Dynamic balancing of planar mechanisms using toric geometry

A mechanism is statically balanced if for any motion, it does not apply forces on the base. Moreover, if it does not apply torques on the base, the mechanism is said to be dynamically balanced. In this paper, a new method for determining the complete set of dynamically balanced planar four-bar mechanisms is presented. Using complex variables to model the kinematics of the mechanism, the static and dynamic balancing constraints are written as algebraic equations over complex variables and joint angular velocities. After elimination of the joint angular velocity variables, the problem is formulated as a problem of factorization of Laurent polynomials. Using tools from toric geometry including toric polynomial division, necessary and sufficient conditions for static and dynamic balancing of planar four-bar mechanisms are derived.     

面向Web Service的动态负载均衡设计与实现

负载均衡是分布式系统的资源管理模块,它的主要功能是合理和透明地在服务器之间分配系统负载,以达到系统的综合性能最优。基于中间件的负载均衡技术在整合异构系统、透明访问和扩展能力等方面具有优势。在中间模块上可以灵活实现多种负载均衡算法,来调整和满足不同应用的负载均衡需求,提高系统的扩展性、配置性、健壮性。本文参考分布式系统中的模型,构建了基于Web Service的负载均衡器,实现了动态负载均衡调度策略。为了准确地描述系统的负载,选择CPU利用率、内存利用率、系统响应时间、输入输出流量和进程数作为衡量系统负载的参量;为了避免因监测服务器而加大均衡器的压力,策略中将负载信息采集程序运行在机群内的各服务结点上,当相邻采集周期的负载差值超过标准值后调用均衡器上的Web服务,报告自身的负载状况,使均衡器根据负载信息进行同步操作,更换服务序列。最后通过仿真实验证明本策略在异构Web平台中具有可行性。     

Maneuver-based motion planning for nonlinear systems with symmetries

In this paper, we introduce an approach for the efficient solution of motion-planning problems for time-invariant dynamical control systems with symmetries, such as mobile robots and autonomous vehicles, under a variety of differential and algebraic constraints on the state and on the control inputs. Motion plans are described as the concatenation of a number of well-defined motion primitives, selected from a finite library. Rules for the concatenation of primitives are given in the form of a regular language, defined through a finite-state machine called a Maneuver Automaton. We analyze the reachability properties of the language, and present algorithms for the solution of a class of motion-planning problems. In particular, it is shown that the solution of steering problems for nonlinear dynamical systems with symmetries and invariant constraints can be reduced to the solution of a sequence of kinematic inversion problems. A detailed example of the application of the proposed approach to motion planning for a small aerobatic helicopter is presented.     

An approximation algorithm for box abstraction of transition systems on real state spaces

Predicate abstraction is a powerful technique for extracting finite-state models from infinite-state systems such as computer software, and is applied to verification of safety properties. Predicate abstraction is also applied to verification of dynamical systems on real state spaces such as hybrid dynamical systems. In this paper, we propose a fast algorithm for computing entire abstract state spaces of transition systems on real state spaces. The method is based on the box abstraction of state spaces, and requires a relatively smaller number of reachability checks and Boolean operations. We also propose a fast method for computing the set of boxes that intersect a given convex polyhedron. This computation is a part of the proposed state-space generation algorithm. Effectiveness of the algorithm is evaluated by the computation time and by the difference of the approximated state space from the exact state space.     

离散非线性时变凸多面体系统族的鲁棒正不变集

动态系统的状态约束和控制约束等问题可归结为状态空间中某些集合的正不变性.利用混合单调分解方法研究离散非线性、时变凸多面体系统族的线性状态约束集合的鲁棒正不变性．对由矩阵凸多面体和加性区间扰动描述的线性时变离散系统族,得到了鲁棒正不变集的充分必要条件;对非线性系统族则得到有关充分条件.这些条件均由系统族的顶点表述,易于检验,同时给出示例.     

集群动态负载平衡系统的性能评价

该文使用随机Petri网对集群动态负载平衡系统建立了一个抽象模型．通过细化模型中的节点本地处理部分对5种动态负载平衡算法的性能进行了分析,并讨论了集群负载特性对动态负载平衡系统性能的影响,最后得出的主要结论有：(1)动态负载平衡算法可以取得比静态负载平衡算法更好的性能;(2)与传统的只考虑CPU就绪队列的负载平衡算法相比,考虑了各种I／O请求队列的负载平衡算法可以取得更好的性能;(3)即使在极端的集群负载特性中。集群动态负载平衡算法仍然能取得比较理想的性能,因此实现即使是十分简单的集群动态负载平衡系统也是很有必要的。     

基于Petri网的集群应用系统性能评价

利用随机Petri网理论给出了一个集群应用软件负载平衡系统的抽象模型,通过细化其中的本地节点处理部分对3种集群动态负载平衡的调度策略和应用系统体系结构对负载平衡系统的影响进行了分析,得到了一些对大部分应用系统的设计起到指导作用的结论.这些结论是:(1)无论是静态负载平衡还是动态负载平衡都能提高集群系统的性能,动态负载平衡会得到更好的性能;(2)在动态负载平衡算法中除了要考虑系统中最重要的等待队列--应用队列外,还要考虑数据库队列;(3)异步体系架构将任务切分到各处理子系统中,有助于将各子系统负载数据综合到负载向量中,能够更准确地衡量系统负载、提高负载平衡系统的性能,优于同步体系结构.     

Hybrid Architecture for Autonomous Load Balancing in Distributed Systems Based on Smooth Fuzzy Function

Due to the rapid advancements and developments in wide area networks and powerful computational resources, the load balancing mechanisms in distributed systems have gained pervasive applications covering wired as well as mobile distributed systems. In large-scale distributed systems, sharing of distributed resources is required for enhancing overall resource utilization. This paper presents a comprehensive study and detailed comparative analysis of different load balancing algorithms employing fuzzy logic and mobile agents. We have proposed a hybrid architecture for integrated load balancing and monitoring in distributed computing systems employing fuzzy logic and autonomous mobile agents. Furthermore, we have proposed a smooth and composite fuzzy membership function in order to model fine grained load information in a system. The simulation study and a detailed qualitative as well as quantitative analysis of algorithmic performances are presented. Lastly, a deployment environment is described.     

面向集群渲染的反馈控制负载均衡算法

针对传统集群渲染系统处理多级任务时无法实现动态负载均衡的问题,引入反馈控制思想,结合负载均衡原理,提出反馈控制负载均衡的新算法。该算法采用全局集中式负载均衡策略调度和分配任务线程,为不同的请求服务等级提供服务响应时间保障,实时动态调整集群系统各节点的负载并使其实现负载均衡。实验结果表明,系统整体性能比传统系统提高约1/3,证明了该算法的可行性和有效性。     

线性控制系统的无界多面体不变集

本文利用非光滑分析方法,讨论了线性控制系统的无界多面体不变集问题.当无界多面体的极方向满足一定条件时,得到了该无界多面体为一类线性控制系统弱不变集的判别方法.然后在更一般的线性控制系统下给出了无界多面体为强不变集的充分条件.最后给出两个应用实例.     

调度服务在虚拟试验系统中的研究与应用

为了提高虚拟试验系统的执行效率,保证系统负载平衡,提高系统的实时性和可靠性,研究并设计了一种实时任务调度服务;该调度服务以节点任务调度算法和迁移策略为基础,提供了一种动态的、可靠的任务分配策略和负载均衡机制,使虚拟试验的任务得到了合理分配和调度;以节点容纳度作为试验参数进行多次试验,试验结果表明:该调度服务在虚拟试验系统中能够动态维持各个节点的负载平衡,降低了任务平均响应时间,提高系统的实时性和可靠性,达到了提高虚拟试验系统整体性能的目的。     

A Competitive Analysis of Load Balancing Strategies for Parallel Ray Tracing

This paper examines the effectiveness of load balancing strategies for ray tracing on large parallel computer systems and cluster computers. Popular static load balancing strategies are shown to be inadequate for rendering complex images with contemporary ray tracing algorithms, and for rendering NTSC resolution images on 128 or more computers. Strategies based on image tiling are shown to be ineffective except on very small numbers of computers. A dynamic load balancing strategy, based on a diffusion model, is applied to a parallel Monte Carlo rendering system. The diffusive strategy is shown to remedy the defects of the static strategies. A hybrid strategy that combines static and dynamic approaches produces nearly optimal performance on a variety of images and computer systems. The theoretical results should be relevant to other rendering and image processing applications.     

Aggregation of a class of interconnected, linear dynamical systems

In this paper, we introduce the notion of a “meaningful” average of a collection of dynamical systems as distinct from an “ensemble” average. Such a notion is useful for the study of a variety of dynamical systems such as traffic flow, power systems, and econometric systems. We also address the associated issue of the existence and computation of such an average for a class of interconnected, linear, time invariant dynamical systems. Such an “average” dynamical system is not only attractive from a computational perspective, but also represents the average behavior of the interconnected dynamical systems. The problem of analysis and control of heirarchical, large scale control systems can be simplified by approximating the lower level dynamics of such systems with such an average dynamical system.     

基于多面体不变集的变终端约束集RMPC

针对一类状态和输入受约束的多胞不确定线性时变系统,提出了一种基于多面体不变集的变终端约束集鲁棒模型预测控制算法.首先采用基于状态反馈增益的多面体不变集计算方法,给出了一种新的控制不变集序列构造方法,然后以控制不变集序列的并集作为终端约束集,结合在线优化和增益切换,实施变终端约束集双模鲁棒预测控制.该算法不仅有效地扩大了...     

A Lagrangian relaxation technique for the general assembly line balancing problem

This paper addresses the problem of balancing assembly or fabrication lines. In order to achieve a given production rate or to optimize the use of workstations, one has to tackle the problem of balancing the production lines. It is well known that this problem belongs to the class of NP-hard problems. In this paper the polyhedron of the feasible solutions of the assembly line balancing problem is first studied. Then a Lagrangian relaxation algorithm that incorporates the set of cycle constraints in the objective function is proposed. These constraints are the complicating restrictions in the model. The relaxed problem has the interesting property that its linear programming relaxation always has integer optimal solutions. The subgradient algorithm is then used to maximize the Lagrangian dual. A heuristic is also used to find primal feasible solutions for the original line balancing integer program. These two bounds are then used to reduce the size of the branch-and-bound tree.     

Disassembly line balancing under high variety of end of life states using a joint precedence graph approach

Disassembly is an important aspect of end of life product treatment, as well as having products disassembled in an efficient and responsible manner. Disassembly line balancing is a technique that enables a product to be disassembled as efficiently and economically viable as possible; however, considering all possible end of life (EOL) states of a product makes disassembly line balancing very difficult. The EOL state and the possibility of multiple recovery options of a product can alter both disassembly tasks and task times for the disassembly of the EOL product. This paper shows how generating a joint precedence graph based on the different EOL states of a product is beneficial to achieving an optimal line balance where traditional line balancing approaches are used. We use a simple example of a pen from the literature to show how a joint disassembly precedence graph is created and a laptop example for joint precedence graph generation and balancing. We run multiple scenarios where the EOL conditions have different probabilities and compare results for the case of deterministic task times. We also consider the possibility where some disassembly task times are normally distributed and show how a stochastic joint precedence graph can be created and used in a stochastic line balancing formulation.     

An adaptive threshold based hybrid load balancing scheme with sender and receiver initiated approach using random information exchange

The primary objective of load balancing for distributed systems is to minimize the job execution time while maximizing the resource utilization. Load balancing on decentralized systems need effective information exchange policy so that with minimum amount of communication the nodes have up to date information about other nodes in the system. Periodic, event‐based and on‐demand information exchange are some important policies used for the same. All these approaches involve a lot of overhead and even sometime leading toward obsolete data with the nodes if there is a delay in the updation. This work presents an adaptive threshold‐based hybrid load balancing scheme with sender and receiver initiated approach (HLBWSR) using random information exchange (RIE). RIE ensures that the information is exchanged in such a way that each node in the system has up‐to‐date state of the other nodes with much reduced communication overhead. Further, the adaptive threshold ensures that almost an average numbers of jobs are executed by all the nodes in the system. The study of the effect of the use of RIE on sender initiated, receiver initiated and hybrid of sender and receiver initiated load balancing approach establishes the superior performance of HLBWSR among its RIE‐based peers. A comparative analysis of HLBWSR, with periodic information exchange strategy, modified estimated load information scheduling algorithm and load balancing on arrival reveals its effectiveness under various test conditions. Copyright © 2016 John Wiley & Sons, Ltd.     

服务器集群负载均衡技术研究及算法比较

简要介绍了负载均衡技术的分类及其发展,重点介绍了服务器集群负载均衡技术及应用.并对评价负载均衡优劣的重要标准之一--负载均衡算法的种类做了详细介绍及优缺点比较.对近年来一些新的负载均衡算法做了介绍.最后,对服务器集群负载均衡技术的发展前景做出了展望和预测.     

Cooperative load balancing in distributed systems

A serious difficulty in concurrent programming of a distributed system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. In this paper, we formulate the static load‐balancing problem in single class job distributed systems as a cooperative game among computers. The computers comprising the distributed system are modeled as M/M/1 queueing systems. It is shown that the Nash bargaining solution (NBS) provides an optimal solution (operation point) for the distributed system and it is also a fair solution. We propose a cooperative load‐balancing game and present the structure of NBS. For this game an algorithm for computing NBS is derived. We show that the fairness index is always equal to 1 using NBS, which means that the solution is fair to all jobs. Finally, the performance of our cooperative load‐balancing scheme is compared with that of other existing schemes. Copyright © 2008 John Wiley & Sons, Ltd.     

Neural learning of chaotic dynamics

In recent years, considerable progress has been made in modeling chaotic time series with neural networks. Most of the work concentrates on the development of architectures and learning paradigms that minimize the prediction error. A more detailed analysis of modeling chaotic systems involves the calculation of the dynamical invariants which characterize a chaotic attractor. The features of the chaotic attractor are captured during learning only if the neural network learns the dynamical invariants. The two most important of these are the largest Lyapunov exponent which contains information on how far in the future predictions are possible, and the Correlation or Fractal Dimension which indicates how complex the dynamical system is. An additional useful quantity is the power spectrum of a time series which characterizes the dynamics of the system as well, and this in a more thorough form than the prediction error does. In this paper, we introduce recurrent networks that are able to learn chaotic maps, and investigate whether the neural models also capture the dynamical invariants of chaotic time series. We show that the dynamical invariants can be learned already by feedforward neural networks, but that recurrent learning improves the dynamical modeling of the time series. We discover a novel type of overtraining which corresponds to the forgetting of the largest Lyapunov exponent during learning and call this phenomenondynamical overtraining. Furthermore, we introduce a penalty term that involves a dynamical invariant of the network and avoids dynamical overtraining. As examples we use the Hénon map, the logistic map and a real world chaotic series that corresponds to the concentration of one of the chemicals as a function of time in experiments on the Belousov-Zhabotinskii reaction in a well-stirred flow reactor.