OOCP∶面向对象的化工过程计算机辅助开发环境（Ⅰ）－面向对象方法建立过程系统模型

从过程系统的角度说明了面向对象概念，完成了对化工过程的多层次抽象，建立了近２０个化工类，并提出了用面向对象方法建立化工单元和过程系统面向方程模型的思想。文中介绍了化工类库的分类方法，类库定义、类库组织形式和面向对象建模机制等，可以看出，面向对象方法能快速、方便地建立新的化工单元和系统的面向方程模型，克服了传统设计方法中面向方程法模拟流程难以建模和利用已有模型产生新单元模型的缺点，为迅速、有效地建立化工模型探索了一条新途径。这也是作者提出的基于Ｗｉｎｄｏｗｓ的面向对象化工过程计算机辅助开发环境─ＯＯＣＰ的核心内容。     

OOCP：面向对象的化工过程计算机辅助开发环境（I）：面向对象…

从过程系统的角度说明了面向对象概念，完成了对化工过程的多层次抽象，建立了近２０个化工类，并提出了用面向对象方法建立化工单元和过程系统面向方程模型的思想。文中介绍了化工类库的分类方法，类库定义，类库组织形式和面向对象建模机制等。     

面向对象的流程工业系统有向无环图建模

提出流程工业系统中有向无环图的面向对象的建模方法。介绍使用面向对象的技术对流程工业系统模进行建模的关键要素的技巧,定义描述流程工业系统中有向无环图模型的建模机制,扩展流程工业系统模型所具有的特性。以流程工业系统出现异常时的诊断实例说明系统模型解决问题的有效性。     

离散事件驱动的流程建模方法与系统实现

在仿真建模中,为了使建模工具简单易用、仿真运行高效,建立了三层仿真元模型体系结构,并在流程定义分析的基础上,结合扩展有向图建模方法,针对模型层研究了离散事件驱动的流程建模方法。基于该流程建模方法及XML文档形式化描述及数据传递技术,采用面向对象的设计方法,开发了适用于离散事件驱动的流程建模系统。该系统提供了简单的可视化流程建模工具,简化了建模流程,易于对模型的理解与交流。     

改进的面向对象Petri网及其应用研究

在面向对象Petri网建模的基础上,在对象内部引入了一个特殊的变迁--控制变迁,在各对象之间引入了控制器,提出了一种基于控制结构的面向对象Petri网(CS-OOPN)模型,并阐述了CS-OOPN建模的步骤.该模型克服了传统的面向对象Petri网建模缺乏流程柔性和系统灵活性的缺点,能更加直观、灵活地描述工作流程.最后通过利用该模型为某集团公司设备采购管理系统建模,以审批部门的CS-OOPN模型为例,求其关联矩阵、可覆盖性树和P不变量,并进行相关性分析,结果表明了CS-OOPN的有效性和强大的建模能力.     

面向对象的地理遥感信息模型——建模过程

地理遥感信息模型是在地形模型、物理模型、数学模型的基础上提出的一种新型模型。面向对象技术是一种新型的系统建模技术。着重论述了面向对象技术应用于地理遥感信息模型建模的可行性,提出以面向对象技术基本理论为指导,构建面向对象的地理遥感信息模型的建模方法、数据结构以及建模优势。     

基于UML的高校学生资助工作管理系统设计

高校学生资助工作意义重大。为了更好地做好高校学生资助工作,从信息化管理的角度出发,对高校学生资助工作的组成、管理流程进行了研究;用面向对象的分析与设计方法,基于UML建模技术描述了高校学生资助工作管理系统,探讨了基于UML的建模过程和方法,设计了较为完善的高校学生资助工作管理系统UML模型,并使用.NET技术实现了该系统。该系统的应用能进一步规范学生资助工作管理行为、提高管理水平和工作效率。同时,为应用UML建模技术进行面向对象软件开发提供了参考范例。     

基于UML的高校学生资助工作管理系统设计

高校学生资助工作意义重大.为了更好地做好高校学生资助工作,从信息化管理的角度出发,对高校学生资助工作的组成、管理流程进行了研究;用面向对象的分析与设计方法,基于UML建模技术描述了高校学生资助工作管理系统,探讨了基于UML的建模过程和方法,设计了较为完善的高校学生资助工作管理系统UML模型,并使用.NET技术实现了该系统.该系统的应用能进一步规范学生资助工作管理行为、提高管理水平和工作效率.同时,为应用UML建模技术进行面向对象软件开发提供了参考范例.     

基于UML的房地产测绘信息管理系统的建模研究

UML是面向对象软件开发中的重要技术。使用UML进行系统建模,就是使用面向对象的方法来分析系统,以此建立面向对象的系统模型。本文针对房地产企业的特点,以房地产测绘信息管理系统为实例,阐述了如何使用UML中的用例图、顺序图和合作图来进行系统分析,以此帮助系统开发人员进行详细需求分析,并能更清楚地了解系统功能和系统流程的分析。     

基于SystemC和POOSL的语音识别系统的系统级建模与性能分析的对比*

根据复杂嵌入式系统的系统级建模的需要,提出了一套复杂嵌入式系统的系统级设计流程和建模方法。该方法主要包括了层次化建模方法、TopDown建模方法、面向对象建模技术和YChart建模方法,并采用提出的系统级建模方法,分别使用SystemC语言和POOSL语言成功地对语音识别系统进行了建模;通过模型的仿真进行了性能分析,并对分别得到的性能分析结果进行了对比,两种语言的系统延迟时间与CPU占用率的性能分析结果基本一致,为建模人员进行复杂嵌入式系统的系统级建模和性能分析提供了指导与参考。     

基于ARMA的微惯性传感器随机误差建模方法

针对微惯性传感器随机误差建模效果不理想,影响微惯性组合导航系统性能的问题,提出了采用自回归滑动平均(ARMA)对微惯性传感器随机误差进行建模的方法。通过对随机误差模型应用于微惯性器件误差建模的深入分析,将Yule-Walker方程引入线性预测问题中,实现AR功率谱密度的估计,建立了基于随机过程有理功率谱密度的ARMA模型建立方法,并给出了ARMA建模准确性的LDA验证准则。通过微惯性传感器实测数据,对随机误差建模方法进行了有效性验证。该方法为微惯性器件的随机误差建模和分析提供了一种新的途径。     

Building Linear Programming Models from Structural Model Components

Model management research investigates the formulation, analysis and interpretation of models. This paper focuses on the formulation aspects of modeling so that the task can be supported by decision support systems (DSS) environments. Given the knowledge intensive nature of the formulation process, the development of a modeling tool requires explicating the knowledge pertaining to modeling. This involves comprehending not only the static knowledge about model components (e.g. decision variables, coefficients, associated indices and constants), but also the process knowledge required to construct models from model pieces. The proposed top-down approach configures equations by exploiting the structural modeling knowledge inherent in equation components. The possible representation of equations at various abstraction levels is introduced, the aim being to uncover the structural model components together with the process knowledge required for their appropriate configuration. As part of developing this conceptual model, the role of semantic and syntactic information in model building is investigated. The paper proposes an approach where the formulation semantics are captured as a simple 'action-resource' view which composes models by identifying and piecing together the equation components. The process of equation construction is illustrated with examples from the linear programming (LP) modeling domain. The proposed top-down approach is contrasted with a bottom-up method.     

Hybrid analytic and simulation models for assembly line design and production planning

Hybrid analytic and simulation models are used in solving complex problems in a variety of domains, but are less commonly used in production system design. This paper reviews hybrid approaches and their applications, proposes a new hybrid modeling class, and illustrates a cost function for selecting analytic or simulation modeling approaches via a problem solving process. To illustrate the new class, a case study is presented, in which a hybrid analytic and simulation modeling approach is used in designing a multi-stage, multi-buffer electronic device assembly line. Development of a robust integrated modeling support environment is proposed as a future direction.     

/spl epsiv/-insensitive fuzzy c-regression models: introduction to /spl epsiv/-insensitive fuzzy modeling

This paper introduces a new epsilon-insensitive fuzzy c-regression models (epsilonFCRM), that can be used in fuzzy modeling. To fit these regression models to real data, a weighted epsilon-insensitive loss function is used. The proposed method make it possible to exclude an intrinsic inconsistency of fuzzy modeling, where crisp loss function (usually quadratic) is used to match real data and the fuzzy model. The epsilon-insensitive fuzzy modeling is based on human thinking and learning. This method allows easy control of generalization ability and outliers robustness. This approach leads to c simultaneous quadratic programming problems with bound constraints and one linear equality constraint. To solve this problem, computationally efficient numerical method, called incremental learning, is proposed. Finally, examples are given to demonstrate the validity of introduced approach to fuzzy modeling.     

On robust nonlinear modeling of a complex process with large number of inputs using m-QRcp factorization and C(p) statistic

The problem of modeling complex processes with a large number of inputs is addressed. A new method is proposed for the optimization of the models in minimum C(p) statistic sense using QR with a modified scheme of column pivoting (m-QRcp) factorization. Two different classes of multilayer nonlinear modeling problems are explored: 1) in the first class of models, each layer comprises multiple linearly parameterized submodels or cells; the individual cells are optimally modeled using QR factorization, and m-QRcp factorization ensures optimal selection of variables across the layers. 2) The nonhomogeneous feed-forward neural network is chosen as the second class of models, where the network architecture and structure are optimized in terms of best set of hidden links (and nodes) using m-QPcp factorization. In both the cases, the optimization is shown to be direct and conclusive. The proposed is a generic approach to the optimal modeling of complex multilayered architectures, which leads to computationally fast and numerically robust parsimonious designs, free from collinearity problems. The method is largely free from heuristics and is amenable to automated modeling.     

Evolving compact and interpretable Takagi-Sugeno fuzzy models with a new encoding scheme.

Developing Takagi-Sugeno fuzzy models by evolutionary algorithms mainly requires three factors: an encoding scheme, an evaluation method, and appropriate evolutionary operations. At the same time, these three factors should be designed so that they can consider three important aspects of fuzzy modeling: modeling accuracy, compactness, and interpretability. This paper proposes a new evolutionary algorithm that fulfills such requirements and solves fuzzy modeling problems. Two major ideas proposed in this paper lie in a new encoding scheme and a new fitness function, respectively. The proposed encoding scheme consists of three chromosomes, one of which uses unique chained possibilistic representation of rule structure. The proposed encoding scheme can achieve simultaneous optimization of parameters of antecedent membership functions and rule structures with the new fitness function developed in this paper. The proposed fitness function consists of five functions that consider three evaluation criteria in fuzzy modeling problems. The proposed fitness function guides evolutionary search direction so that the proposed algorithm can find more accurate compact fuzzy models with interpretable antecedent membership functions. Several evolutionary operators that are appropriate for the proposed encoding scheme are carefully designed. Simulation results on three modeling problems show that the proposed encoding scheme and the proposed fitness functions are effective in finding accurate, compact, and interpretable Takagi-Sugeno fuzzy models. From the simulation results, it is shown that the proposed algorithm can successfully find fuzzy models that approximate the given unknown function accurately with a compact number of fuzzy rules and membership functions. At the same time, the fuzzy models use interpretable antecedent membership functions, which are helpful in understanding the underlying behavior of the obtained fuzzy models.     

Different Zhang functions leading to different ZNN models illustrated via time-varying matrix square roots finding

In view of the great potential in parallel processing and ready implementation via hardware, neural networks are now often employed to solve online nonlinear matrix equation problems. Recently, a novel class of neural networks, termed Zhang neural network (ZNN), has been formally proposed by Zhang et al. for solving online time-varying problems. Such a neural-dynamic system is elegantly designed by defining an indefinite matrix-valued error-monitoring function, which is called Zhang function (ZF). The dynamical system is then cast in the form of a first-order differential equation by using matrix notation. In this paper, different indefinite ZFs, which lead to different ZNN models, are proposed and developed as the error-monitoring functions for time-varying matrix square roots finding. Towards the final purpose of field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) realization, the MATLAB Simulink modeling and verifications of such ZNN models are further investigated for online solution of time-varying matrix square roots. Both theoretical analysis and modeling results substantiate the efficacy of the proposed ZNN models for time-varying matrix square roots finding.     

Evolving Compact and Interpretable Takagi–Sugeno Fuzzy Models With a New Encoding Scheme

Developing Takagi–Sugeno fuzzy models by evolutionary algorithms mainly requires three factors: an encoding scheme, an evaluation method, and appropriate evolutionary operations. At the same time, these three factors should be designed so that they can consider three important aspects of fuzzy modeling: modeling accuracy, compactness, and interpretability. This paper proposes a new evolutionary algorithm that fulfills such requirements and solves fuzzy modeling problems. Two major ideas proposed in this paper lie in a new encoding scheme and a new fitness function, respectively. The proposed encoding scheme consists of three chromosomes, one of which uses unique chained possibilistic representation of rule structure. The proposed encoding scheme can achieve simultaneous optimization of parameters of antecedent membership functions and rule structures with the new fitness function developed in this paper. The proposed fitness function consists of five functions that consider three evaluation criteria in fuzzy modeling problems. The proposed fitness function guides evolutionary search direction so that the proposed algorithm can find more accurate compact fuzzy models with interpretable antecedent membership functions. Several evolutionary operators that are appropriate for the proposed encoding scheme are carefully designed. Simulation results on three modeling problems show that the proposed encoding scheme and the proposed fitness functions are effective in finding accurate, compact, and interpretable Takagi–Sugeno fuzzy models. From the simulation results, it is shown that the proposed algorithm can successfully find fuzzy models that approximate the given unknown function accurately with a compact number of fuzzy rules and membership functions. At the same time, the fuzzy models use interpretable antecedent membership functions, which are helpful in understanding the underlying behavior of the obtained fuzzy models.     

Fast character modeling with sketch-based PDE surfaces

Virtual characters are 3D geometric models of characters. They have a lot of applications in multimedia. In this paper, we propose a new physics-based deformation method and efficient character modelling framework for creation of detailed 3D virtual character models. Our proposed physics-based deformation method uses PDE surfaces. Here PDE is the abbreviation of Partial Differential Equation, and PDE surfaces are defined as sculpting force-driven shape representations of interpolation surfaces. Interpolation surfaces are obtained by interpolating key cross-section profile curves and the sculpting force-driven shape representation uses an analytical solution to a vector-valued partial differential equation involving sculpting forces to quickly obtain deformed shapes. Our proposed character modelling framework consists of global modeling and local modeling. The global modeling is also called model building, which is a process of creating a whole character model quickly with sketch-guided and template-based modeling techniques. The local modeling produces local details efficiently to improve the realism of the created character model with four shape manipulation techniques. The sketch-guided global modeling generates a character model from three different levels of sketched profile curves called primary, secondary and key cross-section curves in three orthographic views. The template-based global modeling obtains a new character model by deforming a template model to match the three different levels of profile curves. Four shape manipulation techniques for local modeling are investigated and integrated into the new modelling framework. They include: partial differential equation-based shape manipulation, generalized elliptic curve-driven shape manipulation, sketch assisted shape manipulation, and template-based shape manipulation. These new local modeling techniques have both global and local shape control functions and are efficient in local shape manipulation. The final character models are represented with a collection of surfaces, which are modeled with two types of geometric entities: generalized elliptic curves (GECs) and partial differential equation-based surfaces. Our experiments indicate that the proposed modeling approach can build detailed and realistic character models easily and quickly.