Delay‐dependent passivity and passification for uncertain Markovian jump systems with time‐varying delays

This paper deals with the problems of passivity analysis and passivity‐based controller design for Markovian jump systems with both time‐varying delays and norm‐bounded parametric uncertainties. Firstly, new delay‐dependent conditions for the considered system to be passive are obtained by using a mode‐dependent Lyapunov functional and by introducing some slack variables. These conditions are expressed by means of LMIs that are easy to check. It is shown through a numerical example that the obtained passivity conditions are less conservative than the existing ones in the literature. Secondly, the passification problem is investigated. On the basis of the obtained passivity conditions, dynamic output‐feedback controllers are designed, which ensure that the resulting closed‐loop system is passive. The effectiveness of the proposed design method is demonstrated by a numerical example. Copyright © 2011 John Wiley & Sons, Ltd.     

Scalable influence maximization for independent cascade model in large-scale social networks

Influence maximization, defined by Kempe et al. (SIGKDD 2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (SIGKDD 2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this article, we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread—it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100–260% increase in influence spread.     

Augmented Lyapunov functional approach for stability of neutral systems with mixed delays

This paper is concerned with the neutral‐delay‐dependent and discrete‐delay‐dependent stability for uncertain neutral systems with mixed delays and norm‐bounded uncertainties. Through constructing a new augmented Lyapunov‐Krasovskii functional and proving its positive definiteness, introducing some slack matrices and using integral inequality, the improved delay‐dependent stability criteria are derived in terms of linear matrix inequalities. Numerical examples are given to illustrate the significant improvement on the conservatism of the delay bound over some existing results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Interactive genetic algorithms with large population and semi-supervised learning

Interactive genetic algorithms are effective methods of solving optimization problems with implicit (qualitative) criteria by incorporating a user's intelligent evaluation into traditional evolution mechanisms. The heavy evaluation burden of the user, however, is crucial and limits their applications in complex optimization problems. We focus on reducing the evaluation burden by presenting a semi-supervised learning assisted interactive genetic algorithm with large population. In this algorithm, a population with many individuals is adopted to efficiently explore the search space. A surrogate model built with an improved semi-supervised learning method is employed to evaluate a part of individuals instead of the user to alleviate his/her burden in evaluation. Incorporated with the principles of the improved semi-supervised learning, the opportunities of applying and updating the surrogate model are determined by its confidence degree in estimation, and the informative individuals reevaluated by the user are selected according to the concept of learning from mistakes. We quantitatively analyze the performance of the proposed algorithm and apply it to the design of sunglasses lenses, a representative optimization problem with one qualitative criterion. The empirical results demonstrate the strength of our algorithm in searching for satisfactory solutions and easing the evaluation burden of the user.     

A fuzzy cerebellar model articulation controller based visual servo system for robot

This paper presents a novel online learning visual servo controller integrating the FCMAC with proportion controller for the control of position of manipulator end-effector. Since the FCMAC has good learning capability and fast learning speed, and can save much computer memory space by fuzzy processing of input space division and memory unit activation, it is used to develop an adaptive control law by learning the relationship between the image feature errors and manipulator input, and the aim of online learning of the FCMAC is to minimize the output of proportion controller. Furthermore, the FCMAC has no need for models of robot manipulator and image feature extraction, so that the capability of proposed controller for tasks under uncertain environment can be improved. Finally, the proposed controller is proved to be effective by the experiment, and compared with BP neural network.     

Patterned paper and alternative materials as substrates for low-cost microfluidic diagnostics

In recent years a strong need has been realised for the creation of low-cost diagnostics for use in impoverished regions of the world. The use of paper has been championed for this purpose by many research groups globally over the past 5?years, leading to the formation of a large body of knowledge. This work reviews and summarises the many techniques developed to date for the patterning of paper substrates to create channels for the flow of liquids. The work also explores methods for increasing the functionality of paper-based microfluidics, the detection mechanisms employed so far and some of the interesting applications addressed. The review will also investigate the recent use of some alternative materials—both as primary substrates and in hybrids—which will become progressively more important in the future.     

Improved stability conditions for systems with interval time-varying delay

The paper is concerned with the stability of linear systems with interval time-varying delay. Through constructing a new augmented Lyapunov-Krasovskii functional (LKF) which contains some quadruple-integral terms and estimating the time derivative of the LKF less conservatively, new stability criteria are derived without introducing any free matrices. Moreover, by proving the positive definiteness of the LKF with some integral inequalities, the constraints on some functional parameters are relaxed and the conservatism of the obtained results are further reduced. Numerical examples are also given to demonstrate the effectiveness and reduced conservatism of the obtained results.     

基于以太网的MVB总线分析仪的设计和实现

简要介绍多功能车辆总线(MVB)的标准以及以太网在工业中应用的优势和前景,通过分析MVB总线信号特点和以太网芯片功能,提出了基于以太网的MVB总线分析仪的硬件设计,网络芯片的驱动设计与实现方案.分析仪的功能模块包括接收模块、发送模块、出错检测管理模块、CPU、存储模块和以太网通信模块等.通过在符合IEC-61375标准的MVB总线测试平台下进行调试,分析仪的各项功能达到了设计目标,可以实际应用于采用MVB总线的车载设备网络或其他工业网络的总线数据分析和网络性能测试.     

高速动车组驾驶室设计中的人机界面分析

在高速动车组驾驶室的人机关系分析与设计中,驾驶室的空间尺度关系、视野、操纵设备的分布和人机环境等因素,将直接影响列车行驶的安全、效率、舒适和健康。应用人机关系分析与研究,合理地配置驾驶室室内环境以及操作和显示设备,将有效地提高司机操作的安全性、便利性和作业效率,从而给司机提供一个方便舒适的操作空间,减少体力、心理疲劳,降低人为误操作带来的安全隐患,提高系统运行的可靠性,使人-机匹配达到最优,以保证车辆行驶的安全。     

应用破圈法求图的最优树的一种新算法

本文依据破圈法的原理提出了求图的最优树的一种新算法,并给出了具体的实现步骤。