ID 原文 译文
38806 数值仿真结果表明:(1)当主信道的信道质量高于窃听信道时，较低的相关对系统安全中断概率性能影响不大，然而强相关却能够带来更低的安全中断概率; Numerical simulation results show that(1) when the channel quality of the main channel is higher than the eavesdropping channel, weak correlation has little effect on the secrecy outage probability of this system, but in strong correlation the secrecy outage probability decreases obviously;
38807 (2)这种相关对系统平均安全容量始终是有害的，且随着相关程度的增强，最大比发射技术相对于发射天线选择技术的优势逐渐变小。 (2) This correlation is always detrimental to the average secrecy capacity, and as the degree of correlation increases, the advantage of the maximum specific transmission technology over antenna selection gradually decreases.
38808 水下图像是水下信息的重要载体和呈现方式，对海洋资源的探索、开发、利用具有至关重要的作用。 Underwater images are important carrier and representation of underwater information, which is crucial for ocean resources' exploration, development, and utilization.
38809 然而，由于客观成像环境和设备的限制，水下图像质量总是差强人意，具有对比度低、细节模糊、颜色偏差等退化现象，严重制约相关领域的发展。 However, due to the limitations of objective imaging environment and imaging equipment, the quality of underwater image is always unsatisfactory, such as low contrast, blurred details, and color deviations, seriously restricting the development of related research areas.
38810 因此，如何通过后期算法对退化的水下图像进行增强和复原越来越受到学者们的关注。 Therefore, how to enhance and restore degraded underwater images through the designed post-processing algorithms has attracted more and more attention.
38811 近些年，随着深度学习技术的快速发展，基于深度学习的水下图像增强与复原技术取得了巨大进展。 In recent years, with the rapid development of deep learning strategy, deep learning-based underwater image enhancement and restoration technology has also made great progresses.
38812 为了更加全面、立体地对现有方法进行梳理与归纳，紧跟最新研究进展，本文着重对深度学习驱动的水下图像增强与复原的方法和模型进行介绍，详细整理现有的水下图像数据集，分析现有基于深度学习方法的关键问题，并对未来发展方向进行展望。 In order to comprehensively summarize the existing methods and keep up with the latest research progress, this paper introduces the methods and models of deep learning driven underwater image enhancement and restoration technology, summarizes the existing underwater image datasets in detail, analyzes the key issues of the existing deep learning-based methods, and prospects for the future development directions.
38813 基于流体动力学模型的烟雾仿真研究是计算机图形学和计算机仿真领域的重要分支。 Physically-based smoke simulation is an essential branch of computer graphics and computer simulation.
38814 本文提出通过追踪涡量的大小和方向并添加涡量修正力进行涡量补偿的格子玻尔兹曼算法实现烟雾的高效真实仿真。 This paper proposed Lattice Boltzmann method with vorticity refinement force calculated with tracked vorticity's magnitude and direction to realize smoke simulation with high performance and realism.
38815 算法从纳维-斯托克斯方程以及亥姆霍兹方程出发分别对烟雾仿真过程中涡量的大小和方向进行追踪，并利用追踪到的涡量信息和对应时刻的速度场信息添加涡量修正力进行涡量补偿和烟雾细节的补充，保证烟雾仿真符合物理规律并提升仿真的真实度。 Vorticity's magnitude and direction were tracked through Navier-Stokes equation and Helmholtz equation. Vorticity refinement force calculated with tracked vorticity and speed field information correspondingly was added to realize vorticity refinement and enrich details of smoke simulation, which made sure that smoke simulation results not only obey physical laws but also have high realism.