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Predicting Human Mobility via Long Short-Term Patterns

Computer Modeling in Engineering & Sciences 2020年第9期124卷 847-864页

作者： Jianwei Chen Jianbo Li Ying Li College of Computer Science and Technology Qingdao UniversityQingdao266071China

Predicting human mobility has great significance in Location based Social Network applications,while it is challenging due to the impact of historical mobility patterns and current *** these challenges,historical patterns tend to be crucial in the prediction ***,it is difficult to capture complex patterns from long historical *** by recent success of Convolutional Neural Network(CNN)-based methods,we propose a Union ConvGRU(UCG)Net,which can capture long short-term patterns of historical trajectories and sequential impact of current ***,we first incorporate historical trajectories into hidden states by a shared-weight layer,and then utilize a 1D CNN to capture short-term pattern of hidden ***,an average pooling method is involved to generate separated hidden states of historical trajectories,on which we use a Fully Connected(FC)layer to capture longterm pattern ***,we use a Recurrent Neural Net-work(RNN)to predict future trajectories by integrating current trajectories and long short-term *** demonstrate that UCG Net performs best in comparison with neural network-based methods.

关键词： Human mobility prediction CNN average pooling

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Seed Selection for Data Offloading Based on Social and Interest Graphs

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Computers, Materials & Continua 2018年第12期57卷 571-587页

作者： Ying Li Jianbo Li Jianwei Chen Minchao Lu Caoyuan Li College of Computer Science and Technology Qingdao UniversityQingdao266071China School of Computer Beijing Institute of TechnologyBeijing100081China Faculty of Engineering and Information Technology University of Technology SydneyNSW 2006Australia

The explosive growth of mobile data demand is becoming an increasing burden on current cellular *** address this issue,we propose a solution of opportunistic data offloading for alleviating overloaded cellular *** principle behind it is to select a few important users as seeds for data *** three critical steps are detailed as *** first explore individual interests of users by the construction of user profiles,on which an interest graph is built by Gaussian graphical *** then apply the extreme value theory to threshold the encounter duration of user ***,a contact graph is generated to indicate the social relationships of ***,a contact-interest graph is developed on the basis of the social ties and individual interests of *** on different graphs,three strategies are finally proposed for seed selection in an aim to maximize overloaded cellular *** evaluate the performance of our algorithms by the trace data of real-word *** demonstrates the effectiveness of the strategy of taking social relationships and individual interests into account.

关键词： Mobile social network social data offloading extreme value model Gaussian graphical model

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Robust H2/H∞ Fuzzy Filtering for Nonlinear Stochastic Systems with Infinite Markov Jump

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Journal of Systems Science & Complexity 2020年第4期33卷 1023-1039页

作者： LIU Yueying HOU Ting College of Mathematics and Systems Science Shandong University of Science and TechnologyQingdao 266590China School of Mathematics and Statistics Shandong Normal UniversityJinan 250358China

Robust fuzzy filterings for a class of nonlinear stochastic systems with infinite Markov jump are explored based on T-S fuzzy model approach in this paper. First of all, the exponentially mean square stable filter with a prescribed H∞ performance is designed via linear matrix inequalities(LMIs). Secondly, H2/H∞ fuzzy filtering is solved in terms of LMIs by minimizing the estimation error energy under the worst case fuzzy disturbance. The corresponding fuzzy filter parameters can be received by convex optimization algorithms. And lastly, two illustrative examples are performed to manifest the validity of the proposed methods.

关键词： H2/H∞ filtering Markov

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