变精度软粗糙集

从集合间的包含程度出发,构造了一种基于包含度的变精度软粗糙集模型。提出带参数的变精度近似算子的定义,得到了它的基本性质和定理,并给出了证明;定义了双精度软粗糙集的近似算子,研究了其性质;讨论了该模型与其他粗糙集模型的关系以及退化条件;举例说明了在信息处理中的应用。     

基于属性约简的MPI运行时参数优化

目前MPI实现提供上百种可供调整的运行时参数,但默认的运行时参数不能发挥程序应有的性能。为了给应用程序提供接近最优的运行时参数,本文提出基于粗糙集理论中的属性约简建立优化模型。首先通过属性约简方法为不同基准程序产生参数组合值,再根据未知应用程序的程序特征通过所建立的优化模型为其预测参数值。实验表明,该方法可以有效地为未知应用程序预测参数值,并且比使用默认参数程序性能提升约20%。     

防止过拟合的属性约简

近年来,机器学习的过拟合问题备受关注,尤其在属性约简中。为解决这一难题,提出一种融合集成策略和去除操作的算法。首先将训练模型数据平分为◢M份;然后将其中M◣-1份采用集成策略进行潜在约简计算;最后将剩余的一份进行提前测试,一旦发生过拟合则将刚加入的属性从潜在约简集中去除。利用提前测试潜在属性约简的方法来防止过拟合现象的发生,几组UCI数据的实验结果说明了新算法的有效性,同时为丰富和发展属性约简提供了一种新的方向。     

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.     

非均匀卵砾石粗糙床面的分形特征

针对传统方法刻画粗糙床面总体特征存在缺陷的问题,将分形几何理论引入非均匀卵砾石粗糙床面特征研究,以卵砾石床面激光扫描试验资料为基础,探讨了分形尺度区宽度与中值粒径d50的关系,分析了覆盖基准面对分形维数的影响,研究了床面整体与局部分形维数的特征与联系,获得了分形维数随床面颗粒组成及高程统计参数的变化规律。结果表明:分形尺度区宽度与床面颗粒中值粒径d50有关,上临界尺度可取为1.5d50;分形维数与覆盖基准面位置有关,覆盖基准面距平均床面的距离越大,分形维数越大且逐渐失真,为有效量化和区分床面粗糙特征,覆盖基准面宜取平均床面;粗糙床面分形维数具有良好的稳定性,局部分形维数变异性很小,并与整体分形维数接近;粗糙床面的分形维数与颗粒大小、级配组成及床面高程统计参数有关,中值粒径越大,颗粒组成越均匀,床面高程极差、标准差、偏度(绝对值)和峰度越大,分形维数越小。     

An approach for selecting seed URLs of focused crawler based on user-interest ontology

Seed URLs selection for focused Web crawler intends to guide related and valuable information that meets a user's personal information requirement and provide more effective information retrieval. In this paper, we propose a seed URLs selection approach based on user-interest ontology. In order to enrich semantic query, we first intend to apply Formal Concept Analysis to construct user-interest concept lattice with user log profile. By using concept lattice merger, we construct the user-interest ontology which can describe the implicit concepts and relationships between them more appropriately for semantic representation and query match. On the other hand, we make full use of the user-interest ontology for extracting the user interest topic area and expanding user queries to receive the most related pages as seed URLs, which is an entrance of the focused crawler. In particular, we focus on how to refine the user topic area using the bipartite directed graph. The experiment proves that the user-interest ontology can be achieved effectively by merging concept lattices and that our proposed approach can select high quality seed URLs collection and improve the average precision of focused Web crawler.     

链图的概念格表示

概念格是基于对象集和属性集之间的二元关系建立的一种层次结构。它与极大二部团存在着一定的联系。将概念格属性约简理论应用于链图,首先给出了链图的概念格表示,其次证明了二部图G=(V1,V2,E)是链图,当且仅当G′=(V1,V2,E)是链图,这里(V1,V2,E)是(V1,V2,E)的约简形式背景。     

PTC型BaTiO3陶瓷的孪晶和孪晶界

用透射电镜和高分辨电镜观察研究了PTC型BaTiO_3陶瓷的孪晶和孪晶界结构,并提出了可能的{111}孪晶及孪晶界的原子结构模型。结果表明,{111}孪晶形成氧八面体共面的共格倾斜孪晶界,在孪晶界处可能存在一定量的A空位且原子密度要低于孪晶体内。同时还发现两种其它特征的孪晶现象。     

云模型在测井曲线分层中的应用

针对由模糊信息导致测井曲线人工分层的不准确,提出了一种基于云模型的测井曲线自动分层方法.而云模型能同时体现概念的随机性和模糊性,利用云变换从测井曲线中提取定性概念,然后对提取的概念进行概念跃升,使得算法输出的概念层次更符合实际.实验表明,该方法能够较准确地提取和表征定性概念,所提取的概念中心与传统测井曲线分层方法得到的概念中心吻合较好,验证了算法的有效性,提高了测井曲线分层的准确性.