There are many Dynamic Multiobjective Optimization Problems （DMOPs） in real life. For such problems， when the environment changes， Dynamic Multi-Objective Evolutionary Algorithm （DMOEA） is required to track the Pareto Front （PF） or Pareto Set （PS） quickly and accurately under the new environment. Aiming at the problem of poor performance of the existing algorithms on population prediction， a dynamic multi-objective optimization algorithm based on Weight Vector Clustering Prediction （WVCP） was proposed. Firstly， the uniform weight vectors were generated in the target space， and the individuals in the population were clustered. According to the clustering results， the distribution of the population was analyzed. Secondly， a time series was established for the center points of clustered individuals. For the same weight vector， the corresponding coping strategies were adopted to supplement individuals according to different clustering situations. If there were cluster centers at all adjacent moments， the difference model was used to predict individuals in the new environment. If there was no cluster center at a certain moment， the centroid of the cluster centers of adjacent weight vectors was used as the cluster center at that moment， and then the difference model was used to predict individuals. In this way， the problem of poor population distribution was solved effectively， and the accuracy of prediction was improved at the same time. Finally， the introduction of individual supplement strategy was beneficial to make full use of historical information. In order to verify the performance of the proposed algorithm， simulation comparison of this algorithm and four representative algorithms was carried out. Experimental results show that the proposed algorithm can solve DMOPs well.

Focusing on the problems of low prediction accuracy and slow convergence speed of network security situation prediction model, a prediction method based on Improved Particle Swarm Optimization Extreme Learning Machine (IPSO-ELM) algorithm was proposed. Firstly, the inertia weight and learning factor of Particle Swarm Optimization (PSO) algorithm were improved to realize the adaptive adjustment of the two parameters with the increase of iteration times, so that PSO had a large search range and fast speed at the initial stage, strong convergence ability and stability at the later stage. Secondly, aiming at the problem that PSO is easy to fall into the local optimum, a particle stagnation disturbance strategy was proposed to re-guide the particles trapped in the local optimum to the global optimal flying. The Improved Particle Swarm Optimization (IPSO) algorithm obtained in this way ensured the global optimization ability and enhanced the local search ability. Finally, IPSO was combined with Extreme Learning Machine (ELM) to optimize the initial weights and thresholds of ELM. Compared with ELM, the ELM combining with IPSO had the prediction accuracy improved by 44.25%. Experimental results show that, compared with PSO-ELM, IPSO-ELM has the fitting degree of prediction results reached 0.99, and the convergence rate increased by 47.43%. The proposed algorithm is obviously better than the comparison algorithms in the prediction accuracy and convergence speed.

Variable Precision Rough Set (VPRS) can effectively process the noise data, but its portability is not good. Aiming at this problem, an improved vulnerability threat assessment model was proposed by introducing the threshold parameter α. First of all, an assessment decision table was created according to characteristic properties of vulnerability. Then, k-means algorithm was used to discretize the continuous attributes. Next, by adjusting the value of β and α, the attributes were reducted and the probabilistic decision rules were concluded. Finally, the test data was matched with the rule base and the vulnerability assessment results were obtained. The simulation results show that the accuracy of the proposed method is 19.66 percentage points higher than that of VPRS method, and the transplantability is enhanced.

The scheme proposed by Zhou et al. (ZHOU M, MU Y, SUSILO W, et al. Privacy enhanced data outsourcing in the cloud. Journal of network and computer applications, 2012, 35(4): 1367-1373) was analyzed, and the shortcoming of no access privilege revocation was shown. To address the shortcoming, an outsourced data encryption scheme with revoking access privilege was proposed. Firstly, the data were divided into several data blocks, and each data block was encrypted separately. Secondly, with the key derivation method, the number of keys stored and managed by the data owner was reduced. Finally, multiple decryption keys were constructed on an encrypted data to revoke access privileges of some users, without affecting the legitimate users. Compared with Zhou's scheme, the proposed scheme not only maintains the advantage of privacy protection to the outsourced data in the scheme, but also realizes access privilege revocation for users. The analysis results show that the proposed scheme is secure under the assumption of the Discrete Logarithm Problem (DLP).

In order to reduce the complexity of signal reconstruction algorithm, and reconstruct the signal with unknown sparsity, a new algorithm named One Projection Subspace Pursuit (OPSP) was proposed. Firstly, the upper and lower bounds of the signal's sparsity were determined based on the restricted isometry property, and the signal's sparsity was set as their integer middle value. Secondly, under the frame of Subspace Pursuit (SP), the projection of the observation onto the support set in each iteration process was removed to decrease the computational complexity of the algorithm. Furthermore, the whole signal's reconstruction rate was used as the index of reconstruction performance. The simulation results show that the proposed algorithm can reconstruct the signals of unknown sparsity with less time and higher reconstruction rate compared with the traditional SP algorithm, and it is effective for signal reconstruction.

In order to improve image retrieval performance, this paper proposed a new image retrieval algorithm based on motif and color features. The color image edge gradient was detected, and by means of edge gradient image transform, a motif image was obtained. Adopting the gravity center of motif image as the datum point, the distances of all points were calculated to the datum point to get the motif center distance histogram. The all motifs of the motif image were projected in four different directions to get motif projective histogram. Color image was uniformly quantized into 64-color space from RGB space to obtain the color histogram. The above three histograms described image features for image retrieval. The experimental results show that the algorithm has high precision and recall.

For the requirements of building Project Information Portal (PIP) data center based on a unified data model, a manageable entity object-oriented data model was proposed. The project data were treated as a series of managerial entity based on management workflows which were decomposed according to the whole life cycle. The conceptual layer data model was designed. The project data could be naturally represented and recorded by using this model. The data organization method was presented based on MongoDB (document-oriented database technology). The cluster storage architecture for PIP was also addressed. The experiments show that it has efficient performance in data writing and querying. It also has high availability and storage capacity scalability.

To solve the problems of single and low rate of convolutional codes and large loss of data rate in the G3 standard, the low voltage power line carrier communication system model based on Orthogonal Frequency Division Multiplexing (OFDM) in the G3 standard was analyzed, and a designing scheme of forward error correction encoder was presented based on RS encoding, convolutional encoding, puncturing and depuncturing, repetition encoding and two dimensional time and frequency interleaving algorithm. Moreover, a method for raising the code rate by puncturing and depuncturing was mainly introduced. The simulation results show that the rate of convolutional codes is raised from 1/2 to 2/3, the data rate is improved without increasing the complexity of decoding, and the effective and reliable communication can be realized, which means the scheme can be widely used in low voltage Power Line Communication (PLC).

For the purpose of reducing multi-path interference for positioning accuracy in Orthogonal Frequency Division Multiplexing (OFDM) systems, a location algorithm based on Back Propagation (BP) neural networks was proposed. MUltiple SIgnal Classification (MUSIC) algorithm was used to estimate the Time Of Arrival (TOA) of the first arrival path and calculate the Time Difference Of Arrival (TDOA). Then BP neural network was used to correct the TDOA. Finally Chan algorithm was used to determine the location of the mobile station. The location algorithm was simulated in multi-path environment. The simulation results show that this algorithm can effectively reduce the effect of the multi-path interference and the performance is better than Least Square (LS) algorithm, Chan algorithm and Taylor algorithm.

Cross-layer design is an effective technique for future mobile communication systems. A cross-layer resource allocation algorithm with partial channel state information was explored to maximize the total system throughput for multi-user MIMO-OFDM （Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing） system. The objective function of the optimization problem was designed based on the power limitation constraint, transmission rate, average queue length and sub-carrier occupancy, Quality of Service (QoS) requirements of different services and queue state information of data link layer. Under the condition of finite-length user buffer in data link layer, the mean feedback model was utilized to describe the feedback process of channel state information, and then the corresponding cross-layer resource allocation criteria could be derived. The simulation results show that compared with the existing schemes, the proposed algorithms obtain reasonable throughput performance and reduce lost package rate while providing better QoS requirement for each user of different services.

The major procedures of text classification such as uniform text format expression, training, testing and classifying based on Naive Bayesian text classification algorithm were implemented using MapReduce programming mode. The experiments were given in Hadoop cloud computing environment. The experimental results indicate basically linear speedup with an increasing number of node computers. A recall rate of 86% was achieved when classifying Chinese Web pages.

With the optimization goal of minimizing the total transmit power, an adaptive subcarrier allocation algorithm based on partial Channel State Information (CSI) under the condition of spatially correlated Rayleigh fading channels was proposed for multiuser STBC-OFDM downlink systems. In the course of algorithm implementation, the Kronecker model was used to express spatially correlated Multiple-Input Multiple-Out-put (MIMO) rayleigh fading channels of each subcarrier, and the dynamic CSIT (CSI at the Transmit) model was utilized to describe the process of CSI feedback; thus, the corresponding subcarrier allocation criteria could be deduced by means of the basic principles of Space-Time Block Code (STBC). The experimental results show that the proposed algorithm not only can effectively reflect system performance effects of the correlation coefficients of antenna correlation matrix and the parameters of delayed feedback, but also has good performance in contrast to subcarrier allocation without CSIT.

In order to improve the splice site recognition accuracy of Fuzzy Support Vector Machine (FSVM), a new method for computing the membership degree of sample was proposed. The initial membership was defined as the distance ratio of the sample to the two cluster centers of positive and negative samples, K-Nearest Neighbor (KNN) was adopted to compute the tightness of the samples, and the multiplication of the tightness and the initial membership degree was used as the ultimate membership. It will not only improve the membership degree of support vector, but also reduce the membership degree of noise sample. This method was applied to recognize the splice site, and the experimental results show that the recognition accuracy of constitutive 5′ and 3′ splice site reaches 94.65% and 88.97% respectively. Compared with the classical support vector machine，the recognition accuracy of constitutive 3′ splice site increases by 7.94%.