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
In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor me...In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.展开更多
In this study, 9 main traits of 774 spring wheat landraces in Tibet were investigated and analyzed. The results show that spring wheat landraces in Tibet have high plant height (with an average of 126.1 cm) and long g...In this study, 9 main traits of 774 spring wheat landraces in Tibet were investigated and analyzed. The results show that spring wheat landraces in Tibet have high plant height (with an average of 126.1 cm) and long growth period (with an average of 135.2 d), with an average spike length of 9.5 cm, average effective tiller number per plant of 5.9, average spikelet number per spike of 19.9, average kernel number per spikelet of 3.5, average spikelet number per spike of 51.8, average kernel weight per spike of 2.0 g, and average 1 000-grain weight of 38.1 g. Specifically, kernel number per spikelet of 2 landraces is larger than 6.0, spikelet number per spike of 2 landraces is larger than 100, kernel weight per spike of 2 landraces is larger than 4.0 g, 1 000-grain weight of 11 landraces is larger than 50 g. There is abundant genetic diversity in those traits except in growth period, and the coefficient variation of 9 traits is in a decreasing order of effective tiller number per plant > kernel weight per spike > kernel number per spike > spike length > kernel number per spikelet > 1 000-grain weight > plant height > spikelet number per spike > growth period. There is different relevance among different traits. Growth period is extremely significantly positively related to yield traits; grain number traits are extremely significantly positively relative to plant height and spike length, but extremely significantly negatively relative to effective tiller number per plant; kernel number per spike is extremely significantly positively relative to kernel weight per spike, but extremely significantly negatively related to 1 000-grain weight; 1 000-grain weight is extremely significantly positively related to kernel weight per spike. Based on principal component analysis, these 9 traits could be included by 5 principal components (grain number, grain weight, spike length, tiller number and growth period). According to the comprehensive evaluation values of these five principal components, 50 landraces including ZM019573, ZM019849, ZM019730, ZM018745, ZM019657, ZM019891, ZM020533, ZM018508, ZM019074 and ZM020026 have good performance.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.
文摘In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.
基金Supported by Special Foundation for Biological Germplasm Resources Innovation&Functional Gene Discovery and Utilization of Xinjiang Production and Construction Corps(2012BB047)"12th Five-Year"Breeding Project of Xinjiang Production and Construction Corps(2011BA002)Fund from Key Laboratory for Cereal Quality Research and Genetic Improvement of Xinjiang Production and Construction Corps(CQG2012-XJ01)
文摘In this study, 9 main traits of 774 spring wheat landraces in Tibet were investigated and analyzed. The results show that spring wheat landraces in Tibet have high plant height (with an average of 126.1 cm) and long growth period (with an average of 135.2 d), with an average spike length of 9.5 cm, average effective tiller number per plant of 5.9, average spikelet number per spike of 19.9, average kernel number per spikelet of 3.5, average spikelet number per spike of 51.8, average kernel weight per spike of 2.0 g, and average 1 000-grain weight of 38.1 g. Specifically, kernel number per spikelet of 2 landraces is larger than 6.0, spikelet number per spike of 2 landraces is larger than 100, kernel weight per spike of 2 landraces is larger than 4.0 g, 1 000-grain weight of 11 landraces is larger than 50 g. There is abundant genetic diversity in those traits except in growth period, and the coefficient variation of 9 traits is in a decreasing order of effective tiller number per plant > kernel weight per spike > kernel number per spike > spike length > kernel number per spikelet > 1 000-grain weight > plant height > spikelet number per spike > growth period. There is different relevance among different traits. Growth period is extremely significantly positively related to yield traits; grain number traits are extremely significantly positively relative to plant height and spike length, but extremely significantly negatively relative to effective tiller number per plant; kernel number per spike is extremely significantly positively relative to kernel weight per spike, but extremely significantly negatively related to 1 000-grain weight; 1 000-grain weight is extremely significantly positively related to kernel weight per spike. Based on principal component analysis, these 9 traits could be included by 5 principal components (grain number, grain weight, spike length, tiller number and growth period). According to the comprehensive evaluation values of these five principal components, 50 landraces including ZM019573, ZM019849, ZM019730, ZM018745, ZM019657, ZM019891, ZM020533, ZM018508, ZM019074 and ZM020026 have good performance.
文摘目的测定青藏高原4个主产地蕨麻的营养成分,并对其品质进行综合评价。方法采用SPSS 19.0分别对青藏高原4个主产地蕨麻的营养成分进行方差多重比较分析、主成分分析(principal component analysis,PCA)和聚类分析(hierarchical cluster analysis,HCA),并对蕨麻品质进行综合评价。结果4个不同主产地蕨麻中部分产地蕨麻的各营养成分之间差异显著(P<0.05),普遍具有高膳食纤维(平均含量>6 g/100 g)、低脂肪和低饱和脂肪(平均含量分别<3 g/100 g和<1.5 g/100 g)、极低钠(平均含量<40 mg/100 g)、氨基酸组成接近联合国粮食及农业组织和世界卫生组织(Food and Agriculture Organization of the United Nations/World Health Organization,FAO/WHO)的理想模式、脂肪酸种类丰富(含10种主要脂肪酸)、富含多种矿物质元素[平均Fe、Mg、K含量≥30%营养素参考值(nutrient reference values,NRV)]等特点。其中青海蕨麻为高蛋白食品(平均含量>20%NRV),四川和西藏蕨麻含有多种维生素(平均维生素B_(1)、维生素B_(2)、叶酸含量≥15%NRV)。PCA综合评价结果表明青海蕨麻排名第一,HCA将4个不同主产地蕨麻分为3类,第Ⅰ类蕨麻综合品质相对更好。结论蕨麻具有良好的营养价值和开发前景,青海蕨麻营养品质最佳,可为其质量控制提供依据。
