The development of internet technology is continuously increasing day by day. Social network is also the internet based technology, running online to deliver more services and strengthen communities. The availability ...
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The development of internet technology is continuously increasing day by day. Social network is also the internet based technology, running online to deliver more services and strengthen communities. The availability ...
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The development of internet technology is continuously increasing day by day. Social network is also the internet based technology, running online to deliver more services and strengthen communities. The availability of large amount of data encouraged the various field of study like data science, data mining and analysis, neural network etc. Currently, in real scenario the two giants Facebook and Google have the tremendous amount of data. The link prediction problem is a key issue with social network analysis. The Link prediction issue is to predicting new link or future link between two nodes in a network. This paper talked about the several method used to address the issue of link prediction based on learning approach mainly it focus on supervised, unsupervised and semi supervised learning based methods.
Robotic throwing and catching of objects is a promising way of material transportation. For successful catch of the flying object accurate prediction of its trajectory in the gripper workspace is required. While most ...
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Robotic throwing and catching of objects is a promising way of material transportation. For successful catch of the flying object accurate prediction of its trajectory in the gripper workspace is required. While most state-of-the-art solutions use physical models to get a trajectory forecast we apply a predictor based on nearest neighbor regression, which does not require exact physical model of the motion. The challenge of such application consist in high volume of calculations that are needed to compare the current trajectory with examples from the database. This issue is critical as the prediction must be real-time. Two approaches for speeding up the procedure are discussed. One approach is based on fast allocation of the small subset from the entire dataset. The current trajectory is compared only with the trajectories from this subset. Another approach is based on the parallelization of computations using graphical processing units. Both approaches are evaluated and compared based on real trajectories. The parallelized version of the algorithm is implemented on the robotic catching system. It provide successful catch for up to 86 % of thrown objects.
In this paper we present two methods for path delay fault testing of circuit-switched Benes Multistage Interconnection Networks (MINs) with centralized control. Although the number of paths is O(n3), the first method ...
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