Adaptive control of siso plants with unmodelled dynamics

In this paper we pursue a twofold aim. First we want to simplify the complexity of the classical Monopoli's scheme, the so-called ‘Augmented error signal control scheme’. Then we also wish to cope with the realistic situation in which the presence of unmodelled dynamics has to be taken into account. This latter problem has been faced in the literature by suitably modifying the adaptation mechanism in order to avoid undesired phenomena as well as to obtain an attractive stability region for the state trajectories starting from any point in a predefined initial condition set. In our case the necessity of introducing any sort of modification in the adaptation mechanism is completely avoided, but we still obtain asymptotic stability of the output error signal.     

Software for dynamic reconfigurable order picking systems

This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Research Center (MHRC), a National Science Foundation sponsored Cooperative Industry/University Research Center. The storage configuration is assumed to be an in-the-aisle order picking system in which stockkeeping units (SKUs) can occupy variable capacity storage locations and stock-splitting is allowed among zones (clusters). The product mix may include multiple product families with different life cycles, correlated demand within families and commonality of demand across families.     

注册表键值设置的实际应用

从初步认识、简单使用、异常情况处理三个方面介绍了注册表及其使用方法。     

Discrete particle swarm optimization based on estimation of distribution for terminal assignment problems

Terminal assignment problem (TEAP) is to determine minimum cost links to form a network by connecting a given set of terminals to a given collection of concentrators. This paper presents a novel discrete particle swarm optimization (PSO) based on estimation of distribution (EDA), named DPSO-EDA, for TEAP. EDAs sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from personal best solutions of all particles into the PSO, and therefore each particle has comprehensive learning and search ability. In the DPSO-EDA, a modified constraint handling method based on Hopfield neural network (HNN) is also introduced to fit nicely into the framework of the PSO and thus utilize the merit of the PSO. The DPSO-EDA adopts the asynchronous updating scheme. Further, the DPSO-EDA is applied to a problem directly related to TEAP, the task assignment problem (TAAP), in order to show that the DPSO-EDA can be generalized to other related combinatorial optimization problems. Simulation results on several problem instances show that the DPSO-EDA is better than previous methods.     

Abstract Models of Storage

This note is a historical survey of Christopher Strachey's influence on the development of semantic models of assignment and storage management in procedural languages.     

遗传算法综述

本文主要回顾了遗传算法的发展历程,并对遗传算法的基本原理及特点作了简要阐述。进一步指出了遗传算法存在的问题及相应的改进措施,讨论了遗传算法在实际中的应用。     

Performance of Reuse Partitioning in Cellular Systems with Mobile Users

Reuse partitioning (RP) is a simple technique that can be used to increase the traffic capacity in a cellular system. With RP, a cell is divided into several concentric regions, each associated with a different cluster size. In this article, a traffic model is developed to analyze the impact of mobile users on a two-region RP system using fixed channel assignment. The influence of user speed and cell size on the new call blocking probability, P_b, and the call dropping probability, P_d, is investigated. A simpler model that can be used to estimate P_b and P_d in some cases is described. The effect on system capacity of reserving some channels for handoff calls is also studied. It is found that even though prioritized handoff can reduce P_d, it may also degrade the capacity. The accuracy of the analysis is verified using simulation for three mobility models.     

面向对象的基于角色访问控制模型的探讨

提出一种应用于分布式系统的面向对象的基于角色访问控制(ORBAC)模型。基于角色访问控制,将权限与角色相关联,用户分配得多个角色从而获得与其关联的权限。引用对象的概念,通过策略控制器集中控制分散管理,为分布式系统提供高效的访问控制机制。模型考虑了静态和动态角色分配,并分析了面向对象访问控制模型的一些动态特性。     

End-to-end delay margin balancing approach for routing in multi-class networks

This paper presents Quality of Service (QoS) based routing and priority class assignment algorithms. It introduces an end-to-end delay margin balancing approach to routing, and uses it to formulate a nonlinear optimization problem. In a single-class network, the formulation is shown to be convex; however in a multi-class priority network, it is only convex within specific regions, and is infeasible otherwise. A centralized off-line computation technique is proposed to calculate both the route configuration and end-to-end priority assignment. A gradient-based solution in the convex region and a heuristic to overcome the multi-class discontinuity are derived. An approximation of the optimization problem is developed for on-line distributed processing is then presented. Using the approximation, arriving traffic flows can use vector routing tables to search for routes. Compared with minimum-hop, minimum-delay, and min-interference routing algorithms, the proposed approach enables the single-class network to accommodate more users of different end-to-end delay requirements. In a multi-class priority network, results show that using the objective function to combine route and priority class assignment further increases the supportable network traffic volume. Mohamed Ashour received his B.Sc. (1991) and M.Sc. (1997) in Electrical Engineering from Ain Shams University, Cairo, Egypt. He worked for Hughes and General Dynamics as a Telecommunications Engineer. Currently, he is a Ph.D. Candidate in the Department of Electrical and Computer Engineering of McGill University, Montreal, Quebec, Canada. His current area of research is focused on traffic engineering, routing, and QoS provisioning in DiffServ and MPLS Networks. He is also interested in multi-class queuing analysis of long-range traffic, and QoS provisioning in ad hoc networks and satellite communications. Tho Le-Ngoc obtained his B. Eng. (with Distinction) in Electrical Engineering in 1976, his M.Eng. in Microprocessor Applications in 1978 from McGill University, Montreal, and his Ph.D. in Digital Communications 1983 from the University of Ottawa, Canada. During 1977–1982, he was with Spar Aerospace Limited as a Design Engineer and then a Senior Design Engineer, involved in the development and design of the microprocessor-based controller of Canadarm (of the Space Shuttle), and SCPC/FM, SCPC/PSK, TDMA satellite communications systems. During 1982–1985, he was an Engineering Manager of the Radio Group in the Department of Development Engineering of SRTelecom Inc., developed the new point-to-multipoint DA-TDMA/TDM Subscriber Radio System SR500. He was the System Architect of this first digital point-to-multipoint wireless TDMA system. During 1985–2000, he was a Professor the Department of Electrical and Computer Engineering of Concordia University. Since 2000, he has been with the Department of Electrical and Computer Engineering of McGill University. His research interest is in the area of broadband digital communications with a special emphasis on Modulation, Coding, and Multiple-Access Techniques. He is a Senior Member of the Ordre des Ingénieur du Quebec, a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the Engineering Institute of Canada (EIC), and a Fellow of the Canadian Academy of Engineering (CAE). He is the recipient of the 2004 Canadian Award in Telecommunications Research, and recipient of the IEEE Canada Fessenden Award 2005.     

IaaS云中面向能源效率优化的虚拟机调度算法(英文)

In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers.The existing VM scheduling schemes propose optimize PMs or network resources utilization,but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously.This paper proposes a VM scheduling scheme meeting multiple resource constraints,such as the physical server size(CPU,memory,storage,bandwidth,etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption.Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem,which is also known as a classic combinatorial optimization and NP-hard problem.Accordingly,we design a twostage heuristic algorithm to solve the issue,and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.