Lexical meaning mainly includes rational meaning,grammatical meaning,and coloring meaning.Mastering the coloring meaning of vocabulary is of great significance for foreign students to use Chinese vocabulary correctly....Lexical meaning mainly includes rational meaning,grammatical meaning,and coloring meaning.Mastering the coloring meaning of vocabulary is of great significance for foreign students to use Chinese vocabulary correctly.This study mainly examines the psychological mechanism of Chinese second language learners mastering the coloring meaning of words,examines the psychological characteristics of students mastering the color meaning of words from the perspectives of second language learning theory and cognitive theory,establishes a cognitive schema for coloring meaning learning,and proposes corresponding learning models and teaching strategies.展开更多
Objective:To explore the meaning of care experienced by people with blindness in hospitals.Methods:Interpretive phenomenology along with the 6-step method of van Manen was used to conduct the study.Using purposeful sa...Objective:To explore the meaning of care experienced by people with blindness in hospitals.Methods:Interpretive phenomenology along with the 6-step method of van Manen was used to conduct the study.Using purposeful sampling,15 people with legal blindness were interviewed.Thematic analysis was used to isolate the meaning of care.Results:Five themes emerged:(a)nurses in the eyes of patients with blindness;(b)negligence in the caring moments;(c)being cared for in ambiguity;(d)Uncoordinated care;and(e)Psychological discomfor t.These sub-themes were condensed into an overarching theme titled as“marginalized patients inside the stereotypical healthcare system.”Conclusions:Lived experiences of patients with blindness revealed that hospitals provide stereotypic or inappropriate care for this minority group in society.Health professionals par ticularly nurses should be skilled to provide person-centered and coordinated care for patients with blindness.展开更多
针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并...针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。展开更多
离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改...离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改进K-means聚类算法,提出了一种名为KLOD(local outlier detection based on improved K-means and least-squares methods)的局部离群点检测方法,以实现对局部离群点的精确检测。首先,利用快速搜索和发现密度峰值方法计算数据点的局部密度和相对距离,并将二者相乘得到γ值。其次,将γ值降序排序,利用肘部法则选择γ值最大的k个数据点作为K-means聚类算法的初始聚类中心。然后,通过K-means聚类算法将数据集聚类成k个簇,计算数据点在每个维度上的目标函数值并进行升序排列。接着,确定数据点的每个维度的离散程度并选择适当的拟合函数和拟合点,通过最小二乘法对升序排列的每个簇的每1维目标函数值进行函数拟合并求导,以获取变化率。最后,结合信息熵,将每个数据点的每个维度目标函数值乘以相应的变化率进行加权,得到最终的异常得分,并将异常值得分较高的top-n个数据点视为离群点。通过人工数据集和UCI数据集,对KLOD、LOF和KNN方法在准确度上进行仿真实验对比。结果表明KLOD方法相较于KNN和LOF方法具有更高的准确度。本文提出的KLOD方法能够有效改善K-means聚类算法的聚类效果,并且在局部离群点检测方面具有较好的精度和性能。展开更多
文摘Lexical meaning mainly includes rational meaning,grammatical meaning,and coloring meaning.Mastering the coloring meaning of vocabulary is of great significance for foreign students to use Chinese vocabulary correctly.This study mainly examines the psychological mechanism of Chinese second language learners mastering the coloring meaning of words,examines the psychological characteristics of students mastering the color meaning of words from the perspectives of second language learning theory and cognitive theory,establishes a cognitive schema for coloring meaning learning,and proposes corresponding learning models and teaching strategies.
基金supported by Ardabil University of Medical Sciences(No.9319.1393-11-21)。
文摘Objective:To explore the meaning of care experienced by people with blindness in hospitals.Methods:Interpretive phenomenology along with the 6-step method of van Manen was used to conduct the study.Using purposeful sampling,15 people with legal blindness were interviewed.Thematic analysis was used to isolate the meaning of care.Results:Five themes emerged:(a)nurses in the eyes of patients with blindness;(b)negligence in the caring moments;(c)being cared for in ambiguity;(d)Uncoordinated care;and(e)Psychological discomfor t.These sub-themes were condensed into an overarching theme titled as“marginalized patients inside the stereotypical healthcare system.”Conclusions:Lived experiences of patients with blindness revealed that hospitals provide stereotypic or inappropriate care for this minority group in society.Health professionals par ticularly nurses should be skilled to provide person-centered and coordinated care for patients with blindness.
文摘针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。
文摘离群点检测任务是指检测与正常数据在特征属性上存在显著差异的异常数据。大多数基于聚类的离群点检测方法主要从全局角度对数据集中的离群点进行检测,而对局部离群点的检测性能较弱。基于此,本文通过引入快速搜索和发现密度峰值方法改进K-means聚类算法,提出了一种名为KLOD(local outlier detection based on improved K-means and least-squares methods)的局部离群点检测方法,以实现对局部离群点的精确检测。首先,利用快速搜索和发现密度峰值方法计算数据点的局部密度和相对距离,并将二者相乘得到γ值。其次,将γ值降序排序,利用肘部法则选择γ值最大的k个数据点作为K-means聚类算法的初始聚类中心。然后,通过K-means聚类算法将数据集聚类成k个簇,计算数据点在每个维度上的目标函数值并进行升序排列。接着,确定数据点的每个维度的离散程度并选择适当的拟合函数和拟合点,通过最小二乘法对升序排列的每个簇的每1维目标函数值进行函数拟合并求导,以获取变化率。最后,结合信息熵,将每个数据点的每个维度目标函数值乘以相应的变化率进行加权,得到最终的异常得分,并将异常值得分较高的top-n个数据点视为离群点。通过人工数据集和UCI数据集,对KLOD、LOF和KNN方法在准确度上进行仿真实验对比。结果表明KLOD方法相较于KNN和LOF方法具有更高的准确度。本文提出的KLOD方法能够有效改善K-means聚类算法的聚类效果,并且在局部离群点检测方面具有较好的精度和性能。
