This paper first analyzes how thermodynamic system state parameters are affected when main steam’s pressure changes under sliding pressure operation .Then a new state distribution equation of sliding pressure operation based on thermo-economy equation of state of thermal power unit is given .With the help of this equation, a new algorithm is proposed to determine the state parameters distribution for variable working condition under sliding pressure operation. Experiment of one supercritical unit is provided to validate the effectiveness of our approach under sliding pressure operation. Simulation results show that this algorithm is beneficial for analyzing the security and economy of thermal power unit with deep fast variable loads. It also has vital important significance for thermal power unit to develop its peak shaving ability

This paper has proposed a new smoothing filter for directional filting, based on the optimal linear estimate and the multi -spike model. Because of differential operators being sensitive to noise in images, We prefer band-pass filters which is insensitive to noise and with a good precision of line localization. In our experiments, it is shown that the images filted by our filter are less noisy and with a better precision than those by the directional filter described in [ Z ] . The experimental results were very satisfactory with many images.

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Data stream is infinite data and quick stream speed, so traditional clustering algorithm can not be applied to data stream clustering directly. As an efficient tool for data analysis, Gaussian mixture model has been widely applied in the fields of signal and information processing. We can use Gaussian mixture model (GMM) simulate arbitrary clustering graphics. There are two critical problems for the clustering analysis technology to select the appropriate value of number of clusters and partition overlapping clusters. Base on an extending method of Gaussian mixture modeling , a new feature mining method named Gaussian Mixture Model with Genetic Algorithms is proposed in this paper. This method is use a probability density based data stream clustering which requires only the newly arrived data, not the entire historical data, and also can choose optimal estimation clusters number value. The algorithm can determine the number of Gaussian clusters and the parameters of each Gaussian through random split and merge operation of Genetic Algorithms. We can get the accurate information each attribute characteristic describe.So that can make an effective date stream mining.

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