For the flexible job shop rescheduling problem with machine breakdown, an improved Imperialist Competition Algorithm (ICA) was proposed. Firstly, a flexible job shop dynamic rescheduling model was established with the maximum completion time, machine energy consumption and total delay time as the objective functions, and linear weighting method was applied to three objectives. Then, the improved ICA was proposed to retain the excellent information for the next generation. A roulette selection mechanism was added after the assimilation and revolutionary steps of the general ICA, so that the excellent genes in the initial empire were able to be retained, and the updated empire quality was better and closer to the optimal solution. Finally, after the machine breakdown, the event-driven rescheduling strategy was adopted to reschedule the unprocessed job procedures after the breakdown point. Through production examples, simulation experiments were carried out on three hypothetical machine breakdown scenarios, and the proposed algorithm was compared with improved Genetic Algorithm (GA) and Genetic and Simulated Annealing Algorithm (GASA). Experimental results show that the proposed improved ICA is effective and feasible.

Focusing on the issues that the sparseness of data is reduced after factorization and the single image feature cannot describe the image content well, a multi-constraint nonnegative matrix factorization based on feature fusion was proposed. The information provided by few known labeled samples and sparseness constraint were considered, and the graph regularization was processed, then the decomposed image features with different sparseness were fused, which improved the clustering performance and effectiveness. Extensive experiments were conducted on both Yale-32 and COIL20 datasets, and the comparisons with four state-of-the-art algorithms demonstrate that the proposed method has superiority in both clustering accuracy and sparseness.

It is difficult to extract suitable face feature for classification, and the face recognition accuracy is low under unconstrained condition. To solve the above problems, a new method based on deep neural network and weighted fusion of face features, namely DLWF, was proposed. First, facial feature points were located by using Active Shape Model (ASM), then different organs of face were sampled according to those facial feature points. The corresponding Deep Belief Network (DBN) was trained by the regional samples to get optimal network parameters. Finally, the similarity vector of different organs was obtained by using Softmax regression. The weighted fusion of multiple regions in the similarity vector method was used for face recognition. The recognition accuracy got to 97% and 88.76% respectively on the ORL and LFW face database; compared with the traditional recognition algorithm including Principal Components Analysis (PCA), Support Vector Machine (SVM), DBN, and Face Identity-Preserving (FIP) + Linear Discriminant Analysis (LDA), no matter under the constrained condition or the unconstrained condition, recognition rates were both improved. The experimental results show that the proposed algorithm has high efficiency in face recognition.

At present, the accuracy of image retrieval is a difficult problem to study, the main reason is the method of feature extraction. In order to improve the precision of image retrieval, a new image retrieval method based on multi-feature called CAUC (Comprehensive Analysis based on the Underlying Characteristics) was presented. First, based on YUV color space, the mean value and the standard deviation were used to extract the global feature from an image that depicted the global characteristics of the image, and the image bitmap was introduced to describe the local characteristics of the image. Secondly, the compactness and Krawtchouk moment were extracted to describe the shape features. Then, the texture features were described by the improved four-pixel co-occurrence matrix. Finally, the similarity between images was computed based on multi-feature fusion, and the images with high similarity were returned.On Corel-1000 image set, the comparative experiments with method which only considered four-pixel co-occurrence matrix showed that the retrieval time of CAUC was greatly reduced without significantly reducing the precision and recall. In addition, compared with the other two kinds of retrieval methods based on multi-feature fusion, CAUC improved the precision and recall with high retrieval speed. The experimental results demonstrate that CAUC method is effective to extract the image feature, and improve retrieval efficiency.

To solve the problem that present traversal methods of hierarchical tree which lead to low efficiency, a new collision detection algorithm based on classified traversal was proposed. Firstly, these objects were classified according to the difference between the balance factors of two tree' nodes. The simultaneous depth-first traversal method was applied to the objects which have similar structure, and the commutative depth-first traversal method was applied to the other objects, which reduced the number of intersect tests. Then, the process of traversal was optimized by using the temporal spatial coherence and priority strategy. Finally, the experimental results show that, compared with the collision detection algorithm based on unified traversal, the proposed algorithm shortens the time of the intersection test. The bigger the number of objects, the more significant the advantage of quickness, it can reduce about 1/5 of the required time.

To solve the slow rendering problem of ray tracing algorithm in irregular scene, an improved grid subdivision ray tracing algorithm was proposed based on the in-depth study and comparison of the recent acceleration algorithms of ray tracing. First, the rectangular bounding box of the scene was set to remove the influence of external light, and the intersect operations was simplified; second, spatial grids were created with a new way to limit the spatial unit number and complexity of storage space within a certain range; finally, the traditional spatial grid algorithm was greatly improved by subdividing grids to eliminate the bad influence on acceleration effects due to ignoring some blank space. The experimental results show that this method can effectively improve the light speed in blank space, it not only increases the time efficiency but also reduces the space lost.

Concerning the problem that how to initialize the weights of deep neural networks, which resulted in poor solutions with low generalization for spam filtering, a classification method of Deep Belief Net (DBN) was proposed based on the fact that the existing spam classifications are shallow learning methods. The DBN was pre-trained with the greedy layer-wise unsupervised algorithm, which achieved the initialization of the network. The experiments were conducted on three datesets named LinsSpam, SpamAssassin and Enron1. It is shown that compared with Support Vector Machines (SVM) which is the state-of-the-art method for spam filtering in terms of classification performance, the spam filtering using DBN is feasible, and can get better accuracy and recall.

In order to improve the speed and accuracy of image retrieval, the drawbacks of image retrieval based on a variety of clustering algorithms were analyzed, then a new partition clustering method for image retrieval was presented in this paper. First, based on the asymmetrical quantization of the color in HSV model, color feature of image was extracted by color coherence vectors. Then, global shape feature of image was extracted based on improved Hu invariant moment. Finally,images were clustered based on contribution according to color and shape features, and image feature index library was established. The methods described above were used for image retrieval based Corel image library. The experimental results show that compared with image retrieval algorithms based on improved K-means algorithms, precision ratio and recall ratio of the proposed algorithm are improved greatly.

Concerning the problem that uncertainty information is difficult to be merged during the decision-making process of multi-source irrigation information, a decision fusion method based on fuzzy rough set and Dempster-Shafer (D-S) evidence theory was proposed. Using the fuzzy rough set theory,the basic probability distribution function was established, the interdependence between irrigation factors and irrigation decision was calculated, and the identification framework of irrigation decision on the multiple fusion irrigation factors was built. Using the improved D-S evidence theory, the multi-source irrigation information was fused at the decision-making level, the expression and synthesis problems of uncertain information were solved. The information of winter wheat such as soil moisture, photosynthetic rate and stomatal conductance in north China was fused in irrigation decision by the application of the methods mentioned above. The results show that the uncertainty of the irrigation decision decreases from 38.0% before fusion to 9.84%. The method can effectively improve the accuracy of irrigation decision and reduce the uncertainty of the irrigation decision.

Concerning the deficiency of the digital image scrambling algorithm in double scrambling, an image scrambling algorithm based on cloud model was proposed. The algorithm used the function value generated by the three-dimensional cloud model to change the positions and values of the image pixels, to achieve a double scrambling. The experimental verification as well as quantitative and qualitative analysis show that the scrambling image renders white noise and realizes the image scrambling. There is no security issue on cyclical recovery. The algorithm can quickly achieve the desired effect, resistant to shear, plus noise, filtering and scaling attacks. This proves that the algorithm is effective and reasonable, and also can be better applied to the image scrambling.

According to the software network communication behavior, a reverse analytical method based on session association was proposed in this paper. The method restored the network traffic communication session and Application Programming Interface (API) sequence session produced by software firstly, then associated the sessions restored. Through the association, a direct mapping was built between two kinds of software network behavior analytical methods based on execution trace analysis and network traffic analysis respectively. The prototype system was designed and completed. Based on the system, the function call list was extracted. The reverse analytical method based on session association makes the reverse analysis of software network behaviors fast and convenient.

In order to simulate broken branches caused by big wind quickly and realistically, a simulation algorithm was proposed. Firstly, a wind model was presented with reference to noise function. Then, mechanics of materials was applied to analyze the movement details of branches so as to get the deformation parameters of branches and the parameters were then introduced into the interpretation on the fractal geometry of trees. At last, on the basis of these models, a visual simulation method for broken branches was designed. This method could obtain visual simulation results of broken branches with different strong wind by modifying the wind model parameters. The simulation results show that the algorithm which provides new ideas for estimating disaster of trees is right and efficient.

In view of the waving motion of trees, a simulation on the broken branches caused by big wind was proposed. After analyzing the motion details using dynamics, the formula of deformation of trees, which was used to interpret character statistics, was obtained. By using Perlin noise function to simulate dynatic wind fields, the simulation of broken branches was realized with different expressions. The experiment result shows that trees move as a whole body conforming to the continuity and consistency of physics until being randomly broken down under wind fields. The method is valid and can be applied in virtual reality to simulate broken branches caused by big wind.

An effective index structure was proposed for high-dimensional data spaces: compact pyramid tree. The basic idea is to divide the data space first into 2d pyramids sharing the center point of the space as a top. Its basic philosophy is: the data invalid in low-dimensional spaces are usually invalid in high-dimensional spaces. In the process of spatial division, the γ division strategy was used to carry out the data compression. It reduced the index structure, and overcame the pyramid technologys shortcomings. The construction method and inquiry algorithm of pyramid tree were given. The experiments prove that compact pyramid technology is an effective spatial division strategy, and has good performance in highdimensional skew space.