In view of the problem that it is difficult for constrained multi-objective optimization algorithms to effectively balance convergence and diversity, a new constrained multi-objective optimization algorithm based on coevolution was proposed. Firstly, a population with certain number of feasible solutions was obtained by using the feasible solution search method based on steady-state evolution. Then, this population was divided into two sub-populations and both convergence and diversity were achieved by coevolution of the two sub-populations. Finally, standard constrained multi-objective optimization problems CF1~CF7, DOC1~DOC7 and the practical engineering problems were used for simulation experiments to test the solution performance of the proposed algorithm. Experimental results show that compared with Nondominated Sorting Genetic Algorithm Ⅱ based on Constrained Dominance Principle (NSGA-Ⅱ-CDP), Two-Phase algorithm (ToP), Push and Pull Search algorithm (PPS) and Two-Archive Evolutionary Algorithm for Constrained multiobjective optimization (C-TAEA), the proposed algorithm achives good results in both Inverted Generational Distance (IGD) and HyperVolume (HV), indicating that the proposed algorithm can effectively balance convergence and diversity.

Aiming at the problem that the non-semantic features of reviews cannot be fully utilized in opinion spam detection, a hierarchical attention mechanism and heterogeneous graph attention network based model, Hierarchical Heterogeneous Graph Attention Network (HHGAN), was proposed. Firstly, the hierarchical attention mechanism was used to learn the word-level and sentence-level document representations to focus on the capturing of the words and sentences that were important to the opinion spam detection. Then, the learned document representations were used as nodes, and the non-semantic features in reviews were selected as meta-paths to construct a heterogeneous graph attention network with a double-layer attention mechanism. Finally, a Multi-Layer Perceptron (MLP) was designed to distinguish the categories of reviews. Experimental results on datasets of restaurant and hotel extracted from yelp.com show that the F1 values of the HHGAN model reach 0.942 and 0.923 respectively, which are better than those of the traditional Convolutional Neural Network (CNN) model and other benchmark models of neural network.

Focused on the issue that adopting the OpenGL selection mechanism to directly act on the data points of Catmull-Clark subdivision mesh may cause the name stack overflowing because of the too large data volume, referencing the intersection theory of subdivision surfaces, a new pickup method for subdivision models was proposed. Through extracting and subdividing the neighboring mesh of pickup objects, the method converted the pickup for data points of subdivision mesh into the pickup for points, edges and faces of the initial mesh and the local subdivision meshes at successive levels, and the pickup for points of the local subdivision mesh at last level. Comparison and analysis experiments of some pickup examples were conducted. The total number of naming objects and the pickup time consumption of the given method were both far less than those of the traditional OpenGL selection method when the subdivision mesh had plenty of data points. The experimental results show that the proposed method can quickly and accurately pick up the data points of subdivision mesh, and it is especially suitable for the complex subdivision models with a large number of data points, and can effectively avoid pickup errors caused by the name stack overflowing.

Body parts segmentation of human point-cloud model is an important research content in action recognition and virtual reconstruction fields. Focused on this issue, a multi-constrained segmentation algorithm based on classified skeleton,geodesic distance, feature points and posture analysis was proposed.By generating the classified skeleton and geodesic distance of point-cloud, the roughly segmented point sets of each body part were got. Feature points were positioned by an algorithm depending on geodesic path and optimized by a curve fit method.According to these feature points and some anatomical features of human body, multiple constraints were constructed and roughly segmented point sets were segmented once again.The experimental results demonstrate that the segmentation effects of human point cloud models with different action, size and precision in standing posture are consistent with visual understanding of human. The point-cloud of body parts obtained through this algorithm can be used for posture analysis and so on.

To solve the problem of preserving detailed features of model about the traditional skeleton driven deformation, a method of subdivision-based skeleton-driven mesh deformation was proposed. Firstly,after that skeleton and control mesh were generated on deformed region, the relationship of between skeleton and control mesh, subdivision surface of control mesh and deformed region were established. Secondly, when the skeleton was modified according to the desired deformation result, the change information of the corresponding subdivision surface was transformed into the alteration of the mesh gradient field for Poisson. Some examples show that the deformation method for different mesh models could get better editing effects and preserve detailed features after the deformation effectively. Compared with the traditional skeleton-driven deformation method, it is proved to be easy to operate, and can be employed to preserve detailed features effectively. The method is suitable for editing the models with complex and rich geometric details.

As regards the poor local optimization ability of Particle Swarm Optimization (PSO), a nonlinear dynamic adjusting inertia weight was put forward to improve the particle swarm path planning algorithm. This algorithm combined the grid method and particle swarm algorithm, introduced the two concepts of safety and smoothness based on path length, and established dynamic adjustment path length of the fitness function. Compared with the traditional PSO. The experimental results show that the improved algorithm has stronger security, real-time and optimization ability.

There is a great demand for publicly verifiable encryption in key escrow, optimistic fair exchange, publicly verifiable secret sharing and secure multiparty computation, but the current schemes are either chosen plaintext secure or chosen ciphertext secure in the random oracle model, which obviously are not secure enough to be applied in the complicated circumstances. Based on the analysis of the current schemes and application of the reality, this paper proposed a new publicly verifiable encryption scheme by combining the CS encryption scheme with the non-interactive zero knowledge proof protocol. The new scheme enabled any third party other than the sender and receiver to verify the validity of the ciphertext, but leaked no information about the message. Finally, without using the random oracle, the adaptively chosen ciphertext security of the scheme is proved in the standard model.

According to the characteristics of parallel BCH decoder, the multiplication of constant coefficient in finite field was realized by using XOR gates to reduce hardware complexity. The part of the error location polynomial was calculated, and then the remaining error location polynomial could be obtained using the theory of affine polynomial and Gray code. The proposed algorithm reduces the system resources occupied.Through timing simulation on Field Programmable Gate Array (FPGA)'s development software ISE10.1, the high-efficiency of the algorithm on time and space has got verified.

We presented a novel approach for empty space skipping for object-order volume rendering. A two-staged space skipping was introduced: the first stage applied bricking on a regular grid, and the second stage used octree to reach a finer granularity. The approach further took into account that rendered volume may exceed the available texture memory and allow fast runtime changes of transfer function. For the bottleneck that heavy workload was assigned to CPU in our approach, a novel algorithm was proposed. The algorithm efficiently computed slicing for texture based volume render in Graphic Processing Unit (GPU); balanced the workload between CPU and GPU. Combining the two approaches above, we can render large volume data efficiently without compromising the image quality.