Investigation on the Chinese Text Sentiment Analysis Based on Convolutional Neural Networks in Deep Learning

Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web monitoring.Text sentiment analysis,an important research topic in the area of natural language processing,is a crucial task in the web monitoring area.The accuracy of traditional text sentiment analysis methods might be degraded in dealing with mass data.Deep learning is a hot research topic of the artificial intelligence in the recent years.By now,several research groups have studied the sentiment analysis of English texts using deep learning methods.In contrary,relatively few works have so far considered the Chinese text sentiment analysis toward this direction.In this paper,a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network(CNN)in deep learning in order to improve the analysis accuracy.The feature values of the CNN after the training process are nonuniformly distributed.In order to overcome this problem,a method for normalizing the feature values is proposed.Moreover,the dimensions of the text features are optimized through simulations.Finally,a method for updating the learning rate in the training process of the CNN is presented in order to achieve better performances.Experiment results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods,e.g.,the support vector machine method.

Analysis of Underlay Cognitive Radio Networks Based on Interference Cancellation Mechanism

In this paper,we investigate the performance of secondary transmission scheme based on Markov ON-OFF state of primary users in Underlay cognitive radio networks.We propose flexible secondary cooperative transmission schemewith interference cancellation technique according to the ON-OFF status of primary transmitter.For maximal ratio combining(MRC)at destination,we have derived exact closed-form expressions of the outage probability in different situations.The numerical simulation results also reveal that the proposed scheme improve the secondary transmission performance compared with traditional mechanism in terms of secondary outage probability and energy efficiency.

Three-Phase Unbalance Prediction of Electric Power Based on Hierarchical Temporal Memory

The difference in electricity and power usage time leads to an unbalanced current among the three phases in the power grid.The three-phase unbalanced is closely related to power planning and load distribution.When the unbalance occurs,the safe operation of the electrical equipment will be seriously jeopardized.This paper proposes a Hierarchical Temporal Memory(HTM)-based three-phase unbalance prediction model consisted by the encoder for binary coding,the spatial pooler for frequency pattern learning,the temporal pooler for pattern sequence learning,and the sparse distributed representations classifier for unbalance prediction.Following the feasibility of spatial-temporal streaming data analysis,we adopted this brain-liked neural network to a real-time prediction for power load.We applied the model in five cities(Tangshan,Langfang,Qinhuangdao,Chengde,Zhangjiakou)of north China.We experimented with the proposed model and Long Short-term Memory(LSTM)model and analyzed the predict results and real currents.The results show that the predictions conform to the reality;compared to LSTM,the HTM-based prediction model shows enhanced accuracy and stability.The prediction model could serve for the overload warning and the load planning to provide high-quality power grid operation.

A Novel GLS Consensus Algorithm for Alliance Chain in Edge Computing Environment

Edge computing devices are widely deployed.An important issue that arises is in that these devices suffer from security attacks.To deal with it,we turn to the blockchain technologies.The note in the alliance chain need rules to limit write permissions.Alliance chain can provide security management functions,using these functions to meet the management between the members,certification,authorization,monitoring and auditing.This article mainly analyzes some requirements realization which applies to the alliance chain,and introduces a new consensus algorithm,generalized Legendre sequence(GLS)consensus algorithm,for alliance chain.GLS algorithms inherit the recognition and verification efficiency of binary sequence ciphers in computer communication and can solve a large number of nodes verification of key distribution issues.In the alliance chain,GLS consensus algorithm can complete node address hiding,automatic task sorting,task automatic grouping,task node scope confirmation,task address binding and stamp timestamp.Moreover,the GLS consensus algorithm increases the difficulty of network malicious attack.

Cooperative Relay Selection Mechanism in Multi-Hop Networks

In this paper, we consider a three-hop relay system based on interference cancellation technique in Underlay cognitive radio (CR) network. Although underlay CR has been shown as a promising technique to better utilize the source of primary users (PUs), its secondary performance will be severely degraded. On one hand, by adapting the Underlay spectrum sharing pattern, secondary users (SUs) would observe the strict power constraints and be interfered by primary users. On the other hand, limited transmit power results in limited transmission range, which greatly degrade the secondary transmission capacity. To solve the problems above, we propose an interference cancellation protocol for multi-hop wireless communication networks in underlay CR, which could develop the long-distance transmission performance and improve the transmission efficiency significantly. As simulation results shows, proposed scheme significantly reduce the secondary outage probability and increase the secondary diversity than the traditional cases.