Different device media can be used to display and output colors in the process of color information transmission. It is difficult to achieve high-fidelity reproduction of color information due to the different color r...
Different device media can be used to display and output colors in the process of color information transmission. It is difficult to achieve high-fidelity reproduction of color information due to the different color reproduction ranges of different devices. This paper designs a gamut mapping algorithm from the source device to the destination device. Based on the design principle of gamut mapping algorithm, the LCUSP algorithm is optimized and improved, and a comprehensive gamut mapping algorithm with both lightness and chroma compression is selected for experiments. The experimental results show that the chroma information of source gamut dark tone area is maintained in the same hue angle plane, which makes up for the poor reproduction ability of the dark tone part of the output device, and the overall reproduction degree is excellent.
key-point-based scene understanding is fundamental for autonomous driving applications. At the same time, optical flow plays an important role in many vision tasks. However, due to the implicit bias of equal attention...
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In the process of exploring quantum algorithms, researchers often need to conduct equivalence checking of quantum circuits with different structures or to reconstruct a circuit in a variational manner, aiming to reduc...
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Reinforcement learning is used to tackle complex tasks with high-dimensional sensory inputs. Over the past decade, a wide range of reinforcement learning algorithms have been developed, with recent progress benefiting...
Reinforcement learning is used to tackle complex tasks with high-dimensional sensory inputs. Over the past decade, a wide range of reinforcement learning algorithms have been developed, with recent progress benefiting from deep learning for raw sensory signal representation. This raises a natural question: how well do these algorithms perform across different robotic manipulation tasks? To objectively compare algorithms, benchmarks use performance metrics. Benchmarks use objective performance metrics to offer a scientific way to compare algorithms. In this paper, we introduce RMBench, the first benchmark for robotic manipulations with high-dimensional continuous action and state spaces. We implement and evaluate reinforcement learning algorithms that take observed pixels as inputs and report their average performance and learning curves to demonstrate their performance and training stability. Our study concludes that none of the evaluated algorithms can handle all tasks well, with soft Actor-Critic outperforming most algorithms in terms of average reward and stability, and an algorithm combined with data augmentation potentially facilitating learning policies. Our code is publicly available at https://***/xiangyanfei212/***, including all benchmark tasks and studied algorithms.
Autonomous navigation of ground robots on uneven terrain is being considered in more and more tasks. However, uneven terrain will bring two problems to motion planning: how to assess the traversability of the terrain ...
Autonomous navigation of ground robots on uneven terrain is being considered in more and more tasks. However, uneven terrain will bring two problems to motion planning: how to assess the traversability of the terrain and how to cope with the dynamics model of the robot associated with the terrain. The trajectories generated by existing methods are often too conservative or cannot be tracked well by the controller since the second problem is not well solved. In this paper, we propose terrain pose mapping to describe the impact of terrain on the robot. With this mapping, we can obtain the SE(3) state of the robot on uneven terrain for a given state in SE(2). Then, based on it, we present a trajectory optimization framework for car-like robots on uneven terrain that can consider both of the above problems. The trajectories generated by our method conform to the dynamics model of the system without being overly conservative and yet able to be tracked well by the controller. We perform simulations and real-world experiments to validate the efficiency and trajectory quality of our algorithm.
Ultraviolet communication (UVC) has emerged as a promising solution for close-range communication within the intricate electromagnetic setting. The atmosphere's attenuation, however, restricts the scope and rate o...
Ultraviolet communication (UVC) has emerged as a promising solution for close-range communication within the intricate electromagnetic setting. The atmosphere's attenuation, however, restricts the scope and rate of communication. We demonstrated a line-of-sight (LOS) UV communication system. In our system, we employed ultraviolet light-emitting diodes (LEDs) in our system to transmit the signal light with a broad radiation angle and use lenses to enhance the communication range. We propose a combined approach of theory and practice to analyze the feasibility of a LOS UVC system.
Environment exploration by autonomous robots through deep reinforcement learning (DRL) based methods has attracted more and more attention. However, existing methods usually focus on robot navigation to single or mult...
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Environment exploration by autonomous robots through deep reinforcement learning (DRL) based methods has attracted more and more attention. However, existing methods usually focus on robot navigation to single or multiple fixed goals, while ignoring the perception and construction of external environments. In this paper, we propose a novel environment exploration task based on DRL, which requires a robot fast and completely perceives all objects of interest, and reconstructs their poses in a global environment map, as much as the robot can do. To this end, we design an auxiliary task aided DRL model, which is integrated with the auxiliary object detection and 6-DoF pose estimation components. The outcome of auxiliary tasks can improve the learning speed and robustness of DRL, as well as the accuracy of object pose estimation. Comprehensive experimental results on the indoor simulation platform AI2-THOR have shown the effectiveness and robustness of our method.
The flexibility of buildings' thermal loads has been widely recognized as the resource in the operation of the heat and electricity integrated energy system(HE- IES). Recently, the data-driven method has proven to...
The flexibility of buildings' thermal loads has been widely recognized as the resource in the operation of the heat and electricity integrated energy system(HE- IES). Recently, the data-driven method has proven to be an effective means of measuring the flexibility in buildings' thermal loads. Nevertheless, measurement errors can hardly be avoided. This paper proposes an optimal dispatch model of the HE-IES that is compatible with the measurement errors of demand flexibility in buildings' thermal loads. First, the measurement errors are modeled as epistemic uncertainty related to the demand response and the multiple error sources are combined through the D-S evidence theory. Then, the representative scenarios are selected and reduced by the LHS and fuzzy clustering method. Last, the scenario-based stochastic scheduling model is developed to dispatch the HE- IES. The results demonstrate that considering the epistemic uncertainties of the thermal load demand response is essential for reducing the wind power curtailments and improving the flexibility of HE-IES.
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