In recent years, graph convolutional networks (GCNs) play an increasingly critical role in skeleton-based human action recognition. However, most GCN-based methods still have two main limitations: 1) They only consider the motion information of the joints or process the joints and bones separately, which are unable to fully explore the latent functional correlation between joints and bones for action recognition. 2) Most of these works are performed in the supervised learning way, which heavily relies on massive labeled training data. To address these issues, we propose a semi-supervised skeleton-based action recognition method which has been rarely exploited before. We design a novel correlation-driven joint-bone fusion graph convolutional network (CD-JBF-GCN) as an encoder and use a pose prediction head as a decoder to achieve semi-supervised learning. Specifically, the correlation-driven joint-bone fusion graph convolution (CD-JBF-GC) can explore the motion transmission between the joint stream and the bone stream, so as to promote both streams to learn more discriminative feature representations. The pose prediction based auto-encoder in the self-supervised training fashion allows the network to learn motion representation from the unlabeled data, which is essential for action recognition. Extensive experiments on two popular datasets, i.e. NTU-RGB+D and Kinetics-Skeleton, demonstrate that our model achieves the state-of-the-art performance for semi-supervised skeleton-based action recognition and is also useful for fully-supervised methods.

In recent years, artificial intelligence has played an increasingly important role in the field of automated control of drones. After AlphaGo used Intensive Learning to defeat the World Go Championship, intensive learning gained widespread attention. However, most of the existing reinforcement learning is applied in games with only two or three moving directions. This paper proves that deep reinforcement learning can be successfully applied to an ancient puzzle game Nokia Snake after further processing. A game with four directions of movement. Through deep intensive learning and training, the Snake (or self-learning Snake) learns to find the target path autonomously, and the average score on the Snake Game exceeds the average score on human level. This kind of Snake algorithm that can find the target path autonomously has broad prospects in the industrial field, such as: UAV oil and gas field inspection, Use drones to search for and rescue injured people after a complex disaster. As we all know, post-disaster relief requires careful staffing and material dispatch. There are many factors that need to be considered in the artificial planning of disaster relief. Therefore, we want to design a drone that can search and rescue personnel and dispatch materials. Current drones are quite mature in terms of automation control, but current drones require manual control. Therefore, the Snake algorithm proposed here to be able to find the target path autonomously is an attempt and key technology in the design of autonomous search and rescue personnel and material dispatching drones.

Transmission line icing is a common natural phenomenon, but it is the most dangerous factor that severely threatens the safety and stability of the power grid operation. Transmission line icing involves many factors, including temperature, humidity, wind speed, light intensity, wire tension, pressure, and wind deflection angle. Because of the high dimensionality, nonlinearity, multi-modality, heterogeneity of the data generated by these factors, it is difficult to establish an accurate prediction model based on these data adopting traditional data mining methods. Firstly, the data demonstrate a geometric growth, and the data dimension becomes higher and higher; secondly the nonlinearity, heterogeneity, and correlation of associated data lead to lower accuracy of the traditional prediction model; thirdly the diversity and complexity of these data makes traditional model processing more difficult and results in computational overhead. How to establish an accurate and effective new model of transmission line icing prediction has become a key problem to be addressed urgently. To address these problems, the paper collects the data monitored by the China Southern Power Grid Online Monitoring System from 2011 to 2016 to study the prediction model of icing level of transmission lines. Since the values affecting the icing level are dynamically changing with time, this paper firstly uses the time series analysis method to process the icing data and proposes an Ensemble Empirical Mode Decomposition (EEMD) method to adaptively decompose the meteorological and mechanical data, which reduces the impact of noise and outliers in high-dimensional data, and maximizes the use of the inherent law of time-frequency to effectively analyze icing data. The feasibility of this method is verified with real data. The experimental results show that the prediction model based on EEMD time-frequency is more accurate than the prediction model based on the original data. Compared with the five prediction models as random forest, support vector machine, BP neural network, Elman neural network and Bayesian network, the accuracy has increased by 0.47%, 2.93%, 1.85%, 0.92% and 1.86% respectively. Additionally, this new method is more sensitive to the serious situation of icing on transmission lines. Compared with the prediction model based on the original data, this method improves the accuracy of prediction for icing level 4 and 5 by 17.5%, 16.67%, 50%, 3.13% and 10.26% respectively.

Normalized cross-correlation is an important mathematical tool in digital signal processing. This paper presents a new algorithm and its systolic structure for digital normalized cross-correlation, based on the statistical characteristic of inner-product. We first introduce a relationship between the inner-product in cross-correlation and a first-order moment. Then digital normalized cross-correlation is transformed into a new calculation formula that mainly includes a first-order moment. Finally, by using a fast algorithm for first-order moment, we can compute the first-order moment in this new formula rapidly, and thus develop a fast algorithm for normalized cross-correlation, which contributes to that arbitrary-length digital normalized cross-correlation being performed by a simple procedure and less multiplications. Furthermore, as the algorithm for the first-order moment can be implemented by systolic structure, we design a systolic array for normalized cross-correlation with a seldom multiplier, in order for its fast hardware implementation. The proposed algorithm and systolic array are also improved for reducing their addition complexity. The comparisons with some algorithms and structures have shown the performance of the proposed method.

Continuous Data Protection (CDP) is an emerging scheme of data backup and recovery for Cyber Security. Compared with traditional data protection techniques, it can provide much higher reliability and allow the data state to be reverted to any point-in-time in the past. CDP continuously captures and logs every disk update, producing large amount of information, thus desperately requires an efficient mechanism of organizing such information (about the disk I/Os) in order to guarantee the performance to be acceptable. However, there are few works which address the organization of history data to improve the recovery efficiency in case of the disaster occurrences. In this paper, three methods of metadata management for Cyber Security are presented, of which two are simple implementations based on MySQL database (named DIR_MySQL and OPT_MySQL respectively) and the other one is designed with consideration of its characteristics. Experimental results show that META_CDP method is far more efficient than the other two and its performance is reasonably acceptable. Furthermore, we discuss in-depth two recovery algorithms, which are full-recovery and incremental recovery. Users can choose the optimal recovery algorithm based on the required recovery time point.