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.     

基于多智能体的备件需求仿真模型?

分析m维修策略在k/N冗余系统的使用特点,提出基于多智能体的备件需求量仿真模型。将工作部件和备件分别用2种Agent表示,即部件(SRU)Agent和备件(SparePart)Agent,并构建各自的状态机模型。采用事件驱动方式控制各个Agent的状态变化,从而间接控制系统的行为。通过算例验证了该方法的适用性。     

Object oriented modelling and simulation of hydropower plants with run-of-river scheme: A new simulation tool

This paper presents the design of a component library for modelling hydropower plants, and describes the development of a new simulation tool for small hydropower plants with a run-of-river scheme. After reviewing the desirable features of simulation, an approach based on an object oriented modelling language, like EcosimPro, is presented. A general model of hydropower plant with run-of-river scheme is created with this component library. It provides the possibility of choosing a specific number of turbines and spillway gates. In this way, several hydropower stations of similar operating characteristics can be simulated using this same general model. It is expected to obtain interesting information in simulation like the reservoir level, water flows, turbine efficiencies, and so on. In addition, a graphical user interface has been designed in order to operate this general model more easily, to configure plant parameters and to simulate the plant behaviour under different conditions. Three real stations have been used as real examples for validating the model and testing the simulator: hydropower stations of Villafranca and El Carpio (Córdoba, Spain) and Marmolejo (Jaén, Spain).     

Batch process modelling with mixtures of Gaussian processes

In recent years Gaussian processes have attracted a significant amount of interest with the particular focus being that of process modelling. This has primarily been a consequence of their good predictive performance and inherent analytical properties. Gaussian processes are a member of the family of non-parametric Bayesian regression models and can be derived from the perspective of neural networks. Their behaviour is controlled through the structure of the covariance function. However, when applied to batch processes, whose data exhibits different variance structures throughout the duration of the batch, a single Gaussian process may not be appropriate for the accurate modelling of its behaviour. Furthermore there are issues with respect to the computational costs of Gaussian processes. The implementation of a Gaussian process model requires the repeated computation of a matrix inverse whose order is the cubic of the number of training data points. This renders the algorithm impractical when dealing with large data sets. To address these two issues, a mixture model of Gaussian processes is proposed. The resulting prediction is attained as a weighted sum of the outputs from each Gaussian process component, with the weights determined by a Gaussian kernel gating network. The model is implemented through a Bayesian approach utilising Markov chain Monte Carlo algorithms. The proposed methodology is applied to data from a bench-mark batch simulation polymerization process, methyl methacrylate (MMA), and the results are compared with those from a single Gaussian process to illustrate the advantages of the proposed mixture model approach.     

An agent-monitored framework for the output-oriented design of experiments in exploratory modelling

Exploratory modelling is an approach for modelling under uncertainty based on the generation and analysis of computational experiments. The results of exploratory modelling are sensitive to the way that experiments are designed, such as the way that the uncertainty space is delineated. This article introduces an agent-monitored framework—i.e. a design metaphor of the interactions among modellers and stakeholders and the simulation process—for controlling the design of experiments based on monitoring model behaviour in the output space. To demonstrate the benefits of the suggested framework in the exploratory modelling process, the article shows how the use of the framework with an output-oriented approach informs the delineation of an appropriate uncertainty space with an illustrative example in the decision-making context. The article concludes that the design of experiments based on feedback from the output space can be a useful approach: to control simulations in exploratory modelling; to build more confidence in final results; and to inform the design of other aspects of experiments, such as selecting policy levers and sampling method.     

Integrated parametric multi-level information and numerical modelling of mechanised tunnelling projects

This paper presents a concept for parametric modelling of mechanized tunnelling within a state of the art design environment, as the basis for design assessments for different levels of details (LoDs). To this end, a parametric representation of each system component (soil with excavation, tunnel lining with grouting, Tunnel Boring Machine (TBM) and buildings) is developed in an information model for three LoDs (high, medium and low) and used for the automated generation of numerical models of the tunnel construction process and soil-structure interaction. The platform enables a flexible, user-friendly generation of the tunnel structure for arbitrary alignments based on predefined structural templates for each component, supporting the design process and at the same time providing an insight into the stability and safety of the design. This model, with selected optimal LoDs for each component, dependent on the objective of the analysis, is used for efficient design and process optimisation in mechanized tunnelling. Efficiency and accuracy are further demonstrated through an error-free exchange of information between Building Information Modelling (BIM) and the numerical simulation and with significantly reduced computational effort. The interoperability of the proposed multi-level framework is enabled through the use of an efficient multi-level representation context of the Industry Foundation Classes (IFC). The results reveal that this approach is a major step towards sensible modelling and numerical analysis of complex tunnelling project information at the early design stages.     

Progressive multi-block modelling for enhanced fault isolation in batch processes

A multi-block progressive modelling approach is proposed for enhanced fault isolation in batch processes. The unfolding of batch data typically leads to matrices with a large number of columns and this complicates contribution analysis. In order to rapidly focus fault isolation in batch processes, it would be desirable to employ multi-block modelling techniques. Multi-block model such as consensus principal component analysis (CPCA) can produce multiple monitoring charts for sub-blocks and block loadings and block scores can be obtained which can represent unique behaviour of each sub-block. CPCA model uses super score which is the same as score from normal principal component analysis (PCA) model and it does not produce enhanced monitoring performance. Multi-block PCA (MBPCA) model using block score for model calculation can represent sub-blocks’ character but block scores are obtained from super loading so it may not be the best way to describe sub-blocks. A new MBPCA model is proposed for better expression of each sub-block. Through progressive modelling and contribution analysis, variables related to or affected by the fault, as well as the associated time information, are gradually identified. This enables a fault propagation path being established. The proposed method is applied to a benchmark simulated penicillin production process, PenSim.     

On the selection of variables for qualitative modelling of dynamical systems

Behavioural modelling of physical systems from observations of their input/output behaviour is an important task in engineering. Such models are needed for fault monitoring as well as intelligent control of these systems. The paper addresses one subtask of behavioural modelling, namely the selection of input variables to be used in predicting the behaviour of an output variable. A technique that is well suited for qualitative behavioural modelling and simulation of physical systems is Fuzzy Inductive Reasoning (FIR), a methodology based on General System Theory. Yet, the FIR modelling methodology is of exponential computational complexity, and therefore, it may be useful to consider other approaches as booster techniques for FIR. Different variable selection algorithms: the method of the unreconstructed variance for the best reconstruction, methods based on regression coefficients (OLS, PCR and PLS) and other methods as Multiple Correlation Coefficients (MCC), Principal Components Analysis (PCA) and Cluster analysis are discussed and compared to each other for use in predicting the behaviour of a steam generator. The different variable selection algorithms previously named are then used as booster techniques for FIR. Some of the used linear techniques have been found to be non-effective in the task of selecting variables in order to compute a posterior FIR model. Methods based on clustering seem particularly well suited for pre-selecting subsets of variables to be used in a FIR modelling and simulation effort.     

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.     

Impact of model plant mismatch on performance of control systems: An application to paper machine control

Model-based controllers based on incorrect estimates of the true plant behaviour can be expected to perform poorly. This work studies the effect of model plant mismatch on the closed loop behaviour and system performance for a certain class of MIMO systems. Performance is measured using a minimum variance index and a closely related user-specified criterion. We study the effect of model plant mismatch on the output variance and performance indices. Under mild assumptions, the performance of each output in a MIMO system can be analysed independently. Moreover, we propose an approach to distinguish the effect of model–plant mismatch from the effect of changes in disturbance characteristics on closed-loop performance. We define a sensitivity measure that relates system performance to model–plant mismatch, and use it to explore this sensitivity for three realistic types of parametric modelling errors. Next, we suggest a quantitative method that compares a system's actual output to its desired response in a transient setting. The performance of the transient response is demonstrably more sensitive to the model–plant mismatch than the steady state performance. The results are illustrated on industrial paper machine data.     

一种基于监测的嵌入式系统设计技术

提出一种嵌入式系统软硬件协同设计方法，它以数据流图为系统模型对嵌入式系统的功能和性能需求进行描述，并通过一种特定的实现结构，使得设计者可以借助快速样机平台和事件驱动式监测技术来精确测定目标系统对系统模型的实现状况，从而使得软硬件协同设计过程特别是系统优化和性能验证能在精确、可靠的测试数据基础上进行．同目前通常使用的以软硬件部件性能估算为基础的软硬件协同设计方法相比，这种以测试为基础的设计技术更能确保设计结果的合理．     

Coloured Petri nets and graphical simulation for the validation of a robotic cell in aircraft industry

This paper proposes a mixed validation approach based on coloured Petri nets and 3D graphic simulation for the design of supervisory systems in manufacturing cells with multiple robots. The coloured Petri net is used to model the cell behaviour at a high level of abstraction. It models the activities of each cell component and its coordination by a supervisory system. The graphical simulation is used to analyse and validate the cell behaviour in a 3D environment, allowing the detection of collisions and the calculation of process times. The motivation for this work comes from the aeronautic industry. The automation of a fuselage assembly process requires the integration of robots with other cell components such as metrological or vision systems. In this cell, the robot trajectories are defined by the supervisory system and results from the coordination of the cell components. The paper presents the application of the approach for an aircraft assembly cell under integration in Brazil. This case study shows the feasibility of the approach and supports the discussion of its main advantages and limits.     

Intelligent Control Paradigm for Dynamic Discrete Event System Simulation

Simulation based control of discrete event systems has been a potential approach to support decision-making in the manufacturing scenario. In this paper, a knowledge intensive simulation modelling approach for a discrete even system is investigated. Based on the proposed simulation model, a robust control mechanism is presented that is believed to add significant value to discrete event dynamic system. The algorithm utilises neural network feedforward control plus robust proportional derivative feedback control to achieve control performance and output stability. The novel simulation approach, as well as the proposed controller, is implemented in an Extend TM environment and the effectiveness and usefulness of the proposed controller are verified, industrially, in the hard disk drive assembly process, a significant component of the Singapore manufacturing economy.     

Semi-quantitative actor-based modelling as a tool to assess the drivers of change and physical variables in participatory integrated assessments

Integrated assessments that aim to support sustainable natural resources management require analysing how biophysical systems are impacted by human actions. These analyses are often performed by modelling the physical system, while human actions are prescribed as scenarios and introduced into the physical models by varying the model input. To achieve a more thorough analysis of the human system component in participatory integrated assessments, we developed a semi-quantitative approach for actor-based modelling which focuses on modelling actions of societal actors based on their problem perceptions but also computes the resulting changes of physical system variables. Our approach is intended to support transdisciplinary research and identification of sustainable development options in problem fields with high levels of uncertainty or ignorance, together with the actors that are being modelled. Actor-based modelling is done using an enhanced version of the DANA software. DANA allows modelling the actors in a specific problem field by representing the perception of each actor by directed graphs, and by computing optimal actions from the perspective of each actor. These perception graphs are semi-quantitative causal maps, which can easily be discussed among stakeholders in a participatory process. DANA was extended to support, in addition to actor modelling, the other two steps in actor-based modelling, modelling of actions and modelling of factors. Modelling of actions refers to determining the actions of each actor under certain scenario assumptions, taking into account the diverse problem perceptions of the individual actors in the problem field, the action of the other actors and exogenous changes. Modelling of factors refers to calculating, in a semi-quantitative way, the resulting changes of physical variables (e.g. pollutant emissions), which may serve as input to physical models. We applied actor-based modelling in an integrated assessment of mobile organic xenobiotics in rivers. Our study shows that actor-based modelling allows generating scientifically better founded and more transparent scenarios of the drivers of change in integrated assessments, in particular because they are based on a structured analysis of the actors' problem perceptions.     

Modelling and simulation of complex manufacturing systems using statechart-based actors

This paper describes a multi-agent architecture based on the actors computational model, for the distributed simulation of discrete event systems whose entities have a complex dynamic behaviour. Complexity is dealt with by exploiting statechart-based actors which constitute the basic building blocks of a model. Actors are lightweight reactive autonomous agents that communicate to one another by asynchronous message passing. The thread-less character of actors saves memory space and fosters efficient execution. The behaviour of actors is specified through “distilled statecharts” that enable hierarchical and modular specifications. Distributed simulation is achieved by partitioning a system model among a set of logical processes (theatres). Timing management and inter-theatre communications rest in a case on the High Level Architecture services. The paper illustrates the practical application of the proposed modelling and simulation methodology by specifying and analysing a complex manufacturing system.     

Gaussian process approach for modelling of nonlinear systems

Parametric modelling principals such as neural networks, fuzzy models and multiple model techniques have been proposed for modelling of nonlinear systems. Research effort has focused on issues such as the selection of the structure, constructive learning techniques, computational issues, the curse of dimensionality, off-equilibrium behaviour, etc. To reduce these problems, the use of non-parametrical modelling approaches have been proposed. This paper introduces the Gaussian process (GP) prior approach for the modelling of nonlinear dynamic systems. The relationship between the GP model and the radial basis function neural network is explained. Issues such as selection of the dimension of the input space and the computation load are also discussed. The GP modelling technique is demonstrated on an example of the nonlinear hydraulic positioning system.     

A hierarchical composite framework of parallel discrete event simulation for modelling complex adaptive systems

As complex adaptive systems(CAS) continue to grow in scale and complexity, and the need for system adaptability increases, systems modelling has become an essential concern. Parallel discrete event simulation became a preferred choice as logical process world view, which bridges complex system modelling and high-performance computing. To resolve the shortcoming of this world view identified with respect to modularity and scalability. A hierarchical composite modelling framework was proposed, which is a three-level architecture intended to support the composition and integration of sub-models. The bottom layer is simulation model component(SMC), which is not a model but implement some simulation-specific support functionality. The middle layer is logical process model(LP), which describes an agent which can react to the current situation by executing a sequence of SMCs. The top layer is CAS system model, which defines a CAS model consist of several LPs and also the interactions between these LPs. The hierarchical composite modelling process and parallel simulation execution strategy are discussed to support the modelling and simulation of a CAS. In order to verify its effectiveness, a complex social opinion system model is proposed based on this hierarchical composite modelling framework. The experimental results confirms the viability of utilizing multi-level architecture for simulating large scale complex adaptive systems.     

Active queue management via event-driven feedback control

Active queue management (AQM) is investigated to avoid incipient congestion in gateways to complement congestion control run by the transport layer protocol such as the TCP. Most existing work on AQM can be categorized as (1) ad-hoc event-driven control and (2) time-driven feedback control approaches based on control theory. Ad hoc event-driven approaches for congestion control, such as RED (random early detection), lack a mathematical model. Thus, it is hard to analyze their dynamics and tune the parameters. Time-driven control theoretic approaches based on solid mathematical models have drawbacks too. As they sample the queue length and run AQM algorithm at every fixed time interval, they may not be adaptive enough to an abrupt load surge. Further, they can be executed unnecessarily often under light loads due to the time-driven nature. To seamlessly integrate the advantages of both event-driven and control-theoretic time-driven approaches, we present an event-driven feedback control approach based on formal control theory. As our approach is based on a mathematical model, its performance is more analyzable and predictable than ad hoc event-driven approaches are. Also, it is more reactive to dynamic load changes due to its event-driven nature. Our simulation results show that our event-driven controller effectively maintains the queue length around the specified set-point. It achieves shorter E2E (end-to-end) delays and smaller E2E delay fluctuations than several existing AQM approaches, which are ad hoc event-driven and based on time-driven control theory, while achieving almost the same E2E delays and E2E delay fluctuations as the two other advanced control theoretic AQM approaches. Further, our AQM algorithm is invoked much less frequently than the tested baselines.     

Comparison of two construction algorithms for local model networks

The divide-and-conquer approach of local model (LM) networks is a common engineering approach to the identification of a complex nonlinear dynamical system. The global representation is obtained from the weighted sum of locally valid, simpler submodels defined over small regions of the operating space. Constructing such networks requires the determination of appropriate partitioning and the parameters of the LMs. This paper focuses on the structural aspect of LM networks. It compares the computational requirements and performances of the Johansen and Foss (J&F) and LOLIMOT tree-construction algorithms. Several useful and important modifications to each algorithm are proposed. The modelling performances are evaluated using real data from a pilot plant of a pH neutralization process. Results show that while J&F achieves a more accurate nonlinear representation of the pH process, LOLIMOT requires significantly less computational effort.     

Neural Network Structures for Optimal Control of LPCVD Reactors

In this paper, a new approach of LPCVD reactor modelling and control is presented, based on the use of neural networks. We first present the development of a hybrid networks model of the reactor. The objective is to provide a simulation model which can be used to compute online the film thickness on each wafer. In the second section, the thermal control of a LPCVD reactor is studied. The objective is to develop a multivariable controller to control a space- and time-varying temperature profile inside the reactor. A neural network is designed using a methodology based on process inverse dynamics modelling. Good control results have been obtained when tracking space-time temperature profiles inside the LPCVD reactor pilot plant. Finally, global software is elaborated to achieve film thickness control in an experimental LPCVD reactor pilot plant, in order to get a defined and uniform deposition thickness on the wafers all along the reactor. Experimental results are presented which confirm the efficiency of the optimal control strategy.