An experimental investigation was performed for active control of coherent structure bursting in the near-wall region of the turbulent boundary layer. By means of synchronous and asynchronous vibrations with double piezoelectric vibrators, the influence of periodic vibration of the double piezoelectric vibrators on the mean velocity profile, drag reduction rate, and coherent structure bursting is analyzed at Re θ = 2766. The case with 100 V/160 Hz-ASYN is superior to other conditions in the experiment and a relative drag reduction rate of 18.54% is exciting. Asynchronous vibration is more effective than synchronous vibration in drag reduction at the same voltage and frequency. In all controlled cases, coherent structures at large scales are regulated while the small-scale structures are stimulated. The fluctuating velocity increases significantly. A periodic regulating effect on the coherent structure can be seen in the ASYN control conditions at the frequency of 160 Hz.

Detecting community structures is an important research topic in social network analysis. Unfortunately, the fundamental factors that drive the generation of social networks (i.e., the network topology and content) and community structures have not been well investigated. In this paper, according to the natural characteristics of social networks, we reveal that individual topics play a core role in community generation. If two individuals are in the same community and are interested in similar topics, it is more likely that a link will form between them. Otherwise, the probability of generating a link depends on the relationships between their communities and the topics they talk about. Based on the above observations, a novel generative community detection model is proposed that simulates the generation of the network topology and network content by considering individual topics. Moreover, our model utilizes a topic model to generate network content. The model is evaluated on two real-world datasets. The experimental results show that the community detection results outperform all the state-of-the-art baselines. In addition to accurate community detection results, we identify each individual topic distribution and the most popular users corresponding to different topics in each community.

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This paper makes an effective combination of rough set and granular computing, and proposes a logic inference form based on rough granular computing. The advantage of rough logic granular computing lies in its demonstration form which is given by array, it can illustrate the formula semantic more directly, and furthermore the reasoning results can be got according to the data information searched momentarily. Chinese classification code: O159 Document identification code: A