Aiming at how to use the food safety standard reference network to find the key standards that have a great impact on food safety inspection and detection from many national food safety standards， a method of finding the important nodes in the food safety standard reference network based on multi-attribute comprehensive evaluation was proposed. Firstly， the importance of standard nodes were evaluated by using the degree centrality， closeness centrality and betweenness centrality in social network analysis as well as the Web page importance evaluation algorithm PageRank respectively. Secondly， the Analytic Hierarchy Process （AHP） was used to calculate the weight of each evaluation index in the importance evaluation， and multi-attribute decision-making method based on TOPSIS （Technique for Order Preference by Similarity to an Ideal Solution） was used to comprehensively evaluate the importance of standard nodes and found out the important nodes. Thirdly， the important nodes obtained from the comprehensive evaluation and the important nodes obtained from the degree based evaluation were deleted from their own reference network respectively， and the connectivity of the reference networks after deleting the important nodes was tested. The worse the connectivity was， the more important the nodes were. Finally， the Louvain community discovery algorithm was used to test the network connectivity， that is to find the communities of the network nodes. The more nodes not included in the communities， the worse the network connectivity. Experimental results show that after deleting the important nodes found by the comprehensive evaluation method based on multi-attribute， more nodes cannot be included in the communities than those of the evaluation method based on degree， proving that the proposed method can better find the important nodes in the reference network. The proposed method helps standard makers quickly grasp the core contents and key nodes when revising and updating standards， and plays a guiding role in the construction of the system of national food safety standards.

Considering the limitations of the feature extraction of high-density crowd images with perspective distortion, a crowd counting model, named LMCNN, that combines Global Feature Perception Network (GFPNet) and Local Association Feature Perception Network (LAFPNet) was proposed. GFPNet was the backbone network of LMCNN, its output feature map was serialized and used as the input of LAFPNet. And the characteristic that the Recurrent Neural Network (RNN) senses the local association features on the time-series dimension was used to map the single spatial static feature to the feature space with local sequence association features, thus effectively reducing the impact of perspective distortion on crowd density estimation. To verify the effectiveness of the proposed model, experiments were conducted on Shanghaitech Part A and UCF_CC_50 datasets. The results show that compared to Atrous Convolutions Spatial Pyramid Network (ACSPNet), the Mean Absolute Error (MAE) of LMCNN was decreased by 18.7% and 20.3% at least, respectively, and the Mean Square Error (MSE) was decreased by 22.3% and 22.6% at least, respectively. The focus of LMCNN is the association between the front and back features on the spatial dimension, and by fully integrating the spatial dimension features and the sequence features in a single image, the crowd counting error caused by perspective distortion is reduced, and the number of people in dense areas can be more accurately predicted, thereby improving the regression accuracy of crowd density.

Aiming at the problem of field segmentation in the reverse analysis of binary protocol message format, a novel algorithm with format keywords as the reverse analysis target was proposed, which can optimally determine the boundaries of binary protocol format keywords by improved n-gram algorithm and optimal path search algorithm. Firstly, by introducing the position factor into n-gram algorithm, a boundary extraction algorithm of format keywords was proposed based on the iterative n-gram-position algorithm, which effectively solved the problems that the n value was difficult to determine and the candidate boundary extraction of format keywords with fixed offset position in the n-gram algorithm. Then, the branch metric was defined based on the hit ratio of frequent item boundaries and the left and right branch information entropies, and the constraint conditions were constructed based on the difference of n-gram-position value change rate between keywords and non-keywords. The boundary selection algorithm of format keywords based on the optimal path search was proposed to determine the joint optimal bound for format keywords. The experimental results of testing on five different types of protocol message datasets such as AIS1, AIS18, ICMP00, ICMP03 and NetBios show that, the proposed algorithm can accurately determine the boundaries of different protocol format keywords, its F values are all above 83%. Compared with the classical algorithms of Variance of the Distribution of Variances (VDV) and AutoReEngine, the F value of the proposed algorithm is increased averagely by about 8 percentage points.

With the improvement of standardization and intelligence level of secondary equipment, a kind of communication network more efficient and smarter is needed in smart substation to meet the substation operation and maintenance requirements, to achieve equipment plug and play, intelligent monitoring, subnet secure isolation and element interchange. For the application needs of substation network unified management, security isolation between subnets and equipment compatibility and interchangeability, a Software Defined Network (SDN)-based substation network architecture was proposed. IEC 61850 and OpenFlow protocols were used for network architecture design. OpenFlow controller was used to control and isolate the individual subnets to implement network device management and subnet secure isolation. The experimental results show that precise traffic control based on service types, and securely data isolation can be implemented with the proposed substation SDN-based network architecture. It has a very important application value for promoting the operation and maintenance level of smart substation.

Concerning the problem that the existing schemes of provable data possession are inefficient and can not support full dynamic update, a novel dynamic provable data possession scheme based on B ⁺ tree was proposed. Bilinear pairing techniques and data version table were introduced to support fine-grained dynamic operations at the block level and to protect user's data privacy in the proposed scheme. The third party auditor could identify the wrong data and locate it accurately by optimizing the system model and designing the retrieved value of data node. In comparison with the scheme based on the Merkel Hash Tree (MHT), theoretical analysis and experimental results show that the proposed scheme can significantly reduce the cost of constructing the authentication data structure, simplify the dynamic update process, and improve the verification efficiency of the third party auditor.

To deal with the discrete series protocol message without session information, a new Separate Protocol Message based Format Signature Construction (SPMbFSC) algorithm was proposed. First, separate protocol message was clustered, then the keywords of the protocol were extracted by improved frequent pattern mining algorithm. At last, the format signature was acquired by filtering and choosing the keywords. Simulation results show that SPMbFSC is quite accurate and reliable, the recognition rate of SPMbFSC for six protocols (DNS, FTP, HTTP, IMAP, POP3 and IMAP) achieves above 95% when using single message as identification unit, and the recognition rate achieves above 90% when using session as identification unit. SPMbFSC has better performance than Adaptive Application Signature (AdapSig) extraction algorithm under the same experimental conditions. Experimental results indicate that the proposed SPMbFSC does not depend on the integrity of session data, and it is more suitable for processing incomplete discrete seriesprotocol message due to the reception limitation.

Aiming at the problems of complex retinal image processing, poor generalization and lack of complete automatic recognition system, a complete retinal image automatic recognition system based on deep neural network was proposed. Firstly, the image was denoised, normalized, and data preprocessed. Then, a compact neural network model named CompactNet was designed. The structure parameters of CompactNet were inherited from AlexNet. The deep network parameters were adjusted adaptively based on the training data. Finally, the performance experiments were conducted on different training methods and various network structures. The experimental results demonstrate that the fine-tuning method of CompactNet is better than the traditional network training method, the classification index can reach 0.87, 0.27 higher than the traditional direct training. By comparing LeNet, AlexNet and CompactNet, CompactNet network model has the highest classification accuracy, and the necessity of preprocessing methods such as data amplification is confirmed by experiments.

Aiming at the problem that the network lifetime was reduced because of the high energy consumption of the nodes covered by multiple radiation sources in large scale Wireless Sensor Network (WSN), a new clustering algorithm based on Evolutionary Game Theory (EGT) was proposed. The non-cooperative game theory model was established by mapping the search space of the optimal node sets to the strategy space of the game and using the utility function of the game as objective function respectively; then the optimization objective was achieved by using Nash equilibrium analysis and the perturb-recover process of equilibrium states. Furthermore, a detailed clustering algorithm was presented to group the optimal node sets into clusters for further location. The proposed algorithm was compared with the nearest-neighbor algorithm and the clustering algorithm based on Discrete Particle Swarm Optimization (DPSO) algorithm in the location accuracy and the network lifetime under the RSSI (Received Signal Strength Indication)/TDOA (Time Difference of Arrival) two rounds cooperative location scheme. Simulation results show that the proposed algorithm decreases the energy consumption of the nodes covered by multiple radiation sources, prolongs the network lifetime and guarantees the precise location.

Concerning the poor robustness and low hiding strength of existing file hiding method based on Universal Serial Bus (USB), a new file hiding method based on capacity disguised and double file system was proposed. By analyzing the characteristics and management mechanism of Nand flash chips, the capacity disguise was achieved to deceive the host by tampering equipment capacity value in Command Status Wrap (CSW). Based on the memory management mechanism of the Flash Translation Layer (FTL), the storage area was divided into two parts including the hiding area and the common area by different marks, and a double file system was established using format function. Request for switching file system was sent by writing specific data, then it was achieved after user authentication to realize secure access to hiding areas. The experimental results and theoretical analysis show that the proposed method can achieve hiding file which is transparent to operating system, moreover, it is not affected by device operation and has better robustness and stronger hiding effect with respect to the methods based on hooking Application Programming Interface (API), modifying File Allocation Table (FAT) or encryption.

The existing methods for monitoring clipboard operations cannot defend kernel-level attacks and satisfy the practical needs due to the simple protection strategy. In order to mitigate these disadvantages, a clipboard operations monitoring technique for document contents based on Virtual Machine Monitor (VMM) was proposed, as well as a classification protection strategy for electronic documents based on clipboard operations monitoring. Firstly, system calls were intercepted and identified in VMM by modifying the shadow registers. Secondly, a mapping table between process identifier and document path was created by monitoring the document open operations, then the document path could be obtained by process identifier when the clipboard operations were intercepted. Finally, clipboard operations were filtered according to classification protection strategy. The experimental results show that the performance loss to Guest OS file system caused by the monitoring system decreases with the increase of the record size; when the record size reaches more than 64 KB, the performance loss is within 10%, which has little effect on the user.

To deal with the difficulties caused by noise disturbance, intensity inhomogeneity and edge blurring in Magnetic Resonance Imaging (MRI) image segmentation, a new multi-threshold MRI image segmentation algorithm based on mixed entropy using Curvelet transformation and Multi-Objective Particle Swarm Optimization (MOPSO) was proposed. First, the high-frequency and the low-frequency subbands were obtained using Curvelet decomposition, which were used to construct the profile-detail gray level matrix model that could represent edge details accurately. Then, with the consideration of both inter-class similarity and intra-class difference of background and object region, two-dimensional reciprocal entropy and reciprocal gray entropy were proposed and combined to define the mixed entropy, which was used as the objective function of MOPSO. The optimal multi-threshold was searched cooperatively to get an accurate segmentation. Finally, in order to speed up the segmentation process, gradient-based multi-threshold estimation algorithms for two-dimensional reciprocal entropy and reciprocal gray entropy were proposed. The experimental results show that the proposed method is more adaptive and accurate when applied to gray uneven and noisy MRI image segmentation in comparison with two-dimensional tsallis entropy, Adaptive Bacterial Foraging (ABF) and improved Otsu multi-threshold segmentation algorithms.

In order to solve the problem that selected accuracy stopping criterion can only be applied in the scenario of batch mode-based active learning, an improved stopping criterion for single-labeling mode was proposed. The matching relationship between each predicted label and the corresponding real label existing in a pre-designed number of learning rounds was used to approximately estimate and calculate the selected accuracy. The higher the match quality was, the higher the selected accuracy was. Then, the variety of selected accuracy could be monitored by moving a sliding-time window. Active learning would stop when the selected accuracy was higher than a pre-designed threshold. The experiments were conducted on 6 baseline data sets with active learning algorithm based on Support Vector Machine (SVM) classifier for indicating the effectiveness and feasibility of the proposed criterion. The experimental results show that when pre-designing an appropriate threshold, active learning can stop at the right time. The proposed method expands the applications of selected accuracy stopping criterion and improves its practicability.

Because the Register Swapping (RS) method does not consider register allocation's effect in reducing soft error of register files, a static register reallocation approach was proposed concerning live variable's effect on soft error. First, this approach introduced live variable's weight to evaluate its impact on soft error of register files, then two rules were put forward to reallocate the live variable after the register swapping phase. This approach can reduce the soft error in the level of live variable further. The experiments and analysis show that this approach can reduce the soft error by 30% further than the RS method, which can enhance the register's reliability.

To improve the accuracy and robustness of image edge detection, a new Canny edge detection algorithm based on Robust Principal Component Analysis (RPCA) was proposed. The image was decomposed into a principal component and a sparse component by RPCA. Then edge information of the principal component was extracted by Canny operator. The proposed algorithm formulated the problem of image edge detection as the edge detection of the principal component of the image. It eliminated the interference of image "stain" on the detection results and suppressed the noise. The experimental results show that the proposed algorithm outperforms Log, Canny and Susan edge detection algorithms in terms of both accuracy and robustness.

Based on Complementary Ensemble Empirical Mode Decomposition (CEEMD)-fuzzy entropy and Echo State Network (ESN) with Leaky integrator neurons (LiESN), a kind of combined forecast method was proposed for improving the precision of short-term power load forecasting. Firstly, in order to reduce the calculation scale of partial analysis for power load series and improve the accuracy of load forecasting, the power load time series was decomposed into a series of power load subsequences with obvious differences in complex degree by using CEEMD-fuzzy entropy, according to the characteristics of each subsequence, and then the corresponding LiESN forecasting submodels were built, the ultimate forecasting results could be obtained by the superposition of the forecasting model. The CEEMD-LiESN method was applied to the instance of short term electricity load forecasting of the New England region. The experimental results show that the proposed combination forecasting method has a high prediction precision.

In order to solve the problem of losing codes and pausing codes in the incremental encoder which conventionally used in the stage speed boom system as speed feedback component and prevent the propagation of fault effect, a fault detection and soft close-loop fault-tolerant control method for encoder faults based on the Takagi-Sugeno Fuzzy Neural Network (T-S FNN) model combined with the data-driven technique was proposed. First, the system of T-S FNN prediction model was established by substracting the system normal operation of historical data, and achieved the residual error information by using measured values of actual encoder and predicted values. Next, encoder fault was detected by using improved Sequential Probability Ratio Test (SPRT) algorithm though the residual error real-time data information, in order to overcome the detection delay and ensure the reliability of fault detection. Then, according to the prediction model output which was used as the output of the encoder failure to accommodate the failure when fault was detected, in order to realize the soft fault-tolerant operation by using close-loop mode. At last, the encoder fault tolerant process for the losing codes and pausing codes was proved by simulation experiment effectively. The simulation results show that the method of this article can detect the encoder fault information rapidly and reliability, and switch from the fault-tolerant mechanism timely and safely by using the reconstruction of the prediction information, in order to realize the soft closed-loop fault-tolerant control of encoder failure and improve the safety and reliability of stage speed boom system operation process.

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.

Aiming at the problem of sample labeling in network traffic feature selection, and the deficiency of traditional semi-supervised methods which can not select a strong correlation feature set, a Semi-supervised Feature Selection based on Extension of Label (SFSEL) algorithm was proposed. The model started from a small number of labeled samples, and the labels of unlabeled samples were extended by K-means algorithm, then MDrSVM (Multi-class Doubly regularized Support Vector Machine) algorithm was combined to achieve feature selection of multi-class network data. Comparison experiments with other semi-supervised algorithms including Spectral, PCFRSC and SEFR on Moore network data set were given, where SFSEL got higher classification accuracy and recall with fewer selection features. The experimental results show that the proposed algorithm has a better classification performance with selecting a strong correlation feature set of network traffic.

An index of network evolution speed and a network evolution model were put forward to analyze the effects of network evolution speed on propagation. The definition of temporal correlation coefficient was modified to characterize the speed of the network evolution; meanwhile, a non-Markov model of temporal networks was proposed. For every active node at a time step, a random node from network was selected with probability r, while a random node from former neighbors of the active node was selected with probability 1-r. Edges were created between the active node and its corresponding selected nodes. The simulation results confirm that there is a monotone increasing relationship between the network model parameter r and the network evolution speed; meanwhile, the greater the value of r, the greater the scope of the spread on network becomes. These mean that the temporal networks with high evolution speed are conducive to the spread on networks. More specifically, the rapidly changing network topology is conducive to the rapid spread of information, but not conducive to the suppression of virus propagation.

In order to reduce the negative impacts of sparse data, a new collaborative filtering recommendation algorithm was put forward based on the number of common rating items among users and the similarity of user interests. The similarity calculations were made to be more credible by combing the number of common rating items among users with the similarity of user interests, so as to provide better recommendation results for the target user. Compared with the method based on Pearson similarity, the new algorithm provides better recommendation results with smaller Mean Absolute Error (MAE). In conclusion, the new algorithm is effective and feasible.

Software tamper resistance is an important method for software protection. Concerning the control-flow tampering invoked by buffer overflow as well as some other software attacks, a software tamper-proofing method based on Function-Level Control-Flow (FLCF) monitoring was proposed. This method described the software's normal behaviors by FLCF and instrumented one guard at every entrance of functions by binary rewriting technology. The monitoring module decided whether the software was tampered or not by comparing the running status received from the guards' reports with the expected condition. A prototype system was realized and its performance was analyzed. The experimental results show that this method can effectively detect the control-flow tampering with less overhead and no false positives. It can be easily deployed and transplanted as its implementation does not need source code or any modifications of underlying devices, and system security is strengthened by isolating the monitoring module with the software being protected.

Concerning the problems of current social networking friends recommended methods, such as no obvious user interest and poor correlation between the users, a collaborative filtering algorithm was proposed based on common users and similar labels. The common concerned users were extracted as joint project data, and the custom labels were added to reflect the users' interest. Then the semantic similarity of the labels was calculated to expand the sparse matrix and improve the collaborative filtering recommendation. The experimental results show that, compared with the traditional collaborative filtering algorithm with single index, the proposed algorithm can better reflect the users' interest, and has significant improvement in the recommended accuracy and the average accuracy.

To deal with the huge spatial and temporal consumption caused by large-scale self-set data, the authors designed a self-set matching mechanism based on artificial immune Network Intrusion Detection System (NIDS). To improve the detection efficiency of the intrusion detection system, an efficient probability matching optimization mechanism was proposed. The authors first proved the relative concentration of the network data, and analyzed the validity of the probability matching mechanism by calculating the Average Search Length (ASL), then verified the fast matching efficiency of the mechanism through simulation experiments. The mechanism has been used in a project application in a new artificial immune network intrusion detection system based on self-set scale simplified mechanism, which has achieved satisfactory matching results.

A new threshold function under non-Gaussian noise background was presented to overcome the limitations of wavelet threshold algorithm under the Gaussian noise background. The shortcomings of conventional function, such as discontinuity of hard threshold function and the invariable dispersion of soft threshold function, can be solved. The new function which employed high order power function was put forward based on Garrote threshold. First, the signal with a class of non-Gaussian noise was decomposed by wavelet. Secondly, each high frequency wavelet coefficient was quantified based on new threshold function. Thirdly, signal was reconstructed by the low frequency coefficients of wavelet decomposition and quantified high frequency coefficients. The simulation results under non-Gaussian noise background indicate that the new threshold function gets higher Signal-to-Noise Ratio (SNR) gains and lower minimum Mean Square Error (MSE) compared to the soft and hard threshold, two types of improved threshold and Garrote threshold.

This paper proposed a new method to describe oracle-bone-script based on the combination of directed stroke and strokes, which aimed at solving the difficulties in input, quantification and jell of oracle-bone-script. Firstly, the oracle-bone-script strokes were classified and analyzed, then the composition of the directed stroke was determined according to the stroke, and an algorithm of the system to describe oracle-bone-script by directed stroke was formed. The input platform for oracle-bone-script was constructured. Users could input oracle-bone-script especially in variant forms or didnot identify oracle-bone-script through designing human-computer instruction. The experimental results prove that this input method can standardize the font, increase and decrease oracle-bone-script library, and freely modify glyphs.

Shortcomings of image encryption schemes based on chaos currently can be mainly classified into three types as follows: firstly, low dimensional chaotic sequences cause degradation of chaos; secondly, the structure of chaotic system adopted is too simple; thirdly, the algorithm is merely related to the structure of chaotic system and key. Concerning the shortcomings above, this paper presented a new image group encryption algorithm. In order to overcome the degradation of chaos, the algorithm generated pixel diffusion matrix by coupling chaotic sequences of high dimensional hyperchaos system with chaotic sequences of one dimensional chaotic mapping by means of matrix tensor product. In the process, the generation of pixel diffusion matrix was controlled by plaintext, thus the algorithm was related to plaintext, which enhanced the security of the algorithm.

The periodic orbits provide a skeleton for the organization of complex chaotic systems, for many important characteristics and dynamic properties of these systems can be determined by solving the periodic orbits, such as the accurate calculation of Lyapunov exponents, estimation of topological entropy and description of a chaotic invariant set. First, the paper reviewed the current commonly used four methods to find periodic orbits, which are NR algorithm, Broyden algorithm, SD algorithm and DL algorithm, and analyzed their characteristics and mutual relations. Second, the paper discussed the advantages, disadvantages and scope of each method with specific examples in detail. Finally, the paper pointed out that DL algorithm is more ideal among the four algorithms, and suggested the future research direction.

The current Magnetic Resonance (MR) image reconstruction algorithms based on compressed sensing (CS-MR) commonly use global regularization parameter, which results in the inferior reconstruction that cannot restore the image edges and smooth the noise at the same time. In order to solve this problem, based on adaptive regularization and compressed sensing, the reconstruction method that used the sparse priors and the local smooth priors of MR image in combination was proposed. Nonlinear conjugate gradient method was used for solving the optimized procedure, and the local regularization parameter was adaptively changed during the iterative process. The regularization parameter can recover the image's edge and simultaneously smooth the noise, making cost function convex within the definition region. The prior information is involved in the regularization parameter to improve the high frequency components of the image. Finally, the experimental results show that the proposed method can effectively restore the image edges and smooth the noise.

According to the characteristics of wireless single-hop broadcast networks, a network coding scheme based on sliding windows named NCBSW was proposed. The scheme designed a coding window which slid in a chronological order in the matrix of data packets waiting for retransmission, and the data packets used to encode were chosen from the sliding window. Meanwhile, the scheme ensured the solvability of coded packets. The simulation results show that the proposed scheme has a better performance as compared to the retransmission approach in wireless broadcasting based on network coding (NCWBR) in terms of the number of retransmission, delay, network overhead and energy consumption.

Ontology is normative, explicit and reusable when defining the domain concept, so it can be combined with topic maps to organize information resource for semantic navigation. An information retrieval model based on topic maps and ontology was proposed and defined formally. Firstly it specified a domain of tourism document. Secondly it defined the ontology and topic maps of tourism document in order to normalize query that user directly input in natural language, and identified the users real meaning of search. Thus, it can expand user' semantic search. Therefore analyzed the effect of the ontology was analyzed, and a valuable function of semantic navigation and sorting the retrieval result correlated with user's query was shown. Finally，the experimental result shows that the topic mapbased ontology information retrieval model can perform better than the traditional model.