To improve the quality and performance of feature point detection and to indirectly improve the model effect of 3D reconstruction, a multi-scale convolution feature point detection model was established. Using the idea of multi-scale model, a method was designed to detect point feature points through supervised learning and convolution operation. Based on the fusion of multi-scale and visual attention mechanisms, identity mapping was used to improve the shortcomings of image feature loss and increase the repetition rate of feature point detection. By testing on different data sets, a large number of accurate and repeated feature points are effectively detected, which effectively reduces the time cost of detection compared with similar methods.

为提高圆锥动静压轴承的综合性能,以单位承载力下功耗最小和平均温升最低为优化目标,考虑几何结构约束条件,采用最优拉丁超立方进行设计空间的布点,进行有限元数值计算.基于计算结果,采用Kriging方法建立目标函数的近似代理模型.在此模型基础上,使用非劣分层遗传算法(NSGA-II)获得Pareto最优解集;最后通过权重系数法求得最优非劣解.结果表明:优化后方案1的两个目标函数值分别较优化前减小了18.8％和10％,优化方案2分别降低了10.9％、32％;轴承无量纲功耗有所降低、无量纲承载力得到提升,温升降低明显,轴承整体性能较优化前有较大提升.

Multi-objective optimization can reveal the complex parameter-objective relationships in the high-dimensional design problems. However, the data-extraction and data-presentation of the high-dimensional complex nonlinear system suffers from the increasing dimensionality. Key features and data-distribution of high-dimensional design spaces:parameter and objective spaces could be obtained by using Self-Organizing Maps (SOM) method, which re-clusters the high-dimensional multi-attribute data existing on the Pareto front into several low-dimensional maps. Correlations among all the design variables can be drawn according the colorized topological structure of the maps. Under the constraints including geometric structure and operating parameters, a low-cost and high accurate Kriging surrogate model was established to optimize a hybrid sliding bearing based on the sequential design method. Correlations between 3 objectives:"friction-to-load" ratio, temperature rise, instability threshold speed and 4 design parameters were extracted by SOM. Optimal feature regions were captured and analyzed. Results show that, within the specific feasible design space, supply pressure, axial bearing land width have important impact on the selected objectives, whereas the other parameters such as deep pocket depth and shallow pocket angle have relatively limited impact. A series of corresponding design decisions and optimization results help to understand the mechanism of the hybrid sliding bearing system in a much more intuitive way.

利用Kriging代理模型提供目标函数无偏预测值和理论置信区间的优势,比较传统试验设计方法和基于Kriging模型的序贯加点方法对模型的影响,结合设计空间的全局搜索和最优解临近区间的局部搜索,引入并行加点准则及相应的收敛条件,得到精度、效率高的代理模型,用2个经典优化测试函数进行验证和评估.结果表明,与传统试验设计方法相比,基于Krig-ing的序贯加点方法得到的模型全局精度更高且能更快地收敛到优化问题的真实最优解.最后,以动静压滑动轴承为优化设计对象,单位承载力下摩擦功耗为目标函数,考虑几何结构及工况等约束条件,采用传统试验设计方案和Kriging序贯加点方案分别建立目标函数的模型,分别进行优化设计,并同传统的复合形优化结果进行对比.3种方案对比结果显示,Kriging加点方案在有限迭代步数下,对于降低单位承载力下的摩擦功耗效果最为显著,验证了该方法快速收敛的特性.