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.     

Sampling framework for accurate curvature estimation in discrete surfaces

Accurate curvature estimation in discrete surfaces is an important problem with numerous applications. Curvature is an indicator of ridges and can be used in applications such as shape analysis and recognition, object segmentation, adaptive smoothing, anisotropic fairing of irregular meshes, and anisotropic texture mapping. In this paper, a new framework is proposed for accurate curvature estimation in discrete surfaces. The proposed framework is based on a local directional curve sampling of the surface where the sampling frequency can be controlled. This local model has a large number of degrees of freedoms compared with known techniques and, so, can better represent the local geometry. The proposed framework is quantitatively evaluated and compared with common techniques for surface curvature estimation. In order to perform an unbiased evaluation in which smoothing effects are factored out, we use a set of randomly generated Bezier surface patches for which the curvature values can be analytically computed. It is demonstrated that, through the establishment of sampling conditions, the error in estimations obtained by the proposed framework is smaller and that the proposed framework is less sensitive to low sampling density, sampling irregularities, and sampling noise.     

An algorithmic framework of discrete particle swarm optimization

Particle swarm optimization (PSO) was originally developed for continuous problem. To apply PSO to a discrete problem, the standard arithmetic operators of PSO are required to be redefined over discrete space. In this paper, a concept of distance over discrete solution space is introduced. Under this notion of distance, the PSO operators are redefined. After reinterpreting the composition of velocity of a particle, a general framework of discrete PSO algorithm is proposed. As a case study, we illustrate the application of the proposed discrete PSO algorithm to number partitioning problem (NPP) step by step. Preliminary computational experience is also presented. The successful application shows that the proposed algorithmic framework is feasible.     

Stochastic Petri net representation of discrete event simulations

In the context of discrete event simulation, the marking of a stochastic Petri net (SPN) corresponds to the state of the underlying stochastic process of the simulation and the firing of a transition corresponds to the occurrence of an event. A study is made of the modeling power of SPNs with timed and immediate transitions, showing that such Petri nets provide a general framework for simulation. The principle result is that for any (finite or) countable state GSMP (generalized semi-Markov process) there exists an SPN having a marking process that mimics the GSMP in the sense that the two processes (and their underlying general state-space Markov chains) have the same finite dimensional distributions     

A unified frame work for discrete event systems

Early fault detection and isolation minimize cost and processing time of industrial systems. The main purpose of this paper is to introduce a novel delta-marking idea to design a generalized interpreted Petri net-based fault detection and isolation scheme (IPN-based diagnoser) for all PN models. This idea overcomes the event detectability problem that restricts the performance of the conventional dignosers to certain types of Petri net model. Furthermore, a unified framework for a discrete event system that comprises a PN-based model, a PN-based supervisor, and a PN-based diagnoser of a process under consideration, is built in this paper. In addition, this paper addresses some issues for industrial systems modeling and supervision. A rapid thermal process (RTP) is used as an industrial process to test the proposed unified framework after revising its PN-model. This scheme is not only developed for RTPs, but also can be employed for industrial processes with certain modifications depending on the nature of their structures. Simulation results have confirmed the effectiveness of the proposed unified framework.     

Using Ada for discrete event simulation

The process interaction approach is proposed for developing a discrete simulation environment in Ada. The introduction of simulation facilities in Ada not only concerns the classical aspect of model building, but allows a new class of problems to be tackled, that is the testing of correctness of programs intended for real-time applications. In this paper attention is focused on the presentation of the process scheduling in the simulation context and on the definition of standard forms of interactions among processes. Simulation facilities are organized by making use of Ada's structuring concepts.     

Structural matching by discrete relaxation

This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this framework to provide a comparative evaluation of a number of contrasting approaches to relational matching. Broadly speaking there are two main aspects to this study. Firstly we focus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realizations of the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter     

《自动控制理论》离散系统部分教学研究

提出了在离散系统教学中利用MATLAB中的SIMULINK工具箱进行现场教学的方法。该方法利用直观地可视化动态仿真结果，进一步加深了学生对离散系统稳定性的理解，在教学过程中取得了良好的效果。[编者按]     

一种基于离散Morse理论的优化模型及其应用

根据Forman 的离散Morse 理论的特点, 提出一种基于离散Morse 理论的优化模型. 该模型在3 维及以上空间点构建离散Morse 函数进行最优化, 得到了问题的最优解或近似最优解. 同时, 证明了所构建的函数确实是复形上的离散Morse 函数. 利用4 个典型的测试函数进行仿真实验, 结果表明了该模型的有效性, 且该模型尤其适用于解决大数据量的优化问题. 从聚类的过程即目标函数的优化过程这一角度考虑, 尝试将优化模型应用于聚类分析. 仿真实验结果表明, 所提出的算法能较好地划分数据点重叠区域的聚类形状, 验证了所提出算法的可行性和有效性.     

Constraint-based modeling of discrete event dynamic systems

Numerous frameworks dedicated to the modeling of discrete event dynamic systems have been proposed to deal with programming, simulation, validation, situation tracking, or decision tasks: automata, Petri nets, Markov chains, synchronous languages, temporal logics, event and situation calculi, STRIPS…All these frameworks present significant similarities, but none offers the flexibility of more generic frameworks such as logic or constraints. In this article, we propose a generic constraint-based framework for the modeling of discrete event dynamic systems, whose basic components are state, event, and time attributes, as well as constraints on these attributes, and which we refer to as CNT for Constraint Network on Timelines. The main strength of such a framework is that it allows any kind of constraint to be defined on state, event, and time attributes. Moreover, its great flexibility allows it to subsume existing apparently different frameworks such as automata, timed automata, Petri nets, and classical frameworks used in planning and scheduling.     

Multiple time scale decomposition of discrete time Markov chains

The multiple time scale decomposition of discrete time, finite state Markov chains is addressed. In [1, 2], the behavior of a continuous time Markov chain is approximated using a fast time scale, ε-independent, continuous time process, and a reduced order perturbed process. The procedure can then be iterated to obtain a complete multiple time scale decomposition. In the discrete time case presented in this paper, the basic approximation has a ‘hybrid’ form. In this form, the fast time scale behavior is approximated using an ε-independent, discrete time Markov chain, and the slow behavior is captured by a perturbed, continuous time process. Further time scale decomposition then involves the continuous time procedure in [1, 2]. This extension to discrete time chains bridges previous multiple time scale decomposition results, which have dealt exclusively with either continuous time or discrete time processes, and provides a uniform framework for the analysis of both types of systems.     

Flexible patient rule induction method for optimizing process variables in discrete type

This paper deals with process optimization, which establishes the optimal settings of process variables to achieve a better quality. To this end, the patient rule induction method (PRIM), widely used in various application areas, could be adopted. However, the PRIM may fail to provide successful solutions when some process variables are in discrete types. Thus, we propose a new PRIM-like method specially to deal with ordinal discrete variables. For an illustrative purpose, the proposed method is applied to a real steel-making process. Also, performance of the proposed method is compared with the original PRIM through an extensive simulation using artificial data sets.     

Geometric properties estimation from discrete curves using discrete derivatives

Accurate geometric properties estimation from discrete curves is an important problem in many application domains, such as computer vision, pattern recognition, image processing, and geometric modeling. In this paper, we propose a novel method for estimating the geometric properties from discrete curves based on derivative estimation. We develop derivative estimation by defining the derivative of a discrete function at a point, which will be called the discrete derivative. Similarly, the second and higher order discrete derivatives at that point are also defined, and their convergence is demonstrated by theory analysis. These definitions of the different order discrete derivatives provide a simple and reliable way to estimate the derivatives from discrete curves. Based on the discrete derivatives, classical differential geometry can be discretized, and the geometric properties are estimated from discrete curves by using differential geometry theory. The proposed method is independent of any analytic curve and estimates the geometric properties directly from discrete data points, which makes it robust to the geometric shapes of discrete curves. Another advantage of the proposed method is the robustness to noise because of the calculation characteristics of the discrete derivatives. The proposed method is evaluated and compared with other existing methods in the experiments with both synthetic and real discrete curves. The test results show that the proposed method has good performance, and is robust to noise and suitable for different curve shapes.     

Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference

In this paper, an intelligent diagnosis for fault gear identification and classification based on vibration signal using discrete wavelet transform and adaptive neuro-fuzzy inference system (ANFIS) is presented. The discrete wavelet transform (DWT) technique plays one of the important roles for signal feature extraction in the proposed system. The abnormal transient signals will show in different decomposition levels and can be used to recognize the various faults by the DWT figure. However, many fault conditions are hard to inspect accurately by the naked eye. In the present study, the feature extraction method based on discrete wavelet transform with energy spectrum is proposed. The different order wavelets are considered to identify fault features accurately. The database is established by feature vectors of energy spectrum which are used as input pattern in the training and identification process. Furthermore, the ANFIS is proposed to identify and classify the fault gear positions and the gear fault conditions in the fault diagnosis system. The proposed ANFIS includes both the fuzzy logic qualitative approximation and the adaptive neural network capability. The experimental results verified that the proposed ANFIS has more possibilities in fault gear identification. The ANFIS achieved an accuracy identification rate which was more satisfactory than traditional vision inspection in the proposed system.     

Diagnosability of fuzzy discrete event systems

In this paper, discrete event systems (DESs) are reformulated as fuzzy discrete event systems (FDESs) and fuzzy discrete event dynamical systems (FDEDSs). These frameworks include fuzzy states, events and IF-THEN rules. In these frameworks, all events occur at the same time with different membership degrees. Fuzzy states and events have been introduced to describe uncertainties that occur often in practical problems, such as fault diagnosis applications. To measure a diagnoser’s fault discrimination ability, a fuzzy diagnosability degree is proposed. If the diagnosability of the degree of the system yields one a diagnoser can be implemented to identify all possible fault types related to a system. For any degree less than one, researchers should not devote their time to distinguish all possible fault types correctly. Thus, two different diagnosability definitions FDEDS and FDES are introduced. Due to the specialized fuzzy rule-base embedded in the FDEDS, it is capable of representing a class of non-linear dynamic system. Computationally speaking, the framework of diagnosability of the FDEDS is structurally similar to the framework of diagnosability of a non-linear system. The crisp DES diagnosability has been turned into the term fuzzy diagnosability for the FDES. The newly proposed diagnosability definition allows us to define a degree of diagnosability in a class of non-linear systems. In addition, a simple fuzzy diagnosability checking method is introduced and some numerical examples are provided to illustrate this theoretical development. Finally, the potential applications of the proposed method are discussed.     

Predictive analytics using statistical,learning, and ensemble methods to support real-time exploration of discrete event simulations

Discrete event simulations (DES) provide a powerful means for modeling complex systems and analyzing their behavior. DES capture all possible interactions between the entities they manage, which makes them highly expressive but also compute-intensive. These computational requirements often impose limitations on the breadth and/or depth of research that can be conducted with a discrete event simulation.This work describes our approach for leveraging the vast quantity of computing and storage resources available in both private organizations and public clouds to enable real-time exploration of discrete event simulations. Rather than directly targeting simulation execution speeds, we autonomously generate and execute novel scenario variants to explore a representative subset of the simulation parameter space. The corresponding outputs from this process are analyzed and used by our framework to produce models that accurately forecast simulation outcomes in real time, providing interactive feedback and facilitating exploratory research.Our framework distributes the workloads associated with generating and executing scenario variants across a range of commodity hardware, including public and private cloud resources. Once the models have been created, we evaluate their performance and improve prediction accuracy by employing dimensionality reduction techniques and ensemble methods. To make these models highly accessible, we provide a user-friendly interface that allows modelers and epidemiologists to modify simulation parameters and see projected outcomes in real time.     

On discrete dynamic output feedback min-max controllers

The paper considers output feedback min-max controllers for non-square discrete time uncertain linear systems. Based on previous work, it is demonstrated that static output feedback min-max controllers are only realizable for a specific class of systems. To broaden this class, a compensator based framework is proposed to introduce additional degrees of freedom. The conditions for the existence of such dynamic output feedback min-max controllers are given and are shown to be relatively mild. Furthermore, a simple parameterization of the available design freedom is proposed. An explicit procedure is described which shows how a Lyapunov matrix, which satisfies both a discrete Riccati inequality and a structural constraint, can be obtained using Linear matrix inequality optimization. This Lyapunov matrix is used to calculate the robustness bounds associated with the closed-loop system. A simple aircraft example is provided to demonstrate the efficacy of the design approach.     

The unified discrete surface Ricci flow

Ricci flow deforms the Riemannian metric proportionally to the curvature, such that the curvature evolves according to a heat diffusion process and eventually becomes constant everywhere. Ricci flow has demonstrated its great potential by solving various problems in many fields, which can be hardly handled by alternative methods so far.This work introduces the unified theoretic framework for discrete surface Ricci flow, including all the common schemes: tangential circle packing, Thurston’s circle packing, inversive distance circle packing and discrete Yamabe flow. Furthermore, this work also introduces a novel schemes, virtual radius circle packing and the mixed type schemes, under the unified framework. This work gives explicit geometric interpretation to the discrete Ricci energies for all the schemes with all back ground geometries, and the corresponding Hessian matrices.The unified frame work deepens our understanding to the discrete surface Ricci flow theory, and has inspired us to discover the new schemes, improved the flexibility and robustness of the algorithms, greatly simplified the implementation and improved the efficiency. Experimental results show the unified surface Ricci flow algorithms can handle general surfaces with different topologies, and is robust to meshes with different qualities, and is effective for solving real problems.     

Smoothed (conditional) perturbation analysis of discrete event dynamical systems

A new sample path analysis approach based on the smoothing property of conditional expectation for estimating the performance sensitivity of discrete event dynamical systems is proposed. Several examples are presented to show how this approach overcomes a difficulty of the ordinary infinitesimal perturbation analysis. The basic message is that one can get more knowledge about the system performance by observing and analyzing the sample path than by using the conventional simulation approach. It is also pointed out that the classical queueing theory approach for getting the performance sensitivity and the sample path based infinitesimal perturbation analysis approach can be unified in the framework of the new approach, the smoothed (conditional) perturbation analysis.     

离散制造企业RFID实施框架研究*

为推进离散制造企业RFID系统的成功实施,提出了面向离散制造企业的RFID实施方法论框架。从应用领域、应用层次、产品生命周期三个维度分析了离散制造企业的RFID应用需求。以信息技术采纳理论为基础,构建了包括RFID技术采纳决策和RFID系统实施两个阶段的RFID实施过程框架,并对各阶段的实施步骤和实施方法进行了阐述。利用上述RFID实施框架在重庆某整车生产厂进行实际应用,验证了可行性。