H ∞ synthesis of unknown input observers for non-linear Lipschitz systems

The problem of unknown input observer design for non-linear Lipschitz systems is considered. A new dynamic framework which is a generalization of previously used linear unknown input observers is introduced. The additional degrees of freedom offered by this dynamic framework are used to deal with the Lipschitz non-linearity. The necessary and sufficient condition that ensures asymptotic convergence of the new observer is presented, and the equivalence between this condition and an H _∞ optimal control problem which satisfies the standard regularity assumptions in the H _∞ optimization theory is shown. Based on these results, a design procedure that is solvable using commercially available software is presented. A simulation example is given to illustrate the proposed design.     

A Parallel SPH Implementation on Multi‐Core CPUs

This paper presents a parallel framework for simulating fluids with the Smoothed Particle Hydrodynamics (SPH) method. For low computational costs per simulation step, efficient parallel neighbourhood queries are proposed and compared. To further minimize the computing time for entire simulation sequences, strategies for maximizing the time step and the respective consequences for parallel implementations are investigated. The presented experiments illustrate that the parallel framework can efficiently compute large numbers of time steps for large scenarios. In the context of neighbourhood queries, the paper presents optimizations for two efficient instances of uniform grids, that is, spatial hashing and index sort. For implementations on parallel architectures with shared memory, the paper discusses techniques with improved cache‐hit rate and reduced memory transfer. The performance of the parallel implementations of both optimized data structures is compared. The proposed solutions focus on systems with multiple CPUs. Benefits and challenges of potential GPU implementations are only briefly discussed.     

Tool allocation in flexible manufacturing systems with tool alternatives

In this paper, a heuristic approach for tool selection in flexible manufacturing systems (FMS) is presented. The proposed approach utilizes the ratio of tool life over tool size (L/S) for tool selection and allocation. The proposed method selects tool types with high L/S ratios by considering tool alternatives for the operations assigned to each machine. The performance of the method is demonstrated in sample problems as static examples, as well as in a simulation study for further analysis. This study also presents a survey of several approaches related to loading and tool allocation problems in FMS, highlights the importance of tooling, and discusses the practical aspects of tool-oriented decision-making. An extended framework, which expands on the L/S concept, is also presented.     

基于多Agent技术的生化网络仿真平台的实现

介绍了基于多agent技术、针对于生化网络的仿真框架，在分析Java相关技术特性的基础上，给出了框架的Java实现，并针对细菌趋药性网络进行了仿真实验，结果表明基于多agent的仿真平台具有良好的特性，在生化网络仿真中具有广泛的应用前景。     

信号与系统仿真通用框架的设计与实现

本文提出了信号与系统仿真的一个通用框架，该框架充分考虑了系统的可扩展性和可重用性，并用几个Java类对核心模块的一些关键算法进行了设计和封装。然后设计了采样定理仿真的实例。仿真结果表明，在这个框架的基础上实现的信号与系统仿真结构清晰，简单易行，效果显著。最后，进一步说明了框架的应用。     

基于高级Petri网的仿真剧情正规校核方法

针对仿真剧情主观校核不理想这一问题，提出了基于高级Petri网的仿真剧情正规校核方法．首先给出仿真剧情的形式化定义，并分析仿真剧情可能存在的错误类型；其次给出仿真剧情到高级Petri网的映射途径，并给出基于高级Petri网的仿真剧情校核准则和算法，此外，还给出实现仿真剧情动态校核的推理规则和机制；最后给出了一个正规校核工具框架．实际应用已经证明了该方法的有效性．     

On Object-Oriented Frameworks and Coordinate Free Formulations of PDEs

. An object-oriented (OO) framework for Partial Differential Equations (PDEs) provides software abstractions for numerical simulation of PDEs. The design of such frameworks is not trivial, and the outcome of the design is highly dependent on which mathematical abstractions one chooses to support. In this paper, coordinate free abstractions for PDEs are advocated. The coordinate free formulation of a PDE hides the underlying coordinate system. Therefore, software based on these concepts has the prospect of being more modular, since the PDE formulation is separated from the representation of the coordinates. Use of coordinate free methods in two independent OO frameworks are presented, in order to exemplify the viability of the concepts. The described applications simulate seismic waves for various classes of rock models and the incompressible Navier-Stokes equations on curvi-linear grids, respectively. In both cases, the possibility to express the equations in a domain independent fashion is crucial. Similarities and differences between the two coordinate free frameworks are discussed. A number of places where such frameworks should be designed for modification is identified. This identification is of interest both for framework developers and for tentative framework users. ID="A1"Correspondance and offprint requests to: K Ahlander, Dept of Information Technology, University of Uppsala, Box 337, SE-75105 Uppsala, Sweden. E-mail: krister@tdb.uu.se     

面向仿真训练系统的过程模型研究

该文首先说明了过程模型的功能，根据功能需求对相关过程建模方法进行分析，找出了适合于仿真系统中过程模型的描述方法，并重点给出了一种基于有限状态机方法的训练过程模型框架、过程模型实时调度方法和应用实例。该过程模型框架由演示过程、物理行为过程、混合过程、过程转移、转移条件等对象类组成，在仿真训练系统开发中，通过定义和扩展这些对象类，对基于有限状态机的过程模型进行控制与调度，准确而全面地描述和管理仿真训练过程，从而有效地解决了仿真训练系统中的过程模型开发问题。     

A neuro-fuzzy hybrid scheme for design and simulation of human machine systems

In this article, a new neuro-fuzzy hybrid approach to human workplace design and simulation is proposed. Problems related to human workplace design such as human-machine modeling, measurement and analysis, workplace layout design and planning, workplace evaluation and simulation are discussed in detail. The complex human-machine interactions in workplace design are described with human and workstation parameters within a comprehensive human-machine system model. Based on this model, procedures and algorithms for workplace design, ergonomic evaluation, and optimization are presented in an integrated framework. With a combination of individual neural and fuzzy techniques, the neuro-fuzzy hybrid scheme implements fuzzy if-then rules block for workplace design and evaluation by trainable neural network architectures. For training and test purposes, simulated assembly tasks are carried out on a self-built multiadjustable laboratory workstation with a flexible PEAK Motus motion measurement and analysis system. The trained fuzzy neural networks are capable of predicting the operator's posture and joint angles of motion associated with a range of workstation configurations. They can also be used for design/layout and adjustment of manual assembly workstations. The developed system provides a unified, intelligent computational framework for human-machine system design and simulation. In the end, case studies for workplace design and simulation are presented to validate and illustrate the developed neuro-fuzzy design scheme and system.