ID 原文 译文
23505 仿真结果表明，该算法提高了服务用户比例，并使得网络资源获得最大收益。 The simulation results show that the algorithm improves the percentage of service users and maximizes the network revenue.
23506 基于单幅高分辨率遥感图像的建筑物几何参数提取结果的准确性，容易受图像背景、图像噪声以及灰度分布相似性的干扰，形成错误的提取结果。 Due to algorithms for buildings' geometric parameters extraction from single high remote sensing image usually suffer from disturbances of background, noise and intensity similarity, resulting in wrong extraction results.
23507 针对这一问题，该文提出一种新的基于形状先验的变分水平集提取方法，该方法同时使用图像边缘信息、区域灰度信息以及包含屋顶和侧立面的形状先验信息，实现单幅遥感图像中建筑物的几何参数的提取。 In this paper, a novel variational level set model is proposed based on shape prior which integrates edge, gray, and shape prior including roof and facade, to extract buildings' geometric parameters from a single high resolution remote sensing image.
23508 实验结果表明，该方法能够更加准确地提取建筑物，最后得到的几何参数比较接近真值，并且由于更加充分地使用了全局形状信息该方法能更好地抵制侧立面的干扰，具有很强的鲁棒性。 Experimental results show that the proposed method can extract buildings' geometric parameters accurately. Moreover, it has strong robustness with respect to the disturbance of facade due to the more sufficient utilization of the whole shape prior.
23509 非凸非光滑优化问题涉及科学与工程应用的诸多领域，是目前国际上的研究热点。 Nonconvex nonsmooth optimization problems are related to many fields of science and engineering applications, which are research hotspots.
23510 该文针对已有基于早期罚函数神经网络解决非光滑优化问题的不足，借鉴 Lagrange 乘子罚函数的思想提出一种有效解决带等式和不等式约束的非凸非光滑优化问题的递归神经网络模型。 For the lack of neural network based on early penalty function for nonsmooth optimization problems, a recurrent neural network model is proposed using Lagrange multiplier penalty function to solve the nonconvex nonsmooth optimization problems with equality and inequality constrains.
23511 由于该网络模型的罚因子是变量，无需计算罚因子的初始值仍能保证神经网络收敛到优化问题的最优解，因此更加便于网络计算。 Since the penalty factor in this network model is variable, without calculating initial penalty factor value, the network can still guarantee convergence to the optimal solution, which is more convenient for network computing.
23512 此外，与传统 Lagrange 方法不同，该网络模型增加了一个等式约束惩罚项，可以提高网络的收敛能力。 Compared with the traditional Lagrange method, the network model adds an equality constraint penalty term, which can improve the convergence ability of the network.
23513 通过详细的分析证明了该网络模型的轨迹在有限时间内必进入可行域，且最终收敛于关键点集。 Through the detailed analysis, it is proved that the trajectory of the network model can reach the feasible region in finite time and finally converge to the critical point set.
23514 最后通过数值实验验证了所提出理论的有效性。 In the end, numerical experiments are given to verify the effectiveness of the theoretic results.