As an original inverter control method, the virtual synchronous generator (VSG) control technology has aroused much attention in recent years since it can achieve better dynamic voltage regulation when micro grid (MG) operates on island or load changes suddenly. Further, aiming at the superior voltage control of VSG, this paper proposed a double closed loop control strategy based on finite-time control (FTC) to improve the dynamic performance of the system. Compared with the traditional proportional integral (PI) control strategy, the proposed strategy can achieve the quick respond speed and strongly robustness when the event of load change occurs. Finally, the simulation and experimental results demonstrate the feasibility and correctness of the proposed control method.

As a promising IoT application, the rural leisure tourism industry can promote the reconstruction of industrial structure in rural areas and realize a sustainable, rapid, and healthy development of rural economy. This paper takes the rural leisure tourism industry in China as an example and aims at building an intelligent and integrated modern IoT use case. Based on the traditional rural leisure tourism, we improve the system by adding the data analysis over a mobile cloud IoT computing platform. In particular, this work investigates the characteristics of the national tourism market under the security requirements from governmental cloud data management policy. Our study shows that the geographical concentration index G of tourists in the Chinese market continues to increase. With the booming of IoT applications in rural leisure tourism, intelligent and integrated tourism guidance and optimized decision‐making will provide tourists with better information and thus make rapid improvement of geographical concentration index.

Modern cloud computing platforms based on virtual machine monitors (VMMs) host a variety of complex businesses which present many network security vulnerabilities. In order to protect network security for these businesses in cloud computing, nowadays, a number of middleboxes are deployed at front-end of cloud computing or parts of middleboxes are deployed in cloud computing. However, the former is leading to high cost and management complexity, and also lacking of network security protection between virtual machines while the latter does not effectively prevent network attacks from external traffic. To address the above-mentioned challenges, we introduce a novel customized network security for cloud service (CNS), which not only prevents attacks from external and internal traffic to ensure network security of services in cloud computing, but also affords customized network security service for cloud users. CNS is implemented by modifying the Xen hypervisor and proved by various experiments which showing the proposed solution can be directly applied to the extensive practical promotion in cloud computing.

Summary Along with the normal network flow, abnormal flow follows, threatening the security and normal use of the computer. Under the impact of the massive network flow, researchers were prompted to study identification methods based on machine learning. Currently, the existing identification methods based on machine learning still have deficiencies, ie, feature redundancy and deficiency in classifier. In order to deal with these problems, this paper proposes an adaptive approach (AEDs) to identify abnormal network flow. The AEDs utilizes the feature extraction algorithm proposed in this paper, which is based on information entropy to filter features and reduce the size of features. Then, a weight construction method based on rough set theory is implemented to construct the weights of given‐case‐based ensemble classifiers, and only the sub‐classifier with weight that is higher than the given threshold will be reserved. On this basis, in order to keep the effectiveness of the classifier, we utilize new flow data to update weights. Moreover, an adaptive detection algorithm is proposed to adaptively monitor the change of entropy of abnormal flow and enable the update of classifier. Finally, experiments have been conducted to illustrate the effectiveness and the feasibility of the approach.

With the advent of social networks and cloud computing, the amount of multimedia data produced and communicated within social networks is rapidly increasing. In the mean time, social networking platform based on cloud computing has made multimedia big data sharing in social network easier and more efficient. The growth of social multimedia, as demonstrated by social networking sites such as Facebook and YouTube, combined with advances in multimedia content analysis, underscores potential risks for malicious use such as illegal copying, piracy, plagiarism, and misappropriation. Therefore, secure multimedia sharing and traitor tracing issues have become critical and urgent in social network. In this paper, we propose a scheme for implementing the Tree-Structured Harr (TSH) transform in a homomorphic encrypted domain for fingerprinting using social network analysis with the purpose of protecting media distribution in social networks. The motivation is to map hierarchical community structure of social network into tree structure of TSH transform for JPEG2000 coding, encryption and fingerprinting. Firstly, the fingerprint code is produced using social network analysis. Secondly, the encrypted content is decomposed by the TSH transform. Thirdly, the content is fingerprinted in the TSH transform domain. At last, the encrypted and fingerprinted contents are delivered to users via hybrid multicast-unicast. The use of fingerprinting along with encryption can provide a double-layer of protection to media sharing in social networks. Theory analysis and experimental results show the effectiveness of the proposed scheme.

Lightcuts is an efficient illumination method for scenes with many complex lights, by hierarchical clustering of lights in a light tree. However, when the light tree is large, it is very time-consuming to traverse in the tree to get the suitable clusters of lights for illumination computation. For this, some methods proposed to use image coherence for neighboring pixels to share clusters of lights, and so save traversal cost in the light tree. However, with the image resolutions reduced, fewer and fewer coherences could be used and so their acceleration efficiency will be reduced dramatically, and they may even decrease the rendering efficiency.