In binocular stereo vision, the camera needs to be calibrated to obtain its internal and external parameters in 3D measurement or precise positioning of the object.Through the study of the camera model with first-order radial distortion, linear formulas of solving internal and external parameters were constructed based on Radial Alignment Constraint (RAC) calibration method. Inclination angle, rotation angle, pitch angle and main distortion elements of lens were taken into consideration in this algorithm, which modified the defects in the traditional RAC calibration method that it only considers radial distortion and some parameters need priori values. The 3D reconstruction experiment of multi-pose binocular camera was carried out by using the obtained internal and external parameters. The experimental results show that,the reprojection error of this calibration method is distributed in[-0.3,0.3], and the similarity between the measurement trajectory and the actual trajectory is 96%, which has a positive effect on reducing the error rate of binocular stereo vision 3D measurement.

Louvain Method (LM) is an algorithm to detect community in complex network based on modularity optimization. Since there is no method to calculate the gain of modularity after nodes leave their community in the existing research, a method was presented to calculate the modularity-gain after nodes leave their community based on the definition of modularity and the method for calculating the modularity-gain after nodes merge. Secondly, aiming at the problem that LM requires large memory space, an improved algorithm was proposed with the strategy of separating isolated nodes. In each iteration of the algorithm, isolated nodes of the input network were separated in advance, only the connected nodes of the input network can actually participate in the iterative process. Isolated nodes and non-isolated nodes were stored respectively when storing communities detected. The experimental results based on real networks showed that the requirement of memory space was reduced by more than 40% in the improved algorithm, and the running time of the algorithm was further reduced. Experimental results indicate that the improved algorithm has more advantages in dealing with real networks.

To minimize damage from network security problem, an improved network security situation assessment model based on Fuzzy Analytic Hierarchy Process (FAHP) was proposed. First, a set of index system in conformity with actual environment which consists of index layer, criterion layer and decision layer was established in consideration of the large-scale network environment in the future. Aiming at the influence on evaluation by data distribution uncertainty and fuzziness in situation assessment, the proposed model used Fuzzy C-Means (FCM) clustering algorithm and the best clustering criterion for data preprocessing to get the optimal cluster number and cluster center. Finally, multi-factor secondary assessment model was established for situation assessment vector. The simulation results show that, compared with the present situation assessment method based on FAHP, the improved method takes the factors which have small weights into consideration better, so the standard deviation is smaller and evaluation results are more objective and accurate.

The conventional threshold function in wavelet noise reduction of chaotic signals has its shortages, such as low resolution of high frequency and restraint limit of quantitative method to hard and soft thresholds. Concerning these shortages, a wavelet packet noise reduction method of chaotic signals was proposed based on a high-order threshold function. The method could further decompose high frequency part by wavelet packet and retained useful high-frequency information, so it was more precious in partial analysis. Furthermore the threshold function was continuous and derivable. There was a smooth transiting area between noise wavelet coefficient and chaotic signal wavelet coefficient. As a result, it is more consistent with the continuous characteristics of the signal. The comparative simulation shows that, compared with soft threshold noise reduction method and semi-soft threshold wavelet packet noise reduction method, the effect of noise reduction to chaotic signals has been significantly improved, and Signal-to-Noise Ratio (SNR) increased 3.7-7dB.

Since traditional gene selection methods may select a large number of irrelevant genes, which leads to low sample prediction accuracy, a new hybrid method based on clustering and Particle Swarm Optimization (PSO) was proposed for gene selection of microarray data in this paper. Firstly, genes were partitioned into a certain number of clusters by using clustering algorithm. Then Extreme Learning Machine (ELM) was applied to validate the classification performance of the genes selected from each cluster, which formed an initial gene pool. Finally, the wrapper approach based on PSO and ELM was used to select compact gene subset with high classification accuracy from the initial gene pool. The better classification accuracy on microarray data was provided with the genes selected by the proposed method. The experiments on two public microarray data sets verify that the proposed method can obtain better classification performance with fewer genes than other classical methods.

To get all the accurate solutions to Nonlinear Systems of Equations (NSE), the algorithm with global convergence was constructed for solving NSE based on the characteristics of Cellular Automata (CA). In the algorithm, the theoretical search space of NSE was divided into the discrete space, the discrete space was defined as the cellular space; each point in the discrete space was a cell in the cellular space, and each cell was a trial solution of NSE; a cellular state consisted of position and increment of position. The cellular space was divided into many nonempty subsets, and states evolution of all cells from one nonempty subset to another realized the search of the cellular space on the theoretical search space. During evolution process of all cells, each cells transition probability from one position to any another position could be simply calculated; each state of cells during evolution corresponded to a state of a finite Markov chain. The stability condition of a reducible stochastic matrix was used to prove the global convergence of the algorithm. The case study shows that the algorithm is efficient.

Phase space reconstruction is an important part in recognizing chaos during ship's sailing, it directly influences the result of chaotic analyzing and the effect of chaos controlling. In order to choose a proper method, this paper reconstructed the ship's sailing data series by two methods, and compared the performances of reconstructions. It is proved that the C-C method does well on processing ship's sailing data series, while the autocorrelation &G-P method does a little bad. Different methods were used to conduct qualitative analysis and quantitative analysis based on the phase space reconstruction, the results of the analysis show the existence of chaotic characteristic in the ship's sailing data series, which provides the following research on ship's chaos control with essential basic data and comparison foundation.