Construction of Lyapunov–Krasovskii functionals for networks of iISS retarded systems in small-gain formulation

This paper tackles a problem of verifying stability of retarded dynamical networks in a dissipative formulation. Subsystems are assumed to be integral input-to-state stable (iISS). Time-delays are allowed to reside in both subsystems and interconnection channels, and may be both discrete and distributed. No assumption is made on the interconnection topology. A small-gain methodology is developed for constructing a Lyapunov–Krasovskii functional to establish iISS of such a network.     

Robust decentralized stabilization of Markovian jump large-scale systems: A neighboring mode dependent control approach

This paper is concerned with the decentralized stabilization problem for a class of uncertain large-scale systems with Markovian jump parameters. The controllers use local subsystem states and neighboring mode information to generate local control inputs. A sufficient condition involving rank constrained linear matrix inequalities is proposed for the design of such controllers. A numerical example is given to illustrate the developed theory.     

Integrated A.I. systems

The broad range of capabilities exhibited by humans and animals is achieved through a large set of heterogeneous, tightly integrated cognitive mechanisms. To move artificial systems closer to such general-purpose intelligence we cannot avoid replicating some subset—quite possibly a substantial portion—of this large set. Progress in this direction requires that systems integration be taken more seriously as a fundamental research problem. In this paper I make the argument that intelligence must be studied holistically. I present key issues that must be addressed in the area of integration and propose solutions for speeding up rate of progress towards more powerful, integrated A.I. systems, including (a) tools for building large, complex architectures, (b) a design methodology for building realtime A.I. systems and (c) methods for facilitating code sharing at the community level.

Benders decomposition for the uncapacitated multiple allocation hub location problem

In telecommunication and transportation systems, the uncapacitated multiple allocation hub location problem (UMAHLP) arises when we must flow commodities or information between several origin–destination pairs. Instead of establishing a direct node to node connection from an origin to its destination, the flows are concentrated with others at facilities called hubs. These flows are transported on links established between hubs, being then splitted and delivered to its final destination. Systems with this sort of topology are named hub-and-spoke (HS) systems or hub-and-spoke networks. They are designed to exploit the scale economies attainable through the shared use of high capacity links between hubs. Therefore, the problem is to find the least expensive HS network, selecting hubs and assigning traffic to them, given the demands between each origin–destination pair and the respective transportation costs. In the present paper, we present efficient Benders decomposition algorithms based on a well known formulation to tackle the UMAHLP. We have been able to solve some large instances, considered ‘out of reach’ of other exact methods in reasonable time.     

Spark平台上利用网络加权Voronoi图的分散迭代社区聚类并行化研究

针对当下数据大规模增长对计算能力需求的急剧增长,传统独立运行的机器在大规模网络社区中执行社区检测操作时无法提供所需的数据处理能力的问题,提出一种网络加权Voronoi图的并行分散迭代社区聚类法(NWVD-PDICCM).利用基于网络加权Voronoi图的分散迭代社区聚类方法(NWVD-DICCM)提取大型网络的有效社区...     

DIDAPPER:具有认知能力的分布式入侵检测系统

近年来,随着网络安全问题日益突出,入侵检测也越来越受到关注。目前,研究入侵检测的课题很多,侧重点也各不相同。该文介绍的ＤＩＤＡＰＰＥＲ系统是一种具有认知能力的分布式入侵检测系统。分布式结构、认知能力和知识的共享是ＤＩＤＡＰＰＥＲ系统的重要特点。流量标本和ＩＰ陷阱是ＤＩＤＡＰＰＥＲ系统所提出的新概念。ＤＩＤＡＰＰＥＲ的分布式三级结构使得它适合于检测大规模网络自动攻击行为,而且有较强的可扩展性和高效性。     

一种新的时变大系统递阶控制求解方法

研究了一种新的大系统求解方法,利用Taylor级数的性质,将大系统时变递阶控制问题转化为代数方程的求解问题.该方法简单,易于编成计算机程序.算例表明了它的有效性.     

具有多项式型非线性项的大规模随机非线性系统的分散输出反馈镇定

基于齐次占优方法研究 具有多项式型非线性项的大规模随机非线性系统的分散输出反馈镇定问题. 本文的主要贡献在于利用高增益齐次占优方法来解决大规模随机非线性系统的分散控制问题. 这种方法能彻底地去掉传统结果中所要求的线性增长条件, 不仅推广了以前的结果还得到了一些新的结果. 仿真验证了输出反馈控制器的有效性.     

Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China

High-speed rail (HSR) has become an essential mode of public transportation in China and is likely to remain so for the foreseeable future. To promote the development of the HSR industry, a high level of passenger satisfaction must be ensured, which means that passenger satisfaction must be assured. Focusing on HSR in-cabin factors that affect the travel experience of HSR passengers, this study aims to determine passenger demands (PDs) and to evaluate passenger satisfaction by using a combination of online review analysis and large-scale group decision-making (LSGDM). By using web crawler technology, online reviews related to HSR were harvested from a microblogging platform to extract PD data and information. The six PDs that reflect the most frequent concerns of passengers were identified by analyzing the online reviews. The level of satisfaction of passengers with respect to these PDs was analyzed based on the online responses from 100 HSR passengers and by adopting the interval-valued two-tuple linguistic representation model. The final degrees of satisfaction and rankings of the PDs were then determined by using the LSGDM approach with the k-means clustering method and a consensus-reaching process. This research thus constructs an index system of HSR passenger satisfaction evaluation based on online-review analysis and evaluates the process by using LSGDM approaches. The conclusions provide insights into the improvements desired by HSR passengers for in-cabin services and to improve passenger satisfaction.     

Efficient closed high-utility pattern fusion model in large-scale databases

High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades since it reveals profit strategies for use in industry for decision-making. Most existing works have focused on mining high-utility itemsets from databases showing large amount of patterns; however exact decisions are still challenging to make from that large amounts of discovered knowledge. Closed High-utility itemset mining (CHUIM) provides a smart way to present concise high-utility itemsets that can be more effective for making correct decisions. However, none of the existing works have focused on handling large-scale databases to integrate discovered knowledge from several distributed databases. In this paper, we first present a large-scale information fusion architecture to integrate discovered closed high-utility patterns from several distributed databases. The generic composite model is used to cluster transactions regarding their relevant correlation that can ensure correctness and completeness of the fusion model. The well-known MapReduce framework is then deployed in the developed DFM-Miner algorithm to handle big datasets for information fusion and integration. Experiments are then compared to the state-of-the-art CHUI-Miner and CLS-Miner algorithms for mining closed high-utility patterns and the results indicated that the designed model is well designed for handling large-scale databases with less memory usage. Moreover, the designed MapReduce framework can speed up the mining performance of closed high-utility patterns in the developed fusion system.