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Quality related fault detection based on dynamic-inner convolutional autoencoder and partial least squares and its application to ironmaking process 认领 引用 被引量：1
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作者 Ping Wu Yuxuan Ni +4 位作者 Huaimin Wang Xuguang Hu Zhenquan Wu Jian Jiang Yaowu Hu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第1期267-276,共10页
Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on li... Partial least squares (PLS) model maximizes the covariance between process variables and quality variables,making it widely used in quality-related fault detection.However,traditional PLS methods focus primarily on linear processes,leading to poor performance in dynamic nonlinear processes.In this paper,a novel quality-related fault detection method,named DiCAE-PLS,is developed by combining dynamic-inner convolutional autoencoder with PLS.In the proposed DiCAE-PLS method,latent features are first extracted through dynamic-inner convolutional autoencoder (DiCAE) to capture process dynamics and nonlinearity from process variables.Then,a PLS model is established to build the relationship between the extracted latent features and the final product quality.To detect quality-related faults,Hotelling's T2 statistic is employed.The developed quality-related fault detection is applied to the widely used industrial benchmark of the Tennessee. 展开更多
关键词 Partial least squares Dynamic-inner convolutional autoencoder Quality-related fault detection Neural networks Safety Dynamic modeling
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Enhancement of the Total Least Squares Method for Feature Extraction in 2D LiDAR Mapped Environments 认领 引用
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作者 Natalia Prieto-Fernández Martín Bayón-Gutiérrez +3 位作者 Sergio Fernández-Blanco álvaro Fernández-Blanco Francisco Carro-De-Lorenzo JoséAlberto Benítez-Andrades 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1043-1059,共17页
Feature-based Simultaneous Localization and Mapping(SLAM)using 2D Light Detection and Ranging(LiDAR)in structured indoor environments commonly relies on the extraction of straight segments and corners from raw scan da... Feature-based Simultaneous Localization and Mapping(SLAM)using 2D Light Detection and Ranging(LiDAR)in structured indoor environments commonly relies on the extraction of straight segments and corners from raw scan data.The quality of these landmarks depends not only on the fitting algorithm,but also on how uncertainty is modeled and propagated from line estimates to derived corner features.Although the magnitude of LiDAR uncertainty has been widely studied,the influence of line parameterization and geometric conditioning on uncertainty propagation has received less attention.In particular,the scale ambiguity inherent to implicit line representations can degrade numerical conditioning and affect the stability of the propagated covariance estimates.This paper proposes a novel Weighted Conformal Total Least Squares(WCTLS)formulation for line extraction from 2D LiDAR data.Unlike conventional approaches,the proposed method enforces a geometrical normalization that removes scale ambiguity and improves the conditioning of the estimation problem.The method is compared with Unweighted Total Least Squares(UTLS)and Weighted Total Least Squares(WTLS)using real indoor datasets and repeated scans acquired from fixed sensor positions.The results show that all three formulations provide equivalent geometric corner locations,whereas the proposed WCTLS method consistently reduces the propagated uncertainty of the estimated corner coordinates.In addition,repeatability analysis over l0o scans per environment shows that WCTLS yields lower median corner ellipse areas and reduced dispersion across scans,without increasing computational complexity. 展开更多
关键词 Feature extraction geometric primitives light detection and ranging(LiDAR)mapping total least squares(TLS)
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Prediction of total nitrogen in water based on UV spectroscopy and Bayesian optimized least squares support vector machine 认领 引用 被引量：1
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作者 ZHENG Peichao YANG Qin +3 位作者 LI Chenglin YIN Xukun WANG Jinmei GUO Lianbo 《Optoelectronics Letters》 EI 2025年第11期698-704,共7页
The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herei... The total nitrogen(TN)is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality.Accurate and rapid methods are crucial for determining the TN content in water.Herein,a fast,highly sensitive,and pollution-free approach is proposed,which combines ultraviolet(UV)absorption spectroscopy with Bayesian optimized least squares support vector machine(LSSVM)for detecting TN content in water.Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values.The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM.To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model,the Bayesian optimization(BO)algorithm was used to optimize the parameters of the LSSVM.Results show that the prediction model performs well in predicting TN concentration,with a high coefficient of prediction determination(R2=0.9413)and a low root mean square error of prediction(RMSE=0.0779 mg/L).Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance. 展开更多
关键词 Bayesian optimization eutrophication total nitrogen tn bayesian optimized least squares support vector machine lssvm least squares support vector machine assessing surface water water quality total nitrogen
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Practical Exploration and Teaching Optimization of Perimeter Calculation for Rectangles and Squares in Life Situations 认领 引用
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作者 DIAN Lulu 《外文科技期刊数据库（文摘版）教育科学》 2026年第1期040-044,共5页
Perimeter calculation for rectangles and squares is a core knowledge point in the "Graphics and Geometry" section of primary school mathematics, and also a key teaching content to help students establish mea... Perimeter calculation for rectangles and squares is a core knowledge point in the "Graphics and Geometry" section of primary school mathematics, and also a key teaching content to help students establish measurement awareness and develop spatial concepts. Primary school students are still dominated by concrete thinking, and it is difficult for them to deeply internalize abstract mathematical concepts and calculation formulas only through classroom lectures. Traditional classroom teaching tends to fall into the misunderstanding of "emphasizing calculation over understanding, emphasizing practice over application", leading to problems such as students mechanically applying formulas, shallow conceptual understanding, and rigid practical application. Based on the concept of life-oriented teaching and following the cognitive development rules of students, this paper carries out teaching practice of perimeter calculation relying on real life scenarios, deeply analyzes the pain points and deficiencies in current classrooms, and puts forward specific optimization paths from the dimensions of situation creation, hands-on operation, application migration and literacy cultivation. It breaks the barrier between mathematics classroom and real life, helps students complete the thinking advancement from intuitive perception to abstract modeling, consolidates the foundation of geometry learning, improves classroom teaching effectiveness, and effectively cultivates students' core mathematical literacy. 展开更多
关键词 life situation rectangle and square perimeter calculation teaching practice teaching optimization
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Study of the nuclear mass model by sequential least squares programming 认领 引用
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作者 Hang Yang Cun-Yu Chen +2 位作者 Xiao-Yu Xu Han-Kui Wang You-Bao Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第7期204-212,共9页
Nuclear mass is an important property in both nuclear and astrophysics.In this study,we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms.The sequential least squ... Nuclear mass is an important property in both nuclear and astrophysics.In this study,we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms.The sequential least squares programming(SLSQP)algorithm augments the precision of this multinomial mass model by reducing the error from 1.863 MeV to 1.631 MeV.These algorithms were further examined using 200 sample mass formulae derived from theδE term of the Eisospin mass model.The SLSQP method exhibited superior performance compared to the other algorithms in terms of errors and convergence speed.This algorithm is advantageous for handling large-scale multiparameter optimization tasks in nuclear physics. 展开更多
关键词 Nuclear mass model Binding energy Magic nuclei Sequential least squares algorithm
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Non-negative least squares variance component estimation of mixed additive and multiplicative random error model 认领 引用
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作者 Hao Xiao Leyang Wang 《Geodesy and Geodynamics》 EI CSCD 2025年第5期617-623,共7页
In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance c... In the variance component estimation(VCE)of geodetic data,the problem of negative VCE is likely to occur.In the ordinary additive error model,there have been related studies to solve the problem of negative variance components.However,there is still no related research in the mixed additive and multiplicative random error model(MAMREM).Based on the MAMREM,this paper applies the nonnegative least squares variance component estimation(NNLS-VCE)algorithm to this model.The correlation formula and iterative algorithm of NNLS-VCE for MAMREM are derived.The problem of negative variance in VCE for MAMREM is solved.This paper uses the digital simulation example and the Digital Terrain Mode(DTM)to prove the proposed algorithm's validity.The experimental results demonstrated that the proposed algorithm can effectively correct the VCE in MAMREM when there is a negative VCE. 展开更多
关键词 Mixed additive and multiplicative random error model Stochastic model Non-negative least squares variance component estimation
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Nonlinear multifunctional sensor signal reconstruction based on least squares support vector machines and total least squares algorithm 认领 引用 被引量：3
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作者 Xin LIU Guo WEI +1 位作者 Jin-wei SUN Dan LIU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第4期497-503,共7页
Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. I... Least squares support vector machines (LS-SVMs) are modified support vector machines (SVMs) that involve equality constraints and work with a least squares cost function, which simplifies the optimization procedure. In this paper, a novel training algorithm based on total least squares (TLS) for an LS-SVM is presented and applied to multifunctional sensor signal reconstruction. For three different nonlinearities of a multifunctional sensor model, the reconstruction accuracies of input signals are 0.001 36%, 0.031 84% and 0.504 80%, respectively. The experimental results demonstrate the higher reliability and accuracy of the proposed method for multifunctional sensor signal reconstruction than the original LS-SVM training algorithm, and verify the feasibility and stability of the proposed method. 展开更多
关键词 Least squares support vector machine Total least squares Multifunctional sensor Signal reconstruction
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Flatness intelligent control via improved least squares support vector regression algorithm 认领 引用 被引量：2
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作者 张秀玲 张少宇 +1 位作者 赵文保 徐腾 《Journal of Central South University》 SCIE EI CAS 2013年第3期688-695,共8页
To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm w... To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression(MLSSVR) was put forward by adding samples' absolute errors in objective function and applied to flatness intelligent control.To solve the poor-precision problem of the control scheme based on effective matrix in flatness control,the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods.Simulation experiment was conducted on 900HC reversible cold roll.The performance of effective matrix method and the effective matrix-predictive control method were compared,and the results demonstrate the validity of the effective matrix-predictive control method. 展开更多
关键词 least squares support vector regression multi-output least squares support vector regression flatness effective matrix predictive control
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On-Line Batch Process Monitoring Using Multiway Kernel Partial Least Squares 认领 引用 被引量：3
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作者 胡益 马贺贺 侍洪波 《Journal of Donghua University(English Edition)》 EI CAS 2011年第6期585-590,共6页
An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partia... An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring. 展开更多
关键词 process monitoring fault detection kernel partial least squares(KPLS) nonlinear process multiway kernel partial least squares(MKPLS)
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Generalized Penalized Least Squares and Its Statistical Characteristics 认领 引用
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作者 DING Shijun TAO Benzao 《Geo-Spatial Information Science》 EI 2006年第4期255-259,共5页
The solution properties of semiparametric model are analyzed, especially that penalized least squares for semiparametric model will be invalid when the matrix B^TPB is ill-posed or singular. According to the principle... The solution properties of semiparametric model are analyzed, especially that penalized least squares for semiparametric model will be invalid when the matrix B^TPB is ill-posed or singular. According to the principle of ridge estimate for linear parametric model, generalized penalized least squares for semiparametric model are put forward, and some formulae and statistical properties of estimates are derived. Finally according to simulation examples some helpful conclusions are drawn. 展开更多
关键词 semiparametric model penalized least squares generalized penalized least squares statistical property ill-posed matrix ridge estimate
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Multi-loop adaptive internal model control based on a dynamic partial least squares model 认领 引用 被引量：4
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作者 Zhao ZHAO Bin HU Jun LIANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2011年第3期190-200,共11页
A multi-loop adaptive internal model control(IMC)strategy based on a dynamic partial least squares(PLS)frame-work is proposed to account for plant model errors caused by slow aging,drift in operational conditions,or e... A multi-loop adaptive internal model control(IMC)strategy based on a dynamic partial least squares(PLS)frame-work is proposed to account for plant model errors caused by slow aging,drift in operational conditions,or environmental changes.Since PLS decomposition structure enables multi-loop controller design within latent spaces,a multivariable adaptive control scheme can be converted easily into several independent univariable control loops in the PLS space.In each latent subspace,once the model error exceeds a specific threshold,online adaptation rules are implemented separately to correct the plant model mismatch via a recursive least squares(RLS)algorithm.Because the IMC extracts the inverse of the minimum part of the internal model as its structure,the IMC controller is self-tuned by explicitly updating the parameters,which are parts of the internal model.Both parameter convergence and system stability are briefly analyzed,and proved to be effective.Finally,the proposed control scheme is tested and evaluated using a widely-used benchmark of a multi-input multi-output(MIMO)system with pure delay. 展开更多
关键词 Partial least squares(PLS) Adaptive internal model control(IMC) Recursive least squares(RLS)
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Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation 认领 引用 被引量：13
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作者 Senlin CHEN Zhenghong GAO +2 位作者 Xinqi ZHU Yiming DU Chao PANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第10期2499-2509,共11页
Common,unsteady aerodynamic modeling methods usually use wind tunnel test data from forced vibration tests to predict stable hysteresis loop.However,these methods ignore the initial unstable process of entering the hy... Common,unsteady aerodynamic modeling methods usually use wind tunnel test data from forced vibration tests to predict stable hysteresis loop.However,these methods ignore the initial unstable process of entering the hysteresis loop that exists in the actual maneuvering process of the aircraft.Here,an excitation input suitable for nonlinear system identification is introduced to model unsteady aerodynamic forces with any motion in the amplitude and frequency ranges based on the Least Squares Support Vector Machines(LS-SVMs).In the selection of the input form,avoiding the use of reduced frequency as a parameter makes the model more universal.After model training is completed,the method is applied to predict the lift coefficient,drag coefficient and pitching moment coefficient of the RAE2822 airfoil,in sine and sweep motions under the conditions of plunging and pitching at Mach number 0.8.The predicted results of the initial unstable process and the final stable process are in close agreement with the Computational Fluid Dynamics(CFD)data,demonstrating the feasibility of the model for nonlinear unsteady aerodynamics modeling and the effectiveness of the input design approach. 展开更多
关键词 Aerodynamics models Forced vibration Input design Least squares support vector machines Nonlinear system System identification Unsteady aerodynamics
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A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression 认领 引用 被引量：13
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作者 ZHANG Xiu-ling ZHANG Shao-yu +1 位作者 TAN Guang-zhong ZHAO Wen-bao 《Journal of Iron and Steel Research International》 SCIE CAS CSCD 2012年第3期25-30,共6页
To adapt to the new requirement of the developing flatness control theory and technology,cubic patterns were introduced on the basis of the traditional linear,quadratic and quartic flatness basic patterns.Linear,quadr... To adapt to the new requirement of the developing flatness control theory and technology,cubic patterns were introduced on the basis of the traditional linear,quadratic and quartic flatness basic patterns.Linear,quadratic,cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns.In order to over-come the defects live in the existent recognition methods based on fuzzy,neural network and support vector regres-sion(SVR)theory,a novel flatness pattern recognition method based on least squares support vector regression(LS-SVR)was proposed.On this basis,for the purpose of determining the hyper-parameters of LS-SVR effectively and enhan-cing the recognition accuracy and generalization performance of the model,particle swarm optimization algorithm with leave-one-out(LOO)error as fitness function was adopted.To overcome the disadvantage of high computational complexity of naive cross-validation algorithm,a novel fast cross-validation algorithm was introduced to calculate the LOO error of LDSVR.Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy,high speed and strong generalization ability. 展开更多
关键词 flatness pattern recognition least squares support vector regression cross-validation
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Correlation-weighted least squares residual algorithm for RAIM 认领 引用 被引量：10
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作者 Dan SONG Chuang SHI +2 位作者 Zhipeng WANG Cheng WANG Guifei JING 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第5期1505-1516,共12页
The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large... The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large-slope faulty satellite and a high False Alarm Risk(FAR)for a small-slope faulty satellite.From the theoretical analysis of the high MDR and FAR cause,the optimal slope is determined,and thereby the optimal test statistic for fault detection is conceived,which can minimize the FAR with the MDR not exceeding its allowable value.To construct a test statistic approximate to the optimal one,the CorrelationWeighted LSR(CW-LSR)algorithm is proposed.The CW-LSR test statistic remains the sum of pseudorange residual squares,but the square for the most potentially faulty satellite,judged by correlation analysis between the pseudorange residual and observation error,is weighted with an optimal-slope-based factor.It does not obey the same distribution but has the same noncentral parameter with the optimal test statistic.The superior performance of the CW-LSR algorithm is verified via simulation,both reducing the FAR for a small-slope faulty satellite with the MDR not exceeding its allowable value and reducing the MDR for a large-slope faulty satellite at the expense of FAR addition. 展开更多
关键词 Correlation analysis Fault detection Least squares residual(LSR)algorithm Receiver autonomous integrity monitoring(RAIM) Slope
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Modeling of Isomerization of C_8 Aromatics by Online Least Squares Support Vector Machine 认领 引用 被引量：16
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作者 李丽娟 苏宏业 褚建 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2009年第3期437-444,共8页
The least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling... The least squares support vector regression (LS-SVR) is usually used for the modeling of single output system, but it is not well suitable for the actual multi-input-multi-output system. The paper aims at the modeling of multi-output systems by LS-SVR. The multi-output LS-SVR is derived in detail. To avoid the inversion of large matrix, the recursive algorithm of the parameters is given, which makes the online algorithm of LS-SVR practical. Since the computing time increases with the number of training samples, the sparseness is studied based on the pro-jection of online LS-SVR. The residual of projection less than a threshold is omitted, so that a lot of samples are kept out of the training set and the sparseness is obtained. The standard LS-SVR, nonsparse online LS-SVR and sparse online LS-SVR with different threshold are used for modeling the isomerization of C8 aromatics. The root-mean-square-error (RMSE), number of support vectors and running time of three algorithms are compared and the result indicates that the performance of sparse online LS-SVR is more favorable. 展开更多
关键词 least squares support vector machine multi-variable online sparseness isomerization
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Real-time object tracking via least squares transformation in spatial and Fourier domains for unmanned aerial vehicles 认领 引用 被引量：7
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作者 Xiaoyuan YANG Ridong ZHU +1 位作者 Jingkai WANG Zhengze LI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2019年第7期1716-1726,共11页
This paper addresses the problem of real-time object tracking for unmanned aerial vehicles. We consider the task of object tracking as a classification problem. Training a good classifier always needs a huge number of... This paper addresses the problem of real-time object tracking for unmanned aerial vehicles. We consider the task of object tracking as a classification problem. Training a good classifier always needs a huge number of samples, which is always time-consuming and not suitable for realtime applications. In this paper, we transform the large-scale least-squares problem in the spatial domain to a series of small-scale least-squares problems with constraints in the Fourier domain using the correlation filter technique. Then, this problem is efficiently solved by two stages. In the first stage, a fast method based on recursive least squares is used to solve the correlation filter problem without constraints in the Fourier domain. In the second stage, a weight matrix is constructed to prune the solution attained in the first stage to approach the constraints in the spatial domain. Then, the pruned classifier is used for tracking. To evaluate proposed tracker’s performance, comprehensive experiments are conducted on challenging aerial sequences in the UAV123 dataset. Experimental results demonstrate that proposed approach achieves a state-ofthe-art tracking performance in aerial sequences and operates at a mean speed of beyond 40 frames/s. For further analysis of proposed tracker’s robustness, extensive experiments are also performed on recent benchmarks OTB50, OTB100, and VOT2016. 展开更多
关键词 Correlation filter Discrete Fourier transform Least squares Object tracking Unmanned aerial vehicle
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Least Squares Evaluations for Form and Profile Errors of Ellipse Using Coordinate Data 认领 引用 被引量：7
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作者 LIU Fei XU Guanghua +2 位作者 LIANG Lin ZHANG Qing LIU Dan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2016年第5期1020-1028,共9页
To improve the measurement and evaluation of form error of an elliptic section, an evaluation method based on least squares fitting is investigated to analyze the form and profile errors of an ellipse using coordinate... To improve the measurement and evaluation of form error of an elliptic section, an evaluation method based on least squares fitting is investigated to analyze the form and profile errors of an ellipse using coordinate data. Two error indicators for defining ellipticity are discussed, namely the form error and the profile error, and the difference between both is considered as the main parameter for evaluating machining quality of surface and profile. Because the form error and the profile error rely on different evaluation benchmarks, the major axis and the foci rather than the centre of an ellipse are used as the evaluation benchmarks and can accurately evaluate a tolerance range with the separated form error and profile error of workpiece. Additionally, an evaluation program based on the LS model is developed to extract the form error and the profile error of the elliptic section, which is well suited for separating the two errors by a standard program. Finally, the evaluation method about the form and profile errors of the ellipse is applied to the measurement of skirt line of the piston, and results indicate the effectiveness of the evaluation. This approach provides the new evaluation indicators for the measurement of form and profile errors of ellipse, which is found to have better accuracy and can thus be used to solve the difficult of the measurement and evaluation of the piston in industrial production. 展开更多
关键词 ellipse form Error profile error least squares method piston
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A multivariate partial least squares approach to joint association analysis for multiple correlated traits 认领 引用 被引量：5
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作者 Yang Xu Wenming Hu +1 位作者 Zefeng Yang Chenwu Xu 《The Crop Journal》 SCIE CSCD 2016年第1期21-29,共9页
Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more acc... Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis. 展开更多
关键词 Association analysis Multiple correlated traits Supersaturated model Multilocus Multivariate partial least squares
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Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division 认领 引用 被引量：8
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作者 邵伟明 田学民 王平 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2014年第7期828-836,共9页
Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensin... Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted.Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects. 展开更多
关键词 Local learning Online soft sensing Partial least squares F-test Multi-output process Process state division
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Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature 认领 引用 被引量：9
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作者 Mohammad Mesbah Ebrahim Soroush Mashallah Rezakazemi 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2017年第9期1238-1248,共11页
Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.... Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature(HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients(R^2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H_2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model. 展开更多
关键词 Hydrate formation temperature(HFT) Natural gas Sour gases Least squares support vector machine Outlier diagnostics Leverage approach
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