The histopathological image is the gold standard for identifying breast cancer, so that the automatic and accurate classification of breast cancer histopathological images is of great clinical application. In order to improve the classification accuracy of breast cancer histopathology images and thus meet the needs of clinical applications, a high-precision breast classification method that incorporates spatial and channel features was proposed. In the method, the histopathological images were processed by using color normalization and the dataset was expanded by using data enhancement, and the spatial feature information and channel feature information of the histopathological images were fused based on the Convolutional Neural Network (CNN) models DenseNet and Squeeze-and-Excitation Network (SENet). Three different BCSCNet (Breast Classification fusing Spatial and Channel features Network) models, BCSCNetⅠ, BCSCNetⅡ and BCSCNetⅢ, were designed according to the insertion position and the number of Squeeze-and-Excitation (SE) modules. The experiments were carried out on the breast cancer histopathology image dataset (BreaKHis), and through experimental comparison, it was firstly verified that color normalization and data enhancement of the images were able to improve the classification accuracy of breast canner, and then among the three designed breast canner classification models, the one with the highest precision was found to be BCSCNetⅢ. Experimental results showed that BCSCNetⅢ had the accuracy of binary classification ranged from 99.05% to 99.89%, which was improved by 0.42 percentage points compared with Breast cancer Histopathology image Classification Network (BHCNet); and the accuracy of multi-class classification ranged from 93.06% to 95.72%, which was improved by 2.41 percentage points compared with BHCNet. It proves that BCSCNet can accurately classify breast cancer histopathological images and provide reliable theoretical support for computer-aided breast cancer diagnosis.

Concerning the problems of 3D point cloud data processing and on-road obstacle detection based on Light Detection And Ranging (LiDAR), a deep learning-based on-road obstacle detection method was proposed. First, the statistical filtering algorithm was applied to eliminate the outliers from the original point cloud, improving the roughness of point clouds. Then, an end-to-end deep neural network named VNMax was proposed, the max pooling was used to optimize the structure of Region Proposal Network (RPN), and an improved target detection layer was built. Finally, training and testing experiments were performed on KITTI dataset. The results show that, by filtering, the average distance between the points in point cloud is reduced effectively. For the car location processing results of easy, medium difficult and hard detection tasks in KITTI dataset, it can be seen that the average precisions of the proposed method are improved by 11.30 percentage points, 6.02 percentage points and 3.89 percentage points, respectively, compared with those of the VoxelNet. Experimental results show that the statistical filtering algorithm is still an effective 3D point cloud data processing method, and the max pooling module can improve the learning performance and object location ability of the deep neural network.

In the ship pipeline layout at three-dimensional environment, aiming at the problems that there are too many constraints, the engineering rules are difficult to quantify and the appropriate optimization evaluation function is hard to determine, a new ship pipeline automatic layout method was proposed. Firstly, the hull and ship equipments were simplified by the Aixe Align Bounding Box (AABB) method, which means that they were discretized into space nodes, and the initial pheromones and energy values of them were given, the obstacles in the space were marked, and the specific quantitative forms for the main pipe-laying rules were given. Secondly, with the combination of Rapidly-exploring Random Tree (RRT) algorithm and Ant Colony Optimization (ACO) algorithm, the direction selection strategy, obstacle avoidance strategy and variable step strategy were introduced to improve the search efficiency and success rate of the algorithm, and then the ACO algorithm was used to optimize the path iteratively by establishing the optimization evaluation function, so as to obtain the comprehensive optimal solution that meets the engineering rules. Finally, the computer simulated cabin space layout environment was used to carry out automatic pipe-laying simulation experiments, which verified the effectiveness and practicability of the proposed method.

Optical flow method is an important and effective deformation registration algorithm based on optical flow field model. Aiming at the problem that the feature quality used by the existing optical flow method is not high enough to make the registration result accurate, combining the features of deep convolutional neural network and optical flow method, a deformable medical image registration algorithm based on Deep Convolution Feature Based Optical Flow (DCFOF) was proposed. Firstly, the deep convolution feature of the image block where each pixel in the image was located was densely extracted by using a deep convolutional neural network, and then the optical flow field was solved based on the deep convolution feature difference between the fixed image and the floating image. By extracting more accurate and robust deep learning features of the image, the optical flow field obtained was closer to the real deformation field, and the registration accuracy was improved. Experimental results show that the proposed algorithm can solve the problem of deformable medical image registration effectively, and has the registration accuracy better than those of Demons algorithm, Scale-Invariant Feature Transform（SIFT） Flow algorithm and professional registration software of medical images called Elastix.

An Ultra WideBand (UWB)/ Pedestrian Dead Reckoning (PDR) fusion positioning algorithm with adaptive coefficient adjustment based on UWB error prediction was proposed in order to improve the UWB performance and reduce the PDR accumulative errors in the indoor Non-Line-Of-Sight (NLOS) positioning scenes and solve the UWB performance degradation caused by environmental factors. On the basis of the creative proposal of predicting the UWB positioning errors in complex environment by Support Vector Machine (SVM) regression model, UWB/PDR fusion positioning performance was improved by adding adaptive adjusted parameters to the conventional Extended Kalman Filter (EKF) algorithm. The experimental results show that the proposed algorithm can effectively predict the current UWB positioning errors in the complex UWB environment, and increase the accuracy by adaptively adjusting the fusion parameters, which makes the positioning error reduced by 18.2% in general areas and reduced by 48.7% in the areas with poor UWB accuracy compared with those of the conventional EKF algorithm, so as to decrease the environmental impact on the UWB performance. In complex scenes of both Line-Of-Sight (LOS) and NLOS including UWB, the positioning error per 100 meters is reduced from meter scale to decimeter scale, which reduces the PDR errors in NLOS scenes.

In order to avoid the flower pollination algorithm falling into local optimum in the identification process of the extreme learning machine, an extreme learning machine algorithm based on cloud quantum flower pollination was proposed. Firstly, cloud model and quantum system were introduced into the flower pollination algorithm to enhance the global search ability of the flower pollination algorithm, so that the particles were able to perform optimization in different states. Then, the cloud quantum flower pollination algorithm was used to optimize the parameters of the extreme learning machine in order to improve the identification accuracy and efficiency of the extreme learning machine. In the experiments, six benchmark functions were used to simulate and compare several algorithms. It is verified by the comparison results that the performance of proposed cloud quantum flower pollination algorithm is superior to those of other three swarm intelligence optimization algorithms. Finally, the improved extreme learning machine algorithm was applied to the identification of oil and gas layers. The experimental results show that, the identification accuracy of the proposed algorithm reaches 98.62%, and compared with the classic extreme learning machine, its training time is shortened by 1.680 2 s. The proposed algorithm has high identification accuracy and efficiency, and can be widely applied to the actual classification field.

For the parallel machine scheduling problem of minimizing the maximum completion time, an Improved Discrete Artificial Bee Colony algorithm (IDABC) was proposed by considering the processing efficiency of the machine and the delivery time of the product as well as introducing the mathematical model of the problem. Firstly, a uniformly distributed population and a generation strategy of the parameters to be optimized were achieved by adopting the population initialization strategy, resulting in the improvement of the convergence speed of population. Secondly, the mutation operator in the differential evolution algorithm and the idea of simulated annealing algorithm were used to improve the local search strategy for the employed bee and the following bee, and the scout bee was improved by using the high-quality information of the optimal solution, resulting in the increasement of the population diversity and the avoidance of trapping into the local optimum. Finally, the proposed algorithm was applied in the parallel machine scheduling problem to analyze the performance and parameters of the algorithm. The experimental results on 15 examples show that compared with the Hybrid Discrete Artificial Bee Colony algorithm (HDABC), IDABC has the accuracy and stability improved by 4.1% and 26.9% respectively, and has better convergence, which indicates that IDABC can effectively solve the parallel machine scheduling problem in the actual scene.

Only one semantic vector can be generated by word-embedding technologies such as Word2vec or GloVe for polysemous word. In order to solve the problem, a sentiment analysis model based on ELMo (Embedding from Language Model) and Multi-Scale Convolutional Neural Network (MSCNN) was proposed. Firstly, ELMo model was used to learn the pre-training corpus and generate the context-related word vectors. Compared with the traditional word embedding technology, in ELMo model, word features and context features were combined by bidirectional LSTM (Long Short-Term Memory) network to accurately express different semantics of polysemous word. Besides, due to the number of Chinese characters is much more than English characters, ELMo model is difficult to train for Chinese corpus. So the pre-trained Chinese characters were used to initialize the embedding layer of ELMo model. Compared with random initialization, the model training was able to be faster and more accurate by this method. Then, the multi-scale convolutional neural network was applied to secondly extract and fuse the features of word vectors, and generate the semantic representation for the whole sentence. Experiments were carried out on the hotel review dataset and NLPCC2014 task2 dataset. The results show that compared with the attention based bidirectional LSTM model, the proposed model obtain 1.08 percentage points improvement of the accuracy on hotel review dataset, and on NLPCC2014 task2 dataset, the proposed model gain 2.16 percentage points improvement of the accuracy compared with the hybrid model based on LSTM and CNN.

The current consistency detection methods of software design and implementation require a large number of template sets and are difficult to generalize. In order to solve these problems, a consistency analysis method of software design and implementation based on control flow was proposed. Firstly, the pseudocode of the design document and the source code of the program were converted into the intermediate representations with the same features, and the design feature and the implementation feature were respectively extracted from the intermediate representations. The features include the function call relationship which can reflect the system structure and the control flow information which can reflect the internal structure of the function. Then, the design feature model and the implementation feature model were respectively established according to the design feature and the implementation feature. Finally, the similarity of the feature model was measured by calculating the feature similarity, so as to obtain the consistency detection result. Experimental results show that this method can correctly detect the inconsistent function call relationship when the function call relationship realized by the software is inconsistent with the design, and can correctly detect the inconsistency of the internal structure of the function when the function call relationship realized by the software is consistent with the design, with the accuracy reached 92.85%. This method can effectively obtain the consistency detection results without any template set, and has superior generality.

The traditional software evaluation methods are lack of consideration for user requirements. For this problem, an evaluation model of software quality based on user group decision-making was proposed. Firstly, it is found that the existing projection measure is not always reasonable in real number and interval vector spaces. Therefore, a new normalized projection measure was proposed to comprehensively measure the proximity between two vectors or matrices and the measure allows the evaluation matrix with hybrid decision-making information. Secondly, the new projection measure was fused in the improved Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) technique. On this basis, a new group decision-making model with hybrid information of real number and interval was developed. And the pseudocode of algorithm was provided. Finally, the new model was applied to software quality evaluation. The requirements of software users were focused and the evaluation information of users group was synthesized in this method. The effectiveness and feasibility of the proposed method were illustrated by a practical example of software quality comprehensive evaluation and the experimental analysis.

Aiming at the inefficiency of neural network inferential calculation executed by mobile intelligent devices using ARM processor, a set of Single precision floating GEneral Matrix Multiply (SGEMM) algorithm optimization scheme based on ARMv8 architecture was proposed. Firstly, it was determined that the computational efficiency of the processor based on ARMv8 architecture executing SGEMM algorithm was limited by the vectorized computation unit usage scheme, the instruction pipeline, and the probability of occurrence of cache miss. Secondly, three optimization techniques:vector instruction inline assembly, data rearrangement and data prefetching were implemented for the three reasons that the computational efficiency was limited. Finally, the test experiments were designed based on three matrix patterns commonly used in the neural network of speech direction and the programs were run on the RK3399 hardware platform. The experimental results show that, the single-core computing speed is 10.23 GFLOPS in square matrix mode, reaching 78.2% of the measured floating-point peak value; the single-core computing speed is 6.35 GFLOPS in slender matrix mode, reaching 48.1% of the measured floating-point peak value; and the single-core computing speed is 2.53 GFLOPS in continuous small matrix mode, reaching 19.2% of the measured floating-point peak value. With the optimized SGEMM algorithm deployed into the speech recognition neural network program, the actual speech recognition speed of program is significantly improved.

To solve the signal demodulation problem of asymmetric Paired Carry Multiple Access (PCMA) composed of the same frequency of main station and small station signals, a framework to realize this kind of signal demodulation was constructed. Parameter estimation is an indispensable part in the realization of two-way signal separation and demodulation for asymmetric PCMA communication systems. For the estimation accuracy of amplitude parameters, a searching amplitude estimation algorithm based on fourth-power method was proposed. Firstly, the demodulation model for asymmetric PCMA systems was established and the basic assumptions were made. Then the phase errors under different assumptions were compared with each other and the influence of phase error on the amplitude estimation algorithm was analyzed. Finally, a new amplitude estimation algorithm was proposed. Experimental results show that, under same Signal-to-Noise Ratio (SNR), the demodulation performance of the small station signal under normal phase error is inferior to its demodulation performance under mean value condition. When the order of magnitude of the Bit Error Rate (BER) is 10 ^-4, the demodulation performance of small station signal is improved by 1 dB with the improved algorithm, proving that the improved algorithm is better than fourth-power method.

To solve traffic congestion problems, a new road condition judgment model was proposed. Firstly, the model was based on YOLOv3 target detection algorithm. Then, according to the eigenvalue matrix corresponding to the picture, the difference between adjacent frames was made by the eigenvalue matrix, and the difference value was compared with preset value to determine whether the current road was in a congested state or a normal traffic state. Secondly, the current calculated road state was compared with previous two calculated road states. Finally, the state statistics method in the model was used to calculate the duration of a state (congestion or patency) of road. The proposed model could analyze the states of three lanes of a road at the same time. Through experiments, the average accuracy of model to judge the state of single lane could reach 80% or more, and it was applicable to both day and night roads.

To solve the problems of external disturbances and the uncertainty of system model parameters for the underactuated quadrotor Unmanned Aerial Vehicle (UAV) existing in actual flight, a flight control scheme based on Extended State Observer (ESO) and integral backstepping sliding mode was designed. Firstly, according to the semi-coupling characteristics and the strict feedback architecture of system, a backstepping control was adopted to design the attitude inner loop and the position outer loop controllers. Then, a sliding mode algorithm with strong anti-jamming ability and integral control were incorporated to enhance system robustness and reduce static error respectively. Finally, ESO was used to eliminate the total internal and external disturbances and to compensate the interference in the control law online. The closed-loop control system was proven to be globally asymptotically stable by the Lyapunov stability analysis. In addition, the effectiveness and robustness of the proposed flight control scheme were verified through simulation analysis.

For traditional SURF (Speeded-Up Robust Feature) algorithm takes a long time for constructing local feature descriptors, an improved SURF algorithm was proposed to meet the real-time requirement. Firstly, the Determinant of Hessian (DoH) matrix was adopted to detect the key points of an image. Non-maximum suppression algorithm and interpolation operation were used to search and position the extreme points. Secondly, gray centroid method was adopted to determine the main direction of the key points. Then a binary descriptor, BRIEF (Binary Robust Independent Elementary Feature), was adopted to describe the key points, and the main direction of the key points was used to construct a directed feature descriptor with the objective of keeping its rotation invariance. Finally, Hamming distance was used to preliminarily determine the matching points. Then, the mismatching points were removed to improve the matching accuracy by ratio detection method and RANSAC (Random Sample Consensus) algorithm. The experimental results show that, when the improved SURF algorithm is applied to the flexible clamping robot, the matching time is reduced from 214.10 ms to 86.29 ms, the matching accuracy is increased by 2.6% compared with traditional SURF algorithm. Therefore, the proposed algorithm can improve the workpiece image matching speed and matching precision of flexible clamping robot effectively.

Focusing on the issue that the LBE (Local Background Enclosure) algorithm is over dependent on depth information and difficult to fully detect the object with complex structure, a RGB-D saliency detection algorithm based on the improved LBE features was proposed. Firstly, a set of segmentations was obtained by multi-level segmentation. Then, the depth saliency map was obtained by computing and merging the LBE features on each level segmentation map. Finally, a saliency map was obtained by adjusting the depth saliency map with color information and prior information. The experimental results show that compared with LBE algorithm, the precision of the proposed algorithm is slightly decreased and the recall is significantly improved, and the obtained saliency maps are much more close to the true values.

In recent years, the amount of data in various industries grows rapidly, which results in the increasing of optimization demands in database storage system. Relational databases are I/O-intensive, take use of relatively free CPU time, data compression technology could save data storage space and I/O bandwidth. However, the compression features of current database systems were designed for traditional storage and computing environments, without considering the impact of virtualized environments or the use of Solid State Drive (SSD) on system performance. To optimize the cache performance of database compression system, a database compression system performance model was proposed, and the impact on the I/O performance of various system environments was analyzed. Take the open source database MySQL as an example, the corresponding cache optimization methods were given based on analysis. Evaluation results on Kernel-based Virtual Machine (KVM) and MySQL database show that the optimized version has an increase of more than 40% in performance under some configurations, even close to superior physical machine performance.

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.

In order to solve the problem of poor robustness of Kernelized Correlation Filter (KCF) in complex scenes, a new object tracking algorithm based on Self-Adaptive Compound Kernel (SACK) was proposed. The tracking task was decomposed into two independent subtasks:position tracking and scale tracking. Firstly, the risk objective function of SACK weight was constructed by using the self-adaptive compound of linear kernel and Gaussian kernel as the kernel tracking filter. The weights of linear kernel and Gaussian kernel were adjusted adaptively by the constructed function according to the response values of kernels, which not only considered the minimum empirical risk function of different kernel response outputs, but also considered the risk function of maximum response value, and had the advantages of local kernel and global kernel. Then, the exact position of object was obtained according to the output response of the SACK filter, and the adaptive update rate based on the maximum response value of object was designed to adaptively update the position tracking filter. Finally, the scale tracker was used to estimate the object scale. The experimental results show that, the success rate and distance precision of the proposed algorithm are optimal on OTB-50 database, which is 6.8 percentage points and 4.1 percentage points higher than those of KCF algorithm respectively, 2 percentage points and 3.2 percentage points higher than those of Bidirectional Scale Estimation Tracker (BSET) algorithm respectively. The proposed algorithm has strong adaptability to complex scenes such as deformation and occlusion.

Probabilistic Soft Logic (PSL), as a kind of declarative rule-based probability model, has strong extensibility and multi-domain adaptability. So far, it requires a lot of common sense and domain knowledge as preconditions for rule establishment. The acquisition of these knowledge is often very expensive and the incorrect information contained therein may reduce the correctness of reasoning. In order to alleviate this dilemma, the C5.0 algorithm and probabilistic soft logic were combined to make the data and knowledge drive the reasoning model together, and a semi-automatic learning method was proposed. C5.0 algorithm was used to extract rules, and artificial rules and optimized adjusted rules were supplemented as improved probabilistic soft logic input. The experimental results show that the proposed method has higher accuracy than the C5.0 algorithm and the PSL without rule learning on student performance prediction. Compared with the past method with pure hand-defined rules, the proposed method can significantly reduce the manual costs. Compared with Bayesian Network (BN), Support Vector Machine (SVM) and other algorithms, the proposed method also shows good results.

There are serious load imbalance and single point performance bottleneck effect in the traditional static topology leader-based distributed consensus algorithm, and the algorithm is unable to work properly when the number of breakdown nodes is larger than 50% of the cluster size. To solve the above problems, a distributed consensus algorithm (Yac) based on dynamic topology and limited voting was proposed. The algorithm dynamically generated the membership subset and Leader nodes to participate in the consensus voting, and varied with time, achieving statistical load balance. With removal of the strong constraints of all the majority of members to participate in voting, the algorithm had a higher degree of failure tolerance. The security constraints of the algorithm were reestablished by the log chain mechanism, and the correctness of the algorithm was proved. The experimental results show that the load concentration effect of single point in the improved algorithm is significantly lower than that of the mainstream static topology leader-based distributed consensus algorithm Zookeeper. The improved algorithm has better fault tolerance than Zookeeper in most cases and maintains the same as Zookeeper in the worst case. Under the same cluster size, the improved algorithm has higher throughput upper limit than Zookeeper.

Due to high complexity of dark channel prior defogging algorithm, a fast defogging algorithm based on dark channel prior was proposed. Firstly, the computing of the dark channel map was accelerated by blocking the image. Secondly, the block effect was eliminated by using the linear interpolation algorithm to smoothing. Then, the transmission map was acquired by dark channel prior. Finally, a clear and haze-free image would be gotten from the atmospheric scattering model. The experimental results show that the defogging effect of the proposed algorithm can be as good as the original defogging algorithm but the complexity can be reduced effectively. The proposed algorithm needs time about one-tenth of the original algorithm and reaches the requirement of near real-time.

Concerning the problem that images captured by optical system suffer unsteady degradation including noise, blurring and geometric distortion when imaging process is affected by defocusing, motion, atmospheric disturbance and photoelectric noise, a generic framework of image restoration based on natural patch likelihood and sparse prior was proposed. Firstly, on the basis of natural image sparse prior model, several patch likelihood models were compared. The results indicate that the image patch likelihood model can improve the restoration performance. Secondly, the image expected patch log likelihood model was constructed and optimized, which reduced the running time and simplified the learning process. Finally, image restoration based on optimized expected log likelihood and Gaussian Mixture Model (GMM) was accomplished through the approximate Maximum A Posteriori (MAP) algorithm. The experimental results show that the proposed approach can restore degraded images by kinds of blur and additive noise, and its performance outperforms the state-of-the-art image restoration methods based on sparse prior in both Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) with a better visual effect.

According to the implicit characteristics of IPTV (Internet Protocol Television) user viewing behavior data, a novel implicit rating model was proposed. Firstly, the main features of IPTV user viewing behavior data were introduced, and a new mixed feature implicit scoring model was proposed, which combined with viewing ratio, user interest bias factor and video type influence factor. Secondly, the strategy of viewing behavior based on viewing time and viewing ratio was proposed. Finally, a distributed model architecture based on Hadoop was designed and implemented. The experimental results show that the proposed novel model effectively improves the quality of the recommended results in the IPTV system, improves the time efficiency, and has good scalability for large amounts of data.

Focusing on the multifarious and time-consuming optimization process of traditional helper thread parameter value enumeration method, a real-time online helper thread prefetching quality assessment method was proposed. First, the help thread prefetching Quality of Service (QoS) target was defined. Second, the dynamic evaluation index of helper thread prefetching quality was analyzed, as well the helper thread prefetching QoS model. Finally, a dynamic and adaptive helper thread prefetching adjustment algorithm was presented. The algorithm was based on phase behavior and dynamic prefetching benefit information to determine the suitable degree of parameter values, and whether to need feedback optimization, so as to realize the adaptive adjustment and control of helper thread prefetching. By applying the adaptive prefeching algorithm, the speed up of Mst's hotspot module was 1.496. The experimental results show that the proposed adaptive prefetching evaluation method can control parameter values adaptively according to the dynamic phase behavior and prefetching benefit information.

Concerning the problem that too many transitional links and complex parameter solving process existed in camera calibration of the surgical navigation based on C-arm, a new method that completely ignored the camera model was proposed. In this method, the camera model was completely ignored, and the transition link in the process of solving mapping parameters was simplified, which increased the efficiency. In addition, the camera calibration was achieved by distinguishing the projection data from the calibration target which has double-layer metal ball. In the calibration point verification experiment, it can be proved that the residual error of each test point was no more than 0.002 pixels; in the navigation validation experiment, probe point and perforation test were successfully implemented with the established preliminary experiment platform. The experimental results verify that the proposed camera calibration method can meet the accuracy requirements of surgical navigation system.

In programs, the path search space is too large because there are too many paths or complicated cycle paths, which directly affect the efficiency and accuracy for path validation. To resolve the above problem, a new program path validation method based on the Satisfiability Modulo Theory (SMT) solver was proposed. Firstly, loop invariants were extracted from the complicated cycle paths by using the method of decision tree, then the No-Loop Control Flow Graph (NLCFG) was constructed. Secondly, the information for basic paths was extracted via traversing Control Flow Graph (CFG) by using basic path method. Finally, the problem of path validation was converted into the problem of constraint solving by using a SMT solver as a constraint solver. Compared with CBMC and FSoft-SMT which were also based on SMT solver, the proposed method reduced validation time on test programs by more than 25% and 15% respectively. As for verification accuracy, the proposed method had significantly improvement. Experimental results show that this method can efficiently resolve the problem with too large path search space, and improve the efficiency and accuracy of path validation.

In order to accurately detect the community structure of complex networks, a community detection algorithm based on signal adaptive transmission was proposed. First, the signal was adaptively passed on complex networks,thereby getting the vector affecting on the entire network of each node, then the topological structure of each node was translated into geometrical relationships of algebra vector space. Thus, according to the nature of the clustering, the community structure of the network was detected. In order to get the feasible spatial vectors, the optimum transfer number was determined, which reduced the searching space, and effectively strengthened the search capability of community detection.The proposed algorithm was tested on computer-generated network, Zachary network and American college football network. Compared with Girvan-Newman (GN) algorithm, spectral clustering algorithm,extremal optimization algorithm and signal transmission algorithm, the results show that the accuracy and precision of the proposed community division algorithm is feasible and effective.

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.

In multi-classifier decision fusion, there is great warp when using limited training data to estimate the probability parameters of classifier. For dealing with this problem, a multi-classifier decision fusion method based on D-S (Dempster-Shafer) Evidential Reasoning (ER) was presented. The method utilized the advantages of D-S theory to describe uncertainty of classifiers. To solve the paradox problem in high conflict circumstance among multiple classifiers, a reliability weighted fusion algorithm was proposed to realize the traffic identification decision fusion. The experimental results show that the accuracy rate of majority voting and Bayes maximum posteriori probability are 78.3% and 81.7% respectively, while the proposed algorithm can improve the accuracy rate up to 82.2%-91.6%, and remain the reject rate between 4.1% and 6.2%.