系统动力学在建设工程风险识别中的应用

首先根据建设工程建设程序,提出了建设工程建设阶段的风险管理.简单综述了系统动力学在项目管理中的应用,然后选取在项目实施期间,以项目承包商的视角,应用系统动力学方法对项目进行风险识别.由于篇幅所限,仅以工期风险为例,详细识别了造成工期风险的原因和风险发生后引起的后果,以及风险发生后通过系统内部调节后对自身的影响,证明了系统动力学在风险识别阶段应用的可行性和优越性.     

An identification method to estimate surface shortwave fluxes on temperature and thickness observations of sea ice in CHINARE 2006

In this paper, a parameter identification method to determine surface shortwave fluxes using temperature and thickness measurements of sea ice in CHINARE 2006 is presented. Adopting a new standard for the calculation of the thermodynamic properties of seawater named TEOS‐10, the surface shortwave fluxes are calculated by the temperature and thickness observations that were measured at Nella Fjord around Zhongshan Station, Antarctica. New simulations for temperature profiles in a different measurement period are performed by three parameterization schemes including the present method, Zillman and Shine. All numerical results are compared with in situ measurements. Results show that better simulations of the surface shortwave radiations and temperature distributions are possible with the identification method than Zillman and Shine. Therefore this method is valid, and the obtained shortwave radiation function can be applied in sea ice modeling. Copyright © 2015 John Wiley & Sons, Ltd.     

Detectability conditions for output-only subspace identification

ABSTRACT

The scope of output-only/blind identification is restricted to stochastic/statistical processes, but for the first time in this study, the detectability conditions for general output-only subspace identification are investigated. This aids the range of input sources to be extended in a much realistic manner, beyond the only stochastic inputs. For this purpose, the subspace framework is assigned to make a connection between the output signal contents and the LTI system order. A few substantial hypotheses and algebraic statements are propounded affirming the sufficiency of the genuine output sequences for the identification purpose. This can be perceived as the cornerstone of state-space model reconstruction. In order to consolidate the notions according to reality, several examples are studied and examined for different input classes with stochastic disturbance.     

基于FUDAN-CCDC模型对新冠肺炎的建模和确诊人数的预测

科学地预测疫情发展趋势对疫情防控至关重要.在新时滞动力学模型(TDD-NCP)的基础上,提出基于随机动力学的时滞卷积模型和离散卷积模型,并基于中国疾病预防控制中心的相关研究结果及公开数据以及Wallinga和Lipsitch的工作,反演出COVID-19的重要参数,拟合了武汉及上海市疫情发展趋势.     

一种面向效率的系统多序参量识别方法

针对社会组织系统演化过程中效率性要求,基于系统演化有效性识别及评估,提出能够反映系统演化过程效率的多序参量识别方法,为提升系统演化过程效率提供可操作的技术参考。通过产出/投入视角分析多序参量形成过程,结合其对系统演化过程的影响,给出基于产出/投入的系统演化过程中多序参量效率原理;应用改进的数据包络分析方法和已有指标体系,建立面向效率的系统演化过程多序参量识别模型。最后,通过算例验证方法的科学有效性。     

Sensitivity analysis and model optimization for reaction-diffusion systems

This paper presents a computational framework for the optimization and sensitivity analysis of a process whose state depends upon several parameter functions. Assuming that the process is described by a system of quasilinear, parabolic, partial differential equations, we show how determining the problem parameters so as to improve an associated objective functional is directly related to knowing the state function sensitivities. An expression for the gradient of the objective functional in terms of the solutions of an adjoint system enables one to bypass the calculation of state function sensitivities. These concepts are illustrated for a simple model of cooperative processes in chemical kinetics. Since sensitivity analysis and model optimization are important tools for investigating parameter dependence and validating mathematical models, research developments in such diverse fields as optimal design theory, chemical kinetics, and parameter identification are important motivations for this paper.This author would like to gratefully acknowledge Dr. M. Delle Donne, EGG, for several helpful discussions.This author was partially supported by NSF Grant No. CMS-80-05677.     

On Regularization of a Source Identification Problem in a Parabolic PDE and its Finite Dimensional Analysis

We consider the inverse problem of identifying a general source term, which is a function of both time variable and the spatial variable, in a parabolic PDE from the knowledge of boundary measurements of the solution on some portion of the lateral boundary. We transform this inverse problem into a problem of solving a compact linear operator equation. For the regularization of the operator equation with noisy data, we employ the standard Tikhonov regularization, and its finite dimensional realization is done using a discretization procedure involving the space $L^2(0,\tau;L^2(Ω))$. For illustrating the specification of an a priori source condition, we have explicitly obtained the range space of the adjoint of the operator involved in the operator equation.     

Optimal Conditional Estimation: Average Case Setting

We consider an estimation problem which appears in the identification of systems by means of restricted complexity models: find the optimal approximation to an element of a linear normed space (a system) based on noisy information, subject to the restriction that approximations (models) can be selected from a prescribed subspace M of the problem element space. In contrast to the worst-case optimization criterion, which may be pessimistic, in this paper the quality of an identification algorithm is measured by its local average performance. Two types of local average errors are considered: for a given information (measurement) y and for a given unknown element x, the latter in two versions. For a wide spectrum of norms in the measurement space, we define an optimal algorithm and give expressions for its average errors which show the dependence on information, information errors, unmodelled dynamics, and norm in the measurement space.     

Direct and Inverse Problems for a Phase-Field Model with Memory

We consider a semilinear integrodifferential system in non-normal form. Such a system is a generalization of the one that arises in the phase-field theory with memory. We prove an abstract existence and uniqueness theorem and a continuous dependence result for the direct problem. Reformulating the direct problem in a suitable way we prove that the identification problem admits a unique solution.     

非线性动力学系统的辨识：时频滤波与骨架曲线

利用非平稳信号的时频分析方法研究了一类非线性系统的频率特性和阻尼特性随运动形态的变化规律,得到了能简洁、直观地反映系统基本非线性动力学特性的广义骨架线性系统（简称GSLS）和骨架曲线。在此基础上,利用时频滤波方法根据系统非平稳响应信号对非线性系统进行辨识。该项工作为非线性系统反问题的研究提供了一条新的途径。