Carotid artery stenosis, Carotid Intima Media Thickness (CIMT) or carotid artery plaque may lead to stroke. For large-scale preliminary screening of stroke, an improved Variational AutoEncoder (VAE) based on medical data was proposed to predict and identify abnormal carotid arteries. Firstly, for the missing values in medical data, K-Nearest Neighbor ( KNN), Mixture of mean, mode and KNN (M KNN) method and improved VAE were respectively used to impute the missed values to obtain the complete dataset, improving the application range of the data. Secondly, the feature attributes were analyzed and the features were ranked in order of importance. Thirdly, four supervised algorithms, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting Tree (XGBT), were combined with Genetic Algorithm (GA) to build the abnormal carotid artery identification models. Finally, based on the improved VAE, a semi-supervised abnormal carotid artery prediction model was built. Compared to the performance of baseline model, the performance of the semi-supervised model based on the improved VAE improves significantly with sensitivity of 0.893 8, specificity of 0.927 2, F1-measure of 0.910 5 and classification accuracy of 0.910 5. Experimental results show that this semi-supervised model can be used to identify the abnormal carotid arteries and thus serves as a tool to recognize high-risk groups of stroke, preventing and reducing the occurrence of stroke.

To solve the high storage cost and low retrieval speed problems in malicious Structure Query Language (SQL) detection faced by Nearest Neighbor (NN) method, a Hash learning based Malicious SQL Detection (HMSD) method was proposed. In this algorithm, Hash learning was used to learn the binary coding representation for SQL statements. Firstly, the SQL statements were presented as real-valued features by washing and deleting the duplicated SQL statements. Secondly, the isotropic hashing was used to learn the binary coding representation for SQL statements. Lastly, the retrieval procedure was performed and the detection speed was improved by using binary coding representation. Experimental results show that on the malicious SQL detection dataset Wafamole, the dataset is randomly divided so that the training set contains 10 000 SQL statements and the test set contains 30 000 SQL statements, at the length of 128 bits, compared with nearest neighbor method, the proposed algorithm has the detection accuracy increased by 1.3%, the False Positive Rate (FPR) reduced by 0.19%,the False Negative Rate (FNR) decreased by 2.41%, the retrieval time reduced by 94%, the storage cost dropped by 97.5%; compared with support vector machine method, the proposed algorithm has the detection accuracy increased by 0.17%, which demonstrate that the proposed algorithm can solve the problems of nearest neighbor method in malicious SQL detection.

Limited and heterogeneous cache capacity is one of the key parameters which affect the cache efficiency in Device-to-Device (D2D) caching networks. However, most of existing literatures assume all users have homogeneous cache capability. In this regard, cache placement optimization is necessary for practical scenarios with heterogeneous cache capacities. Considering the mobility and random distribution of terminal users, users with different cache capacities were modeled as mutually independent homogeneous Poisson point processes with stochastic geometry. Moreover, the average cache hit ratio was derived with considering both self-offloading and D2D-offloading cases. Finally, a Joint Cache Placement (JCP) algorithm based on coordinate gradient optimization was proposed to obtain the optimal cache placement scheme which can maximize the cache hit ratio. Simulation results show that the proposed JCP can achieve larger cache hit ratio than the existing cache placement schemes.

To solve the problems of updating, modifying, upgrading and maintaining the function of robot by offline and static method, SoftMan was introduced for robot platform, and the architecture of robot system, whose managing center is host SoftMan, was built. The host SoftMan was mainly researched. Firstly, the architecture of host SoftMan was constructed. Then the descriptive unification model of knowledge and behavior of host SoftMan was put forward, the knowledge model was constructed and implemented based on data structure, and the design specifications and reference realization of the algorithm were given for its main service behaviors. Finally, the robot system was unified with the SoftMan system. Through the test, the function of robot was successfully replaced online and dynamically, verifying the correctness and feasibility of the method of designing and implementing the host SoftMan.

This thesis focused on the traditional and message-based SoftMan's communication approach which has some problems in the aspects of expression ability, communication efficiency and quality. Based on the early research in SoftMan system and its communication theory, as well as SoftMan cogmatics model and context awareness mechanism, this thesis proposed the Context driven SoftMan Knowledge Communication (CSMKC) framework by learning from mature Agent communication language specification. First, the message layer, the knowledge layer and the scenarios layer in knowledge communication framework were designed; second, from the three aspects of implementation of message layer, knowledge layer and scenarios layer, the key points of knowledge communication achievements of scenario-driven SoftMan were introduced; finally, different SoftMan's communication in knowledge grade and the maintenance of scenario context were realized basically. The experimental results show that when the later content has high dependence on communication scenario, compared with the traditional message-based SoftMan communication approach, the communication overhead per unit time of CSMKC reduces by 46.15% averagely. Thus, the higher dependence on the scene, the more obvious CSMKC advantages in terms of reducing communication while accomplishing a task in the system.