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We introduce a new method for discrete-decision-variable optimization via simulation that combines the stochastic branch-and-bound method and the nested partitions method in the sense that we take advantage of the partitioning structure of stochastic branch and bound, but estimate the bounds based on the performance of sampled solutions as the nested partitions method does. Our Empirical Stochastic...
This paper presents a novel optimization algorithm called self-organizing potential field network (SOPFN). The SOPFN algorithm is derived from the idea of the vector potential field. In the proposed network, the neuron with the best weight is considered as the target with the attractive force, while the neuron with the worst weight is considered as the obstacle with the repulsive force. The competitive...
To overcome the challenges of recovery polices generation in the presence of inaccurate failure detection, a failure recovery model for microrebootable distributed systems based on discounted Partially Observable Markov Decision Processes is presented in this paper. Thus the reasonable recovery policies are generated by solving the POMDP model. To tackle the problem of computational complexity of...
Detection of linear structure is a very important problem in image processing and computer vision. The task of finding lines in 2D images has long being studied, but the work in 3D space does not have any promising work yet. This paper investigates the issue of line detection for range images. It proposes an approach to find a wire-frame composed of lines that can represent precisely and comprehensively...
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