目前使用的系统动力学模型往往由于输入数据与实际系统不同步,引起对预测和调控的误差和失败。动态数据驱动应用系统(Dynamic Data Driven Application System,DDDAS)以动态运作方式,集实时模拟、实时测量、自动反馈和控制管理于一体能...目前使用的系统动力学模型往往由于输入数据与实际系统不同步,引起对预测和调控的误差和失败。动态数据驱动应用系统(Dynamic Data Driven Application System,DDDAS)以动态运作方式,集实时模拟、实时测量、自动反馈和控制管理于一体能够有效克服传统模拟存在的问题。本文简述了DDDAS提出的历史背景和基本概念,以该系统在农田温室气体排放、农田定量灌溉和河流污染监控中的具体应用为例,进一步阐明了DDDAS在农业和环境科学中应用的思路和方法。并提出DDDAS应用中需要解决的一些具体问题。展开更多
Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to thi...Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.展开更多
Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed ...Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.展开更多
文摘目前使用的系统动力学模型往往由于输入数据与实际系统不同步,引起对预测和调控的误差和失败。动态数据驱动应用系统(Dynamic Data Driven Application System,DDDAS)以动态运作方式,集实时模拟、实时测量、自动反馈和控制管理于一体能够有效克服传统模拟存在的问题。本文简述了DDDAS提出的历史背景和基本概念,以该系统在农田温室气体排放、农田定量灌溉和河流污染监控中的具体应用为例,进一步阐明了DDDAS在农业和环境科学中应用的思路和方法。并提出DDDAS应用中需要解决的一些具体问题。
基金Supported by the High Technology Research and Development Programme of China(2006AA12A106)~~
文摘Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.
文摘Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.
