No abstract

Improving the accuracy of wind speed forecast can reduce the randomness and uncertainty of the wind power output and effectively improve a system's wind power accommodation. However, the highdimensional historical wind speed information should be taken into account in the wind speed forecast, which increases the complexity of the model and reduces the efficiency and accuracy of a forecast. Feature selection by the Filter method can effectively reduce the feature dimension, but losing all the information of low-importance features. Although the feature reduction can retain the partial information of all features, it causes the loss of the partial information of high-importance features. In order to reduce the information loss caused by traditional FS and FR, short-term wind speed forecast with low information loss based on OSVD feature generation is proposed. First, the original wind speed series is denoised by OVMD. Then, based on the 96-dimensional original wind speed feature set, the OSVD is used to generate features. Furthermore, the extended original feature set EFS is obtained by combining the initial feature set with the features generated by OSVD. Gini importance is used to measure the importance of all features in EFS, and the forward feature selection is combined with random forests to determine the optimal subset. Finally, the optimal model determined by the new method is compared with seven models to verify the advancement of the new method. The experiments show that it reduces the information loss. Thus, the model has a higher forecast accuracy than the traditional model. INDEX TERMS Short-term wind speed forecast, feature selection, feature reduction, low information loss, variational mode decomposition, singular value decomposition. The associate editor coordinating the review of this manuscript and approving it for publication was Shuaihu Li. which makes the safe and reliable operation of a power grid enormously challenging and restricts the development of wind power. Accurate and efficient wind speed forecast (WSF) can reduce the negative impact of wind power uncertainty [4], [5]. WSF methods can generally be divided into physical methods [6], [7], statistical methods [8]-[11], artificial intelligence (AI) methods [12]-[17], and others. The physical method is based on a numerical weather prediction (NWP) model, which uses a series of meteorological data (wind direction, temperature, and humidity) and terrain information to establish WSFM [6], [7]. Unlike physical methods, statistical methods only use historical

No abstract

Action recognition has attracted increasing interest in computer vision due to its potential applications in many vision systems. One of the main challenges in action recognition is to extract powerful features from videos. Most existing approaches exploit either hand-crafted techniques or learning-based methods to extract features from videos. However, these methods mainly focus on extracting the dynamic motion features, which ignore the static form features. Therefore, these methods cannot fully capture the underlying information in videos accurately. In this article, we propose a novel feature representation method for action recognition, which exploits hierarchical sparse coding to learn the underlying features from videos. The learned features characterize the form and motion simultaneously and therefore provide more accurate and complete feature representation. The learned form and motion features are considered as two modalities, which are used to represent both the static and motion features. These modalities are further encoded into a global representation via a pairwise dictionary learning and then fed to an SVM classifier for action classification. Experimental results on several challenging datasets validate that the proposed method is superior to several state-of-the-art methods.