To improve the performance of k-Nearest Neighbor (kNN) model on class-imbalanced data, a new kNN classification algorithm was proposed. Different from the traditional kNN, for the learning process, the majority set was partitioned into several clusters by using partitioning method (such as K-Means), then each cluster was merged with the minority set as a new training set to train a kNN model, therefore a classifier library was constructed consisting of serval kNN models. For the prediction, using a partitioning method (such as K-Means), a model was selected from the classifier library to predict the class category of a sample. By this way, it is guaranteed that the kNN model can efficiently discover local characteristics of the data, and also fully consider the effect of imbalance of the data on the performance of the classifier. Besides, the efficiency of kNN was also effectively promoted. To further enhance the performance of the proposed algorithm, Synthetic Minority Over-sampling TEchnique (SMOTE) was applied to the proposed algorithm. Experimental results on KEEL data sets show that the proposed algorithm effectively enhances the generalization performance of kNN method on evaluation measures of recall, g-mean, f-measure and Area Under ROC Curve (AUC) on majority set partitioned by random partition strategy, and it also shows great superiority to other state-of-the-art methods.

In the traditional workflow-based collaborative design, the difficulties of communication and task coordination among different professional designers lead to low efficiency of product design. In order to solve this problem, the "A Meta-model with Three Levels" data model of complex product and data-driven collaborative design technology of complex product based on data driven were proposed. Firstly, multi-dimensional and multi-granularity data modeling and ontology description were used to complete the information modeling of complex products. Then the semantic retrieval technology based on ontology was used to complete the data subscription of the collaborative design process task. Finally, a complex product task collaboration technology based on data subscription/publishing was implemented. The experimental results show that the data-driven collaborative design technology solves the difficulties of communication and task coordination among different professional designers in the traditional collaborative design process, and achieves a spiral of rise product collaborative design process, thereby improving the efficiency of complex product design.

Concerning the signature security problem in reality, based on the Waters'technology, a universal designated verifier signcryption scheme in the standard model was proposed. Signcryption is a cryptographic primitive which performs encryption and signature in a single logical step. Universal designated verifier signature allowed a signature holder who had a signature of a signer, to convince a designated verifier that he was in possession of a signer's signature, while the verifier could not transfer such conviction to anyone else, only allowed the designated verifier to verify the existence of the signature. The scheme by combining universal designated verifier and signcryption eliminated the signer and signture holders for signature transmission required for a secure channel. Under the assumption of Computational Bilinear Diffie-Hellman (CBDH) problem, the scheme was proved to be safe. Compared with the existing schemes, the proposed scheme has better computational efficiency.

An effective noise detection algorithm based on two-threshold and iteration for noise images with impulse noise was proposed to decrease the influence of impulse noise. The selective method of two-threshold was reliable in theory, and the two-step iteration method guaranteed the accuracy of noise detection. Moreover, the median filtering algorithm with selected pixels made sure that the details of image were not blurred at the same time. The experimental results show that the proposed algorithm is more robust and adaptive, and it has lower leak-detection and better filtering effect.