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The two problems restrict the development of directly buried pipeline installation without compensation technology in the heating engineering.Therefore, the intensity of elbow itself is analyzed to optimize the elbow .A new model of elbow is provided .This paper uses ANSYS finite element analysis software to establish a new type of elbow model.We compare and analyze the stress of ordinary elbow which add block and do not add block ; two different types which both have the same wall thickness and new type elbow which have 5 different wall thickness for each elbow.The results show that:(1) the new type elbow can reduce the stress greatly;(2) with the increasing diameter of elbow, the stress of inner elbow decreases gradually. Therefore, this new type of elbow can truly realize non-compensating installation in heating system.

To improve the dynamic response and of compensation characteristic three-phase shunt active power filter(APF),a novel control strategy was proposed based on supply voltage detection. Fundamental wave current controller and dc side voltage controller was designed in detail. This method can not only compensate the parameter is determined and position distortion load, also can compensate the uncertain load.The superiority of the control architecture proposed is verified completely by simulation results.

In this study, the sample data was based on 2190 branch length samples of 30 trees from dahurian larch (Larix gmeliniiRupr.) plantations located in Wuying forest bureau in Heilongjiang Province. A second order polynomial equation with linear mixed-effects was used for modeling branch length of larch tree. The LME procedure in S-Plus is used to fit the mixed-effects models for the branch length data. The results showed that the polynomial model with three random parameters could significantly improve the model performance. The fitted mixed effects model was also evaluated using mean error, mean absolute error, mean percent error, and mean absolute percent error. The mixed model was found to predict branch length better than the original model fitted using ordinary least squares based on all errors. The application of mixed branch length model not only showed the mean trends of branch length, but also showed the individual difference based on variance-covariance structure.

Model for predicting wood density of Larch was established using near-infrared spectroscopy (NIR) combined with support vector machine (SVM). A hundred and seventeen Larch samples were used in the study. Wood density of samples was measured according to standard test methods for physical and mechanical properties of wood. Support vector machines for regression (SVR) was used for model building. Radial basis function (RBF) was used as kernel function to establish a model for predicting wood density. For the train set, the coefficient of determination (R2) and the mean square error (MSE) were 0.8504 and 0.6460×10-3, while the R2 and MSE was 0.8520 and 0.4451×10-3, respectively, for the test set. Results showed that using SVM in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.

Back-propagation (BP) algorithm of artificial neural network (ANN) was applied to tree height prediction of Larch plantation in northeast China by taking logsigmoid function of logsig and linear function of purelin in Matlab as the neural functions. One input variable of tree diameter and one output variable of tree height was used in the model with one hidden layer of 5 hidden neurons. Model developed was evaluated graphically and statistically. Results showed that model performs well with mean square error (MSE) of 0.130901 and model precision of 97.6%. The graphical comparisons between the actual measured data and the network predicted output clearly demonstrate very good agreement between the actual and predicted performance.