ID 原文 译文
14215 仿真结果证明了所提策略的可行性与有效性。 Simulation results have proved the feasibility and validity of the proposed strategy.
14216 针对线性加权多核图聚类方法限制了共识核的表达能力和再生核希尔伯特空间中噪声污染的问题,提出一种鲁棒多核子空间图聚类算法(RSMKL),旨在增强核的表达能力和提高核空间中噪声的鲁棒性。 The current graph-based multiple kernel clustering usually adopts a linearly weighted strategy, which limits the representational capacity of the consensus kernel and ignores noise pollution in Reproducing Kernel Hilbert Space(RKHS). To solve the problem, a Robust Self-weighted Multiple Kernel Learning(RSMKL)graph-based subspace clustering algorithm is proposed,which is aimed at enhancing the representational capacity of the kernel and improving the robustness to noise in RKHS.
14217 该算法利用一种新颖的非线性自加权核融合策略来生成最佳的共识核,同时在核空间利用低秩约束模型来消除噪声对关系图质量的影响。 This algorithm adopts a novel nonlinear self-weighted kernel fusion strategy to generate the optimal consensus kernel, and then uses Low-Rank Representation(LRR)in RKHS to remove the influence of noise on the quality of affinity graphs.
14218 最后,提出一种基于交替方向乘子的迭代优化算法求解目标函数。 Finally, an alternating direction method of multipliers with iterative optimization is proposed to solve the objective function.
14219 与5种同类流行算法在5个常用数据集上比较,实验结果表明RSMKL在聚类精度(ACC)、标准互信息(NMI)和聚类纯度(Purity)上具有更好的聚类性能。 The experimental results on five common data sets show that, compared with five popular homogeneous algorithms,RSMKL possesses better clustering performance on the indexes of ACC. NMI and Purity.
14220 针对相互速度障碍法(RVO)避障速度理论上可选而实际应用中不可达的问题,综合考虑相互速度障碍法和动态窗口法(DWA)的优缺点,将两种算法进行融合,对可选避障速度进行运动学约束。 To solve the problem that the velocity of Reciprocal Velocity Obstacle(RVO) avoidance is optional in theory but is inaccessible in practical application, after comprehensively considering the advantages and disadvantages of RVO algorithm and Dynamic Window Approach(DWA),this paper fuses the two algorithms, so as to add kinematic constraints to the optional velocity of obstacle avoidance.
14221 进一步,针对复杂环境下多移动机器人相遇过程中膨胀半径可能导致移动机器人避障失败的问题,又提出了膨胀半径可变的自适应相互速度障碍法(ARVO)。 Furthermore, when multiple mobile robots are encountering in complex environment, the conventional expansion radius may lead to failure in obstacle avoidance. To solve the problem, an Adaptive Reciprocal Velocity Obstacle(ARVO) algorithm with variable expansion radius is proposed.
14222 该方法可以通过判断周边环境的复杂度对膨胀半径进行调整,既保障了移动机器人与周边障碍物的安全距离,又适当扩大了可选速度范围。 ARVO algorithm can adjust the expansion radius according to the complexity of the environment, which not only ensures a safe distance between the mobile robot and the surrounding obstacles, but also increases the range of optional speed.
14223 最后,通过机器人操作系统(ROS)中的Gazebo仿真平台验证了算法的良好性能。 Finally, the performance of the algorithm is verified by Gazebo simulation platform in Robot Operating System(ROS).
14224 针对由于传统W-H算法计算量大,检测效率不高,海面小目标检测难度较大的问题,提出了基于重排频谱时频脊的小目标检测新算法。 It is difficult to detect small targets on the sea surface due to large amount of calculation and low detection efficiency of the traditional W-H algorithm. To solve the problem, a new small target detection algorithm based on time-frequency ridge of rearranged spectrum is proposed.