Unsupervised relation extraction aims to extract the semantic relations between entities from unlabeled natural language text. Currently， unsupervised relation extraction models based on Variational Auto-Encoder （VAE） architecture provide supervised signals to train model through reconstruction loss， which offers a new idea to complete unsupervised relation extraction tasks. Focusing on the issue that this kind of models cannot understand contextual information effectively and relies on dataset inductive biases， a Prompt-based learning based Unsupervised Relation Extraction （PURE） model was proposed， including a relation extraction module and a link prediction module. In the relation extraction module， a context-aware Prompt template function was designed to fuse the contextual information， and the unsupervised relation extraction task was converted into a mask prediction task， so as to make full use of the knowledge obtained during pre-training phase to extract relations. In the link prediction module， supervised signals were provided for the relation extraction module by predicting the missing entities in the triples to assist model training. Extensive experiments on two public real-world relation extraction datasets were carried out. The results show that PURE model can use contextual information effectively and does not rely on dataset inductive biases， and has the evaluation index B-cubed F1 improved by 3.3 percentage points on NYT dataset compared with the state-of-the-art VAE architecture-based model UREVA （Variational Autoencoder-based Unsupervised Relation Extraction model）.

Aiming at the problem that the heuristic algorithms have unstable path lengths and are easy to fall into local minimum in the process of robot path planning， an Adaptively Adjusted Harris Hawk Optimization （AAHHO） algorithm was proposed. Firstly， the convergence factor adjustment strategy was used to adjust the balance between the global search stage and the local search stage， and the natural constant was used as the base to improve the search efficiency and convergence accuracy. Then， in the global search phase， the elite cooperation guided search strategy was adopted， by three elite Harris hawks cooperatively guiding other individuals to update the positions， so that the search performance was enhanced， and the information exchange among the populations was enhanced through the three optimal positions. Finally， by simulating the intraspecific competition strategy， the ability of the Harris hawks to jump out of the local optimum was improved. The comparative experimental results of function testing and robot path planning show that the proposed algorithm is superior to comparison algorithms such as IHHO（Improve Harris Hawk Optimization） and CHHO（Chaotic Harris Hawk Optimization）， in both function testing and path planning， and it has better effectiveness， feasibility and stability in robot path planning.

Due to the wide coverage of the Wide Area Network (WAN), the single-controller deployment of Software Defined-Wide Area Network (SD-WAN) cannot meet its needs in capacity, load and security, the deployment of multiple controllers becomes necessary. However, the static configuration of the whole network after the deployment of multiple controllers was difficult to be adapted to the change of dynamic network flow, which can easily lead to load unbalance of controllers, reducing the network performance. To solve this problem, a multi-controller deployment algorithm named SC-cSNN (Spectral Clustering-closeness of the Shared Nearest Neighbors) was proposed to reduce the propagation delay between the controller and the switch, and a dynamic switch migration method based on features such as time-delay, capacity and security was proposed to solve the problem of controller overload. Simulation results indicate that compared with existing controller deployment algorithms based on k-means and spectral clustering, the multi-controller deployment algorithm and the dynamic switch migration method can effectively minimize the average maximum delay between the controller and the switch and solve the problem of controller overload.

The Simultaneous Localization And Mapping (SLAM) is an essential skill for mobile robots exploring in unknown environments without external referencing systems. As the sparse map constructed by feature-based Visual SLAM (VSLAM) algorithm is not suitable for robot application, an efficient and compact map construction algorithm based on octree structure was proposed. First, according to the pose and depth data of the keyframes, the point cloud map of the scene corresponding to the image was constructed, and then the map was processed by the octree map technique, and a map suitable for the application of the robot was constructed. Comparing the proposed algorithm with RGB-Depth SLAM (RGB-D SLAM) algorithm, ElasticFusion algorithm and Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM) algorithm on publicly available benchmark datasets, the results show that the proposed algorithm has high validity, accuracy and robustness. Finally, the autonomous mobile robot was built, and the improved VSLAM system was applied to the mobile robot. It can complete autonomous obstacle avoidance and 3D map construction in real-time, and solve the problem that the sparse map cannot be used for obstacle avoidance and navigation.

Focused on the issue that the quantum secret sharing is limited to the maximally entangled state, a scheme for quantum state sharing of an arbitrary unknown N-qubit state by using entangled state as quantum channel was proposed. The sender Alice used the Bell basis measurement and then the receiver Bob or Charlie used the single particle measurement. The participants chose the right joint unitary operation according to the results from Alice and the signal measurement, which could realize arbitrary N-qubit secret sharing. The eavesdropping analysis shows explicitly that the scheme is secure and it can resist the external eavesdropper and internal dishonest participant.

The method for iterative probability relaxation segmentation used in cell division can overcome the difficult issues on account of complicated cellular structure and phenomenon of serious adhesion, while the general segmentation algorithm cannot make it effectively. In addition, because of tense embedded resources under the environment of Linux system, the iterative relaxation cellular segmentation algorithm has been improved and then added to the embedded cellular segmentation system based on Qt and OpenCV. The experimental results demonstrate that the improved algorithm can effectively solve the difficult problem of cell division efficaciously and the naked eye can clearly distinguish the difference between the nucleus, cytoplasm and glands. The improved algorithm increases the processing speed and can be transplanted to the embedded facilities convenient for carrying, diagnosing and treating.

The deficiency of Gaussian Mixture Model (GMM) is the high computation cost and cannot deal with the shadow and ghosting. An improved foreground detection algorithm based on GMM is proposed in this paper. By analyzing the stability of the background, intermittent or continuous frame updating is chose to update the parameters of the GMM.It can efficiently reduce the runtime of the algorithm. In the background updating,the updating rate is associated with the weight and this makes it change with the weight.The background pixels which appear after the objects moving set a larger updating rate.It can improve the stability of the background and solve the problem of ghosting phenomenon and the transformation of background and foreground.After objects detection,the algorithm eliminates the shadow based on the RGB color space distortion model and treats the result by Gauss Pyramid filtering and morphological filtering.Through the whole process,a better contour is obtained. The experimental results show that this algorithm has improved the calculation efficiency and accurately segmented the foreground object.

Methods for manifold embedding have the following issues: on one hand, neighborhood graph is constructed in such high-dimensionality of original space that it tends to work poorly; on the other hand, appropriate values for the neighborhood size and heat kernel parameter involved in graph construction are generally difficult to be assigned. To address these problems, a new semi-supervised dimensionality reduction algorithm called SparsE Discriminant Analysis (SEDA) was proposed. Firstly, SEDA set up a sparse graph to preserve the global information and geometric structure of the data based on sparse representation. Secondly, it applied both sparse graph and Fisher criterion to seek the optimal projection. The experimental results on a broad range of data sets show that SEDA is superior to many popular dimensionality reduction methods.

The error concealment is an important technique in the video transmission, and it can ensure the reconstruction video quality and efficiently recover the data loss and the data errors in the transmission process caused by severe transmission environments. In order to enhance the error resilience of AVS-P2, the paper proposed a new adaptive temporal-spatial error concealment method based on the redundancy motion vectors. To conceal a lost block, the paper used the spatial error concealment for the I-frame macroblocks, and used the temporal error concealment for the non-I-frame macroblocks. At the same time, according to the motion intensity of the macroblocks, it used the default error concealment of AVS-P2 and error concealment method based on redundancy motion vectors, respectively. Lastly, the proposed algorithm was realized based on the platform of the AVS-P2 RM52_20080721. The simulation results show that the proposed method is significantly better than the existing techniques in terms of both objective and subjective quality of reconstruction video.

According to the research of distributed SIP flooding attack detection and defense, in combination with the characteristics of IP-based distributed flood attack and SIP messages, the two-level defense architecture against SIP distributed flooding attacks (TDASDFA) was presented. Two-level defensive components made up TDASDFA logically: the First level Defense Subsystem (FDS) and the Second level Defense Subsystem (SDS). FDS coarse-grained detected and defended SIP signaling stream to filter out non-VoIP messages and discard SIP messages of the IP addresses exceeding the specified rate to ensure service availability| SDS fine-grained detected and defended SIP messages using a mitigation method based on security level to identify the cunning attacks and low-flow attacks with obvious features of malicious DoS attacks. FDS and SDS detected and defended network status in real-time together to weaken SIP distributed flooding attacks. The experimental results show that TDASDFA can detect and defend SIP distributed flooding attacks, and reduces the probability of SIP proxy server or IMS server being attacked when the network is on the abnormity.

In order to inspect the size and defects of elastomer workpiece in real-time, a video preprocessing IP core based on Processor Local Bus (PLB) was proposed and implemented. The structure of IP core and the hardware implementation of convolution were investigated. LOG filter was designed by System Generator, and ultimately the edge detection algorithm was implemented on Field Programmable Gate Array (FPGA). The experimental results indicate that compared to the traditional implementation of edge detection by software, method of IP core achieves higher speed, and needs only 40ms. It can satisfy the requirement of online inspection system of elastomer workpiece in real-time video preprocessing.

The existing methods of malware feature selection and reduction methods were studied. Current attribute reduction methods of malware do not take advantage of the information of feature selection evaluation function. So a method was proposed to order all features based on their value of information gain and their size, and used attributes order reduction method to get a reduction. An analysis of spatial and temporal complexity was given, and the overall design was given. Test results show that the application of attributes order reduction can obtain fewer reduction results in less time, and get higher classification accuracy using the reduction result.