Matrix expression and vaccination control for epidemic dynamics over dynamic networks

This paper investigates epidemic dynamics over dynamic networks via the approach of semi-tensor product of matrices. First, a formal susceptible-infected-susceptible epidemic dynamic model over dynamic networks (SISED-DN) is given. Second, based on a class of determinate co-evolutionary rule, the matrix expressions are established for the dynamics of individual states and network topologies, respectively. Then, all possible final spreading equilibria are obtained for any given initial epidemic state and network topology by the matrix expression. Third, a sufficient and necessary condition of the existence of state feedback vaccination control is presented to make every individual susceptible. The study of illustrative examples shows the effectiveness of our new results.     

基于内容分析的网络新闻中社会网络自动抽取

本文针对网络新闻报道,提出了一种基于文本内容分析的社会网络自动抽取方法。此方法在对输入文章进行分词标注、共指消解等预处理之后,通过名词合并及主动词识别,得到存在关系的命名实体之间的关系指向和关系描述,最后通过有向图把存在关系的命名实体进行连接,形成由命名实体、实体间关系指向、实体间关系描述构成的关系网络。试验结果表明该方法对新闻中的命名实体关系抽取比较有效。     

网络安全可视化综述

随着互联网技术的发展,网络渗入到人们生活的方方面面。一方面,电子商务、社交网络、线上娱乐、信息化办公等各种网络应用为人们的生活带来了诸多便利;另一方面,网络与人们生活的不可分割性为网络攻击和网络犯罪提供了可乘之机。攻击者通过各种各样的网络攻击获取他人隐私,牟取非法利益。近年来,网络攻击的数量越来越多,攻击的规模越来越大,攻击的复杂度也越来越高。因此,网络安全比以往任何时期都显得重要。然而传统的网络安全保障机制,如入侵检测,防御系统,网络防火墙等,因其智能性、动态性、全局性等的缺乏,都不足以应对越发复杂和高强度的网络攻击。因此,网络安全可视化应运而生,成为近年来网络安全研究的一个热点。与传统网络安全保障机制不同,网络安全可视化技术不仅能有效处理海量网络数据信息,捕获网络的全局态势,而且能通过对图形图像模式的分析帮助网络管理人员快速识别潜在的攻击和异常事件,即时预测安全事件,甚至是发现新的攻击类型。可视化技术为网络安全研究方法带来了变革,优秀的网络安全可视化方案层出不穷。网络安全可视化建立在对网络数据分析的基础之上,网络数据对网络安全分析十分重要,而大数据时代的到来进一步凸显了数据的重要性。因此,本文从数据角度出发,根据所处理的网络数据的类型,对网络安全可视化工作进行系统的整理、分类和对比。此外,本文还深入分析网络安全可视化研究面临的挑战并探讨未来该领域的研究方向。     

城市需水的智能预测方法研究

选取对城市用水影响较大的因素作为预测指标,通过HP滤波分析其指标及用水量的趋势成分及波动成分。应用多元线性回归法对趋势性成分进行模拟,应用模糊神经网络对波动性成分进行网络训练,以获得城市需水的总预测值。以大连市1980~2000年用水及其相关因子为例建模,以2001~2007年指标对模型进行检验,预测了大连市2010、2020年的需水值,为城市水资源规划提供一定的理论支持。     

Implementation of Envelope Analysis on a Wireless Condition Monitoring System for Bearing Fault Diagnosis

 Envelope analysis is an effective method for characterizing impulsive vibrations in wired condition monitoring (CM) systems. This paper depicts the implementation of envelope analysis on a wireless sensor node for obtaining a more convenient and reliable CM system. To maintain CM performances under the constraints of resources available in the cost effective Zigbee based wireless sensor network (WSN), a low cost cortex-M4F microcontroller is employed as the core processor to implement the envelope analysis algorithm on the sensor node. The on-chip 12 bit analog-to-digital converter (ADC) working at 10 kHz sampling rate is adopted to acquire vibration signals measured by a wide frequency band piezoelectric accelerometer. The data processing flow inside the processor is optimized to satisfy the large memory usage in implementing fast Fourier transform (FFT) and Hilbert transform (HT). Thus, the envelope spectrum can be computed from a data frame of 2048 points to achieve a frequency resolution acceptable for identifying the characteristic frequencies of different bearing faults. Experimental evaluation results show that the embedded envelope analysis algorithm can successfully diagnose the simulated bearing faults and the data transmission throughput can be reduced by at least 95% per frame compared with that of the raw data, allowing a large number of sensor nodes to be deployed in the network for real time monitoring.     

Performance comparison of reduced models for leak detection in water distribution networks

This paper presents a methodology for comparing the performance of model-reduction strategies to be used with a diagnostic methodology for leak detection in water distribution networks. The goal is to find reduction strategies that are suitable for error-domain model falsification, a model based data interpretation methodology. Twelve reduction strategies are derived from five strategy categories. Categories differ according to the manner in which nodes are selected for deletion. A node is selected for deletion according to: (1) the diameter of the pipes; (2) the number of pipes linked to a node; (3) the angle of the pipes in the case of two-pipe nodes; (4) the distribution of the water demand; and, (5) a pair-wise combination of some categories.The methodology is illustrated using part of a real network. Performance is evaluated first by judging the equivalency of the reduced network with the initial network (before the application of any reduction procedure) and secondly, by assessing the compatibility with the diagnostic methodology. The results show that for each reduction strategy the equivalency of networks is verified. Computational time can be reduced to less than 20% of the non-reduced network in the best case. Results of diagnostic performance show that the performance decreases when using reduced networks. The reduction strategy with the best diagnostic performance is that based on the angle of two-pipe nodes, with an angle threshold of 165°. In addition, the sensitivity of the performance of the reduced networks to variation in leak intensity is evaluated. Results show that the reduction strategies where the number of nodes is significantly reduced are the most sensitive.Finally this paper describes a Pareto analysis that is used to select the reduction strategy that is a good compromise between reduction of computational time and performance of the diagnosis. In this context, the extension strategy is the most attractive.     

E-ARK: Harmony of Social/Cultural Records Across Europe

E-ARK (European Archiving of Records and Knowledge Preservation) is currently introducing standardization, new tools, and infrastructure into pan-European digital archival systems. The results of this pilot project can be applied to higher education where research data centers are required to preserve data for re-use in accordance with new open policies.

We elucidate how the E-ARK best practice survey has informed the development of new information packages for ingest, preservation, and re-use, and show how these can be applied to the Arts, Humanities, and Social Sciences with practical exemplars such as census data.     

An integrated neuro-genetic approach incorporating the Taguchi method for product design

Product design is a multidisciplinary activity that requires the integration of concurrent engineering approaches into a design process that secures competitive advantages in product quality. In concurrent engineering, the Taguchi method has demonstrated an efficient design approach for product quality improvement. However, the Taguchi method intuitively uses parameters and levels in measuring the optimum combination of design parameter values, which might not guarantee that the final solution is the most optimal. This work proposes an integrated procedure that involves neural network training and genetic algorithm simulation within the Taguchi quality design process to aid in searching for the optimum solution with more precise design parameter values for improving the product development. The concept of fractals in computer graphics is also considered in the generation of product form alternatives to demonstrate its application in product design. The stages in the general approach of the proposed procedures include: (1) use of the Taguchi experimental design procedure, (2) analysis of the neural network and genetic algorithm process, and (3) generation of design alternatives. An electric fan design is used as an example to describe the development and explore the applicability of the proposed procedures. The results indicate that the proposed procedures could enhance the efficiency of product design efforts by approximately 7.8%. It is also expected that the proposed design procedure will provide designers with a more effective approach to product development.     

A new robust observer-based adaptive type-2 fuzzy control for a class of nonlinear systems

In this paper, a novel robust observer-based adaptive controller is presented using a proposed simplified type-2 fuzzy neural network (ST2FNN) and a new three dimensional type-2 membership function is presented. Proposed controller can be applied to the control of high-order nonlinear systems and adaptation of the consequent parameters and stability analysis are carried out using Lyapunov theorem. Moreover, a new adaptive compensator is presented to eliminate the effect of the external disturbance, unknown nonlinear functions approximation errors and sate estimation errors. In the proposed scheme, using the Lyapunov and Barbalat's theorem it is shown that the system is stable and the tracking error of the system converges to zero asymptotically. The proposed method is simulated on a flexible joint robot, two-link robot manipulator and inverted double pendulums system. Simulation results confirm that in contrast to other robust techniques, our proposed method is simple, give better performance in the presence of noise, external disturbance and uncertainties, and has less computational cost.     

Optimization of neural network model using modified bat-inspired algorithm

The success of an artificial neural network (ANN) strongly depends on the variety of the connection weights and the network structure. Among many methods used in the literature to accurately select the network weights or structure in isolate; a few researchers have attempted to select both the weights and structure of ANN automatically by using metaheuristic algorithms. This paper proposes modified bat algorithm with a new solution representation for both optimizing the weights and structure of ANNs. The algorithm, which is based on the echolocation behaviour of bats, combines the advantages of population-based and local search algorithms. In this work, ability of the basic bat algorithm and some modified versions which are based on the consideration of the personal best solution in the velocity adjustment, the mean of personal best and global best solutions through velocity adjustment and the employment of three chaotic maps are investigated. These modifications are aimed to improve the exploration and exploitation capability of bat algorithm. Different versions of the proposed bat algorithm are incorporated to handle the selection of the structure as well as weights and biases of the ANN during the training process. We then use the Taguchi method to tune the parameters of the algorithm that demonstrates the best ability compared to the other versions. Six classifications and two time series benchmark datasets are used to test the performance of the proposed approach in terms of classification and prediction accuracy. Statistical tests demonstrate that the proposed method generates some of the best results in comparison with the latest methods in the literature. Finally, our best method is applied to a real-world problem, namely to predict the future values of rainfall data and the results show satisfactory of the method.