The orthodontic treatment often relies on the experience of doctors in traditional methods, and there are often fewer doctors with good experience, which is not conducive to improving the efficiency of patient consultation. Therefore, it has become the mainstream research direction in recent years to assist doctors in improving the efficiency of diagnosis by simulating the dental orthodontic process through computers. The orthodontic process is a multiobjective and high-dimensional path planning problem. To optimize the movement path of multiobjective orthodontics and compensate for the movement efficiency of invisible appliances, a preovercorrection orthodontic motion path scheme based on an improved multi-PSO algorithm was proposed to reduce the dimension disaster and the movement cost and improve the success rate of orthodontic surgery. Firstly, the solution set of the multiobjective particle swarm optimization (MOPSO) algorithm is introduced into the multi-PSO path planning algorithm to obtain the orthodontic movement path. Secondly, by analyzing the movement efficiency of the invisible appliance, tooth displacement compensation is designed and evaluated, and the final orthodontic scheme was generated for patients through the overcorrection method. Finally, the scheme is visualized by VTK visualization. The experimental results show that compared with the multi-PSO algorithm, the improved algorithm can reduce the length of the motion path by 10%, and the rotation angle is reduced by 4%. Meanwhile, the preovercorrection scheme designed can provide movement allowance for the orthodontic process, which guarantees that the optimal orthodontic path obtained by the scheme conforms to the clinical experiment results, ensuring that the tooth can move according to the expected path in the clinical experiment and assuring the success rate of orthodontic treatment.
How to generate the path planning of mobile robots quickly is a problem in the field of robotics. The Q-learning(QL) algorithm has recently become increasingly used in the field of mobile robot path planning. However, its selection policy is blind in most cases in the early search process, which slows down the convergence of optimal solutions, especially in a complex environment. Therefore, in this paper, we propose a continuous local search Q-Learning (CLSQL) algorithm to solve these problems and ensure the quality of the planned path. First, the global environment is gradually divided into independent local environments. Then, the intermediate points are searched in each local environment with prior knowledge. After that, the search between each intermediate point is realized to reach the destination point. At last, by comparing other RL-based algorithms, the proposed method improves the convergence speed and computation time while ensuring the optimal path.
In photography, accurate exposure is key to taking high-quality photos, particularly images with uneven exposure levels. Global exposure operations are usually difficult to effectively strengthen the various regions of the image. To achieve a balanced exposure level in different regions of the image, we propose a novel algorithm inspired by the luminance masking frequently employed by professional photographers. We use reinforcement learning to adaptively generate guiding regions and adjusting parameters as a basis for multi-step exposure fusion, to enhance the overall quality of the image. Firstly, reinforcement learning is employed to automatically segment the single image to be enhanced into multiple sub-images, with corresponding appropriate adjusting parameters for each sub-image generated. Then, the input image is enhanced using the local adjusting parameters, yielding a set of images with varying enhancing degrees. Finally, these images are fused in an exposure process to obtain the final result. Experimental results show that our method not only generates intuitive and interpretable guiding regions, but also its performance is comparable to that of other contemporaneous methods.INDEX TERMS Deep reinforcement learning, Image enhancement, Multi-step decision.
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