私有云平台的虚拟机内存调度策略

在私有云平台中,现有的方法无法灵活地对虚拟机内存资源进行有效的监控和分配。针对以上问题,提出了内存实时监测和动态调度(MMS)模型,利用libvirt函数库和Xen提供的libxc函数库实现了对虚拟机内存紧缺、内存空闲时的实时监测和动态调度,并且提出虚拟机迁移策略,有效地缓解宿主机的内存紧缺问题。最后选取一台物理机作为主控节点,两台物理机作为子节点,利用Eucalyptus搭建一个小型的私有云平台。结果显示,当宿主机处于内存紧缺状态时,MMS系统通过启动虚拟机迁移策略有效地释放了内存空间;当虚拟机占用内存逼近初始最大内存时,MMS为其分配新的最大内存;当占用内容降低时,MMS系统对部分空闲的内存资源进行了回收,而且释放内存不超过150MB(最大内存512MB)时,其对虚拟机性能的影响不大。结果表明该模型对私有云平台中虚拟机内存进行实时监测和动态调度是有效的。     

Free-energy principle inspired visual quality assessment: An overview

The free energy principle was proposed several years ago as a unified justification for some brain theories. The process of human perception, cognition, action, and learning can be well explained using the free energy principle. The free energy principle suggests that the human perception and understanding of a given scene can be modeled as an active inference process, and the human brain tries to explain the scene using an internal generative model. The discrepancy between the given image or view and its best internal generative model explainable part is upper bounded by the free energy of the inference process. It was then conjectured that perceptual quality of the input image is closely related to free energy value of the process. Following this framework, dozens of visual quality assessment techniques have been proposed in the last few years and many have achieved state of the art performance. In this paper, we first give an overview of the free energy principle and then review the free energy principle inspired visual quality assessment metrics with a comparison in terms of algorithm design and performance.     

New results in realization theory for linear time-varying analytic systems

The realization of linear time-varying systems specified by an analytic weighting pattern is approached in a novel manner using an algebraic framework defined over the ring of analytic functions. Realizations are given by a state representation consisting of a first-order vector differential equation and an output equation, both with analytic coefficients. Various new criteria for realizability are derived, including conditions given in terms of the finiteness of modules over the ring of analytic functions generated by the elementary rows or columns of a (generalized) Hankel matrix. These results are related to local criteria for realizability specified in terms of the rank of matrix functions, as developed in the work of Silverman and Meadows [5], [8], [9] and Kalman [7]. It is shown that the construction of minimal realizations reduces to the problem of computing a basis for a finite free module defined over the ring of analytic functions. A minimal realization algorithm is then derived using a constructive procedure for computing bases for finite free modules over a Bezout domain. The Silverman-Meadows realization algorithm [5] is a special case of the procedure given here. In the last part of the paper, the realization algorithm is applied to the problem of system reduction.     

Min–max Kalman filtering

The problem of H_∞-optimal state estimation of linear continuous-time systems that are measured with an additive white noise is addressed. The relevant cost function is the expected value of the standard H_∞ performance index, with respect to the measurement noise statistics. The solution is obtained by applying the matrix version of the maximum principle to the solution of the min–max problem in which the estimator tries to minimize the mean square estimation error and the exogenous disturbance tries to maximize it while being penalized for its energy. The solution is given in terms of two coupled Riccati difference equations from which the filter gains are derived. In the case where an infinite penalty is imposed on the energy of the exogenous disturbance, the celebrated Kalman filter is recovered. In the stationary case, where all the signals are stationary, an upper-bound on the solutions of the coupled Riccati equations is obtained via a solution of coupled linear matrix inequalities. The resulting filter then guarantees a bound on the estimation error covariance matrix. An illustrative example is given where the velocity of a maneuvering target has to be estimated utilizing noisy measurements of the position.     

State-constrained optimal control problems governed by coupled nonlinear wave equations with memory

This work considers the optimal control problems governed by coupled nonlinear wave equations with memory, where the state constraint is a type of integral. First, we investigate the existence of the optimal solution for the optimal control problem, and then we deduce the maximum principle for the optimal solution with state constraint. Moreover, we give a concrete example which is covered by the main result.     

Design of minimal order stable observers for linear functions of the state via realization theory

Abstract--The design of a minimal order stable observer and a minimal order observer with arbitrary poles that estimates a vector linear function of the state of a multivariable system is discussed. It is shown that both problems can be solved in a straightforward manner using partial realization theory, and several new results are given. These include a strong bound for the dimension of the minimal order stable observer and a simple necessary condition to design the minimal order observer with arbitrary poles that estimates a vector linear function of the state of a multiple-output system. Necessary and sufficient conditions are given for designing a minimal order observer with arbitrary poles for the case of estimating a vector linear function of the state of a single-output system and the case of estimating a scalar linear function of the state of a multiple-output system. A procedure to carry out the design in each of these cases is described. No restrictions whatsoever (except stability) are placed on the possible values of the observer poles. A significant observation of this paper is that the dynamics of the observer are constrained (in all cases) only by the gain matrix in the feedback law to be estimated and the output structure of the given system.     

A novel energy demand modelling approach for CNC machining based on function blocks

Energy efficiency remains one of the major issues in the machining domain. Today's machining systems are confronted with a number of new challenges, such as turbulent product demand and variations in production resources. Rapid and flexible energy modelling in a distributed and collaborative machining environment emerges as a new research area. Energy demand models in such an environment need to be practical, accurate, effective, scalable and reusable. Energy analysis and optimisation cannot be carried out once for all at the beginning. Instead, it is an on-going process. In this paper, the function block technique, i.e. IEC 61499, is used for the development of energy demand models as it brings advantages such as modularity, encapsulation, extensibility and reusability. A brief review on energy modelling and research on function blocks are given in the first part. A novel energy demand modelling approach based on function blocks is then proposed and elaborated. Three types of function blocks have been developed, i.e. machine tool dependent function blocks, state transition function blocks, and service interface function blocks. The first type, as the fundamental building blocks, is divided into two sub-types, machine component function block and machining state function block. Two case studies, based on a small 3-axis milling machine and an industrial production line respectively, are presented to demonstrate the possible applications using the function block-based model. Comprehensive discussions are given thereafter, including a pilot application of a distributed process planning system and a unique energy evaluation scheme. A confidence level associated energy rating system is proposed as the first step to turn energy consumption figures into useful indicators. The energy demand model based on function blocks developed here enhances the energy modelling and their practical implementations.     

基于能量的受迫哈密顿系统的镇定及在电力系统中的应用

有两个目的,第一目的是考虑电力系统的励磁镇定。将该系统表示为具有耗散的受迫哈密顿系统的形式,随后基于能量构造了李雅普诺夫函数来考察受迫系统的稳定性。第二个目的是提出广义哈密顿系统的模型,广义哈密顿系统具有外部能量供给、内部能量供给及能量消耗,状态空间被描述成具有二次张量场的流形,得到了广义哈密顿系统与坐标无关的全局模型。     

Stochastic minimax optimal control strategy for uncertain quasi-Hamiltonian systems using stochastic maximum principle

A stochastic minimax optimal control strategy for uncertain quasi-Hamiltonian systems is proposed based on the stochastic averaging method, stochastic maximum principle and stochastic differential game theory. First, the partially completed averaged Itô stochastic differential equations are derived from a given system by using the stochastic averaging method for quasi-Hamiltonian systems with uncertain parameters. Then, the stochastic Hamiltonian system for minimax optimal control with a given performance index is established based on the stochastic maximum principle. The worst disturbances are determined by minimizing the Hamiltonian function, and the worst-case optimal controls are obtained by maximizing the minimal Hamiltonian function. The differential equation for adjoint process as a function of system energy is derived from the adjoint equation by using the Itô differential rule. Finally, two examples of controlled uncertain quasi-Hamiltonian systems are worked out to illustrate the application and effectiveness of the proposed control strategy.     

非线性非齐次Markov跳变系统的贝叶斯滤波

针对具有时变不确定转移概率的非线性非齐次Markov跳变系统, 提出一种贝叶斯状态估计方法.该方法首次采用带约束高斯概率密度函数来刻画转移概率的真实特性. 然后,基于参考概率空间法, 将实际的概率测度投影到理想概率空间, 得出信息变量的递归表达式. 同时, 在贝叶斯框架内给出转移概率矩阵的最大后验估计式. 进一步, 采用粒子逼近法求解转移概率矩阵的最大后验估计, 解决非线性函数的多重积分问题, 进而获取状态估计值. 最后, 通过一个仿真示例表明该方法的有效性.     

逆向工程的关键技术及其应用研究

全面论述了逆向工程的应用领域;讨论了坐标点的测量技术,说明了各种测量方法的特点和适用范围;阐述了逆向工程中曲面重建技术及存在的主要问题,提出了一种用Ｂ样条曲面重构复杂雕塑曲面的算法。该算法首先利用部分测量数据构造张量积的Ｂ样条网格曲面,通过确定适当的检查点,并计算检查点到所构造的Ｂ样条网格曲面间的距离,从而控制重构曲面的构造精度;文章最后给出了与通用ＣＡＤ／ＣＡＭ集成一体的曲面自动重建模块的主要功能。     

Molecular docking with multi-objective Particle Swarm Optimization

The molecular docking problem is to find a good position and orientation for docking a small molecule (ligand) to a larger receptor molecule. In the first part of this paper we propose a new algorithm for solving the docking problem. This algorithm – called ClustMPSO – is based on Particle Swarm Optimization (PSO) and follows a multi-objective approach for comparing the quality of solutions. For the energy evaluation the algorithm uses the binding free energy function that is provided by the Autodock 3.05 tool. The experimental results show that ClustMPSO computes a more diverse set of possible docking conformations than the standard Simulated Annealing and Lamarckian Genetic Algorithm that are incorporated into Autodock. Moreover, ClustMPSO is significantly faster and more reliable in finding good solutions. In the second part of this paper a new approach for the prediction of a docking trajectory is proposed. In this approach the ligand is “un-docked” via a controlled random walk that can be biased into a given direction and where only positions are accepted that have an energy level that is below a given threshold.     

Artificial memory optimization

To solve some complicated optimization problems, an artificial memory optimization (AMO) is constructed based on the human memory mechanism. In AMO, a memory cell is used to trace an alternative solution of a problem to be solved; memorizing and forgetting rules of the human memory mechanism are used to control state transition of each memory cell; the state of a memory cell consists of two components, one is the solution state which associates with an alternative solution being traced; another is the memory state which associates with the memory information resulting from tracing results, where the memory residual value (MRV) is stored; the states of memory cells are divided into three types: instantaneous, short- and long-term memory state, each of which can be strengthened or weakened by accepted stimulus strength. If the solution state of a memory cell has transferred to a good position, its MRV will increase, and then the memory cell is not easily to be forgotten; when the solution state of a memory cell is at sticky state, its MRV will decrease until the memory cell is forgotten; this will effectively prevent invalid iteration. In the course of evolution, a memory cell may strive to evolve from the instantaneous, short-term memory state to long-term memory state, it makes search to be various. Because AMO has 6 operators at the curent version, it has wider adaptability to solve different types of optimization problems. Besides, these operators are automatically dispatched according to their executing efficiency. Results show that AMO possesses of strong search capability and high convergence speed when solving some complicated function optimization problems.     

An energy function-based design method for discrete hopfield associative memory with attractive fixed points

An energy function-based autoassociative memory design method to store a given set of unipolar binary memory vectors as attractive fixed points of an asynchronous discrete Hopfield network (DHN) is presented. The discrete quadratic energy function whose local minima correspond to the attractive fixed points of the network is constructed via solving a system of linear inequalities derived from the strict local minimality conditions. The weights and the thresholds are then calculated using this energy function. If the inequality system is infeasible, we conclude that no such asynchronous DHN exists, and extend the method to design a discrete piecewise quadratic energy function, which can be minimized by a generalized version of the conventional DHN, also proposed herein. In spite of its computational complexity, computer simulations indicate that the original method performs better than the conventional design methods in the sense that the memory can store, and provide the attractiveness for almost all memory sets whose cardinality is less than or equal to the dimension of its elements. The overall method, together with its extension, guarantees the storage of an arbitrary collection of memory vectors, which are mutually at least two Hamming distances away from each other, in the resulting network.     

延迟离散Hopfield型神经网络异步收敛性

离散Ｈｏｐｆｉｅｌｄ型神经网络的一个重要性质是异步运动方式下总能收敛到稳定态。同步运行方式下总能收敛到周期不超过２的极限环,它是该模型可以用于联想记忆设计,组合设计计算的理论基础,文中给出了延迟离散Ｈｏｐｆｉｅｌｄ型网络的收敛性定理,在异步运动方式下,证明了对称连接权阵的收敛性定理,推广了已有的离散Ｈｏｐｆｉｅｌｄ型网络的收敛性结果,给出了能量函数极大值点与延迟离散Ｈｏｐｆｉｅｌｄ型网络的稳定态的     

Guaranteed Cost Consensus for High-dimensional Multi-agent Systems With Time-varying Delays

Guaranteed cost consensus analysis and design problems for high-dimensional multi-agent systems with timevarying delays are investigated. The idea of guaranteed cost control is introduced into consensus problems for high-dimensional multi-agent systems with time-varying delays, where a cost function is defined based on state errors among neighboring agents and control inputs of all the agents. By the state space decomposition approach and the linear matrix inequality (LMI), sufficient conditions for guaranteed cost consensus and consensualization are given. Moreover, a guaranteed cost upper bound of the cost function is determined. It should be mentioned that these LMI criteria are dependent on the change rate of time delays and the maximum time delay, the guaranteed cost upper bound is only dependent on the maximum time delay but independent of the Laplacian matrix. Finally, numerical simulations are given to demonstrate theoretical results.      

Entropic thermoelasticity at finite strains. Aspects of the formulation and numerical implementation

This paper presents a formulation of isotropic finite strain thermoelasticity and addresses some aspects of its numerical implementation. On the theoretical side, a Eulerian setting of isotropic thermoelasticity is discussed, based on the Finger tensor as a strain measure. Novel aspects are a direct representation of the Eulerian thermoelastic moduli in terms of the Finger tensor and a rigorous decomposition of the thermoelastic response functions into decoupled volumetric and isochoric contributions based on a multiplicative split of the Finger tensor into spherical and unimodular parts. An algorithmic procedure for the computation of the stress and thermoelastic moduli, based on a representation of the free energy in terms of eigenvalues of the unimodular part of the Finger tensor, is developed and applied to model problems of strictly entropic and modified entropic thermoelasticity. Furthermore, two algorithms for the solution of the coupled problem are discussed, based on operator splits of the global field equations of thermoelasticity. The paper concludes with some representative numerical simulations of thermoelastic processes in rubber-like materials.     

不确定拟哈密顿系统的随机最优控制

本文提出了不确定拟哈密顿系统、基于随机平均法、随机极大值原理和随机微分对策理论的一种随机极大极小最优控制策略.首先,运用拟哈密顿系统的随机平均法,将系统状态从速度和位移的快变量形式转化为能量的慢变量形式,得到部分平均的It随机微分方程;其次,给定控制性能指标,对于不确定拟哈密顿系统的随机最优控制,根据随机微分对策理论,将其转化为一个极小极大控制问题;再根据随机极大值原理,建立关于系统与伴随过程的前向-后向随机微分方程,随机最优控制表达为哈密顿控制函数的极大极小条件,由此得到最坏情形下的扰动参数与极大极小最优控制;然后,将最坏扰动参数与最优控制代入部分平均的It随机微分方程并完成平均,求解与完全平均的It随机微分方程相应的Fokker-Planck-Kolmogorov(FPK)方程,可得受控系统的响应量并计算控制效果;最后,将上述不确定拟哈密顿系统的随机最优控制策略应用于一个两自由度非线性系统,通过数值结果说明该随机极大极小控制策略的控制效果.     

An algorithm for solving fuzzy relation equations with max-T composition operator

This paper studies the problem of solving a max-T composite finite fuzzy relation equation, where T is a special class of pseudo-t-norms. If the equation is solvable, then its set of feasible solutions is determined by the greatest solution and a finite number of minimal solutions. Some necessary conditions are presented for the minimal solutions in terms of the maximum solution and zero value. Under these conditions, some minimal solutions of the system can be obtained easily. Some procedures are also proposed in order to simplify the original system. The simplified system is then decomposed (if possible) into several subsystems with smaller dimensions, which are very easy to solve. Furthermore, a method is presented to solve each subsystem. By combining the method and those procedures, an efficient algorithm is proposed to obtain the set of feasible solutions of the original system. Two examples are also given to illustrate the algorithm.     

关于二元延迟3步前馈逆有限自动机的结构

前馈逆有限自动机的结构是有限自动机可逆性理论中的基本问题.对延迟步数≥3的前馈逆结构的刻划,则是一个长期的未解决问题.研究了二元延迟3步前馈逆有限自动机的结构.对于自治有限自动机M_a的状态图为圈的二元延迟3步弱可逆半输入存储有限自动机C(M_af ),给出了其长3极小输出权分别为1,2,8三种情形下结构的一种刻画.由于C(M_af )延迟3步弱可逆当且仅当它是延迟3步弱逆,因此,得到了二元延迟3步前馈逆有限自动机结构的一种部分刻画.