Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing an...Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and Mallat decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.展开更多
Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal c...Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.展开更多
This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characte...This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.展开更多
An NT-MT combined method based on nodal test (NT) and measurement test (MT) is developed for gross error detection and data reconciliation for industrial application. The NT-MT combined method makes use of both NT and...An NT-MT combined method based on nodal test (NT) and measurement test (MT) is developed for gross error detection and data reconciliation for industrial application. The NT-MT combined method makes use of both NT and MT tests and this combination helps to overcome the defects in the respective methods. It also avoids any artificial manipulation and eliminates the huge combinatorial problem that is created in the combined method based on the nodal test in the case of more than one gross error for a large process system. Serial compensation strategy is also used to avoid the decrease of the coefficient matrix rank during the computation of the proposed method. Simulation results show that the proposed method is very effective and possesses good performance.展开更多
Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance con...Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.展开更多
This paper describes a broad perspective of the application of graph theory to establishment of GPS control networks whereby the GPS network is considered as a connected and directed graph with three components.In thi...This paper describes a broad perspective of the application of graph theory to establishment of GPS control networks whereby the GPS network is considered as a connected and directed graph with three components.In this algorithm the gross error detection is undertaken through loops of different spanning trees using the "Loop Law" in which the individual components Δ X, Δ Y and Δ Z sum up to zero.If the sum of the respective vector components ∑X,∑Y and ∑Z in a loop is not zero and if the error is beyond the tolerable limit (ε>w),it indicates the existence of gross errors in one of the baselines in the loop and therefore the baseline must be removed or re_observed.After successful screening of errors by graph theory,network adjustment can be carried out.In this paper,the GPS data from the control network established as reference system for the HP Dam at Baishan county in Liaoning province is presented to illustrate the algorithm.展开更多
This paper systematically studies the statistical diagnosis and hypothesis testing for the semiparametric linear regression model according to the theories and methods of the statistical diagnosis and hypothesis testi...This paper systematically studies the statistical diagnosis and hypothesis testing for the semiparametric linear regression model according to the theories and methods of the statistical diagnosis and hypothesis testing for parametric regression model.Several diagnostic measures and the methods for gross error testing are derived.Especially,the global and local influence analysis of the gross error on the parameter X and the nonparameter s are discussed in detail;at the same time,the paper proves that the data point deletion model is equivalent to the mean shift model for the semiparametric regression model.Finally,with one simulative computing example,some helpful conclusions are drawn.展开更多
The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-...The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-erful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in ef-ficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the pres-ence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be es-timated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of de-cision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a con-tinuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.展开更多
The dominant and recessive effect made by exceptional interferer is analyzed in measurement system based on responsive character, and the gross error model of fuzzy clustering based on fuzzy relation and fuzzy equipol...The dominant and recessive effect made by exceptional interferer is analyzed in measurement system based on responsive character, and the gross error model of fuzzy clustering based on fuzzy relation and fuzzy equipollance relation is built. The concept and calculate formula of fuzzy eccentricity are defined to deduce the evaluation rule and function ofgruss error, on the base of them, a fuzzy clustering method of separating and discriminating the gross error is found, utilized in the dynamic circular division measurement system, the method can identify and eliminate gross error in measured data, and reduce measured data dispersity. Experimental results indicate that the use of the method and model enables repetitive precision of the system to improve 80% higher than the foregoing system, to reach 3.5 s, and angle measurement error is less than 7 s.展开更多
A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Ak...A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.展开更多
Digital elevation model(DEM)matching techniques have been extended to DEM deformation detection by substituting a robust estimator for the least squares estimator,in which terrain changes are treated as gross errors.H...Digital elevation model(DEM)matching techniques have been extended to DEM deformation detection by substituting a robust estimator for the least squares estimator,in which terrain changes are treated as gross errors.However,all existing methods only emphasise their deformation detecting ability,and neglect another important aspect:only when the gross error can be detected and located,can this system be useful.This paper employs the gross error judgement matrix as a tool to make an in-depth analysis of this problem.The theoretical analyses and experimental results show that observations in the DEM matching algorithm in real applications have the ability to detect and locate gross errors.Therefore,treating the terrain changes as gross errors is theoretically feasible,allowing real DEM deformations to be detected by employing a surface matching technique.展开更多
A new idea and a distinctive method have been proposed, which concern real errors and their estimators. By using the idea of 'Quasi-Stable Adjustment' created by Prof. Zhou Jiangwen for reference, the rank-def...A new idea and a distinctive method have been proposed, which concern real errors and their estimators. By using the idea of 'Quasi-Stable Adjustment' created by Prof. Zhou Jiangwen for reference, the rank-deficient equations on real errors are resolved by adding the conditions under which the minimum of the norm of the real errors of the quasi-accurate observations is restrained.展开更多
Because of the sensitivity of the Kalman framework to gross errors, proper techniques for detection of gross errors are necessary. By integrating the selection of quasi-accurate observations and the Kalman framework, ...Because of the sensitivity of the Kalman framework to gross errors, proper techniques for detection of gross errors are necessary. By integrating the selection of quasi-accurate observations and the Kalman framework, a new filter called the quasi-accurate filter (QUAF) is developed. The expansibility and implementation scheme of the new algorithm are then discussed in detail, and the reliability matrix for the Kalman filter is proposed to analyze the reliability of the filters with different detection technologies. Finally, the experimental results from a real world case study are used to validate the conclusions. The QUAF carries out the preliminary selection of the quasi-accurate observations (QAOs) using the innovation of the Kalman filter, and use the check QAOs to determine reasonable observations. This causes the QUAF to handle more easily and possess wider expansibility. QUAF can be reformulated to the special cases of several common detection methods, such as the innovation method, robust estimation and quasi-accurate detection (QUAD). Since only reasonable observations are used, the QUAF has better detection accuracy and stronger avoidance of gross errors than the innovation method and robust estimation. Meanwhile, compared with QUAD methods, QUAF introduces the state-predicted model, requiring fewer quasi-accurate observations and making it more suitable for systems with complicated observation structures or sparse observations.展开更多
基金Supported by Specialized Research Fundfor the Doctoral Programof Higher Educationin China(No.20040290503) Science and Technology Fundationof CUMT(No.2005B020) .
文摘Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and Mallat decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.
基金The National Natural Science Foundation of China(No 60504033)
文摘Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.
基金The National Natural Science Foundation of China(No.61273236)the Natural Science Foundation of Jiangsu Province(No.BK2010239)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061)
文摘This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.
基金Supported by the National Creative Research Groups Science Foundation of China (No.60421002) and the National "TenthFive-Year" Science and Technology Research Program of China (2004BA204B08).
文摘An NT-MT combined method based on nodal test (NT) and measurement test (MT) is developed for gross error detection and data reconciliation for industrial application. The NT-MT combined method makes use of both NT and MT tests and this combination helps to overcome the defects in the respective methods. It also avoids any artificial manipulation and eliminates the huge combinatorial problem that is created in the combined method based on the nodal test in the case of more than one gross error for a large process system. Serial compensation strategy is also used to avoid the decrease of the coefficient matrix rank during the computation of the proposed method. Simulation results show that the proposed method is very effective and possesses good performance.
基金Supported by the National High Technology Research and Development Program of China (2007AA40702 and 2007AA04Z191)
文摘Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.
文摘This paper describes a broad perspective of the application of graph theory to establishment of GPS control networks whereby the GPS network is considered as a connected and directed graph with three components.In this algorithm the gross error detection is undertaken through loops of different spanning trees using the "Loop Law" in which the individual components Δ X, Δ Y and Δ Z sum up to zero.If the sum of the respective vector components ∑X,∑Y and ∑Z in a loop is not zero and if the error is beyond the tolerable limit (ε>w),it indicates the existence of gross errors in one of the baselines in the loop and therefore the baseline must be removed or re_observed.After successful screening of errors by graph theory,network adjustment can be carried out.In this paper,the GPS data from the control network established as reference system for the HP Dam at Baishan county in Liaoning province is presented to illustrate the algorithm.
基金Supported by the National Natural Science Foundation of China （No. 40604001）,the National High Technology Research and Development Program of China （No. 2007AA12Z312）.Acknowledgement The authors thank Prof. Tao Benzao and Prof. Wang Xingzhou for several helpful suggestions during the preparation of this manuscript.
文摘This paper systematically studies the statistical diagnosis and hypothesis testing for the semiparametric linear regression model according to the theories and methods of the statistical diagnosis and hypothesis testing for parametric regression model.Several diagnostic measures and the methods for gross error testing are derived.Especially,the global and local influence analysis of the gross error on the parameter X and the nonparameter s are discussed in detail;at the same time,the paper proves that the data point deletion model is equivalent to the mean shift model for the semiparametric regression model.Finally,with one simulative computing example,some helpful conclusions are drawn.
基金Supported by the National High Technology Research and Development Program of China (2006AA04Z176)
文摘The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-erful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in ef-ficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the pres-ence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be es-timated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of de-cision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a con-tinuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.
基金This project is supported by National Natural Science Foundation of China (No.59575081,No.59735120).
文摘The dominant and recessive effect made by exceptional interferer is analyzed in measurement system based on responsive character, and the gross error model of fuzzy clustering based on fuzzy relation and fuzzy equipollance relation is built. The concept and calculate formula of fuzzy eccentricity are defined to deduce the evaluation rule and function ofgruss error, on the base of them, a fuzzy clustering method of separating and discriminating the gross error is found, utilized in the dynamic circular division measurement system, the method can identify and eliminate gross error in measured data, and reduce measured data dispersity. Experimental results indicate that the use of the method and model enables repetitive precision of the system to improve 80% higher than the foregoing system, to reach 3.5 s, and angle measurement error is less than 7 s.
基金Project supported by the National Creative Research Groups Science Foundation of China (No. 60421002)the National "Tenth Five-Year" Science and Technology Research Program of China (No.2004BA204B08)
文摘A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.
基金Supported by National High Technology Research and Development Program of China （863 Program） （2006AA040308）, National Natural Science Foundation of China （60736021）, and the National Creative Research Groups Science Foundation of China （60721062）
基金This research is supported by the National High Technology Plan(863)of the People’s Republic of China,Project No.2009AA12Z207.
文摘Digital elevation model(DEM)matching techniques have been extended to DEM deformation detection by substituting a robust estimator for the least squares estimator,in which terrain changes are treated as gross errors.However,all existing methods only emphasise their deformation detecting ability,and neglect another important aspect:only when the gross error can be detected and located,can this system be useful.This paper employs the gross error judgement matrix as a tool to make an in-depth analysis of this problem.The theoretical analyses and experimental results show that observations in the DEM matching algorithm in real applications have the ability to detect and locate gross errors.Therefore,treating the terrain changes as gross errors is theoretically feasible,allowing real DEM deformations to be detected by employing a surface matching technique.
文摘A new idea and a distinctive method have been proposed, which concern real errors and their estimators. By using the idea of 'Quasi-Stable Adjustment' created by Prof. Zhou Jiangwen for reference, the rank-deficient equations on real errors are resolved by adding the conditions under which the minimum of the norm of the real errors of the quasi-accurate observations is restrained.
文摘Because of the sensitivity of the Kalman framework to gross errors, proper techniques for detection of gross errors are necessary. By integrating the selection of quasi-accurate observations and the Kalman framework, a new filter called the quasi-accurate filter (QUAF) is developed. The expansibility and implementation scheme of the new algorithm are then discussed in detail, and the reliability matrix for the Kalman filter is proposed to analyze the reliability of the filters with different detection technologies. Finally, the experimental results from a real world case study are used to validate the conclusions. The QUAF carries out the preliminary selection of the quasi-accurate observations (QAOs) using the innovation of the Kalman filter, and use the check QAOs to determine reasonable observations. This causes the QUAF to handle more easily and possess wider expansibility. QUAF can be reformulated to the special cases of several common detection methods, such as the innovation method, robust estimation and quasi-accurate detection (QUAD). Since only reasonable observations are used, the QUAF has better detection accuracy and stronger avoidance of gross errors than the innovation method and robust estimation. Meanwhile, compared with QUAD methods, QUAF introduces the state-predicted model, requiring fewer quasi-accurate observations and making it more suitable for systems with complicated observation structures or sparse observations.
