The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command La...The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command Language codes.The optimization procedure was executed on Isight platform,on which the linear dimensionless method was introduced to establish the weighted multi-objective function.The extreme processing method was applied and proved effective to normalize the objectives.The bracket was optimized under the typical single loads and design waves,accompanied by the different proportions of weights in the objective function,in which the safety factor function was further established,including yielding,buckling,and fatigue strength,and the weight minimization and safety maximization of the bracket were obtained.The findings of this study illustrate that the dimensionless objectives share equal contributions to the multi-objective function,which enhances the role of weights in the optimization.展开更多
目的时空融合是解决当前传感器无法兼顾遥感图像的空间分辨率和时间分辨率的有效方法。在只有一对精细—粗略图像作为先验的条件下,当前的时空融合算法在预测地物变化时并不能取得令人满意的结果。针对这个问题,本文提出一种基于线性模...目的时空融合是解决当前传感器无法兼顾遥感图像的空间分辨率和时间分辨率的有效方法。在只有一对精细—粗略图像作为先验的条件下,当前的时空融合算法在预测地物变化时并不能取得令人满意的结果。针对这个问题,本文提出一种基于线性模型的遥感图像时空融合算法。方法使用线性关系表示图像间的时间模型,并假设时间模型与传感器无关。通过分析图像时间变化的客观规律,对模型进行全局和局部约束。此外引入一种多时相的相似像素搜寻策略,更灵活地选取相似像素,消除了传统算法存在的模块效应。结果在两个数据集上与STARFM(spatial and temporal adaptive reflectance fusion model)算法和FSDAF(flexible spatiotemporal data fusion)算法进行比较,实验结果表明,在主要发生物候变化的第1个数据集,本文方法的相关系数CC(correlation coefficient)分别提升了0.25%和0.28%,峰值信噪比PSNR(peak signal-to-noise ratio)分别提升了0.1531 d B和1.379 d B,均方根误差RMSE(root mean squared error)分别降低了0.05%和0.69%,结构相似性SSIM(structural similarity)分别提升了0.79%和2.3%。在发生剧烈地物变化的第2个数据集,本文方法的相关系数分别提升了6.64%和3.26%,峰值信噪比分别提升了2.0860 d B和2.5107 d B,均方根误差分别降低了1.45%和2.08%,结构相似性分别提升了11.76%和11.2%。结论本文方法根据时间变化的特点,对时间模型进行优化,同时采用更加灵活的相似像素搜寻策略,收到了很好的效果,提升了融合结果的准确性。展开更多
基金This work was financially supported by the Key Research and Development Project of Shandong Province(Grant No.2020CXGC010702).
文摘The shape and size optimization of brackets in hull structures was conducted to achieve the simultaneous reduction of mass and high stress,where the parametric finite element model was built based on Patran Command Language codes.The optimization procedure was executed on Isight platform,on which the linear dimensionless method was introduced to establish the weighted multi-objective function.The extreme processing method was applied and proved effective to normalize the objectives.The bracket was optimized under the typical single loads and design waves,accompanied by the different proportions of weights in the objective function,in which the safety factor function was further established,including yielding,buckling,and fatigue strength,and the weight minimization and safety maximization of the bracket were obtained.The findings of this study illustrate that the dimensionless objectives share equal contributions to the multi-objective function,which enhances the role of weights in the optimization.
文摘目的时空融合是解决当前传感器无法兼顾遥感图像的空间分辨率和时间分辨率的有效方法。在只有一对精细—粗略图像作为先验的条件下,当前的时空融合算法在预测地物变化时并不能取得令人满意的结果。针对这个问题,本文提出一种基于线性模型的遥感图像时空融合算法。方法使用线性关系表示图像间的时间模型,并假设时间模型与传感器无关。通过分析图像时间变化的客观规律,对模型进行全局和局部约束。此外引入一种多时相的相似像素搜寻策略,更灵活地选取相似像素,消除了传统算法存在的模块效应。结果在两个数据集上与STARFM(spatial and temporal adaptive reflectance fusion model)算法和FSDAF(flexible spatiotemporal data fusion)算法进行比较,实验结果表明,在主要发生物候变化的第1个数据集,本文方法的相关系数CC(correlation coefficient)分别提升了0.25%和0.28%,峰值信噪比PSNR(peak signal-to-noise ratio)分别提升了0.1531 d B和1.379 d B,均方根误差RMSE(root mean squared error)分别降低了0.05%和0.69%,结构相似性SSIM(structural similarity)分别提升了0.79%和2.3%。在发生剧烈地物变化的第2个数据集,本文方法的相关系数分别提升了6.64%和3.26%,峰值信噪比分别提升了2.0860 d B和2.5107 d B,均方根误差分别降低了1.45%和2.08%,结构相似性分别提升了11.76%和11.2%。结论本文方法根据时间变化的特点,对时间模型进行优化,同时采用更加灵活的相似像素搜寻策略,收到了很好的效果,提升了融合结果的准确性。
基金The National Science Foundation of China(10701066)Basic and Cutting-edge Technology Research Projects of Henan Province (092300410137)+1 种基金The Natural Science Foundation of Henan Education Committee(2008-755-65)The NationalScience Foundation of the Education Department of Henan province (2008A110022)
