In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
Convex feasibility problems are widely used in image reconstruction,sparse signal recovery,and other areas.This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery...Convex feasibility problems are widely used in image reconstruction,sparse signal recovery,and other areas.This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery.We rst derive the projection formulas for a vector onto the feasible sets.The centralized circumcentered-reection method is designed to solve the convex feasibility problem.Some numerical experiments demonstrate the feasibility and e ectiveness of the proposed algorithm,showing superior performance compared to conventional alternating projection methods.展开更多
Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The t...Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.展开更多
This paper establishes an evaluation model of the customer satisfaction index for the wellhead blowout preventers of China＇s petroleum industry based on evaluation models of the customer satisfaction index at home an...This paper establishes an evaluation model of the customer satisfaction index for the wellhead blowout preventers of China＇s petroleum industry based on evaluation models of the customer satisfaction index at home and aboard, and by considering the consuming situation in China and the features of the China＇s petroleum industry. For the existence of： （1） multiple correlations among the factors in the model; （2） the variables need to be explained, but that are hard to observe; （3） the customer satisfaction degree of observation variables appears the shape of skewness or two or three peaks, the correlations between the satisfaction index and its factors cannot be described by common multiple regression. This paper uses a partial least squares （PLS） method based on principal components and typical correlative analysis to solve the problem. When PLS is used in the model of the customer satisfaction index of the wellhead blowout preventers, the latent variables and the explanation degree coefficient of the manifest variable to the corresponding latent variables are estimated by PLS path analysis, and the influencing coefficient among the latent variables in the model is estimated by PLS regression analysis. PLS is also be used to calculate and analyze the model and disclose the correlations among the structural variables as well as the correlation between structural variables and its corresponding observation variables, evaluating results of which provide useful information for petroleum industry to improve the product quality and to the enhancement of the customer satisfaction to the product.展开更多
Cigarette market is a kind of monopoly market which is closed loop running, it depends on the plan mechanism to schedule producing, supplying and selling, but the “bullwhip effect” still exists. So it has a fundamen...Cigarette market is a kind of monopoly market which is closed loop running, it depends on the plan mechanism to schedule producing, supplying and selling, but the “bullwhip effect” still exists. So it has a fundamental significance to do sales forecasting work. It needs to considerate the double trend characteristics, history sales data and other main factors that affect cigarette sales. This paper depends on the panel data of A province’s cigarette sales, first we established three single forecasting models, after getting the predicted value of these single models, then using the combination forecasting method which based on PLS to predict the province’s cigarette sales of the next year. The results show that the prediction accuracy is good, which could provide a certain reference to cigarette sales forecasting in A province.展开更多
A spatially adaptive (SA) two-dimensional (2-D) numerical wave flume is presented based on the quadtree mesh system,in which a new multiple particle level set (MPLS) method is proposed to solve the problem of interfac...A spatially adaptive (SA) two-dimensional (2-D) numerical wave flume is presented based on the quadtree mesh system,in which a new multiple particle level set (MPLS) method is proposed to solve the problem of interface tracking,in which common intersection may be traversed by multiple interfaces.By using the adaptive mesh technique and the MPLS method,mesh resolution is updated automatically with time according to flow characteristics in the modeling process with higher resolution around the free surface and the solid boundary and lower resolution in less important area.The model has good performance in saving computer memory and CPU time and is validated by computational examples of small amplitude wave,second-order Stokes wave and cnoidal wave.Computational results also indicate that standing wave and wave overtopping are also reasonably simulated by the model.展开更多
Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM...Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.展开更多
PLS （Partial Least Squares regression） is introduced into an automatic estimation of fundamental stellar spectral parameters. It extracts the most correlative spectral component to the parameters （Teff, log g and [...PLS （Partial Least Squares regression） is introduced into an automatic estimation of fundamental stellar spectral parameters. It extracts the most correlative spectral component to the parameters （Teff, log g and [Fe/H]）, and sets up a linear regression function from spectra to the corresponding parameters. Considering the properties of stellar spectra and the PLS algorithm, we present a piecewise PLS regression method for estimation of stellar parameters, which is composed of one PLS model for Teff, and seven PLS models for log g and [Fe/H] estimation. Its performance is investigated by large experiments on flux calibrated spectra and continuum normalized spectra at different signal-to-noise ratios （SNRs） and resolutions. The results show that the piecewise PLS method is robust for spectra at the medium resolution of 0.23 nm. For low resolution 0.5 nm and 1 nm spectra, it achieves competitive results at higher SNR. Experiments using ELODIE spectra of 0.23 nm resolution illustrate that our piecewise PLS models trained with MILES spectra are efficient for O ～ G stars： for flux calibrated spectra, the systematic offsets are 3.8%, 0.14 dex, and -0.09 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.44 dex and 0.38 dex, respectively; for continuum normalized spectra, the systematic offsets are 3.8%, 0.12dex, and -0.13 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.49 dex and 0.41 dex, respectively. The PLS method is rapid, easy to use and does not rely as strongly on the tightness of a parameter grid of templates to reach high precision as Artificial Neural Networks or minimum distance methods do.展开更多
数字化教学能力是教育数字化转型时期教师必备的关键能力,深入探究并分析教师数字化教学能力的影响因素,是教育管理者和教师应对技术挑战、提升教学质量、培养创新人才的关键环节。基于此,文章依托统一接受和使用技术(Unified Theory of...数字化教学能力是教育数字化转型时期教师必备的关键能力,深入探究并分析教师数字化教学能力的影响因素,是教育管理者和教师应对技术挑战、提升教学质量、培养创新人才的关键环节。基于此,文章依托统一接受和使用技术(Unified Theory of Acceptance and Use of Technology,UTAUT)模型,采用偏最小二乘结构方程模型(Partial Least Squares Structural Equation Modeling,PLS-SEM)和模糊集定性比较分析(Fuzzy-Set Qualitative Comparative Analysis,fsQCA)方法,对教师数字化教学能力的影响因素及其组合效应进行了实证分析。其中,PLS-SEM分析结果表明,绩效期望、努力期望、社群影响、便利条件和自我效能感对教师数字化教学意愿有显著的正向影响,并进一步正向影响教师的数字化教学能力;教师的自我效能感对数字化教学能力有显著的直接影响,且影响效应最强。而fs QCA分析结果显示,存在四条激发教师数字化教学能力的路径,在这些路径中数字化教学意愿和自我效能感是两个重要的前因变量,这弥补了结构方程模型分析的相对不足。文章通过研究,旨在为教育数字化转型时期教师数字化教学能力的提升提供实证依据。展开更多
This paper depends on the panel data of Anhui province and its 17 cities’ cigarette sales. First we established three single forecasting models (Holter-Wintel Season product model, Time series model decomposing model...This paper depends on the panel data of Anhui province and its 17 cities’ cigarette sales. First we established three single forecasting models (Holter-Wintel Season product model, Time series model decomposing model and Partial least square regression model), after getting the predicted value of cigarette sales from these single models, we then employ the combination forecasting method based on Time Series method and PLS to predict the province and its 17 cities’ cigarette sales of the next year. The results show that the accuracy of prediction is good which could provide a reliable reference to cigarette sales forecasting in Anhui province and its 17 cities.展开更多
水稻类病斑突变体在研究水稻细胞程序性死亡和广谱抗病性中具有重要作用,已报道的水稻类病斑主要发生在叶片上,少量发生在颖壳上。本研究中首次报道了水稻的一种穗叶类病斑突变体pls1(Panicle and leaf spot 1),其从三叶期叶片开始出现...水稻类病斑突变体在研究水稻细胞程序性死亡和广谱抗病性中具有重要作用,已报道的水稻类病斑主要发生在叶片上,少量发生在颖壳上。本研究中首次报道了水稻的一种穗叶类病斑突变体pls1(Panicle and leaf spot 1),其从三叶期叶片开始出现红褐色斑点,随生育进程扩大,并扩展到其他器官。与以往报道的水稻类病斑突变体不同的是,pls1抽穗后稻穗枝梗和颖壳逐渐产生红褐色病斑,成熟期稻穗干枯,严重影响产量,是一种新类型的水稻类病斑。结合图位克隆和全基因组重测序发现pls1突变体产生了173403 bp的大片段缺失,导致7个基因缺失和1个基因启动子缺失。这8个基因中4个编码醇溶蛋白,另外3个在叶片和穗部表达量较低,只有Os12g0268000在叶片和稻穗中较其他器官有较高的表达量,推测PLS1为Os12g0268000,基因功能注释显示其编码色胺5-羟化酶。pls1突变体叶片中活性氧、过氧化氢、超氧阴离子过量积累,抗氧系统相关酶氧化物歧化酶、抗坏血酸过氧化物酶、过氧化氢酶和谷胱甘肽还原酶活性提高,发生细胞程序性死亡和叶绿体降解,降低光合能力。褪黑素在植物耐盐性中起重要作用。进一步的功能分析发现,缺失PLS1会抑制水稻中褪黑素合成相关酶基因OsTDC1、OsTDC3、OsSNAT1、OsASMT1和OsCOMT的表达,进而导致pls1突变体的耐盐性下降。综上,穗叶类病斑突变体pls1是一种新类型的水稻类病斑突变体,将为水稻类病斑研究提供新的种质材料;耐盐性的分析揭示了色胺5-羟化酶的新功能,为研究其在细胞程序性死亡和耐盐性中的机制提供了新视角。展开更多
煤炭灰分值是衡量煤炭质量的关键指标之一,灰分含量和性质对燃烧设备、环境、后续的加工利用都有着极大影响。针对目前煤炭灰分检测方法的滞后性、劳动密集型问题,提出了一种基于XRF光谱的预处理(Preprocessing,PRE)与偏最小二乘法(Part...煤炭灰分值是衡量煤炭质量的关键指标之一,灰分含量和性质对燃烧设备、环境、后续的加工利用都有着极大影响。针对目前煤炭灰分检测方法的滞后性、劳动密集型问题,提出了一种基于XRF光谱的预处理(Preprocessing,PRE)与偏最小二乘法(Partial Least Squares,PLS)相结合的XRF煤炭灰分智能预测算法。通过将XRF技术获取煤炭样品的光谱数据输入PLS主模型初步预测灰分,再将相关校正参数输入补偿优化模型中,最终将两者相加得到预测灰分值。试验结果表明:相对于偏最小二乘法回归、神经网络回归模型,PRE-PLS模型决定系数为0.9951,均方根误差为0.9411,平均绝对误差为0.7332%,表明该模型具备较高的精度,能够胜任现场检测工作,为生产提供可靠指导。展开更多
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
基金Supported by the Natural Science Foundation of Guangxi Province(Grant Nos.2023GXNSFAA026067,2024GXN SFAA010521)the National Natural Science Foundation of China(Nos.12361079,12201149,12261026).
文摘Convex feasibility problems are widely used in image reconstruction,sparse signal recovery,and other areas.This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery.We rst derive the projection formulas for a vector onto the feasible sets.The centralized circumcentered-reection method is designed to solve the convex feasibility problem.Some numerical experiments demonstrate the feasibility and e ectiveness of the proposed algorithm,showing superior performance compared to conventional alternating projection methods.
基金Supported by the National Natural Science Foundation of China under Grant No.51975138the High-Tech Ship Scientific Research Project from the Ministry of Industry and Information Technology under Grant No.CJ05N20the National Defense Basic Research Project under Grant No.JCKY2023604C006.
文摘Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.
文摘This paper establishes an evaluation model of the customer satisfaction index for the wellhead blowout preventers of China＇s petroleum industry based on evaluation models of the customer satisfaction index at home and aboard, and by considering the consuming situation in China and the features of the China＇s petroleum industry. For the existence of： （1） multiple correlations among the factors in the model; （2） the variables need to be explained, but that are hard to observe; （3） the customer satisfaction degree of observation variables appears the shape of skewness or two or three peaks, the correlations between the satisfaction index and its factors cannot be described by common multiple regression. This paper uses a partial least squares （PLS） method based on principal components and typical correlative analysis to solve the problem. When PLS is used in the model of the customer satisfaction index of the wellhead blowout preventers, the latent variables and the explanation degree coefficient of the manifest variable to the corresponding latent variables are estimated by PLS path analysis, and the influencing coefficient among the latent variables in the model is estimated by PLS regression analysis. PLS is also be used to calculate and analyze the model and disclose the correlations among the structural variables as well as the correlation between structural variables and its corresponding observation variables, evaluating results of which provide useful information for petroleum industry to improve the product quality and to the enhancement of the customer satisfaction to the product.
文摘Cigarette market is a kind of monopoly market which is closed loop running, it depends on the plan mechanism to schedule producing, supplying and selling, but the “bullwhip effect” still exists. So it has a fundamental significance to do sales forecasting work. It needs to considerate the double trend characteristics, history sales data and other main factors that affect cigarette sales. This paper depends on the panel data of A province’s cigarette sales, first we established three single forecasting models, after getting the predicted value of these single models, then using the combination forecasting method which based on PLS to predict the province’s cigarette sales of the next year. The results show that the prediction accuracy is good, which could provide a certain reference to cigarette sales forecasting in A province.
基金The Innovative Research Groups of the National Natural Science Foundation of China under contract No.51021004the National Natural Science Foundation for Youth of China under contract No. 51109018+2 种基金the Open Foundation of Water & Sediment Science and Water Hazard Prevention Hunan Provincial Key Laboratory under contract No. 2011SS05the Open Foundation of Port,Coastal and offshore Engineering Hunan Provincial Key Discipline under contract No. 20110815001the Open Foundation of State Key Laboratory of Hydraulic Engineering Simulation and Safety under contract No.HSSKLTJU-201208.
文摘A spatially adaptive (SA) two-dimensional (2-D) numerical wave flume is presented based on the quadtree mesh system,in which a new multiple particle level set (MPLS) method is proposed to solve the problem of interface tracking,in which common intersection may be traversed by multiple interfaces.By using the adaptive mesh technique and the MPLS method,mesh resolution is updated automatically with time according to flow characteristics in the modeling process with higher resolution around the free surface and the solid boundary and lower resolution in less important area.The model has good performance in saving computer memory and CPU time and is validated by computational examples of small amplitude wave,second-order Stokes wave and cnoidal wave.Computational results also indicate that standing wave and wave overtopping are also reasonably simulated by the model.
基金National Natural Science Foundation of China ( No. 61074079) Shanghai Leading Academic Discipline Project,China ( No.B504)
文摘Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.
基金Supported by the National Natural Science Foundation of China
文摘PLS （Partial Least Squares regression） is introduced into an automatic estimation of fundamental stellar spectral parameters. It extracts the most correlative spectral component to the parameters （Teff, log g and [Fe/H]）, and sets up a linear regression function from spectra to the corresponding parameters. Considering the properties of stellar spectra and the PLS algorithm, we present a piecewise PLS regression method for estimation of stellar parameters, which is composed of one PLS model for Teff, and seven PLS models for log g and [Fe/H] estimation. Its performance is investigated by large experiments on flux calibrated spectra and continuum normalized spectra at different signal-to-noise ratios （SNRs） and resolutions. The results show that the piecewise PLS method is robust for spectra at the medium resolution of 0.23 nm. For low resolution 0.5 nm and 1 nm spectra, it achieves competitive results at higher SNR. Experiments using ELODIE spectra of 0.23 nm resolution illustrate that our piecewise PLS models trained with MILES spectra are efficient for O ～ G stars： for flux calibrated spectra, the systematic offsets are 3.8%, 0.14 dex, and -0.09 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.44 dex and 0.38 dex, respectively; for continuum normalized spectra, the systematic offsets are 3.8%, 0.12dex, and -0.13 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.49 dex and 0.41 dex, respectively. The PLS method is rapid, easy to use and does not rely as strongly on the tightness of a parameter grid of templates to reach high precision as Artificial Neural Networks or minimum distance methods do.
文摘数字化教学能力是教育数字化转型时期教师必备的关键能力,深入探究并分析教师数字化教学能力的影响因素,是教育管理者和教师应对技术挑战、提升教学质量、培养创新人才的关键环节。基于此,文章依托统一接受和使用技术(Unified Theory of Acceptance and Use of Technology,UTAUT)模型,采用偏最小二乘结构方程模型(Partial Least Squares Structural Equation Modeling,PLS-SEM)和模糊集定性比较分析(Fuzzy-Set Qualitative Comparative Analysis,fsQCA)方法,对教师数字化教学能力的影响因素及其组合效应进行了实证分析。其中,PLS-SEM分析结果表明,绩效期望、努力期望、社群影响、便利条件和自我效能感对教师数字化教学意愿有显著的正向影响,并进一步正向影响教师的数字化教学能力;教师的自我效能感对数字化教学能力有显著的直接影响,且影响效应最强。而fs QCA分析结果显示,存在四条激发教师数字化教学能力的路径,在这些路径中数字化教学意愿和自我效能感是两个重要的前因变量,这弥补了结构方程模型分析的相对不足。文章通过研究,旨在为教育数字化转型时期教师数字化教学能力的提升提供实证依据。
文摘This paper depends on the panel data of Anhui province and its 17 cities’ cigarette sales. First we established three single forecasting models (Holter-Wintel Season product model, Time series model decomposing model and Partial least square regression model), after getting the predicted value of cigarette sales from these single models, we then employ the combination forecasting method based on Time Series method and PLS to predict the province and its 17 cities’ cigarette sales of the next year. The results show that the accuracy of prediction is good which could provide a reliable reference to cigarette sales forecasting in Anhui province and its 17 cities.
文摘煤炭灰分值是衡量煤炭质量的关键指标之一,灰分含量和性质对燃烧设备、环境、后续的加工利用都有着极大影响。针对目前煤炭灰分检测方法的滞后性、劳动密集型问题,提出了一种基于XRF光谱的预处理(Preprocessing,PRE)与偏最小二乘法(Partial Least Squares,PLS)相结合的XRF煤炭灰分智能预测算法。通过将XRF技术获取煤炭样品的光谱数据输入PLS主模型初步预测灰分,再将相关校正参数输入补偿优化模型中,最终将两者相加得到预测灰分值。试验结果表明:相对于偏最小二乘法回归、神经网络回归模型,PRE-PLS模型决定系数为0.9951,均方根误差为0.9411,平均绝对误差为0.7332%,表明该模型具备较高的精度,能够胜任现场检测工作,为生产提供可靠指导。
