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The internet is an abundant source of news every day. Thus, efficient algorithms to extract keywords from the text are important to obtain information quickly. However, the precision and recall of mature keyword extraction algorithms need improvement. TextRank, which is derived from the PageRank algorithm, uses word graphs to spread the weight of words. The keyword weight propagation in TextRank focuses only on word frequency. To improve the performance of the algorithm, we propose Semantic Clustering TextRank (SCTR), a semantic clustering news keyword extraction algorithm based on TextRank. Firstly, the word vectors generated by the Bidirectional Encoder Representation from Transformers (BERT) model are used to perform k-means clustering to represent semantic clustering. Then, the clustering results are used to construct a TextRank weight transfer probability matrix. Finally, iterative calculation of word graphs and extraction of keywords are performed. The test target of this experiment is a Chinese news library. The results of the experiment conducted on this text set show that the SCTR algorithm has greater precision, recall, and F1 value than the traditional TextRank and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms.

With the rapid development of Internet of Things (IoT) technology, Network Function Virtualization (NFV) is introduced in the edge network to provide flexible and personalized service. However, there still exist some problems to be solved, such as high cost, unbalanced load, and low availability. Therefore, a reliability-and-energy-balanced Service Function Chain (SFC) mapping and migration method is presented for IoT applications. First, aiming at improving network performance and reducing expenditure, an SFC mapping algorithm based on cost optimization, load balancing, and reliability is proposed to map SFC requests onto the network and provide backup. Second, aiming at optimizing resource configuration, an SFC migration method based on energy consumption and quality of service is proposed to integrate network resources. Simulation results show that the proposed method outperforms the compared algorithms by 15.5% and 24.55% in the acceptance ratio of SFC requests and the overall costs, respectively.

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In order to improve the performance of external disturbance rejection of permanent magnet synchronous motor (PMSM) in speed control, sliding mode control with extended state observer is adopted in this paper. First, an exponential function-based sliding mode reaching law (ESMRL) is developed. The ESMRL can dynamically adapt to the variations of the controlled system, which decrease the reaching time in reaching stage and void chattering in sliding motion stage while maintaining high tracking accuracy of the servo system. Then, an extended state observer (ESO) is introduced to the controller to simultaneously estimate external disturbance and compensate the uncertainties. Simulation results demonstrate that the proposed method has better suppression of chattering effect and disturbance rejection ability while ensuring dynamic performance.

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