opposite. Based on this feature, the upper and lower edges of the jitter projection ratio can be used as a flame from the edge shape regular interference. Then, the six striking flame features were chosen in order to create training samples. When fire classification ability was not affected, the feature classification table gained by experiment was used to reduce attributes of the training samples. And the reduced information systems attributes were applied to train a support vector machine model, and the fire detection was realized. Finally, this fire detection algorithm was compared to the traditional Support Vector Machine (SVM) fire detection algorithm. The results show that the presented algorithm reduces redundant attributes, eliminates the dimension of fire image features space, and decreases the data of training and testing in classifier in case rough set as a SVM classifier prefix system. While ensuring recognition accuracy, the algorithm improves fire detection speed.

ecause of the asymmetry between SIP protocol and H.323 protocol, there are some difficulties in the course of realizing SGW(Signal Gate Way) in SoftSwitch. Through discussing the following problems such as call setting up, signal converting, media logical channel setting up and media ability exchange, relevant methods about address exchange and the inconsistency of signal interpretation were put forward to realize the SGW in SoftSwitch.