Model order reduction of MIMO bilinear systems by multi-order Arnoldi method

In this paper, we present a time domain model order reduction method for multi-input multi-output (MIMO) bilinear systems by general orthogonal polynomials. The proposed method is based on a multi-order Arnoldi algorithm applied to construct the projection matrix. The resulting reduced model can match a desired number of expansion coefficient terms of the original system. The approximate error estimate of the reduced model is given. And we also briefly discuss the stability preservation of the reduced model in some cases. Additionally, in combination with Krylov subspace methods, we propose a two-sided projection method to generate reduced models which capture properties of the original system in the time and frequency domain simultaneously. The effectiveness of the proposed methods is demonstrated by two numerical examples.     

云计算下基于属性的访问控制方法

云计算作为一种新型的服务模式,近年来受到各个企业的青睐.虽有云有三种部署模式,企业可以自己选择部署方式,但是云中数据存储和传输安全还是企业最为担心的.因此,运用了一种基于属性的控制策略方法(Attribute Based Access Control, ABAC)来保证只有授权用户才有权限来操作云中的数据.另外运用粗糙集属性约简的方法来减除冗余属性保留权威属性减少策略冲突问题的出现,加快访问时间.     

基于能量反馈协调控制的电动汽车底盘减振方法

针对电动汽车底盘振动控制及其能量反馈的设计要求,建立其1/4馈能式主动悬架模型,从能量角度使用基于线性二次最优调节器(Linear Quadratic Regulator, LQR)的控制方法,使悬架在控制力作动下达到车身振动能量尽量小并获得一定的振动反馈能量.在理论分析基础上,使用与Simulink建立联合仿真系统,分析LQR控制器协调底盘在复杂工况下的振动控制及其能量反馈的综合效果.分析表明,所用LQR协调控制方法能有效协调减振及其能量反馈.     

改进的变精度粗糙集在概念格构造中的应用研究

根据可变精度粗糙集的R-上、下分布约简算法的优势,结合概念格形式背景的特点,将二者有机地结合,提出了基于变精度粗糙集的概念格约减算法。分析了变精度粗糙集模型中的R值的选取算法、可辨识矩阵属性约简,以及传统算法中存在的问题,并进行了改进。最后,为了验证改进后算法的有效性,设计了基于变精度粗糙集的概念格生成系统,通过一个实例演示了构造概念格的整个过程,并通过实验证明了算法的有效性。     

绿色计算背景下的网络管理研究

随着IT技术的不断发展,IT行业在整个节能减排环节中占据的地位越来越高,由此提出了绿色计算、绿色网络等概念。从绿色网络的概念入手,分析网络中主要设备的节能方式和可能性,提出了在网络管理中增加能耗管理的功能,研究并提出了实现能耗管理应具备的MIB信息,并设计了能耗监视、控制和统计的实施方案,从而可以对网络中的能耗进行定量监视和控制,为绿色节能网络的建立提供全面的监控和管理。     

新的文本分类特征选择方法研究

特征降维是文本分类过程中的一个重要环节。在现有特征选择方法的基础上,综合考虑特征词在正类和负类中的分布性质,综合四种衡量特征类别区分能力的指标,提出了一个新的特征选择方法,即综合比率(CR)方法。实验采用K-最近邻分类算法(KNN)来考查CR方法的有效性,实验结果表明该方法能够取得比现有特征选择方法更优的降维效果。     

Identification and data-driven model reduction of state-space representations of lossless and dissipative systems from noise-free data

We illustrate procedures to identify a state-space representation of a lossless or dissipative system from a given noise-free trajectory; important special cases are passive systems and bounded-real systems. Computing a rank-revealing factorization of a Gramian-like matrix constructed from the data, a state sequence can be obtained; the state-space equations are then computed by solving a system of linear equations. This idea is also applied to perform model reduction by obtaining a balanced realization directly from data and truncating it to obtain a reduced-order model.     

Efficient parallel implementation of Ewald summation in molecular dynamics simulations on multi-core platforms

We present a multi-step computation method to implement the Ewald summation for long-range electrostatic interactions in molecular dynamic simulations on a multi-core machine. Our methodology is based on the OpenMP programming model. It partitions computations of real-space summation among threads so that the global force of a single particle cannot be modified by more than one thread simultaneously. It requires neither a private copy of the force array for each thread nor an inspector at runtime. Compared with some other methods that can parallelise reduction operations on a force-array, our method achieves relatively higher speedups and lower L2 cache miss and bus utilisation ratios.     

A time-efficient pattern reduction algorithm for k-means clustering

This paper presents an efficient algorithm, called pattern reduction (PR), for reducing the computation time of k-means and k-means-based clustering algorithms. The proposed algorithm works by compressing and removing at each iteration patterns that are unlikely to change their membership thereafter. Not only is the proposed algorithm simple and easy to implement, but it can also be applied to many other iterative clustering algorithms such as kernel-based and population-based clustering algorithms. Our experiments—from 2 to 1000 dimensions and 150 to 10,000,000 patterns—indicate that with a small loss of quality, the proposed algorithm can significantly reduce the computation time of all state-of-the-art clustering algorithms evaluated in this paper, especially for large and high-dimensional data sets.     

A survey of multilinear subspace learning for tensor data

Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract useful information from these massive data. This paper surveys the field of multilinear subspace learning (MSL) for dimensionality reduction of multidimensional data directly from their tensorial representations. It discusses the central issues of MSL, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, examining the algorithmic aspects of typical MSL solutions, and categorizing both unsupervised and supervised MSL algorithms into taxonomies. Lastly, the paper summarizes a wide range of MSL applications and concludes with perspectives on future research directions.