Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
In this paper, an integrated validation method and process are developed for multivariate dynamic systems. The principal component analysis approach is used to address multivariate correlation and dimensionality reduc...In this paper, an integrated validation method and process are developed for multivariate dynamic systems. The principal component analysis approach is used to address multivariate correlation and dimensionality reduction, the dynamic time warping and correlation coefficient are used for error assessment, and the subject matter experts (SMEs)’ opinions and principal component analysis coefficients are incorporated to provide the overall rating of the dynamic system. The proposed method and process are successfully demonstrated through a vehicle dynamic system problem.展开更多
Software watermarking is an efficient tool to verify the copyright of software. Public key cryptosystem-based watermarking is widely researched. However, the popular public key cryptosystem is not secure under quantum...Software watermarking is an efficient tool to verify the copyright of software. Public key cryptosystem-based watermarking is widely researched. However, the popular public key cryptosystem is not secure under quantum algorithm. This paper proposes a novel soft-ware watermarking scheme based on multivariate public key cryptosystem. The copyright information generated by copyright holder is transformed into copyright numbers using multivariate quadratic polynomial equations inspired by multivariate public key cryptosystem （MPKC）. Every polynomial is embedded into the host program independently. Based on the security performance of MPKC, the robustness and invisibility of the proposed scheme is significantly improved in comparison with the RSA-based watermarking method.展开更多
In this paper,a new method for simultaneous spectrophotometric analysis of multi- component with double-system was developed.The multivariate calibration method,principal component analysis-partial least squares(PCA-P...In this paper,a new method for simultaneous spectrophotometric analysis of multi- component with double-system was developed.The multivariate calibration method,principal component analysis-partial least squares(PCA-PLS),was described and applied to the processing of measurement data.A demonstration,simultaneous determination of cobalt,nickel,copper,zinc and iron with double-system(5-Br-PADAP and PAR as chromogenic chelate reagents,respectively) was given.The results showed that the method with douhle-system gave better precision than those with single system and MLR(in this paper,AKC method was selected)did not give satis- fied precision in any situation.展开更多
Multivariate seemingly unrelated regression system is raised first and the two stage estimation and its covariance matrix are given. The results of the literatures[1-5] are extended in this paper.
The design of optical instruments is an active subject due to improvement in lens techniques, fabrication technology, and data handling capacity. Much remains to do to expand its application to phytopathology, which w...The design of optical instruments is an active subject due to improvement in lens techniques, fabrication technology, and data handling capacity. Much remains to do to expand its application to phytopathology, which would be in particular quite useful to improve crop growth monitoring in countries like Mali. An optical multimodal system for plant samples has been developed to improve the characterization of leaf disease symptoms, provide information on their effects, and avoid their spread. Potentially inexpensive components (laser, lens, turntables camera and sample, filter, lens, camera and computer) have been selected, assembled and aligned on an optical table into a multimodal system operating in transmission, reflection, diffusion and fluorescence. The illumination and observation angles can be adjusted to optimize viewing conditions in the four modes. This scientific contribution has been an initiation into the design and implementation of an optical instrument. Initial results are shown and will now be extended in cooperation with agronomic laboratories in African countries for tests on specific plant diseases in relation with prevailing climate conditions.展开更多
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 significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and a...The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.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
In this paper, a geometric approach to fault detection and isolation （FDI） is applied to a Multiple-Input Multipie-Output （MIMO） model of a frame and the FDI results are compared to the ones obtained in the Single...In this paper, a geometric approach to fault detection and isolation （FDI） is applied to a Multiple-Input Multipie-Output （MIMO） model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output （SISO）, Multiple-Input Single-Output （MISO）, and Single-Input Multiple-Output （SIMO） cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX （Auto Regressive with exogenous input） and VARX （Vector ARX） models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs, the classification errors reduce, even in the case of applying only one of the inputs in the computations. Furthermore, increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also, it is shown that by using probabilistic space in the distance function, fault diagnosis scheme has better performance in comparison with the deterministic one.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 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
A new algorithm for constructing an inverse of a multivariable linear system is presented. This algorithm makes the constructing an inverse of the higher order matrices into searching for the equivalent normal form o...A new algorithm for constructing an inverse of a multivariable linear system is presented. This algorithm makes the constructing an inverse of the higher order matrices into searching for the equivalent normal form of the lower order matrices. Consequently, the calculation is more simple efficient and programmed than previous methods. Another result of the paper is that the lower reduced inverse system is obtained, by selecting special bases of the observable space of the original systems, it reveals the effect of the observability of the original systems on the order of the inverse systems.展开更多
AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analy...AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analysis.METHODS:The causal effects of several behavioral factors,including screen time,education time,time spent outdoors,and physical activity,on the risk of HM using univariable Mendelian randomization(MR)and MVMR analyses were first assessed.Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM.RESULTS:MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM.Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM.Additionally,MR analyses identified 13 serum metabolites significantly associated with HM,of which 12 were lipids and one was an amino acid derivative.Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM,with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC(p-16:0;30.83%).CONCLUSION:This study suggests that in addition to outdoor time,moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity.These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.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
A new method for calculating the failure probabilityof structures with random parameters is proposed based onmultivariate power polynomial expansion, in which te uncertain quantities include material properties, struc...A new method for calculating the failure probabilityof structures with random parameters is proposed based onmultivariate power polynomial expansion, in which te uncertain quantities include material properties, structuralgeometric characteristics and static loads. The structuralresponse is first expressed as a multivariable power polynomialexpansion, of which the coefficients ae then determined by utilizing the higher-order perturbation technique and Galerkinprojection scheme. Then, the final performance function ofthe structure is determined. Due to the explicitness of theperformance function, a multifold integral of the structuralfailure probability can be calculated directly by the Monte Carlo simulation, which only requires a smal amount ofcomputation time. Two numerical examples ae presented toillustate te accuracy ad efficiency of te proposed metiod. It is shown that compaed with the widely used first-orderreliability method （ FORM） and second-order reliabilitymethod （ SORM）, te results of the proposed method are closer to that of the direct Monte Carlo metiod,and it requires much less computational time.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
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported by the National Natural Science Fundation of China (No. 51075262)the New Century Excellent Talents in University Program (No. NCET-08-0361)the Fund for the Doctoral Program of Higher Education (No. 200802480036), China
文摘In this paper, an integrated validation method and process are developed for multivariate dynamic systems. The principal component analysis approach is used to address multivariate correlation and dimensionality reduction, the dynamic time warping and correlation coefficient are used for error assessment, and the subject matter experts (SMEs)’ opinions and principal component analysis coefficients are incorporated to provide the overall rating of the dynamic system. The proposed method and process are successfully demonstrated through a vehicle dynamic system problem.
基金Supported by National Key Basic Research Program(973 Program)of China(2014CB340600)Nature Science Foundation of Guangxi Province(2014GXNSFBA118268,2014GXNSFBA118010)+2 种基金Project of Guangxi Key Laboratory of Multi-source Information Mining&Security(MIMS13-06)Key Project of Yulin Normal University(2013YJZD04)Education Project of Yulin Normal University(2012SYJG30,15JSJYYB11,13YJJG30)
文摘Software watermarking is an efficient tool to verify the copyright of software. Public key cryptosystem-based watermarking is widely researched. However, the popular public key cryptosystem is not secure under quantum algorithm. This paper proposes a novel soft-ware watermarking scheme based on multivariate public key cryptosystem. The copyright information generated by copyright holder is transformed into copyright numbers using multivariate quadratic polynomial equations inspired by multivariate public key cryptosystem （MPKC）. Every polynomial is embedded into the host program independently. Based on the security performance of MPKC, the robustness and invisibility of the proposed scheme is significantly improved in comparison with the RSA-based watermarking method.
基金This project was supported by the Natural Science Foundation of Jiangxi Province
文摘In this paper,a new method for simultaneous spectrophotometric analysis of multi- component with double-system was developed.The multivariate calibration method,principal component analysis-partial least squares(PCA-PLS),was described and applied to the processing of measurement data.A demonstration,simultaneous determination of cobalt,nickel,copper,zinc and iron with double-system(5-Br-PADAP and PAR as chromogenic chelate reagents,respectively) was given.The results showed that the method with douhle-system gave better precision than those with single system and MLR(in this paper,AKC method was selected)did not give satis- fied precision in any situation.
基金Supported by the NSF of Henan Province(0611052600)
文摘Multivariate seemingly unrelated regression system is raised first and the two stage estimation and its covariance matrix are given. The results of the literatures[1-5] are extended in this paper.
文摘The design of optical instruments is an active subject due to improvement in lens techniques, fabrication technology, and data handling capacity. Much remains to do to expand its application to phytopathology, which would be in particular quite useful to improve crop growth monitoring in countries like Mali. An optical multimodal system for plant samples has been developed to improve the characterization of leaf disease symptoms, provide information on their effects, and avoid their spread. Potentially inexpensive components (laser, lens, turntables camera and sample, filter, lens, camera and computer) have been selected, assembled and aligned on an optical table into a multimodal system operating in transmission, reflection, diffusion and fluorescence. The illumination and observation angles can be adjusted to optimize viewing conditions in the four modes. This scientific contribution has been an initiation into the design and implementation of an optical instrument. Initial results are shown and will now be extended in cooperation with agronomic laboratories in African countries for tests on specific plant diseases in relation with prevailing climate conditions.
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.
基金supported by the funding from the Humanities and Social Science Fund of Ministry of Education of China(No.22YJCZH028)National Natural Science Foundation of China(Grant No.72303001)+3 种基金Fundamental Research Funds for the Central Universities(No.JUSRP124043)Anhui Provincial Excellent Young Scientists Fund for Universities(No.2024AH030001)Anhui Education Department Excellent Young Teachers Fund(No.YQYB2024021)Basic Research Program of Jiangsu(No.BK20251593)。
文摘The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies.
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.
文摘In this paper, a geometric approach to fault detection and isolation （FDI） is applied to a Multiple-Input Multipie-Output （MIMO） model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output （SISO）, Multiple-Input Single-Output （MISO）, and Single-Input Multiple-Output （SIMO） cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX （Auto Regressive with exogenous input） and VARX （Vector ARX） models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs, the classification errors reduce, even in the case of applying only one of the inputs in the computations. Furthermore, increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also, it is shown that by using probabilistic space in the distance function, fault diagnosis scheme has better performance in comparison with the deterministic one.
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.
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A new algorithm for constructing an inverse of a multivariable linear system is presented. This algorithm makes the constructing an inverse of the higher order matrices into searching for the equivalent normal form of the lower order matrices. Consequently, the calculation is more simple efficient and programmed than previous methods. Another result of the paper is that the lower reduced inverse system is obtained, by selecting special bases of the observable space of the original systems, it reveals the effect of the observability of the original systems on the order of the inverse systems.
基金Supported by the Central High Level Hospital Clinical Research Funding(No.BJ-2024-089).
文摘AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analysis.METHODS:The causal effects of several behavioral factors,including screen time,education time,time spent outdoors,and physical activity,on the risk of HM using univariable Mendelian randomization(MR)and MVMR analyses were first assessed.Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM.RESULTS:MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM.Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM.Additionally,MR analyses identified 13 serum metabolites significantly associated with HM,of which 12 were lipids and one was an amino acid derivative.Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM,with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC(p-16:0;30.83%).CONCLUSION:This study suggests that in addition to outdoor time,moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity.These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.
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.
基金The National Natural Science Foundation of China(No.51378407,51578431)
文摘A new method for calculating the failure probabilityof structures with random parameters is proposed based onmultivariate power polynomial expansion, in which te uncertain quantities include material properties, structuralgeometric characteristics and static loads. The structuralresponse is first expressed as a multivariable power polynomialexpansion, of which the coefficients ae then determined by utilizing the higher-order perturbation technique and Galerkinprojection scheme. Then, the final performance function ofthe structure is determined. Due to the explicitness of theperformance function, a multifold integral of the structuralfailure probability can be calculated directly by the Monte Carlo simulation, which only requires a smal amount ofcomputation time. Two numerical examples ae presented toillustate te accuracy ad efficiency of te proposed metiod. It is shown that compaed with the widely used first-orderreliability method （ FORM） and second-order reliabilitymethod （ SORM）, te results of the proposed method are closer to that of the direct Monte Carlo metiod,and it requires much less computational time.
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.
