hfg

The German HFG , ULM Pattern and the Development of Designing in the Third World

德国HfG，ULM模式与后进国家设计发展的关系

Main tasks in ECG feature extraction are analyzed and identified according to the goal of constructing feature HFG .

以构建特征层次因子图为目标，分析并确定了心电图特征提取的主要任务。

HFG inference can be performed by transforming HFG to standard factor graph and applying inference algorithms of factor graph .

层次因子图的推理可通过将层次因子图转化为标准的因子图，再应用因子图推理方法进行实现。

Methods are discussed and determined for computing messages in Feature HFG and performing inference in HFG for automated ECG diagnosis .

分析并确定了疾病层次因子图中相关消息计算以及心电图自动诊断因子图推理实现的方法。

With the goal of constructing noise HFG , the variables and their relationships are analyzed , and main tasks are identified in ECG signal preprocessing .

以构建噪声层次因子图为目标，对心电信号预处理所涉及变量及变量间关系进行分析，确定了心电信号预处理的主要任务。

Methods Eighteen Wistar rats were divided into three groups : normal control group ( NCG ), hepatic fibrosis group ( HFG ) and hepatic cirrhosis group treated with complex pathogens ( HCG ) .

方法将18只Wistar大鼠分为三组：正常对照组（NCG）、复合因素致肝纤维化组（HFG）、肝硬化组（HCG）。

Feature HFG is constructed based on the proposed methods for ECG feature extraction and the messages involved in inference are computed . ( 5 ) Disease HFG based methods are studied for ECG disease diagnosis .

基于本文提出的心电图特征提取方法，构建了特征层次因子图，完成了推理所需相关消息的计算。（5）对基于疾病层次因子图的心电图疾病诊断方法进行了研究。

Firstly , according to the characteristics of ECG diagnosis , a strategy for model construction with combination of domain knowledge and sample data is proposed and hierarchical construction method based on top-down idea is adopted to construct HFG for automated ECG diagnosis .

首先，针对心电图自动诊断问题的特点，提出领域知识与样本数据相结合的建模策略，并采用自顶向下的分级建造方法，进行心电图自动诊断层次因子图的构建。

The relative competitive adsorption ability of three proteins on DF and graphite is HFG > IgG > HSA , but on DLC , the sequence is HFG ≈ HSA > IgG . Comparing with HSA , HFG has no apparent competitive adsorption superiority to DLC .

三种蛋白在DF和石墨表面的相对竞争吸附能力为HFG>IgG>HSA，而对于DLC，这一顺序则为HFG≈HSA>IgG，HFG对HSA没有表现出明显的竞争吸附优势。