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Phase transition extracted by principal component analysis in the disordered Moore–Read state
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作者 Na Jiang Shuaixin Fu +1 位作者 Zhengzhi Ma Lian Wang 《Chinese Physics B》 2025年第4期552-558,共7页
We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of t... We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of the ground state wave functions with different disorder strengths can be distilled.As the disorder strength increases,the Moore–Read state will be destroyed.We explore the phase transition by analyzing the overlaps between the random sample wave functions and the topologically distilled state.The cross-point between the amplitudes of the principal component and its counterpart is the phase transition point.Additionally,the origin of the second component comes from the excited states,which is different from the Laughlin state. 展开更多
关键词 fractional quantum Hall phase transition principal component analysis
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FUZZY WITHIN-CLASS MATRIX PRINCIPAL COMPONENT ANALYSIS AND ITS APPLICATION TO FACE RECOGNITION 被引量:3
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作者 朱玉莲 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2008年第2期141-147,共7页
Matrix principal component analysis (MatPCA), as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of sampl... Matrix principal component analysis (MatPCA), as an effective feature extraction method, can deal with the matrix pattern and the vector pattern. However, like PCA, MatPCA does not use the class information of samples. As a result, the extracted features cannot provide enough useful information for distinguishing pat- tern from one another, and further resulting in degradation of classification performance. To fullly use class in- formation of samples, a novel method, called the fuzzy within-class MatPCA (F-WMatPCA)is proposed. F-WMatPCA utilizes the fuzzy K-nearest neighbor method(FKNN) to fuzzify the class membership degrees of a training sample and then performs fuzzy MatPCA within these patterns having the same class label. Due to more class information is used in feature extraction, F-WMatPCA can intuitively improve the classification perfor- mance. Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effective and competitive than MatPCA. The experimental analysis on face image databases indicates that F-WMatPCA im- proves the recognition accuracy and is more stable and robust in performing classification than the existing method of fuzzy-based F-Fisherfaces. 展开更多
关键词 face recognition principal component analysis (PCA) matrix pattern PCA(MatPCA) fuzzy K-nearest neighbor(FKNN) fuzzy within-class MatPCA(F-WMatPCA)
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Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis 被引量:6
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作者 杨洪星 付洪波 +3 位作者 王华东 贾军伟 Markus W Sigrist 董凤忠 《Chinese Physics B》 SCIE EI CAS CSCD 2016年第6期290-295,共6页
Laser-induced breakdown spectroscopy(LIBS) is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis(PCA) and support vector machine(SVM) is... Laser-induced breakdown spectroscopy(LIBS) is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis(PCA) and support vector machine(SVM) is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy(91.38% for the real rock) is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated. 展开更多
关键词 laser-induced breakdown spectroscopy(LIBS) principal component analysis(PCA) support vector machine(SVM) lithology identification
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Improved Face Recognition Method Using Genetic Principal Component Analysis 被引量:2
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作者 E.Gomathi K.Baskaran 《Journal of Electronic Science and Technology》 CAS 2010年第4期372-378,共7页
An improved face recognition method is proposed based on principal component analysis (PCA) compounded with genetic algorithm (GA), named as genetic based principal component analysis (GPCA). Initially the eigen... An improved face recognition method is proposed based on principal component analysis (PCA) compounded with genetic algorithm (GA), named as genetic based principal component analysis (GPCA). Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL (Olivetti Research Labs) face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods. 展开更多
关键词 EIGENFACES EIGENVECTORS face recognition genetic algorithm principal component analysis.
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Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis 被引量:2
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作者 Byeongcheol Kang Hyungsik Jung +1 位作者 Hoonyoung Jeong Jonggeun Choe 《Petroleum Science》 SCIE CAS CSCD 2020年第1期182-195,共14页
Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir mode... Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models.For stable convergence in ensemble Kalman filter(EnKF),increasing ensemble size can be one of the solutions,but it causes high computational cost in large-scale reservoir systems.In this paper,we propose a preprocessing of good initial model selection to reduce the ensemble size,and then,EnKF is utilized to predict production performances stochastically.In the model selection scheme,representative models are chosen by using principal component analysis(PCA)and clustering analysis.The dimension of initial models is reduced using PCA,and the reduced models are grouped by clustering.Then,we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data.One representative model with the minimum error is considered as the best model,and we use the ensemble members near the best model in the cluster plane for applying EnKF.We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time. 展开更多
关键词 Channel reservoir CHARACTERIZATION MODEL selection scheme EGG MODEL principal component analysis(PCA) ENSEMBLE KALMAN filter(EnKF) History matching
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Energy-efficient Scheme for Multiple Access Network Selection Using Principal Component Analysis 被引量:2
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作者 王莉 王景尧 +2 位作者 魏翼飞 马跃 满毅 《China Communications》 SCIE CSCD 2011年第3期133-144,共12页
This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly... This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones. 展开更多
关键词 multiple access network selection information entropy quality of service principal component analysis
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Grey Relational Analysis Coupled with Principal Component Analysis Method For Optimization Design of Novel Crash Box Structure 被引量:1
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作者 Shuang Wang Dengfeng Wang 《Journal of Beijing Institute of Technology》 EI CAS 2019年第3期577-584,共8页
Crashworthiness and lightweight optimization design of the crash box are studied in this paper. For the initial model, a physical test was performed to verify the model. Then, a parametric model using mesh morphing te... Crashworthiness and lightweight optimization design of the crash box are studied in this paper. For the initial model, a physical test was performed to verify the model. Then, a parametric model using mesh morphing technology is used to optimize and decrease the maximum collision force (MCF) and increase specific energy absorption (SEA) while ensure mass is not increased. Because MCF and SEA are two conflicting objectives, grey relational analysis (GRA) and principal component analysis (PCA) are employed for design optimization of the crash box. Furthermore, multi-objective analysis can convert to a single objective using the grey relational grade (GRG) simultaneously, hence, the proposed method can obtain the optimal combination of design parameters for the crash box. It can be concluded that the proposed method decreases the MCF and weight to 16.7% and 29.4% respectively, while increasing SEA to 16.4%. Meanwhile, the proposed method in comparison to the conventional NSGA-Ⅱ method, reduces the time cost by 103%. Hence, the proposed method can be properly applied to the optimization of the crash box. 展开更多
关键词 CRASH box optimization maximum COLLISION force (MCF) specific energy absorption (SEA) GREY RELATIONAL analysis (GRA) principal component analysis (PCA)
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Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network 被引量:4
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作者 LYU Defeng HU Yuwen 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2021年第5期867-875,共9页
In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based... In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness. 展开更多
关键词 AEROENGINE remaining useful life(RUL) principal component analysis(PCA) one-dimensional convolution neural network(1D-CNN) time series prediction state parameters
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Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor 被引量:2
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作者 Li Tang He-Ping Pan Yi-Yong Yao 《Journal of Electronic Science and Technology》 CAS CSCD 2020年第4期341-349,共9页
On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feat... On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate. 展开更多
关键词 Empirical mode decomposition(EMD) k-nearest neighbor(KNN) principal component analysis(PCA) time series
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Selection of tree species by principal component analysis for abandoned farmland in southeastern Horqin Sandy Land,China 被引量:1
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作者 Peng Meng Jing Liu Xuefeng Bai 《Journal of Forestry Research》 SCIE CAS CSCD 2022年第2期475-486,共12页
With changes in global climate and land use,the area of desertified farmland in southeastern Horqin Sandy Land(HSL)has increased in recent years,and farmlands are being abandoned.These abandoned farmlands(AFs)nega-tiv... With changes in global climate and land use,the area of desertified farmland in southeastern Horqin Sandy Land(HSL)has increased in recent years,and farmlands are being abandoned.These abandoned farmlands(AFs)nega-tively impact the local ecology.Therefore,the aim of the present study was to select suitable trees and shrubs for those AFs to prevent and control the desertification tendency.In this study,three AFs were fenced for 2 years,then 37 arbor and shrub species or varieties of 21 families were planted in the fenced AFs and grown for 10 years.The ecological adaptability of the species was evaluated and ranked using a principal component analysis.The results showed that the biodiversity of the AFs significantly improved after 2 years of fencing;the Shannon-Wiener index and species rich-ness of perennial grasses and forbs were 1.45 and 3.6 times higher,respectively,than for the unfenced AF.Among all species planted in fenced AFs,nine tree species had posi-tive comprehensive F(CF)values;Pinus sylvestris(Russian Shira steppe provenance),Populus alba‘Berolinensis’and Gleditsia triacanthos had CF greater than 1,and the first(PC1),second(PC2)and third(PC3)principal component values(F_(1),F_(2),F_(3))were all positive.Among the shrubs,only Lespedeza bicolor and Rosa xanthina f.normalis had CF greater than 0.All these results suggest that fencing improves biodiversity and that planting trees and shrubs that have higher CF values on the basis of fencing is an effective way to green and beautify AFs in HSL. 展开更多
关键词 Horqin Sandy Land Fenced abandoned farmland principal component analysis Tree species selection
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Compressive Sensing Sparse Sampling Method for Composite Material Based on Principal Component Analysis
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作者 Sun Yajie Gu Feihong +1 位作者 Ji Sai Wang Lihua 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2018年第2期282-289,共8页
Signals can be sampled by compressive sensing theory with a much less rate than those by traditional Nyquist sampling theorem,and reconstructed with high probability,only when signals are sparse in the time domain or ... Signals can be sampled by compressive sensing theory with a much less rate than those by traditional Nyquist sampling theorem,and reconstructed with high probability,only when signals are sparse in the time domain or a transform domain.Most signals are not sparse in real world,but can be expressed in sparse form by some kind of sparse transformation.Commonly used sparse transformations will lose some information,because their transform bases are generally fixed.In this paper,we use principal component analysis for data reduction,and select new variable with low dimension and linearly correlated to the original variable,instead of the original variable with high dimension,thus the useful data of the original signals can be included in the sparse signals after dimensionality reduction with maximize portability.Therefore,the loss of data can be reduced as much as possible,and the efficiency of signal reconstruction can be improved.Finally,the composite material plate is used for the experimental verification.The experimental result shows that the sparse representation of signals based on principal component analysis can reduce signal distortion and improve signal reconstruction efficiency. 展开更多
关键词 principal component analysis COMPRESSIVE sensing SPARSE REPRESENTATION SIGNAL RECONSTRUCTION
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Beam position monitor troubleshooting by using principal component analysis in Shanghai Synchrotron Radiation Facility 被引量:1
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作者 陈之初 冷用斌 +2 位作者 袁任贤 阎映炳 赖龙伟 《Nuclear Science and Techniques》 SCIE CAS CSCD 2014年第2期7-12,共6页
Beam position monitors(BPMs)have been widely used in all kinds of measurement systems,feedback systems and other areas in particle accelerator field these days.The malfunction of a single BPM can cause serious consequ... Beam position monitors(BPMs)have been widely used in all kinds of measurement systems,feedback systems and other areas in particle accelerator field these days.The malfunction of a single BPM can cause serious consequences such as the failure of the orbit feedback and the transverse feedback.A troubleshooting has been made to prevent the defective BPMs from affecting the accuracy and stability of the storage ring in Shanghai Synchrotron Radiation Facility(SSRF).Different types of malfunctions have been successfully identified by using the idea of principal component analysis(PCA). 展开更多
关键词 上海同步辐射装置 主成分分析法 光位置检测器 故障排除 反馈系统 粒子加速器 BPM 测量系统
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Biomass estimation of Shorea robusta with principal component analysis of satellite data
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作者 Nilanchal Patel Arnab Majumdar 《Journal of Forestry Research》 SCIE CAS CSCD 2010年第4期469-474,524,共7页
Spatio-temporal assessment of the above ground biomass (AGB) is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of tre... Spatio-temporal assessment of the above ground biomass (AGB) is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal (Shorea robusta C. F. Gaertn). Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components (PCs) of the bands, vegetation indices (VI), and the PCs of the VIs respectively and multiple linear regression (MLR) analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs. 展开更多
关键词 above ground biomass spectral response modeling vegetation indices principal component analysis linear and multiple regression analysis.
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Principal component analysis and cluster analysis based orbit optimization for earth observation satellites
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作者 卫晓娜 DONG Yun-feng +3 位作者 LIU Feng-rui TIAN Lu HAO Zhao SHI Heng 《Journal of Chongqing University》 CAS 2016年第3期83-94,共12页
This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverag... This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time. 展开更多
关键词 satellite orbit multi-objective optimization index normalization method principal component analysis cluster analysis
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Cardiovascular age of aviation personnel: based on the principal component analysis of heart rate and blood pressure variability
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作者 牛有国 王守岩 +2 位作者 张玉海 王兴邦 张立藩 《Journal of Medical Colleges of PLA(China)》 CAS 2004年第1期64-70,共7页
Objective: To introduce a method to calculate cardiovascular age, a new, accurate and much simpler index for assessing cardiovascular autonomic regulatory function, based on statistical analysis of heart rate and bloo... Objective: To introduce a method to calculate cardiovascular age, a new, accurate and much simpler index for assessing cardiovascular autonomic regulatory function, based on statistical analysis of heart rate and blood pressure variability (HRV and BPV) and baroreflex sensitivity (BRS) data. Methods: Firstly, HRV and BPV of 89 healthy aviation personnel were analyzed by the conventional autoregressive (AR) spectral analysis and their spontaneous BRS was obtained by the sequence method. Secondly, principal component analysis was conducted over original and derived indices of HRV, BPV and BRS data and the relevant principal components, PCi orig and PCi deri (i=1, 2, 3,...) were obtained. Finally, the equation for calculating cardiovascular age was obtained by multiple regression with the chronological age being assigned as the dependent variable and the principal components significantly related to age as the regressors. Results: The first four principal components of original indices accounted for over 90% of total variance of the indices, so did the first three principal components of derived indices. So, these seven principal components could reflect the information of cardiovascular autonomic regulation which was embodied in the 17 indices of HRV, BPV and BRS exactly with a minimal loss of information. Of the seven principal components, PC2 orig , PC4 orig and PC2 deri were negatively correlated with the chronological age ( P <0 05), whereas the PC3 orig was positively correlated with the chronological age ( P <0 01). The cardiovascular age thus calculated from the regression equation was significantly correlated with the chronological age among the 89 aviation personnel ( r =0.73, P <0 01). Conclusion: The cardiovascular age calculated based on a multi variate analysis of HRV, BPV and BRS could be regarded as a comprehensive indicator reflecting the age dependency of autonomic regulation of cardiovascular system in healthy aviation personnel. 展开更多
关键词 flying personnel heart rate variability blood pressure variability baroreflex sensitivity age principal components analysis multiple regression analysis
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Network Traffic Signature Generation Mechanism Using Principal Component Analysis
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作者 牟澄 黄小红 +1 位作者 吴军 马严 《China Communications》 SCIE CSCD 2013年第11期95-106,共12页
The Deep Packet Inspection(DPI)method is a popular method that can accurately identify the flow data and its corresponding application.Currently,the DPI method is widely used in common network management systems.Howev... The Deep Packet Inspection(DPI)method is a popular method that can accurately identify the flow data and its corresponding application.Currently,the DPI method is widely used in common network management systems.However,the major limitation of DPI systems is that their signature library is mainly extracted manually,which makes it hard to efficiently obtain the signature of new applications.Hence,in this paper,we propose an automatic signature extraction mechanism using Principal Component Analysis(PCA)technology,which is able to extract the signature automatically.In the proposed method,the signatures are expressed in the form of serial consistent sequences constructed by principal components instead of normally separated substrings in the original data extracted from the traditional methods.Extensive experiments based on numerous sets of data have been carried out to evaluate the performance of the proposed scheme,and the results prove that the newly proposed method can achieve good performance in terms of accuracy and efficiency. 展开更多
关键词 network traffic classification aut-omatic signature generation deep packet in-spection principal component analysis
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Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means
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作者 Bo-Han Wu Ran-Hong Xie +3 位作者 Li-Zhi Xiao Jiang-Feng Guo Guo-Wen Jin Jian-Wei Fu 《Petroleum Science》 SCIE EI CSCD 2023年第5期2747-2758,共12页
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig... In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method. 展开更多
关键词 Tight sandstone Integrated reservoir classification principal component analysis Simulated annealing genetic algorithm Fuzzy cluster means
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Analysis and Optimisation of Halomonas Growth Factors Based on PCA and RSM 被引量:2
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作者 Shi Ke Huang Guofu +4 位作者 Xu Huachun Xue Jianliang Sun Jingkuan Xiao Xinfeng Li Lin 《China Petroleum Processing & Petrochemical Technology》 SCIE CAS 2019年第4期81-87,共7页
The biomass of petroleum-degrading bacteria, such as Halomonas spp., is crucial to the alleviation of severe oil spills through bioremediation. In this paper, the bacterium(HDMP1) was isolated and identified. Growth f... The biomass of petroleum-degrading bacteria, such as Halomonas spp., is crucial to the alleviation of severe oil spills through bioremediation. In this paper, the bacterium(HDMP1) was isolated and identified. Growth factors were analysed and optimised through the single-factor experiments, the factor analysis(FA), the principal component analysis(PCA), and the response surface methodology(RSM). Results indicated that HDMP1 was identified as genus Halomonas. In the single-factor experiments, the range of suitable growth conditions for HDMP1 covered: a salt concentration of 2%-4%, a medium pH value of approximately 9, an inoculum concentration of 1.0%, a substrate concentration of 1.0%-1.4%, and a rotation rate of 140 r/min. The evaluation by FA and PCA indicated that three significant growth factors were the salt concentration, the pH value, and the rotation rate. A maximum biomass of HDMP1 was obtained under the conditions covering a salt concentration of 3.5%, a medium pH of 8, and a rotation rate of 151 r/min by optimization. 展开更多
关键词 BIOMASS factor analysis principal component analysis growth factors response surface methodology
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RIEMANN BOUNDARY VALUE PROBLEMS WITH GIVEN PRINCIPAL PART 被引量:2
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作者 李卫峰 杜金元 《Acta Mathematica Scientia》 SCIE CSCD 2009年第1期25-32,共8页
In this article, the authors discuss the Riemann boundary value problems with given principal part. First, authors consider a special case and give a classification of the solution class Rn by the way. And then, they ... In this article, the authors discuss the Riemann boundary value problems with given principal part. First, authors consider a special case and give a classification of the solution class Rn by the way. And then, they consider the general case. The solvable conditions for this problem and its solutions is obtained when it is solvable. 展开更多
关键词 Riemann boundary value problems principal part Riemann-Hilbert analysis for orthogonal polynomials
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Environmental Characteristics of Groundwater:an Application of PCA to Water Chemistry Analysis in Yulin 被引量:4
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作者 DONG Dong-lin WU Qiang ZHANG Rui SONG Ying-xia CHEN Shu-ke LI Pei LIU Shou-qiang BI Cen-cen LV Zhen-qi HUANG Song-lin 《Journal of China University of Mining and Technology》 EI 2007年第1期73-77,共5页
For our investigation into the water quality in Yulin city, we collected 76 typical water samples to be tested for particle quality. By applying a Romani type classification method the groundwater of Yulin city was cl... For our investigation into the water quality in Yulin city, we collected 76 typical water samples to be tested for particle quality. By applying a Romani type classification method the groundwater of Yulin city was classified into nine categories by type, i.e., Ca-HCO3, Na-HCO3, Na-HCO3-SO4-Cl, Na-HCO3-SO4, Na-Cl, Na-Cl-HCO3, Na-Ca- HCO3, Ca-Cl-HCO3 and Ca-HCO3-SO4-Cl. A principal component analysis was carried out in order to analyze the groundwater environment. From this analysis we considered that the information collected could be represented by 21 indices from which we extracted seven principal components, which, respectively, accounted for 37.4%, 13.0%, 8.1%, 7.2%, 6.3%, 5.9% and 4.6% of the total variation. The results show that the groundwater environment of this region is largely determined by characteristic components of the natural groundwater background. One part of the water was polluted by leaching/eluviation of solid waste generated from coal mining. Another part of the ground water was contaminated by acid mine water from the coal layer and from improper irrigation. In addition, geological and hydrogeological conditions also cause changes in the water environment. 展开更多
关键词 water environment principal component analysis component loading groundwater type Yulin city
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