To optimize Text-to-SQL generation performance based on heterogeneous graph encoder， SELSQL model was proposed. Firstly， an end-to-end learning framework was employed by the model， and the Poincaré distance metric in hyperbolic space was used instead of the Euclidean distance metric to optimize semantically enhanced schema linking graph constructed by the pre-trained language model using probe technology. Secondly， K-head weighted cosine similarity and graph regularization method were used to learn the similarity metric graph so that the initial schema linking graph was iteratively optimized during training. Finally， the improved Relational Graph ATtention network （RGAT） graph encoder and multi-head attention mechanism were used to encode the joint semantic schema linking graphs of the two modules， and Structured Query Language （SQL） statement decoding was solved using a grammar-based neural semantic decoder and a predefined structured language. Experimental results on Spider dataset show that when using ELECTRA-large pre-training model， the accuracy of SELSQL model is increased by 2.5 percentage points compared with the best baseline model， which has a great improvement effect on the generation of complex SQL statements.

Aiming at the difference of health system resilience in urban areas and the random evolution of demand for emergency medical supplies， a multi-stage dynamic allocation model for emergency medical supplies based on resilience assessment was proposed. Firstly， combined with the entropy method and the K-means algorithm， the resilience assessment system and classification method of area’s health system were established. Secondly， the random evolution characteristic of demand state was designed as a Markov process， and triangular fuzzy numbers were used to deal with the fuzzy demand， thereby constructing a multi-stage dynamic allocation model of emergency medical supplies. Finally， the proposed model was solved by the binary Artificial Bee Colony （ABC） algorithm， and the effectiveness of the model was analyzed and verified by an actual example. Experimental results show that the proposed model can realize the dynamic allocation of supplies to stabilize the demand changes and prioritize the allocation of areas with weak resilience， reflecting the fairness and efficiency of emergency management requirements.

The existing image super-resolution reconstruction methods are affected by texture distortion and details blurring of generated images. To address these problems， a new image super-resolution reconstruction network based on multi-channel attention mechanism was proposed. Firstly， in the texture extraction module of the proposed network， a multi-channel attention mechanism was designed to realize the cross-channel information interaction by combining one-dimensional convolution， thereby achieving the purpose of paying attention to important feature information. Then， in the texture recovery module of the proposed network， the dense residual blocks were introduced to recover part of high-frequency texture details as many as possible to improve the performance of model and generate high-quality reconstructed images. The proposed network is able to improve visual effects of reconstructed images effectively. Besides， the results on benchmark dataset CUFED5 show that the proposed network has achieved the 1.76 dB and 0.062 higher in Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） compared with the classic Super-Resolution using Convolutional Neural Network （SRCNN） method. Experimental results show that the proposed network can increase the accuracy of texture migration， and effectively improve the quality of generated images.

Before an emergency occurs， the hospitals need to maintain a certain amount of emergency resource redundancy. Aiming at the problem of configuration optimization of hospital emergency resource redundancy under emergencies， firstly， based on the utility theory， by analyzing the utility performance of the hospital emergency resource redundancy， the emergency resource redundancy was defined and classified， and the utility function conforming to the marginal law was determined. Secondly， the redundancy configuration model of hospital emergency resources with maximal total utility was established， and the upper limit of emergency resource storage and the lower limit of emergency rationality were given as the constraints of the model. Finally， the combination of particle swarm optimization and sequential quadratic programming method was used to solve the model. Through case analysis， four optimization schemes for the emergency resource redundancy of the hospital were obtained， and the demand degree of the hospital emergency level to the hospital emergency resource redundancy was summarized. The research shows that with the emergency resource redundancy configuration optimization model， the emergency rescue of hospitals under emergencies can be carried out well， and the utilization efficiency of hospital emergency resources can be improved.

To solve the problem that high dimension of descriptor decreases the matching speed of Scale Invariant Feature Transform (SIFT) algorithm, an improved SIFT algorithm was proposed. The feature point was acted as the center, the circular rotation invariance structure was used to construct feature descriptor in the approximate size circular feature points' neighborhood, which was divided into several sub-rings. In each sub-ring, the pixel information was to maintain a relatively constant and positions changed only. The accumulated value of the gradient within each ring element was sorted to generate the feature vector descriptor when the image was rotated. The dimensions and complexity of the algorithm was reduced and the dimensions of feature descriptor were reduced from 128 to 48. The experimental results show that, the improved algorithm can improve rotating registration repetition rate to more than 85%. Compared with the SIFT algorithm, the average matching registration rate increases by 5%, the average time of image registration reduces by about 30% in the image rotation, zoom and illumination change cases. The improved SIFT algorithm is effective.

In order to decrease the influence caused by low bandwidth and high latency on Media Access Control (MAC) layer in Underwater Acoustic Sensor Network (UWASN), an Evolutionary Game Theory based MAC (EGT-MAC) protocol was proposed. In EGT-MAC, each sensor node adopted two strategies including spatial multiplexing and temporal multiplexing. With the replication kinetics equation, each strategy got an evolutionary stable strategy and reached stable equilibrium of evolution. In this way, it improved channel utilization rate and data transmission efficiency to achieve performance optimization for MAC protocol. The simulation results show that EGT-MAC can improve the network throughput as well as the transmission rate of data packet.

In order to improve the performance of face recognition under non-uniform illumination conditions, a face recognition method based on Patterns of Monogenic Oriented Magnitudes (PMOM) was proposed. Firstly, multi-scale monogenic filter was used to get monogenic magnitude maps and orientation maps of a face image. Secondly, a new operator named PMOM was proposed to decompose the monogenic orientation and magnitude into several PMOM maps by accumulating local energy along several orientations, then Local Binary Pattern (LBP) was used to get LBP feature map from each PMOM map. Finally, LBP feature maps were divided into several blocks, and the concatenated histogram calculated over each block was used as the face feature. The experimental results on the CAS-PEAL and the YALE-B face databases show that the proposed approach improves the performance significantly for the image face with illumination variations. Other advantages of our approach include its simplicity and generality. Its parameter setting is simple and does not require any training steps or lighting assumption and can be implemented easily.

Concerning the disadvantages of traditional face recognition methods, such as high dimension of extracted feature, higher computational complexity, a new method of face recognition combining monogenic filtering with Local Quantiztative Pattern (LQP) was proposed. Firstly, the multi-modal monogenic features of local amplitude, local orientation and local phase were extracted by a multi-scale monogenic filter; secondly, the LQP operator was used to get LQP feature maps by encoding the three kinds of monogenic features in each pixel; finally, the LQP feature maps were divided into several blocks, from which spatial histograms were extracted and connected as the face feature. ORL and CAS-PEAL face databases were used to test the proposed algorithm and the recognition rates were higher than all the other methods used in the experiments. The results validate that the proposed method has higher recognition accuracy and can reduce the computational complexity significantly.

As the multimodal complex problem has many local optima, it is difficult for the basic Particle Swarm Optimization (PSO) to effectively solve this kind of problem. To conquer this defect, firstly, Monte Carlo method was used to simulate the fly trajectory of particle, and the reason for falling into local optima was concluded. Then, by defining distance, average distance and maximal distance between particles, an adaptive control factor named Adaptive Rejection Factor (ARF) for controlling local optimum position and global optimum position was proposed to increase the ability for escaping from local optima. In order to test the proposed strategy, three test benchmarks including Rosenbrock, Ackley and Griewank were selected to conduct the analysis of convergence property and statistical property. The 60 times independent runs show that the improved PSO based on ARF (ARFPSO) has the best value of 53.82, 2.1203 and 5.32E-004, which is better than the both contrast algorithms. The results show that ARFPSO can effectively avoid premature phenomenon, and the complexity analysis of the algorithm also shows that the introduced strategy does not increase computational complexity.

Concerning the problem that Ontology Web Language for Services (OWL-S) process model lacks capacity for dynamic interaction and timing characteristics, a formalization method based on temporal description logic for process model was proposed. It described the atomic processes and composite processes of the OWL-S process model, and then the dynamic semantic of OWL-S process model was obtained. Finally, the formal modeling of OWL-S process model was realized. The experimental results show that the proposed method is feasible, and it provides the foundation for the analysis and validation.

Considering the multi-sourcing and heterogeneous data streams in three dimensional underwater acoustic sensor networks (3D UWASNs), this paper gives formalized definition and modeling for describing the characters and attributes of burst and presents an evolutionary game theory based burst detection algorithm for reducing processing time and improving detection performance through optimized choosing the size of slide window. We demonstrate through simulations that our burst detection algorithm consumes less processing time than traditional algorithms in the same condition of data distribution, burst probability or maximum slide window size.

In view of the kinematic model of mobile robot, a tracking controller of global asymptotic stability was proposed. The design of tracking controller was divided into two parts: The first part designed the control law of angular velocity by using global fast terminal sliding mode in order to asymptotically stabilize the tracking error of the heading angle; the second part designed the control law of linear velocity by using the Lyapunov method in order to asymptotically stabilize the tracking error of the planar coordinate. By combining Lyapunov stability theorem and two control laws, the mobile robot can track the desired trajectory in a global asymptotic sense when the angular velocity and the linear velocity satisfy these control laws. The experimental results show that the mobile robot can track desired trajectory effectively. It is helpful for promoting the practical application.

To cope with Multi-objective Programming on Manufacturing Cloud Service Composition (MOP-MCSC) problem in cloud manufacturing (CMfg) system, a mathematical model and a solution algorithm were proposed and studied. Firstly, inspired by the resource service composition technology in manufacturing grid, a QoS-aware MOP-MCSC model in CMfg system had been explored and described. Secondly, by analyzing the characteristics of manufacturing cloud services according to the domain knowledge of manufacturing, an eight-dimensional QoS evaluation criterion with corresponding quantitative calculation formulas was defined. Then, the QoS expression of manufacturing cloud service was eventually formulated. Lastly, the MOP-MCSC model was built, and an Adaptive Mutation Particle Swarm Optimization (AMPSO) was designed to realize this model. The simulation experimental results suggest that the proposed algorithm could solve the MOP-MCSC problem efficiently and effectively with a better performance than conventional particle swarm optimization.

The authors proposed a short-time spectral amplitude estimation approach based on speech absence probability information and the masking properties of the human auditory system to improve the performance of speech enhancement algorithm in low signal to noise ratio input environments. Meanwhile, the hardware design and the algorithm optimization based on the TMS320C5502 digital signal processor embedded system were introduced. The system testing results show that the hardware platform works stably and reliably, and the optimization can significantly improve system processing speed. Besides, the output signal achieves a good balance between noise reduction and speech distortion.

Focused on the requirements of low cost and low power in Wireless Sensor Network (WSN), this paper proposed a range-free localization algorithm based on Support Vector Machine (SVM) classification regions. First, SVM constructed a binary decision tree classifier via learning of the training data. Then the classifier determined the certain classification region where the unknown nodes were located. Finally, the study used the region's center point as the estimated position of the unknown node. The proposed algorithm required mere connectivity information (i.e., hop counts only), so as to reduce the network cost and communication load. The simulation results show that this algorithm alleviates the coverage holes and border problem significantly while certain location accuracy is assured.

Context-Based Adaptive Variable-Length Coding (CAVLC) algorithm was adopted as an entropy coding method in baseline and extended profile of H.264/AVC standard, but the detailed syntax on which was not explicitly stipulated. A profound analysis on the CAVLC coding algorithm of H.264 standard was performed based on the principle of CALVC decoding method. A high-speed and low power-consumption CAVLC coder for H.264/AVC standard was presented according to the former analysis, in which multi-clock domain processing and parallel processing techniques were adopted to improve the performance of the system, and arithmetic were used to replace some static code table to reduce memory consumption. The detailed design and FPGA realization method on each sub-blocks are also concerned. Finally, FPGA verification and realization indicates that the maximum coding system clock can up to 107.97MHz, and the coding delay is less than 36 clock cycles, which can adequately meet the needs of some high-definition and real-time applications.

An algorithm to compute an initial trust value for the new entities based on its capability was presented, and then the computation algorithms of direct trustworthiness based on the degree of interaction satisfaction were designed. The recommendation trustworthiness based on the recommendation opinions came from trusted entities. Finally, the integrated trustworthiness computation model was given. Based on this model, the trust relationships can be built among entities in P2P network service environment and the decision of trust can be made based on the trustworthiness, and the risk can be reduced and the stability of network can be promoted.

In wireless sensor networks, nodes closer to the base station are prone to die because of excessive flow, in which becomes a hot issue. Forming clusters can not only address this issue, but also reduce energy consumption of the whole network. However, cluster head is much more expensive. An unequal energy-balancing algorithm based on least-clustering (EBUC) was proposed in this paper, in which a least coverage model was established at first to optimize the cluster head number. On this basis, how to adjust the number of cluster member and aggregation coefficient to balance energy consumption was studied. The simulation results prove that, through varying the number of cluster members and restricting aggregation coefficient, the algorithm can efficiently prolong network lifetime.

An energy-efficient adaptive time-slot scheduling algorithm（EATA）based on clustering for MAC protocol of wireless sensor networks was described. In this algorithm, the cluster head adjusts the time-slot of nodes according to their weighted traffic load in a proper order and frequency, thus the idle listening was reduced, and the energy consumption balancing among nodes was achieved with a low latency. Simulation results show that the algorithm EATA improves the network energy usability and increases networks lifetime.