Control logic injection attack against Programmable Logic Controller （PLC） manipulate the physical process by tampering with the control program， thereby achieving the purpose of affecting the control process or destroying the physical facilities. Aiming at PLC control logic injection attacks， an intrusion detection method based on automatic whitelist rules generation was proposed， called PLCShield （Programmable Logic Controller Shield）. Based on the fact that PLC control program carries comprehensive and complete physical process control information， the proposed method mainly includes two stages： firstly， by analyzing the PLC program’s configuration file， instruction function， variable attribute， execution path and other information， the detection rules such as program attribute， address， value range and structure were extracted； secondly， combining actively requesting a “snapshot” of the PLC’s running and passively monitoring network traffic was used to obtain real-time information such as the current running status of PLC and the operation and status in the traffic， and the attack behavior was identified by comparing the obtained information with the detection rules. Four PLCs of different manufacturers and models were used as research cases to verify the feasibility of PLCShield. Experimental results show that the attack detection accuracy of the proposed method can reach more than 97.71%. The above prove that the proposed method is effective.

When measuring the importance of attributes in Tensor-based Multiple Clustering algorithm （TMC）， the relevance of attribute combinations within object tensors are ignored， and the selected and unselected feature space are incompletely separated because of the fixed weight strategy under different feature space selection. For above problems， a Multiple Clustering algorithm based on Dynamic Weighted Tensor Distance （DWTD-MC） was proposed. Firstly， a self-association tensor model was constructed to improve the accuracy of attribute importance measurement of each feature space. Then， a multi-view weight tensor model was built to meet the task requirements of multiple clustering analysis by dynamic weighting strategy under different feature space selection. Finally， the dynamic weighted tensor distance was used to measure the similarity of data points， generating multiple clustering results. Simulation results on real datasets show that DWTD-MC outperforms comparative algorithms such as TMC in terms of Jaccard Index （JI）， Dunn Index （DI）， Davies-Bouldin index （DB） and Silhouette Coefficient （SC）. It can obtain high quality clustering results while maintaining low redundancy among clustering results， as well as meeting the task requirements of multiple clustering analysis.

To deal with the under-resourced labeled pronunciation data in mispronunciation detection, some other data were used to improve the discriminability of feature in the framework of Tandem system. Taking Chinese learning of English as object, unlabeled data, native Mandarin data and native English data which can be relatively easily accessed were selected as the assisted data. The experiments show that these types of data can effectively improve the performance of system, and the unlabeled data performs the best. And the effect to system performance was discussed with different length of frame context, the shallow and deep neural network typically represented by Multi-Layer Perception (MLP) and Deep Neural Network (DNN), and different structure of Tandem feature. Finally the strategy of merging multiple data streams was used to further improve the system performance, and the best system performance was achieved by combining the DNN based unlabeled data stream and native English stream. Compared with the baseline system, the recognition accuracy is increased by 7.96%, and the diagnostic accuracy of mispronunciation type is increased by 14.71%.

The exiting model representation cannot meet the requirements of multistage modeling, so model share, reuse and management can hardly achieve in multistage modeling process. Therefore, a hierarchical model management framework based on universal relation model presentation was presented. Firstly, the requirements of model representation in model management and the limitations of exiting model representation were analyzed, then a model representation based on universal relation was investigated to set the mapping relation between layers of conceptual model and mathematical model, and the integrative model representation and the hierarchical model management framework including conceptual model, mathematical model and physical model were set up. At last, the logic of modelbase in management framework was designed and the physical model generation based on universal relation was investigated. The model of different modeling process could be managed uniformly based on an integrative model representation.