English language classes in most Chinese universities are bound to be large because of the phenomenal expansion, in students＇ enrollment. Thus, most English language teachers are confronting the challenge of teaching...English language classes in most Chinese universities are bound to be large because of the phenomenal expansion, in students＇ enrollment. Thus, most English language teachers are confronting the challenge of teaching larger classes than before. The main purpose of this article is to state the main problems of teaching English reading comprehension that lie in the large classes and analyze how to overcome the constraints caused by the fact of having many different students in the large class. The goal of this article is to show it is possible to create an interactive learning environment in large English intensive reading classes.展开更多
Aiming at finding out some typical features of classroom discourse of the CE classrooms in China context so as to be of assistance to college English majors and their teachers, the present study, by following a four-p...Aiming at finding out some typical features of classroom discourse of the CE classrooms in China context so as to be of assistance to college English majors and their teachers, the present study, by following a four-part process of Recording, Viewing, Transcribing and Analyzing, has made some detailed study on the classroom discourse of several top-quality Comprehensive English classes. And finally, the features of those selected top-quality CE classes and their implications can be described in this paper.展开更多
This paper aims to present some issues about metaphor comprehension process mainly from psycholinguistic point of view.By discussing these issues,more information about the nature of metaphor comprehension can be learnt.
为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical de...为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。展开更多
文摘English language classes in most Chinese universities are bound to be large because of the phenomenal expansion, in students＇ enrollment. Thus, most English language teachers are confronting the challenge of teaching larger classes than before. The main purpose of this article is to state the main problems of teaching English reading comprehension that lie in the large classes and analyze how to overcome the constraints caused by the fact of having many different students in the large class. The goal of this article is to show it is possible to create an interactive learning environment in large English intensive reading classes.
文摘Aiming at finding out some typical features of classroom discourse of the CE classrooms in China context so as to be of assistance to college English majors and their teachers, the present study, by following a four-part process of Recording, Viewing, Transcribing and Analyzing, has made some detailed study on the classroom discourse of several top-quality Comprehensive English classes. And finally, the features of those selected top-quality CE classes and their implications can be described in this paper.
文摘This paper aims to present some issues about metaphor comprehension process mainly from psycholinguistic point of view.By discussing these issues,more information about the nature of metaphor comprehension can be learnt.
文摘为解决近年来用户行业变化特性加剧导致的难以准确辨识用户档案信息变动的问题,文中提出一种基于数据驱动的负荷特征异常辨识方法。首先,提出一种两阶段行业典型负荷形态构建方法,利用基于层次密度的含噪声应用空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)提取用户在不同场景下的典型日负荷曲线,并利用改进的K-means算法对提取出的典型日负荷曲线进行聚类分析,构建行业的典型负荷形态;其次,提出一种多维场景负荷特征异常智能研判方法,通过构造用户的负荷特征,使用熵权法评估行业典型场景的相对重要性,并采用单分类支持向量机(one-class support vector machine,OCSVM)算法量化每个场景下的用户负荷特征的异常程度,通过加权计算得到用户的综合嫌疑得分并排序,从而实现对负荷特征异常用户的准确辨识。最后,采用某地区实际用户数据进行算例验证。仿真结果表明,所提方法在行业典型负荷场景构建及负荷特征异常辨识方面表现出良好的可行性与实用价值。
