In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems ...In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.展开更多
Numerical simulation modeling is a hotspot in the geological engineering computing field. Tak- ing a fast Langrangian analysis of continua in 3 dimensions （FLAC3D） numerical modeling on com- puting the geo-deformati...Numerical simulation modeling is a hotspot in the geological engineering computing field. Tak- ing a fast Langrangian analysis of continua in 3 dimensions （FLAC3D） numerical modeling on com- puting the geo-deformation information caused by the mining subsidence in a coalmine for example, a new GIS-Excel modeling method is proposed to build geologic strata within the simulation range combined with the coal-seam dip angle of the underground mining working-planes. First of all, the coal-seam model of the numerical computing is built by using the geographic information system （GIS） according to the stripe-through principle and the calculating formula on the size of the model blocks in the paper defined, then the FLAC3D numerical computing model of all geologic strata with- in the simulation range is also built based on the calculating formula of thickness of each stratum and the Excel fast computing advantages. The GIS-Excel method is good at the higher modeling accuracy, seldom making mistakes and consuming less time. The reliability and validity of the method is veri- fied well by its practical applications in the coalmine area.展开更多
Emotions are becoming increasingly important in human-centered interaction architectures. Recognition of facial expressions, which are central to human-computer interactions, seems natural and desirable. However, faci...Emotions are becoming increasingly important in human-centered interaction architectures. Recognition of facial expressions, which are central to human-computer interactions, seems natural and desirable. However, facial expressions include mixed emotions, continuous rather than discrete, which vary from moment to moment. This paper represents a novel method of recognizing facial expressions of various internal states via manifold learning, to achieve the aim of humancentered interaction studies. A critical review of widely used emotion models is described, then, facial expression features of various internal states via the locally linear embedding （LLE） are extracted. The recognition of facial expressions is created with the pleasure-displeasure and arousal-sleep dimensions in a two-dimensional model of emotion. The recognition result of various internal state expressions that mapped to the embedding space via the LLE algorithm can effectively represent the structural nature of the two-dimensional model of emotion. Therefore our research has established that the relationship between facial expressions of various internal states can be elaborated in the two-dimensional model of emotion, via the locally linear embedding algorithm.展开更多
In this paper,we are devoted to the asymptotic behavior for a nonlinear parabolic type equation of higher order with additive white noise.We focus on the Ginzburg-Landau population equation perturbed with additive noi...In this paper,we are devoted to the asymptotic behavior for a nonlinear parabolic type equation of higher order with additive white noise.We focus on the Ginzburg-Landau population equation perturbed with additive noise.Firstly,we show that the stochastic Ginzburg-Landau equation with additive noise can be recast as a random dynamical system.And then,it is proved that under some growth conditions on the nonlinear term,this stochastic equation has a compact random attractor,which has a finite Hausdorff dimension.展开更多
Existing groupwise dimension reduction requires given group structure to be non-overlapped. This confines its application scope. We aim at groupwise dimension reduction with overlapped group structure or even unknown ...Existing groupwise dimension reduction requires given group structure to be non-overlapped. This confines its application scope. We aim at groupwise dimension reduction with overlapped group structure or even unknown group structure. To this end, existing groupwise dimension reduction concept is extended to be compatible with overlapped group structure. Then, the envelope method is ameliorated to deal with overlapped groupwise dimension reduction. As an application, Gaussian graphic model is employed to estimate the structure between predictors when the group structure is not given, and the amended envelope method is used for groupwise dimension reduction with graphic structure. Furthermore, the rationale of the proposed estimation procedure is explained at the population level and the estimation consistency is proved at the sample level. Finally, the finite sample performance of the proposed methods is examined via numerical simulations and a body fat data analysis.展开更多
基金supported by National High Technology Research and Development Program of China (863 Program)(No. 2009AA04Z162)National Nature Science Foundation of China(No. 60825302, No. 60934007, No. 61074061)+1 种基金Program of Shanghai Subject Chief Scientist,"Shu Guang" project supported by Shang-hai Municipal Education Commission and Shanghai Education Development FoundationKey Project of Shanghai Science and Technology Commission, China (No. 10JC1403400)
文摘In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.
基金Supported by the National Natural Science Foundation of China(No.41271436)
文摘Numerical simulation modeling is a hotspot in the geological engineering computing field. Tak- ing a fast Langrangian analysis of continua in 3 dimensions （FLAC3D） numerical modeling on com- puting the geo-deformation information caused by the mining subsidence in a coalmine for example, a new GIS-Excel modeling method is proposed to build geologic strata within the simulation range combined with the coal-seam dip angle of the underground mining working-planes. First of all, the coal-seam model of the numerical computing is built by using the geographic information system （GIS） according to the stripe-through principle and the calculating formula on the size of the model blocks in the paper defined, then the FLAC3D numerical computing model of all geologic strata with- in the simulation range is also built based on the calculating formula of thickness of each stratum and the Excel fast computing advantages. The GIS-Excel method is good at the higher modeling accuracy, seldom making mistakes and consuming less time. The reliability and validity of the method is veri- fied well by its practical applications in the coalmine area.
基金supported by research funds from Chosun University,2008
文摘Emotions are becoming increasingly important in human-centered interaction architectures. Recognition of facial expressions, which are central to human-computer interactions, seems natural and desirable. However, facial expressions include mixed emotions, continuous rather than discrete, which vary from moment to moment. This paper represents a novel method of recognizing facial expressions of various internal states via manifold learning, to achieve the aim of humancentered interaction studies. A critical review of widely used emotion models is described, then, facial expression features of various internal states via the locally linear embedding （LLE） are extracted. The recognition of facial expressions is created with the pleasure-displeasure and arousal-sleep dimensions in a two-dimensional model of emotion. The recognition result of various internal state expressions that mapped to the embedding space via the LLE algorithm can effectively represent the structural nature of the two-dimensional model of emotion. Therefore our research has established that the relationship between facial expressions of various internal states can be elaborated in the two-dimensional model of emotion, via the locally linear embedding algorithm.
基金the grant of China Scholarship Council,National Natural Science Foundation of P.R.China(No.11101370,No.11302150,No.11211130093)the "521" talent program of Zhejiang Sci-Tech University(No.11430132521304)Zhejiang Provincial Natural Science Foundation(LY13A010014)
文摘In this paper,we are devoted to the asymptotic behavior for a nonlinear parabolic type equation of higher order with additive white noise.We focus on the Ginzburg-Landau population equation perturbed with additive noise.Firstly,we show that the stochastic Ginzburg-Landau equation with additive noise can be recast as a random dynamical system.And then,it is proved that under some growth conditions on the nonlinear term,this stochastic equation has a compact random attractor,which has a finite Hausdorff dimension.
基金supported by a grant from the University Grant Council of Hong Kong of ChinaNational Natural Science Foundation of China (Grant No. 11371013)Tian Yuan Foundation for Mathematics
文摘Existing groupwise dimension reduction requires given group structure to be non-overlapped. This confines its application scope. We aim at groupwise dimension reduction with overlapped group structure or even unknown group structure. To this end, existing groupwise dimension reduction concept is extended to be compatible with overlapped group structure. Then, the envelope method is ameliorated to deal with overlapped groupwise dimension reduction. As an application, Gaussian graphic model is employed to estimate the structure between predictors when the group structure is not given, and the amended envelope method is used for groupwise dimension reduction with graphic structure. Furthermore, the rationale of the proposed estimation procedure is explained at the population level and the estimation consistency is proved at the sample level. Finally, the finite sample performance of the proposed methods is examined via numerical simulations and a body fat data analysis.
