Traffic prediction is a fundamental task in Intelligent Transportation System （ITS）， as accurate Traffic Flow Forecasting （TFF） can significantly improve the utilization efficiency of public resources. To address the limitations of insufficient utilization of contextual information， imbalanced graph fusion techniques， and consideration of only static spatial relationships in existing multi-graph neural network models， a TFF model based on Spatio-Temporal Multi-Graph Fusion （STMGF） was proposed. Firstly， different spatial correlations across different regions were extracted by the model through the fusion of spatial graphs， semantic graphs， and spatial-semantic graphs. Spatial attention mechanism and graph attention mechanism were utilized to dynamically learn the importance of different graph structures for different neighbors. Then， a multi-kernel temporal attention mechanism was employed to capture both local and global temporal dependencies. Finally， a multi-layer perceptron was utilized to predict traffic flow， obtaining the final prediction values. The validity of the model was verified on NYCTaxi dataset and NYCBike dataset. Experimental results showed that the Root Mean Square Errors （RMSE） of the proposed model STMGF were 8.46%， 2.70%， and 2.20% lower than those of Spatio-Temporal Graph Convolutional Network （STGCN）， Attention based Spatial-Temporal Graph Neural Network （ASTGNN）， and Meta-graph Convolutional Recurrent Network （MegaCRN）， respectively in the 36 steps forecast task of the NYCBike dataset.

Obtaining reliable labels is time-consuming and laborious caused by the characteristics of ultrasonic images such as strong noise， low quality and blurred boundary. Therefore， a semi-supervision and multi-scale cascaded attention based ultrasound carotid plaque segmentation method was proposed. Firstly， a semi-supervised segmentation method of Uncertainty Rectified Pyramid Consistency （URPC） was used to make full use of unlabeled data to train the model， so as to reduce the time-consuming and laborious labeling pressure. Then， a dual encoder structure based on edge detection was proposed， and the edge detection encoder was used to assist the ultrasonic plaque image feature encoder to fully acquire the edge information. In addition， a Multi-Scale Fusion Module （MSFM） was designed to improve the extraction of irregularly shaped plaques by adaptive fusion of multi-scale features， and a Cascaded Channel Spatial Attention （CCSA） module was combined to better focus on the plaque region. Finally， the proposed method was evaluated on the ultrasonic carotid plaque image dataset. Experimental results show that the Dice index and IoU （Intersection over Union） index of the proposed method on the dataset are 2.8 and 6.3 percentage points higher than those of the supervised method CA-Net （Comprehensive Attention convolutional neural Network） respectively， and 1.8 and 1.3 percentage points higher than those of the semi-supervised method Cyclic Prototype Consistency Learning （CPCL） respectively. It can be seen that this method can effectively improve the segmentation accuracy of ultrasound carotid plaque image.

Aiming at the problem of low data availability caused by existing disturbance mechanisms that do not consider the semantic relationship of location points， a Trajectory Location Privacy protection Mechanism based on Differential Privacy was proposed， namely DP-TLPM. Firstly， the sliding windows were used to extract trajectory dwell points to generate the fuzzy regions， and the regions were sampled using exponential and Laplacian mechanisms. Secondly， a road network matching algorithm was proposed to eliminate possible semantic free location points in the sampled points， and the trajectory was segmented and iteratively matched by using Error Ellipse Matching （EEM）. Finally， a disturbance trajectory was formed based on the matched location points， which was sent to the server by the user. The mechanism was evaluated comprehensively by confusion quality and Root Mean Square Error （RMSE）. Compared with the GeoInd algorithm， the data quality loss of the DP-TLPM is reduced by 24% and the confusion quality of the trajectories is improved by 52%， verifying the effectiveness of DP-TLPM in terms of both privacy protection strength and data quality.

The target detection model of anesthesia resuscitation is often used to help medical staff to perform resuscitation detection on anesthetized patients. The targets of facial actions during patient resuscitation are small and are not obvious， and the existing Single Shot multibox Detector （SSD） is difficult to accurately detect the facial micro-action features of patients in real time. Aiming at the problem that the original model has low detection speed and is easy to have missed detection， an anesthesia resuscitation object detection method based on improved SSD was proposed. Firstly， the backbone network VGG （Visual Geometry Group）16 of the original SSD was replaced by the lightweight backbone network MobileNetV2， and the standard convolutions were replaced by the depthwise separable convolutions. At the same time， the calculation method of first increasing and then reducing the dimension of the extracted features from patient photos was used to reduce computational cost， thereby improving detection speed of the model. Secondly， the Coordinate Attention （CA） mechanism was integrated into the feature layers with different scales extracted by the SSD， and the ability of the feature map to extract key information was improved by weighting the channel and location information， so that the network positioning and classification performance was optimized. Finally， comparative experiments were carried out on three datasets： CEW（Closed Eyes in the Wild）， LFW（Labeled Faces in the Wild）， and HAPF（Hospital Anesthesia Patient Facial）. Experimental results show that the mean Average Precision （AP） of the proposed model reaches 95.23%， and the detection rate of photos is 24 frames per second， which are 1.39 percentage points higher and 140% higher than those of the original SSD model respectively. Therefore， the improved model has the effect of real-time accurate detection in anesthesia resuscitation detection， and can assist medical staff in resuscitation detection.

In recent years， the Grid-based distributed Xin’anjiang hydrological Model （GXM） has played an important role in flood forecasting， but when simulating the flooding process， due to the vast amount of data and calculation of the model， the computing time of GXM increases exponentially with the increase of the model warm-up period， which seriously affects the computational efficiency of GXM. Therefore， a parallel computing algorithm of GXM based on grid flow direction division and dynamic priority Directed Acyclic Graph （DAG） scheduling was proposed. Firstly， the model parameters， model components， and model calculation process were analyzed. Secondly， a parallel algorithm of GXM based on grid flow direction division was proposed from the perspective of spatial parallelism to improve the computational efficiency of the model. Finally， a DAG task scheduling algorithm based on dynamic priority was proposed to reduce the occurrence of data skew in model calculation by constructing the DAG of grid computing nodes and dynamically updating the priorities of computing nodes to achieve task scheduling during GXM computation. Experimental results on Dali River basin of Shaanxi Province and Tunxi basin of Anhui Province show that compared with the traditional serial computing method， the maximum speedup ratio of the proposed algorithm reaches 4.03 and 4.11， respectively， the computing speed and resource utilization of GXM were effectively improved when the warm-up period is 30 days and the data resolution is 1 km.

Current research on Person Re-Identification （Re-ID） mainly concentrates on short-term situations with person’s clothing usually unchanged. However， more common practical cases are long-term situations， in which a person has higher possibility to change his clothes， which should be considered by Re-ID models. Therefore， a method of person re-identification with cloth changing based on joint loss capsule network was proposed. The proposed method was based on ReIDCaps， a capsule network for cloth-changing person re-identification. In the method， vector-neuron capsules that contain more information than traditional scalar neurons were used. The length of the vector-neuron capsule was used to represent the identity information of the person， and the direction of the capsule was used to represent the clothing information of the person. Soft Embedding Attention （SEA） was used to avoid the model over-fitting. Feature Sparse Representation （FSR） mechanism was adopted to extract discriminative features. The joint loss of label smoothing regularization cross-entropy loss and Circle Loss was added to improve the generalization ability and robustness of the model. Experimental results on three datasets including Celeb-reID， Celeb-reID-light and NKUP prove that the proposed method has certain advantages compared with the existing person re-identification methods.

Owing to that different users focus on attributes of the same item is not exactly the same, individuals' weight distribution for goods attributes are not the same. A method of the generalized interval-valued trapezoidal fuzzy soft set was proposed to deal with this kind of recommendation problems. First, the concept of generalized interval-valued trapezoidal fuzzy soft set was established by combining the concepts of generalized interval-valued trapezoidal fuzzy set and soft set, some basic operations on a generalized interval-valued trapezoidal fuzzy soft set were defined, such as “and” operation, and “or” operation. Using these operations, as well as the center of gravity method of the generalized interval-valued trapezoidal fuzzy numbers, commodities could be ranked. A group preference model from the preferences of the group members could be constructed. Finally, this paper used the car recommendation as an example to introduce the group preference aggregation algorithm and this numerical example was given to illustrate the feasibility and effectiveness of the proposed method.

In order to solve the system integration problems of high-speed railway integrated dispatching system in highly-distributed, highly heterogeneous environment, system integration framework based on Service Oriented Architecture (SOA) was proposed. The high-speed railway integrated dispatching system structure and its distributed SOA application were constructed based on Service Component Architecture (SCA) and Service Data Object (SDO) technology. The integration of power dispatching subsystem and other scheduling subsystems was achieved based on SCA and SDO technology on Java EE platform. The method fully embodies the openness and cross-platform features of SOA, and it is easy to implement.

According to the theory of sparse representation, images can be sparse-represented by using an appropriately redundant dictionary. The completeness can enable using very few big coefficients to capture the important information of images, and cause more robust to noise. Regarding image de-noising, considering the human visual characteristics, this paper studied the effective representation of characteristics and edge information of noisy image based on complete dictionary. For more effective feature retaining of images, a method of feature-retaining de-noising via sparse representation was proposed, which made the Structural SIMilarity (SSIM) as fidelity measure of the information. The experimental results indicate that the proposed algorithm has a better efficiency of de-noising, enhances the capacity of retaining feature, and gets a better visual effect of de-noised image.

The identity-based ring signcryptions have both high efficiency of identity-based cryptosystem and signcryption technology and the ambiguity of ring signature. In the fair exchange protocol based on signcryption proposed by Luo, et al (Luo M, Zou C H, Hu J, 〖WTBX〗et al.〖WTBZ〗Signcryption-based fair exchange protocol. Journal of Communications, 2010, 31(8A): 146-150), the fairness and efficiency are not good enough. The new scheme introduced a more efficient identity-based ring signcryption and dealt with the decryption and the signature-and-identity binding separately. A new fair exchange protocol was proposed based on this new scheme. The analysis shows that this scheme overcomes the fairness defect of the original scheme and has a better efficiency, which makes it a good application in electronic payment and contract signing.

By analyzing the certificateless verifiable ring signature scheme (LUO DAWEN, HE MINGXING, LI XIAO. Certificateless verifiable ring signature scheme. Computer Engineering,2009, 35(15): 135-137) and the verifiable proxy ring signature scheme (LUO DAWEN, HE MINGXING, LI XIAO.A verifiable proxy ring signature scheme.Journal of Southwest University for Nationalities:Natural Science Edition, 2009, 35(3):608-611), it was found that these convertible ring signature schemes were susceptible to non-repudiation attack, i.e., any member in the ring can impersonate others identity to sign the message and the verifier believed the signature was signed by the latter. To address the above problems, improved schemes were proposed by using the private key of the signer to have a secret value. The security analysis proves that the improved schemes overcome the security defect of the original scheme and satisfy all security requirements of verifiable ring signature.

Phase space reconstruction is an important part in recognizing chaos during ship's sailing, it directly influences the result of chaotic analyzing and the effect of chaos controlling. In order to choose a proper method, this paper reconstructed the ship's sailing data series by two methods, and compared the performances of reconstructions. It is proved that the C-C method does well on processing ship's sailing data series, while the autocorrelation &G-P method does a little bad. Different methods were used to conduct qualitative analysis and quantitative analysis based on the phase space reconstruction, the results of the analysis show the existence of chaotic characteristic in the ship's sailing data series, which provides the following research on ship's chaos control with essential basic data and comparison foundation.

The image segmentation effect of balloon force Snake model largely depends on the initial parameters' selection. A new method based on Genetic Algorithm (GA), which is efficient, parallel and global searching, was proposed to solve the selection of optimal parameters. In this paper, the parallel genetic computation was used to calculate optimal parameter, the energy function of Snake was used as an object function, and the image similarity function was used as the criteria to stop genetic iterating. The results of real medical images prove that the proposed method can avoid the trivial of selecting parameters artificially through a large number of experiments, also solve the problem of not ideal result caused by unsuitable parameters' values, and it can get excellent segmentation effect.

User requirement is not considered in existing spectrum allocation algorithms for the cognitive radio, which results in excessive allocation of resources to users with less demand. To solve the above problem, two spectrum allocation algorithms, which included spectrum allocation algorithm based on user requirement and joint proportional fairness algorithm, were proposed. They both consider user requirement as the needed factor of spectrum allocation. Numerical results indicate that these two algorithms are better satisfied with users requirement than the previous algorithms.

A demand-based spectrum allocation algorithm between cognitive radio cells was proposed. Based on the combination of graph coloring model and the concept of spectrum quality grading, the proposed algorithm allocated the available spectrum to cognitive radio cells to meet demand maximally under QoS guarantee. Simulation results show that, compared with the original algorithm, the proposed algorithm can better satisfy demand in the network.