基于多层核主成分提取估计器需要将调制信号的训练样本根据各自的频率进行分层。
The estimator based on kernel principal component extraction requires to stratify the training samples of interested signals with respect to their respective frequencies.
调查在分层逐级抽样法与随机抽样法相结合的抽样基础完成，共收集有效样本1005个。
The survey was completed on the basis of combining stratified sampling and random sampling, collecting 1005 valid samples in total.
更大样本的队列研究、分层分析、全基因测序有助于明确NKG2D基因在UC 发病过程中的作用。
Research of larger sample's queue, analysis from different layer and DNA sequence will help to determine the function of NKG2D in the process of UC.
应用推荐