A structured modeling technology

This paper presents the methodological background and implementation of a structured modeling environment developed to meet the requirements of modeling activities undertaken to support intergovernmental negotiations aimed at improving European air quality. Although the motivation for the reported work came from the actual complex application presented in the paper, the actual scope of the paper covers a wide range of issues related to model-based decision-making support. The paper starts with a summary of the context of modeling composed of: the role of models in decision-making support; modeling paradigms; and state-of-the-art aspects of modeling complex problems. The modeling process is then characterized, and the requirement analysis for implementation of structured modeling is specified. The main part of the paper presents the structured modeling technology which was developed to support the implementation of the structured modeling principles for modeling complex problems.     

Geometric means of structured matrices

The geometric mean of positive definite matrices is usually identified with the Karcher mean, which possesses all properties—generalized from the scalar case—a geometric mean is expected to satisfy. Unfortunately, the Karcher mean is typically not structure preserving, and destroys, e.g., Toeplitz and band structures, which emerge in many applications. For this reason, the Karcher mean is not always recommended for modeling averages of structured matrices. In this article a new definition of a geometric mean for structured matrices is introduced, its properties are outlined, algorithms for its computation, and numerical experiments are provided. In the Toeplitz case an existing mean based on the Kähler metric is analyzed for comparison.     

The informational value of employee online reviews

This paper investigates the informational value of online reviews posted by employees for their employer, a rather untapped source of online information from employees, using a sample of 349,550 reviews from 40,915 UK firms. We explore this novel form of electronic Word-of-Mouth (e-WOM) from different perspectives, namely: (i) its information content as a tool to identify the drivers of job satisfaction/dissatisfaction, (ii) its predictive ability on firm financial performance and (iii) its operational and managerial value. Our approach considers both the rating score as well as the review text through a probabilistic topic modeling method, providing also a roadmap to quantify and exploit employee big data analytics. The novelty of this study lies in the coupling of structured and unstructured data for deriving managerial insights through a battery of econometric, financial and operational research methodologies. Our empirical analyses reveal that employee online reviews have informational value and incremental predictability gains for a firm's internal and external stakeholders. The results indicate that when models integrate structured and unstructured big data there are leveraged opportunities for firms and managers to enhance the informativeness of decision support systems and in turn, gain competitive advantage.     

Parallel simulation of individual-based, physiologically structured population models

A general scheme for parallel simulation of individual-based, structured population models is proposed. Algorithms are developed to simulate such models in a parallel computing environment. The simulation model consists of an individual model and a population model that incorporates the individual dynamics. The individual model is a continuous time representation of organism life history for growth with discrete allocations for reproductive processes. The population model is a continuous time simulation of a nonlinear partial differential equation of extended McKendrick-von Foerster-type.

As a prototypical example, we show that a specific individual-based, physiologically structured model for Daphnia populations is well suited for parallelization, and significant speed-ups can be obtained by using efficient algorithms developed along our general scheme. Because the parallel algorithms are applicable to generic structured populations which are the foundation for populations in a more complex community or food-web model, parallel computation appears to be a valuable tool for ecological modeling and simulation.     

Building confidence in models for multiple audiences: The modelling cascade

This paper reports on a model building process developed to enable multiple audiences, particularly non-experts, to appreciate the validity of the models being built and their outcomes. The process is a four stage reversible cascade. This cascade provides a structured, auditable/transparent, formalized process from “real world” interviews generating a rich qualitative model through two intermediate steps before arriving at a quantitative simulation model. There are a number of advantages of the cascade process including; achieving comprehensiveness, developing organizational learning, testing the veracity of multiple perspectives, modeling transparency, achieving common understanding across many audiences and promoting confidence building in the models. The paper, based on extensive work with organizations, discusses both the cascade process and its inherent benefits.     

Edge vibration of a pre-stressed semi-infinite strip with traction-free edge and mixed face boundary conditions

Edge-vibration, and associated resonance phenomena, is investigated in respect of a semi-infinite strip composed of pre-stressed incompressible elastic material. The strip is assumed to have a traction free outer edge, with the upper and lower edges subject to some simple mixed boundary conditions. The frequency of the modes of free edge-vibration are shown to depend on the surface wave speed. Moreover, when the normal pre-stress approaches one of two critical values, associated with the vanishing of the surface wave speed, the edge spectrum density of the boundary value problem increases significantly. This problem then provides an example for which the famous Weyls hypothesis, stating the edge spectrum is secondary in comparison with the whole bodys spectrum, is not true. However, the corresponding theorems statement is valid only with imposition of the Shapiro-Lopatinsky condition, which is not satisfied in this case. Variation of the pre-stress is also shown to greatly influence the resonance frequency arising in the forced vibration problem, to the extent that the phenomenon of resonance may be totally removed.     

The Theory of SML Schema-Directed Query

SML is a modeling language for the structured modeling framework, which represents the semantics as well as the mathematical structure of a model. This paper uses an SML approach to improve the object based universal relation data model. By this approach, both the relational structure of a database and the objects in relations are automatically derived by the associated SML schema. The interpretation part of an SML schema allows users to easily learn the meanings of the data before performing universal relation queries; the queries are then computed by using the automatically derived objects. With a goal of making queries simpler, this paper presents theories, table naming conventions, a confirmation approach, and a unified example illustrating many different concepts. It helps lay the foundation for the eventual development of a remarkably easy user interface for ad hoc query in computer-based modeling systems. We are hopeful that the results may in the future contribute to real applications in databases as well as in management science/operations research.     

Empirical Modeling of a Hydraulic Actuator in a Vehicle Control System

The pressure information of a hydraulic actuator plays a key role in modern vehicle control and fault diagnosis. The difficulty in measuring pressure directly naturally motivates such an indirect approach as an observer whose accuracy depends heavily upon the availability of a high fidelity model of a hydraulic actuator. Notwithstanding its success in understanding the dynamics of the first principle model of a hydraulic actuator, it is not suited to controller/observer design due to its complexity. This paper presents an alternative to the first principle modeling methodology: an empirical approach to hydraulic actuator modeling. Linear and nonlinear system identification techniques are applied to obtain low-order models of a hydraulic actuator. Experimental results show how the empirical models reproduce the key features of a hydraulic actuator with sufficient accuracy.     

Newton-Like Iteration Based on a Cubic Polynomial for Structured Matrices

We recall Newtons iteration for computing the inverse or Moore–Penrose generalized inverse of a matrix. Then we specialize this approach to the case of structured matrices where all input, output and intermediate auxiliary matrices are represented in a compressed form, via their short displacement generators. We design a new Newton-like iteration based on a cubic polynomial and show its effectiveness by some numerical experiments for matrices from the Toeplitz-like class and the Cauchy-like class.     

多物理场中不同结构特征FCI的力学行为分析

流道插件（FCI）是ITER中包层模块的重要部件,起到电绝缘和热绝缘的作用,FCI的力学行为是对复杂的磁-热-流-固多物理场共同作用的响应.将有限体积法和有限元方法相结合,对包层流道中的流场、温度场以及FCI的应力应变场进行求解,分析了磁场效应对结构的影响,以及不同FCI壁厚和间隙流宽度等结构特征对包层的影响.计算结果表明,强磁场虽然会产生较强的MHD效应,但可以降低第一壁温度和FCI结构热应力;较厚的FCI可以降低第一壁上的最高温度,但也会增加FCI上的温度梯度和热应力;而较宽的间隙有利于降低第一壁上的最高温度,但会增加FCI的最大Mises应力.     

Using FFT-based techniques in polynomial and matrix computations: recent advances and applicatons

Computations with univariate polynomials, like the evaluation of product, quotient, remainder, greatest common divisor, etc, are closely related to linear algebra computations performed with structured matrices having the Toeplitz-like or the Hankel-like structures.

The discrete Fourier transform, and the FFT algorithms for its computation, constitute a powerful tool for the design and analysis of fast algorithms for solving problems involving polynomials and structured matrices.

We recall the main correlations between polynomial and matrix computations and present two recent results in this field: in particular, we show how Fourier methods can speed up the solution of a wide class of problems arising in queueing theory where certain Markov chains, defined by infinite block Toeplitz matrices in generalized Hessenberg form, must be solved. Moreover, we present a new method for the numerical factorization of polynomials based on a matrix generalization of Koenig's theorem. This method, that relies on the evaluation/interpolation technique at the Fourier points, reduces the problem of polynomial factorization to the computation of the LU decomposition of a banded Toeplitz matrix with its rows and columns suitably permuted. Numerical experiments that show the effectiveness of our algorithms are presented     

Rejoinder on: Queueing models for the analysis of communication systems

In this rejoinder, we respond to the comments and questions of three discussants of our paper on queueing models for the analysis of communication systems. Our responses are structured around two main topics: discrete-time modeling and further extensions of the presented queueing analysis.     

无穷维线性系统的渐近调节器设计

§1.引言设(?)是 Banach 空间,A 是(?)上 C_0类半群无穷小生成无。B∈L(R~n;(?))。E∈L(R~q;(?))未知。C 是值域为 R~m 的 A 有界且 A 界为零的线性算子。即 D(C)(?)D(A),存在正常数 a,b,对任一x∈D(A),有     

Phase-type distributions in stochastic automata networks

Stochastic automata networks (Sans) are high-level formalisms for modeling very large and complex Markov chains in a compact and structured manner. To date, the exponential distribution has been the only distribution used to model the passage of time in the evolution of the different San components. In this paper we show how phase-type distributions may be incorporated into Sans thereby providing the wherewithal by which arbitrary distributions can be used which in turn leads to an improved ability for more accurately modeling numerous real phenomena.     

Concepts and fuzzy models for behavior-based robotics

In this paper, we propose a modeling paradigm that uses fuzzy sets to represent concepts on which control modules of a behavior-based autonomous robot operate. The primitives defined in the modeling paradigm are expressive enough to represent the knowledge needed by planning, coordination, and reactive control of a multi-robot control system. At the same time, it provides a well-founded tool to represent in a compact way the data interpretations needed to reason effectively about what is happening in the world and what is desired to happen. This modeling paradigm makes the design of behavior, planning, and coordination modules easy, since its primitives are simple and expressive. Moreover, it provides a sound framework to deal with uncertainty in sensing and world modeling.     

Structural and computational properties of possibly singular semiseparable matrices

A classical result of structured numerical linear algebra states that the inverse of a nonsingular semiseparable matrix is a tridiagonal matrix. Such a property of a semiseparable matrix has been proved to be useful for devising linear complexity solvers, for establishing recurrence relations among its columns or rows and, moreover, for efficiently evaluating its characteristic polynomial. In this paper, we provide sparse structured representations of a semiseparable matrix A which hold independently of the fact that A is singular or not. These relations are found by pointing out the band structure of the inverse of the sum of A plus a certain sparse perturbation of minimal rank. Further, they can be used to determine in a computationally efficient way both a reflexive generalized inverse of A and its characteristic polynomial.     

Variable selection and model choice in structured survival models

We aim at modeling the survival time of intensive care patients suffering from severe sepsis. The nature of the problem requires a flexible model that allows to extend the classical Cox-model via the inclusion of time-varying and nonparametric effects. These structured survival models are very flexible but additional difficulties arise when model choice and variable selection are desired. In particular, it has to be decided which covariates should be assigned time-varying effects or whether linear modeling is sufficient for a given covariate. Component-wise boosting provides a means of likelihood-based model fitting that enables simultaneous variable selection and model choice. We introduce a component-wise, likelihood-based boosting algorithm for survival data that permits the inclusion of both parametric and nonparametric time-varying effects as well as nonparametric effects of continuous covariates utilizing penalized splines as the main modeling technique. An empirical evaluation of the methodology precedes the model building for the severe sepsis data. A software implementation is available to the interested reader.     

Efficient Management of Multiple Sets to Extract Complex Structures from Mathematical Programs

Most of the applied models written with an algebraic modeling language involve simultaneously several dimensions such as materials, location, time or uncertainty. The information about dimensions available in the algebraic formulation is usually sufficient to retrieve different block structures from mathematical programs. These structured problems can then be solved by adequate solution techniques. To illustrate this idea we focus on stochastic programming problems with recourse. Taking into account both time and uncertainty dimensions of these problems, we are able to retrieve different customized structures in their constraint matrices. We applied the Structure Exploiting Tool to retrieve the structure from models built with the GAMS modeling language. The underlying mathematical programs are solved with the decomposition algorithm that applies interior point methods. The optimization algorithm is run in a sequential and in a parallel computing environment.