DNA微阵列数据特征提取的分类方法研究

常用的排列方法从DNA微数据中选择的基因集合往往会包含相关性较高的基因,而且使用单个基因评价方法也不能真正反映由此得到的特征集合分类能力的优劣。另外,基因数量远多于样本数量是进行疾病诊断面临的又一挑战。为此,提出一种DNA微阵列数据特征提取方法用于组织分类。该方法运用K-means方法对基因进行聚类分析,获取各子类DNA微阵列数据中心,用排列法去除对分类无关的子类,然后利用ICA方法提取剩余子类集合的特征,用SVMs方法构造分类器对组织进行分类。真实的生物学数据实验表明,该方法通过提取一种复合基因,能综合评价基因分类能力,减少特征数,提高分类器的分类准确性。     

蛇纹石有价成分的提取与富集

蛇纹石作为一种重要的非金属矿物,广泛用于多种工业领域。本文介绍了蛇纹石的基本性质和结构,综述了国内外蛇纹石有价成分提取和富集的研究现状,主要介绍了氧化镁、氢氧化镁等镁系列产品、硅系列产品以及重金属镍和贵金属钯提取工艺。系统介绍了镍的提取技术,如还原焙烧—磁选法、还原焙烧—氨浸法、常压酸浸法和加压酸浸法等。提出了开发综合利用蛇纹石的重要意义及相关研究方向。     

近红外光谱法快速预测烟梗中常规化学成分的研究

采用近红外光谱法对烟梗中常规化学成分进行快速预测,其相对误差较小,预测能力和稳定性较好,具备推广价值。     

Two Projection Methods for Use in the Analysis of Multivariate Process Data With an Illustration in Petrochemical Production

Principal components analysis (PCA) is often used in the analysis of multivariate process data to identify important combinations of the original variables on which to focus for more detailed study. However, PCA and other related projection techniques from the standard multivariate repertoire are not explicitly designed to address or to exploit the strong autocorrelation and temporal cross-correlation structures that are often present in multivariate process data. Here we propose two alternative projection techniques that do focus on the temporal structure in such data and that therefore produce components that may have some analytical advantages over those resulting from more conventional multivariate methods. As in PCA, both of our suggested methods linearly transform the original p-variate time series into uncorrelated components; however, unlike PCA, they concentrate on deriving components with particular temporal correlation properties, rather than those with maximal variance. The first technique finds components that exhibit distinctly different autocorrelation structures via modification of a signal-noise decomposition method used in image analysis. The second method draws on ideas from common PCA to produce components that are not only uncorrelated as in PCA, but that also have approximately zero temporally lagged cross-correlations for all time lags. We present the technical details for these two methods, assess their performance through simulation studies, and illustrate their use on multivariate output measures from a fluidized catalytic cracking unit used in petrochemical production, contrasting the results obtained with those from standard PCA.     

跨越既有线路的大跨度钢箱梁架设技术探讨

钢箱梁具有拼装快、自重轻、跨度大、承载能力高、便于维修的特点,在高速公路桥梁工程中日益受到重视。现结合西安某高速公路匝道桥跨越西三高速公路处钢箱梁架设的实例,从架设方案、控制措施、受力分析等方面进行了详细计算和论证,可为大跨径钢箱梁架设施工提供参考。     

缓解女性生理期疼痛的内裤设计及性能评价

为开发缓解女性生理期腹部疼痛且适用于日常穿着的功能性智能内裤,应用感性工学原理,访谈20名女性目标人群,确定基本设计要素。基于人体工程学和中医针灸学原理,确定内裤的腹部功能位置,并采用嵌入式工艺将柔性纳米增强远红外芯片制作在内裤腹部功能位置。经远红外热像分析得出,柔性纳米增强远红外内裤腹部渗入人体穴位温度达到25.8℃,并刺激按摩穴位;经穿着评价试验得出,穿着该内裤5天可以显著缓解生理期腹部疼痛。     

广东省电子元器件产业发展现状及发展战略

广东省电子元器件产业作为广东省电子信息产业的基石,支撑着广东省电子信息产业的快速发展。一直以来,广东省电子元器件产业都位居全国第一,但近两年已经被江苏省超越。该文将深入分析广东省电子元器件产业的现状,剖析其存在的问题,围绕其面临的发展形势,指出其应采取的发展战略。     

基于SVM的说话人识别参数选择方法

针对支持向量机(SVM)计算复杂度高的问题,采用归一化和主元分析变换算法对语音数据进行预处理,并把K倍交叉验证与网络搜索法相结合应用到语音识别中。分析结果表明,与遗传算法和粒子群优化算法相比,该方法可以在识别率基本不变的情况下有效提高 SVM的参数寻优效率。     

Aromatic Compounds in Wines Produced During Fermentation: Effect of Three Red Cultivars

The aromatic compounds produced during the fermentation of the red grape cultivars Caiño Tinto, Caiño Longo, and Caiño Bravo were analysed by gas chromatography (FID) on the wines of 2002 and 2003 vintages. In both years, significant differences (p < 0.001) were observed between the wines with respect to the concentrations of aromatic compounds. Caiño Longo wines had the highest concentrations of acetates and esters. The concentrations of ethyl ester and acetates in Caíño Bravo wines were comparatively very low. Principal components analysis confirmed these results: the wines made from the different cultivars and the vintages were clearly different.     

Effect of waviness and roughness components on transverse profiles of turned surfaces

This paper presents the authors method of simultaneous analysis of roughness and waviness irregularity components, with the aim of better defining the key qualities and characteristics.