Adaptive prescribed time control for quadrotor formation with stochastic links failure

The formation control for multiple quadrotors subject to maintaining the formation configuration and collision avoidance in the situation of stochastic links failure is investigated in this paper. First, the distributed formation controller is designed, the position controller is developed to manage the desired formation of position, and the attitude controller is developed to control the translation and rotation movements of the quadrotor. Then, in order to avoid the collisions between multiple quadrotors and the obstacles, a potential energy function method is introduced into the quadrotor formation control combined with the nest adaptive control. Inspired by the design of event trigger controller, a communication compensation controller is designed to ensure the stability of quadrotor formation under the condition of random communication interruption and recovery. Moreover, a prescribed time function is designed, which means the convergence time of the formation system can be set in advance. The prescribed time stability of the formation control system is proved by Lyapunov theory. Finally, the simulation results verify the effectiveness and superiority of this method.     

Volt-VAR control for distribution networks with high penetration of DGs: An overview

Continuously improving the ability to accept distributed renewable energies is the trend of future grid development, and a large number of papers have been published in recent years to study the problem of Volt-VAR control (VVC) for distribution networks with high penetration of distributed generations. This paper summarizes the relevant modeling and solution methods for VVC problems, mainly including VVC based on multiple time scales, hierarchical partitioning, multi-stage and network reconstruction, in conjunction with the operational characteristics of distribution networks containing distributed renewable energies; meanwhile, it analyzes the advantages and disadvantages of traditional optimization methods, heuristic intelligent algorithms and random variable processing methods used to solve VVC problems, and then introduces the application of model-free deep reinforcement learning as a latest decision method in VVC of distribution networks. Most of the models and methods compiled in this article are from the research results of the last three years and have some reference value.     

Improved inter-view correlations for low complexity MV-HEVC

The more advanced multi-view extension, MV-HEVC, effectively exploits visual similarities between multi-view videos and enables high compression efficiency. Each view in the multi-view sequence depends on the captured scene, the distance between cameras and recording angles. Increasing the distance between dependent viewpoints generates an inter-view disparity. This impacts the inter-view similarities, affects the disparity estimation and further increases the computational complexity of the MV-HEVC encoder. In this paper, an efficient earlier disparity estimation is proposed for low complexity MV-HEVC. This algorithm is based on reducing the complexity of disparity estimation by eliminating the inter-view offset. Moreover, the inter-view similarities are controlled by considering the reliability of each coding unit size in the search range. This reliability is estimated by reducing the number of searching points within a new limited window. For reliable motion estimation, we further proposed an earlier decision of coding units splitting in the dependent views according to those in the reference views. Experimental results show that the proposed algorithm can achieve an average encoding time saving of 20.37%–40,61% with marginal performance degradation.     

Stabilization of linear systems with input delay and saturation—A parametric Lyapunov equation approach

This paper studies the problem of stabilizing a linear system with delayed and saturating feedback. It is known that the eigenstructure assignment‐based low‐gain feedback law (globally) stabilizes a linear system in the presence of arbitrarily large delay in its input, and semi‐globally stabilizes it when the input is also subject to saturation, as long as all its open‐loop poles are located in the closed left‐half plane. Based on a recently developed parametric Lyapunov equation‐based low‐gain feedback design method, this paper presents alternative, but simpler and more elegant, feedback laws that solve these problems. The advantages of this new approach include its simplicity, the capability of giving explicit conditions to guarantee the stability of the closed‐loop system, and the ease in scheduling the low‐gain parameter on line to achieve global stabilization in the presence of actuator saturation. Copyright © 2009 John Wiley & Sons, Ltd.     

Several multi-criteria programming methods for classification

In this paper, we propose a general optimization-based model for classification. Then we show that some well-known optimization-based methods for classification, which were developed by Shi et al. [Data mining in credit card portfolio management: a multiple criteria decision making approac. In: Koksalan M, Zionts S, editors. Multiple criteria decision making in the new millennium. Berlin: Springer; 2001. p. 427–36] and Freed and Glover [A linear programming approach to the discriminant problem. Decision Sciences 1981; 12: 68–79; Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 1981; 7: 44–60], are special cases of our model. Moreover, three new models, MCQP (multi-criteria indefinite quadratic programming), MCCQP (multi-criteria concave quadratic programming) and MCVQP (multi-criteria convex programming), are developed based on the general model. We also propose algorithms for MCQP and MCCQP, respectively. Then we apply these models to three real-life problems: credit card accounts, VIP mail-box and social endowment insurance classification. Extensive experiments are done to compare the efficiency of these methods.     

Agent-based negotiation and decision making for dynamic supply chain formation

Modern businesses are facing the challenge of effectively coordinating their supply chains from upstream to downstream services. It is a complex problem to search, schedule, and coordinate a set of services from a large number of service resources under various constraints and uncertainties. Existing approaches to this problem have relied on complete information regarding service requirements and resources, without adequately addressing the dynamics and uncertainties of the environments. The real-world situations are complicated as a result of ambiguity in the requirements of the services, the uncertainty of solutions from service providers, and the interdependencies among the services to be composed. This paper investigates the complexity of supply chain formation and proposes an agent-mediated coordination approach. Each agent works as a broker for each service type, dedicated to selecting solutions for each service as well as interacting with other agents in refining the decision making to achieve compatibility among the solutions. The coordination among agents concerns decision making at strategic, tactical, and operational level. At the strategic level, agents communicate and negotiate for supply chain formation; at the tactical level, argumentation is used by agents to communicate and understand the preferences and constraints of each other; at the operational level, different strategies are used for selecting the preferences. Based on this approach, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

测试稿件

复杂网络理论是时间序列分析中一种有力的工具。但在面对高频数据时,现有建网方法是低效的。因此,提出利用时间序列符号化技术压缩原始序列,并构造网络的方法。该方法使用最小二乘估计时序分段斜率,提取序列的局部特征,并构造字典判断节点是否邻接。模拟试验表明,所建网络的标度指数、集群系数与过程的Hurst指数高度相关,可以精确地捕捉原过程的复杂性特征     

基于混合聚类的城市道路多时段控制研究*

多方案控制具有简单实用、可靠性好,稳定性高,性价比高等的优点,在城市交通控制中得到了广泛应用。本研究在利用指数平滑异同移动平均法对缺失交通数据进行修补的基础上,运用快速聚类与系统聚类相结合的混合聚类方法对交通数据进行分析,并以经典的Silhouttte评价指标作为聚类终止条件,同时兼顾/考虑了交通数据时序性,消除聚类结果中的三类“奇异点”,最后利用交通信号配时软件SYNCHRO制定了相应时段的控制方案。相关的案例分析显示,优化后的多时段控制方案可以更好地适应交通流的波动,车均延误减少9.79%,其中时序性考虑可以有效改善控制方案频繁切换对交通流的扰动,避免其负效应对混合聚类改善效果的影响。     

Robust stabilisation and L2 -gain analysis for switched systems with actuator saturation under asynchronous switching

Robust stabilisation and L₂-gain analysis for a class of switched systems with actuator saturation are studied in this paper. The switching signal of the controllers lags behind that of the system modes, which leads to the asynchronous switching between the candidate controllers and the subsystems. By combining the piecewise Lyapunov function method with the convex hull technique, sufficient conditions in terms of LMIs for the solvability of the robust stabilisation and weighted L₂-gain problems are presented respectively under the dwell time scheme. Finally, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed results.     

基于云计算的营销决策支持系统

设计并实施了一种基于云计算的营销决策支持系统,和其他管理决策子系统发生交互,共同构成了完整的现代企业经营决策支持系统.系统的数据库采用分布式设计,使得系统既有独立处理本地数据库的能力,也可读取异地数据库中的数据.系统模型库包含多种营销因素的决策模型,并采用模型组合的思想,将复杂的决策问题通过模型之间的组合来实现.在流程设计上充分考虑人机交互,将用户的经验判断纳入到决策过程中.完成了系统的开发,并用仿真数据进行了系统测试,结果表明系统基本运行稳定,各子模块衔接良好,与其他子系统实现了数据共享.