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Search Results: 1 - 10 of 106400 matches for " 林梦雷 "
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区间直觉模糊信息系统中的信息粒度
杨伟萍,
计算机应用 , 2012,
Abstract: ?区间直觉模糊信息系统比一般信息系统更能全面、细致、直观地描述和刻画决策信息,对其进行不确定性研究具有重要的意义。利用信息粒度对区间直觉模糊信息系统的不确定性进行了刻画,给出了区间直觉模糊粒度结构的交、并、差、补等四种运算。提出了区间直觉模糊粒度结构上的三种偏序关系,并建立了它们之间的联系。定义了区间直觉模糊信息粒度和区间直觉模糊信息粒度的公理化,并研究它们的性质。
覆盖族动态变化时粗集计算的矩阵方法
艺东,张燕兰,
计算机应用 , 2015,
Abstract: ?在覆盖信息系统中覆盖个数动态变化的背景下,针对如何有效、快速地计算集合的上、下近似集的问题,通过引入特征函数的概念,定义了一个关系矩阵,提出了集合的覆盖近似算子、正域、负域、边界域的矩阵表达式.其次,在覆盖信息系统中覆盖个数变化的条件下,利用矩阵方法研究和讨论了集合近似集的增量更新方法.这些结果丰富了覆盖粗糙集的动态知识更新理论,同时也为动态覆盖信息系统中知识更新提供了一种新的方法.
优势关系下的集值序值信息系统
耀进,李进金,
计算机应用 , 2011,
Abstract: ?集值序值信息系统分为合取/析取集值序值信息系统,分别深入分析了合取/析取集值序值信息系统已有优势关系的局限性,并对其不合理情形进行了讨论,进而提出了对对象进行更好划分的新优势关系。在此基础上,分别构建了优势关系下的合取/析取集值序值信息系统的粗集模型,并通过典型例子验证了该方法的有效性。
基于目标用户近邻修正的协同过滤算法*
张佳,耀进,,刘景华
模式识别与人工智能 , 2015, DOI: 10.16451/j.cnki.issn1003-6059.201509005
Abstract: 在基于用户的协同过滤算法中,用户评分倾向性和评分矩阵的稀疏性致使难以准确可靠地搜寻目标用户的近邻.基于此,文中提出基于目标用户近邻修正的协同过滤算法.首先定义积极评分和消极评分两类用户群体,选择从目标用户评分倾向性一致的用户群体中寻找其近邻.然后对与目标用户共同评分项数量少而相似度可能高的近邻进行修正,为目标用户寻找更准确的近邻集合.实验表明,文中算法在一定程度上能有效提高推荐质量.
基于协同过滤的药物重定位算法
耀进*张佳,,李进金
南京大学学报(自然科学) , 2015, DOI: 10.13232/j.cnki.jnju.2015.04.021
Abstract: 药物重定位是指发现已上市或批准药物的新用途,受到了广泛的关注.为此,提出一种基于协同过滤的药物重定位算法.首先,收集药物及疾病的描述信息以构建药物?疾病关联矩阵.其次,根据药物对疾病有适应症和有副作用的相关信息,设计了一种刻画药物之间趋同程度的度量方法,该方法同时考虑了不同药物在适应症和副作用上的相似度.然后,搜寻目标药物的近邻以预测药物对疾病的评分.最后,采用平均绝对偏差和覆盖率二项评价指标衡量系统的预测质量.另外,针对某种特定疾病,利用新的协同过滤模型预测药物在该疾病上的未评分项,根据预测的评分信息发现对该疾病有治疗作用的药物.实验结果表明,该算法不仅能提高系统的预测质量,而且能够发现有治疗作用的药物?疾病组合,验证了所提算法能有效地应用于药物重定位.
小麦抗逆相关转录因子DREB密码子偏好性特征分析
,冯瑞云,郝雅萍,,,王慧杰,杨生权
- , 2019, DOI: 10.7606/j.issn.1009-1041.2019.01.01
Abstract: DREB(dehydration responsive element binding)转录因子在植物抵抗干旱、高盐、低温等非生物逆境中发挥着重要作用。为探究普通小麦抗逆相关转录因子DREB密码子的偏好性特征,本研究运用CodonW、CHIPS和CUSP软件程序分析了小麦DREB基因的密码子使用特性,并与13种植物的DREB密码子偏性进行比较。结果表明,小麦DREB基因主要偏好以GC结尾的密码子;根据RSCU值,确定小麦DREB基因的高频密码子有14个;不同作物间DREB基因的密码子选用偏好性存在一定差异;基于DREB编码序列的聚类分析比基于密码子使用偏性聚类分析更能准确地反映物种间的亲缘关系;属于真核生物的酵母菌比属于原核生物的大肠杆菌更适宜作为DREB基因表达的异源受体。小麦DREB与模式植物基因组之间密码子使用偏性差异较小,拟南芥可能比烟草和番茄更适合作为该基因转基因研究的理想受体。小麦DREB密码子偏性分析为该基因的异源表达及分子遗传研究提供了一定的理论依据。
胆总管转角与胆总管扩张程度相关性的MRCP研究
张仕勇,敬宗,黄小华,力行,,刘念
- , 2015, DOI: 10.13609/j.cnki.1000-0313.2015.07.012
Abstract: 【摘要】目的:采用MRCP技术研究胆总管转角与胆总管扩张程度的相关性。方法:连续性搜集符合纳入标准的321例患者的MRI和临床资料,其中胆总管无扩张218例,轻度扩张55例,中度扩张20例,重度扩张28例,测量肝外胆管转角大小。采用单因素方差分析和Dunnett-t比较胆总管无扩张与不同程度扩张患者间转角差异,采用Spearman秩相关分析胆总管转角与胆总管扩张程度的相关性。结果:胆总管无扩张、轻、中、重度扩张患者平均肝外胆管转角分别为(121.40±17.76)°、(118.66±21.65)°、(117.44±23.51)°、(104.68±24.06)°,胆总管重度扩张患者转角小于胆总管无扩张患者(P<0.05),胆总管转角与胆总管扩张程度的相关性系数为-0.162(P<0.05)。结论:肝外胆管转角与胆总管扩张程度呈负相关,肝外胆管转角随着胆管扩张加重而减小
亲子依恋与初中生人际宽恕的关系:一个有调节的中介模型
Parent-child Attachment and Interpersonal Forgiveness among Junior High School Students: A Moderated Mediation Model

苗灵童,赵凯莉,,,刘燊,
- , 2018, DOI: 10.16187/j.cnki.issn1001-4918.2018.03.02
Abstract: 采用问卷法对浙江省四所初中的502名在校学生进行调查,探讨亲子依恋与人际宽恕的关系,提出一个有调节的中介模型,考察共情的中介作用和自尊的调节作用。结果发现:(1)亲子依恋对初中生人际宽恕既有直接预测作用,又可通过共情间接影响人际宽恕;(2)亲子依恋通过共情的间接效应受到自尊的调节,即相对于低自尊个体,共情对高自尊初中生亲子依恋与人际宽恕关系的影响更显著。本研究表明,安全的依恋关系有利于培养初中生的共情,进而促进人际宽恕,但是较低的自尊水平会阻碍共情作用的发挥。结果提示培养良好的亲子关系和提高自尊水平对于初中生宽恕品质的发展有重要作用。
The present study investigated the relationship between parent-child attachment and interpersonal forgiveness, proposed a moderated mediation model, and examined the mediation effect of empathy and the moderation effect of self-esteem. 502 students in four junior high schools in Zhejiang Province were recruited by questionnaire measures. The results were as follows:(1) Parent-child attachment has both a direct predictive effect on interpersonal forgiveness of junior high school students and indirect influence on interpersonal forgiveness through empathy; (2) the mediation effect of empathy was moderated by self-esteem. To be detailed, empathy had a significant effect on this link of parent-child attachment and interpersonal forgiveness for junior high school students with high self-esteem than for those with low self-esteem. This study shows that a secure attachment relationship is conducive to cultivating junior high school students' empathy and promote interpersonal forgiveness, but lower levels of self-esteem can hinder the exertion of empathy. The results suggest that fostering good parent-child relationship and improving self-esteem level play an important role in the development of junior high school students' forgiveness quality.
一种基于模糊信息熵的协同过滤推荐方法
A method of collaborative filtering recommendation based on fuzzy information entropy

耀进,张佳,,王娟
LIN Yaojin
, ZHANG Jia, LIN Menglei, WANG Juan

- , 2016, DOI: 10.6040/j.issn.1672-3961.1.2016.165
Abstract: 摘要: 针对评分数据的稀疏性制约协同过滤推荐性能的情况,提出一种新的相似性度量方法。首先,定义了用户的模糊信息熵以反映用户评分偏好的不确定程度;其次,利用两两用户的模糊互信息衡量用户之间的相似程度;最后,同时考虑用户之间的模糊互信息和用户的模糊信息熵,并设计一种基于模糊信息熵的相似性度量方法以计算用户之间的相似性。在两个公开数据集上的试验结果表明:基于模糊信息熵的相似性度量方法能够降低数据稀疏性的影响,并能显著提高推荐系统的推荐性能。
Abstract: The performance of collaborative filtering was restricted by the sparsity of rating data. To solve this problem, a novel similarity measure based on fuzzy mutual information was proposed. First, the definition of user fuzzy information entropy was given to reflect the uncertainty degree of rating preference. Then, the fuzzy mutual information between users was introduced to measure the similarity degree between users. Finally, the fuzzy information entropy based on similarity measure method was designed to calculate the similarity between users by considering not only the fuzzy mutual information between users but also user fuzzy information entropy. Experimental results on two benchmark data sets showed that the fuzzy information entropy based similarity measure method could reduce the influence of the data sparsity, and the recommendation performance of systems had significant improvements
基于信息熵的协同过滤算法
Entropy-based collaborative filtering algorithm

张佳,耀进,,刘景华,李慧宗
ZHANG Jia
, LIN Yaojin, LIN Menglei, LIU Jinghua, LI Huizong

- , 2016, DOI: 10.6040/j.issn.1672-3961.2.2015.047
Abstract: 摘要: 针对用户评分数据的稀疏性制约着系统的推荐质量的问题,提出了一种基于信息熵的协同过滤算法。首先定义了用户信息熵以反映用户评分分布和倾向程度;然后,利用大间隔的方法计算目标用户与其他用户的间隔距离,结合目标用户的信息熵,得到目标用户的近邻选择范围;最后,同时考虑用户的信息熵和用户间的相似性大小得到目标用户的近邻集合,以降低数据稀疏性对推荐结果的影响。试验结果表明:基于信息熵的协同过滤算法能够有效地提高推荐质量。
Abstract: In the recommender system, the recommended quality was restricted by the sparsity of user rating data. To solve this problem, a novel entropy-based collaborative filtering algorithm was proposed. First, the definition of user entropy was given to reflect the rating distribution of users and their rating tendency degree. Then, the method of large margin was introduced to calculate the margin distance, and the neighbor selection range was determined via combining both of the active users entropy and margin distance with other users. Finally, neighbors were obtained by making full of the user entropy and the similarity between users, which could degrade the influence of the sparse rating data. Experimental results on two data sets showed that the proposed algorithm could improve the recommended quality effectively
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