No abstract

As a kind of green, clean and renewable energy, wind power generation has been widely utilized in various countries in the world. With the rapid development of wind energy, it is also facing prominent problems. Because wind power generation is intermittent, unstable and stochastic, it has caused serious difficulties for power grid dispatch. At present, the important method to solve this problem is to predict wind speed and wind power. Grey model is suitable for uncertain systems with poor information and needs less operation data, so it can be used for wind speed and wind power prediction. However, the traditional grey system model has the disadvantage of low prediction accuracy. Therefore, firstly the GM (1,1) for wind speed prediction is improved by background value optimization in this paper. In order to comprehensively reveal the inherent uncertainty of wind speed random series, the fractional order grey system models with different orders are constructed. Secondly, in order to overcome the shortcoming of single grey model, each grey model is effectively united, and a combination prediction model based on neural network is presented. The two NWP outputs, i.e. ECMWF and GRAPES-MESO, have been added to the prediction model for reducing the uncertainty. The structure parameters of the neural network are optimized by trial and error. Thirdly, the support vector regression model is established to fit the scatter operation data of wind speedpower, and the parameters of the model are optimized by the particle swarm algorithm. Then the power prediction value is obtained by the fitted wind speed-power relationship and the corresponding result of the grey combination model for wind speed prediction. Finally, wind speed and wind power are predicted based on the actual operation data. In addition, the prediction model based on ARIMA is also constructed as a benchmark model. The results show that the proposed grey combination model improves the prediction accuracy.INDEX TERMS Grey model, background value, support vector regression, fractional order, particle swarm algorithm, wind speed prediction, wind power prediction, combination model.

Background: Microarray Comparative Genomic Hybridization (array CGH) provides a means to examine DNA copy number aberrations. Various platforms, brands and underlying technologies are available, facing the user with many choices regarding platform sensitivity and number, localization, and density distribution of probes.

No abstract

No abstract

No abstract

ObjectivesTo explore the change and its significance of cytokines in patients with pulmonary tuberculosis complicated with COPD.MethodsThe immune function of 152 cases of pulmonary tuberculosis with COPD was detected to compare with 150 cases of patients with pulmonary tuberculosis, 157 cases of patients with COPD and 50 cases of healthy volunteers who were in the hospital during the same period. T lymphocyte cell population in peripheral blood was detected by flow cytometry. The serum levels of sIL-2R, IL-6, IFN-γ, TNF-α were measured using ELISA.ResultsThe percentage of CD4+ T cells in TB patients with or without COPD and COPD patients without TB was significantly lower than that in control group. The percentage of CD4+ T cells in patients with TB and COPD was significantly lower than that in the non-COPD TB patients. The percentage of CD8+ T cells was higher in the TB patients group than that in control group. The CD4+/CD8+ ratio in the TB patients group was significantly lower than that in control group. The concentrations of sIL-2R, IL-6, TNF-α, IFN-γ in TB patients with or without COPD and COPD patients without TB were significantly higher than those in control group. In addition, sIL-2R, IL-6, TNF-α concentrations in the patients with TB and COPD were higher than those in the non-COPD TB patients. The concentrations of sIL-2R, IL-6, TNF-α, IFN-γ in COPD patients with TB were significantly higher than those in COPD patients without TB. There was a significant negative correlation between serum levels of TNF-α, IL-6 and FEV1 (%, predicted) in COPD without TB group.ConclusionsThe patients with pulmonary tuberculosis complicated with COPD were impaired in cellular immunity, and its extent of immune impairment is more serious than those of the patients with pulmonary tuberculosis and the patients with COPD.

Different types of outliers have existed in the monitoring data of wind turbines, which are not conducive to the follow-up data mining. However, the complex inner characteristics of the monitoring data pose major challenges to detect the outliers. To address this problem, an unsupervised outlier detection approach combining stacked denoising autoencoder (SDAE) and density-grid-based clustering method is proposed. First, the characteristics of the outliers in supervisory control and data acquisition data caused by different reasons are analyzed. Then, the SDAE is utilized to extract features by training the original data. Furthermore, the density-grid-based clustering method is applied to achieve the clustering results. Window width is added to classify the outliers as isolated outliers, missing data, and fault data according to the duration of abnormal data. The monitoring data of four wind turbines are sampled as the training data to demonstrate the effectiveness of the proposed method. The results show that the proposed model can effectively identify the isolated outliers, missing data, and fault information in the high dimensional data set by unsupervised learning.INDEX TERMS Density-grid based clustering, outlier detection, stacked denoising autoencoder, unsupervised learning.