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
A Bayesian method is used to evaluate the component safety failure model parameter of the safe arming system of an air faced missile in flight. It was proved that Bayes estimation of the model parameter is coinciden...A Bayesian method is used to evaluate the component safety failure model parameter of the safe arming system of an air faced missile in flight. It was proved that Bayes estimation of the model parameter is coincident with the physical explanation of the prior probability density distribution of the random parameter.展开更多
A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited...A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.展开更多
Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the t...Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.展开更多
The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-pr...The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.展开更多
A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introdu...A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introduced to solve this equation efficiently.The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method.Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.展开更多
Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description...Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description—minimum description length, this paper discusses a case: where there is multi-collinearity in the coefficient matrix, principal component estimation is used to estimate and select the original parameters, so as to reduce its multi-collinearity and improve its credibility. From the viewpoint of minimum description length, this paper discusses the approach of selecting principal components and uses this approach to solve a practical problem.展开更多
A mixed distribution of empirical variances, composed of two distributions the basic and contaminating ones, and referred to as PERG mixed distribution of empirical variances, is considered. In the paper a robust inve...A mixed distribution of empirical variances, composed of two distributions the basic and contaminating ones, and referred to as PERG mixed distribution of empirical variances, is considered. In the paper a robust inverse problem solution is given, namely a (new) robust method for estimation of variances of both distributions—PEROBVC Method, as well as the estimates for the numbers of observations for both distributions and, in this way also the estimate of contamination degree.展开更多
Depending on analyzing the abuse of equivalent weights,a set of self-contained theory system on robust estimation based on equivalent variance-covariance is established,which includes ρ function, φ function,equivale...Depending on analyzing the abuse of equivalent weights,a set of self-contained theory system on robust estimation based on equivalent variance-covariance is established,which includes ρ function, φ function,equivalent variance-covariance function,influence function and breakdown point.And an example is given to verify that the robust models proposed in this paper are reliable and correct.展开更多
The article deals with optimality of combining ridge and principal component estimator.It is proved that combining ridge and principal componet estimate has several types of minimumvariance properties in the class of ...The article deals with optimality of combining ridge and principal component estimator.It is proved that combining ridge and principal componet estimate has several types of minimumvariance properties in the class of reduced dimension estimates and it also has minimum of orthogo-nal unchanged norm of covariance in the same class.展开更多
The estimation of glacier flow velocity on a short-term scale is very important for further glacier dynamics research.In this study,10 Sentinel-1 ascending images and 10 Sentinel-1 descending images of Urumqi Glacier ...The estimation of glacier flow velocity on a short-term scale is very important for further glacier dynamics research.In this study,10 Sentinel-1 ascending images and 10 Sentinel-1 descending images of Urumqi Glacier No.1 in 2017 were used to calculate the glacier flow velocity in a high coherence period by DIn SAR technology and MAI technology,while the offset tracking technology was used to estimate the glacier flow velocity in a low coherence period.Then,the monthly three-dimensional flow velocity of the glacier was calculated by the Helmert variance component estimation method.Finally,the accuracy of the estimated glacier flow velocity on a monthly scale was evaluated.The results showed that:(1)the monthly scale motion velocity of Urumqi Glacier No.1 in May,June,July,and August 2017 was 0.273 m/month,0.657 m/month,0.582 m/month,and 0.392 m/month,respectively.(2)The accuracy of glacier surface velocity from May 2017 to August 2017 was 0.033 m/month,0.026 m/month,0.034 m/month and 0.037 m/month,respectively.(3)The accuracy of glacier surface flow velocity from May 2017 to August 2017 was 0.018 m/month,0.031 m/month,0.029 m/month and 0.030 m/month,respectively.Therefore,the research methodology based on the Sentinel-1 ascending and descending data and presented in this paper was applicable to the estimation of monthly-scale flow velocity of mountain glaciers.展开更多
For the two seemingly unrelated regression system, this paper proposed a new type of estimator called pre-test principal components estimator (PTPCE) and discussed some properties of PTPCE.
Progeny testing of oil palm interspecific hybrids was conducted in four trials in Kluang, Ulu Paka, Teluk Intan and Carey Island, Malaysia. The interspecific hybrids （O × P） were created according to the North ...Progeny testing of oil palm interspecific hybrids was conducted in four trials in Kluang, Ulu Paka, Teluk Intan and Carey Island, Malaysia. The interspecific hybrids （O × P） were created according to the North Carolina Model I （NCM I） mating design, using Elaeis oleifera （0） and Elaeis guineensis var. pisifera （P） as the maternal and paternal parents, respectively. Differences among O, P and O-within-P were determined by the performance （bunch yield, components and vegetative traits） of the progenies. There were significant differences among P for fresh fruit bunch （FFB） yield, bunch number （BN） and average bunch weight （ABW） in Kluang, Ulu Paka and Teluk Intan, but not in Carey Island. FFB yield was generally higher on coastal soils （Teluk Intan and Carey Island） than inland soils （Kluang and Ulu Paka）. Heritability was calculated based on the intraclass correlation. Heritability estimates for these three yield components were variable, depending on the breeding material and environment in which the materials were tested. Fruit to bunch （F/B） and oil to bunch （O/B） of parthenocarpic fruits were important in determining the overall O/B of the interspecific hybrids. The O x P hybrids in Kluang showed the lowest height increment with only a mean of 14.0 cm/year, whereas in Ulu Paka and Teluk Intan, the values were higher at 24.0 cm/year and 25.0 cm/year, respectively. The study showed that the FFB yields ofoil palm interspecific hybrids performed better in coastal soils than inland soils.展开更多
Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis...Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.展开更多
Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have ...Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.展开更多
Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating...Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating the four raw materials. But the chemical compositions of raw materials vary from time to time, resulting in difficulties to control the oxide compositions to a predefined value. Therefore, a novel algorithm to estimate the chemical compositions of the raw materials is developed. The paper mainly consists of two parts. In model construction part, a novel constrained least square model is proposed to overcome the deviation introduced by long-term drift of the material components, and the model parameters are estimated with an online strategy. And in validation part, the approach is implemented to two examples including datasets from simulation model and the actual industrial process. The final results show the effectiveness of the proposed method.展开更多
In the assembly process of large volume product,engineering constraints limit the relative pose of components and serve as a standard for judging assembly quality.However,in the traditional process of target pose esti...In the assembly process of large volume product,engineering constraints limit the relative pose of components and serve as a standard for judging assembly quality.However,in the traditional process of target pose estimation,a general method is needed for establishing the correlation between engineering constraints and product pose,and it is difficult to evaluate pose by constraints comprehensively.Therefore,the process of target pose estimation and evaluation is separated.In this paper,a pose coordination model based on multi-constraints is proposed,which includes pre-processing,pose estimation,pose adjustment and evaluation.Firstly,engineering constraints are decoupled into 4 types of Minimum Geometrical Reference Constraints(MGRC),and the inequalities for solving target pose are formulated.Then the Constraint Coordination Index(CCI)is defined as the optimization objective to solve the target pose.Finally,with CCI as the numerical index,the target pose is evaluated to illustrate the quality of assembly.Taking the simulation experiment of wing-fuselage jointing as an example,the external and internal parameters of model are analyzed,and the pose estimation based on multi-constraints reduces the CCI by 12%,compared with the point-set-registration method.展开更多
基金supported by the National Natural Science Foundation of China(No.42174011)。
文摘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.
文摘A Bayesian method is used to evaluate the component safety failure model parameter of the safe arming system of an air faced missile in flight. It was proved that Bayes estimation of the model parameter is coincident with the physical explanation of the prior probability density distribution of the random parameter.
文摘A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.
基金Supported by the National Natural Science Foundation of China （No.60574047） and the Doctorate Foundation of the State Education Ministry of China （No.20050335018）.
文摘Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.
基金supported by the National Natural Science Foundation of China(No.41874001 and No.41664001)Support Program for Outstanding Youth Talents in Jiangxi Province(No.20162BCB23050)National Key Research and Development Program(No.2016YFB0501405)。
文摘The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.
文摘A new algorithm for linear instantaneous independent component analysis is proposed based on maximizing the log-likelihood contrast function which can be changed into a gradient equation.An iterative method is introduced to solve this equation efficiently.The unknown probability density functions as well as their first and second derivatives in the gradient equation are estimated by kernel density method.Computer simulations on artificially generated signals and gray scale natural scene images confirm the efficiency and accuracy of the proposed algorithm.
基金Project(40074001) supported by National Natural Science Foundation of China Project (SD2003 -10) supported by the Open ResearchFund Programof the Key Laboratory of Geomatics and Digital Technilogy ,Shandong Province
文摘Gauss-Markov model is frequently used in data analysis; the analysis and estimation of its parameters is always a hot issue. Based on the information theory and from the viewpoint of optimal information on description—minimum description length, this paper discusses a case: where there is multi-collinearity in the coefficient matrix, principal component estimation is used to estimate and select the original parameters, so as to reduce its multi-collinearity and improve its credibility. From the viewpoint of minimum description length, this paper discusses the approach of selecting principal components and uses this approach to solve a practical problem.
文摘A mixed distribution of empirical variances, composed of two distributions the basic and contaminating ones, and referred to as PERG mixed distribution of empirical variances, is considered. In the paper a robust inverse problem solution is given, namely a (new) robust method for estimation of variances of both distributions—PEROBVC Method, as well as the estimates for the numbers of observations for both distributions and, in this way also the estimate of contamination degree.
文摘Depending on analyzing the abuse of equivalent weights,a set of self-contained theory system on robust estimation based on equivalent variance-covariance is established,which includes ρ function, φ function,equivalent variance-covariance function,influence function and breakdown point.And an example is given to verify that the robust models proposed in this paper are reliable and correct.
文摘The article deals with optimality of combining ridge and principal component estimator.It is proved that combining ridge and principal componet estimate has several types of minimumvariance properties in the class of reduced dimension estimates and it also has minimum of orthogo-nal unchanged norm of covariance in the same class.
基金funded by the Basic scientific research fund projects(Youth Project)of the Educational Department of Liaoning Province in 2023(Grants No.JYTQN2023451)Liaoning Institute of Science and Technology doctoral research initiation fund project in 2023(Grants No.2307B27)+2 种基金Basic Research Project of Higher Education Institutions of Liaoning Provincial Department of Education(Grants No.2024JYTYB-12)the Basic scientific research fund projects(Youth Project)of the Educational Department of Liaoning Province in 2023(Grants No.JYTQN2024-21)Liaoning Institute of Science and Technology doctoral research initiation fund project in 2023(Grants No.2307B26)。
文摘The estimation of glacier flow velocity on a short-term scale is very important for further glacier dynamics research.In this study,10 Sentinel-1 ascending images and 10 Sentinel-1 descending images of Urumqi Glacier No.1 in 2017 were used to calculate the glacier flow velocity in a high coherence period by DIn SAR technology and MAI technology,while the offset tracking technology was used to estimate the glacier flow velocity in a low coherence period.Then,the monthly three-dimensional flow velocity of the glacier was calculated by the Helmert variance component estimation method.Finally,the accuracy of the estimated glacier flow velocity on a monthly scale was evaluated.The results showed that:(1)the monthly scale motion velocity of Urumqi Glacier No.1 in May,June,July,and August 2017 was 0.273 m/month,0.657 m/month,0.582 m/month,and 0.392 m/month,respectively.(2)The accuracy of glacier surface velocity from May 2017 to August 2017 was 0.033 m/month,0.026 m/month,0.034 m/month and 0.037 m/month,respectively.(3)The accuracy of glacier surface flow velocity from May 2017 to August 2017 was 0.018 m/month,0.031 m/month,0.029 m/month and 0.030 m/month,respectively.Therefore,the research methodology based on the Sentinel-1 ascending and descending data and presented in this paper was applicable to the estimation of monthly-scale flow velocity of mountain glaciers.
文摘For the two seemingly unrelated regression system, this paper proposed a new type of estimator called pre-test principal components estimator (PTPCE) and discussed some properties of PTPCE.
文摘Progeny testing of oil palm interspecific hybrids was conducted in four trials in Kluang, Ulu Paka, Teluk Intan and Carey Island, Malaysia. The interspecific hybrids （O × P） were created according to the North Carolina Model I （NCM I） mating design, using Elaeis oleifera （0） and Elaeis guineensis var. pisifera （P） as the maternal and paternal parents, respectively. Differences among O, P and O-within-P were determined by the performance （bunch yield, components and vegetative traits） of the progenies. There were significant differences among P for fresh fruit bunch （FFB） yield, bunch number （BN） and average bunch weight （ABW） in Kluang, Ulu Paka and Teluk Intan, but not in Carey Island. FFB yield was generally higher on coastal soils （Teluk Intan and Carey Island） than inland soils （Kluang and Ulu Paka）. Heritability was calculated based on the intraclass correlation. Heritability estimates for these three yield components were variable, depending on the breeding material and environment in which the materials were tested. Fruit to bunch （F/B） and oil to bunch （O/B） of parthenocarpic fruits were important in determining the overall O/B of the interspecific hybrids. The O x P hybrids in Kluang showed the lowest height increment with only a mean of 14.0 cm/year, whereas in Ulu Paka and Teluk Intan, the values were higher at 24.0 cm/year and 25.0 cm/year, respectively. The study showed that the FFB yields ofoil palm interspecific hybrids performed better in coastal soils than inland soils.
文摘Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.
文摘Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.
基金Supported by the National Key R&D Program of China(2016YFB0303401)the National Natural Science Foundation of China(61333010,61503138).
文摘Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating the four raw materials. But the chemical compositions of raw materials vary from time to time, resulting in difficulties to control the oxide compositions to a predefined value. Therefore, a novel algorithm to estimate the chemical compositions of the raw materials is developed. The paper mainly consists of two parts. In model construction part, a novel constrained least square model is proposed to overcome the deviation introduced by long-term drift of the material components, and the model parameters are estimated with an online strategy. And in validation part, the approach is implemented to two examples including datasets from simulation model and the actual industrial process. The final results show the effectiveness of the proposed method.
基金supported by the Special Research on Civil Aircraft of China(No.MJZ-2017-J-96)the Equipment Pre-research Project of China(No.41423010401)。
文摘In the assembly process of large volume product,engineering constraints limit the relative pose of components and serve as a standard for judging assembly quality.However,in the traditional process of target pose estimation,a general method is needed for establishing the correlation between engineering constraints and product pose,and it is difficult to evaluate pose by constraints comprehensively.Therefore,the process of target pose estimation and evaluation is separated.In this paper,a pose coordination model based on multi-constraints is proposed,which includes pre-processing,pose estimation,pose adjustment and evaluation.Firstly,engineering constraints are decoupled into 4 types of Minimum Geometrical Reference Constraints(MGRC),and the inequalities for solving target pose are formulated.Then the Constraint Coordination Index(CCI)is defined as the optimization objective to solve the target pose.Finally,with CCI as the numerical index,the target pose is evaluated to illustrate the quality of assembly.Taking the simulation experiment of wing-fuselage jointing as an example,the external and internal parameters of model are analyzed,and the pose estimation based on multi-constraints reduces the CCI by 12%,compared with the point-set-registration method.
