To reduce the running energy consumption of the central air conditioner and stabilize and control the return temperature of chilled water effectively, an optimal control method of return water temperature was proposed, and the actual load demand was judged according to the deviation between the measured value of return water temperature and the set value. Firstly, the inertia weight of Particle Swarm Optimization (PSO) algorithm was made decline exponentially which made updating speed of particles match each stage of optimization process. Then, aiming at uncertain disturbance of parameters of the model, the thoughts of Artificial Immune (AI) algorithm were introduced in Particle Swarm Optimization (PSO) algorithm to form AI-PSO algorithm which could expand the diversity of particles and enforce their ability to get rid of local optimum. Finally, three parameters of Proportional Integral Differential (PID) controller were optimized with AI-PSO algorithm, and through this controller, the frequency of chilled water pump was adjusted to make return water temperature steady near set value. The experimental results show that the proposed strategy can reduce operating frequency of chilled water pump more effectively while meeting indoor load demand, in addition, energy saving effect and control quality are much better.

Parameter-free locality preserving projection does not need to set parameters and has stable performance, but the algorithm cannot effectively maintain the local structure of the sample and ignores the role of non-local samples. Moreover, this method exists the Small Size Sample (SSS) problem. A complete parameter-free local neighborhood preserving and non-local maximization algorithm was proposed. In order to make full use of the nearest neighbor samples and non-nearest neighbor samples, which were divided by whether the distance between two samples is no more than 0.5 or not, the neighbor scatter matrix and non-nearest neighbor scatter matrix were constructed. Then, the objective function of the algorithm was to seek a set of projection vectors such that the neighbor scatter matrix was maximized and non-nearest neighbor scatter matrix was minimized simultaneously. As to solve the objective function, the high dimensional samples were projected to a low dimensional subspace by Principal Component Analysis (PCA) algorithm, which was proved without lossing any effective discriminant information according to two theorems. In order to solve the SSS problem, the objective function was converted to differential form. The experimental results on face database and palmprint database illustrate that the proposed method outperforms Parameter-free locality preserving projection with average recognition rate, which proves the effectiveness of the proposed algorithm.

Div + CSS is popular in Web page layout. In this layout, a lot of data records of Web pages gather in a layer in the form of repetition structure. To mine data from Web well, this paper proposed a new kind of Web data mining algorithm, computed tree edit distance through string edit distance, improved string edit distance algorithm,used string edit distance to access similarity between one tree and another, and then found repeated patterns in Web pages and mined data. By testing pages of different features of repeated patterns, this algorithm is proved to extract Web data successfully with the feature whether the root and upper layer nodes are the same or the lowest layer nodes are the same.

Deploying sensor nodes’ has to meet certain coverage and connectivity in the energy constrained wireless sensor networks. Concerning this, a new square regionbased coverage and connectivity probability computational method was proposed in this paper. The new method can not only depict the relations among coverage rate, connectivity rate, the number of nodes, the sensing (communication) range of nodes and the size of network, but also calculate the number of sensor nodes scattered for maintaining certain coverage rate and connectivity rate. The simulation results show that the errorrate of deployment is less than the value obtained from the theoretical analysis.