The task of hand reconstruction aims to estimate the shape and pose of hands from the input images. Deep learning algorithms have shown remarkable performance in the field of computer vision in recent years, promoting...
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As an essential component of modern industry, steel strips play an indispensable role in various fields and serve as a crucial raw material in industrial production. However, due to various factors such as production ...
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Computing offload is a key technology in mobile edge computing (MEC). By offloading tasks to the edge servers, it solves the problem that terminal devices in the edge network cannot handle high computing and time sens...
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Recently, the use of neural radiance fields (NeRF) for three-dimensional reconstruction of humans has achieved high-quality view rendering. However, existing methods often rely on multiple or single videos as input, r...
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Currently, the advanced geological prediction for the main tunnel from the parallel tunnel often employs parallel deduction of detection conclusions, which faces issues such as a lack of diverse geological exploration...
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Image feature matching is an important research field in computer vision that can be widely applied to advanced vision tasks, such as 3D reconstruction and visual tracking. Considering the low matching accuracy and po...
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The multitude of advantages offered by distributed power generation units are recognized as crucial factors in enhancing the security of distribution networks. Ensuring the optimal size and placement of distributed ge...
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Locating 3D objects from a single RGB image via perspective projection is a long-standing problem in computer vision. Inspired by differentiable geometry methods in the field of camera relocation, we apply this neural...
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Accurate short-term power load forecasting is important to ensure the reliability and economy of the power grid. Convolutional neural networks are widely used, but they are difficult to capture the multi-scale tempora...
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In this paper,a stochastic linear quadratic optimal tracking scheme is proposed for unknown linear discrete-time(DT)systems based on adaptive dynamic programming(ADP)***,an augmented system composed of the original sy...
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In this paper,a stochastic linear quadratic optimal tracking scheme is proposed for unknown linear discrete-time(DT)systems based on adaptive dynamic programming(ADP)***,an augmented system composed of the original system and the command generator is constructed and then an augmented stochastic algebraic equation is derived based on the augmented ***,to obtain the optimal control strategy,the stochastic case is converted into the deterministic one by system transformation,and then an ADP algorithm is proposed with convergence *** the purpose of realizing the ADP algorithm,three back propagation neural networks including model network,critic network and action network are devised to guarantee unknown system model,optimal value function and optimal control strategy,***,the obtained optimal control strategy is applied to the original stochastic system,and two simulations are provided to demonstrate the effectiveness of the proposed algorithm.
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