Stochastic Optimal Design for Unknown Linear Discrete‐Time System Zero‐Sum Games in Input‐Output form Under Communication Constraints

In this paper, stochastic optimal strategy for unknown linear discrete‐time system quadratic zero‐sum games in input‐output form with communication imperfections such as network‐induced delays and packet losses, otherwise referred to as networked control system (NCS) zero‐sum games, relating to the H_∞ optimal control problem is solved in a forward‐in‐time manner. First, the linear discrete‐time zero sum state space representation is transformed into a linear NCS in the state space form after incorporating random delays and packet losses and then into the input‐output form. Subsequently, the stochastic optimal approach, referred to as adaptive dynamic programming (ADP), is introduced which estimates the cost or value function to solve the infinite horizon optimal regulation of unknown linear NCS quadratic zero‐sum games in the presence of communication imperfections. The optimal control and worst case disturbance inputs are derived based on the estimated value function in the absence of state measurements. An update law for tuning the unknown parameters of the value function estimator is derived and Lyapunov theory is used to show that all signals are asymptotically stable (AS) and that the estimated control and disturbance signals converge to optimal control and worst case disturbances, respectively. Simulation results are included to verify the theoretical claims.     

基于贪婪路由协议的动态单/多链路故障恢复策略

针对现有的贪婪方法不能有效处理拓扑结构中链路故障的问题,提出单链路故障和多链路故障本地化恢复策略。首先,通过利用克莱因伯格的贪婪嵌入给出单链路故障恢复策略;然后,将其扩展到多链路故障的情况;最后,在基于Python/C++的仿真环境下对提出的技术进行评估。实验结果表明,该技术仅需要非常有限的资源,且造成的路由质量损耗也有限,可以实现快速切换,可依网络生成树中链路数目扩展。该技术的可扩展性、简单性和低开销使其适合于大型网络。     

基于辅助学习与富信息策略的Tri-training算法

针对Tri-training算法利用无标记样例时会引入噪声且限制无标记样例的利用率而导致分类性能下降的缺点,提出了AR-Tri-training(Tri-training with assistant and rich strategy)算法.提出辅助学习策略,结合富信息策略设计辅助学习器,并将辅助学习器应用在Tri-training训练以及说话声识别中.实验结果表明,辅助学习器在Tri-training训练的基础上不仅降低每次迭代可能产生的误标记样例数,而且能够充分地利用无标记样例以及在验证集上的错分样例信息.从实验结果可以得出,该算法能够弥补Tri-training算法的缺点,进一步提高测试率.     

Information systems use as strategy practice: A multi-dimensional view of strategic information system implementation and use

Information systems (IS) are strategic in so far as they are used to realize strategic intent. Yet, while much has been said about aligning IS functionality with the strategic intent and how to organizationally implement strategically aligned systems, less is known of how to successfully implement strategic change associated with system use – a truly critical challenge within strategic IS implementation. Drawing on a strategy-as-practice perspective we address this gap by developing a multi-dimensional view of IS strategy, conceptualizing three key challenges in the IS strategy process, to explain how and why a paper mill, despite successfully implementing a strategic production management system, failed to produce intended strategic change. We call this outcome strategy blindness: organizational incapability to realize the strategic intent of implemented, available system capabilities. Using a longitudinal case study we investigate how cognitive rigidity of key actors and fixed, interrelated practices shaped the implementation of the new production system. We also identify core components and dynamics that constitute a richer multi-dimensional view of the IS strategy implementation (alignment) process. In particular, we identify three salient factors that contribute to strategy blindness – mistranslation of intent, flexibility of the IT artifact and cognitive entrenchment – and discuss how they affect strategic implementation processes. We conclude by discussing implications of our findings for IS strategy theory and practice, especially the contribution of strategy-as-practice to this stream of research.     

企业云应用演进策略多时段分析

随着云计算技术的发展,企业急需一种有效方法将原本孤立的应用系统向云平台迁移。将网络层次分析法(ANP)及其超级矩阵收敛过程应用于企业云应用演进计划制定,提出一个演进策略的多时段分析方法。通过对企业云应用体系结构的建模与仿真分析,构建ANP多属性决策模型,解析超级矩阵从初始状态到极限状态的收敛过程,挖掘各云应用系统的分时段关键性影响因素,为企业的整体演进策略提供支持。最后,通过一个案例展示多时段分析方法的具体过程,提取出相关关键因素,为企业策略的动态规划提供理论依据。     

基于卷积树核的中文微博情感要素识别

情感要素识别是情感分析的关键子任务之一,其目的是识别出文本情感所作用的情感对象。文本情感要素识别属于最细粒度的情感分析,吸引了大量研究者的关注。中文微博由于其语言简短灵活、文本不规范、噪声较大等特点,给中文微博情感分析研究工作带来了新的挑战。目前大部分情感要素识别方法都是基于规则的方法或者基于扁平化特征的统计学习方法,区分噪声的能力不强,性能提升有限。针对中文微博的特点,提出一种基于卷积树核的情感要素识别算法,即首先对句子进行词性标注与依存关系分析,将句子中的名词作为候选情感要素;然后基于两种不同的修剪策略对依存树进行修剪,以获取每个候选情感要素的结构化信息;最后采用卷积树核计算依存树的相似度,并在此基础上识别句子中的情感要素。NLPCC2012和NLPCC2013中文微博情感分析评测任务中的实验验证了该方法的性能,其准确率相比于传统方法有显著提升。     

一种改进的基于分解的多目标进化算法

利用基于分解的多目标进化算法框架(MOEA/D),将混合策略的进化算法用于求解分解后的若干单目标优化子问题,提出了一种带局部搜索的基于分解的多目标混合策略进化算法(LMS-MOEA/D)。算法利用均匀设计产生子问题的聚合权重向量,混合交叉策略能够充分利用不同交叉算子的优势;同时算法针对演化过程收敛的特点,结合局部搜索策略,获得逼近Pareto前沿的最优解集。最后通过实验验证算法在多样性和收敛性方面的有效性。     

在线视频点播系统节能方法与策略研究

目前随着海量数据应用的快速发展,如何减少海量数据存储造成的巨大能耗成为研究热点。分析了在线视频点播的海量存储系统,根据正态分布分析视频访问的时间段,结合磁盘的特点提出一种基于视频点播的节能策略。策略中将磁盘的状态划分为不同的等级,降级时根据历史时间间隔调整时间窗口,同时根据视频的种类制定分布,以最终满足系统的性能和节能需求。根据计算,使用节能策略后系统平均节能达到44%。     

一种MMC型VSC-HVDC系统预充电的控制策略

模块化多电平换流器(Modular Multilevel Converter,MMC)是一种新型的电压源换流器拓扑,很适合于柔性直流输电(Voltage Source Converter Topology Applied in Voltage Source Converter-high Voltage Direct Current,VSC-HVDC)场合。在系统正常运行之前,MMC中的电容需进行预充电操作,以建立额定的电容电压和直流母线电压。首先介绍了传统启动方式存在的缺陷,为弥补这些不足,结合MMC启动时的数学模型,在详细分析换流器电容预充电动态过程的基础上,采用载波移相(Carrier Phase Shifted Modulation,CPSM)策略提出了一种适用于MMC可控阶段的新型启动方法。设计了电容电压参考值给定方式、内环电流控制以及外环电压控制,该预充电策略在限制过电流和避免过电压的同时,兼顾启动的速度和效率,提高了柔性直流输电系统启动的快速性和可靠性。由在Matlab/Simulink中所构建模型的仿真结果可以看出,所提出的控制策略正确、有效。     

基于无传感器的PMSM电流控制策略的研究

提出一种新型的滑模观测器,并分别研究在4种不同的电流控制策略下,基于该新型滑模观测器的PMSM伺服系统的控制问题。该新型滑模观测器引进了Sigmoid函数作为控制函数用来抑制抖振,并根据PMSM的反电动势模型构建了反电势观测器来提取所需的连续信号,因此取消了传统的一阶低通滤波器和相角补偿环节。为了对电机的转子位置和转速进行更加精确的估算,引入了转子位置锁相环结构。在Matlab/Simulink平台基础上建立了4种不同的电流控制策略,基于新型滑模观测器PMSM无位置传感器三闭环控制系统仿真模型,分别对反电动势估算、速度估算、位置估算和突加负载扰动进行了仿真分析。仿真结果表明,该新型滑模观测器在4种不同的电流控制策略下,对电机转子位置和转速的估算、电磁转矩以及定子三相电流有着不同的影响,验证了该新型滑模观测器算法的可行性。