Aiming at the problem that traditional single detection algorithm models have low detection rate and slow detection speed on different types of attacks in industrial control system, an intrusion detection model combining optimized Support Vector Machine (SVM) and K-means++algorithm was proposed. Firstly, the original dataset was preprocessed by Principal Component Analysis (PCA) to eliminate its correlation. Secondly, an adaptive mutation process was added to Particle Swarm Optimization (PSO) algorithm to avoid falling into local optimal solution during the training process. Thirdly, the PSO with Adaptive Mutation (AMPSO) algorithm was used to optimize the kernel function and penalty parameters of the SVM. Finally, a K-means algorithm improved by density center method was united with the optimized support vector machine to form the intrusion detection model, achieving anomaly detection of industrial control system. The experimental results show that the proposed method can significantly improve the detection speed and the detection rate of various attacks.

For the problem of estimation error and feedback delay in the process of acquiring channel information in Multiple-Input Multiple-Output (MIMO) systems, a robust interference alignment algorithm based on weight adjustment was proposed to improve the system performance at low Signal-to-Noise Ratio (SNR). Firstly, the system model was reconstructed by considering the influence of channel error on the basis of the ideal channel; secondly, the signal space at the receiving terminal was decomposed by matrix projection and divided into two parts, including desired signal subspace and interference signal subspace; thirdly, considering the interaction between the desired signal and the interference signal, the weighted sum of the power leaked into the corresponding subspaces was used as the objective function, and the iterative idea was used to calculate the precoding and interference suppression matrix. Finally, the calculated pre-coding and interference suppression matrix was used to derive the sum-rate expression of the channel error. Simulation results show that compared with the robust minimum interference leakage algorithm, when the SNR is 10 dB and the channel error variance value is 0.05, the spectrum efficiency of the system is increased by 25% and the energy efficiency is increased by 38%. Therefore, the proposed algorithm can effectively improve system performance at low SNR.

Concerning the problems including repetitive query, storage and processing in the process of complex event query processing in Internet of Things (IOT), an Event Sharing Mechanism (ESM) was proposed. Firstly, in order to realize the query and detection of complex events, a semantic event definition about IOT and semantic descriptions of event operators were presented. Secondly, research on the IOT ESM was done from the following three aspects: the definition of public subqueries, the design of public internal query structure and the sharing of event resources. Through rewriting the query expression, building the Directed Acyclic Graph (DAG) related to the query expression and using the improved Continuous, one of the parameter contexts, on each node to handle event streams, the sharing of public events' query, storage and processing was implemented. Finally, a Semantics Formal Query-plan Processing Model (SFQPM) based on ESM was also designed, which could process query expressions and predicates automatically, and fulfill the automation of complex event detection and processing. The simulation results show that, compared with the method based on BTree (Binary Tree), the proposed SFQPM has high efficiency and reliability in processing, and can process massive and real-time IOT event streams timely and efficiently. In addition, a case study was given to verify the effectiveness and feasibility of the proposed SFQPM.

During the post-earthquake transitional phase, there are relief goods recycling and environmental protection problems. In the premise of meeting the basic demand of people in disaster area, the Location-Routing Problem (LRP) model of emergency logistics facilities with forward and reverse directions was built. First, according to the characteristics that the recycled materials could be partially transported, a mathematical model was established in which the objective function was minimum time of emergency system. Second, a two-phase heuristic algorithm was used to solve the model. Finally, the example analyses verified the feasibility of the model and algorithm. The experimental results show that, compared with the traditional one-way LRP model, the objective function value of the proposed method decreases by 51%. The proposed model can effectively improve the efficiency of emergency logistics system operation and provide auxiliary decision support for emergency management department.

To further reduce the great computational complexity for High Efficiency Video Coding (HEVC) intra prediction, a novel algorithm was proposed in this paper. First, in Coding Unit (CU) level, the minimum Sum of Absolute Transformed Difference (SATD) of current CU was used to decide an early termination for the split of this CU at each depth level: if the minimum SATD of this CU is smaller than the given threshold value. Meanwhile, based on statistical analysis, the probabilities of each candidate prediction modes being optimal mode were used to further reduce the number of candidate modes which have almost no chance to be selected as the best mode. The experimental results show that, the proposed algorithm can save an average of 30.5% of the encoding time with negligible loss of coding efficiency (only 0.02dB Y-PSNR(Y-Peak Signal-to-Noise Ratio) loss) compared with the reference model HM10.1. Besides, the proposed algorithm is easy to provide software and hardware implementations, and it is also easy to be combined with other methods to further reduce the great computational complexity for HEVC intra coding.