For the problems of limited bandwidth resources， the existence of external disturbance and parameter uncertainty， a non-fragile dissipative control scheme for event-triggered networked systems was proposed. Firstly， based on the Networked Control System （NCS） model， a non-periodic sampling event-triggered scheme was proposed， and a delay closed-loop system model was established. Then， a novel bilateral Lyapunov functional was constructed by using the structure characteristics of sawtooth wave. Finally， the sufficient conditions to ensure the stability of the system were derived by using methods such as Jensen inequality， free weight matrix and convex combination， and the gain of the feedback controller was calculated. The results of numerical simulation show that the proposed bilateral functional is less conservative than the unilateral functional， the event-triggered mechanism can save bandwidth compared with the common sampling mechanism， and the proposed controller is feasible.

The location of books is one of the critical technologies to realize the intelligent development of libraries， and the accurate book spine segmentation algorithm has become a major challenge to achieve this goal. Based on the above solution， an improved book spine segmentation algorithm based on improved DeepLabv3+ network was proposed， aiming to solve the difficulties in book spine segmentation caused by dense arrangement， skew angles of books， and extremely similar book spine textures. Firstly， to extract more dense pyramid features of book images， the Atrous Spatial Pyramid Pooling （ASPP） in the original DeepLabv3+ network was replaced by the multi-dilation rate and multi-scale DenseASPP （Dense Atrous Spatial Pyramid Pooling） module. Secondly， to solve the problem of insensitivity of the original DeepLabv3+ network to the segmentation boundaries of objects with large aspect ratios， Strip Pooling （SP） module was added to the branch of the DenseASPP module to enhance the strip features of book spines. Finally， based on the Multi-Head Self-Attention （MHSA） mechanism in ViT （Vision Transformer）， a global information enhancement-based self-attention mechanism was proposed to enhance the network’s ability to obtain long-distance features. The proposed algorithm was tested and compared on an open-source database， and the experimental results show that compared with the original DeepLabv3+ network segmentation algorithm， the proposed algorithm improves the Mean Intersection over Union （MIoU） by 1.8 percentage points on the nearly vertical book spine database and by 4.1 percentage points on the skewed book spine database， and the latter MIoU of the proposed algorithm achieves 93.3%. The above confirms that the proposed algorithm achieves accurate segmentation of book spine targets with certain skew angles， dense arrangement， and large aspect ratios.

As the core of steel production， hot rolling process has demands of strict production continuity and complex production technology. The random arrival of rush orders and urgent delivery requirements have adverse impacts on production continuity and quality stability. Aiming at those kind of dynamic events of rush order insertion， a hot rolling rescheduling optimization method was proposed. Firstly， the influence of order disturbance factor on the scheduling scheme was analyzed， and a mathematical model of hot rolling rescheduling was established with the optimization objective of minimizing the weighted sum of tardiness of orders and jump penalty of slabs. Then， an Estimation of Distribution Algorithm （EDA） for hot rolling rescheduling was designed. In this algorithm， aiming at the insertion processing of rush orders， an integer encoding scheme was proposed based on the insertion position， the probability model based on the characteristics of the model was designed， and the fitness function based on the penalty value was defined by considering the targets and constraints comprehensively. The feasibility and validity of the model and the algorithm were verified by the simulation experiment on the actual production data.

In recent years， the single image Super-Resolution （SR） reconstruction methods based on Convolutional Neural Network （CNN） have become mainstream. Under normal circumstances， the deeper network layers of the reconstruction model have， the more features are extracted， and the better reconstruction effect is. However， as the number of network layers increases， the reconstruction model will not only have the vanishing gradient problem， but also significantly increase the number of parameters and increase the difficulty of training. To solve the above problems， a single image SR reconstruction method based on dense Inception was proposed. In the proposed method， the image features were extracted by introducing the Inception-Residual Network （Inception-ResNet） structure， and the simplified dense network was adopted globally. And only the path that each module outputs to the reconstruction layer was constructed， avoiding the increase of computation amount caused by the generation of redundant data. When the magnification was 4， the dataset Set5 was used to test the model performance. The results show that， the Structural SIMilarity （SSIM） of the proposed model is 0.013 6 higher than that of accurate image Super-Resolution using Very Deep convolutional network （VDSR）， and the proposed method has the SSIM 0.002 9 higher and the model parameters 78% smaller than Multi-scale residual Network for Image Super-Resolution （MSRN）. The experimental results show that， under the premise of ensuring the depth and width of the model， the proposed method significantly reduces the number of parameters and the difficulty of training. In the meantime， the proposed method can achieve better Peak Signal-to-Noise Ratio （PSNR） and SSIM than the comparison methods.

Because of randomness, the robustness of Label Propagation Algorithm (LPA) is severely hampered. To improve the robustness, a LPA based on potential function of data field (LPAP) was proposed. The potential of every node was calculated, and local extreme potential was searched. Only the node with extreme potential was labeled initially, and the label was updated according to the sum potential of its neighbors with equal label during iteration. When there were no nodes changing its label, iteration stopped. The experimental results show that the average distinct community partition of LPAP is 4.0% of that of LPA, 12.9% of that of Balanced Propagation Algorithm (BPA), and the average Variation of Information (VOI) of LPAP is 45.1% of that of LPA, 73.3% of that of BPA. LPAP is significantly more robust, and is suitable for community detection in large network.

Concerning the situation that the ecological security has a lot of uncertaint factors and is dynamic, the Set Pair Analysis (SPA) theory and Markov chain were combined for dynamic assessment of ecological security, and a dynamic assessment method of ecological security was proposed combining the state assessment and trend analysis. The method adopted connection degree of SPA to represent uncertain knowledge, used the connection number of SPA to classify ecological security level, and built the comprehensive evaluation model of the system. Through the analysis of the development trend of ecological security, using the ergodicity of Markov chain and statistical quality of Monte Carlo method, the state of ecological security in the next moment could be predicted. This method uses limited assessment data and historical data, dynamically assesses the development and changes of ecological security, and provides a theoretical basis for the safety management.

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.

Images captured in hazy weather suffer from poor contrast and low visibility. This paper proposed a single image defogging algorithm to remove haze by combining with the characteristics of HSI color space. Firstly, the method converted original image from RGB color space to HSI color space. Then, based on the different affect to hue, saturation and intensity, a defogged model was established. Finally, the range of weight in saturation model was obtained by analyzing original images saturation, then the range of weight in intensity model was also estimated, and the original image was defogged. In comparison with other algorithms, the experimental results show that the running efficiency of the proposed method is doubled. And the proposed method effectively enhances clarity, so it is appropriate for single image defogging.

Complex Event Processing (CEP) is currently an advanced analytical technology which deals with high velocity event streams in a real-time way and primarily gets applied in Event Driven Architecture (EDA) system domain. It is helpful to realize intelligent business in many applications. For the sake of reporting its research status, the paper introduced the basic meaning and salient feature of CEP, and proposed a system architecture model composed of nine parts. Afterwards, the main constituents of the model were reviewed in terms of key technology and its formalization. In order to illustrate how to use CEP in the logistic Internet of things, an application framework with CEP infiltrating in it was also proposed here. It can be concluded that CEP has many merits and can play an important role in application fields. Finally, the shortcomings of this research domain were pointed out and future works were discussed. The paper systematically analyzed the CEP technology in terms of theory and practice so as to further develop CEP technology.

The convergence speed of Artificial Glowworm Swarm Optimization (AGSO) algorithm declines, even falls into local minimums, when some glowworms gather in non whole extreme points or some glowworms wander around aimlessly. Concerning this problem, an AGSO algorithm based on adaptive t distribution mixed mutation was proposed. Adaptive t distribution mutation and optimization adjustment mutation was introduced into the AGSO algorithm to improve the diversity of glowworm swarm, and prevent the AGSO algorithm from falling into local minimums. Mutation control factor was defined. Combining history status information, the description of adaptive t distribution mixed mutation was given. The mutation method could enhance ability of global exploration and local development. The emulation results of representative test functions and many application examples show that the proposed algorithm is reliable and efficient. Meanwhile, this algorithm is better than tradition algorithm in terms of speed and precision for seeking the optimum.

In Wireless Sensor Network (WSN), the network coverage range is wide, the communication range of sensor nodes is limited, and the long distance transmission is easy to cause data loss problem. To solve these problems, a routing algorithm based on game theory for WSN was proposed, through establishing the network Quality of Service (QoS) and the nodes residual energy of nodes as the utility function of game model, and resolving the Nash equilibrium. The simulation results show that the proposed game model can optimize network service quality, reduce the energy consumption of nodes and prolong the survival time of the entire network.

It is difficult to get the correct segmentation results for the intensity inhomogeneity images, and the segmentation results are very sensitive to the initial contours. Thus, a fast and stable approach was proposed to overcome these disadvantages. First, an Adaptive Distance Preserving Level Set (ADPLS) method was utilized to get a better initial contour. Second, the Local Binary Fitting (LBF) model was used for a further segmentation. The experimental results show that the improved model can achieve good performance and is better to solve the contradiction among the segmentation speed, accuracy and stability.

Non-negative Matrix Factorization (NMF) has the disadvantage of slow convergence, which is mainly due to that the base image (base matrix) contains lots of noise points. Besides, the coefficient matrix is significantly dependent, which is not conducive to distinguish between different images. In view of the above shortcomings, a new algorithm called Smoothness and Principal Components Based Non-Negative Matrix Factorization (SPNMF) was proposed in this paper. SPNMF had two novelties. On one hand, a constant matrix was added to the base matrix to enhance the smoothness and stabilize the noise points, which caused good convergence; on the other hand, to improve the discrimination, the variance between the different columns of the coefficient matrix as a penalty term was added to the loss function of NMF. The experimental results on the PIE face database and FERET face database show that the proposed method not only has higher recognition performance compared with the traditional algorithms, but also is two to four times faster than NMF, making the face recognition system based on NMF more practical.

In order to get rid of the noise of flight data more effectively, based on discussing the principle of Stationary Wavelet Transform (SWT), a new denoising method was proposed, which combined correlation of wavelet coefficient with wavelet shrinkage. Firstly, signals were decomposed by using SWT; secondly, wavelet coefficient was dealt with by using methods of coefficient correlation and wavelet shrinkage in sequence; at last, denoised signal was reconstructed through inverse wavelet transform. The results of experiments show that the proposed method can raise Signal-to-Noise Ratio (SNR), decrease Mean Squared Error (MSE) and preserve the shape of signal; and it can be applied to flight data effectively.

Argumentation model is the theoretical basis to establish group argumentation environment. Based on Issue-Based Information System (IBIS) model and Toulmin' argument schema, a group argumentation model was proposed, which was able to evaluate the argumentative utterance. With this model, the group argumentative information could be structured as a graph which consisted of utterance nodes and semantic links. A method of evaluating utterance nodes based on Language Weighted Aggregation (LWA) operator and node reduction was proposed. A group argumentation on the issue of system architecture design was illustrated as an example to show the usability and effectiveness of the proposed model.

This paper firstly analyzed some security problems in Lee-Chiu's remote authentication scheme with smart cards, and proposed an improved scheme. Compared with Lee-Chiu's scheme, this scheme can not only provide mutual authentication between clients and sever, but also avoid replay attack, guessing attack and forgery attack, so it can improve the security of the application systems.

A domain-based fast feature modeling approach was proposed. Inspired by the approach of Domain Engineering in software engineering, for a particular field of feature modeling design, this approach is on how to use the knowledge of such field to improve geometric modeling system. As a result, the entire efficiency of the geometric modeling system was improved by optimizing software architecture and algorithm design. Base on this novel method, the demand of geometric modeling in Computer-Aided Architectural Design (CAAD) was discussed, and a system framework for domain-based geometric modeling was created. Several examples of this method in industry modeling design and digital entertainment fields prove the effective.

A new approach for automatic stitching of aerial image sequence was proposed, based on the SIFT (Scale Invariant Feature Transform) features. It consisted of two core steps: image registration and mosaic. The variations between two consecutive frames in aerial sequence are always very notable, thus the present commonlyused featurebased methods are not very suitable for their registration. Concerning this, an iterative registration method using the SIFT features was put forward. Moreover, for image mosaic, a new color fusion method was proposed to achieve smooth stitching results based on human visual features. Finally, experiments on real aerial sequences prove the effectiveness of the proposed stitching approach.