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Accurate wind speed prediction plays an important role in the planning and development of wind power plant. Wind speed prediction can reduce the impact of wind power grid connection and improve the control of wind power systems. In this study, wind speed is considered as a geographical variable. We firstly used exploratory methods to examine the spatio-temporal dependence on wind field. The variogram was taken into account in analysing the spatial structure in the rectangular grid. Then the exponential model was adopted to fit the anisotropic feature of variogram. The de-trended data have been computed by using a geostatistical kriging estimator. The results were compared with the leave-one-out cross-validation (LOOCV) method. The accuracy of the proposed method was verified to obtain the best estimated results.

The prediction of wind speed plays a key role in the determination of the optimal reserve capacity for different time instants and the reduction of the standby cost of wind power grid. Short-term wind speed prediction can reduce the impact of wind power grid and improve the control of wind turbines. The geostatistics kriging as an unbiased estimation method applied to predict the wind speed in a two-dimensional vertical rectangular grid. The wind data were as a geographical variable considered. We firstly used exploratory methods to examine the spatial variability of wind speed and its dependence on geographical variables. The covariance and variogram models took into account in analysing the spatial structure of wind speed at three different instants of time. Then the product model adopted to fit wind data due to the variogram. The accuracy of the method was assessed through a cross validation. The prediction results were comparable with the observed values. The prediction error was about ±0.15 and the correlation coefficient was about 0.85. This study demonstrated that the kriging method could be success fully used in the short-time wind speed prediction in space and time direction.

In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

To study the effect of body mass index (BMI) on lung function indexes in Chinese population. A cross-sectional study was performed on 10, 592 participants. The linear relationship between lung function and BMI was evaluated by multivariate linear regression analysis, and the correlation between BMI and lung function was assessed by Pearson correlation analysis. Correlation analysis showed that BMI was positively related with the decreasing of forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and FEV1/FVC (P