Advanced Materials Research Vols. 989-994

Abstract: The group customer line service has become one of the key businesses for communication operators, and the line PTN technology development currency, the PTN technology application, and the development trend are researched. According to the PTN technology and client group line error correction algorithm, the multi granularity hash correction algorithm is used for data video aware, and when the PTN data is changed fast, the fuzzy block effect happened. The customer line service performance is bad. An improved group customer line correction algorithm is proposed based on PTN technology. The hidden Markov model is used for packet loss rate prediction, and the multiple steps are selected in random, and the data stream iteration algorithm is designed. The tamper detection algorithm is obtained. PTN technology group customer line correction is realized. Simulation results show that the new method can reduce error transmission rate of the PTN group customer line, the customer loss and delay of the data transmission can be controlled, and the peak signal to noise ratio is improved, the error correction performance is better, and it can be effectively applied to communications operator service.

Abstract: The location mobile social networks information privacy protection under the environment of cloud storage and data security of encryption is researched, the traditional data encryption to rank has higher in the length of ciphertext and private key attributes of large matrix, it defines the length of ciphertext data on customer privacy private property and it cannot be revoked. The encryption complexity and security are not good. An improved encryption algorithm based on key attributes of reduced rank agent is proposed, the proxy ReEncryption technology is taken, the ReEncrypt decryption scheme is designed, and private key attributes of bilinear mapping and reduced rank processing is taken, and a communication channel between the user, CSP, trusted third party and data users is established, and the algorithm is obtained. The simulation is taken for testing the customer information data for privacy protection, the simulation results show that it can ensure the length of ciphertext is relatively small, and it has low computational complexity with more security, and it has the very good practical value in encryption communication and privacy protection fields.

Abstract: Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. Parameters selection of support vector machine is a important problems in network intrusion detection. In order to improve network intrusion detection precision, this paper proposed a network intrusion detection model based on parameters of support vector machine (SVM) by genetic algorithm. The performance of the model was tested by KDD Cup 99 data. Compared with other network intrusion detection models, the proposed model has significantly improved the detection precision of network intrusion.

Abstract: The active shape model (ASM) is a statistical parametric model, which is mainly used in image feature extraction. On the basis of the analysis of the original ASM texture model, a new texture modeling method was proposed in this paper. The improved method fully utilized the gray level information of adjacent points in the neighborhood of sampling points, improved the original face modeling method which only used the one-dimensional gray information with model matching accuracy problems. The experiments on Weizmann face database Indicate that, the improved method can obviously improve the feature point positioning accuracy, and accelerate the speed of face model fitting.

Abstract: The design of data mining system in database is researched. Vast amounts of information contained in the database, and the data show the diversity of characteristics, resulting in lower efficiency of data mining in database, which database brought greater difficulties to information query. To avoid these shortcomings, database performance optimization method based on cloud computing is proposed. The model of cloud computing data relationship is established to describe the connection between related data inthe database, thus providing the basis for data query. The load state of data nodes is calculated to enable rapid information inquiryin the database. Experimental results show that using this algorithm to optimize data inquiry in database can improve the efficiency of informationinquiry indatabase effectively.

Abstract: In order to use minimal cost to compensate signal distortion caused by fiber dispersion and carrier phase noise etc, this paper mainly puts forward 2 different self-adaption compensation algorithms in algorithm part of digital signal processing, through test and comparative analysis, it indicates that the performance of the best matching and the nature expression based on GCT is the best.

Abstract: There are various evaluation indicators in command information system. It is important to determine the weight of each indicator because it has a direct impact on the final result for evaluation and decision making. The reasonable and accurate attribute weight is helpful to ascertain the status or effect on the policy decision. With analyzing the deficiency of attribute weighting algorithms based on the rough sets theory, the new attribute weight algorithm is proposed in the paper. The proposed algorithm considers objective weight and subjective weight. The objective weight includes three factors, named as the importance of the attribute itself, the increment of mutual information, and its own information entropy. The subjective weight is obtained by the experts with prior knowledge in the field. Experiment results prove that the new method not only overcomes the deficiency of the existing weight methods, but also is more in line with the actual situation.

Abstract: The leaning and evolutionary (L&E) algorithm of Agent for task oriented is deeply researched in this paper. Based on the relationship between tasks and the executive Agent, the importance of the research has been elaborated. Moreover, the algorithm is improved by considering the effect of environment and network structure. Reinforcement leaning and complex network have been introduced into the nonlinear genetic algorithm. Finally, some simulations of equipment acquisition tasks are made to test the validity and capability of the algorithm.

Abstract: In order to analyze the gap of function network between Major depressive disorder and health person, this paper studies with modeling approach. This paper analyzes the function network of Major depressive disorder with the model based on anatomical distance and the number of common neighbor. The result shows that the distribution of the optimal brain function network is linear in all volunteer. And the slope of the linear relationship in the patients is less than health, so we hope this point can be as secondary evidence to determine the person whether fall ill. And we also propose two models and those models of brain function are based on anatomical distance or the number of common neighbor. Create the evaluation criteria for select the optimal brain function model network in each class model based on select the maximum value in the proportion of the common edges of two network accounted all edges. Select the model that can simulate the real brain function network by comparison with real data fMRI network. Finally, the results show the best model only is based on anatomical distance .

Abstract: Classification of moving military vehicle in battlefield is an important part of information acquirement. Support vector machine is a pattern classification method which is suitable to solve the small sample, non-linear classification problems. This paper uses one-versus-one multi-class SVM to classify military vehicle. This method is based on multi-sensor data including noise signal, the magnetic field disturbance signal, and vibration signal. The parameters of the SVM are determined by using the cross-validation method. The Simulation experiment results show that, compared to AdaBoost algorithm and two-class SVM, the one-versus-one multi-class SVM has higher accuracy.