The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first dis...The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first discuss the relation between zero coprime equivalence and unimodular equivalence for polynomial matrices.Then,we investigate the zero coprime equivalence problem for several classes of polynomial matrices,some novel findings and criteria on reducing these matrices to their Smith normal forms are obtained.Finally,an example is provided to illustrate the main results.展开更多
The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic tim...The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.展开更多
Oil–water two-phase flow patterns in a horizontal pipe are analyzed with a 16-electrode electrical resistance tomography(ERT) system. The measurement data of the ERT are treated as a multivariate time-series, thus th...Oil–water two-phase flow patterns in a horizontal pipe are analyzed with a 16-electrode electrical resistance tomography(ERT) system. The measurement data of the ERT are treated as a multivariate time-series, thus the information extracted from each electrode represents the local phase distribution and fraction change at that location. The multivariate maximum Lyapunov exponent(MMLE) is extracted from the 16-dimension time-series to demonstrate the change of flow pattern versus the superficial velocity ratio of oil to water. The correlation dimension of the multivariate time-series is further introduced to jointly characterize and finally separate the flow patterns with MMLE. The change of flow patterns with superficial oil velocity at different water superficial velocities is studied with MMLE and correlation dimension, respectively, and the flow pattern transition can also be characterized with these two features. The proposed MMLE and correlation dimension map could effectively separate the flow patterns, thus is an effective tool for flow pattern identification and transition analysis.展开更多
The analysis result of absolute degree of grey incidence for multivariate time series is often inconsistent with the qualitative analysis. To overcome this shortage, a multivariate absolute degree of grey incidence ba...The analysis result of absolute degree of grey incidence for multivariate time series is often inconsistent with the qualitative analysis. To overcome this shortage, a multivariate absolute degree of grey incidence based on distribution characteristics of points is proposed. Based on the geometric description of multivariate time se- ries, the neighborhood extrema are extracted in the different regions, and a characteristic point set is constructed. Then according to the distribution of the characteristic point set, a characteristic point sequence reflecting the ge- ometric features of multivariate time series is obtained. The incidence analysis between multivariate time series is transformed into the relational analysis between characteristic point sequences, and a grey incidence model is established. The model possesses the properties of translational invariance, transpose and rank transform invari- ance, and satisfies the grey incidence analysis axioms. Finally, two cases are studied and the results prove the ef- fectiveness of the model.展开更多
[Objective] The plankton and macrobenthos samples in municipal polluted river were analyzed by different methods, so as to explore the method suitable for biological data analysis in heavy polluted area. [Method] Shan...[Objective] The plankton and macrobenthos samples in municipal polluted river were analyzed by different methods, so as to explore the method suitable for biological data analysis in heavy polluted area. [Method] Shannon-Wiener diversity index, cluster analysis of multivariate statistical analysis and MDS （Non-matric Multi- dimentional Scaling）analysis were used to analyze biological data of phytoplankton, zooplankton and Zoobenthos collected from the representative municipal polluted river in Pearl River Delta. The sediment samples were also collected to determine. Pb, Cd, Hg, Cr, As, Cu, Ni, Zn, as well as CODe, and NH3-N of porewater. Hakanson potential ecological risk index method was used to evaluate the ecological risk. [Re- suit] Shannon-Wiener diversity index analysis results can not effectively reflect the difference of pollution status of various stations in heavy polluted area; despite the presence of some problems, multivariate analysis method is superior to the Shannon-Wiener diversity index method in biological monitoring of heavy polluted river in the city. [Conclusion] The paper provided theoretical basis for biological data analysis in heavy polluted area.展开更多
This study addresses the public concerns of potential adverse health effects from ambient fine particulate matter as well as socioeconomic factors. Heart attack, high blood pressure, and heart disease mortality rates ...This study addresses the public concerns of potential adverse health effects from ambient fine particulate matter as well as socioeconomic factors. Heart attack, high blood pressure, and heart disease mortality rates were investigated against fine particulate matter and socioeconomic status, for all counties in the United States in 2013. Multivariate multiple regressions as well as multivariate geostatistical predictions show that these are significant factors towards assessing the causal inferences between exposure to air pollution and socioeconomic status and the three mortality rates.展开更多
This paper considers the upper orthant and extremal tail dependence indices for multivariate t-copula. Where, the multivariate t-copula is defined under a correlation structure. The explicit representations of the tai...This paper considers the upper orthant and extremal tail dependence indices for multivariate t-copula. Where, the multivariate t-copula is defined under a correlation structure. The explicit representations of the tail dependence parameters are deduced since the copula of continuous variables is invariant under strictly increasing transformation about the random variables, which are more simple than those obtained in previous research. Then, the local monotonicity of these indices about the correlation coefficient is discussed, and it is concluded that the upper extremal dependence index increases with the correlation coefficient, but the monotonicity of the upper orthant tail dependence index is complex. Some simulations are performed by the Monte Carlo method to verify the obtained results, which are found to be satisfactory. Meanwhile, it is concluded that the obtained conclusions can be extended to any distribution family in which the generating random variable has a regularly varying distribution.展开更多
In this paper, we consider the Straight Line Type Node Configuration C （SLTNCC） in multivariate polynomial interpolation as the result of different kinds of transformations of lines （such as parallel translations, ...In this paper, we consider the Straight Line Type Node Configuration C （SLTNCC） in multivariate polynomial interpolation as the result of different kinds of transformations of lines （such as parallel translations, rotations）. Corresponding to these transformations we define different kinds of interpolation problems for the SLTNCC. The expression of the confluent multivariate Vandermonde determinant of the coefficient matrix for each of these interpolation problems is obtained, and from this expression we conclude the related interpolation problem is unisolvent. Also, we give a kind of generalization of the SLTNCC in Section 5. As well, we obtain an expression of the interpolating polynomial for a kind of interpolation problem discussed in this paper.展开更多
Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was foun...Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was found that the molar refractivity of the C3′substituent of the C13 side chain has significant correlation with its activity. We deduce that structural changes in the C3′substituents may be critical to the anticancer function. It would be useful to the design and synthesis of taxol like compounds with improved activities.展开更多
Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and...Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical solution with regard to the phenomena involved.In situations where measured data or numerical hypothetical results are available, neural networks stand out in mapping the nonlinear interactions and relationships between the system’s predictors and dependent responses. In addition, unlike most computational tools, no mathematical relationship assumption between the dependent and independent variables has to be made. Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configuration is not known a priori. This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines（MARS）, as an alternative to neural networks, to approximate the relationship between the inputs and dependent response, and to mathematically interpret the relationship between the various parameters. In this paper, the Back propagation neural network（BPNN） and MARS models are developed for assessing pile drivability in relation to the prediction of the Maximum compressive stresses（MCS）, Maximum tensile stresses（MTS）, and Blow per foot（BPF）. A database of more than four thousand piles is utilized for model development and comparative performance between BPNN and MARS predictions.展开更多
Multivariate statistical techniques,cluster analysis,non-parametric tests,and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area,China,from 200...Multivariate statistical techniques,cluster analysis,non-parametric tests,and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area,China,from 2003–2008 to investigate spatio-temporal variations and identify potential pollution sources.Using cluster analysis,the twelve months of the year were classified into three periods of lowflow （LF）,normal-flow （NF）,and high-flow （HF）;and the 37 monitoring sites were divided into low pollution （LP）,moderate pollution （MP）,and high pollution （HP）.Dissolved oxygen （DO）,potassium permanganate index （COD Mn ）,and ammonia-nitrogen （NH 4 ＋-N） were identified as significant variables affecting temporal and spatial variations by non-parametric tests.Factor analysis identified that the major pollutants in the HP region were organic matters and nutrients during NF,heavy metals during LF,and petroleum during HF.In the MP region,the identified pollutants primarily included organic matter and heavy metals year-around,while in the LP region,organic pollution was significant during both NF and HF,and nutrient and heavy metal levels were high during both LF and HF.The main sources of pollution came from domestic wastewater and agricultural activities and runoff;however,they contributed differently to each region in regards to pollution levels.For the HP region,inputs from wastewater treatment plants were significant;but for MP and LP regions,water pollution was more likely from the combined effects of agriculture,domestic wastewater,and chemical industry.These results provide fundamental information for developing better water pollution control strategies for the Three Gorges area.展开更多
In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar re...In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.展开更多
Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares ...Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.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
This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the infl...This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.展开更多
Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were ...Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.展开更多
The Cretaceous Mamu Formation coal samples located within the Northern Anambra Basin were collected, analysed and interpreted using multivariate statistical approach to determine the major, trace and rare elements ass...The Cretaceous Mamu Formation coal samples located within the Northern Anambra Basin were collected, analysed and interpreted using multivariate statistical approach to determine the major, trace and rare elements association with a view to determine the source of the terrestrial rocks, palaeoweathering/climatic conditions and tectonic setting. The dominant oxides such as SiO2, A12O3, Fe2O3, TiO2 and CaO were identified in the coal to suggest terrigenous origin. The ratio of SiO2/Al2O3 of 4.8 suggests that the coal was formed from low land peat associated with freshwater continental marine or blackish water with low salinity as indicated by Sr/Ba and CaO + MgO/K2O + Na2。ratios. The major oxides also revealed stable condition of deposition, low degree of tectonic setting but constant subsidence in the basin. The condition of deposition was acidic in nature as indicated by TiO2/Zr plot. Based on the abundance of Zr, Zn, Ba, Ni, Co, Sr, V, and Y, moderate salinity, sub-oxic to oxic bottom water condition was prevalent and also indication of marine influence. Based on the ratios of La/Yb;La/Sm and Gd/Yb, LREE has higher enrichment than HREE. Humid climatic conditions were observed at the coal formation stage while weak laterization to kaolinization was also evidence.展开更多
BACKGROUNDSpontaneous bacterial peritonitis (SBP) is a detrimental infection of the asciticfluid in liver cirrhosis patients, with high mortality and morbidity. Earlydiagnosis and timely antibiotic administration have...BACKGROUNDSpontaneous bacterial peritonitis (SBP) is a detrimental infection of the asciticfluid in liver cirrhosis patients, with high mortality and morbidity. Earlydiagnosis and timely antibiotic administration have successfully decreased themortality rate to 20%-25%. However, many patients cannot be diagnosed in theearly stages due to the absence of classical SBP symptoms. Early diagnosis ofasymptomatic SBP remains a great challenge in the clinic.AIMTo establish a multivariate predictive model for early diagnosis of asymptomaticSBP using positive microbial cultures from liver cirrhosis patients with ascites.METHODSA total of 98 asymptomatic SBP patients and 98 ascites liver cirrhosis patients withnegative microbial cultures were included in the case and control groups,respectively. Multiple linear stepwise regression analysis was performed toidentify potential indicators for asymptomatic SBP diagnosis. The diagnosticperformance of the model was estimated using the receiver operatingcharacteristic curve.RESULTSPatients in the case group were more likely to have advanced disease stages,cirrhosis related-complications, worsened hematology and ascites, and higher mortality. Based on multivariate analysis, the predictive model was as follows: y (P) = 0.018 + 0.312 × MELD (model of end-stage liver disease) + 0.263 × PMN(ascites polymorphonuclear) + 0.184 × N (blood neutrophil percentage) + 0.233 ×HCC (hepatocellular carcinoma) + 0.189 × renal dysfunction. The area under thecurve value of the established model was 0.872, revealing its high diagnosticpotential. The diagnostic sensitivity was 73.5% (72/98), the specificity was 86.7%(85/98), and the diagnostic efficacy was 80.1%.CONCLUSIONOur predictive model is based on the MELD score, polymorphonuclear cells,blood N, hepatocellular carcinoma, and renal dysfunction. This model mayimprove the early diagnosis of asymptomatic SBP.展开更多
Classification models for multivariate time series have drawn the interest of many researchers to the field with the objective of developing accurate and efficient models.However,limited research has been conducted on...Classification models for multivariate time series have drawn the interest of many researchers to the field with the objective of developing accurate and efficient models.However,limited research has been conducted on generating adversarial samples for multivariate time series classification models.Adversarial samples could become a security concern in systems with complex sets of sensors.This study proposes extending the existing gradient adversarial transformation network(GATN)in combination with adversarial autoencoders to attack multivariate time series classification models.The proposed model attacks classification models by utilizing a distilled model to imitate the output of the multivariate time series classification model.In addition,the adversarial generator function is replaced with a variational autoencoder to enhance the adversarial samples.The developed methodology is tested on two multivariate time series classification models:1-nearest neighbor dynamic time warping(1-NN DTW)and a fully convolutional network(FCN).This study utilizes 30 multivariate time series benchmarks provided by the University of East Anglia(UEA)and University of California Riverside(UCR).The use of adversarial autoencoders shows an increase in the fraction of successful adversaries generated on multivariate time series.To the best of our knowledge,this is the first study to explore adversarial attacks on multivariate time series.Additionally,we recommend future research utilizing the generated latent space from the variational autoencoders.展开更多
Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental...Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.展开更多
基金Supported by the National Natural Science Foundation of China(12271154)the Natural Science Foundation of Hunan Province(2022JJ30234)the Postgraduate Scientific Research Innovation Project of Hunan Province(CX20231032)。
文摘The zero coprime system equivalence is one of important research in the theory of multidimensional system equivalence,and is closely related to zero coprime equivalence of multivariate polynomial matrices.We first discuss the relation between zero coprime equivalence and unimodular equivalence for polynomial matrices.Then,we investigate the zero coprime equivalence problem for several classes of polynomial matrices,some novel findings and criteria on reducing these matrices to their Smith normal forms are obtained.Finally,an example is provided to illustrate the main results.
文摘The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.
基金Projects(61227006,61473206) supported by the National Natural Science Foundation of ChinaProject(13TXSYJC40200) supported by Science and Technology Innovation of Tianjin,China
文摘Oil–water two-phase flow patterns in a horizontal pipe are analyzed with a 16-electrode electrical resistance tomography(ERT) system. The measurement data of the ERT are treated as a multivariate time-series, thus the information extracted from each electrode represents the local phase distribution and fraction change at that location. The multivariate maximum Lyapunov exponent(MMLE) is extracted from the 16-dimension time-series to demonstrate the change of flow pattern versus the superficial velocity ratio of oil to water. The correlation dimension of the multivariate time-series is further introduced to jointly characterize and finally separate the flow patterns with MMLE. The change of flow patterns with superficial oil velocity at different water superficial velocities is studied with MMLE and correlation dimension, respectively, and the flow pattern transition can also be characterized with these two features. The proposed MMLE and correlation dimension map could effectively separate the flow patterns, thus is an effective tool for flow pattern identification and transition analysis.
基金Supported by the National Natural Science Foundation of China(71101043,70901041,71171113)the Joint Research Project of National Natural Science Foundation of China and Royal Society of UK(71111130211)+4 种基金the Major Program of National Funds of Social Science of China(10ZD&014,11&ZD168)the Doctoral Fundof Ministry of Education of China(20093218120032,200802870020)the Qinglan Project for Excellent Youth Teacherin Jiangsu Province(China)Research Funding in Nanjing University of Aeronautics and Astronautics(NR2011002)the Central University Scientific Research Expenses of HoHai University(2011B09914,2010B11114)~~
文摘The analysis result of absolute degree of grey incidence for multivariate time series is often inconsistent with the qualitative analysis. To overcome this shortage, a multivariate absolute degree of grey incidence based on distribution characteristics of points is proposed. Based on the geometric description of multivariate time se- ries, the neighborhood extrema are extracted in the different regions, and a characteristic point set is constructed. Then according to the distribution of the characteristic point set, a characteristic point sequence reflecting the ge- ometric features of multivariate time series is obtained. The incidence analysis between multivariate time series is transformed into the relational analysis between characteristic point sequences, and a grey incidence model is established. The model possesses the properties of translational invariance, transpose and rank transform invari- ance, and satisfies the grey incidence analysis axioms. Finally, two cases are studied and the results prove the ef- fectiveness of the model.
基金Supported by National Natural Science Foundation of China(41001341)Natural Science Foundation of Guangdong Province(9152800001000007)+1 种基金Open Fund ofState Key Laboratory of Subtropical Building Science(2011KB12)Basic Scientific Research Expenses Project of Central Universities(2012ZM0082)~~
文摘[Objective] The plankton and macrobenthos samples in municipal polluted river were analyzed by different methods, so as to explore the method suitable for biological data analysis in heavy polluted area. [Method] Shannon-Wiener diversity index, cluster analysis of multivariate statistical analysis and MDS （Non-matric Multi- dimentional Scaling）analysis were used to analyze biological data of phytoplankton, zooplankton and Zoobenthos collected from the representative municipal polluted river in Pearl River Delta. The sediment samples were also collected to determine. Pb, Cd, Hg, Cr, As, Cu, Ni, Zn, as well as CODe, and NH3-N of porewater. Hakanson potential ecological risk index method was used to evaluate the ecological risk. [Re- suit] Shannon-Wiener diversity index analysis results can not effectively reflect the difference of pollution status of various stations in heavy polluted area; despite the presence of some problems, multivariate analysis method is superior to the Shannon-Wiener diversity index method in biological monitoring of heavy polluted river in the city. [Conclusion] The paper provided theoretical basis for biological data analysis in heavy polluted area.
文摘This study addresses the public concerns of potential adverse health effects from ambient fine particulate matter as well as socioeconomic factors. Heart attack, high blood pressure, and heart disease mortality rates were investigated against fine particulate matter and socioeconomic status, for all counties in the United States in 2013. Multivariate multiple regressions as well as multivariate geostatistical predictions show that these are significant factors towards assessing the causal inferences between exposure to air pollution and socioeconomic status and the three mortality rates.
基金The National Natural Science Foundation of China(No.11001052,11171065)the National Science Foundation of Jiangsu Province(No.BK2011058)the Science Foundation of Nanjing University of Posts and Telecommunications(No.JG00710JX57)
文摘This paper considers the upper orthant and extremal tail dependence indices for multivariate t-copula. Where, the multivariate t-copula is defined under a correlation structure. The explicit representations of the tail dependence parameters are deduced since the copula of continuous variables is invariant under strictly increasing transformation about the random variables, which are more simple than those obtained in previous research. Then, the local monotonicity of these indices about the correlation coefficient is discussed, and it is concluded that the upper extremal dependence index increases with the correlation coefficient, but the monotonicity of the upper orthant tail dependence index is complex. Some simulations are performed by the Monte Carlo method to verify the obtained results, which are found to be satisfactory. Meanwhile, it is concluded that the obtained conclusions can be extended to any distribution family in which the generating random variable has a regularly varying distribution.
文摘In this paper, we consider the Straight Line Type Node Configuration C （SLTNCC） in multivariate polynomial interpolation as the result of different kinds of transformations of lines （such as parallel translations, rotations）. Corresponding to these transformations we define different kinds of interpolation problems for the SLTNCC. The expression of the confluent multivariate Vandermonde determinant of the coefficient matrix for each of these interpolation problems is obtained, and from this expression we conclude the related interpolation problem is unisolvent. Also, we give a kind of generalization of the SLTNCC in Section 5. As well, we obtain an expression of the interpolating polynomial for a kind of interpolation problem discussed in this paper.
文摘Abstract Using the method of stepwise multivariate linear regression (SMLR), the quantitative structure activity relationships (QSAR) of two isomeric series of taxol and its derivatives have been studied. It was found that the molar refractivity of the C3′substituent of the C13 side chain has significant correlation with its activity. We deduce that structural changes in the C3′substituents may be critical to the anticancer function. It would be useful to the design and synthesis of taxol like compounds with improved activities.
文摘Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical solution with regard to the phenomena involved.In situations where measured data or numerical hypothetical results are available, neural networks stand out in mapping the nonlinear interactions and relationships between the system’s predictors and dependent responses. In addition, unlike most computational tools, no mathematical relationship assumption between the dependent and independent variables has to be made. Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configuration is not known a priori. This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines（MARS）, as an alternative to neural networks, to approximate the relationship between the inputs and dependent response, and to mathematically interpret the relationship between the various parameters. In this paper, the Back propagation neural network（BPNN） and MARS models are developed for assessing pile drivability in relation to the prediction of the Maximum compressive stresses（MCS）, Maximum tensile stresses（MTS）, and Blow per foot（BPF）. A database of more than four thousand piles is utilized for model development and comparative performance between BPNN and MARS predictions.
基金supported by the National Water Special Project (No.2009ZX07526-005)the Strategic Environmental Assessment Project (No.HP1080901)
文摘Multivariate statistical techniques,cluster analysis,non-parametric tests,and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area,China,from 2003–2008 to investigate spatio-temporal variations and identify potential pollution sources.Using cluster analysis,the twelve months of the year were classified into three periods of lowflow （LF）,normal-flow （NF）,and high-flow （HF）;and the 37 monitoring sites were divided into low pollution （LP）,moderate pollution （MP）,and high pollution （HP）.Dissolved oxygen （DO）,potassium permanganate index （COD Mn ）,and ammonia-nitrogen （NH 4 ＋-N） were identified as significant variables affecting temporal and spatial variations by non-parametric tests.Factor analysis identified that the major pollutants in the HP region were organic matters and nutrients during NF,heavy metals during LF,and petroleum during HF.In the MP region,the identified pollutants primarily included organic matter and heavy metals year-around,while in the LP region,organic pollution was significant during both NF and HF,and nutrient and heavy metal levels were high during both LF and HF.The main sources of pollution came from domestic wastewater and agricultural activities and runoff;however,they contributed differently to each region in regards to pollution levels.For the HP region,inputs from wastewater treatment plants were significant;but for MP and LP regions,water pollution was more likely from the combined effects of agriculture,domestic wastewater,and chemical industry.These results provide fundamental information for developing better water pollution control strategies for the Three Gorges area.
基金supported by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),UTS under grant numbers 321740.2232335,323930,and 321740.2232357
文摘In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.
基金Supported by the National High-Tech Development Program of China(No.863-511-920-011,2001AA411230).
文摘Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.
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.
基金supported by The Hong Kong Polytechnic University through the project RU3Ythe Research Grant Council through the project PolyU 5128/13E+1 种基金National Natural Science Foundation of China(Grant No.51778313)Cooperative Innovation Center of Engineering Construction and Safety in Shangdong Blue Economic Zone
文摘This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.
基金supported by the Major State Basic Research Development Program (No. 2010CB428800)the Geological Survey Projects Foundation of Institute of Hydrogeology and Environmental Geology (No. SK201308)
文摘Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.
文摘The Cretaceous Mamu Formation coal samples located within the Northern Anambra Basin were collected, analysed and interpreted using multivariate statistical approach to determine the major, trace and rare elements association with a view to determine the source of the terrestrial rocks, palaeoweathering/climatic conditions and tectonic setting. The dominant oxides such as SiO2, A12O3, Fe2O3, TiO2 and CaO were identified in the coal to suggest terrigenous origin. The ratio of SiO2/Al2O3 of 4.8 suggests that the coal was formed from low land peat associated with freshwater continental marine or blackish water with low salinity as indicated by Sr/Ba and CaO + MgO/K2O + Na2。ratios. The major oxides also revealed stable condition of deposition, low degree of tectonic setting but constant subsidence in the basin. The condition of deposition was acidic in nature as indicated by TiO2/Zr plot. Based on the abundance of Zr, Zn, Ba, Ni, Co, Sr, V, and Y, moderate salinity, sub-oxic to oxic bottom water condition was prevalent and also indication of marine influence. Based on the ratios of La/Yb;La/Sm and Gd/Yb, LREE has higher enrichment than HREE. Humid climatic conditions were observed at the coal formation stage while weak laterization to kaolinization was also evidence.
基金Supported by the Digestive Medical Coordinated Development Center of Beijing Municipal Administration,No.XXZ0403.
文摘BACKGROUNDSpontaneous bacterial peritonitis (SBP) is a detrimental infection of the asciticfluid in liver cirrhosis patients, with high mortality and morbidity. Earlydiagnosis and timely antibiotic administration have successfully decreased themortality rate to 20%-25%. However, many patients cannot be diagnosed in theearly stages due to the absence of classical SBP symptoms. Early diagnosis ofasymptomatic SBP remains a great challenge in the clinic.AIMTo establish a multivariate predictive model for early diagnosis of asymptomaticSBP using positive microbial cultures from liver cirrhosis patients with ascites.METHODSA total of 98 asymptomatic SBP patients and 98 ascites liver cirrhosis patients withnegative microbial cultures were included in the case and control groups,respectively. Multiple linear stepwise regression analysis was performed toidentify potential indicators for asymptomatic SBP diagnosis. The diagnosticperformance of the model was estimated using the receiver operatingcharacteristic curve.RESULTSPatients in the case group were more likely to have advanced disease stages,cirrhosis related-complications, worsened hematology and ascites, and higher mortality. Based on multivariate analysis, the predictive model was as follows: y (P) = 0.018 + 0.312 × MELD (model of end-stage liver disease) + 0.263 × PMN(ascites polymorphonuclear) + 0.184 × N (blood neutrophil percentage) + 0.233 ×HCC (hepatocellular carcinoma) + 0.189 × renal dysfunction. The area under thecurve value of the established model was 0.872, revealing its high diagnosticpotential. The diagnostic sensitivity was 73.5% (72/98), the specificity was 86.7%(85/98), and the diagnostic efficacy was 80.1%.CONCLUSIONOur predictive model is based on the MELD score, polymorphonuclear cells,blood N, hepatocellular carcinoma, and renal dysfunction. This model mayimprove the early diagnosis of asymptomatic SBP.
文摘Classification models for multivariate time series have drawn the interest of many researchers to the field with the objective of developing accurate and efficient models.However,limited research has been conducted on generating adversarial samples for multivariate time series classification models.Adversarial samples could become a security concern in systems with complex sets of sensors.This study proposes extending the existing gradient adversarial transformation network(GATN)in combination with adversarial autoencoders to attack multivariate time series classification models.The proposed model attacks classification models by utilizing a distilled model to imitate the output of the multivariate time series classification model.In addition,the adversarial generator function is replaced with a variational autoencoder to enhance the adversarial samples.The developed methodology is tested on two multivariate time series classification models:1-nearest neighbor dynamic time warping(1-NN DTW)and a fully convolutional network(FCN).This study utilizes 30 multivariate time series benchmarks provided by the University of East Anglia(UEA)and University of California Riverside(UCR).The use of adversarial autoencoders shows an increase in the fraction of successful adversaries generated on multivariate time series.To the best of our knowledge,this is the first study to explore adversarial attacks on multivariate time series.Additionally,we recommend future research utilizing the generated latent space from the variational autoencoders.
基金supposed by the Program for Science and Technology of Shandong Province (2011GHY11521)the Department of Education of Shandong Province (No. J11LB07)the Natural Science Foundation of Qingdao City (Nos. 12-1-3-52-(1)-nsh and 12-1-4-16-(7)-jch)
文摘Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.
