Based on the grey correlation analysis theory, this paper puts forward a grey dynamic three-dimensional correlation space analysis method for multi-attribute decision problems. Firstly, this paper constructs a grey three-dimensional correlation space, which contains a time-axis and different time sections corresponding to the projection surfaces of objects' properties. Points in the time sections represent research objects, and their positions are determined by the objects' attribute values in different investigation periods. Secondly, based on the grey three-dimensional correlation space, we build a new grey correlation degree algorithm for dynamic multi-attribute decision problems. This correlation degree algorithm considers the difference between research objects as well as the fluctuations of their own attribute values, and provides a new method the grey decision theory. Lastly, an example of industrial development verifies the validity and practicability of our model and its algorithm.

For the model of active contours with group similarity (ACGS), a rank constraint for a group of evolving contours is defined to keep the shape similarity. ACGS obtains robust results in extracting a single object with missing or misleading features. However, with one initial contour, it could not extent to multiple objects segmentation because low-rank property will not hold in some image sequences. Besides, ACGS is affected by nontarget objects. In this paper, an active contour model based on block similarity of shapes is proposed to extend the ACGS model to realize multiple objects extraction. For a sequence of image with multiple objects, a model for multiple objects extraction is constructed by combining sparse decomposition and ACGS; second, a block low-rank constraint is proposed to constrain the similarity of these evolving contours in every block; finally, segmentation results are obtained through iterative evolutions. Experimental results show the proposed method could segment images with multiple targets, and it improves the robust segmentation performance of sequence of image when the features of multiobjects are missing or misleading.

Increasing the breadth and depth of student participation in teaching has been a hot issue of educational reform in China, but it is difficult for China's traditional high school teaching model of information technology to mobilize the enthusiasm and initiative of the students involved in teaching. Combined with the characteristics of participatory teaching, the corresponding teaching model is proposed based on the requirements of information technology course in high school. This model divides the students participation involved in teaching into three stages: participation in teaching design, participation in teaching process and reflection after school.The results show that student interest in learning is enhanced, academic achievement and the ability of operational application, cooperation and exchange, analysis and evaluation is significantly improved.