According to the theory of alternating magnetohydrodynamics and magnetic boundary renewal method,mathematical models were proposed for electromagnetic stirring in secondary cooling region( SEMS) of slab caster. The ma...According to the theory of alternating magnetohydrodynamics and magnetic boundary renewal method,mathematical models were proposed for electromagnetic stirring in secondary cooling region( SEMS) of slab caster. The magnetic fields and flow fields of melt were simulated with SEMS. It's shown that the electromagnetic forces with inward and sidelong components produced by travel magnetic field at the wide faces of slab make the melt whirling in horizontal section,and the convection of the melt is strengthened obviously there. In addition,magnetic flux density attenuates from the edge to the center of slab,and the profile of the melt velocity along slab thickness in the center of the horizontal section takes a two-opposite-peak configuration. Ultimately,the stirring intensity and features are determined by the electromagnetic parameters,coil arrangement and stirring types.展开更多
This paper proposes a new method for model predictive control （MPC） of nonlinear systems to calculate stability region and feasible initial control profile/sequence, which are important to the implementations of MPC...This paper proposes a new method for model predictive control （MPC） of nonlinear systems to calculate stability region and feasible initial control profile/sequence, which are important to the implementations of MPC. Different from many existing methods, this paper distinguishes stability region from conservative terminal region. With global linearization, linear differential inclusion （LDI） and linear matrix inequality （LMI） techniques, a nonlinear system is transformed into a convex set of linear systems, and then the vertices of the set are used off-line to design the controller, to estimate stability region, and also to determine a feasible initial control profile/sequence. The advantages of the proposed method are demonstrated by simulation study.展开更多
According to the hierarchical characteristics of monthly rainfall in different regions, the paper takes the geographical factors and seasonal factors into the hierarchical linear model as the level effect. Through clu...According to the hierarchical characteristics of monthly rainfall in different regions, the paper takes the geographical factors and seasonal factors into the hierarchical linear model as the level effect. Through clustering methods we select two more representative regional meteorological data. We establish three-layer model by transforming the interactive structure date into nested structure data. According the model theory we perform the corresponding model calculations, optimization and analysis, accordingly to interpret the level effects, and residual test. The results show that most of the difference in Monthly Rainfall was respectively explained by Variables (Meteorological factors, seasonal effects, geographic effects) in different levels.展开更多
In this paper we prove that a class of trust region methods presented in part I is superlinearly convergent. Numerical tests are reported thereafter. Results by solving a set of typical problems selected from literatu...In this paper we prove that a class of trust region methods presented in part I is superlinearly convergent. Numerical tests are reported thereafter. Results by solving a set of typical problems selected from literatures have demonstrated that our algorithm is effective.展开更多
A class of trust region methods for solving linear inequality constrained problems is proposed in this paper. It is shown that the algorithm is of global convergence.The algorithm uses a version of the two-sided proje...A class of trust region methods for solving linear inequality constrained problems is proposed in this paper. It is shown that the algorithm is of global convergence.The algorithm uses a version of the two-sided projection and the strategy of the unconstrained trust region methods. It keeps the good convergence properties of the unconstrained case and has the merits of the projection method. In some sense, our algorithm can be regarded as an extension and improvement of the projected type algorithm.展开更多
The aim of this brief paper is to give several results concerning the regional controllability of distributed systems governed by semi-linear parabolic equations. We concentrate on the determination of a control achie...The aim of this brief paper is to give several results concerning the regional controllability of distributed systems governed by semi-linear parabolic equations. We concentrate on the determination of a control achieving internal and boundary regional controllability. The approach is based on an extension of the Hilbert Uniqueness Method (HUM) and Schauder’s fixed point theorem. We give a numerical example developed in internal and boundary sub region. These numerical illustrations show the efficiency of the approach and lead to conjectures.展开更多
由于不同的照明条件、复杂的大气环境等因素,相同端元的光谱特征在图像的不同位置呈现出可见的差异,这种现象被称为端元的光谱变异性。在相当大的场景中,端元的变异性可能很大,但在适度的局部同质区内,变异性往往很小。扰动线性混合模型...由于不同的照明条件、复杂的大气环境等因素,相同端元的光谱特征在图像的不同位置呈现出可见的差异,这种现象被称为端元的光谱变异性。在相当大的场景中,端元的变异性可能很大,但在适度的局部同质区内,变异性往往很小。扰动线性混合模型(Perturbed Linear Mixing Model,PLMM)在解混的过程中可以减轻端元变异性造成的不利影响,但是对缩放效应造成的变异性的处理能力较弱。为此,本文改进了扰动线性混合模型,引入了尺度因子以处理缩放效应造成的变异性,并结合超像素分割算法划分局部同质区,然后设计出基于局部同质区共享端元变异性的解混算法(Shared Endmember Variability in Unmixing,SEVU)。与扰动线性混合模型,扩展线性混合模型(Extended Linear Mixing Model,ELMM)等算法相比,所提SEVU算法在合成数据集上平均端元光谱角距离(mean Spectral Angle Distance,mSAD)和丰度均方根误差(abundance Root Mean Square Error,aRMSE)最优,分别为0.0855和0.0562;在Jasper Ridge和Cuprite真实数据集上mSAD是最优的,分别为0.0603和0.1003。在合成数据集和两个实测数据集上的实验结果验证了SEVU算法的有效性。展开更多
基金Item Sponsored by National Key Fundamental Research Development Project of China(G1998061510)National High Technology Research and Development Project of China(2001AA337040)
文摘According to the theory of alternating magnetohydrodynamics and magnetic boundary renewal method,mathematical models were proposed for electromagnetic stirring in secondary cooling region( SEMS) of slab caster. The magnetic fields and flow fields of melt were simulated with SEMS. It's shown that the electromagnetic forces with inward and sidelong components produced by travel magnetic field at the wide faces of slab make the melt whirling in horizontal section,and the convection of the melt is strengthened obviously there. In addition,magnetic flux density attenuates from the edge to the center of slab,and the profile of the melt velocity along slab thickness in the center of the horizontal section takes a two-opposite-peak configuration. Ultimately,the stirring intensity and features are determined by the electromagnetic parameters,coil arrangement and stirring types.
基金This work was supported by an Overseas Research Students Award to Xiao-Bing Hu.
文摘This paper proposes a new method for model predictive control （MPC） of nonlinear systems to calculate stability region and feasible initial control profile/sequence, which are important to the implementations of MPC. Different from many existing methods, this paper distinguishes stability region from conservative terminal region. With global linearization, linear differential inclusion （LDI） and linear matrix inequality （LMI） techniques, a nonlinear system is transformed into a convex set of linear systems, and then the vertices of the set are used off-line to design the controller, to estimate stability region, and also to determine a feasible initial control profile/sequence. The advantages of the proposed method are demonstrated by simulation study.
文摘According to the hierarchical characteristics of monthly rainfall in different regions, the paper takes the geographical factors and seasonal factors into the hierarchical linear model as the level effect. Through clustering methods we select two more representative regional meteorological data. We establish three-layer model by transforming the interactive structure date into nested structure data. According the model theory we perform the corresponding model calculations, optimization and analysis, accordingly to interpret the level effects, and residual test. The results show that most of the difference in Monthly Rainfall was respectively explained by Variables (Meteorological factors, seasonal effects, geographic effects) in different levels.
文摘In this paper we prove that a class of trust region methods presented in part I is superlinearly convergent. Numerical tests are reported thereafter. Results by solving a set of typical problems selected from literatures have demonstrated that our algorithm is effective.
文摘A class of trust region methods for solving linear inequality constrained problems is proposed in this paper. It is shown that the algorithm is of global convergence.The algorithm uses a version of the two-sided projection and the strategy of the unconstrained trust region methods. It keeps the good convergence properties of the unconstrained case and has the merits of the projection method. In some sense, our algorithm can be regarded as an extension and improvement of the projected type algorithm.
文摘The aim of this brief paper is to give several results concerning the regional controllability of distributed systems governed by semi-linear parabolic equations. We concentrate on the determination of a control achieving internal and boundary regional controllability. The approach is based on an extension of the Hilbert Uniqueness Method (HUM) and Schauder’s fixed point theorem. We give a numerical example developed in internal and boundary sub region. These numerical illustrations show the efficiency of the approach and lead to conjectures.
文摘由于不同的照明条件、复杂的大气环境等因素,相同端元的光谱特征在图像的不同位置呈现出可见的差异,这种现象被称为端元的光谱变异性。在相当大的场景中,端元的变异性可能很大,但在适度的局部同质区内,变异性往往很小。扰动线性混合模型(Perturbed Linear Mixing Model,PLMM)在解混的过程中可以减轻端元变异性造成的不利影响,但是对缩放效应造成的变异性的处理能力较弱。为此,本文改进了扰动线性混合模型,引入了尺度因子以处理缩放效应造成的变异性,并结合超像素分割算法划分局部同质区,然后设计出基于局部同质区共享端元变异性的解混算法(Shared Endmember Variability in Unmixing,SEVU)。与扰动线性混合模型,扩展线性混合模型(Extended Linear Mixing Model,ELMM)等算法相比,所提SEVU算法在合成数据集上平均端元光谱角距离(mean Spectral Angle Distance,mSAD)和丰度均方根误差(abundance Root Mean Square Error,aRMSE)最优,分别为0.0855和0.0562;在Jasper Ridge和Cuprite真实数据集上mSAD是最优的,分别为0.0603和0.1003。在合成数据集和两个实测数据集上的实验结果验证了SEVU算法的有效性。
