For mining research teams more rationally, a teacher-student relationship based research team mining algorithm was proposed. First, the BiLSTM-CRF neural network model was used to extract the teacher and classmate named entities from the acknowledgement parts of academic dissertations. Secondly, the guidance and cooperation network between teachers and students was constructed. Thirdly, the Leuven algorithm was improved, and the teacher-student relationship based Leuven algorithm was proposed to mine the research teams. The performance comparison was performed to the label propagation algorithm, the clustering coefficient algorithm and the Leuven algorithm on the datasets such as American College football dataset. Moreover, the operating efficiency of the teacher-student relationship based Leuven algorithm was compared to the operating efficiency of the original Leuven algorithm on three academic dissertation datasets with different scales. Experimental results show that the larger the data size, the more obvious performance improvement of the teacher-student relationship based Leuven algorithm. Finally, based on the academic dissertation dataset of National University of Defense Technology, the performance of the teacher-student relationship based Leuven algorithm was validated. Experimental results show that research teams mined by the proposed algorithm are more reasonable compared to academic paper cooperation network based mining method in the aspects of team cooperation closeness, team scale, team internal relationship and team stability.

To deal with the difficulty of input disturbance system in achieving adaptive control, a design method for the Fully Online Sequential Extreme Learning Machine (FOS-ELM) controller with Proportion-Integral-Derivative (PID) compensation was proposed. Firstly, a dynamic linear model of the system was established, then the FOS-ELM algorithm was used to design the controller and learn its parameters. Secondly, by calculating the output error of the system and combining with the system control error, the PID parameters of the system compensation were designed. Finally, the FOS-ELM controller parameters for PID compensation were adjusted online and used for system control. The experiment was carried out on engine Air Fuel Ratio (AFR) control system. The results show that the proposed method can achieve the adaptive control, reduce the disturbance caused by system disturbance input, and obviously improve the effective control rate of the system at the same time. When the positive and negative interference coefficients are 0.2, the effective control rate is increased from less than 53% to over 93%. In addtion, the proposed method is easy to implement and has strong robustness and practical value.

To overcome the disadvantage of high consumption of energy and bandwidth when the existing cloud storage technology which is applied to the smartphone, based on the Dropbox platform as a cloud service provider, an efficient and safe File Management system based on Cloud Instances (FM-CI) was designed. FM-CI supported download, compress, encrypt, convert operations, and file transfer between two smartphone users' cloud storage spaces. In addition, due to frequent open cloud instances may still increase the cost of the user, a protocol for users to share their idle instances and a file transfer scheme for sharing instance was designed. Simulation results show that, FM-CI can efficiently complete the file operations with less time and bandwidth, and the performance of FM-CI is better than those of the latest cloud storage schemes.

Semi-Supervised Support Vector Machine using label mean (meanS3VM) for image classification selects a small number of unlabeled instances randomly to train the classifier, and the classification accuracy is low; meanwhile, the parameter's determination always derives much oscillation of the results. In allusion to the above problems, meanS3VM image classification method based on mean shift was proposed. The smoothed image acquired by mean shift was used as original segmented image to reduce diversities of image features; an instance in each smoothed area was randomly selected as unlabeled instance to ensure that it carried useful information for classification and had a more efficient classifier; and the parameters value were also investigated and improved, the grid search method was used for sensitive parameters, the parameter ep was estimated by combining with Support Vector Machine (SVM) mean shift results, so that there will be a better and more stable result. The experimental results indicate that the classification rate of the proposed method to ordinary and noise image can be averagely increased more than 1% and 5%, and it has higher efficiency and avoids the oscillation of the results effectively, which is suitable for image classification.

For the difficulty of complex non-linear system modeling, a new system modeling algorithm based on the Takagi-Sugeno (T-S) Fuzzy Radial Basis Function (RBF) neural network optimized by improved Particle Swarm Optimization (PSO) algorithm was proposed. In this algorithm, the good interpretability of T-S fuzzy model and the self-learning ability of RBF neural network were combined together to form a T-S fuzzy RBF neural network for system modeling, and the network parameters were optimized by the improved PSO algorithm with dynamic adjustment of the inertia weight combined with recursive least square method. Firstly, the proposed algorithm was used to do the approximation simulation of a non-linear multi-dimensional function, the Mean Square Error (MSE) of the approximation model was 0.00017, the absolute error was not greater than 0.04, which shows higher approximation precision; the proposed algorithm was also used to build a dynamic flow soft measurement model and to finish related experimental study, the average absolute error of the dynamic flow measurement results was less than 0.15L/min, the relative error is 1.97%, these results meet measurement requirements well and are better than the results of the existing algorithms. The above simulation results and experimental results show that the proposed algorithm is of high modeling precision and good adaptability for complex non-linear system.

Considering the data security and integrity on the processing of HD video data stream transmission, acrypto DSP with special implementation for DES, SHA1, MD5, RSA are introduced. In order to improve the performance and decrease the cost, the DSP has 11 pipeline stages, and two parallel execution clusters (each cluster contains 3 function units). In order to improve throughput, special instructions are customized for complex operations. The methods of realizing the symmetrical encryption, public-key encryption and authentication algorithm based on such DSP are presented. In order to improve the throughput, The simulation experiment results show that the performance can well satisfy the requirement of real time HD video data stream applications.