Rough function model and rough membership function

Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower （upper） approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.     

基于遗传算法的一种粗糙集知识约简算法

知识约简是粗糙集理论研究的重要内容之一,是在保持信息系统分类能力不变的基础上,删除冗余知识.文中从系统的信息表出发,根据可辨别下三角矩阵,利用遗传算法,提出一种基于遗传算法的粗糙集知识约简算法,并通过实验分析说明,这种算法可以解决现有启发式算法无法解决的部分问题.     

基于克隆选择的粗糙集属性约简方法

基于免疫克隆选择的原理，提出了一种新的粗糙集属性约简方法，将属性集合的分类近似质量作为进化目标，利用免疫反应的分布性特点通过局部并行搜索实现全局优化，并采用抗体更新和亲和力抑制手段来维持群体的多样性，保证了各抗体局部优化解的稳定性，从而获得了多个优化约简集合，通过机械故障诊断数据的实例应用，表明该方法可以获得多个符合分类质量要求的属性约简集合，因此满足了设备故障诊断的特征优化选择要求。     

Rough set-based feature selection method

A new feature selection method is proposed based on the discern matrix in rough set in this paper. The main idea of this method is that the most effective feature, if used for classification, can distinguish the most number of samples belonging to different classes. Experiments are performed using this method to select relevant features for artificial datasets and real-world datasets. Results show that the selection method proposed can correctly select all the relevant features of artificial datasets and drastically reduce the number of features at the same time. In addition, when this method is used for the selection of classification features of real-world underwater targets,the number of classification features after selection drops to 20% of the original feature set, and the classification accuracy increases about 6% using dataset after feature selection.     

转炉流程轴承钢连铸坯非金属夹杂物的行为

探讨了BOF-LF-VD-CC流程生产的轴承钢连铸坯非金属夹杂物的物性、分布、特点,并通过示踪剂追踪分析了轴承钢夹杂物的来源,为轴承钢夹杂物的去除,提高转炉流程轴承钢质量提供了依据.研究表明,该生产流程可以获得T[O]的平均值为9.3×10-4%;显微夹杂体积分数为0.031%;>50μm的大型夹杂为1.5 mg/10 kg的洁净度较高的轴承钢连铸坯.夹杂物主要为<5μm的显微夹杂,它的分布特点决定了连铸坯中夹杂物的分布特点,显微夹杂主要是多元素复合脱氧产物与中间包液面二次氧化产物吸附集聚形成的复合物,其次是结晶器液面对钢液的污染,钢包液面影响较小.     

RS-GA-KNN算法识别灵长类动物DNA序列剪接位点

以灵长类动物DNA序列的剪接位点识别资料为研究对象,将选定样本序列中各碱基编码作为原始变量数据,用粗糙集方法和遗传算法对原始变量数据进行变量筛选,即以粗糙集方法选取的变量为基础,用遗传算法进行变量的二次搜索,从样本序列各碱基中挑选出保守性强的碱基对应的变量构成变量集,采用最近邻聚类识别灵长类动物DNA序列剪接位点类型,总识别准确率达90．66％,明显高于直接使用原始变量数据或将粗糙集理论方法和遗传算法单独用于变量选取的识别结果．     

杂质和粗糙界面散射影响下NIS结的隧道谱和束缚态

在正常金属/绝缘层/s波超导隧道结中,考虑绝缘层中的体杂质散射以及粗糙界面散射,运用Blon-der-Tinkham-Klapwijk(BTK)理论和Bogoliubov-de Gennes(BdG)方程,计算系统的微分电导.计算表明:(1)电导峰的位置与绝缘层的厚度密切相关,当绝缘层厚度具有几个相干长度时,隧道谱中的能级谐振峰数目增多,从而在超导能隙内形成一些束缚态;(2)粗糙界面散射和绝缘层中的杂质散射均能有效地压低电导峰,由此可解释理论与实验间尚存的偏差.     

基于粗糙集理论的知识库分解与规则获取

粗糙集理论可以通过对数据的约简从例子中学习,获取决策规则。但是,当知识库规模较大、条件属性个数较多时,存在提取规则速度慢、规则长度长等缺点。本文介绍了粗糙集理论的基本概念,提出了一种基于粗糙集的知识库分解算法。首先引入决策属性支持度的概念,在此基础上定义了一个属性选择量度,选择最佳目标属性对知识库进行分解,直到所有对象都被精确分类,从而得到具有一定支持度的规则集,得到的规则长度短,提取速度快。通过对一个简单实例的分析,证明了该算法的可行性。     

奥氏体不锈钢焊缝热裂敏感性研究

应用变形速率连续可变式热裂纹试验方法（ＶＤＲ法）研究了碳、钼和稀土元素对奥氏体不锈钢焊缝热裂敏感性的影响。试验结果表明,降低奥氏体不锈钢熔敷金属中的碳含量并加入适量的钼可显著提高焊缝的抗热裂性能。稀土元素对焊缝的抗热裂性也有显著影响,加入适量稀土能减少焊缝中夹杂物的数量和大小,改变其成分及分布,提高抗裂性。但是如果加入过量,焊缝中会形成大量稀土夹杂物,降低奥氏体不锈钢焊缝的抗裂性。     

A coupled model of TiN inclusion growth in GCr15SiMn during solidification in the electroslag remelting process

TiN inclusions observed in an ingot produced by electroslag remelting (ESR) are extremely harmful to GCr15SiMn steel. There-fore, accurate predictions of the growth size of these inclusions during steel solidification are significant for clean ESR ingot production. On the basis of our previous work, a coupled model of solute microsegregation and TiN inclusion growth during solidification has been estab-lished. The results demonstrate that compared to a non-coupled model, the coupled model predictions of the size of TiN inclusions are in good agreement with experimental results using scanning electron microscopy with energy disperse spectroscopy (SEM-EDS). Because of high cooling rate, the sizes of TiN inclusions in the edge area of the ingots are relatively small compared to the sizes in the center area. Dur-ing the ESR process, controlling the content of Ti in the steel is a feasible and effective method of decreasing the sizes of TiN inclusions.