Development of novel EST-SSR markers in common carp by data mining from public EST sequences

Expressed sequence tags (ESTs) are a source for microsatellite development. In the present study, EST-derived microsatelltes (EST-SSRs) were generated and characterized in the common carp (Cyprinus carpio) by data mining from updated public EST databases and by subsequent testing for polymorphism. About 5.5% (555) of 10,088 ESTs contain repeat motifs of various types and lengths with CA being the most abundant dinucleotide one. Out of the 60 EST-SSRs for which PCR primers were designed, 25 loci showed polymorphism in a common carp population with the alleles per locus ranging from 3 to 17 (mean 7). The observed (H-O) and expected (HE) heterozygosities of these EST-SSRs were 0.13-1.00 and 0.12-0.91, respectively. Six EST-SSR loci significantly deviated from the Hardy-Weinberg equilibrium (HWE) expectation, and the remaining 19 loci were in HWE. Of the 60 primer sets, the rates of polymorphic EST-SSRs were 42% in common carp, 17% in crucian carp (Carassius auratus), and 5% in silver carp (Hypophthalmichthys molitrix), respectively. These new EST-SSR markers would provide sufficient polymorphism for population genetic studies and genome mapping of the common carp and its closely related fishes. (c) 2007 Published by Elsevier B.V.

Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams

IEEE

基于SPC和数据挖掘的总装过程质量控制研究

对于一个企业来说，质量是产品和服务的生命。质量受企业生产经营管理活动中多种因素的影响，是企业各项工作的综合反映。目前企业产品质量指标的检测大多是在产品生产出来后才进行的，检测需要成本，有时还需要进行破坏性试验，如测量产品的抗拉强度，要做拉断检验。这样滞后的质量数据对生产过程的实时质量控制帮助不大，而且当发现产品质量不合格时，损失已无法挽回，这样极大地影响了企业的生产质量和效益。贯彻预防原则是现代质量管理的核心与精髓，要保证和提高产品质量，必须把影响质量的各个指标全面系统地管理起来。因而，如何将这些生产过程参数与产品质量特性关联起来成为企业生产故障预测及诊断的瓶颈问题。 本文首先从功能结构组织，数据库逻辑结构设计，类关系等多个方面描述了制造执行系统（MES）平台统计过程控制（SPC）子系统开发的相关工作。并针对所开发的SPC子系统在异常原因识别方面的不足，将数据挖掘与统计过程控制（SPC）技术结合起来，对变速箱总装线质量信息进行统计分析和深度挖掘，提出一种生产过程在线质量控制和诊断模型。该模型运用SPC对生产异常状态进行监测，并基于数据挖掘技术对大量过程检测数据进行分析，找到最有可能出现问题的工序和加工设备，将控制图异常状态与生产过程参数关联起来，实现异常状态的实时检测与诊断。研究表明，数据挖掘的理论和方法适合于质量控制领域，可以为产品质量控制提供一种新的途径。