Robust Fusion(强大的融合)研究综述
Robust Fusion 强大的融合 - This would contribute to a more robust fusion by taking advantage of the complementary properties of the two-scale observed focus maps, i. [1] For a robust fusion, a cooperative redundancy validation method is proposed to detect the outliers. [2] Among the 120 rostral levels instrumented percutaneously, robust fusion was noted in 25 (20. [3] We did not observe detectable GPcl-mediated fusion with NPC1 or its GPcl binding domain at any pH tested, while robust fusion was consistently observed with GP from lymphocytic choriomeningitis virus at low pH. [4] The method relies on a robust fusion of the multiple candidate textures for each surface patch, and optionally on a geometric criterion to remove the shadows of the known objects. [5] The technique allows for robust fusion while avoiding many of the complications associated with more traditional anterior and posterior approaches. [6]这将通过利用两尺度观察到的焦点图的互补特性来促进更稳健的融合，即。 [1] 对于鲁棒融合，提出了一种协作冗余验证方法来检测异常值。 [2] 在经皮检测的 120 个头端水平中，有 25 个（20. [3] 在任何测试的 pH 值下，我们都没有观察到可检测到的 GPcl 介导的与 NPC1 或其 GPcl 结合结构域的融合，而在低 pH 值下与淋巴细胞性脉络丛脑膜炎病毒的 GP 一致地观察到强融合。 [4] 该方法依赖于每个表面块的多个候选纹理的稳健融合，并且可选地依赖于几何标准来去除已知对象的阴影。 [5] 该技术允许稳健融合，同时避免与更传统的前路和后路方法相关的许多并发症。 [6]
robust fusion algorithm 稳健的融合算法
A robust fusion algorithm based on Radial Basis Function (RBF) neural network with Takagi–Sugeno (TS) fuzzy model is proposed in view of the data loss, data distortion or signal saturation which is usually occurred in the process of infrared flame detecting with multiple sensors. [1] The results from both the simulation and the cadaver trial have shown the effectiveness of the proposed robust fusion algorithm. [2] This paper provides a robust fusion algorithm for accurate position estimation under uncertain large errors in range measurements. [3]针对红外火焰检测过程中经常出现的数据丢失、数据失真或信号饱和等问题，提出一种基于径向基函数（RBF）神经网络与Takagi-Sugeno（TS）模糊模型的鲁棒融合算法。传感器。 [1] 仿真和尸体试验的结果都表明了所提出的鲁棒融合算法的有效性。 [2] 本文提供了一种鲁棒的融合算法，用于在距离测量中不确定的大误差下进行准确的位置估计。 [3]