Concerning the problem that the interpretable mapping relationship between data features and data categories do not be revealed by general feature selection algorithms. on the basis of Gene Expression Programming (GEP),by introducing the initialization methods, mutation strategies and fitness evaluation methods,an improved Feature Selection classification algorithm based on Layer Distance for GEP(FSLDGEP) was proposed. Firstly,the selection probability was defined to initialize the individuals in the population directionally, so as to increase the number of effective individuals in the population. Secondly, the layer neighborhood of the individual was proposed, so that each individual in the population would mutate based on its layer neighborhood, and the blind and unguided problem in the process of mutation was solved。Finally, the dimension reduction rate and classification accuracy were combined as the fitness value of the individual, which changed the population evolutionary mode of single optimization goal and balanced the relationship between the above two. The 5-fold and 10-fold verifications were performed on 7 datasets, the functional mapping relationship between data features and their categories was given by the proposed algorithm, and the obtained mapping function was used for data classification. Compared with Feature Selection based on Forest Optimization Algorithm (FSFOA), feature evaluation and selection based on Neighborhood Soft Margin (NSM), Feature Selection based on Neighborhood Effective Information Ratio (FS-NEIR)and other comparison algorithms, the proposed algorithm has obtained the best results of the dimension reduction rate on Hepatitis, Wisconsin Prognostic Breast Cancer (WPBC), Sonar and Wisconsin Diagnostic Breast Cancer (WDBC) datasets, and has the best average classification accuracy on Hepatitis, Ionosphere, Musk1, WPBC, Heart-Statlog and WDBC datasets. Experimental results shows that the feasibility, effectiveness and superiority of the proposed algorithm in feature selection and classification are verified.

In order to solve the Knapsack Problem with a single Continuous variable (KPC) efficiently, a novel S-shape transfer function based on Gauss error function was proposed, and a new approach of transforming a real vector into a 0-1 vector by using the proposed transfer function was given, thereby a New Binary Particle Swarm Optimization algorithm (NBPSO) was proposed. Then, based on the second mathematical model of KPC and the combination of NBPSO and the effective algorithm to deal with the infeasible solutions of KPC, a new approach to solve KPC was proposed. For validating the performance of NBPSO in solving KPS, NBPSO was utilized to solve four kinds of large-scale KPC instances, and the obtained calculation results were compared with those of Binary Particle Swarm Optimization algorithms (BPSOs) based on other S and V-shape transfer functions, Single-population Binary Differential Evolution with Hybrid encoding (S-HBDE), Bi-population Binary Differential Evolution with Hybrid encoding (B-HBDE) and Binary Particle Swarm Optimization algorithm (BPSO). The comparison results show that NBPSO is superior to the comparison algorithms in average calculation result and stability, illustrating that NBPSO has the performance better than other algorithms.

To solve the Split Delivery Vehicle Routing Problem (SDVRP) more reasonably, overcome the shortcoming that the traditional two-stage solution method of first route and then optimization is easy to fall into local optimization, and handle the problem that the intelligent optimization algorithm fails to integrate competition and cooperation organically in the optimization stage, an Improved Pyramid Evolution Strategy (IPES) was proposed with the shortest delivery path and the least delivery vehicles as the optimization objectives. Firstly, based on the pyramid, the encoding and decoding methods and hierarchical cooperation strategy were proposed to solve SDVRP. Secondly, according to the characteristics such as the random of genetic algorithm, high parallelism of "survival of the fittest" and self-adaption, as well as the different labor division of different layers of pyramid structure, an adaptive neighborhood operator suitable for SDVRP was designed to make the algorithm converge fast to the optimum. Finally, the optimal solution was obtained. Compared with the piecewise solving algorithm, clustering algorithm, particle swarm algorithm, artificial bee colony algorithm, taboo search algorithm,the results of four simulation experiments show that, when solving the optimal path of each case, the proposed IPES has the solution accuracy improved by at least 0.92%, 0.35%, 3.07%, 9.40% respectively, which verifies the good performance of IPES in solving SDVRP.

For the problems such as poor unmixing results and sensitivity to noise of traditional Nonnegative Matrix Factorization (NMF) applied to hyperspectral unmixing, a Manifold Regularized Sparse NMF with superpixel (MRS-NMF) algorithm for hyperspectral unmixing was proposed. Firstly, the manifold structure of hyperspectral image was constructed by superpixel segmentation based on entropy. The original image was divided into k-superpixel blocks, and the data points in each superpixel block with same property were labeled the same label. Weight matrices were defined between any two data points with the similar label in a superpixel block, and then the weight matrices were applied to the objective function of NMF to construct the manifold regularization constraint. Secondly, a quadratic parabola function was added to the objective function to complete the sparse constraint. Finally, the multiplicative iterative update rule was used to solve the objective function to obtain the solution formulas of endmember matrix and abundance matrix. At the same time, maximum iteration times and tolerate error threshold were set to get the final results by iterative operation. The proposed method makes full use of spectral and spatial information of hyperspectral images. Experimental results show that on synthetic data the unmixing accuracies of endmember and abundance based on proposed MRS-NMF are 0.016-0.063 and 0.01-0.05 respectively higher than those based on traditional methods like Graph-regularized L1/2-Nonnegative Matrix Factorization (GLNMF), L1/2NMF and Vertex Component Analysis-Fully Constrained Least Squares (VCA-FCLS); while on real hyperspectral images, the average unmixing accurary of endmember based on proposed MRS-NMF is 0.001-0.0437 higher than that of traditional GLNMF, Vertex Component Analysis (VCA) and Minimum Volume Constrained Nonnegative Matrix Factorization (MVCNMF). This proposed algorithm improves the accuracy of unmixing effectively with good robustness to noise.

With the rapid development of the Internet of Things (IoT), the research and application of Low-power and Lossy Network (LLN) has become a trend of development. Firstly, the basic principle and structure of IPv6 over Low-power Wireless Personal Area Network (6LoWPAN) and Routing Protocol for Low-power and lossy network (RPL) were introduced. Secondly, the main security threats of RPL routing in LLN and the corresponding solutions were summarized, classified and compared through the different strategies adopted by the protocol. Then, the research status of existing secure RPL at home and abroad was introduced and analyzed. At the same time, the existing security threats and solutions were summarized. Finally, the security issues and development trends that needed to be further studied in large-scale, mobile, self-organizing and low-power RPL were proposed.

Aiming at the illumination consistency of complex scenes in Augmented Reality (AR) system and analyzing the marker images by High-Dynamic Range (HDR) technology, an improved measurement algorithm for illumination direction based on the analysis of halo in HDR images was proposed. In order to improve the immersion and reality of virtual objects, after researching and analyzing the existing illumination recovery algorithms, a camera calibration method was proposed by utilizing the projection invariance of the quadratic curve pair. In order to get detailed light information, HDR was used to process marker image to improve accuracy. Refering to Lambert illumination model, the light information of image was analyzed to classify the shooting angle, and the improvement of traditional light source direction measuring was realized, part of the directions of the light sources outside of the photography ball reflection range was measured. The shooting 1 and shooting 2 of the single point light source were tested and analyzed. The experimental results show that this method is simple, robust, and can measure the direction of partial illumination outside the mirror ball reflection range no matter whether the marker is partially shaded or not.

Concerning that the existing methods cannot well detect the salient object boundary and entire region, a new method based on super-pixel segmentation was proposed. Firstly, the bilateral filtering was employed on original images to reduce the local color difference and make the image smoother and more homogeneous; at the same time, the information of salient object edge was retained. The initial detection of salient object's edge was implemented by calculating the pixel' difference within the local window; super-pixel segmentation was adopted to filtered image so that the pixels with the same or similar color were divided into the same super-pixel block, based on this, the local contrast, global contrast and spatial distribution of super-pixel block were considered synthetically to calculate the salient value of each super-pixel block. Finally, the results of the above two parts were fused and optimized by guided filtering. The experiments were conducted on the international open data set MSRA-1000 compared with other seven methods. The average accuracy rate, average recall, and F-measure value of the proposed method are 81.57%, 77.13% and 80.50% respectively. The experimental results show that the proposed method can exact salient object in images effectively and robustly.

Since the solution set obtained by 3-approximation algorithm of cluster vertex deletion problem is probably big, a new approximation algorithm was proposed through analyzing the characteristics of cluster. In the new algorithm, the number of P3 related to each vertex in the graph was counted by examining first-order neighbors and second-order neighbors of each vertex, and then the vertex with maximum number of P3 was selected into the solution set to eliminate P3 as soon as possible, which led to a smaller vertex deletion set. In order to verify the performance of this new algorithm, several sets of randomized simulation were designed. The simulation results show that the new algorithm outperforms the classic 3-approximation algorithm.

The data link capacity of 1090ES (1090 MHz Extended Squitter) could be expended by modulating 1090 MHz signal with phase information, thus RS (Reed-Solomon) calibration technology of 1090ES expansion system based on 8PSK (8 Phase Shift Keying) phase modulation was studied. Firstly, the total length of the RS code symbols was designed as 54 according to the characteristics of RS code and the data link structure of 1090ES expansion system. Secondly, error performance with different RS code coding efficiency was discussed, and its influence on performance of the 1090ES expansion system was analyzed, thereby, the optimum selection of RS code coding efficiency range was determined as 0.6-0.7. Finally, the concrete analysis of the error performance in the selected encoding efficiency range was given, and then the experimental results show that the length of information symbols could be chosen as 32, 34 or 36. Furthermore, Matlab simulation analysis shows that the designed RS code can effectively improve the error performance of 1090ES expansion system with RS(54, 32) as an example.

Focusing on the issue that the label kernel functions do not take the correlation between labels into consideration in the multi-label feature extraction method, two construction methods of new label kernel functions were proposed. In the first method, the multi-label data were transformed into single-label data, and thus the correlation between labels could be characterized by the label set; then a new label kernel function was defined from the perspective of loss function of single-label data. In the second method, mutual information was used to characterize the correlation between labels, and a new label kernel function was proposed from the perspective of mutual information. Experiments on three real-life data sets using two multi-label classifiers demonstrated that the best method of all measures was feature extraction method with label kernel function based on loss function and the performance of five evaluation measures on average increased by 10%; especially on the data set Yeast, the evaluation measure Coverage reached a decline of about 30%. Closely followed by feature extraction method with label kernel function based on mutual information and the performance of five evaluation measures on average increased by 5%. The theoretical analysis and simulation results show that the feature extraction methods based on new output kernel functions can effectively extract features, simplify learning process of multi-label classifiers and, moreover, improve the performance of multi-label classification.

The null models of complex networks generated by random scrambling algorithm often can't tell when null models can be stable because of the difference of successful scrambling probabilities of different order null models. Focusing on the issue, the concept of "successful scrambling times" was defined and used to replace the usual "try scrambling times" to set the algorithm. The index of the proposed successful scrambling times could be added only when the randomly selected edges could meet the scrambling conditions of corresponding null models, and thus be successfully scrambled. The generation experiments of null models of every order show that every index can be stable in a small scale of successful scrambling times. Further quantitative analyses show that, according to the corresponding orders, 0-order, 1-order and 2-order null models with good quality can be got by setting successfully scrambling times to be 2 times, 1 times and 1 times of actual networks' edge number respectively.

Concerning of the low accurate rate of active defense technology, a heuristic detection system of Trojan based on the analysis of trajectory was proposed. Two kinds of typical Trojan trajectories were presented, and by using the behavioral data on Trojan trajectory the danger level of the suspicious file was detected with the decision rules and algorithm. The experimental results show that the performance of detecting unknown Trojan of this system is better than that of the traditional method, and some special Trojans can also be detected.

The access request for computer network has the characteristics of real-time and dynamic change. In order to detect network intrusion in real time and be adapted to the dynamic change of network access data, a real-time detection framework for network intrusion was proposed based on data stream. First of all, misuse detection model and anomaly detection model were combined. A knowledge base was established by the initial clustering which was made up of normal patterns and abnormal patterns. Secondly, the similarity between network access data and normal pattern and abnormal pattern was measured using the dissimilarity between data point and data cluster, and the legitimacy of network access data was determined. Finally, when network access data stream evolved, the knowledge base was updated by reclustering to reflect the state of network access. Experiments on intrusion detection dataset KDDCup99 show that, when initial clustering samples are 10000, clustering samples in buffer are 10000, adjustment coefficient is 0.9, the proposed framework achieves a recall rate of 91.92% and a false positive rate of 0.58%. It approaches the result of the traditional non-real-time detection model, but the whole process of learning and detection only scans network access data once. With the introduction of knowledge base update mechanism, the proposed framework is more advantageous in the real-time performance and adaptability of intrusion detection.

Concerning the low accuracy of tagging Chinese ambiguity words, a combined tagging method of rules and statistical model was proposed in this paper. Firstly, three kinds of traditional statistical models, including Hidden Markov Model (HMM), Maximum Entropy (ME) and Condition Random Field (CRF), were used to tagging problem of the ambiguity words. Then, the improved mutual information algorithm was applied to learn Part Of Speech (POS) tagging rules. Tagging rules were got through the calculation of correlation between the target words and the nearby word units. Finally, rules were combined with statistical model algorithm to tag Chinese ambiguity words. The experimental results show that after adding the rule algorithm, the average accuracy of POS tagging promotes by 5%.

Building an interpretable and large-scale protein-compound interactions model is an very important subject. A new chemical interpretable model to cover the protein-compound interactions was proposed. The core idea of the model is based on the hypothesis that a protein-compound interaction can be decomposed as protein fragments and compound fragments interactions, so composing the fragments interactions brings about a protein-compound interaction. Firstly, amino acid oligomer clusters and compound substructures were applied to describe protein and compound respectively. And then the protein fragments and the compound fragments were viewed as the two parts of a bipartite graph, fragments interactions as the edges. Based on the hypothesis, the protein-compound interaction is determined by the summation of protein fragments and compound fragments interactions. The experiment demonstrates that the model prediction accuracy achieves 97% and has the very good explanatory.

The near-surface defects are hard to identify in ultrasonic phased array Non-Destructive Testing (NDT), thus a new intelligent identification method based on fractal theory was proposed to solve this problem. A box-counting dimension algorithm based on linear interpolation was described to calculate the box-counting dimension of 140 groups of ultrasonic A-Scan time domain signals. Then the distribution of box-counting dimension was analyzed using the statistical method. The experimental results show that ultrasonic A-Scan signal is obviously fractal and it is effective to analyze the A-Scan signal with the fractal approach. This method has the potential to identify near-surface defects since the values of the box counting dimension of defective signals are different from those of defective signals. As a result, the detection rate of near-surface defects can be improved and the omission rate caused by man-made factors can be reduced in ultrasonic phased array automatic testing.

In integrated support engineering, the number of components in reliability block diagram is large, the level of mastering the principle of system is required to be high and the operational data is always incomplete. To resolve these problems, a method that identifies the reliability structure of system using the information of operational data and the reliability of the units was proposed. The system reliability was estimated by using the system performance information. In addition, all reliability structure models was traversed and the theoretical reliability was calculated with the system's units reliability information, then the deviations between the estimated value of system reliability and all the reliability theoretical values were calculated, and the identification results by the first N reliability structure models of the lowest deviation was outputted after sorting the deviations. The calculation results of a given example show that the combined system based on the voting reliability structure can be identified with the probability of around 80%, decreases to 3% of the scope out of all possible forms, it can significantly reduce the workload of the researcher to identify the system reliability structure.

To eliminate the influence of visual difference on color image filtering, a method to compute the visual sensitivity value of three primary colors was given. The filter algorithm detected pixels by rough set first, then used visual sensitivity to correct the test results, finally filtered the noise pixels by the improved Directional Distance Filter (DDF). The experimental results show that the new filter, compared with several other representative vector filters, provides better performance on color keeping, edge detail preservation, and noise filtering rate. In addition, the normalized mean square error of our method is the smallest and the Peak Signal-to-Noise Ratio (PSNR) is the largest under different noise density.

The current disadvantages of Mobile Ad Hoc Network (MANET) test method include simple model, high cost and difficult duplicate. In this paper, a large-scale MANET hybrid emulation testing method based on NS2 (LHEN) was proposed to solve these problems. By making use of simulation function of NS2, the authors could complete encapsulation and decapsulation of real packets and virtual packets with Tap agent, thus they could achieve the communication between virtual environment and real environment through network objects and NS2 real-time scheduler. A real node movement could be emulated by controlling network wireless signal strength, and then a real network environment was established. Finally, the authors constructed a large scale MANET respectively for contrast experiments through method of hybrid emulation and simulation. The experimental results show that the performance is almost consistent and mean difference value is lower than 18.7%, which means LHEN is able to be applied in some indicators test and verification for a large scale MANET.

An effective registration approach was proposed for multiple-view images captured on a turntable. In combination with binocular stereo, a complete system for 3D reconstruction was proposed. The calibration target was mounted on a step-motor-controlled turntable and it was acquired under multiple views. The coordinate transformation between the turntable and the camera coordinate systems was computed by the angular point information on the calibration target. The coordinate transformation among multiple views was used for data registration at the fixed viewpoint. The experimental results show that the proposed registration method achieves high precision and can be effectively used in 3D reconstruction.

Considering the huge amount of information in network comments, using computers to recognize the semantic orientation of the comments automatically will make great significance. In this paper, by using the semantic orientation analysis based on HowNetbased semantic similarity method, the research improved related methods to satisfy the semantic orientation recognition of network comments. The experimental results show that the improved method is more reasonable, and to some degree the accuracy rate of semantic orientation recognition has been improved.

The recent Risk Minimization based Ontology Mapping (RiMOM), based on multi-strategy, in spite of certain improvements, has the shortcomings in complexity and redundancy, and limitations in similarity selection of structure characteristics. Therefore, a comprehensive method of computing ontology similarity based on association rules was proposed. First, the "tree" model of association rules was constructed to compute the structure similarity. Then the similarity of instances, properties, and names were also computed. Finally, a comprehensive method was used to achieve the optimal calculation of the ontology similarity. The experimental results show that this method has higher recall and precision ratio and less time complexity than RiMOM.

In view of the characteristics of Ka-band mobile satellite channel, a model was built up with taking full consideration of the decline influenced by weather and surrounding environment in this paper. Then, the satellite channel with Bipolarity Phase Shift Keying (BPSK) modulation was simulated, and the probability model of baseband channel bit error distribution was established. Meanwhile, the probability of error occurrences was fitted by using the least-squares method. According to the results, the occurrence number of channel errors obeys Poisson distribution. Moreover, during the digital baseband simulation of Ka-band mobile satellite channels, it is found that the system error with BPSK modulation consists of the burst error and the random error that obeys the Poisson distribution.

A new localization algorithm based on reputation mechanism was proposed to improve the robustness of the node positioning system in Wireless Sensor Network (WSN). This algorithm introduced a monitoring mechanism and reputation model to filter out malicious beacon nodes giving the false location information, used Beta distribution to update and integrate the reputation of the beacon nodes. Through the cluster head node, the proposed algorithm collected and judged which beacon nodes were reliable, increased the malicious beacon nodes detection rates while the positioning error was reduced. Finally, the simulation and detailed analysis prove its efficiency and robustness. The algorithm is efficient in self-positioning of sensor nodes in distributed WSN, and the localization accuracy and security are greatly improved.

Due to lack of sufficient semantic information, it is difficult to interoperate among XML data files with different schemas. Facing the needs to integrate XML data in the field of Well-Engineering, this paper proposes a model-independent semantic integration method for XML data using domain global ontology. This method builds semantic mapping between domain ontology and paths of XML schema to shielding the pattern differences; then, XML data is stored as relational data using model-mapping method; finally, the SPARQL query will be rewriting into SQL statements to realize semantic query. By semantic annotating to XML schema and using relational database to store and query XML date, this method solves the semantic integration for domain XML data effectively.

Based on a thorough research on the time-domain security of BLP(Bell-LaPadula) model,an incorrect understanding on the time-domain security of BLP model was pointed out,and a new theoretical proof about the time-domain security of BLP model was given.The BLP model accords well with the requirement of confidentiality was indicated.

An approach for planar pattern tracking was introduced. The tracker could locate a planar pattern accurately in real time using a Webcamera. The tracking result could be used for computing camera's position and placing virtual object in the video. Some common tracking failures caused by "motion blur", "fast motion", "small target" were discussed. An algorithm, combined "LK tracking" techniques with "Hungarian algorithm-based feature correspondence", was used to solve such problems. A method based on non-linear optimization is used to suppress the influence of noise, when computing the camera's position. The experiments show that this method can track the planar pattern robustly in some difficult situations. The curve of the camera's computed position is smooth and accurate.

Most of current music visualizations are monotone. In this article, a aquarelle-style music visualization integrated perfectly with music was implemented. It adopts some key technologies, such as aquarelle effect based on alpha channel, tidy physical model, piecemeal joint, and composite anti-aliasing, etc. The example of music visualization by bloom, stalk, dimple and goldfish shows that this method can get good effect with low CPU occupancy and small memory request.

The view maintenance process in the loosely coupled environment was implemented as a conceptual transaction, by the definitions of concurrency dependency and same-source dependency. Based on schema changes, the VMSCNF algorithm was proposed to solve the consistency of view maintenance processes in the non-FIFO network environments. Finally, a preliminary experiment was carried out to verify the efficiency of the proposed algorithm.