In order to define the relationship between yield and important agronomic traits of two lines hybrid Uangyou 2111, the principal component analysis method was used to analyze the expadmental data of six test points in...In order to define the relationship between yield and important agronomic traits of two lines hybrid Uangyou 2111, the principal component analysis method was used to analyze the expadmental data of six test points in Yunnan Province. The results showed that the main factors influencing the production of Liangyou 2111 were grain number, grains seed number, panicle length, growth padod and panicle rate; then were 1 O00-grain weight, seed setting rate, effective panicle and highest stem tillers number; again was plant height. Therefore, when hybrid rice of Uangyou 2111 will be planted widely in yunnan province, we should focus on en- sudng the panicle traits, especially increase grain number and grain seed number, and coordinately develop other traits to achieve high yield.展开更多
[Objectivc] This study aimed to investigate the chilling tolerance of seedlings of different cotton genotypes and screen appropriate indicators for assess- ing chilling tolerance, to establish reliable mathematical ev...[Objectivc] This study aimed to investigate the chilling tolerance of seedlings of different cotton genotypes and screen appropriate indicators for assess- ing chilling tolerance, to establish reliable mathematical evaluation model for chilling tolerance of cotton, thus providing theoretical basis for breeding and promoting new chilling-tolerant cotton germplasms and large-scale evaluation of chilling tolerance of cotton varieties. [Method] Fifteen cotton varieties （lines） were used as experimental materials. The photosynthetic gas exchange parameters, chlorophyll fluorescence ki- netic parameters, chlorophyll content, relative soluble sugar content, malonaldehyde content, relative proiine content, relative conductivity and other 12 physiological indi- cators of seedling leaves under low temperature treatment （5 ℃, 12 h） and recovery treatment （25 ℃. 24 h） were determined; based on the chilling tolerance coefficient （CTC） of various individual indicators, the comprehensive evaluation of chilling toler- ance was conducled by using principal component analysis, hierarchical cluster anal- ysis and stepwise regression analysis. [Result] The results showed that the 12 indi- vidual physiological indicators could be classified into 7 independent comprehensive components by principal component analysis; 15 cotton varieties （lines） were clus- tered into three categories by using membership function method and hierarchical cluster analysis; the mathematical model for evaluating chilling tolerance of cotton seedlings was established： D =0.275 -0.244Fo1 ＋0.206Fv/Fm1＋0.326g,%-0.056SS ＋ 0.225MDA＋O.O38REC （FF=0.995）, and the evaluation accuracy of the equation was higher than 94.25%,0. Six identification indicators closely related to chilling tolerance were screened, including Fo,, Fv/Fm1, Seedling leaves of cotton varieties （lines） gs2, SS, MDA, and REC. [Conclusion] with high chilling tolerance are less dam- aged under low temperature stress, and are able to maintain relatively high photo- synthetic electron transport capacity and high stomatal conductance after recovery treatment, which is contributed to gas exchange and recovery of photosynthetic ca- pacity. Determination of the six indicators under the same stress condition can be adopted for rapid identification and prediction of the chilling tolerance of other cotton varieties, which provides basis for the breeding, promotion, identification and screen- ing of chilling tolerant germplasms.展开更多
[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experim...[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.展开更多
Objective] The alm was to survey 10 characters of 8 fresh edibIe soy-bean varieties, analyze maln Ioading factors using principal component analysis, and estabIish muItipIe regression equation on fresh pod yield. [Met...Objective] The alm was to survey 10 characters of 8 fresh edibIe soy-bean varieties, analyze maln Ioading factors using principal component analysis, and estabIish muItipIe regression equation on fresh pod yield. [Methods] Through princi-pal component analysis on 10 characters of 8 fresh edibIe soybean varieties, char-acters reIated to fresh pod yield of fresh edibIe soybean were cIarified. [Results] Af-ter the principal components analysis, pod weight per pIant, 100-seed weight and pod number per pIant of fresh edibIe soybean were chosen to study their reIation with the yield of fresh edibIe soybean, moreover, it was demonstrated that the reIa-tion was Iinear reIation, thus it was suitabIe for muItivariate regression analysis. Fi-nal y, the mathematical expression formuIa about fresh pod yield was estabIished. [Conclusions] There were three characters affecting fresh pod yield, nameIy, pod weight per pIant, 100-seed weight and pod number per pIant, the mathematical equation was y=816.732＋4.145X6-0.718X8-0.985X9 （X6： pod weight per pIant; X8： 100-seed weight; X9： pod number per pIant）.展开更多
[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering anal...[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.展开更多
[Objective] The aim was to indentify diseased leaves of broad bean by vibra- tional spectroscopy. [Method] In this paper, broad bean rust, fusarium rhizome rot, broad bean zonate spot, yellow leaf curl virus and norma...[Objective] The aim was to indentify diseased leaves of broad bean by vibra- tional spectroscopy. [Method] In this paper, broad bean rust, fusarium rhizome rot, broad bean zonate spot, yellow leaf curl virus and normal leaves were studied using Fourier transform infrared spectroscopy combined with chemometrics. [Result] The spectra of the samples were similar, only with minor differences in absorption inten- sity of several peaks. Second derivative analyses show that the significant difference of all samples was in the range of 1 200-700 cm2. The data in the range of 1 200- 700 cm＇ were selected to evaluate correlation coefficients, hierarchical cluster analy- sis （HCA） and principal component analysis （PCA）. Results showed that the correla- tion coefficients are larger than 0.928 not only between the healthy leaves, but also between the same diseased leaves. The values between healthy and diseased leaves, and among diseased leaves, are all declined. HCA and PCA yielded about 73.3% and 82.2% accuracy, respectively. [Conclusion] This study demonstrated that FTIR techniques might be used to detect crop diseases.展开更多
With the development of industry in China, the emission issues of indus- trial wastewater has got more and more attention. Excessive levels of pollutants in wastewater are urgent problem to be solved. Together with th...With the development of industry in China, the emission issues of indus- trial wastewater has got more and more attention. Excessive levels of pollutants in wastewater are urgent problem to be solved. Together with the emissions of do- mestic wastewater, the discharge amount of pollutants has exceeded standard in many cities, which not only pollutes the water resources, but also greatly threatens the environment, and does great harm to people＇s health. The principal component analysis was conducted based on the principal components extracted from the data of major pollutants emission conditions in the wastewater of major cities from the China Statistical Yearbook 2014.展开更多
The objective of this study is to analyze soil physical and chemical properties,soil comprehensive functions and impact factors after different years of reclamation.Based on the survey data taken from 216 soil samplin...The objective of this study is to analyze soil physical and chemical properties,soil comprehensive functions and impact factors after different years of reclamation.Based on the survey data taken from 216 soil sampling points in the Fengxian Reclamation Area of the Changjiang (Yangtze) River Estuary,China in April 2009 and remotely sensed TM data in 2006,while by virtue of multivariate analysis of variance (MANOVA),geo-statistical analysis (GA),prin-cipal component analysis (PCA) and canonical correspondence analysis (CCA),it was concluded that:1) With the in-crease in reclamation time,soil moisture,soil salinity,soil electric conductivity and soil particle size tended to decline,yet soil organic matter tended to increase.Soil available phosphorous tended to increase in the early reclamation period,yet it tended to decline after about 49 years of reclamation.Soil nitrate nitrogen,soil ammonia nitrogen and pH changed slightly in different reclamation years.Soil physical and chemical properties reached a steady state after about 30 years of reclamation.2) According to the results of PCA analysis,the weighted value (0.97 in total) that represents soil nutrient factors (soil nitrate nitrogen,soil organic matter,soil available phosphorous,soil ammonia nitrogen,pH and soil particle size) were higher than the weighted value (0.48 in total) of soil limiting factors (soil salinity,soil elec-tric conductivity and soil moisture).The higher the F value is,the better the soil quality is.3) Different land use types play different roles in the soil function maturity process,with farmlands providing the best contribution.4) Soil physi-cal and chemical properties in the reclamation area were mainly influenced by reclamation time,and then by land use types.The correlation (0.1905) of the composite index of soil function (F) with reclamation time was greater than that with land use types (-0.1161).展开更多
To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance...To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.展开更多
On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process m...On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.展开更多
A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,wher...A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.展开更多
It is an effective way in realizing urban coordinated and sustainable development to establish a series of in- dicators and to evaluate urban environmental and socioeconomic development. According to the characteristi...It is an effective way in realizing urban coordinated and sustainable development to establish a series of in- dicators and to evaluate urban environmental and socioeconomic development. According to the characteristics of Harbin City in Northeast China, an indicator system including five subsystems and 37 indicators was established for comprehensive evaluation on urban sustainable development. The development indexes of all urban subsystems and complex system were calculated quantitatively using the comprehensively integrated methods composed of Principle Component Analysis, Analytic Hierarchy Process and weighed index method, and then the comprehensive level of ur- ban sustainable development and the degree of urban interior coordination were analyzed. The results indicated that 1) the overall urban development presented an uptrend, however, the interior development was not well balanced from 1996 to 2006; 2) the development in each subsystem presented a strong fluctuation; and 3) the development in re- sources subsystem showed a downtrend. Based on those results, the suggestions of urban sustainable development were put forward at the end.展开更多
Human activity and urbanization result in urban-rural environmental gradients. Understanding effect of the gradients on soil properties is necessary for management of the soils around urban areas. In this study, soil ...Human activity and urbanization result in urban-rural environmental gradients. Understanding effect of the gradients on soil properties is necessary for management of the soils around urban areas. In this study, soil quality of some vegetable fields was characterized along an urban-rural gradient in Shaoxing County, Zhejiang Province. Fifteen soil physical and chemical properties were evaluated by using principal component analysis.Results showed that there was a great variation in the soil quality along the gradient. From rural to urban zones, soil organic matter, water-stable aggregates, cation exchangeable capacity (CEC), total N and P, and available K increased, whereas soil pH value decreased. In addition, Pb, Cu, Ni, Co, Zn and Cr in the soils tended to be accumulated toward the urban zone. Sequential chemical extraction showed that mobility of all the heavy metals in the soils tended to increase from the rural to the urban zones. The variation of soil properties accounted for by the first principal component was significantly explained by the difference in application rates of municipal wastes.展开更多
A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a n...A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.展开更多
The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but m...The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.展开更多
In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation ...In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.展开更多
Objective To establish gas chromatography-mass spectrometry(GC-MS)fingerprint method for the petroleum ether fraction of Shenqi Jiangtang Granules(SQJTG)and evaluate the product quality.Methods The GC-MS fingerprint o...Objective To establish gas chromatography-mass spectrometry(GC-MS)fingerprint method for the petroleum ether fraction of Shenqi Jiangtang Granules(SQJTG)and evaluate the product quality.Methods The GC-MS fingerprint of petroleum ether fraction of SQJTG was established by GC-MS,and the chemical components corresponding to the fingerprint peaks were structurally identified on NIST2014.The batch consistency of SQJTG products was evaluated based on the chemical composition of petroleum ether parts by using fingerprint similarity evaluation and Principal components analysis(PCA)technology.At the same time,Hotelling's T2 and DMODX statistics are used to set the control range for the quality of different batches of products.Results Twenty-two components were identified from the petroleum ether part of SQJTG,accounting for 60.94%of the total components separated.The similarity of fingerprints of petroleum ether parts of 24 batches of SQJTG was greater than 0.95.The PCA of 24 batches of samples were all under the control limits of Hotellin’s T2 and DMODX statistics,indicating that the petroleum ether parts of different batches of SQJTG were consistent.Conclusion The developed GC-MS fingerprint method can be used to evaluate the quality of SQJTG.展开更多
Physicochemical characterization of 82 Algerian honeys, collected between 2005 and 2010, from different botanical and geographical origins were analyzed. The studied parameters were： water content, pH, free acidity ...Physicochemical characterization of 82 Algerian honeys, collected between 2005 and 2010, from different botanical and geographical origins were analyzed. The studied parameters were： water content, pH, free acidity （FA）, electrical conductivity （EC）, ash content, hydroxymethylfurfuraldehyde （HMF）, proline content, specific rotatory power and color. Most of the measured parameters had showed values in the range of the international standards, with a particular richness in proline and ash content. Chemometrics-based approach reveals that the discriminated groups were Citrus, Ziziphus and forest even with over represented groups like Eucalyptus. Principle component analysis （PCA） enabled to extract three principal components explaining nearly 65% of total variance, PCj and PC2 were related to botanical origin whereas PC3 to honey age. Analysis of variance showed that the studied variables were almost different depending on botanical, geographical origin and season. The current study also shows the presence of diverse honey varieties in Algeria. The collected data will contribute to the creation of products with protected geographical or/and botanical origins.展开更多
Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the t...Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.展开更多
基金Supported by Yunnan Agricultural Development BureauYunnan Modern Agricultural Rice Industry Technology System~~
文摘In order to define the relationship between yield and important agronomic traits of two lines hybrid Uangyou 2111, the principal component analysis method was used to analyze the expadmental data of six test points in Yunnan Province. The results showed that the main factors influencing the production of Liangyou 2111 were grain number, grains seed number, panicle length, growth padod and panicle rate; then were 1 O00-grain weight, seed setting rate, effective panicle and highest stem tillers number; again was plant height. Therefore, when hybrid rice of Uangyou 2111 will be planted widely in yunnan province, we should focus on en- sudng the panicle traits, especially increase grain number and grain seed number, and coordinately develop other traits to achieve high yield.
基金Supported by"11thFive-Year Plan"National Science and Technology Support Program(2009BADA4B01-3)~~
文摘[Objectivc] This study aimed to investigate the chilling tolerance of seedlings of different cotton genotypes and screen appropriate indicators for assess- ing chilling tolerance, to establish reliable mathematical evaluation model for chilling tolerance of cotton, thus providing theoretical basis for breeding and promoting new chilling-tolerant cotton germplasms and large-scale evaluation of chilling tolerance of cotton varieties. [Method] Fifteen cotton varieties （lines） were used as experimental materials. The photosynthetic gas exchange parameters, chlorophyll fluorescence ki- netic parameters, chlorophyll content, relative soluble sugar content, malonaldehyde content, relative proiine content, relative conductivity and other 12 physiological indi- cators of seedling leaves under low temperature treatment （5 ℃, 12 h） and recovery treatment （25 ℃. 24 h） were determined; based on the chilling tolerance coefficient （CTC） of various individual indicators, the comprehensive evaluation of chilling toler- ance was conducled by using principal component analysis, hierarchical cluster anal- ysis and stepwise regression analysis. [Result] The results showed that the 12 indi- vidual physiological indicators could be classified into 7 independent comprehensive components by principal component analysis; 15 cotton varieties （lines） were clus- tered into three categories by using membership function method and hierarchical cluster analysis; the mathematical model for evaluating chilling tolerance of cotton seedlings was established： D =0.275 -0.244Fo1 ＋0.206Fv/Fm1＋0.326g,%-0.056SS ＋ 0.225MDA＋O.O38REC （FF=0.995）, and the evaluation accuracy of the equation was higher than 94.25%,0. Six identification indicators closely related to chilling tolerance were screened, including Fo,, Fv/Fm1, Seedling leaves of cotton varieties （lines） gs2, SS, MDA, and REC. [Conclusion] with high chilling tolerance are less dam- aged under low temperature stress, and are able to maintain relatively high photo- synthetic electron transport capacity and high stomatal conductance after recovery treatment, which is contributed to gas exchange and recovery of photosynthetic ca- pacity. Determination of the six indicators under the same stress condition can be adopted for rapid identification and prediction of the chilling tolerance of other cotton varieties, which provides basis for the breeding, promotion, identification and screen- ing of chilling tolerant germplasms.
文摘[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.
文摘Objective] The alm was to survey 10 characters of 8 fresh edibIe soy-bean varieties, analyze maln Ioading factors using principal component analysis, and estabIish muItipIe regression equation on fresh pod yield. [Methods] Through princi-pal component analysis on 10 characters of 8 fresh edibIe soybean varieties, char-acters reIated to fresh pod yield of fresh edibIe soybean were cIarified. [Results] Af-ter the principal components analysis, pod weight per pIant, 100-seed weight and pod number per pIant of fresh edibIe soybean were chosen to study their reIation with the yield of fresh edibIe soybean, moreover, it was demonstrated that the reIa-tion was Iinear reIation, thus it was suitabIe for muItivariate regression analysis. Fi-nal y, the mathematical expression formuIa about fresh pod yield was estabIished. [Conclusions] There were three characters affecting fresh pod yield, nameIy, pod weight per pIant, 100-seed weight and pod number per pIant, the mathematical equation was y=816.732＋4.145X6-0.718X8-0.985X9 （X6： pod weight per pIant; X8： 100-seed weight; X9： pod number per pIant）.
基金Supported by Fund of Sichuan Provincial Administration of traditional Chinese Medicine(2008-12)~~
文摘[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.
基金Supported by National Natural Science Foundation of China(30960179)Natural Science Foundation of Yunnan Province(2007A048M)~~
文摘[Objective] The aim was to indentify diseased leaves of broad bean by vibra- tional spectroscopy. [Method] In this paper, broad bean rust, fusarium rhizome rot, broad bean zonate spot, yellow leaf curl virus and normal leaves were studied using Fourier transform infrared spectroscopy combined with chemometrics. [Result] The spectra of the samples were similar, only with minor differences in absorption inten- sity of several peaks. Second derivative analyses show that the significant difference of all samples was in the range of 1 200-700 cm2. The data in the range of 1 200- 700 cm＇ were selected to evaluate correlation coefficients, hierarchical cluster analy- sis （HCA） and principal component analysis （PCA）. Results showed that the correla- tion coefficients are larger than 0.928 not only between the healthy leaves, but also between the same diseased leaves. The values between healthy and diseased leaves, and among diseased leaves, are all declined. HCA and PCA yielded about 73.3% and 82.2% accuracy, respectively. [Conclusion] This study demonstrated that FTIR techniques might be used to detect crop diseases.
文摘With the development of industry in China, the emission issues of indus- trial wastewater has got more and more attention. Excessive levels of pollutants in wastewater are urgent problem to be solved. Together with the emissions of do- mestic wastewater, the discharge amount of pollutants has exceeded standard in many cities, which not only pollutes the water resources, but also greatly threatens the environment, and does great harm to people＇s health. The principal component analysis was conducted based on the principal components extracted from the data of major pollutants emission conditions in the wastewater of major cities from the China Statistical Yearbook 2014.
基金Under the auspices of Ministry of Education,China (No.108148)State Key Laboratory of Urban and Regional Ecology (No.SKLURE2010-2-2)+2 种基金National Basic Research Program of China (No.2010CB951203)Key Research Program of Shanghai Science & Technology (No.08231200700,08231200702)111 Project,Ministry of Education,China (No.B08022)
文摘The objective of this study is to analyze soil physical and chemical properties,soil comprehensive functions and impact factors after different years of reclamation.Based on the survey data taken from 216 soil sampling points in the Fengxian Reclamation Area of the Changjiang (Yangtze) River Estuary,China in April 2009 and remotely sensed TM data in 2006,while by virtue of multivariate analysis of variance (MANOVA),geo-statistical analysis (GA),prin-cipal component analysis (PCA) and canonical correspondence analysis (CCA),it was concluded that:1) With the in-crease in reclamation time,soil moisture,soil salinity,soil electric conductivity and soil particle size tended to decline,yet soil organic matter tended to increase.Soil available phosphorous tended to increase in the early reclamation period,yet it tended to decline after about 49 years of reclamation.Soil nitrate nitrogen,soil ammonia nitrogen and pH changed slightly in different reclamation years.Soil physical and chemical properties reached a steady state after about 30 years of reclamation.2) According to the results of PCA analysis,the weighted value (0.97 in total) that represents soil nutrient factors (soil nitrate nitrogen,soil organic matter,soil available phosphorous,soil ammonia nitrogen,pH and soil particle size) were higher than the weighted value (0.48 in total) of soil limiting factors (soil salinity,soil elec-tric conductivity and soil moisture).The higher the F value is,the better the soil quality is.3) Different land use types play different roles in the soil function maturity process,with farmlands providing the best contribution.4) Soil physi-cal and chemical properties in the reclamation area were mainly influenced by reclamation time,and then by land use types.The correlation (0.1905) of the composite index of soil function (F) with reclamation time was greater than that with land use types (-0.1161).
基金Project(52005358)supported by the National Natural Science Foundation of ChinaProject(2018YFB1307902)supported by the National Key R&D Program of China+1 种基金Project(201901D111243)supported by the Natural Science Foundation of Shanxi Province,ChinaProject(2019-KF-25-05)supported by the Natural Science Foundation of Liaoning Province,China。
文摘To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.
文摘On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.
基金Projects(61273163,61325015,61304121)supported by the National Natural Science Foundation of China
文摘A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.
基金Under the auspices of the Major State Basic Research Development Program of China (973 Program) (No. 2005CB 724207)
文摘It is an effective way in realizing urban coordinated and sustainable development to establish a series of in- dicators and to evaluate urban environmental and socioeconomic development. According to the characteristics of Harbin City in Northeast China, an indicator system including five subsystems and 37 indicators was established for comprehensive evaluation on urban sustainable development. The development indexes of all urban subsystems and complex system were calculated quantitatively using the comprehensively integrated methods composed of Principle Component Analysis, Analytic Hierarchy Process and weighed index method, and then the comprehensive level of ur- ban sustainable development and the degree of urban interior coordination were analyzed. The results indicated that 1) the overall urban development presented an uptrend, however, the interior development was not well balanced from 1996 to 2006; 2) the development in each subsystem presented a strong fluctuation; and 3) the development in re- sources subsystem showed a downtrend. Based on those results, the suggestions of urban sustainable development were put forward at the end.
基金Project supported by the National Key Basic Research Support Foundation (NKBRSF) of China (No. 1999011809).
文摘Human activity and urbanization result in urban-rural environmental gradients. Understanding effect of the gradients on soil properties is necessary for management of the soils around urban areas. In this study, soil quality of some vegetable fields was characterized along an urban-rural gradient in Shaoxing County, Zhejiang Province. Fifteen soil physical and chemical properties were evaluated by using principal component analysis.Results showed that there was a great variation in the soil quality along the gradient. From rural to urban zones, soil organic matter, water-stable aggregates, cation exchangeable capacity (CEC), total N and P, and available K increased, whereas soil pH value decreased. In addition, Pb, Cu, Ni, Co, Zn and Cr in the soils tended to be accumulated toward the urban zone. Sequential chemical extraction showed that mobility of all the heavy metals in the soils tended to increase from the rural to the urban zones. The variation of soil properties accounted for by the first principal component was significantly explained by the difference in application rates of municipal wastes.
基金Project(9140A18010210KG01) supported by the Departmental Pre-Research Fund of China
文摘A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was proposed.This method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers' individual requirements.The PCA technique was used to reduce and orthogonalize the module parameters that affect the product performance.Then,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product data.The performance values of a newly configured product can be predicted by means of the trained SVM models.This PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample conditions.The applicability of the proposed method was verified on a family of plate electrostatic precipitators.
基金Supported by the 973 project of China （2013CB733600）, the National Natural Science Foundation （21176073）, the Doctoral Fund of Ministry of Education （20090074110005）, the New Century Excellent Talents in University （NCET-09-0346）, ＂Shu Guang＂ project （09SG29） and the Fundamental Research Funds for the Central Universities.
文摘The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.
基金Support by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)Zhejiang Provincial Science and Technology Planning Projects of China(2014C31019)
文摘In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.
基金We thank for the funding support from the National Key Research and Development Program of China(No.2019YFC1711200).
文摘Objective To establish gas chromatography-mass spectrometry(GC-MS)fingerprint method for the petroleum ether fraction of Shenqi Jiangtang Granules(SQJTG)and evaluate the product quality.Methods The GC-MS fingerprint of petroleum ether fraction of SQJTG was established by GC-MS,and the chemical components corresponding to the fingerprint peaks were structurally identified on NIST2014.The batch consistency of SQJTG products was evaluated based on the chemical composition of petroleum ether parts by using fingerprint similarity evaluation and Principal components analysis(PCA)technology.At the same time,Hotelling's T2 and DMODX statistics are used to set the control range for the quality of different batches of products.Results Twenty-two components were identified from the petroleum ether part of SQJTG,accounting for 60.94%of the total components separated.The similarity of fingerprints of petroleum ether parts of 24 batches of SQJTG was greater than 0.95.The PCA of 24 batches of samples were all under the control limits of Hotellin’s T2 and DMODX statistics,indicating that the petroleum ether parts of different batches of SQJTG were consistent.Conclusion The developed GC-MS fingerprint method can be used to evaluate the quality of SQJTG.
文摘Physicochemical characterization of 82 Algerian honeys, collected between 2005 and 2010, from different botanical and geographical origins were analyzed. The studied parameters were： water content, pH, free acidity （FA）, electrical conductivity （EC）, ash content, hydroxymethylfurfuraldehyde （HMF）, proline content, specific rotatory power and color. Most of the measured parameters had showed values in the range of the international standards, with a particular richness in proline and ash content. Chemometrics-based approach reveals that the discriminated groups were Citrus, Ziziphus and forest even with over represented groups like Eucalyptus. Principle component analysis （PCA） enabled to extract three principal components explaining nearly 65% of total variance, PCj and PC2 were related to botanical origin whereas PC3 to honey age. Analysis of variance showed that the studied variables were almost different depending on botanical, geographical origin and season. The current study also shows the presence of diverse honey varieties in Algeria. The collected data will contribute to the creation of products with protected geographical or/and botanical origins.
基金Supported by the National Natural Science Foundation of China （No.60574047） and the Doctorate Foundation of the State Education Ministry of China （No.20050335018）.
文摘Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.
