Enhanced utilization bound of Rate-Monotonic scheduling in Controller Area Networks

When priorities to message streams are assigned using Rate Monotonic (RM) for a Controller Area Network (CAN), the utilization bound is known to be about 25% for CAN 2.0A and 29% for CAN 2.0B. In this letter, we present a higher utilization bound than the existing ones with a reasonable constraint. The new utilization bounds are approximately 34% for CAN 2.0A and 41% for CAN 2.0B if no single message stream's utilization exceeds 46% or 27% of the total utilization for CAN 2.0A or CAN 2.0B, respectively.     

试议港口集卡调度算法模型分析

随着我国集装箱吞吐量的快速增长,集装箱港口对作业效率的要求也越来越高。在现有作业技术水平和硬件设备、设施的基础上实现作业效率的提高,已经成为集装箱港口发展所面临的重要问题。从集装箱港口作业的一个环节——集卡作业入手,分析提高集卡利用率,进而带动整个港口作业效率的提高。     

一种基于优先级的Ad hoc网络QoS模型*

提出一种基于优先级业务分类的Ad hoc网络QoS模型,它能够为多种优先级多种类别的业务提供不同等级的服务。源端判断业务是否允许接入,中间节点只负责转发允许接入的业务。在资源不足的情况下,能为最高优先级业务提供相对稳定的带宽资源,高优先级业务能获得比低优先级业务更高的调度权限。通过OPNET下的仿真,证明该模型能满足不同级别QoS的要求。     

在嵌入式系统中实现热备份

在要求高可靠性的系统设计中要求系统能够实现双机热备份,从而保证在出现问题时能够快速恢复服务。详细解读了在开发的调度机中如何实现热备份。     

An energy-efficient real-time scheduling scheme on dual-channel networks

The recent evolution of wireless sensor networks have yielded a demand to improve energy-efficient scheduling algorithms and energy-efficient medium access protocols. This paper proposes an energy-efficient real-time scheduling scheme that reduces power consumption and network errors on dual channel networks. The proposed scheme is based on a dynamic modulation scaling scheme which can scale the number of bits per symbol and a switching scheme which can swap the polling schedule between channels. Built on top of EDF scheduling policy, the proposed scheme enhances the power performance without violating the constraints of real-time streams. The simulation results show that the proposed scheme enhances fault-tolerance and reduces power consumption.     

Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions

We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.     

基于经济的网格资源调度算法研究

网格使构建虚拟组织,在全世界范围内共享资源成为现实.但是,在网格环境下的资源管理和调度是一项相当复杂的工作,这主要是由于资源分布在世界各地,隶属于不同的组织,各个资源拥有者都有不同的使用和访问策略、开销模型、不断变化的负载和可用性.为了满足网格计算环境中资源调度的复杂需要,可以将现实世界中的经济原理和模型应用到网格环境下的任务调度中,并据此提出了几种经济资源调度算法.     

多模型自适应控制的空分系统整体能耗优化

运用多模型自适应控制技术,针对空分系统的整体能耗优化,就空分系统模型集的建立、控制器的设计、切换算法等方面进行了分析。空分系统作为一个实时变化的有机整体,在整体运行过程中存在的操作环境改变对系统的影响,当系统发生变化及波动时,根据相应切换算法切换到可以满足生产变化的模型,采用适合的控制器,以达到实时监控、安全生产的效果,最大限度地优化了系统运行。     

考虑EV充放电意愿的园区综合能源系统双层优化调度

随着电动汽车的普及度越来越高,工业园区的电动汽车用户日益增多,其充放电行为对园区综合能源系统规划和运行带来极大挑战。本文提出了考虑电动汽车充放电意愿的园区综合能源系统双层优化调度。首先基于动态实时电价、电池荷电量、电池损耗补偿、额外参与激励等因素建立充放电意愿模型,在此基础上得到改进的电动汽车充放电模型;然后,以园区综合能源系统总成本最小和电动汽车充电费用最小为目标建立双层优化调度模型,通过KKT条件将内层模型转化为外层模型的约束条件,从而快速稳定的实现单层模型的求解;最后,进行仿真求解,设置3种不同场景,对比了所提模型与一般充放电意愿模型,验证了文中所提引入EV充放电意愿模型的园区综合能源系统双层优化调度的有效性和可行性,具有一定的经济效益。     

Finite-time optimal tracking control for a class of nonlinear systems with prescribed performance

In this paper, a finite-time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite-time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite-time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.