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Si X.,Beijing Jiaotong University | Si X.,Key Laboratory of Communication and Information Systems | Liu Y.,Beijing Jiaotong University | Liu Y.,Key Laboratory of Communication and Information Systems
Journal of Internet Technology | Year: 2011

Internet chat rooms make it convenient for people to communicate with others anywhere in the world about any topics. Only by understanding the discipline of opinion formation under the influence of internet chat rooms, can we succeed in monitoring and guiding opinions effectively, and thereby securing internal networks. We propose a multi-agent model permitting neutral opinions to investigate the influence of internet chat rooms on network public opinion evolution. The populations with Internet Chat Room always reach consensus instead of fragmentation. The convergence time decays remarkably as more and more people participate in discussion in Internet Chat Room. Moreover, Internet Chat Room also can help group advertise their opinion. Our investigation demonstrates that internet chat rooms strengthen our communication between each other, and accelerate the process of opinion spreading. Source


Liu Y.,Beijing Jiaotong University | Liu Y.,Key Laboratory of Communication and Information Systems | Si X.-M.,Beijing Jiaotong University | Si X.-M.,Key Laboratory of Communication and Information Systems | And 3 more authors.
International Journal of Modern Physics C | Year: 2012

Community structure is another important feature besides small-world and scale-free property of complex networks. Communities can be coupled through specific fixed links between nodes, or occasional encounter behavior. We introduce a model for opinion evolution with multiple cluster-coupled patterns, in which the interconnectivity denotes the coupled degree of communities by fixed links, and encounter frequency controls the coupled degree of communities by encounter behaviors. Considering the complicated cognitive system of people, the CODA (continuous opinions and discrete actions) update rules are used to mimic how people update their decisions after interacting with someone. It is shown that, large interconnectivity and encounter frequency both can promote consensus, reduce competition between communities and propagate some opinion successfully across the whole population. Encounter frequency is better than interconnectivity at facilitating the consensus of decisions. When the degree of social cohesion is same, small interconnectivity has better effects on lessening the competence between communities than small encounter frequency does, while large encounter frequency can make the greater degree of agreement across the whole populations than large interconnectivity can. © 2012 World Scientific Publishing Company. Source


Xiong F.,Beijing Jiaotong University | Xiong F.,Key Laboratory of Communication and Information Systems | Liu Y.,Beijing Jiaotong University | Liu Y.,Key Laboratory of Communication and Information Systems | And 2 more authors.
International Journal of Modern Physics C | Year: 2011

Based on the voter model, we present a new opinion formation model which takes into account the evolution of both opinions and individual inclinations. A memory-based inclination is developed gradually during the process of social interaction; however, if the individual inclination gets strong enough, it will react to opinion dynamics. We assume that an individual inclination increases with the number of times the individual has held its most frequent opinion in the past interactions. As a result of inclination choices the transition rate following neighbors decreases, thus slowing down the microscopic dynamics. Analytical and simulation results indicate the system under the action of opinion inclinations evolves to a more polarized state for average opinion. The appearance of extremists holding the minority opinion is observed in the final state, where one opinion predominates. It is also found that the stable opinion and relaxation time depend on network topology and memory length. Moreover, this model is not only valid to the voter model, but can also be applied to other spin systems. © 2011 World Scientific Publishing Company. Source


Cheng J.-J.,Key Laboratory of Communication and Information Systems | Liu Y.,Key Laboratory of Communication and Information Systems | Cheng H.,Key Laboratory of Communication and Information Systems | Zhang Y.-C.,Key Laboratory of Communication and Information Systems | And 2 more authors.
Journal of Convergence Information Technology | Year: 2011

With the vast amount of digitized materials now available on the Internet, it is almost a hard job to monitor the growth trends of network consensus in a timely manner. To alleviate the problem, we present a novel approach for predicting online topic growth trends based on the artificial neural networks (ANNs). Our technique consists of two steps. First, find out hot topics and utilize the preprocess module to obtain the time series from the initial data. Second, based on the selected Back Propagation neural network (BPNN), the historical data has been trained with a preset precision, and then we can detect the mapping relationship between the test (output) and some previous data (input) in time series, with which we can predict the topic growth trends in a short time. The results of our empirical tests show that this approach is more effective than existing method ARIMA in growth trends forecasting. Source


Shen B.,Key Laboratory of Communication and Information Systems | Zhang W.-Y.,Key Laboratory of Communication and Information Systems | Qi D.-P.,Key Laboratory of Communication and Information Systems | Wu X.-Y.,Key Laboratory of Communication and Information Systems
International Journal of Distributed Sensor Networks | Year: 2015

Subway tunnel cracks directly reflect the structural integrity of a tunnel, and as such the detection of subway tunnel cracks is always an important task in tunnel structure monitoring. This paper presents a convenient, fast, and automated crack detection method based on a wireless multimedia sensor subway tunnel network. This method primarily provides a solution for image acquisition, image detection and identification of cracks. In order to quickly obtain a surface image of the tunnel, we used special train image sensor nodes to provide the high speed and high performance processing capability with a large-capacity battery. The proposed process can significantly reduce the amount of data transmission by compressing the binary image obtained by initial processing of the original image. We transferred the data compressed by the sensor to an appropriate station and uploaded them to a database when the train passes through the station. We also designed a fast, easy to implement fracture identification and detection image processing algorithm that can solve the image identification and detection problem. In real subway field tests, this method provided excellent performance for subway tunnel crack detection, and recognition. © 2015 Bo Shen et al. Source

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