吉尔布雷思夫妇设计了一个分类体系，标记了17种基本手势，他们将其称为动作分类体系。
Frank and Lillian Gilbreth devised a classification scheme to label 17 basic hand motions which they called therbligs.
导航器插件的行为由我们给它绑定的内容、动作，以及使用CNF(过滤器、分类器等)时插件中设置的其他选项进行管理。
The behavior of our navigator plug-in will be governed by the content we bind to it, the actions we bind to it, and the other options we set in our plug-in when using CNF (filters, sorters, etc.).
这里展示的分类机制是服务器端通过调用命令动作来分类的。
The sorting mechanism demonstrated here does a server side sort by invoking a command action.
此外，可以基于要采取的动作将各事件分类为不同的类型。
Furthermore, events may be classified into different types based on the action that is to be taken.
它是一个你实现不同的动作的函数分类，比如移动单位和播放它们的动画。
It is an assortment of functions that you use for scripting different actions, like sliding units and playing their animations.
吉尔布雷思夫妇设计了一个分类体系，分析了17种手的动作，他们将这套体系称为动作分类体系。
Frank and Lillian Gibreth devised a classification scheme to lable 17 basic hand motions which they called therbligs.
基于此，提出基于主元分析(pca)和改进K均值相结合的动作电位分类方法。
This paper proposes a method based on Principal Component Analysis (PCA) combined with improved K-means for action potential classification.
其次，设计了一个三层的BP神经网络，利用AR系数对手臂的各种肢体动作进行运动模式的分类。
Second, a three-layer BP neural network was designed to classify the muscle movement of forearm with AR model coefficient.
通过对4类不同的动作肌电信号进行分类，验证了该方法的有效性和高识别率。
The application of the method to the classification of four action EMG signals proves its effectivity and high recognition rate.
当你将生词分类记忆的时候，例如，把食品类和动作类单词放在一起，这样记起来就会容易一些。
It is easier to study new words when you organize them into groups like foods or action verbs.
该方法采用PCA提取动作电位特征，使用改进K均值算法实现动作电位分类。
The action potentials' features are extracted by PCA, the action potential classification is implemented by the improved K-means algorithm.
针对屏幕的动作是那些你想要在屏幕上对所有的项目执行的动作，如把图像“按照时间分类”。
Screen-specific actions are those things you would like to perform on all items in a screen, such as "Sort by time" for all images.
它使得用户在没有额外的工作站或服务器来支持分析软件的情况下能分析和分类相机抓获的行为动作。
It allows users to analyze and classify behaviors captured on camera without additional workstations or servers to support analytics software.
它使得用户在没有额外的工作站或服务器来支持分析软件的情况下能分析和分类相机抓获的行为动作。
It allows users to analyze and classify behaviors captured on camera without additional workstations or servers to support analytics software.
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