This letter proposes an ensemble neural network (Ensem-NN) for skeleton-based action recognition. The Ensem-NN is introduced based on the idea of ensemble learning, “two heads are better than one”. According to the property of skeleton sequences, we design one-dimensional (1D) convolution neural network (CNN) with residual structure as Base-Net. From entirety to local, from focus to motion, we designed four different subnets based on the Base-Net to extract diverse features. The first subnet is a Two-stream Entirety Net, which performs on the entirety skeleton and explores both temporal and spatial features. The second is a Body-part Net, which can extract fine-grained spatial and temporal features. The third is an Attention Net, in which a channel-wised attention mechanism can learn important frames and feature channels. Frame-difference Net, as the fourth subnet, aims at exploring motion features. Finally, the four subnets are fused as one ensemble network. Experimental results show that the proposed Ensem-NN performs better than state-of-the-art methods on three widely used datasets.

Quantum signal processing offers a possible solution to store and process massive signals efficiently. As the foundation of quantum signal processing, quantum signal representation (QSR) that stores color information using basis states, is convenient to retrieve accurately signals from quantum systems. But the existing QSRs are only focus on integer signals (i.e., images) and are difficult to be directly operated by quantum Fourier transform (QFT) and quantum wavelet transform (QWT). Thus, we propose four models of QSR for integer, real, and complex signals. Furthermore, we design quantum implementation circuits of type conversions of QSR with complexity $O$(1). Type conversions of QSR are illustrated by general processes of QFT and Haar QWT, and demonstrate that the proposed QSRs are suitable for QFT and QWT.

SOH (state of health) estimation is important for battery management. Since the electrochemical reaction inside LIBS (lithium-ion battery system) is extremely complex and the external working environment is uncertain, it is difficult to achieve accurate determination of SOH. To improve the accuracy of SOH estimation, we propose a SOH estimation method for lithium-ion battery based on XGBoost algorithm with accuracy correction. We extract several features, including average voltage, voltage difference, current difference, and temperature difference, to describe the aging process of batteries. Due to the higher prediction accuracy and generalization ability of ensemble learning algorithm, the XGBoost model is established to estimate the SOH of lithium-ion battery. Then, the estimation values are corrected by Markov chain. Compared with the methods by XGBoost, random forest, k-nearest neighbor algorithm (KNN), SVM, linear regression, our proposed method shows an accuracy improvement by 10% to 20%. Additionally, the errors of our method are also superior to the others in terms of the average absolute error, root mean square error, and root mean square error.