Mix separately , then combine and mix part a : b = 2 : 1 by volume and stir well . mechanical mixing equipment is recommended 各组分搅拌均匀，再将两组分按a组分： b组分2 ： 1 （体积比）混合，并搅拌均匀，推荐使用机器搅拌。
Mix separately , then combine and mix part a : b = 3 : 2 by volume and stir well . mechanical mixing equipment is recommended 各组分搅拌均匀，再将两组分按a组分（液体） ： b组分（基料） 3 ： 2 （体积比）混合，并搅拌均匀，需使用机器搅拌。
Mix part a and part b separately and combine in the following proportions ; part a : part b = 4 : 1 by volume . mechanical mixing is recommended 各组分搅拌均匀，再将两组分按a组分： b组分4 ： 1 （体积比）混合，并搅拌均匀。推荐使用机器搅拌。
Caution : do not use before driving or operating machinery . not recommended if trying to conceive or for pregnant or lactating women . keep out of the reach of children 警告：孕妇，哺乳期间妇女忌用。此产品可令人昏睡，驾驶或使用机器前勿服。避免让儿童接触。
Tracking test and visit customer to realize problem and opinion of operate units , provide report and failure analysis , give feedback to engineering department 通过追踪及定期回访客户，了解客户在使用机器时存在的问题和意见，及时报告和反馈给研发等相关部门，提供失效分析报告。
Therefore , several different segmentation approaches are investigated in this paper , and machine - learning techniques are also used to meet the challenge of automated image segmentation . according to their different characteristics , images can be roughly separated into two classes : textures and non - textures . non - texture images usually have simple content , and can be properly segmented by gray level threshold 有鉴于此，本文首先针对具体的图像分析任务，研究了对于纹理图像和非纹理图像的分割技术；接着，尝试了使用机器学习技术来解决图像分割研究中遇到的困难，从而为因应图像分割所面临的挑战指出一个可能的努力方向。
We attempt to exploit various machine - learning techniques to learn the heuristic knowledge from users " experiences , so that the image segmentation system can have some human ability in adaptively selecting optimal algorithm and corresponding parameters . learning based image segmentation system can be classified i 希望使用机器学习的技术，通过从用户对训练图像集的分割和评价中学习相应的启发式知识，以此使系统能够根据图像的特征，为不同的图像灵活的选择参数或算法，从而自动实现令人满意的分割。
