In this paper, multi-weighted and directed complex dynamic networks (MWDCDNs) under the control of hybrid impulses are analyzed. Two cases are considered. In the first case, impulses can be only applied on one fixed n...
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ISBN:
(纸本)9781665426480
In this paper, multi-weighted and directed complex dynamic networks (MWDCDNs) under the control of hybrid impulses are analyzed. Two cases are considered. In the first case, impulses can be only applied on one fixed node. In the other case, impulses are applied on different nodes according to whether they are synchronized or de-synchronized in MWDCDNs with unbounded time delay. By limiting impulsive interval and Chebyshev distance between the normalized left eigenvectors (NLEVec) of multiple coupling matrices corresponding to the zero eigenvalue, we obtain some criteria for the synchronization of MWDCNs. Some numerical simulation are also given.
The Petri-net-based information flow analysis offers an effective approach for detecting information leakage by the concept of non-interference. Although the related studies propose efficient solutions, they lack quan...
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With the increasing popularity of social network, more and more people tend to store and transmit information in visual format, such as image and video. However, the cost of this convenience brings about a shock to tr...
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With the increasing popularity of social network, more and more people tend to store and transmit information in visual format, such as image and video. However, the cost of this convenience brings about a shock to traditional video servers and expose them under the risk of overloading. Among the huge amount of online videos, there are quite a number of Near-Duplicate Videos (NDVs). Although many works have been proposed to detect NDVs, few researches are investigated to compress these NDVs in a more effective way than independent compression. In this work, we utilize the data redundancy of NDVs and propose a video coding method to jointly compress NDVs. In order to employ the proposed video coding method, a number of pre-processing functions are designed to explore the correlation of visual information among NDVs and to suit the video coding requirements. Experimental results verify that the proposed video coding method is able to effectively compress NDVs and thus save video data storage.
In this paper,the finite time cluster consensus(FnTCC) of fractional-order multi-agent systems(FOMAS)with directed topology is *** fractional-order system is converted into an integer-order system by defining a neighb...
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In this paper,the finite time cluster consensus(FnTCC) of fractional-order multi-agent systems(FOMAS)with directed topology is *** fractional-order system is converted into an integer-order system by defining a neighborhood-based error variable,and suitable control rules are designed for the obtained first-order multi-agent *** to the exponential finite-time stability theorem,suitable Lyapunov functions are ***,the settling time function is *** simulation results prove the feasibility and validity of our theory.
We attack the sensor network deployment problem. We define the deployment problem as the problem of deciding how many sensor nodes should be deployed in the sensor field over how many phases during its lifetime. We ta...
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We attack the sensor network deployment problem. We define the deployment problem as the problem of deciding how many sensor nodes should be deployed in the sensor field over how many phases during its lifetime. We target the optimal deployment strategy that meets user-defined availability requirement with minimum total cost taking into consideration node failures and changing field trip to sensor node cost ratio. We model WSN availability and total cost as functions of the deployment plan, then, we formalize the deployment problem as a 2D optimization problem. Our modeling enables us to explore cost-benefit tradeoffs, we believe, this is a solid step toward bringing cost as an explicit dimension in the design space of WSN protocols. We compare the performance of the optimized solution (denoted as pro-active) to more ad-hoc solutions: on-demand and at-front. The former strategy schedules future deployments only on demand. The latter strategy deploys all nodes at front with no later field trips. Using extensive simulations, we show that proactive outperforms at-front and on-demand in terms of total cost per availability unit in all application scenarios. For example, using pro-active costs $7 compared to $40 and $280 per total uptime in case of on-demand and at-front, respectively.
This paper introduces an automatic Web service composition method based on logical inference of Horn clauses and Petri nets. The Web service composition problem is transformed into the logical inference problem of Hor...
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One of the most promising advantages of Web service technology is the possibility of creating value-added services by combining existing ones. A major challenge is how to discover and select concrete service according...
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One of the most promising advantages of Web service technology is the possibility of creating value-added services by combining existing ones. A major challenge is how to discover and select concrete service according to user requirements. This paper addresses the topic of service discovery composite Web services. The main feature is that we take the process model as well as service profile into account. Firstly, the process models of Web services are translated into Petri nets. Based on this, we propose a service matchmaking algorithm, via comparing the functionality compatibility and process consistency, thus leading to more accurate matchmaking.
Traffic forecasting plays a crucial role in intelligent transportation systems and finds application in various domains. Accurate traffic forecasting remains challenging due to the time-varying correlations within the...
Traffic forecasting plays a crucial role in intelligent transportation systems and finds application in various domains. Accurate traffic forecasting remains challenging due to the time-varying correlations within the data and the heterogeneous correlations between regions. Although various dynamic spatial-temporal graph models have been proposed to address these challenges in recent years, most of them are burdened by high computation costs and not intuitive to understand. In this paper, we propose a spatial-temporal graph model, Spatial-Temporal Dynamic Graph Diffusion Convolutional Network (SDGDN) that provides an effective and efficient approach to traffic forecasting. From the perspective of traffic flow transition probabilities, SDGDN learns dynamic graph structures to capture the time-varying traffic transition relationships. Besides dynamic graph structures, static node features are employed in diffusion convolution to better capture heterogeneous regional features. Furthermore, we utilize temporal encoding and also generate varying graphs in each stacked layer to enhance the forecasting performance. Experiments results on five real-world datasets demonstrate that SDGDN outperforms most baseline models in terms of both performance and computation efficiency.
To improve the availability of data in the cloud and avoid vendor lock-in risk, multi-cloud storage is attracting more and more attentions. However, accessing data from the cloud usually has some disadvantages such as...
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Recently, the deep neural network has achieved great performance in many areas. During analysis, all learned features are used at once, some of which could bring negative affect to specific classes. Recently, cognitiv...
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Recently, the deep neural network has achieved great performance in many areas. During analysis, all learned features are used at once, some of which could bring negative affect to specific classes. Recently, cognitive studies show that a human visual cognition process is hierarchical and dynamic, i.e., when meeting different targets, human brain intends to pay attention to different parts. Therefore, in this paper, we introduce this kind of mechanism into deep belief network (DBN) and propose a new general-to-specialized algorithm. Firstly, hierarchical knowledge networks are constructed based on the original learned DBN through pruning and retraining. Because these networks are learned for different discriminate ability, we call them as the general network and the specialized network separately. Secondly, a general-to-specialized analysis method is proposed which is proved theoretically. When predicting the class of an input sample, we select the corresponding specialized networks according to the preliminary analysis result and then make in-depth analysis. Experiments on four benchmark datasets are performed to test the proposed algorithm. The results show that our algorithm is feasible, valid and robust.
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