Controlling electroosmotic flow by polymer coating: a dissipative particle dynamics study

We have performed dissipative particle dynamics (DPDs) simulations of electroosmotic flow (EOF) through a polymer-grafted nanopore. In this model, charged particles including salt ions and counterions are not included explicitly, and EOF is created using an effective boundary condition. The screening effect of polymer layer on EOF is investigated in detail under different solvent qualities and boundary electroosmotic velocities. Results show that the solvent quality has a significant effect on the conformational properties of polymer chains and the flow characteristics of the solvent. The polymer layer undergoes a collapsed transition when decreasing the solvent quality from good to poor. Under different solvent qualities, enhancing the EOF leads to a different variation tendency of the layer thickness. The solvent-induced permeability change is inconsistent with the steady velocity away from the surface. The minimum value of the solvent permeability occurs at an intermediate solvent quality. However, the layer thickness drops gradually to a smallest value (corresponding to the largest effective pore radius) in the poor solvent condition. It is also found that the polymer inclination and stretching length exhibit a complex behavior under the combined effect of solvent quality and electroosmosis-induced shear.     

Fast structural optimization with frequency constraints by genetic algorithm using adaptive eigenvalue reanalysis methods

Structural optimization with frequency constraints is highly nonlinear dynamic optimization problems. Genetic algorithm (GA) has greater advantage in global optimization for nonlinear problem than optimality criteria and mathematical programming methods, but it needs more computational time and numerous eigenvalue reanalysis. To speed up the design process, an adaptive eigenvalue reanalysis method for GA-based structural optimization is presented. This reanalysis technique is derived primarily on the Kirsch’s combined approximations method, which is also highly accurate for case of repeated eigenvalues problem. The required number of basis vectors at every generation is adaptively determined and the rules for selecting initial number of basis vectors are given. Numerical examples of truss design are presented to validate the reanalysis-based frequency optimization. The results demonstrate that the adaptive eigenvalue reanalysis affects very slightly the accuracy of the optimal solutions and significantly reduces the computational time involved in the design process of large-scale structures.     

基于NiosⅡ和HDMI的高清雷达显示系统

针对高分辨率雷达图像显示的应用,利用Altera公司提供的片上系统开发工具设计了一个基于Nios Ⅱ软核处理器和高清多媒体接口(HDMI)芯片的图像显示系统;可将并行输入的灰度图像信号转换成CEA-861-D建议规定的多种视频格式,在HDMI(DVI)显示器上滚动输出图像,分辨率最高可达1920x1080p@60Hz;通过软硬件协同设计的方法提高系统的灵活性和可扩展性。     

3G通讯模块设备驱动设计与实现

随着国家3G牌照的发放,支持3G移动通讯的设备开发已经成为通讯产业和嵌入式系统发展的热点.嵌入式Linux因其源代码开放、可定制等特点而在嵌入式系统开发中得到广泛应用,而基于3G通讯模块的设备驱动是开发3G终端设备和服务的基础.文中讨论了嵌入式Linux环境下的设备文件管理、USB系统结构,详细分析了USB驱动骨架,并结合arm系列S3C2440处理器和支持TD-SCDMA的通讯模块LC6311,实现嵌入式Linux系统下的3G通讯模块设备驱动开发,并给出了测试验证实例.     

Fault tolerant control for singular systems with actuator saturation and nonlinear perturbation

In this paper, the problem of robust fault tolerant control for a class of singular systems subject to both time-varying state-dependent nonlinear perturbation and actuator saturation is investigated. A sufficient condition for the existence of a fixed-gain controller is first proposed which guarantees the regularity, impulse-free and stability of the closed-loop system under all possible faults. An optimization problem with LMI constraints is formulated to determine the largest contractively invariant ellipsoid. An adaptive fault tolerant controller is then developed to compensate for the failure effects on the system by estimating the fault and updating the design parameter matrices online. Both of these two controllers are in the form of a saturation avoidance feedback with the advantage of relatively small actuator capacities compared with the high gain counterpart. An example is included to illustrate the proposed procedures and their effectiveness.     

Dependency mining-based causal message logging

In fault-tolerant computing, the approach of causal message logging provides on-demand stable logging and enables the independent recovery of nodes. It imposes the requirement that the dependency between non-deterministic events needs to be known for nodes. The dependency information is disseminated through messages. A central problem in traditional causal message logging is that if the message logging progress of nodes cannot be effectively tracked, some redundant dependency information will be piggybacked on the messages. In this paper, a dependency mining based approach is proposed. It tries to detect the message logging progress of nodes only from the dependency between non-deterministic events, without needing to piggyback an additional dependency vector or dependency matrix on each message.     

Fast Algorithm for Walsh Hadamard Transform on Sliding Windows

This paper proposes a fast algorithm for Walsh Hadamard Transform on sliding windows which can be used to implement pattern matching most efficiently. The computational requirement of the proposed algorithm is about 1.5 additions per projection vector per sample, which is the lowest among existing fast algorithms for Walsh Hadamard Transform on sliding windows.     

基于条件熵和变论域方法的属性约简算法

深入分析了论域变化与决策表条件信息熵的变化原理,在此基础上提出一种新的决策表属性约简算法. 该算法通过不断将决策表中一定属性集下协调的部分从论域中去除,把不协调的部分作为新的论域做进一步处理,逐步减少参与属性约简的对象数,可以在一定程度上降低求取属性约简的计算量,实验数据表明该算法是高效的.     

Stability analysis for discrete delayed Markovian jumping neural networks with partly unknown transition probabilities

This paper addresses the analysis problem of asymptotic stability for a class of uncertain neural networks with Markovian jumping parameters and time delays. The considered transition probabilities are assumed to be partially unknown. The parameter uncertainties are considered to be norm-bounded. A sufficient condition for the stability of the addressed neural networks is derived, which is expressed in terms of a set of linear matrix inequalities. A numerical example is given to verify the effectiveness of the developed results.     

A robust scheduling method based on a multi-objective immune algorithm

A robust scheduling method is proposed to solve uncertain scheduling problems. An uncertain scheduling problem is modeled by a set of workflow models, and then a scheduling scheme (solution) of the problem can be evaluated by workflow simulations executed with the workflow models in the set. A multi-objective immune algorithm is presented to find Pareto optimal robust scheduling schemes that have good performance for each model in the set. The two optimization objectives for scheduling schemes are the indices of the optimality and robustness of the scheduling results. An antibody represents a resource allocation scheme, and the methods of antibody coding and decoding are designed to deal with resource conflicts during workflow simulations. Experimental tests show that the proposed method can generate a robust scheduling scheme that is insensitive to uncertain scheduling environments.