Block Diagonal Representation (BDR) model can efficiently cluster data by using linear representation, but it cannot make good use of non-linear manifold information commonly appeared in high-dimensional data. To solve this problem, the improved Block Diagonal Representation based on Neighbor Graph (BDRNG) clustering algorithm was proposed to perform the linear fitting of the local geometric structure by the neighbor graph and generate the block-diagonal structure by using the block-diagonal regularization. In BDRNG algorithm, both global information and local data structure were learned at the same time to achieve a better clustering performance. Due to the fact that the model contains the neighbor graph and non-convex block-diagonal representation norm, the alternative minimization was adopted by BDRNG to optimize the solving algorithm. Experimental results show that:on the noise dataset, BDRNG can generate the stable coefficient matrix with block-diagonal form, which proves that BDRNG is robust to the noise data; on the standard datasets, BDRNG has better clustering performance than BDR, especially on the facial dataset, BDRNG has the clustering accuracy 8% higher than BDR.

In the existing French Named Entity Recognition (NER) research, the machine learning models mostly use the character morphological features of words, and the multilingual generic named entity models use the semantic features represented by word embedding, both without taking into account the semantic, character morphological and grammatical features comprehensively. Aiming at this shortcoming, a deep neural network based model CGC-fr was designed to recognize French named entity. Firstly, word embedding, character embedding and grammar feature vector were extracted from the text. Then, character feature was extracted from the character embedding sequence of words by using Convolution Neural Network (CNN). Finally, Bi-directional Gated Recurrent Unit Network (BiGRU) and Conditional Random Field (CRF) were used to label named entities in French text according to word embedding, character feature and grammar feature vector. In the experiments, F1 value of CGC-fr model can reach 82.16% in the test set, which is 5.67 percentage points, 1.79 percentage points and 1.06 percentage points higher than that of NERC-fr, LSTM(Long Short-Term Memory network)-CRF and Char attention models respectively. The experimental results show that CGC-fr model with three features is more advantageous than the others.

In cloud environment, the code of pivotal business in Virtual Machine (VM) can be modified by malicious software in many ways, which can pose a threat to its stable operation. Traditional measurement systems based on host are liable to be bypassed or attacked. To solve the problem that it is difficult to obtain a complete virtual machine running process code and verify its integrity at Virtual Machine Monitor (VMM) layer, a paging-measurement method based on hardware virtualization was proposed. The Kernel-based Virtual Machine (KVM) was used as the VMM to capture the system calls of virtual machine process in VMM and regarde it as the trigger point of the measurement process; the semantic differences of different virtual machine versions were solved by using relative address offset, then the paging-measurement method could verify the code integrity of running process in virtual machine transparently at VMM layer. The implemented prototype system of VMPMS (Virtual Machine Paging-Measurement System) can effectively measure the virtual machine process code with acceptable performance loss.

The performance requirements of application layer multicast are not identical in different business areas, and the network environment is more complex as follows: the multicast node is diversified, the communication channel is complex, the node scale is large, the amount of data is magnified and so on. The multicast programs should be optimized by analyzing the existing application layer multicast and combining new applications demands. By analyzing the evaluating indicator of application layer multicast, application layer multicast optimization method would be divided into the coding features optimization, the hierarchical clustering optimization, the node performance optimization, the optimal parent selection optimization and the routing information maintenance optimization. Through comparing the performance indicators of different types of optimization methods, the applicable environments were introduced separately, and further research directions were discussed finally.

With the development on technology of CAD/CAM, the product design, virtual manufacturing and digital prototyping can all be done in the computer, which makes the design of large and complex assembly an essential part in the product design. As these assembly models tend to have a huge number of data, it is extremely inconvenient to process on ordinary PCs. In order to speed up, the large scale assembly model needs simplifying. On the premise of maintaining the style and facade of the system, two simplification methods were proposed: (1) by removing invisible parts from the assembly, (2) by removing the invisible features from the assembly. The proposed methods were based on an algorithm which can directly detect invisible parts or features by pre-rendering the model from multiple view directions and reading the rendered results from the frame buffer. The experimental results show that our methods can correctly remove the invisible parts or features correctly from assembly for simplification.

With the increasing of product complexity,more requirements are raised to evaluation of quality.Directed toward large complex aerial product,evaluation architecture and model for product quality were proposed with synthesized theory.On basis of that,the key process and function of the evaluation system for quality of super-large aeronautic product were designed.At last,the implementation technology and the key function implementation of the evaluation system for quality of super-large aeronautic product based on.NET were presented in detail.