Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department t...Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.展开更多
In this paper we reparameterize covariance structures in longitudinal data analysis through the modified Cholesky decomposition of itself. Based on this modified Cholesky decomposition, the within-subject covariance m...In this paper we reparameterize covariance structures in longitudinal data analysis through the modified Cholesky decomposition of itself. Based on this modified Cholesky decomposition, the within-subject covariance matrix is decomposed into a unit lower triangular matrix involving moving average coefficients and a diagonal matrix involving innovation variances, which are modeled as linear functions of covariates. Then, we propose a penalized maximum likelihood method for variable selection in joint mean and covariance models based on this decomposition. Under certain regularity conditions, we establish the consistency and asymptotic normality of the penalized maximum likelihood estimators of parameters in the models. Simulation studies are undertaken to assess the finite sample performance of the proposed variable selection procedure.展开更多
An effective solution method of fractional ordinary and partial differential equations is proposed in the present paper.The standard Adomian Decomposition Method(ADM)is modified via introducing a functional term invol...An effective solution method of fractional ordinary and partial differential equations is proposed in the present paper.The standard Adomian Decomposition Method(ADM)is modified via introducing a functional term involving both a variable and a parameter.A residual approach is then adopted to identify the optimal value of the embedded parameter within the frame of L^(2) norm.Numerical experiments on sample problems of open literature prove that the presented algorithm is quite accurate,more advantageous over the traditional ADM and straightforward to implement for the fractional ordinary and partial differential equations of the recent focus of mathematical models.Better performance of the method is further evidenced against some compared commonly used numerical techniques.展开更多
The moving-mean method is one of the conventional approaches for trend-extraction from a data set. It is usually applied in an empirical way. The smoothing degree of the trend depends on the selections of window lengt...The moving-mean method is one of the conventional approaches for trend-extraction from a data set. It is usually applied in an empirical way. The smoothing degree of the trend depends on the selections of window length and weighted coefficients, which are associated with the change pattern of the data. Are there any uniform criteria for determining them? The present article is a reaction to this fundamental problem. By investigating many kinds of data, the results show that: 1) Within a certain range, the more points which participate in moving-mean, the better the trend function. However, in case the window length is too long, the trend function may tend to the ordinary global mean. 2) For a given window length, what matters is the choice of weighted coefficients. As the five-point case concerned, the local-midpoint, local-mean and global-mean criteria hold. Among these three criteria, the local-mean one has the strongest adaptability, which is suggested for your usage.展开更多
日负荷数据聚类是实现用户用电特性分析的重要方式。用于聚类的降维采样数据的指标权重会影响聚类结果,因此提出一种基于CRITIC赋权的奇异值分解(singular value decomposition,SVD)降维方法C-SVD与改进加权模糊C均值聚类(fuzzy C-means...日负荷数据聚类是实现用户用电特性分析的重要方式。用于聚类的降维采样数据的指标权重会影响聚类结果,因此提出一种基于CRITIC赋权的奇异值分解(singular value decomposition,SVD)降维方法C-SVD与改进加权模糊C均值聚类(fuzzy C-means,FCM)算法相结合的日负荷数据聚类方法,同时针对传统FCM易受初始聚类中心影响的问题,提出一种自适应确定初始聚类中心的密度‒距离中心点选择(density-distance centersr selection,DDCS)方法。首先,采用SVD对负荷数据进行降维处理;其次,使用CRITIC赋权法对降维指标进行权重配置;然后,使用DDCS法确定初始聚类中心;最后,使用加权FCM算法对负荷数据进行聚类。仿真算例表明,与传统方法相比,所提方法鲁棒性强,能够明显提升负荷数据聚类结果的准确性。展开更多
A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest si...A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest norm. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms, such as N-FINDR, that it generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which the traditional simplex-based algorithms cannot do by themselves. Experimental results of both artificial simulated images and practical remote sensing images demonstrate the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.展开更多
云平台多容器集群数据量大、涉及种类多,导致异常状态监控难度大,为此提出基于Prometheus的监控算法。在云平台中,利用小波分解法获取多容器集群数据的实时状态序列,结合二叉树分解描述法划分不同类型的集群数据特征。根据Prometheus技...云平台多容器集群数据量大、涉及种类多,导致异常状态监控难度大,为此提出基于Prometheus的监控算法。在云平台中,利用小波分解法获取多容器集群数据的实时状态序列,结合二叉树分解描述法划分不同类型的集群数据特征。根据Prometheus技术具备的分布式储存管理特点划分监控空间,并设定监控类中心,对比多容器集群数据与该节点中心相似性,相似性最强的数据即异常。仿真实验证明,方法监控异常状态数据入侵信号在800~1200测试点位间出现大幅度变动,与实际number format exception (NFE)异常状态数据入侵监控结果十分接近,CPU耗用率较低,最小值为15%,对异常监控的响应耗时平均值为1.7 s,可为云平台稳定运行提供帮助。展开更多
基金Project(61873283)supported by the National Natural Science Foundation of ChinaProject(KQ1707017)supported by the Changsha Science&Technology Project,ChinaProject(2019CX005)supported by the Innovation Driven Project of the Central South University,China。
文摘Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.
文摘In this paper we reparameterize covariance structures in longitudinal data analysis through the modified Cholesky decomposition of itself. Based on this modified Cholesky decomposition, the within-subject covariance matrix is decomposed into a unit lower triangular matrix involving moving average coefficients and a diagonal matrix involving innovation variances, which are modeled as linear functions of covariates. Then, we propose a penalized maximum likelihood method for variable selection in joint mean and covariance models based on this decomposition. Under certain regularity conditions, we establish the consistency and asymptotic normality of the penalized maximum likelihood estimators of parameters in the models. Simulation studies are undertaken to assess the finite sample performance of the proposed variable selection procedure.
文摘An effective solution method of fractional ordinary and partial differential equations is proposed in the present paper.The standard Adomian Decomposition Method(ADM)is modified via introducing a functional term involving both a variable and a parameter.A residual approach is then adopted to identify the optimal value of the embedded parameter within the frame of L^(2) norm.Numerical experiments on sample problems of open literature prove that the presented algorithm is quite accurate,more advantageous over the traditional ADM and straightforward to implement for the fractional ordinary and partial differential equations of the recent focus of mathematical models.Better performance of the method is further evidenced against some compared commonly used numerical techniques.
文摘The moving-mean method is one of the conventional approaches for trend-extraction from a data set. It is usually applied in an empirical way. The smoothing degree of the trend depends on the selections of window length and weighted coefficients, which are associated with the change pattern of the data. Are there any uniform criteria for determining them? The present article is a reaction to this fundamental problem. By investigating many kinds of data, the results show that: 1) Within a certain range, the more points which participate in moving-mean, the better the trend function. However, in case the window length is too long, the trend function may tend to the ordinary global mean. 2) For a given window length, what matters is the choice of weighted coefficients. As the five-point case concerned, the local-midpoint, local-mean and global-mean criteria hold. Among these three criteria, the local-mean one has the strongest adaptability, which is suggested for your usage.
文摘集中供热系统实际调控方式受环境因素等影响,常偏离预定调控方案,核实集中供热系统实际调控方式是实现系统节能优化的前提。本文综合采用ACF(Autocorrelation Function)自相关函数、相关性分析和STL(Seasonal-Trend Decomposition Using LOESS)时间序列分解等数据挖掘技术有效结合,提出了一套通过运行数据识别系统实际调控方式的方法。将提出的方法应用于实际案例的换热站中,结果表明可以识别出该换热站实际调控方式,验证了所提方法的可行性。最终确定该换热站实际调控方式与预定方案之间存在差异,并总结导致实际调控方式产生差异的原因。
基金Supported in part by the National Natural Science Foundation of China (Grant No. 60672116)the National High-Tech Research & Development Program of China (Grant No. 2009AA12Z115)the Shanghai Leading Academic Discipline Project (Grant No. B112)
文摘A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest norm. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms, such as N-FINDR, that it generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which the traditional simplex-based algorithms cannot do by themselves. Experimental results of both artificial simulated images and practical remote sensing images demonstrate the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.
文摘云平台多容器集群数据量大、涉及种类多,导致异常状态监控难度大,为此提出基于Prometheus的监控算法。在云平台中,利用小波分解法获取多容器集群数据的实时状态序列,结合二叉树分解描述法划分不同类型的集群数据特征。根据Prometheus技术具备的分布式储存管理特点划分监控空间,并设定监控类中心,对比多容器集群数据与该节点中心相似性,相似性最强的数据即异常。仿真实验证明,方法监控异常状态数据入侵信号在800~1200测试点位间出现大幅度变动,与实际number format exception (NFE)异常状态数据入侵监控结果十分接近,CPU耗用率较低,最小值为15%,对异常监控的响应耗时平均值为1.7 s,可为云平台稳定运行提供帮助。
