多种动态模拟和复杂三维动画控制的框架结构

本文提出了一种新的适用于多种动态模拟和复杂三维动画控制的面向对象的框架结构，它采用混合层次模型，其角色按部件层次组织其静态结构，角色中包含有决定其形状、样式和行为变化的属性，它按类的层次结构定义，属性的动态赋值通过引进的动态约束机制实现.该框架结构已用于SIMUKIT──用于多种动态模拟视觉效果自动生成的集成工具环境中，并在SGI工作站上初步实现，实验表明该框架结构对解决多种动态模拟生成是可行的.     

Component-based analysis of embedded control applications

The widespread use of embedded systems requires the creation of industrial software technology that will make it possible to engineer systems being correct by construction. That can be achieved through the use of validated (trusted) components, verification of design models, and automatic configuration of applications from validated design models and trusted components. This design philosophy has been instrumental for developing COMDES—a component-based framework for distributed embedded control systems. A COMDES application is conceived as a network of embedded actors that are configured from instances of reusable, executable components—function blocks (FBs). System actors operate in accordance with a timed multitasking model of computation, whereby I/O signals are exchanged with the controlled plant at precisely specified time instants, resulting in the elimination of I/O jitter. The paper presents an analysis technique that can be used to validate COMDES design models in SIMULINK. It is based on a transformation of the COMDES design model into a SIMULINK analysis model, which preserves the functional and timing behaviour of the application. This technique has been employed to develop a feasible (light-weight) analysis method based on runtime observers. The latter are conceived as special-purpose actors running in parallel with the application actors, while checking system properties specified in Linear Temporal Logic. Observers are configured from reusable FBs that can be exported to SIMULINK in the same way as application components, making it possible to analyze system properties via simulation. The discussion is illustrated with an industrial case study—a Medical Ventilator Control System, which has been used to validate the developed design and analysis methods.     

Advantages of Model Driven Engineering for studying complex systems

The evaluation of the emergent behaviour in complex systems requires an analytical framework which allows the observation of different phenomena that take place at different levels. In order to observe the dynamics of complex systems, it is necessary to perform simulations so that both local and the emergent behaviour can be observed. To this end, the way in which complex system simulators are built must be examined so that it will be feasible to model large scale scenarios. In this paper, the use of Model Driven Engineering methodology is proposed to deal with this issue. Among other benefits, it is shown that this methodology allows the representation and simulation of a complex system providing support for the analysis. This analysis is supported by a metamodel which describes the system components that are under study. The application of this methodology to the development of large scale simulators is explored through a case study. This case study analyses a complex socio-technical system: a power grid.     

Fault detection and isolation of the three-tank system using the modal interval analysis

Analytical redundancy is a widely used technique for fault detection. It consists of comparing the behaviour of a real system with a reference obtained by simulation of its model. The main problem is that there are always imprecisions and uncertainties which are not represented in the model so the behaviour of the real system and the behaviour of the model are not exactly the same. One way to represent these uncertainties in the model is using interval models. The results of the simulation of these types of models may be represented by envelopes. This paper proposes an approach to generate envelopes based on interval techniques of the modal interval analysis. As an example, this approach is used to detect and isolate faults in a physical system formed by three interconnected tanks.     

Agent-Based Simulation of Animal Behaviour

In the biological literature on animal behaviour, in addition to real experiments and field studies, also simulation experiments are a useful source of progress. Often specific mathematical modelling techniques are adopted and directly implemented in a programming language. Modelling more complex agent behaviours is less adequate using the usually adopted mathematical modelling techniques. The literature on AI and Agent Technology offers more specific methods to design and implement (also more complex) intelligent agents and agent societies on a conceptual level. One of these methods is the compositional multi-agent system design method DESIRE. In this paper it is shown how (depending on the complexity of the required behaviour) a simulation model for animal behaviour can be designed at a conceptual level in an agent-based manner. Different models are shown for different types of behaviour, varying from purely reactive behaviour to pro-active, social and adaptive behaviour. The compositional design method for multi-agent systems DESIRE and its software environment supports the conceptual and detailed design, and execution of these models. A number of experiments reported in the literature on animal behaviour have been simulated for different agent models.     

Modelling selective visual attention for autonomous virtual characters

Autonomous virtual characters (AVCs) are becoming more prevalent both for real‐time interaction and also as digital actors in film and TV production. AVCs require believable virtual human animations, accompanied by natural attention generation, and thus the software that controls the AVCs needs to model when and how to interact with the objects and other characters that exist in the virtual environment. This paper models automatic attention behaviour using a saliency model that generates plausible targets for combined gaze and head motions. The model was compared with the default behaviour of the Second Life (SL) system in an object observation scenario while it was compared with real actors' behaviour in a conversation scenario. Results from a study run within the SL system demonstrate a promising attention model that is not just believable and realistic but also adaptable to varying task, without any prior knowledge of the virtual scene. Copyright © 2011 John Wiley & Sons, Ltd.     

Holonic multilevel simulation of complex systems: Application to real-time pedestrians simulation in virtual urban environment

Simulation, which creates abstractions of the system is an appropriate approach for studying complex systems that are inaccessible through direct observation and measurement. The problem with simulation of great numbers of interacting entities is that it is difficult to create a reliable and tractable abstraction of the real system. Indeed, simulating large numbers of entities requires great computing resources. A solution to avoid this problem is to use macroscopic models. However, this type of model may be unavailable or not reliable for the problem at hand and it does not allow the observation of individual behaviours. In this paper, a multilevel simulation model is proposed to allow the use of both microscopic and macroscopic techniques. This model is based upon Holonic Multi-Agent Systems which offer a promising approach for developing applications in complex domains characterised by a hierarchical structure. The proposed approach provides a generic scheduling model for multilevel simulations: dynamically adapting the level of simulated behaviours while being as faithful as possible to the simulated model. It does not only manage the level of entities’ behaviour but also of behaviours classically assigned to the environmental part of a simulation. A set of physics-based indicators is also introduced to dynamically determine the most suitable level for each entity and to maintain the best trade-off between simulation accuracy and constraints (dependent on the model or the experimental context).     

Bifurcation Analysis of Railway Vehicles

To identify the long-time behaviour of nonlinear dynamical systems with respect to the influence of one or more system parameters, numerical bifurcation analysis is an ideal computer-aided method. The objective of the paper is to describe a software environment for such an analysis that is based on the principles of path-following or continuation. A specific viewpoint is the application to ‘realistic’, i.e. detailed and complex simulation models of railway vehicles following a multibody system approach. Stationary as well as periodic behaviour is considered. Three major topics are of primary interest: The integration of a bifurcation software into a software package for the simulation of arbitrary mechanical systems; the direct calculation of periodic solutions (limit cycles); and the handling of differential algebraic equations (DAEs). The algorithms are applied finally to the ‘realistic’ simulation model of a high-speed railway passenger car.     

模糊环境中工作流任务分配的多级模型

对工作流实例中各用户型任务进行自动优化分配是提高工作流管理系统运行效率的关键技术之一 .在详细分析了影响用户型任务分配的多种因素及其语意模糊性后,提出了一个可组合的任务分配多级模型以及相应的任务分配方法,详细讨论了具有自适应能力的影响因素权重设计方法 .最后,通过与其他任务分配方法对比,分析了该任务分配方法的性能 .     

Automated FMEA based diagnostic symptom generation

The comprehensive on-board diagnosis of faults in many aerospace and other engineered systems requires real time execution using limited computational resources, and must also provide verifiable behaviour. This paper shows how a diagnostic system satisfying these requirements can be automatically generated from the model based simulation used to produce an automated Failure Modes and Effect Analysis (FMEA). The resulting diagnostic system comprises a set of efficiently evaluated symptoms and their associated faults. The symptoms are complete in that they include all necessary observations required to determine applicable system operating states, unlike other work that finesses this problem by having models for each operating state and producing diagnostics for each operating state separately. The symptoms are also efficient because they abstract complex system behaviour based on observations available to the diagnostic system and only preserve sufficient symptom detail to isolate faults given these available observations.This work has been done in the context of diagnosing autonomous aircraft, and is illustrated with examples from that domain. The models used as a basis for automated generation of diagnostics were originally produced to automate the production of a FMEA report, and the paper also considers the relationship between FMEA and diagnostics that provides verification of the failure effects predicted by the simulation and hence validation of the generated symptoms.     

Simulation optimisation using a genetic algorithm

The paper presents an attempt to apply genetic algorithms (GAs) to the problem of optimising an existing simulation model. A simple real-coded GA is presented and used to change the simulation model parameters. With each new parameter set proposed, a simulation run is performed. From the statistics gathered by running the simulation, a goal function is constructed that measures the quality of these parameters. Because of its nature and the stochastic and unpredictable behaviour of the complex simulation model, the goal function used leads to a highly non-linear, noisy and mixed (discrete and continuous) programming optimisation problem. A GA successfully works on it, and as a result gives a parameter set that measures substantially better than the nominal one. This demonstrates the capability of GAs to solve hard inverse problems even in the area of complex simulation model optimisation.     

Metamodels and emergent behaviour in models of conflict

In this paper, we develop a simplified mathematical model (a metamodel) of a simulation model of conflict, based on ideas drawn from the analysis of more general physical systems, such as found in fluid dynamics modelling. We show that there is evidence from the analysis of historical conflicts to support the kind of emergent behaviour implied by this approach. We then apply this approach to the development of a metamodel of a particular complexity based simulation model of conflict (ISAAC), developed for the US Marine Corps.The approach we have illustrated here is very generic, and is applicable to any simulation model which has complex interactions similar to those found in fluid dynamic modelling, or in simulating the emergent behaviour of large numbers of simple systems which interact with each other locally.     

An agent-based simulation model for analyzing the governance of the Brazilian Financial System

Regulation can play an important role in effectively managing systemic risk while providing accountability to all affected governments. IMF points out weak governance structures as one of the main causes for financial/economical crisis. However, research in this area is still limited. One of the reasons is the inherent complexity of the public sector governance notion. In this research, the regulatory governance of the financial sector is conceived as a complex system, in which governance is perceived as a phenomenon resulting from the interactions among all the actors that influence or are influenced by regulatory activities within the financial sector. An agent-based simulation was developed to analyze and evaluate the emergent behaviors from the governance in the Brazilian finance sector under different macroeconomics variables and different attitudes, perceptions and desires of economic and political actors. The agent-based model is combined with an econometric model, which is intended to characterize the macroeconomic environment. The regulatory environment is modeled by computational agents using BDI (beliefs–desires–intentions) architecture. The agents have beliefs about their environment and desires they want to satisfy, thus leading them to create intentions to act. The agents’ behavior was modeled using fuzzy rules built by means of content analysis of newspapers and in-depth interviews with experts from the financial area. Computational experiments demonstrate the potential of the agent-based model simulation in the study of complex environments involving regulatory governance.     

Modelling chaotic behaviour of SIP retransmission mechanism

The Session Initiation Protocol (SIP) retransmission mechanism can cause SIP network collapse with short-term overload. In this paper, we investigate with a fluid modelling approach the chaotic behaviour of the SIP retransmission mechanism in SIP networks. We capture the complex correlation structure in SIP systems through a detailed and novel queuing analysis. To dimension a buffer size which can avoid unnecessary message drop in a SIP server, we develop a sufficient condition for a stable SIP system analytically based on our fluid model. We also apply our fluid model to the simulation of a complex SIP system. We compare the simulation results achieved through our fluid models with those based on OPNET^® event-driven approach to demonstrate the validity of our approach.     

Analysis and characterization of complex reactor behaviour: A case study

This paper analyzes the steady state and dynamic behaviour of a reactor for production of silicon metal, a submerged arc furnace. The furnace behaviour has been analyzed through simulation studies using a detailed, industrially proven, mechanistic simulation model. The analysis reveals that the silicon furnace has changing and complex dynamic behaviour, including inverse responses and slow modes, especially close to optimality. The paper analyzes the causes of the changing dynamic behaviour. It also shows how the margins to optimality can be deduced from the dynamic response to changes in the carbon coverage input.     

Modelling virtual cities dedicated to behavioural animation

In order to populate virtual cities, it is necessary to specify the behaviour of dynamic entities such as pedestrians or car drivers. Since a complete mental model based on vision and image processing cannot be constructed in real time using purely geometrical information, higher levels of information are needed in a model of the virtual environment. For example, the autonomous actors of a virtual world would exploit the knowledge of the environment topology to navigate through it. In this article, we present a model of virtual urban environments using structures and information suitable for behavioural animations. Thanks to this knowledge, autonomous virtual actors can behave like pedestrians or car drivers in a complex city environment. A city modeler has been designed, using this model of urban environment, and enables complex urban environments for behavioural animation to be automatically produced.     

An analysis of ‘ industrial dynamics ’

In this paper a linearized version of an ‘ industrial dynamics ’ simulation mode discussed by Forrester (1061) is presented and analysed. It is shown that at least for the stop change in final demand treated by Forrester (19R1) the behaviour of the linearized model presented here is not dissimilar to the behaviour of Forrester's nonlinear model. Analysis of the linearized model shows that the poor transient behaviour of the system is due primarily to the modes associated with the exponential smoothing of the demand.     

Animation using NURBS

All animation, whether generated by pen or processor, is concerned with the changing behaviour of synthetic actors through time. Computer animation has allowed an animator to regard different actors as functionally equivalent entities for the purpose of generating their behaviours. This has opened the door to visually impressive effects, such as actors changing their appearance, thereby becoming other actors. For NURBS to become a tool which is useful to the writer of animation packages, it is necessary to provide this ability to in-between objects of different appearances through time. This paper aims to present a technique for creating higher dimensional NURBS to model object deformation, as well as to illustrate its advantages.