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A smoothing neural network (SNN) can be proposed for solving a kind of constrained convex problems, which has wide applications in image restoration. The optimization model is a nonsmooth convex problem with convex constraint defined by a class of affine equalities. Thanks to the smoothing methods, the SNN can be modeled by a differential equation instead of a differential inclusion and it can be...
This paper researches the application of trajectory optimization method in trajectory design of multi-constraint boost-phase terminal. For the boost-glide vehicle, the multi-constraint parameters are deduced from the analysis of the trajectory characteristic. Considering the process constraints, the boost-phase trajectory optimization model is constructed based on the max-velocity. The optimal control...
In seismic data processing, Prestack Kirchhoff time migration is a forming method, and widely used. In this paper, we introduced how to port the original CUDA program to OpenCL, and how to implement and optimize Prestack Kirchhoff Time Migration algorithm on OpenCL and General Purpose GPU, and how to optimize the OpenCL program to get the competitive performance comparing with the original CUDA version...
This paper proposes a new approach for the multi-sensor management with multi-target. To meet the need of the engineering practice, we transform the problem of the multi-sensor management into two consecutive optimization processes with different objectives. First optimization objective is the maximum number of the tracking-target obtained by assigning one sensor to each target, while the second is...
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