Understanding the strategies to optimize/suppress information spreads under intense competition could provide important insights in a broad range of settings including viral marketing,emergency response and informatio...
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Understanding the strategies to optimize/suppress information spreads under intense competition could provide important insights in a broad range of settings including viral marketing,emergency response and information system ***,most of existing studies about competitive influence diffusion mainly focus on two-information competition *** date,the competitive influence maximization problem considering the mechanism of multi-information competition is still not well *** this paper,we conducted computational experiments to study the competitive influence maximization with multi-information competition *** applying an information diffusion model called limited attention model(LAM),we carried on two computational experiments to validate the model and investigate the relation between seed selection methods and the properties of information *** experimental results show that 1)the LAM model could reproduce the features of empirical distribution in Chinese social media;2)the eigenvector centrality-based heuristic is a reasonable seed selection method for competitive influence maximization *** results of this paper can provide significant potential implications for information system design and management.
In this paper, aiming at the indoor scene under monitoring by visual sensor network (VSN), an object recognition approach based on structural feature is presented. Firstly, we regard the output of existing line segmen...
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In this paper, aiming at the indoor scene under monitoring by visual sensor network (VSN), an object recognition approach based on structural feature is presented. Firstly, we regard the output of existing line segment detector LSD with proper parameters as the preliminary extraction result and it still will be further restored and split. Then, we give an inference model based on structural features of object including line segment ontology characteristics and relative relationship between the line segments. Finally, the objects are recognized with position information through inference. The effectiveness of the approach is verified, and the results show that our approach does not rely on segmentation and has robustness on partial defect and structural deformation to some extent.
In this paper, a model-free and effective approach is proposed to solve infinite horizon optimal control problem for affine nonlinear systems based on adaptive dynamic programming technique. The developed approach, re...
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In this paper, a model-free and effective approach is proposed to solve infinite horizon optimal control problem for affine nonlinear systems based on adaptive dynamic programming technique. The developed approach, referred to as the actor-critic structure, employs two multilayer perceptron neural networks to approximate the state-action value function and the control policy, respectively. It uses data collected arbitrarily from any reasonable sampling distribution for policy iteration. In the policy evaluation phase, a novel objective function is defined for updating the critic network, and thus makes the critic network converge to the Bellman equation directly rather than iteratively. In the policy improvement phase, the action network is updated to minimize the outputs of the critic network. The two phases alternate until no more improvement of the control policy is observed, such that the optimal control policy is achieved. Two simulation examples are provided to show the effectiveness of the approach.
Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal wi...
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Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.
According to the actual circumstance of the intersection,reasonably adjusting traffic light time can help to ease traffic pressure and save transportation *** this paper,Interval type-2 fuzzy sets and matched-degree a...
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According to the actual circumstance of the intersection,reasonably adjusting traffic light time can help to ease traffic pressure and save transportation *** this paper,Interval type-2 fuzzy sets and matched-degree are applied to the time of intersection signal adjusting,and five words are used to cover the range of adjusting lights’time,finally according to the average number of stranded vehicles,a query table is built to inquire and control the signal time during a certain period.
With the continuous development of data storage technology,the complexity,category and size of data increase sharply;on the other hand,the wide expansion of its application domain brings great challenges on the tradit...
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With the continuous development of data storage technology,the complexity,category and size of data increase sharply;on the other hand,the wide expansion of its application domain brings great challenges on the traditional data processing and analysis *** this paper,the data analysis and processing technology and its development status are introduced and by multi-dimensional data visualization analysis method and combining the knowledge of domain experts,the equipment purchasing fund data of a large enterprise is analyzed to provide reliable management decision to the investment and investment volume of business fund.
Researches on office building energy consumption have been hot in these years, but few researchers consider the classification of office energy consumption performance which can evaluate user behaviors in order to off...
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Researches on office building energy consumption have been hot in these years, but few researchers consider the classification of office energy consumption performance which can evaluate user behaviors in order to offer a clear analysis of energy consumption and improve their energy saving consciousness. In this paper, we propose a novel hierarchical classification algorithm for evaluating energy consumption behaviors at a real energy managementsystem, which combines fuzzy c-means clustering with GA (genetic algorithm)-based SVM (support vector machine) to fully utilize collected samples. The experiment results with real energy consumption data show that the proposed algorithm works well to distinguish the abnormal behaviors and classify energy consumption behaviors accurately on normal offices.
The leader-following output consensus problem of multi-agent systems (MAS) is studied in this paper. Each agent is modeled by a single-input single-output (SISO) system which can be further described by a controllable...
The leader-following output consensus problem of multi-agent systems (MAS) is studied in this paper. Each agent is modeled by a single-input single-output (SISO) system which can be further described by a controllable and observable linear state space model. An observer is constructed to estimate the agent's state, and the estimated state is shared with neighbor agents via the noisy communication channels. Similar to the previous work, in the proposed protocol a time-varying gain is employed to attenuate the noise's effect. However, in this paper, each agent is allowed to have its own time-varying gain. Some sufficient conditions on the time-varying gain are given for ensuring the consensus in the mean square sense. Finally, a simulation example is presented to verify the theoretical results.
Assessment and analysis of the intersection status can help to make the right choice of interventions,it’s can bring convenience to solve the traffic *** this paper,interval type-2 fuzzy sets is applied in the analys...
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Assessment and analysis of the intersection status can help to make the right choice of interventions,it’s can bring convenience to solve the traffic *** this paper,interval type-2 fuzzy sets is applied in the analysis of the intersection state and congestion intervention,dynamic fuzzy comprehensive evaluation and footprint of uncertainty are used to assess the intersection *** with the example of intersection congestion,through corresponding intervention measures to improve the intersections crowded *** the linguistic dynamic orbits of road status figured out.
Traffic control is an effective and efficient method for the problem of traffic *** complex urban traffic networks,it is necessary to design a high-level controller to regulate the traffic *** the parallel control fra...
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Traffic control is an effective and efficient method for the problem of traffic *** complex urban traffic networks,it is necessary to design a high-level controller to regulate the traffic *** the parallel control framework for complex traffic networks,we design a demand-balance MPC controller based on the MFD-based multi-subnetwork model,which can optimize the network traffic mobility and the network traffic throughput by regulating the input traffic flows of the *** transferring traffic flows among subnetworks are indirectly controlled by the demand-balance MPC controller,and a global optimality can be achieved for the entire traffic *** simulation results show the effectiveness of the proposed controller in improving the network traffic throughput.
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