Non-negative Matrix Factorization (NMF) has the disadvantage of slow convergence, which is mainly due to that the base image (base matrix) contains lots of noise points. Besides, the coefficient matrix is significantly dependent, which is not conducive to distinguish between different images. In view of the above shortcomings, a new algorithm called Smoothness and Principal Components Based Non-Negative Matrix Factorization (SPNMF) was proposed in this paper. SPNMF had two novelties. On one hand, a constant matrix was added to the base matrix to enhance the smoothness and stabilize the noise points, which caused good convergence; on the other hand, to improve the discrimination, the variance between the different columns of the coefficient matrix as a penalty term was added to the loss function of NMF. The experimental results on the PIE face database and FERET face database show that the proposed method not only has higher recognition performance compared with the traditional algorithms, but also is two to four times faster than NMF, making the face recognition system based on NMF more practical.

As for the present research on the dissemination of Internet Public Opinion (IPO), some research use mathematic statistics or intelligent learning to analyze the growing or descending process of a topic related text, and some use cellular automata or Hidden Markov Model (HMM) to find the tendency propagation of the subject of IPO. However, all of them lack the analyses of the impacts of the subject attributes in IPO on its tendency propagation. Based on the systematic synergy of IPO space, the synergistic transition probability between states on whole cells space of discussed IPO was computed firstly, and then it was compared with a local state probability in 9 neighbors of a central cell to decide whether the state of central cell should be converted. After several iterative operations, the degree (magnetisability) which expressed the tendency propagation upon to "+" or "-" was obtained. Through observing the magnetisability-time variable curve, one can clearly handle its evolution. Therefore, a new model and an algorithm of extensive synergistic cellular automata model were presented. The simulation results show that the order-variable parameters of society adaptability can express the subject's group psychology, and it goes towards the majority opinion. Similarly, the order-variable parameters of preference fast make tendency propagation close to the direction of preference, i.e "+" or "-". The model is relatively closer to the real situation of dissemination of IPO.

Due to the defects of field bus, such as low communication rate and high cost, a new control system based on TCP/IP protocol was designed to embed control system with Server/Client structure. The communication network system, software structure and control method were described in detail. XML language was used to transfer frame data through TCP/IP Ethernet. Message queue scheduling algorithm and data clustering at memory sharing buffer technology were introduced to enhance the system’s real-time response performance and meet the function of warning and control. The experimental result shows that it adopts industrial Ethernet bus with the advantages of high rate of communication, good compatibility and low cost. This design is a feasible and efficient method for computer aided driving and control system in vehicle.

The design methods and load balancing algorithm of EJB clustering system was analyzed,and a new approach based on the BP neural networks was presented to solve the load balancing algorithm problem. How to design the BP Neural Network model was researched.The example shows that the system based on this algorithm is proved to reach better effects.