社交网络影响的不良信息扩散建模及最优控制策略

针对已有不良信息传播模型没有考虑不同社交网络间信息扩散情况,利用图论中的连通性原理,建立了多个社交网络间不良信息扩散的动力学模型,并且将优化控制理论应用到模型中。通过最优控制原理,证明了最优控制策略的存在性,进一步得到了不良信息扩散的优化控制模型。实验结果表明,引入优化控制措施可以有效抑制不良信息扩散规模,而且控制策略的强度可以根据需要进行动态调整。另外,通过模拟不同社交网络间是否有信息相互传递,发现社交网络间的信息传递会增大不良信息扩散的规模和速度。     

等几何分析中的复杂物理区域投影算子及误差分析

针对等几何分析中复杂物理区域上的偏微分方程求解问题,提出了多片参数域上双三次样条投影映射的方法.首先基于多片参数化,构造了复杂物理域上的一个投影映射;其次对于物理域上的光滑函数,讨论了该投影映射的逼近误差,理论分析表明该投影映射可达到最优逼近阶;最后基于投影映射的思想,给出了一类适用于基于等几何分析在复杂物理区域上二阶椭圆方程求解的样条空间.数值算例的结果表明,该方法求解的逼近误差阶可达到最优.     

一种用于电子邮件网络中的综合利用网络拓扑与传播参数的免疫方法设计*

电子邮件网络中的传播型攻击是非常严重的网络安全问题。研究界提出了很多种网络免疫方法来解决这个问题,基于节点介数(node betweenness,NB)的方法是目前最好的方法。综合利用电子邮件网络的网络拓扑与传播型攻击的传播参数设计了一种网络免疫方法。在生成的电子邮件网络拓扑模型以及Enron电子邮件网络真实拓扑数据的仿真表明,该方法比NB方法更有效。在某些仿真场景下,本免疫方法能够比NB方法达到50%的改进。     

Modeling and entropy based control of urban transportation network

This paper shows the similarity between transportation networks and thermodynamic systems. In particular, by regarding the vehicles as the energy stored in the system, it is demonstrated that transportation systems can have a similar notion of entropy. This transportation entropy is the measure of disorder and it is not only a suitable notion for evaluating the system performances, but also very useful for the control issue. With this in mind, by using dissipativity approach and choosing the entropy as storage function, a traffic signal control strategy is presented by means of Linear Matrix Inequality (LMI). This generation of dissipativity decreases the disorder and consequently renders the system better organized. Finally, a four-intersection system is studied in order to illustrate the performance of the results.     

Multimedia application to an extended public transportation network in South Korea: optimal path search in a multimodal transit network

Multimedia Tools and Applications - Various traffic network usage formats have arisen along with the popularization of automobiles and the continued service extension of busses, subways, and other...     

两种典型网络中三种病毒传播模型的传播特性研究

传染性疾病在人类社会的流行,计算机蠕虫病毒在Internet上的频频爆发,都给人类社会造成了巨大的损失。因此,病毒传播研究一直是国际上科学家所关注的焦点。近年来兴起的复杂网络研究为人类认识病毒传播特征、抑制和防御病毒传播提供了一条新的途径。本文研究了两种典型网络中三种病毒传播模型的传播特性。     

Analysis of time-varying control strategies for optimal disturbancerejection and robustness

Analysis of linear time-invariant systems that are controlled by time-varying controllers is given. The problems of disturbance rejection and robustness are formulated and analyzed using functional analytic methods, which reveal the properties that are common to these problems. The theoretical development presented uses the theory and techniques of nuclear operators and their relation with the duality theory of tensor products of Banach spaces. The authors show how time-varying compensation offers no advantage over time-invariant compensation for the problem of disturbance rejection over general signal spaces, in both continuous and discrete time, and for the problem of L_∞ robust stabilization of time-invariant plants. In addition, another application of the theory for the problem of norm minimization subject to a norm constraint is presented     

Active vibration control of smart piezoelectric beams: Comparison of classical and optimal feedback control strategies

This paper presents a numerical study concerning the active vibration control of smart piezoelectric beams. A comparison between the classical control strategies, constant gain and amplitude velocity feedback, and optimal control strategies, linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controller, is performed in order to investigate their effectiveness to suppress vibrations in beams with piezoelectric patches acting as sensors or actuators. A one-dimensional finite element of a three-layered smart beam with two piezoelectric surface layers and metallic core is utilized. A partial layerwise theory, with three discrete layers, and a fully coupled electro-mechanical theory is considered. The finite element model equations of motion and electric charge equilibrium are presented and recast into a state variable representation in terms of the physical modes of the beam. The analyzed case studies concern the vibration reduction of a cantilever aluminum beam with a collocated asymmetric piezoelectric sensor/actuator pair bonded on the surface. The transverse displacement time history, for an initial displacement field and white noise force disturbance, and point receptance at the free end are evaluated with the open- and closed-loop classical and optimal control systems. The case studies allow the comparison of their performances demonstrating some of their advantages and disadvantages.     

Modelling plant control strategies and their applications into a knowledge-based system

The high complexity of a plant control system related structuring the domain expert knowledge into a knowledge base could decrease task. This paper presents a strategy adopted to model the application of control strategies employed in surveyed companies. Control engineering plays an important part in any industrial plant. Good control and optimisation of correct control strategies is therefore very crucial for the effective running of a control task related establishment of any kind. The control strategy approaches, must be followed right from system identification, modelling, validation, test and implementation. These stages are not always transparent when dealing with a system whose mathematical model does not exist or is difficult to obtain. When faced with this kind of problem, control enhancement techniques, such as knowledge-based, and intelligent system are always an obvious alternative.     

Modelling of non-minimum phase effects in discrete-time norm optimal iterative learning control

The subject of this article is the modelling of the influence of non-minimum phase discrete-time system dynamics on the performance of norm optimal iterative learning control (NOILC) algorithms with the intent of explaining the observed phenomenon and predicting its primary characteristics. It is established that performance in the presence of one or more non-minimum phase plant zeros typically has two phases. These consist of an initial fast monotonic reduction of the L ₂ error norm (mean square error) followed by a very slow asymptotic convergence. Although the norm of the tracking error does eventually converge to zero, the practical implications over a finite number of trials is apparent convergence to a non-zero error. The source of this slow convergence is identified using the singular value distribution of the system's all pass component. A predictive model of the onset of slow convergence behaviour is developed as a set of linear constraints and shown to be valid when the iteration time interval is sufficiently long. The results provide a good prediction of the magnitude of error norm where slow convergence begins. Formulae for this norm and associated error time series are obtained for single-input single-output systems with several non-minimum phase zeros outside the unit circle using Lagrangian techniques. Numerical simulations are given to confirm the validity of the analysis.     

Modelling and multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.