With the continuous improvement of information technology, the amount of image-text data with orientation on various social platforms is growing rapidly, and the sentiment analysis with image and text fusion is widely concerned. The single sentiment analysis method can no longer meet the demand of multi-modal data. Aiming at the technical problems of image and text sentiment feature extraction and fusion, firstly, the widely used image and text emotional analysis datasets were listed, and the extraction methods of text features and image features were introduced. Then, the current fusion modes of image features and text features were focused on and the problems existing in the process of image-text sentiment analysis were briefly described. Finally, the research directions of sentiment analysis in the future were summarized and prospected for. In order to have a deeper understanding of image-text fusion technology, literature research method was adopted to review the study of image-text sentiment analysis, which is helpful to compare the differences between different fusion methods and find more valuable research schemes.

The data in internet social media has the characteristics of fast transmission, high user participation and complete coverage compared with traditional media under the background of the rise of various platforms on the internet.There are various topics that people pay attention to and publish comments in, and there may exist deeper and more fine-grained sub-topics in the related information of one topic. A survey of sub-topic detection based on internet social media, as a newly emerging and developing research field, was proposed. The method of obtaining topic and sub-topic information through social media and participating in the discussion is changing people’s lives in an all-round way. However, the technologies in this field are not mature at present, and the researches are still in the initial stage in China. Firstly, the development background and basic concept of the sub-topic detection in internet social media were described. Secondly, the sub-topic detection technologies were divided into seven categories, each of which was introduced, compared and summarized. Thirdly, the methods of sub-topic detection were divided into online and offline methods, and the two methods were compared, then the general technologies and the frequently used technologies of the two methods were listed. Finally, the current shortages and future development trends of this field were summarized.

Aiming at the lag consensus problem of first-order and second-order hybrid heterogeneous multi-agent systems, a distributed lag consensus control protocol based on pinning control was proposed. Firstly, the lag consensus analysis was transformed into stability verification. Then, the stability of closed loop system was analyzed by using graph theory and Lyapunov stability theory. Finally, the sufficient conditions for solvability of lag consensus based on Linear Matrix Inequality (LMI) were given under fixed and switching topologies respectively, so that leader-follower lag consensus of heterogeneous multi-agent system was achieved. The numerical simulation results show that, the proposed lag consensus control method can make the heterogeneous mult-agent systems achieve leader-follower lag consensus.

Aiming at the problems that a database is scanned for multiple times and a record is compared for many times to count in most frequent pattern mining algorithms for privacy-preserving, an Incremental Bitmap-based Randomized Response with Partial Hiding (IBRRPH) algorithm was proposed. Firstly, the bitmap technique was used to represent the transaction in the database, and the "and" operator for bit was used to speed up the support degree calculating. Secondly, an incremental update model was introduced by analyzing incremental access relationship, so that the mining result before was used to the maximum limit during incremental updating. The contrast experiment of performance to the algorithm proposed by Gu et al. (GU C, ZHU B P, ZHANG J K. Improved algorithm of privacy preserving association rule mining. Journal of Nanjing University of Aeronautics & Astronautics, 2015, 47(1):119-124) was done aiming at the increment range from 1000 to 40000. The experimental results show that the efficiency of the IBRRPH algorithm is improved over 21% compared to the algorithm proposed by Gu et al.

Since the hyponymy between symptoms has strong structural features, an automatic hyponymy extracting method based on symptom components was proposed. Firstly, it was found that symptoms can be divided into eight parts: atomic symptoms, adjunct words, and so on, and the composition of these parts satisfied certain constructed rules. Then, the lexical analysis system and Conditional Random Field (CRF) model were used to segment symptoms and label the parts of speech. Finally, the hyponymy extraction was considered as a classification problem. Symptom constitution features, dictionary features and general features were selected as the features of different classification algorithms to train the models. The relationship between symptoms were divided into hyponymy and non-hyponymy. The experimental results show that when these features are selected simultaneously, precision, recall and F1-measure of Support Vector Machine (SVM) are up to 82.68%, 82.13% and 82.40%, respectively. On this basis, by using the above hyponymy extracting algorithm, 20619 hyponymies were extracted, and the knowledge base of symptom hyponymy was built.

It is difficult for robot to move in wild environment because of pit areas, so a visual coping method was put forward to detect those pit areas. Firstly, according to project requirements, a part of suspected areas with small size were removed, as well as some the suspected areas with edge gradient. Secondly, the oval similarity was calculated to determine gray level segmentation threshold, and the similarity threshold was confirmed by analyzing the oval similarity curve, which was used to separate pit areas from the suspected pit areas. At last, the simulation results on 200 pictures with different angles, scenes and pit umbers show that the proposed method can be applied to extract pit area in complex environment, and is also not sensitive to outline regularity of pit area; besides, it can adapt to complex environment.

Due to the high radiation dose to the patient when acquiring lung four Dimensional Computed Tomography (4D-CT) data, this paper proposed a method for deriving the phase-binned 4D-CT image sets through deformable registration of the images acquired at some known phases. First, Active Demons registration algorithm was employed to estimate the motion field between inhale and exhale phases. Then, images at an intermediate phase were reconstructed by a linear interpolation of the deformation coefficients. The experiment results showed that the images at intermediate phases could be reconstructed efficiently. The quantitative analysis of landmark point displacements showed that 3 mm accuracy was achievable. The different maps of reconstructed and acquired images illustrated the similar level of success. The proposed method can accurately reconstruct images at intermediate phases of lung 4D-CT data.

Aiming at the problem of extracting the useful signal in the complex background of chaotic noise rapidly and accurately, the phase space reconstruction theory based on complex nonlinear system was proposed, and the method of improved Extreme Learning Machine (ELM) was used to predict the single step error and detect the weak signal. The improved K-means clustering algorithm was used to select the optimal family as training set, the improved extreme learning machine chose the weight value and the offset to improve the detection accuracy and speed. The one step prediction model of chaotic noise sequence with Lorenz system was established, and the weak target signals (including periodic signal and transient signal) that lost in the chaotic noise were detected, then the IPIX radar data of Canada Mc Master University were used, and the floater signal in sea clutter noise was extracted to do the experimental research. The results show that the method can effectively detect the very weak signal in chaos background noise, at the same time, the influence of noise was restrained to the chaotic background signal, compared with the traditional algorithms such as Radial Basis Function (RBF), the prediction accuracy is increased by 25%, the detection threshold is increased by -5 dB, the training time is reduced by 77.1 s, it has more obvious advantages in practical application.

Maximizing customer satisfaction is directly related to the enterprise profit and market competitiveness for the supermarket as a service enterprise, so it is important to optimize the retail checkout operation. Firstly, the retail checkout scheduling problem was described by a triplet of α/β/γ, maximizing customer satisfaction was taken as the first goal and minimizing operating cost was taken as the second goal with machine usage restriction and the rule of First In First Out (FIFO). The corresponding mathematical model was established, and then an algorithm was designed using plant growth simulation algorithm. 〖BP(〗Finally, the actual data was used to simulate, and the results prove that the study has effectiveness and feasibility. 〖BP)〗Finally, a numerical simulation of actual cases was used to verify the effectiveness and feasibility of the method.

Dynamic trusted measurement is a hot and difficult research topic in trusted computing. Concerning the measurement difficulty invoked by the dynamic nature of operating system kernel, a Dynamic Trusted Kernel Measurement (DTKM) model was proposed. Dynamic Measurement Variable (DMV) was presented to describe and construct dynamic data objects and their relations, and the method of semantic constraint was proposed to measure the dynamic integrity of kernel components. In DTKM, the collection of memory data was implemented in real-time, and the dynamic integrity was verified by checking whether the constructed DMV was consistent with semantic constraints which were defined based on the security semantics. The nature analysis and application examples show that DTKM can effectively implement dynamic measurement of the kernel and detect the illegal modification of the kernel dynamic data.

Privilege deduction relation makes the authorization management easier, and at the same time it also causes the calculation of valid privileges more difficult. Therefore, it is important for authorization and access control to calculate deduction relations between privileges correctly and efficiently. Based on the resource hierarchy and operation hierarchy, the rule of privilege deduction was given in this paper. According to the fact that privilege query happens more frequently than privilege update, a new method of calculating deduction relations based on reachability matrix of privilege deduction was proposed. The experimental results show that the new method is more efficient than the way calculating deduction relations directly.

The model, which is available to Command Protection Engineering (CPE) site selection, was built taking the complex characteristics into consideration. The weight of evaluation index was determined by improved Analytic Hierarchy Process (AHP), and was modified by entropy method. According to the characteristic of the evaluation index of CPE site selection, the combined method of the spatial information analysis and multi-expert group decision was adopted to achieve spatial and non-spatial index value. Grey Correlation Analysis (GCA) was used to improve Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), avoided the limitation that traditional TOPSIS adopts Euclidean distance alone. This model can achieve an ideal evaluation result through an example.

Chinese word segmentation is one of the fundamental key techniques for Chinese Information Processing. In this paper, the authors firstly studied current segmentation algorithms, then, modifid the traditional Maximum Match (MM) algorithm. With the consideration of both word-coverage rate and sentence-coverage rate, a character Directed Graph with ambiguity mark was implemented for searching all possible segmentation sequences. This method compared with the classic MM algorithms and omni-segmentation algorithm and the experiment result shows that the Directed Graph based algorithm can achieve higher coverage rate and lower complexity.

Text classification has become a research focus in the field of natural language processing. After the review of traditional text classification models, a method using N-gram language models to classify Chinese text was presented. This model doesn′t present documents with bag of words, but regards documents as random observation sequences. With the bi-gram model, a text classifier based on word level was implemented. The performance of the N-gram model classifier was compared with that of the traditional models (Vector Space Model and Naive Bayes Model). Experiment result shows that the accuracy and the stability of the N-gram model classifier are better than others.