The effects of the fuel supply per cycle and the exhaust gas recirculation(EGR) rate on the power output, the fuel economy and the exhaust emission behavior of the homogeneous charge compression ignition(HCCI) ethanol engine were investigated experimentally on a singlecylinder test engine modified from a conventional production ergine. The results show that the indicated thermal efficiency of the HCCI ethanol engine can reach as high as 60%, the peak indicated mean effective pressure Pmi is 0.6 MPa. The proper combination of the equivalent airfuel ratio a and the EGR rate can effectively control the autoignition timing of the HCCI combustion and thus the NOx emission level. However, the introduction of the EGR results in the increased HC and CO emission levels of the engine.

To overcome the low C/N ratio in biodenitrification process of wastewater treatment, the removal of NH+4N by biofilm with starch based biodegradable carrier was studied.Several factors that affect simultaneous nitrification and denitrification,such as pH,DO and temperature,were investigated. Results show that,at C/N = 4,the starch based biodegradable carrier can provide plenty organic carbon source to denitrify microorganism. Under the conditions of pH = 8~8.5,DO = 1 mg/L and T = 28 ℃, the removal efficiencies of NH3N and TN are 93% and 76% respectively.

To provide better dynamic analysis of social network,a new algorithm,Dynamic Social Network Analysis Algorithm Based on Events (DSNE),was proposed.The new algorithm takes full use of the dynamic information of the entities and events based on latent space and twophases clustering method.It can accurately identify the core node of each cluster,and observe the moving trend of node positions at each timestep.The feasibility,efficiency and veracity of the proposed algorithm were verified by experiments.

A method to recognise head rice and break rice based on computer vision system was proposed,which can replace human visual observation.A computer vision based detection system was developed,which uses image processing methods,such as gray transformation,threshold segmentation and smooth processing etc,to get the image of rice kernel shape.The kernel shape was characterized by sixteen parameters,such as the area,perimeter,length and width etc.The data from the kernel profile were processed by the principal components analysis method.The first three principal components,standing for all the parameters of the shape,were taken as the input eigenvalues to train the BP neural net. Recognition experiments show that the accurate ratio of distinguishing head rice is 98.67%,and the accurate ratio of distinguishing break rice is 92.09%.