一个模拟网格上并行作业调度的离散事件系统

为评估计算网格上并行作业的无中心式调度,设计了基于离散事件的模拟系统J3S.离散事件包括作业提交、开始和终止,作业状态可以是等待、运行或完成.事件引起作业状态的变化 ,并触发网格层次或本地层次调度.作业由网格工作负荷模型产生,该模型基于并行计算机工作负荷模型构建.网格层次调度通过网格调度器之间的协作完成.本地层次调度模拟改进的装填法,使作业可有不同的调度优先级.网格资源性能可有差异,作业执行性能依赖作业指派.模块化实现使J3S可模拟网格上并行作业的无中心式调度的各种场景.     

采用双层协调机制的服务社区模型

Web服务的动态组合技术作为有效实现服务增值功能的重要方法已成为业界研究和发展的焦点,纵观已有的研究成果,其协调机制可分为集中和分散两种.但实际应用中,这两种机制都存在不足.针对这一问题,提出了一种采用双层协调机制的服务社区模型,并讨论了该模型的体系结构、注册原理、发现算法、替换规则等问题,最后与传统模型进行对比分析.结果表明,这种采用传统集中和分散相结合协调机制的模型,将UDDl分层设计与管理,可以更好地支持存储和管理资源;社区的内部自治性有利于提高服务发现和替换的效率,从而更有效地支持Web服务的动态组合过程.     

一类多通道不确定时滞大系统分散鲁棒H∞控制：LMI方法

研究多通道不确定时滞大系统的鲁棒分散H_∞控制问题. 假定不确定性是时不变、范数有界, 且存在于系统、时滞和输出矩阵中. 主要针对动态输出反馈控制问题. 基于Lyapunov稳定性理论, 通过设定Lyapunov矩阵为合适的块对角结构, 采用矩阵替换的方法推导出了使多通道不确定时滞大系统可鲁棒镇定, 且满足一定的扰动水平的时滞依赖充分条件即线性矩阵不等式(LMI) 有可行解, 并且给出了具有期望阶数的分散鲁棒控制器的设计方法. 数值例子说明了本文提出方法的有效性.     

不确定广义大系统分散鲁棒H∞保性能控制

针对一类状态矩阵和控制矩阵存在参数不确定的广义大系统,研究其分散鲁棒H∞保性能控制问题,系统中不确定项具有数值界,可不满足匹配条件.基于广义系统的有界实引理,应用线性矩阵不等式(LMI)方法,给出了不确定广义大系统存在分散鲁棒H∞保件能控制器的一个LMI条件,并用这个线性矩阵不等式系统的可行解提供了一组分散鲁棒H∞保性能控制律的参数化表示,最后用例子说明该方法的应用.     

广义互联大系统的渐近稳定与分散镇定

利用Lyapunov函数方法、系统分解法以及矩阵不等式等方法研究一类具有脉冲行为的广义互联大系统的渐近稳定性及分散镇定问题,在子系统正则且零解渐近稳定的条件下,给出广义线性互联大系统的渐近稳定莉定的一个充分条件,并设计适当的反馈律,以实现广义互联大系统的镇定。通过仿真实例说明主要结果。     

基于SERCOS总线的分布式控制系统设计

SERCOS总线是目前用于数字伺服和传动数据通信的唯一国际标准,具有开放性、兼容性、实时性等优点。本文设计了基于SERCOS总线的分布式控制系统,采用上位机和下位机结构,利用配置了SERCOS接口卡的PC机作为上位机,通过光纤环路向下位机发送指令。下位机由DSP控制器、电机驱动和反馈电路等组成,对电机进行位置闭环控制。实验结果表明,SERCOS总线成功地实现了上位机对电机的实时监控。     

考虑风电不确定性的交直流配电网低碳分布鲁棒优化调度

为增加配电网风电的消纳能力,减少碳排放,建立了一种交直流配电网低碳分布鲁棒优化调度模型。分析风电预测误差和预测出力历史数据之间的正相关性,采用混合Copula函数,建立它们之间的联合概率分布,得到风电预测误差的条件概率分布。将交直流配电网解耦为交流和直流子网,以各自综合运行成本最小为优化目标,在交流子网优化模型中引入碳交易机制,建立交直流配电网分散协调优化模型。以得到的风电预测误差的条件概率分布为参考,构建了基于K-L散度的分布鲁棒模糊集。利用拉格朗日对偶理论,将优化模型转化为单层优化目标模型,并利用交替方向乘子法进行分散协调优化求解。基于修改后33节点交直流配电网模型的仿真结果表明所提模型能有效减少配电网侧碳排放量,显著提高风电消纳能力。     

DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

From AlphaGo to ChatGPT, the field of AI has launched a series of remarkable achievements in recent years. Analyzing, comparing, and summarizing these achievements at the paradigm level is important for future AI innovation, but has not received sufficient attention. In this paper, we give an overview and perspective on machine learning paradigms. First, we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective. Accordingly, we give an overview on three basic and twelve extended learning paradigms, such as Ensemble Learning, Transfer Learning, etc., with figures in unified style. We further analyze three advanced paradigms, i.e., AlphaGo, AlphaFold and ChatGPT. Second, to enable more efficient and effective scientific discovery, we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science (DeSci) movement based on decentralized autonomous organization (DAO). To this end, we design the Hanoi framework, which integrates human factors, parallel intelligence based on a combination of artificial systems and the natural world, and the DAO to inspire AI innovations.      

Adaptive NN output‐feedback decentralized stabilization for a class of large‐scale stochastic nonlinear strict‐feedback systems

In this paper, the decentralized adaptive neural network (NN) output‐feedback stabilization problem is investigated for a class of large‐scale stochastic nonlinear strict‐feedback systems, which interact through their outputs. The nonlinear interconnections are assumed to be bounded by some unknown nonlinear functions of the system outputs. In each subsystem, only a NN is employed to compensate for all unknown upper bounding functions, which depend on its own output. Therefore, the controller design for each subsystem only need its own information and is more simplified than the existing results. It is shown that, based on the backstepping method and the technique of nonlinear observer design, the whole closed‐loop system can be proved to be stable in probability by constructing an overall state‐quartic and parameter‐quadratic Lyapunov function. The simulation results demonstrate the effectiveness of the proposed control scheme. Copyright © 2010 John Wiley & Sons, Ltd.     

Combination framework of rendezvous algorithm for multi‐agent systems with limited sensing ranges

In this paper, we present a novel rendezvous algorithm framework ‐ the combination framework, which is named after the term convex combination in computational geometry. The convergence of the proposed algorithms is proved based on the novel tools from the recent results in graph theory and consensus study. It has been observed that the circumcenter algorithm can be considered as a special case of the combination framework. Moreover, the relevant analysis provides us a novel viewpoint on designing and the rendezvous rate of rendezvous algorithms for networked multi‐agent systems with limited sensing ranges. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society