Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to thi...Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.展开更多
The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very effectiv...The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very effective algorithm to estimate the finite mixture model parameters. However, EM algorithm can not guarantee to find the global optimal solution, and often easy to fall into local optimal solution, so it is sensitive to the determination of initial value to iteration. Traditional EM algorithm select the initial value at random, we propose an improved method of selection of initial value. First, we use the k-nearest-neighbor method to delete outliers. Second, use the k-means to initialize the EM algorithm. Compare this method with the original random initial value method, numerical experiments show that the parameter estimation effect of the initialization of the EM algorithm is significantly better than the effect of the original EM algorithm.展开更多
The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition...The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.展开更多
Wireless Sensor Networks (WSNs) are mainly deployed for data acquisition, thus, the network performance can be passively measured by exploiting whether application data from various sensor nodes reach the sink. In thi...Wireless Sensor Networks (WSNs) are mainly deployed for data acquisition, thus, the network performance can be passively measured by exploiting whether application data from various sensor nodes reach the sink. In this paper, therefore, we take into account the unique data aggregation communication paradigm of WSNs and model the problem of link loss rates inference as a Maximum-Likelihood Estimation problem. And we propose an inference algorithm based on the standard Expectation-Maximization (EM) techniques. Our algorithm is applicable not only to periodic data collection scenarios but to event detection scenarios. Finally, we validate the algorithm through simulations and it exhibits good performance and scalability.展开更多
Based on Vector Aitken （VA） method, we propose an acceleration Expectation-Maximization （EM） algorithm, VA-accelerated EM algorithm, whose convergence speed is faster than that of EM algorithm. The VA-accelerated ...Based on Vector Aitken （VA） method, we propose an acceleration Expectation-Maximization （EM） algorithm, VA-accelerated EM algorithm, whose convergence speed is faster than that of EM algorithm. The VA-accelerated EM algorithm does not use the information matrix but only uses the sequence of estimates obtained from iterations of the EM algorithm, thus it keeps the flexibility and simplicity of the EM algorithm. Considering Steffensen iterative process, we have also given the Steffensen form of the VA-accelerated EM algorithm. It can be proved that the reform process is quadratic convergence. Numerical analysis illustrate the proposed methods are efficient and faster than EM algorithm.展开更多
A new expectation-maximization（EM） algorithm is proposed to estimate the parameters of the truncated multinormal distribution with linear restriction on the variables. Compared with the generalized method of moments...A new expectation-maximization（EM） algorithm is proposed to estimate the parameters of the truncated multinormal distribution with linear restriction on the variables. Compared with the generalized method of moments（GMM） estimation and the maximum likelihood estimation（MLE） for the truncated multivariate normal distribution, the EM algorithm features in fast calculation and high accuracy which are shown in the simulation results. For the real data of the national college entrance exams（NCEE）, we estimate the distribution of the NCEE examinees＇ scores in Anhui, 2003, who were admitted to the university of science and technology of China（USTC）. Based on our analysis, we have also given the ratio truncated by the NCEE admission line of USTC in Anhui, 2003.展开更多
The use of a general EM(expectation-maximization) algorithm in multi-spectral image classification is known to cause two problems:singularity of the variance-covariance matrix and sensitivity of randomly selected init...The use of a general EM(expectation-maximization) algorithm in multi-spectral image classification is known to cause two problems:singularity of the variance-covariance matrix and sensitivity of randomly selected initial values.The former causes computation failure;the latter produces unstable classification results.This paper proposes a modified approach to resolve these defects.First,a modification is proposed to determine reliable parameters for the EM algorithm based on a k-means algorithm with initial centers obtained from the density function of the first principal component,which avoids the selection of initial centers at random.A second modification uses the principal component transformation of the image to obtain a set of uncorrelated data.The number of principal components as the input of the EM algorithm is determined by the principal contribution rate.In this way,the modification can not only remove singularity but also weaken noise.Experimental results obtained from two sets of remote sensing images acquired by two different sensors confirm the validity of the proposed approach.展开更多
This paper discusses the maximum likelihood estimate of β under linear inequalities A0β≥ a in a linear model with missing data, proposes the restricted EM algo rithm and proves the convergence.
The conventional method for testing hypotheses is to find an exact or asymptotic distributionof a test statistic. But when the model is complex and the sample size is small, difficulty often arises. Thispaper aims to ...The conventional method for testing hypotheses is to find an exact or asymptotic distributionof a test statistic. But when the model is complex and the sample size is small, difficulty often arises. Thispaper aims to present a method for finding maximum probability with the help of EM algorithm. For any fixedsample size, this method can be used not only to obtain an accurate test but also to check the real level ofa test which is build by large sample theory. Especially, while doing this, one needs neither the accurate norasymptotic distribution of the test statistic. So the method is easily performed and is especially useful for small samples.展开更多
基金Supported by the High Technology Research and Development Programme of China(2006AA12A106)~~
文摘Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.
文摘The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very effective algorithm to estimate the finite mixture model parameters. However, EM algorithm can not guarantee to find the global optimal solution, and often easy to fall into local optimal solution, so it is sensitive to the determination of initial value to iteration. Traditional EM algorithm select the initial value at random, we propose an improved method of selection of initial value. First, we use the k-nearest-neighbor method to delete outliers. Second, use the k-means to initialize the EM algorithm. Compare this method with the original random initial value method, numerical experiments show that the parameter estimation effect of the initialization of the EM algorithm is significantly better than the effect of the original EM algorithm.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(62325104).
文摘The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.
文摘Wireless Sensor Networks (WSNs) are mainly deployed for data acquisition, thus, the network performance can be passively measured by exploiting whether application data from various sensor nodes reach the sink. In this paper, therefore, we take into account the unique data aggregation communication paradigm of WSNs and model the problem of link loss rates inference as a Maximum-Likelihood Estimation problem. And we propose an inference algorithm based on the standard Expectation-Maximization (EM) techniques. Our algorithm is applicable not only to periodic data collection scenarios but to event detection scenarios. Finally, we validate the algorithm through simulations and it exhibits good performance and scalability.
基金Supported by the National Natural Science Foundation of China(No.11071253,11471335,11626130)
文摘Based on Vector Aitken （VA） method, we propose an acceleration Expectation-Maximization （EM） algorithm, VA-accelerated EM algorithm, whose convergence speed is faster than that of EM algorithm. The VA-accelerated EM algorithm does not use the information matrix but only uses the sequence of estimates obtained from iterations of the EM algorithm, thus it keeps the flexibility and simplicity of the EM algorithm. Considering Steffensen iterative process, we have also given the Steffensen form of the VA-accelerated EM algorithm. It can be proved that the reform process is quadratic convergence. Numerical analysis illustrate the proposed methods are efficient and faster than EM algorithm.
基金Supported by the National Natural Science Foundation(Grant No.11571337,11271347,71172214)
文摘A new expectation-maximization（EM） algorithm is proposed to estimate the parameters of the truncated multinormal distribution with linear restriction on the variables. Compared with the generalized method of moments（GMM） estimation and the maximum likelihood estimation（MLE） for the truncated multivariate normal distribution, the EM algorithm features in fast calculation and high accuracy which are shown in the simulation results. For the real data of the national college entrance exams（NCEE）, we estimate the distribution of the NCEE examinees＇ scores in Anhui, 2003, who were admitted to the university of science and technology of China（USTC）. Based on our analysis, we have also given the ratio truncated by the NCEE admission line of USTC in Anhui, 2003.
基金supported by the National High-tech R&D Program of China(2007AA12Z226 and SS2012AA120804)the National Natural Science Foundation of China(40674015 and 41074009)+2 种基金the Doctoral Fund of Ministry of Education of China(20100022110008)the Fundamental Research Funds for the Central Universities(2-9-2011-227)the Open Research Fund of Key Laboratory of Digital Earth Science,Center for Earth Observation and Digital Earth,Chinese Academy of Sciences (2010LDE002)
文摘The use of a general EM(expectation-maximization) algorithm in multi-spectral image classification is known to cause two problems:singularity of the variance-covariance matrix and sensitivity of randomly selected initial values.The former causes computation failure;the latter produces unstable classification results.This paper proposes a modified approach to resolve these defects.First,a modification is proposed to determine reliable parameters for the EM algorithm based on a k-means algorithm with initial centers obtained from the density function of the first principal component,which avoids the selection of initial centers at random.A second modification uses the principal component transformation of the image to obtain a set of uncorrelated data.The number of principal components as the input of the EM algorithm is determined by the principal contribution rate.In this way,the modification can not only remove singularity but also weaken noise.Experimental results obtained from two sets of remote sensing images acquired by two different sensors confirm the validity of the proposed approach.
基金We would like to thank the referees for many useful suggestions on the earlier draft of the manuscript.This work was supported by the National Natural Foundation of China(Grant Nos.10431010,10329102&10371015)the Science and Technology Keystone Fund of MOE,China(Grant Nos.104070&00041)+1 种基金EYTP,the Distinguished Young Scholars Science Research Program of Jilin Province(Grant No.20030113)Young Teacher's Foundation of Northeast Normal University,China.
文摘This paper discusses the maximum likelihood estimate of β under linear inequalities A0β≥ a in a linear model with missing data, proposes the restricted EM algo rithm and proves the convergence.
基金This work was supported by the National Natural Science Foundation of China （51507015, 61773402, 61540037, 71271215, 61233008, 51425701, 70921001, 51577014）, the Natural Science Foundation of Hunan Province （2015JJ3008）, the Key Laboratory of Renewable Energy Electric-Technology of Hunan Province （2014ZNDL002）, and Hunan Province Science and Technology Program（2015NK3035）.
基金This work was supported by the National Natural Science Foundation of China(Grant No.10071004).
文摘The conventional method for testing hypotheses is to find an exact or asymptotic distributionof a test statistic. But when the model is complex and the sample size is small, difficulty often arises. Thispaper aims to present a method for finding maximum probability with the help of EM algorithm. For any fixedsample size, this method can be used not only to obtain an accurate test but also to check the real level ofa test which is build by large sample theory. Especially, while doing this, one needs neither the accurate norasymptotic distribution of the test statistic. So the method is easily performed and is especially useful for small samples.
