A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis ...A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.展开更多
Investigation of genetic diversity of geographically distant wheat genotypes is </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">useful ...Investigation of genetic diversity of geographically distant wheat genotypes is </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues ></span><span style="font-family:""> </span><span style="font-family:Verdana;">1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).展开更多
为了解决多源挥发性有机物(Volatile Organic Compounds,VOCs)数据存在数据维度高、数据关系复杂、数据存在异常的问题,建立了基于核主成分分析(Kernel Principal Component Analysis,KPCA)、孤立森林(Isolated Forest,IF)、加权随机森...为了解决多源挥发性有机物(Volatile Organic Compounds,VOCs)数据存在数据维度高、数据关系复杂、数据存在异常的问题,建立了基于核主成分分析(Kernel Principal Component Analysis,KPCA)、孤立森林(Isolated Forest,IF)、加权随机森林(Weighted Random Forest,WRF)混合方法的VOCs数据清洗模型。首先对研究区域进行网格划分,建立了基于KPCA-IF的VOCs降维异常数据识别模型,通过KPCA方法对多源混合VOCs数据降维,使用IF算法识别异常数据并进行剔除。然后设计了基于WRF的VOCs数据补偿算法,对降维与异常识别后的数据集进行缺失值回归填补。最后,以西安市为例,选取空气质量数据、气象数据等多源VOCs数据进行数据清洗。结果表明,该混合模型可有效对多源VOCs数据降维,进行数据清洗的平均绝对误差为5.08、均方根误差为10.24、中值绝对误差为3.54,均优于对比模型,证明了KPCA-IF-WRF混合模型的鲁棒性更强、精确度更高,具有科学性和可行性。展开更多
基金The National Natural Science Foundation ofChina(No60504033)
文摘A novel nonlinear combination process monitoring method was proposed based on techniques with memo- ry effect （multivariate exponentially weighted moving average （MEWMA）） and kernel independent component analysis （KICA）. The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently devel- oped statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of mea- surements and it is a two-phase algorithm., whitened kernel principal component analysis （KPCA） plus indepen- dent component analysis （ICA）. The application to the fluid catalytic cracking unit （FCCU） simulated process in- dicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear rela- tionship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for lonu-term performance deterioration.
文摘Investigation of genetic diversity of geographically distant wheat genotypes is </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">useful approach in wheat breeding providing efficient crop varieties. This article presents multivariate cluster and principal component analyses (PCA) of some yield traits of wheat, such as thousand-kernel weight (TKW), grain number, grain yield and plant height. Based on the results, an evaluation of economically valuable attributes by eigenvalues made it possible to determine the components that significantly contribute to the yield of common wheat genotypes. Twenty-five genotypes were grouped into four clusters on the basis of average linkage. The PCA showed four principal components (PC) with eigenvalues ></span><span style="font-family:""> </span><span style="font-family:Verdana;">1, explaining approximately 90.8% of the total variability. According to PC analysis, the variance in the eigenvalues was </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">greatest (4.33) for PC-1, PC-2 (1.86) and PC-3 (1.01). The cluster analysis revealed the classification of 25 accessions into four diverse groups. Averages, standard deviations and variances for clusters based on morpho-physiological traits showed that the maximum average values for grain yield (742.2), biomass (1756.7), grains square meter (18</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">373.7), and grains per spike (45.3) were higher in cluster C compared to other clusters. Cluster D exhibited the maximum thousand-kernel weight (TKW) (46.6).
文摘为了解决多源挥发性有机物(Volatile Organic Compounds,VOCs)数据存在数据维度高、数据关系复杂、数据存在异常的问题,建立了基于核主成分分析(Kernel Principal Component Analysis,KPCA)、孤立森林(Isolated Forest,IF)、加权随机森林(Weighted Random Forest,WRF)混合方法的VOCs数据清洗模型。首先对研究区域进行网格划分,建立了基于KPCA-IF的VOCs降维异常数据识别模型,通过KPCA方法对多源混合VOCs数据降维,使用IF算法识别异常数据并进行剔除。然后设计了基于WRF的VOCs数据补偿算法,对降维与异常识别后的数据集进行缺失值回归填补。最后,以西安市为例,选取空气质量数据、气象数据等多源VOCs数据进行数据清洗。结果表明,该混合模型可有效对多源VOCs数据降维,进行数据清洗的平均绝对误差为5.08、均方根误差为10.24、中值绝对误差为3.54,均优于对比模型,证明了KPCA-IF-WRF混合模型的鲁棒性更强、精确度更高,具有科学性和可行性。
