As the integration of wind power into electrical energy network increasing, accurate forecast of wind speed becomes highly important in the case of large-scale wind power connected into the grid. In order to improve the accuracy of wind speed forecast and the generalization ability of the model, Extreme Gradient Boosting (XGBoost) as an improvement from gradient boosting decision tree (GBDT) is trained and deployed in the cheaper central processing unit (CPU) devices instead of graphics processing unit (GPU) devices, thus, a wind speed forecast model based on Extreme Gradient Boosting is proposed in this paper. Firstly, the historical data is taken as a part of the input vectors for the model. Moreover, considering the monthly change of wind speed characteristics, the dataset of wind power is divided into four parts by month so that the models are constructed in different complexity by month. Finally, compared with back propagation neural networks (BPNN) and linear regression (LR) models, the experimental results show that the improved XGBoost model can promote the forecast accuracy effectively. INDEX TERMS Short-term wind speed forecasting, XGBoost, time series, historical characteristics, power grid.

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Multiple vehicles cooperative control is an important field of research for unmanned aerial vehicles (UAVs). In this paper, the difficulties and challenges for cooperation of multiple networked UAVs were analyzed. Based on OODA model, the cooperation process of multiple networked UAVs was presented. Furthermore, three key technologies for multiple UAVs cooperative control were proposed, which are multiple vehicles cooperative information sensing under dynamic environment, multiple vehicles distributed mission decision under dynamic network and multiple vehicle autonomous online path planning and path coordination. Finally, the main research content and trend of these key technologies were discussed.

Alzheimer disease (AD) is mainly manifested as insidious onset, chronic progressive cognitive decline and non-cognitive neuropsychiatric symptoms, which seriously affects the quality of life of the elderly and causes a very large burden on society and families. This paper uses graph theory to analyze the constructed brain network, and extracts the node degree, node efficiency, and node betweenness centrality parameters of the two modal brain networks. The T test method is used to analyze the difference of graph theory parameters between normal people and AD patients, and brain regions with significant differences in graph theory parameters are selected as brain network features. By analyzing the calculation principles of the conventional convolutional layer and the depth separable convolution unit, the computational complexity of them is compared. The depth separable convolution unit decomposes the traditional convolution process into spatial convolution for feature extraction and point convolution for feature combination, which greatly reduces the number of multiplication and addition operations in the convolution process, while still being able to obtain comparisons. Aiming at the special convolution structure of the depth separable convolution unit, this paper proposes a channel pruning method based on the convolution structure and explains its pruning process. Multimodal neuroimaging can provide complete information for the quantification of Alzheimer’s disease. This paper proposes a cascaded three-dimensional neural network framework based on single-modal and multi-modal images, using MRI and PET images to distinguish AD and MCI from normal samples. Multiple three-dimensional CNN networks are used to extract recognizable information in local image blocks. The high-level two-dimensional CNN network fuses multi-modal features and selects the features of discriminative regions to perform quantitative predictions on samples. The algorithm proposed in this paper can automatically extract and fuse the features of multi-modality and multi-regions layer by layer, and the visual analysis results show that the abnormally changed regions affected by Alzheimer’s disease provide important information for clinical quantification.

With the growing concerns on the energy crisis and the strong support of policies, the development and construction of the multi-energy infrastructure are booming in the world. The post-evaluation for the multi-energy infrastructure investment projects can discover problems, giving advice for improvement, and guide the future project process management, which plays a significant role in achieving closed-loop and lean management. In this paper, a comprehensive overview on the post-evaluation indicators of overall multienergy infrastructure construction processes is illustrated. Then, the post-evaluation indicators on economic benefits are surveyed from the financial and national economic perspectives. Furthermore, the indicators to evaluate the impact on society and environment induced by the multi-energy infrastructure are thoroughly analyzed and investigated. Also, the post-evaluation indicators on the sustainability of the multi-energy infrastructure investment projects are summarized. Lastly, some challenges with great importance in the future research are presented. This review contributes to finding out the problems existing in the multi-energy infrastructure projects and analyzing the causes of the problems, which helps to guide the enterprise's future investment direction, decision-making, and realize an accurate investment.