Aiming at the problem of inconsistency between the network policies of control layer and flow rules of data layer in Software Defined Network (SDN), a detection model for Verifying control-data plane Consistency (VeriC) was proposed. Firstly, the function of the packet processing subsystem was realized through the VeriC pipeline on the switch, and the function is sampling the data packet, and updating the tag field in the sampled data packet when the packet passing through the switch. Then, after the update was completed, the tag values were sent to the server and stored in the real tag value group. Finally, the real tag value group and the stored correct tag value group were sent to the verification subsystem to perform the consistency verification. As it failed, the two groups of tag values were sent to the localization subsystem to locate the switch with flow table entry error. A fat tree topology with 4 Pod was generated by ns-3 simulator, where the accuracies of consistency detection and faulty machine location of VeriC are higher than those of VeriDP, and the overall performance of VeriC is higher than that of 2MVeri model. Theoretical analysis and simulation results show that VeriC detection model can not only perform consistency detection and accurately locate the faulty switch, but also take shorter time to locate the faulty switch compared to other comparison detection models.

Because of the uncertainty and complexity of remote sensing image data, it is difficult for traditional unsupervised algorithms to create an accurate classification model for them. Pattern recognition methods based on fuzzy set theory can express the fuzziness of data effectively. In these methods, type-2 fuzzy set can better describe inter-class hybrid uncertainty. Furthermore, semi-supervised method can use prior knowledge to deal with the generalization problem of algorithm to data. Therefore, a remote sensing image classification method based on Semi-Supervised Adaptive Interval Type-2 Fuzzy C-Means (SS-AIT2FCM) was proposed. Firstly, by integrating the semi-supervised and evolution theory, a novel fuzzy weight index selection method was proposed to improve the robustness and generalization of the adaptive interval type-2 fuzzy C-means clustering algorithm. The proposed algorithm was more suitable for the classification of remote sensing data with severe spectral aliasing, large coverage areas and abundant features. In addition, by performing soft constrained supervision on small number of labeled samples, the iterative process of the algorithm was optimized and guided, and the greatest expression of the data was obtained. In the experiments, SPOT5 multi-spectral remote sensing image data of the Summer Palace in Beijing and Landsat TM multi-spectral remote sensing image data of the Hengqin Island in Guangdong were used to compare the results of the existing fuzzy classification algorithms and SS-AIT2FCM. The experimental results show that the proposed method obtains more accurate classification and clearer boundaries of classes, and has good data generalization ability.

ElectroCardioGram (ECG) heart beat classification plays an important role in clinical diagnosis.However, there is a serious imbalance of the available data among four types of ECG, which restricts the improvement of heart beat classification performance. In order to solve this problem, a class information extracting method based on Convolutional Neural Network (CNN) was proposed. Firstly, an general CNN model based on equivalent data of four ECG types was constructed. And then based on the general CNN model, four CNN models that more effectively express the propensity information of the four heart beat categories were constructed. Finally, the outputs of the four categories of CNN models were combined to discriminate the heart beat type. The experimental results show that the average sensitivity of the proposed method is 99.68%, the average positive detection rate is 98.58%, and the comprehensive index is 99.12%; which outperform the two-stage cluster analysis method.

Concerning the "data sparsity" problem of moving object's trajectory prediction, i.e., the available historical trajectories are far from enough to cover all possible query trajectories that can obtain predicted destinations, a Trajectory Prediction Algorithm suffer from Data Sparsity based on Iterate Grid Partition and Entropy Estimation (TPDS-IGP&EE) was proposed. Firstly, the moving region of trajectories was iteratively divided into a two-dimensional plane grid graph, and then the original trajectories were mapped to the grid graph so that each trajectory could be represented as a grid sequence. Secondly, an L-Z entropy estimator was used to calculate the entropy value of trajectory sequence, and a new trajectory space was generated by doing trajectory synthesis based on trajectory entropy. At last combining with the Sub-Trajectory Synthesis (SubSyn) algorithm, sparse trajectory prediction was implemented. The experimental results show when trajectory completed percentage increases towards 90%, the coverage of the Baseline algorithm decreases to almost 25%. TPDS-IGP&EE algorithm successfully coped with it as expected with only an unnoticeable drop in coverage, and could constantly answer almost 100% of query trajectories. And TPDS-IGP&EE algorithm's prediction accuracy was generally 4% higher than Baseline algorithm. At the same time, the prediction time of Baseline algorithm to 100 ms was too long, while the prediction time of TPDS-IGP&EE algorithm could be negligible (10 μs). TPDS-IGP&EE algorithm can make an effective prediction for the sparse trajectory, providing much wider predicting range, faster predicting speed and better predicting accuracy.

In order to reveal logic attack strategy information from alarms generated by intrusion detection system and reconstruct attack scenario, a hybrid model of alarm correlation was proposed, which was based on attack graph and alert similarity analysis. This model combined the advantages of attack graph and alert data analysis. First of all, it described the causal relationship between alarms, according to the initial attack graph defined by the prior knowledge of intrusion attack. Afterwards, it used the similarity analysis of the alert data to repair the defects of the initial attack graph. And then it implemented alert correlation. The experimental results show that the model can not only recover attack scenario but also be able to fully repair the attack graph in the absence of a single attack step.

Due to the complexity and the dynamic changes of the coal mine environment, the concentrations of harmful gases are difficult to be accurately monitored. The traditional monitoring methods use a single sensor to pick-up information, and the collected data have simple data form, low reliability, big error and so on. Concerning these problems, a new method was proposed in this paper, that is, sampling a variety of heterogeneous gases sources, and then taking advantage of the strong classification algorithm to filter, lastly fusing the above obtained information. As experiments state, the new method significantly improve the reliability of the mine monitoring system.

According to the requirement of modern postal logistics, a kind of intelligent control system for post parcel servo was established. This system was structured by integrated OMRON Programmable Logic Controller (PLC), touch panel and IFIX supervisory control and data acquisition software. The function of variable-frequency driver for post parcel delivery was analyzed. The communication between PLC and variable-frequency driver was realized by Modbus Remote-Terminal-Unit (RTU) protocol. And the data exchange process was implemented by OLE for Process Control (OPC) and data services program. Application shows that automatic and intelligent control of post parcel delivery has been achieved with variable frequency technology, and the design of control system is reasonable and reliable.