Software for dynamic reconfigurable order picking systems

This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Research Center (MHRC), a National Science Foundation sponsored Cooperative Industry/University Research Center. The storage configuration is assumed to be an in-the-aisle order picking system in which stockkeeping units (SKUs) can occupy variable capacity storage locations and stock-splitting is allowed among zones (clusters). The product mix may include multiple product families with different life cycles, correlated demand within families and commonality of demand across families.     

Studies of substrate specificities of lipases from different sources

Although lipases are widely applied in a wide variety of reactions, available information on the factors that are responsible for the substrate specificities of lipases from different sources is scarce. In this paper, nine lipase‐producing microorganism strains were isolated from oil‐containing samples. The properties of these enzymes, including pH optima, temperature optima, thermal stability, and pH stability, vary significantly with the different sources from which these lipases were isolated. The substrate specificities of the nine lipases, including fatty acid and positional specificities, were also studied. The fatty acid specificities of the nine lipases were observably different toward 15 substrates with different carbon chain lengths, different saturation degrees and different side chains. The lipases from S₃ Penicillium citrinum (PCL), MJ₁ Aspergillus niger (ANL), MJ₂ Aspergillus oryzae (AOL), YM Bacillus coughing (BCL), S₉ Geotrichum candidum (GCL), and S₁₁ Candida lypolytica (CLL) showed the strongest specificities to short‐chain esters, and the other lipases showed strong selectivity for medium‐ or long‐chain and branched esters. The positional specificities were examined by analyzing the hydrolytic products of triolein catalyzed by the nine lipases, using TLC. The lipases can be mainly classified into two groups by their specificities for the ester bond at position 2 of triglycerides; one group can selectively hydrolyze the ester bond at position 2 of the triglycerides, the other group cannot. All these nine lipases were divided into four groups by hierarchical clustering analysis on the basis of the results of fatty acid and positional specificities.     

Influence of polymer molecular weight on selected thermodynamic properties of polymer/solvent systems and the application of the UNIFAC theory

The intrinsic viscosities of eleven narrow molecular weight distribution polystyrene samples ranging from number-average molecular weight 1820 to 1 292 000 have been determined in pure solvents of tetrahydrofuran and chloroform with a Ubbelohde No. 1 viscometer at 25°C. By combining the original and modified Fox-Flory viscosity treatments, respectively, with the Flory-Huggins Lattice theory, the interaction parameters were calculated. With the aid of original and modified UNIFAC methods the solvent activity, clustering function of the solvent, and the interaction parameter were computed and compared with the experimental data. It was established that the influence imposed by the polymer molecular weight on the activity of the solvent, the clustering function of solvent, and interaction parameter is insignificant. The modified UNIFAC method satisfactorily predicted the solvent activity as illustrated by previous investigators. However, both UNIFAC methods failed to predict the interaction parameter.     

主题分析技术在文档聚类中的应用

为解决高频特征对文章的主题信息反映不够全面,无法获得高质量聚类结果的问题,同时为获得聚类后各类别反映信息的精确描述,采用词汇链反映文章所描述的主题信息,并依据文本间词汇链的相似度进行聚类.将聚类后属于同一类别并反映相同主题信息的词汇链进行融合,通过分析各词汇链所描述的主题信息在不同类别内的分布来抽取能够充分反映各类别主题的关键词集合.实验证明该方法比应用高频特征进行聚类的效果好,同时由于分析了主题信息在各类别内的分布情况,使抽取的类别关键词能够很好地体现每个类别所侧重描述的信息.     

中国信息服务业空间集聚研究

通过产业集聚的EG指数对中国各省份信息服务业的空间集聚程度进行研究,得出集聚程度比较显著.在此基础上,找到这一服务业的两个相关指标,利用产业集中度CRn判断出信息服务业主要集中于北京、广东等六个地区,然后对两指标数据进行无量纲化处理,加权求和得到新的综合指标,最后对这一新指标进行了系统聚类分析,得知这一产业主要集中在北京和广东两个地区.     

用聚类预报模型预报三峡水库来水量的探讨

为了开展三峡水库入库来水量长期预报,从北半球500 hPa高度场、太平洋海温场、北半球100 hPa高度场、北半球地面气压场与74项环流特征指数作为预报因子,采用回归方程求解和回归效果检验,建立回归聚类预报物理模型,对三峡水库6～8月各月平均来水量进行了长期预报分析。结果表明,三峡水库6~8月份各月来水量回归聚类预报的技术路线是可行的,正、负距平符号一致的概率能达到80%以上,效果较好,基本上可以满足生产需要。回归聚类预报物理模型具有一定的应用推广价值。     

PSO和K均值混合分类算法在远程教学系统中的应用

现行的多数网络教学系统在学生按需学习和＂因材施教＂上存在局限性,本文用PSO和K均值的混合分类算法对传统的网络教学系统进行改进,根据获取的用户基本信息和兴趣特征进行聚类,充分体现＂以学习者为中心＂个性化教学模式。实验结果表明,该算法具有较好的全局收敛性,寻优能力明显提升。     

BK-means:骨架初始解K-means

K-means是典型的启发式聚类算法,容易受到初始解的影响而无法获得高质量的聚类结果。骨架是近年来启发式算法设计的研究热点,它是指所有全局最优解中相同的部分,对于提高启发式算法性能具有重要意义。给出的骨架初始解K-means算法(BK-means)的基本思想是:首先利用K-means算法得到一组局部最优解(聚类结果),通过对局部最优解求交得到骨架簇。利用骨架簇构造骨架初始解及新的搜索空间。最后以骨架初始解引导K-means算法在新的搜索空间中搜索聚类结果。在15组仿真数据集和4组实际数据集上的实验结果表明,BK-means算法具有获得高内聚、高分离的聚类结果能力。     

一种基于图论的聚类算法NeiMu

提出一种新的基于图论的聚类算法NeiMu。该算法首先分析数据中的对象,寻找每个对象的k近邻,根据k近邻关系构造k近邻有向图,然后通过k近邻有向图中的k-互邻居关系构造k-聚类图,发现数据中的自然聚类。算法的特点是根据数据之间的互为k近邻关系确定数据中的自然簇,而不必引入其他方法来划分小簇,从而能够保证对象不会被错误聚类,仅会与其他小簇一起融合到一个大簇中。这一优点可以有效保证NeiMu算法的聚类质量。而且,NeiMu算法给出的这种类似自底向上的层次聚类结果还有利于用户根据渐变的结果确定最佳的k值。实验结果表明,该算法对密度变化大的数据、大小相差大的数据、任意分布形状的数据均具有很好的聚类质量,对孤立点也很健壮。