Forecasting the output power of solar PV systems is required for the good operation of the power grid and the optimal management of energy fluxes occurring in the solar system. Before forecasting the solar system’s output, it is essential to focus on the prediction of solar irradiance. In this paper, the solar radiation data collected for two years in a certain place in Jiangsu in China are investigated. The objective of this paper is to improve the ability of short-term solar radiation prediction. Firstly, missing data are recovered through the means of matrix completion. Then the completed data are denoised via robust principal component analysis. To reduce the influence of weather types on solar radiation, spectral clustering is adopted by fusing sparse subspace representation and k-nearest-neighbor to partition the data into three clusters. Next, for each cluster, four neural networks are established to predict the short-term solar radiation. The experimental results show that the proposed method can enhance the solar radiation accuracy.

A Comprehensive Application of Machine Learning Techniques for Short-Term Solar Radiation Prediction

With the proliferation of massive varieties and unknown malicious viruses, it is difficult to achieve effective and timely defence by anti-virus technology with virus signature matching as the core. Hence, the research of malicious code detection and defence is investigated. Firstly, it introduces the defence technology and detection technology. The simulation results show that the default infection probability of the simulation program is 15.1%. The default detection probability is 60.1%, the default number of nodes is 1000, the default time step is 1000 ms, and the default initial infected node is only node 0, that is, only the malicious code on node 0 is activated. After start-up, the whole P2P starts to run, and the number of infected nodes increases. Finally, all nodes are infected. The infection speed becomes faster and faster. When the number of infected nodes reaches about half, the infection speed begins to slow down. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/tje2.12153

A study on detection and defence of malicious code under network security over biomedical devices



In order to explore the study on information leakage tracking algorithms in online social networks, solve the problem of information leakage in the current online social network. a deterministic leaker tracking algorithm based on digital fingerprints is proposed



: First, the basic working principle of the algorithm is that the platform uses plug-ins to embed a unique user-identifying information before users try to obtain digital media such as images and videos shared by others on the platform.



Secondly, because the scale of users in social networks is extremely large and dynamic, while ensuring the uniqueness of digital fingerprints, it is also necessary to ensure the coding efficiency and scalability of digital fingerprint code words.



Simulation experiments show that: 10 experiments are performed on 10,000 to 100,000 nodes, the Hamming distance threshold d is set to be 3, and the length of the hash code and the binary random sequence code are both 64 bits.



Compared with the traditional linear search, the proposed digital fingerprint fast detection scheme has better performance



It is proved that an index table based on hash code and user ID is established and combines with community structure, to improve the detection efficiency of digital fingerprints


Study on Information Leakage Tracking Algorithms in Online Social Networks

Research on community finding is very helpful to control virus spreading in networks.Most of the proposed community-finding algorithms are not suitable for very large networks because of their time-complexity.Combined with the advantage of solving the clustering of unknown distributed data set of the spectral clustering,and the ability of mo-dularity function in finding good community number in large networks,a community-finding algorithm based on spectral clustering was proposed.Experimental results indicate that the new algorithm is efficient and effective at finding good community structure in large networks.

Community-finding Algorithm in Complex Networks Based on Spectral Clustering

This paper discussed the characteristics of user’s relationship networks on the individual micro-blog.We tracked the data of individual micro-blog from registering to a certain stabilization period,extracted representative data on the three time points,built three complex networks.We measured the properties of the networks on the different three time points,and found the changes of users'behavior and influence.The paper used K-means clustering algorithm to analyze the characteristics of users.From the purpose of using micro-blog perspective,K-means clustering classified the users into three types(ordinary social type,personal interest type,information dissemination type).Thus,by optimizing the algorithm and using the result of clustering,service providers can reduce unnecessary recommendations of pages and application to meet the needs of users so as to acquire commercial value.

Structure and Cluster Analysis on Micro-Blog User’s Relationship Networks

The text data of the social network platforms take the form of short texts, and the massive text data have high-dimensional and sparse characteristics, which does not make the traditional clustering algorithm perform well. In this paper, a new community detection method based on the sparse subspace clustering (SSC) algorithm is proposed to deal with the problem of sparsity and the high-dimensional characteristic of short texts in online social networks. The main ideal is as follows. First, the structured data including users’ attributions and user behavior and unstructured data such as user reviews are used to construct the vector space for the network. And the similarity of the feature words is calculated by the location relation of the feature words in the synonym word forest. Then, the dimensions of data are deduced based on the principal component analysis in order to improve the clustering accuracy. Further, a new community detection method of social network members based on the SSC is proposed. Finally, experiments on several data sets are performed and compared with the K-means clustering algorithm. Experimental results show that proper dimension reduction for high dimensional data can improve the clustering accuracy and efficiency of the SSC approach. The proposed method can achieve suitable community partition effect on online social network data sets.

https://www.mdpi.com/1999-5903/11/12/254/pdf

Research on Community Detection of Online Social Network Members Based on the Sparse Subspace Clustering Approach

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Research on Community Detection of Online Social Network Members Based on the Sparse Subspace Clustering Approach