采用UMT-2型微观多功能测试系统,在试验环境为牛血清,法向加载应力0.8 MPa,加载时间从5 s到60 m in变化条件下分别测定了牛膝关节软骨在静态加载和滑行时的法向位移以及滑行时的摩擦系数.结果表明:上、下软骨间的法向位移与加载时间呈...采用UMT-2型微观多功能测试系统,在试验环境为牛血清,法向加载应力0.8 MPa,加载时间从5 s到60 m in变化条件下分别测定了牛膝关节软骨在静态加载和滑行时的法向位移以及滑行时的摩擦系数.结果表明:上、下软骨间的法向位移与加载时间呈非线性增加;滑行时法向位移影响软骨间摩擦系数,启动摩擦系数高于平坦滑行过程中的摩擦系数;滑行速率对软骨摩擦系数的影响较小,加载时间越长,法向位移越大,所对应的启动摩擦系数越大;加载时法向位移和滑行时启动摩擦系数具有类似的变化趋势.展开更多
A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared...A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.展开更多
文摘采用UMT-2型微观多功能测试系统,在试验环境为牛血清,法向加载应力0.8 MPa,加载时间从5 s到60 m in变化条件下分别测定了牛膝关节软骨在静态加载和滑行时的法向位移以及滑行时的摩擦系数.结果表明:上、下软骨间的法向位移与加载时间呈非线性增加;滑行时法向位移影响软骨间摩擦系数,启动摩擦系数高于平坦滑行过程中的摩擦系数;滑行速率对软骨摩擦系数的影响较小,加载时间越长,法向位移越大,所对应的启动摩擦系数越大;加载时法向位移和滑行时启动摩擦系数具有类似的变化趋势.
基金supporting by grant fund under the Strategic Scholarships for Frontier Research Network for the PhD Program Thai Doctoral degree
文摘A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.
