To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on princip...To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.展开更多
Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality dat...Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.展开更多
In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effect...In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.展开更多
The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic suppor...The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.展开更多
【目的】探究樱桃番茄Solanum lycopersicum var. cerasiforme种质资源在银川平原地区的适应性,评价适合银川平原地区新品种选育的优良樱桃番茄育种材料。【方法】以收集到的100份樱桃番茄种质资源为研究对象,对其主要表型性状进行测定...【目的】探究樱桃番茄Solanum lycopersicum var. cerasiforme种质资源在银川平原地区的适应性,评价适合银川平原地区新品种选育的优良樱桃番茄育种材料。【方法】以收集到的100份樱桃番茄种质资源为研究对象,对其主要表型性状进行测定,利用多元统计法、灰色关联度分析法和DTOPSIS法3种不同的评价方法进行适应性综合评价。基于主成分计算出综合得分,灰色关联度法计算出加权关联度,DTPOSIS法计算出相对贴近度。【结果】100份樱桃番茄的主要表型性状的变异系数在17.78%~306.46%之间,大部分性状间存在显著或极显著相关性。26个表型性状综合成了10个主成分,累计贡献率达71.901%。以3种评价方法对各种质进行排名,结果既有统一性,也有差异性,共有4份材料均排在前10名,分别是T55、T83、T42和T87,表明T55、T83、T42和T87是表现优良的种质,其中T55的表现最为优异。【结论】T55是最适宜银川平原地区栽培的种质材料,可作为重要的育种基础材料;上述3种方法对樱桃番茄的评价结果略有不同,但无巨大差异,说明方法可行,有利于种质资源评价方面的研究。展开更多
本研究旨在利用多元统计学方法对中国不同地区(合肥、哈尔滨、黄山、苏州和成都)传统发酵香肠的风味和品质特性进行分析。结果表明,不同地区发酵香肠的水分含量、水分活度、pH值、L^(*)值、a^(*)值和b^(*)值具有显著差异。电子舌结果表...本研究旨在利用多元统计学方法对中国不同地区(合肥、哈尔滨、黄山、苏州和成都)传统发酵香肠的风味和品质特性进行分析。结果表明,不同地区发酵香肠的水分含量、水分活度、pH值、L^(*)值、a^(*)值和b^(*)值具有显著差异。电子舌结果表明,苏州和成都发酵香肠的鲜味、丰富度和咸味高于哈尔滨、合肥和黄山。电子鼻结果表明,W5S传感器在不同地区发酵香肠中具有最高的响应值。通过气相色谱-质谱联用技术在发酵香肠中共鉴定出121种挥发性化合物,筛选出26种关键挥发性化合物,其中桉叶油醇、柠檬醛和苯甲醛分别是哈尔滨、苏州和成都发酵香肠中最关键的挥发性化合物,而(E)-2-壬醛是合肥和黄山发酵香肠中最关键的挥发性化合物。偏最小二乘判别分析结果表明,黄山、苏州和合肥发酵香肠具有相似的气味特征,而黄山和合肥发酵香肠则具有相似的滋味特征。结合变量投影重要性(variable importance in projection,VIP)分析,筛选出25种差异挥发性化合物(P<0.05,VIP值>1)。关键挥发性化合物与滋味的相关性结果表明,1-辛烯-3-醇、己醛、庚醛和(E)-2-壬醛在调节滋味感知方面发挥着重要作用。综上,本研究可为提升传统特色发酵香肠风味提供理论指导。展开更多
基金supported by the National Natural Science Foundation of China(71401052)the Key Project of National Social Science Fund of China(12AZD108)+2 种基金the Doctoral Fund of Ministry of Education(20120094120024)the Philosophy and Social Science Fund of Jiangsu Province Universities(2013SJD630073)the Central University Basic Service Project Fee of Hohai University(2011B09914)
文摘To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.
基金Project (2012ZX07501002-001) supported by the Ministry of Science and Technology of China
文摘Multivariate statistical techniques,such as cluster analysis(CA),discriminant analysis(DA),principal component analysis(PCA) and factor analysis(FA),were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River(SSHR) basin in China,obtained during two years(2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites.The results showed that most of physicochemical parameters varied significantly among the sampling sites.Three significant groups,highly polluted(HP),moderately polluted(MP) and less polluted(LP),of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics.DA identified p H,F,DO,NH3-N,COD and VPhs were the most important parameters contributing to spatial variations of surface water quality.However,DA did not give a considerable data reduction(40% reduction).PCA/FA resulted in three,three and four latent factors explaining 70%,62% and 71% of the total variance in water quality data sets of HP,MP and LP regions,respectively.FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities(point sources:industrial effluents and wastewater treatment plants;non-point sources:domestic sewage,livestock operations and agricultural activities) and natural processes(seasonal effect,and natural inputs).PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters(about 80% reduction) as the most important parameters to explain 72% of the data variation.Thus,this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and,in water quality assessment,identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.
基金Project(2003AA430200) supported by the National High-Tech Research and Development Program of China
文摘In order to improve reliability of the excavator's hydraulic system, a fault detection approach based on dynamic principal component analysis(PCA) was proposed. Dynamic PCA is an extension of PCA, which can effectively extract the dynamic relations among process variables. With this approach, normal samples were used as training data to develop a dynamic PCA model in the first step. Secondly, the dynamic PCA model decomposed the testing data into projections to the principal component subspace(PCS) and residual subspace(RS). Thirdly, T2 statistic and Q statistic performed as indexes of fault detection in PCS and RS, respectively. Several simulated faults were introduced to validate the approach. The results show that the dynamic PCA model developed is able to detect overall faults by using T2 statistic and Q statistic. By simulation analysis, the proposed approach achieves an accuracy of 95% for 20 test sample sets, which shows that the fault detection approach can be effectively applied to the excavator's hydraulic system.
基金supported by the National Natural Science Foundation of China(71171008)
文摘The uncertainty analysis is an effective sensitivity analysis method for system model analysis and optimization. However,the existing single-factor uncertainty analysis methods are not well used in the logistic support systems with multiple decision-making factors. The multiple transfer parameters graphical evaluation and review technique（MTP-GERT） is used to model the logistic support process in consideration of two important factors, support activity time and support activity resources, which are two primary causes for the logistic support process uncertainty. On this basis,a global sensitivity analysis（GSA） method based on covariance is designed to analyze the logistic support process uncertainty. The aircraft support process is selected as a case application which illustrates the validity of the proposed method to analyze the support process uncertainty, and some feasible recommendations are proposed for aircraft support decision making on carrier.
文摘【目的】探究樱桃番茄Solanum lycopersicum var. cerasiforme种质资源在银川平原地区的适应性,评价适合银川平原地区新品种选育的优良樱桃番茄育种材料。【方法】以收集到的100份樱桃番茄种质资源为研究对象,对其主要表型性状进行测定,利用多元统计法、灰色关联度分析法和DTOPSIS法3种不同的评价方法进行适应性综合评价。基于主成分计算出综合得分,灰色关联度法计算出加权关联度,DTPOSIS法计算出相对贴近度。【结果】100份樱桃番茄的主要表型性状的变异系数在17.78%~306.46%之间,大部分性状间存在显著或极显著相关性。26个表型性状综合成了10个主成分,累计贡献率达71.901%。以3种评价方法对各种质进行排名,结果既有统一性,也有差异性,共有4份材料均排在前10名,分别是T55、T83、T42和T87,表明T55、T83、T42和T87是表现优良的种质,其中T55的表现最为优异。【结论】T55是最适宜银川平原地区栽培的种质材料,可作为重要的育种基础材料;上述3种方法对樱桃番茄的评价结果略有不同,但无巨大差异,说明方法可行,有利于种质资源评价方面的研究。
文摘本研究旨在利用多元统计学方法对中国不同地区(合肥、哈尔滨、黄山、苏州和成都)传统发酵香肠的风味和品质特性进行分析。结果表明,不同地区发酵香肠的水分含量、水分活度、pH值、L^(*)值、a^(*)值和b^(*)值具有显著差异。电子舌结果表明,苏州和成都发酵香肠的鲜味、丰富度和咸味高于哈尔滨、合肥和黄山。电子鼻结果表明,W5S传感器在不同地区发酵香肠中具有最高的响应值。通过气相色谱-质谱联用技术在发酵香肠中共鉴定出121种挥发性化合物,筛选出26种关键挥发性化合物,其中桉叶油醇、柠檬醛和苯甲醛分别是哈尔滨、苏州和成都发酵香肠中最关键的挥发性化合物,而(E)-2-壬醛是合肥和黄山发酵香肠中最关键的挥发性化合物。偏最小二乘判别分析结果表明,黄山、苏州和合肥发酵香肠具有相似的气味特征,而黄山和合肥发酵香肠则具有相似的滋味特征。结合变量投影重要性(variable importance in projection,VIP)分析,筛选出25种差异挥发性化合物(P<0.05,VIP值>1)。关键挥发性化合物与滋味的相关性结果表明,1-辛烯-3-醇、己醛、庚醛和(E)-2-壬醛在调节滋味感知方面发挥着重要作用。综上,本研究可为提升传统特色发酵香肠风味提供理论指导。
