This paper overviews recent advances in the field of switched fuzzy systems, switched systems whose subsystems are fuzzy systems, followed by the comparative study for this kind of systems. Starting with the basic ide...
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This paper presents a distributed multi sensor data processing and fusion system providing sophisticated surveillance capabilities in the urban environment. The system enables visual/non-visual event detection, situat...
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The main purpose of this paper is to make table tennis robots complete the hit table tennis ball action by imitating human’s behavior. The main strategy is to record a video of action which people played the table te...
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The main purpose of this paper is to make table tennis robots complete the hit table tennis ball action by imitating human’s behavior. The main strategy is to record a video of action which people played the table tennis, then analysis the video of the racket trajectory. The racket in the image is extracted by image processing when the each frame is captured in the video. Then three-dimensional coordinates of the center of the racket and racket posture are obtained via PnP positioning approach based on the intrinsic parameters of the camera. The table tennis robot will off-line learn to complete the imitation of basic actions of the racket trajectory and postures. A large number of experimental data is used to establish basic actions of table tennis robot.
For a freeway traffic system with strict repeatable pattern, iterative learning control (ILC) has been successfully applied to local ramp metering for a macroscopic freeway environment by formulating the original ramp...
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For a freeway traffic system with strict repeatable pattern, iterative learning control (ILC) has been successfully applied to local ramp metering for a macroscopic freeway environment by formulating the original ramp metering problem as an output tracking, disturbance rejection, and error compensation problem. In this paper, we address the freeway traffic ramp-metering system under a nonstrict repeatable pattern. ILC-based ramp metering and ILC add-on to ALINEA strategies are modified to deal with the presence of iteration-dependent parameters, iteration-dependent desired trajectory, and input constraints. Theoretical analysis and extensive simulations are used to verify the effectiveness of the proposed approaches.
It is very difficult to capture the target in the microgravity environment, simply by relying on ground operators. Based on shared control theory, this paper effectively combines the decision-making ability of ground ...
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It is very difficult to capture the target in the microgravity environment, simply by relying on ground operators. Based on shared control theory, this paper effectively combines the decision-making ability of ground operators and the independent ability of space robot, through making full use of independent control ability of the space robot, and reducing operators' workload and working time, to achieve more valid capture for targets. Shared control is more accurate capture of the target compared with teleoperation in experimental.
Self-organizing methods can efficiently search, route and replicate content in complex, dynamic networks. Furthermore, they make the assumption that decisions of the nodes of the networks rely only on local informatio...
Self-organizing methods can efficiently search, route and replicate content in complex, dynamic networks. Furthermore, they make the assumption that decisions of the nodes of the networks rely only on local information and therefore the global optimum is not known. For evaluation purposes, however, it is important to compute the global optimum to serve as a theoretical bound. In this paper we define a formal model describing the problem of content placement and use an integer linear programming (ILP) based optimization method. With this method we discover the Quality of Service (QoS) bounds of a self-organizing content delivery system. We further demonstrate how to balance between run-time complexity and accuracy of the model by applying a use case.
It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we ...
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It has been shown that the Universum data, which do not belong to either class of the classification problem of interest, may contain useful prior domain knowledge for training a classifier [1], [2]. In this work, we design a novel boosting algorithm that takes advantage of the available Universum data, hence the name UBoost. UBoost is a boosting implementation of Vapnik's alternative capacity concept to the large margin approach. In addition to the standard regularization term, UBoost also controls the learned model's capacity by maximizing the number of observed contradictions. Our experiments demonstrate that UBoost can deliver improved classification accuracy over standard boosting algorithms that use labeled data alone.
Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a d...
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Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a deterministic or stochastic/fuzzy model yields quite poor performance in reality. In real environments, assembly line balancing robustness is a more appropriate decision selection guide. A robust model based on the α worst case scenario is developed to compensate for the drawbacks of traditional robust criteria. A robust genetic algorithm is used to solve the problem. Comprehensive computational experiments to study the effect of the solution procedure show that the model generates more flexible robust solutions. Careful tuning the value of α allows the decision maker to balance robustness and conservativeness of as- sembly line task element assignments.
Mobility is an important issue in the research of mobile delay-tolerant networks (DTNs). A simple grid model has been frequently used to simulate urban road networks in geographical restricted mobility models. However...
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ISBN:
(纸本)9781467358088
Mobility is an important issue in the research of mobile delay-tolerant networks (DTNs). A simple grid model has been frequently used to simulate urban road networks in geographical restricted mobility models. However, by analyzing graph attributes of some urban road networks in main cities of Europe and USA, we discovered the discrepancy between real road network samples and the grid model. Based on the finding, we proposed a random graph-based road network model, called the Grid Model with Random Edges (GRE). The GRE model extends the basic grid model with new probabilistic parameters and thus has better capabilities to approximate real-world road networks. The model was validated through optimizing model parameter values using a genetic algorithm and comparing graph attributes of road networks generated by the model. It was demonstrated that the GRE model has better capability on approximating real road networks than the grid model, thus providing a better foundation for mobility modeling in mobile DTNs.
Utility services provided by cloud computing rely on virtual customer communities forming spontaneously and evolving continuously. Clarifying the explicit boundaries of these communities is thus essential to the quali...
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