A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to el...A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.展开更多
In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr...In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.展开更多
An alternative method is proposed in this letter for describing the arbitrary shape and size for granules in 2D image.After image binarization, the edge points on contour are detected, by which the centroid of the sha...An alternative method is proposed in this letter for describing the arbitrary shape and size for granules in 2D image.After image binarization, the edge points on contour are detected, by which the centroid of the shape in question is sought using the moment calculation.Using Principal Component Analysis(PCA), the major and minor diameters are computed.Based on the signature curve-fitting, the first-order derivative is taken so as to seek all the characteristic vertices.By connecting the vertices found, the simplified polygon is formed and utilized for shape and size descriptive purposes.The developed algorithm is run on two given real particle images, and the execution results indicate that the computed parameters can technically well describe the shape and size for the original particles, being able to provide a ready-to-use database for machine vision system to perform related data processing tasks.展开更多
The authors present their analysis of the differential equation d X(t)/ d t = AX(t)-X T (t)BX(t)X(t) , where A is an unsymmetrical real matrix, B is a positive definite symmetric real matrix, X ∈...The authors present their analysis of the differential equation d X(t)/ d t = AX(t)-X T (t)BX(t)X(t) , where A is an unsymmetrical real matrix, B is a positive definite symmetric real matrix, X ∈R n; showing that the equation characterizes a class of continuous type full feedback artificial neural network; We give the analytic expression of the solution; discuss its asymptotic behavior; and finally present the result showing that, in almost all cases, one and only one of following cases is true. 1. For any initial value X 0∈R n, the solution approximates asymptotically to zero vector. In this case, the real part of each eigenvalue of A is non positive. 2. For any initial value X 0 outside a proper subspace of R n, the solution approximates asymptotically to a nontrivial constant vector (X 0) . In this case, the eigenvalue of A with maximal real part is the positive number λ=‖(X 0)‖ 2 B and (X 0) is the corresponding eigenvector. 3. For any initial value X 0 outside a proper subspace of R n, the solution approximates asymptotically to a non constant periodic function (X 0,t) . Then the eigenvalues of A with maximal real part is a pair of conjugate complex numbers which can be computed.展开更多
The tourism is a key branch in the world wide economy nowadays, and revenues account for one third of total income in the world. Many nations are trying to improve their tourism sector attracting more tourists every y...The tourism is a key branch in the world wide economy nowadays, and revenues account for one third of total income in the world. Many nations are trying to improve their tourism sector attracting more tourists every year in order to impact social welfare. This study addresses two research questions： （1） What are the factors that impact on tourism sector？ and （2） Does the tourism really impact on social welfare of the communities？ The objectives of this work are to analyze the variables that impact on the tourism in the Mexican providence of Michoacan and also to find out if the tourism sector is impacting on social welfare of the province, with the propose of answering this questions 41 variables were selected being 63 municipalities of Michoacan province in the case of study. Analysis Factorial of Correspondences （AFC） through the analysis of principal components methodology is employed in this article. The analysis is divided into five phases： （1） reliability testing; （2） the calculation of a matrix that expresses the joint variability of the variables; （3） extraction of the optimal number of factors; （4） the rotation of solutions for the ease of interpretation; and （5） the estimation of the scores graphically. The results showed that the variables that impact on tourism are several the most representative tourism infrastructure and complementary services restaurants, lodging with category five- and four- star travel, visitors foreign share of the Economic Active Population （EAP） in the tertiary sector, percentage of EAP women, percentage of economically active men and Gross Domestic Product （GDP） per capita among others. However the analysis of the Human Development Index （HDI） is not associated with the tourism variables展开更多
By using principal component analysis, this paper selected some appropriate influencing indicators, and constructed multiple linear regression models to predict the development of energy-saving environmental protectio...By using principal component analysis, this paper selected some appropriate influencing indicators, and constructed multiple linear regression models to predict the development of energy-saving environmental protection industry（ESEPl） in Shanghai. The Influencing Factors can be categorized into comprehensive economic factors and environmental factors, and GDP of the second industries and the total industries GDP in comprehensive economic factors have the strongest correlation, while in the environmental index factors, the total discharge of waste water has the strongest correlation. On the basis of influencing factors study, the regression model shows that by the end of 2020, the industry investment will reach 89.788 billion RMB, which proves that the development of ESEPI in Shanghai would grow continuously and dramatically.展开更多
Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then p...Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then principal components are extracted through reconstructing multi effects. Moreover, combining with the optimal estimation theory, the method of singular value diagnosis in dam safety monitoring effect values is proposed. After dam monitoring information matrix is obtained, single effect state estimation matrix and multi effect fusion estimation matrix are constructed to make diagnosis on singular values to reduce false alarm rate. And the diagnosis index is calculated by PCA. These methods have already been applied to an actual project and the result shows the ability of the monitoring effect reflecting dam evolution behavior is improved as dam safety monitoring effect fusion estimation can take accurate identification on singular values and achieve data reduction, filter out noise and lower false alarm rate effectively.展开更多
The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techn...The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techniques. However, the application of this extended procedure is often hampered by its complexity in computation and by lack of some appropriate theory. In this paper, by use of the empirical processes we established a large sample theory for the robust PP estimators of the principal components and dispersion matrix.展开更多
We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 reg...We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 regularization to restrict the sparsity of the residual term, an L_2 regularization term to restrict the sparsity of the representation coefficients, and an L_2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.展开更多
基金Supported in part by Chinese Recruitment Program of Global Young Expert,Alexander von Humboldt Research Fellowship of Germany,the Foundamental Research Funds for the Central Universitiesthe National Natural Science Foundation of China (61074020)
文摘A closed-loop subspace identification method is proposed for industrial systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem. Using the orthogonal projection approach to eliminate the noise influence, consistent estimation is guaranteed for the deterministic part of such a system. A strict proof is given for analyzing the rank condition for such orthogonal projection, in order to use the principal component analysis (PCA) based singular value decomposition (SVD) to derive the extended observability matrix and lower triangular Toeliptz matrix of the plant state-space model. In the result, the plant state matrices can be retrieved in a transparent manner from the above matrices. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed subspace identification method.
基金Project(51209167) supported by Youth Project of the National Natural Science Foundation of ChinaProject(2012JM8026) supported by Shaanxi Provincial Natural Science Foundation, China
文摘In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.
基金Supported by the Ningbo Natural Science Foundation (No.2006A610016)
文摘An alternative method is proposed in this letter for describing the arbitrary shape and size for granules in 2D image.After image binarization, the edge points on contour are detected, by which the centroid of the shape in question is sought using the moment calculation.Using Principal Component Analysis(PCA), the major and minor diameters are computed.Based on the signature curve-fitting, the first-order derivative is taken so as to seek all the characteristic vertices.By connecting the vertices found, the simplified polygon is formed and utilized for shape and size descriptive purposes.The developed algorithm is run on two given real particle images, and the execution results indicate that the computed parameters can technically well describe the shape and size for the original particles, being able to provide a ready-to-use database for machine vision system to perform related data processing tasks.
文摘The authors present their analysis of the differential equation d X(t)/ d t = AX(t)-X T (t)BX(t)X(t) , where A is an unsymmetrical real matrix, B is a positive definite symmetric real matrix, X ∈R n; showing that the equation characterizes a class of continuous type full feedback artificial neural network; We give the analytic expression of the solution; discuss its asymptotic behavior; and finally present the result showing that, in almost all cases, one and only one of following cases is true. 1. For any initial value X 0∈R n, the solution approximates asymptotically to zero vector. In this case, the real part of each eigenvalue of A is non positive. 2. For any initial value X 0 outside a proper subspace of R n, the solution approximates asymptotically to a nontrivial constant vector (X 0) . In this case, the eigenvalue of A with maximal real part is the positive number λ=‖(X 0)‖ 2 B and (X 0) is the corresponding eigenvector. 3. For any initial value X 0 outside a proper subspace of R n, the solution approximates asymptotically to a non constant periodic function (X 0,t) . Then the eigenvalues of A with maximal real part is a pair of conjugate complex numbers which can be computed.
文摘The tourism is a key branch in the world wide economy nowadays, and revenues account for one third of total income in the world. Many nations are trying to improve their tourism sector attracting more tourists every year in order to impact social welfare. This study addresses two research questions： （1） What are the factors that impact on tourism sector？ and （2） Does the tourism really impact on social welfare of the communities？ The objectives of this work are to analyze the variables that impact on the tourism in the Mexican providence of Michoacan and also to find out if the tourism sector is impacting on social welfare of the province, with the propose of answering this questions 41 variables were selected being 63 municipalities of Michoacan province in the case of study. Analysis Factorial of Correspondences （AFC） through the analysis of principal components methodology is employed in this article. The analysis is divided into five phases： （1） reliability testing; （2） the calculation of a matrix that expresses the joint variability of the variables; （3） extraction of the optimal number of factors; （4） the rotation of solutions for the ease of interpretation; and （5） the estimation of the scores graphically. The results showed that the variables that impact on tourism are several the most representative tourism infrastructure and complementary services restaurants, lodging with category five- and four- star travel, visitors foreign share of the Economic Active Population （EAP） in the tertiary sector, percentage of EAP women, percentage of economically active men and Gross Domestic Product （GDP） per capita among others. However the analysis of the Human Development Index （HDI） is not associated with the tourism variables
基金This research work was financially supported by the Shanghai Board of Education （2012-SHNGE-06ZD） , China Postdoctoral Science Foundation funded project （2013M531157） , and The Ministry of Education of Youth Fund Project of Humanities and Social Sciences Research （14YJC790152）
文摘By using principal component analysis, this paper selected some appropriate influencing indicators, and constructed multiple linear regression models to predict the development of energy-saving environmental protection industry（ESEPl） in Shanghai. The Influencing Factors can be categorized into comprehensive economic factors and environmental factors, and GDP of the second industries and the total industries GDP in comprehensive economic factors have the strongest correlation, while in the environmental index factors, the total discharge of waste water has the strongest correlation. On the basis of influencing factors study, the regression model shows that by the end of 2020, the industry investment will reach 89.788 billion RMB, which proves that the development of ESEPI in Shanghai would grow continuously and dramatically.
基金the water saving project funding of Ministry of Water Resources of P.R.China(code:200970)the research funding of North China University of Water Conservancy and Electric Power of 2006+1 种基金the project of Henan Excellent Teacher Funding of 2006,Henan Science and Technology project(092102310197)Henan natural science research project of Education Department(2009A170004)
基金supported by the National Natural Science Foundation of China (Grant Nos. 51079046, 50909041, 50809025, and 50879024)the National Science and Technology Support Plan (Grant Nos. 2008BAB29B03and 2008BAB29B06)+6 种基金the Special Fund of State Key Laboratory of China (Grant Nos. 2009586012, 2009586912, and 2010585212)the Fundamental Research Funds for the Central Universities (Grant Nos. 2009B08514, 2010B20414, 2010B01414, and 2010B14114)the China Hydropower Engineering Consulting Group Co. Science and Technology Support Pro-ject (Grant No. CHC-KJ-2007-02)Jiangsu Province "333 High-Level Personnel Training Project" (Grant No. 2017-B08037)Graduate Innovation Program of Universities in Jiangsu Province (Grant No. CX09B_ 163Z)Dominant Discipline Construction Program Funded Projects of University in Jiangsu ProvineScience Foundation for the Excellent Youth Scholars of Ministry of Education of China (Grant No. 20070294023)
文摘Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then principal components are extracted through reconstructing multi effects. Moreover, combining with the optimal estimation theory, the method of singular value diagnosis in dam safety monitoring effect values is proposed. After dam monitoring information matrix is obtained, single effect state estimation matrix and multi effect fusion estimation matrix are constructed to make diagnosis on singular values to reduce false alarm rate. And the diagnosis index is calculated by PCA. These methods have already been applied to an actual project and the result shows the ability of the monitoring effect reflecting dam evolution behavior is improved as dam safety monitoring effect fusion estimation can take accurate identification on singular values and achieve data reduction, filter out noise and lower false alarm rate effectively.
基金The researcb was partially supported by the National Natural Science Foundation of China under Grant No.19631040.
文摘The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techniques. However, the application of this extended procedure is often hampered by its complexity in computation and by lack of some appropriate theory. In this paper, by use of the empirical processes we established a large sample theory for the robust PP estimators of the principal components and dispersion matrix.
基金supported by the National Natural Science Foundation of China(No.61401425)
文摘We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 regularization to restrict the sparsity of the residual term, an L_2 regularization term to restrict the sparsity of the representation coefficients, and an L_2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.
